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Voisard C, de l'Escalopier N, Ricard D, Oudre L. Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients. J Neuroeng Rehabil 2024; 21:104. [PMID: 38890696 PMCID: PMC11184826 DOI: 10.1186/s12984-024-01405-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.
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Affiliation(s)
- Cyril Voisard
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France.
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France.
| | - Nicolas de l'Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Chirurgie Orthopédique, Traumatologique et Réparatrice des Membres, Service de Santé des Armées, HIA Percy, Clamart, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, Paris, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France
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Michaud M, Guérin A, Dejean de La Bâtie M, Bancel L, Oudre L, Tricot A. The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde ®. SENSORS (BASEL, SWITZERLAND) 2024; 24:2413. [PMID: 38676029 PMCID: PMC11054238 DOI: 10.3390/s24082413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/04/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024]
Abstract
The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.
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Affiliation(s)
- Mona Michaud
- Sysnav, 27200 Vernon, France; (M.M.); (A.G.); (L.B.)
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, 91190 Gif-sur-Yvette, France
| | - Alexandre Guérin
- Sysnav, 27200 Vernon, France; (M.M.); (A.G.); (L.B.)
- DataShape, Inria Saclay Ile-de-France, 91120 Palaiseau, France
- Laboratoire de Mathématiques d’Orsay, Université Paris-Saclay, CNRS, 91405 Orsay, France
| | | | | | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, 91190 Gif-sur-Yvette, France
| | - Alexis Tricot
- Sysnav, 27200 Vernon, France; (M.M.); (A.G.); (L.B.)
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Jiao Y, Hart R, Reading S, Zhang Y. Systematic review of automatic post-stroke gait classification systems. Gait Posture 2024; 109:259-270. [PMID: 38367457 DOI: 10.1016/j.gaitpost.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 01/11/2024] [Accepted: 02/12/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Gait classification is a clinically helpful task performed after a stroke in order to guide rehabilitation therapy. Gait disorders are commonly identified using observational gait analysis in clinical settings, but this approach is limited due to low reliability and accuracy. Data-driven gait classification can quantify gait deviations and categorise gait patterns automatically possibly improving reliability and accuracy; however, the development and clinical utility of current data driven systems has not been reviewed previously. RESEARCH QUESTION The purpose of this systematic review is to evaluate the literature surrounding the methodology used to develop automatic gait classification systems, and their potential effectiveness in the clinical management of stroke-affected gait. METHOD The database search included PubMed, IEEE Xplore, and Scopus. Twenty-one studies were identified through inclusion and exclusion criteria from 407 available studies published between 2015 and 2022. Development methodology, classification performance, and clinical utility information were extracted for review. RESULTS AND SIGNIFICANCE Most of gait classification systems reported a classification accuracy between 80%-100%. However, collated studies presented methodological errors in machine learning (ML) model development. Further, many studies neglected model components such as clinical utility (e.g., predictions don't assist clinicians or therapists in making decisions, interpretability, and generalisability). We provided recommendations to guide development of future post-stroke automatic gait classification systems to better assist clinicians and therapists. Future automatic gait classification systems should emphasise the clinical significance and adopt a standardised development methodology of ML model.
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Affiliation(s)
- Yiran Jiao
- Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand
| | - Rylea Hart
- Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand
| | - Stacey Reading
- Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand
| | - Yanxin Zhang
- Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.
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Ribeiro M, Yordanova YN, Noblet V, Herbet G, Ricard D. White matter tracts and executive functions: a review of causal and correlation evidence. Brain 2024; 147:352-371. [PMID: 37703295 DOI: 10.1093/brain/awad308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 08/17/2023] [Accepted: 08/25/2023] [Indexed: 09/15/2023] Open
Abstract
Executive functions are high-level cognitive processes involving abilities such as working memory/updating, set-shifting and inhibition. These complex cognitive functions are enabled by interactions among widely distributed cognitive networks, supported by white matter tracts. Executive impairment is frequent in neurological conditions affecting white matter; however, whether specific tracts are crucial for normal executive functions is unclear. We review causal and correlation evidence from studies that used direct electrical stimulation during awake surgery for gliomas, voxel-based and tract-based lesion-symptom mapping, and diffusion tensor imaging to explore associations between the integrity of white matter tracts and executive functions in healthy and impaired adults. The corpus callosum was consistently associated with all executive processes, notably its anterior segments. Both causal and correlation evidence showed prominent support of the superior longitudinal fasciculus to executive functions, notably to working memory. More specifically, strong evidence suggested that the second branch of the superior longitudinal fasciculus is crucial for all executive functions, especially for flexibility. Global results showed left lateralization for verbal tasks and right lateralization for executive tasks with visual demands. The frontal aslant tract potentially supports executive functions, however, additional evidence is needed to clarify whether its involvement in executive tasks goes beyond the control of language. Converging evidence indicates that a right-lateralized network of tracts connecting cortical and subcortical grey matter regions supports the performance of tasks assessing response inhibition, some suggesting a role for the right anterior thalamic radiation. Finally, correlation evidence suggests a role for the cingulum bundle in executive functions, especially in tasks assessing inhibition. We discuss these findings in light of current knowledge about the functional role of these tracts, descriptions of the brain networks supporting executive functions and clinical implications for individuals with brain tumours.
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Affiliation(s)
- Monica Ribeiro
- Service de neuro-oncologie, Hôpital La Pitié-Salpêtrière, Groupe Hospitalier Universitaire Pitié Salpêtrière-Charles Foix, Sorbonne Université, 75013 Paris, France
- Université Paris Saclay, ENS Paris Saclay, Service de Santé des Armées, CNRS, Université Paris Cité, INSERM, Centre Borelli UMR 9010, 75006 Paris, France
| | - Yordanka Nikolova Yordanova
- Service de neurochirurgie, Hôpital d'Instruction des Armées Percy, Service de Santé des Armées, 92140 Clamart, France
| | - Vincent Noblet
- ICube, IMAGeS team, Université de Strasbourg, CNRS, UMR 7357, 67412 Illkirch, France
| | - Guillaume Herbet
- Praxiling, UMR 5267, CNRS, Université Paul Valéry Montpellier 3, 34090 Montpellier, France
- Département de Neurochirurgie, Hôpital Gui de Chauliac, Centre Hospitalier Universitaire de Montpellier, 34295 Montpellier, France
- Institut Universitaire de France
| | - Damien Ricard
- Université Paris Saclay, ENS Paris Saclay, Service de Santé des Armées, CNRS, Université Paris Cité, INSERM, Centre Borelli UMR 9010, 75006 Paris, France
- Département de neurologie, Hôpital d'Instruction des Armées Percy, Service de Santé des Armées, 92140 Clamart, France
- Ecole du Val-de-Grâce, 75005 Paris, France
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Martino Cinnera A, Picerno P, Bisirri A, Koch G, Morone G, Vannozzi G. Upper limb assessment with inertial measurement units according to the international classification of functioning in stroke: a systematic review and correlation meta-analysis. Top Stroke Rehabil 2024; 31:66-85. [PMID: 37083139 DOI: 10.1080/10749357.2023.2197278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 03/24/2023] [Indexed: 04/22/2023]
Abstract
OBJECTIVE To investigate the usefulness of inertial measurement units (IMUs) in the assessment of motor function of the upper limb (UL) in accordance with the international classification of functioning (ICF). DATA SOURCES PubMed; Scopus; Embase; WoS and PEDro databases were searched from inception to 1 February 2022. METHODS The current systematic review follows PRISMA recommendations. Articles including IMU assessment of UL in stroke individuals have been included and divided into four ICF categories (b710, b735, b760, d445). We used correlation meta-analysis to pool the Fisher Z-score of each correlation between kinematics and clinical assessment. RESULTS A total of 35 articles, involving 475 patients, met the inclusion criteria. In the included studies, IMUs have been employed to assess the mobility of joint functions (n = 6), muscle tone functions (n = 4), control of voluntary movement functions (n = 15), and hand and arm use (n = 15). A significant correlation was found in overall meta-analysis based on 10 studies, involving 213 subjects: (r = 0.69) (95% CI: 0.69/0.98; p < 0.001) as in the d445 (r = 0.71) and b760 (r = 0.64) ICF domains, with no heterogeneity across the studies. CONCLUSION The literature supports the integration of IMUs and conventional clinical assessment in functional evaluation of the UL after a stroke. The use of a limited number of wearable sensors can provide additional kinematic features of UL in all investigated ICF domains, especially in the ADL tasks when a strong correlation with clinical evaluation was found.
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Affiliation(s)
- Alex Martino Cinnera
- Scientific Institute for Research, Hospitalization and Health Care IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
| | - Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "eCampus", Novedrate, Italy
| | | | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, University of Ferrara, Italy
| | - Giovanni Morone
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Giuseppe Vannozzi
- Scientific Institute for Research, Hospitalization and Health Care IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Movement, Human and Health Sciences, University of Rome "Foro Italico", Rome, Italy
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Ilg W, Milne S, Schmitz-Hübsch T, Alcock L, Beichert L, Bertini E, Mohamed Ibrahim N, Dawes H, Gomez CM, Hanagasi H, Kinnunen KM, Minnerop M, Németh AH, Newman J, Ng YS, Rentz C, Samanci B, Shah VV, Summa S, Vasco G, McNames J, Horak FB. Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2023:10.1007/s12311-023-01625-2. [PMID: 37955812 DOI: 10.1007/s12311-023-01625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/20/2023] [Indexed: 11/14/2023]
Abstract
With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.
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Affiliation(s)
- Winfried Ilg
- Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research, Otfried-Müller-Straße 25, 72076, Tübingen, Germany.
- Centre for Integrative Neuroscience (CIN), Tübingen, Germany.
| | - Sarah Milne
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, VIC, Australia
- Department of Paediatrics, Melbourne University, Melbourne, VIC, Australia
- Physiotherapy Department, Monash Health, Clayton, VIC, Australia
- School of Primary and Allied Health Care, Monash University, Frankston, VIC, Australia
| | - Tanja Schmitz-Hübsch
- Experimental and Clinical Research Center, a cooperation of Max-Delbrueck Center for Molecular Medicine and Charité, Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
| | - Lukas Beichert
- Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Enrico Bertini
- Research Unit of Neuromuscular and Neurodegenerative Disorders, Bambino Gesu' Children's Research Hospital, IRCCS, Rome, Italy
| | | | - Helen Dawes
- NIHR Exeter BRC, College of Medicine and Health, University of Exeter, Exeter, UK
| | | | - Hasmet Hanagasi
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | | | - Martina Minnerop
- Institute of Neuroscience and Medicine (INM-1)), Research Centre Juelich, Juelich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jane Newman
- NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne, UK
- Wellcome Centre for Mitochondrial Research, Newcastle University, Newcastle upon Tyne, UK
| | - Yi Shiau Ng
- Wellcome Centre for Mitochondrial Research, Newcastle University, Newcastle upon Tyne, UK
| | - Clara Rentz
- Institute of Neuroscience and Medicine (INM-1)), Research Centre Juelich, Juelich, Germany
| | - Bedia Samanci
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Vrutangkumar V Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
| | - Susanna Summa
- Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Unit, Neurological Science and Neurorehabilitation Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Gessica Vasco
- Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Unit, Neurological Science and Neurorehabilitation Area, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - James McNames
- APDM Precision Motion, Clario, Portland, OR, USA
- Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
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Voisard C, de l'Escalopier N, Vienne-Jumeau A, Moreau A, Quijoux F, Bompaire F, Sallansonnet M, Brechemier ML, Taifas I, Tafani C, Drouard E, Vayatis N, Ricard D, Oudre L. Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram. Front Neurol 2023; 14:1237162. [PMID: 37780706 PMCID: PMC10540441 DOI: 10.3389/fneur.2023.1237162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023] Open
Abstract
Background Quantifying gait using inertial measurement units has gained increasing interest in recent years. Highly degraded gaits, especially in neurological impaired patients, challenge gait detection algorithms and require specific segmentation and analysis tools. Thus, the outcomes of these devices must be rigorously tested for both robustness and relevancy in order to recommend their routine use. In this study, we propose a multidimensional score to quantify and visualize gait, which can be used in neurological routine follow-up. We assessed the reliability and clinical coherence of this method in a group of severely disabled patients with progressive multiple sclerosis (pMS), who display highly degraded gait patterns, as well as in an age-matched healthy subjects (HS) group. Methods Twenty-two participants with pMS and nineteen HS were included in this 18-month longitudinal follow-up study. During the follow-up period, all participants completed a 10-meter walk test with a U-turn and back, twice at M0, M6, M12, and M18. Average speed and seven clinical criteria (sturdiness, springiness, steadiness, stability, smoothness, synchronization, and symmetry) were evaluated using 17 gait parameters selected from the literature. The variation of these parameters from HS values was combined to generate a multidimensional visual tool, referred to as a semiogram. Results For both cohorts, all criteria showed moderate to very high test-retest reliability for intra-session measurements. Inter-session quantification was also moderate to highly reliable for all criteria except smoothness, which was not reliable for HS participants. All partial scores, except for the stability score, differed between the two populations. All partial scores were correlated with an objective but not subjective quantification of gait severity in the pMS population. A deficit in the pyramidal tract was associated with altered scores in all criteria, whereas deficits in cerebellar, sensitive, bulbar, and cognitive deficits were associated with decreased scores in only a subset of gait criteria. Conclusions The proposed multidimensional gait quantification represents an innovative approach to monitoring gait disorders. It provides a reliable and informative biomarker for assessing the severity of gait impairments in individuals with pMS. Additionally, it holds the potential for discriminating between various underlying causes of gait alterations in pMS.
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Affiliation(s)
- Cyril Voisard
- Université Paris Saclay, Université Paris Cité, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Gif-sur-Yvette, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Nicolas de l'Escalopier
- Université Paris Cité, Université Paris Saclay, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Paris, France
- Service de Chirurgie Orthopédique, Traumatologique et Réparatrice des Membres, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Aliénor Vienne-Jumeau
- Université Paris Saclay, Université Paris Cité, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Gif-sur-Yvette, France
| | - Albane Moreau
- Université Paris Saclay, Université Paris Cité, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Gif-sur-Yvette, France
| | - Flavien Quijoux
- Université Paris Saclay, Université Paris Cité, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Gif-sur-Yvette, France
| | - Flavie Bompaire
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
- Université Paris Cité, Université Paris Saclay, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Paris, France
| | - Magali Sallansonnet
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Marie-Laure Brechemier
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Irina Taifas
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Camille Tafani
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Eve Drouard
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
| | - Nicolas Vayatis
- Université Paris Saclay, Université Paris Cité, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Gif-sur-Yvette, France
| | - Damien Ricard
- Service de Neurologie, Service de Santé des Armées, Hôpital d'Instruction des Armées Percy, Clamart, France
- Université Paris Cité, Université Paris Saclay, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Paris, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, Paris, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, Ecole Normale Supérieure Paris Saclay, Centre National de la Recherche Scientifique, Service de Santé des Armées, Institut National de la Santé et de la Recherche Médicale, Centre Borelli, Gif-sur-Yvette, France
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Prigent G, Aminian K, Cereatti A, Salis F, Bonci T, Scott K, Mazzà C, Alcock L, Del Din S, Gazit E, Hansen C, Paraschiv-Ionescu A. A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings. Med Biol Eng Comput 2023; 61:2341-2352. [PMID: 37069465 PMCID: PMC10412496 DOI: 10.1007/s11517-023-02826-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/08/2023] [Indexed: 04/19/2023]
Abstract
Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.
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Affiliation(s)
- Gaëlle Prigent
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
| | - Tecla Bonci
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Kirsty Scott
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Claudia Mazzà
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
| | - Lisa Alcock
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Clint Hansen
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - for the Mobilise-D consortium
- Laboratory of Movement Analysis and Measurement (LMAM), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Electronics and Telecommunications, Politecnico Di Torino, Turin, Italy
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
- Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, Sassari, Italy
- Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine, University of Sheffield, Sheffield, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Neurology, University Medical Center Schleswig-Holstein Campus Kiel, Kiel, Germany
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9
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Werner C, Gönel M, Lerch I, Curt A, Demkó L. Data-driven characterization of walking after a spinal cord injury using inertial sensors. J Neuroeng Rehabil 2023; 20:55. [PMID: 37120519 PMCID: PMC10149024 DOI: 10.1186/s12984-023-01178-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/19/2023] [Indexed: 05/01/2023] Open
Abstract
BACKGROUND An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson's disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity. METHODS The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters. RESULTS Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient's walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors. CONCLUSIONS In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions.
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Affiliation(s)
- Charlotte Werner
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland.
- Rehabilitation Engineering Laboratory, ETH Zurich, Zurich, Switzerland.
| | - Meltem Gönel
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Irina Lerch
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - László Demkó
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
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10
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Liang W, Wang F, Fan A, Zhao W, Yao W, Yang P. Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094229. [PMID: 37177436 PMCID: PMC10180901 DOI: 10.3390/s23094229] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/20/2023] [Accepted: 04/22/2023] [Indexed: 05/15/2023]
Abstract
Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force/torque, and muscle activity/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.
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Affiliation(s)
- Wenqi Liang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Fanjie Wang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ao Fan
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Wenrui Zhao
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Wei Yao
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
| | - Pengfei Yang
- Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China
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11
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Jung S, de l’Escalopier N, Oudre L, Truong C, Dorveaux E, Gorintin L, Ricard D. A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment. SENSORS (BASEL, SWITZERLAND) 2023; 23:4000. [PMID: 37112339 PMCID: PMC10145775 DOI: 10.3390/s23084000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/03/2023] [Accepted: 04/13/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a novel approach to creating a graphical summary of a subject's activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
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Affiliation(s)
- Sylvain Jung
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
- Université Sorbonne Paris Nord, L2TI, UR 3043, F-93430 Villetaneuse, France
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
- ENGIE Lab CRIGEN, F-93249 Stains, France
| | - Nicolas de l’Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Charles Truong
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190 Gif-sur-Yvette, France
| | - Eric Dorveaux
- AbilyCare, 130 Rue de Lourmel, F-75015 Paris, France
| | - Louis Gorintin
- Novakamp, 10-12 Avenue du Bosquet, F-95560 Baillet en France, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006 Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, F-92190 Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, F-75005 Paris, France
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12
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Bargiotas I, Wang D, Mantilla J, Quijoux F, Moreau A, Vidal C, Barrois R, Nicolai A, Audiffren J, Labourdette C, Bertin-Hugaul F, Oudre L, Buffat S, Yelnik A, Ricard D, Vayatis N, Vidal PP. Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall. J Neurol 2023; 270:618-631. [PMID: 35817988 PMCID: PMC9886639 DOI: 10.1007/s00415-022-11251-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 02/03/2023]
Abstract
Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.
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Affiliation(s)
- Ioannis Bargiotas
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France. .,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.
| | - Danping Wang
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Juan Mantilla
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Flavien Quijoux
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,ORPEA Group, Puteaux, France
| | - Albane Moreau
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Catherine Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Otorhinolaryngology (ENT), AP-HP, Hôpital Universitaire Pitié Salpêtrière, Paris, 75013, France
| | - Remi Barrois
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Alice Nicolai
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Julien Audiffren
- Department of Neuroscience, University of Fribourg, Fribourg, Switzerland
| | - Christophe Labourdette
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | | | - Laurent Oudre
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Stephane Buffat
- Laboratoire d'accidentologie de biomécanique et du comportement des conducteurs, GIE Psa Renault Groupes, Nanterre, France
| | - Alain Yelnik
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Physical and Rehabilitation Medicine (PRM), AP- HP, GH St Louis, Lariboisière, F. Widal, Paris, 75010, France
| | - Damien Ricard
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Service of Neurology, AP-HP, Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, 92140, France.,École d'application du Val-de-Grâce, Service de Santé des Armée, Paris, France
| | - Nicolas Vayatis
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France
| | - Pierre-Paul Vidal
- Centre Borelli, CNRS, SSA, INSERM, Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, Gif-sur-Yvette, 91190, France.,Centre Borelli, CNRS, SSA, INSERM, Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, Paris, 75006, France.,Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China
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13
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Zahn A, Koch V, Schreff L, Oschmann P, Winkler J, Gaßner H, Müller R. Validity of an inertial sensor-based system for the assessment of spatio-temporal parameters in people with multiple sclerosis. Front Neurol 2023; 14:1164001. [PMID: 37153677 PMCID: PMC10157085 DOI: 10.3389/fneur.2023.1164001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/17/2023] [Indexed: 05/10/2023] Open
Abstract
Background Gait variability in people with multiple sclerosis (PwMS) reflects disease progression or may be used to evaluate treatment response. To date, marker-based camera systems are considered as gold standard to analyze gait impairment in PwMS. These systems might provide reliable data but are limited to a restricted laboratory setting and require knowledge, time, and cost to correctly interpret gait parameters. Inertial mobile sensors might be a user-friendly, environment- and examiner-independent alternative. The purpose of this study was to evaluate the validity of an inertial sensor-based gait analysis system in PwMS compared to a marker-based camera system. Methods A sample N = 39 PwMS and N = 19 healthy participants were requested to repeatedly walk a defined distance at three different self-selected walking speeds (normal, fast, slow). To measure spatio-temporal gait parameters (i.e., walking speed, stride time, stride length, the duration of the stance and swing phase as well as max toe clearance), an inertial sensor system as well as a marker-based camera system were used simultaneously. Results All gait parameters highly correlated between both systems (r > 0.84) with low errors. No bias was detected for stride time. Stance time was marginally overestimated (bias = -0.02 ± 0.03 s) and gait speed (bias = 0.03 ± 0.05 m/s), swing time (bias = 0.02 ± 0.02 s), stride length (0.04 ± 0.06 m), and max toe clearance (bias = 1.88 ± 2.35 cm) were slightly underestimated by the inertial sensors. Discussion The inertial sensor-based system captured appropriately all examined gait parameters in comparison to a gold standard marker-based camera system. Stride time presented an excellent agreement. Furthermore, stride length and velocity presented also low errors. Whereas for stance and swing time, marginally worse results were observed.
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Affiliation(s)
- Annalena Zahn
- Department of Neurology, Klinikum Bayreuth GmbH, Bayreuth, Germany
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen, Erlangen, Germany
- *Correspondence: Annalena Zahn
| | - Veronika Koch
- Fraunhofer Institute for Integrated Circuits (IIS), Digital Health Systems, Erlangen, Germany
| | - Lucas Schreff
- Department of Neurology, Klinikum Bayreuth GmbH, Bayreuth, Germany
- Bayreuth Center of Sport Science, University of Bayreuth, Bayreuth, Germany
| | - Patrick Oschmann
- Department of Neurology, Klinikum Bayreuth GmbH, Bayreuth, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen, Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen, Erlangen, Germany
- Fraunhofer Institute for Integrated Circuits (IIS), Digital Health Systems, Erlangen, Germany
| | - Roy Müller
- Department of Neurology, Klinikum Bayreuth GmbH, Bayreuth, Germany
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen, Erlangen, Germany
- Bayreuth Center of Sport Science, University of Bayreuth, Bayreuth, Germany
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14
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Prieto-Avalos G, Sánchez-Morales LN, Alor-Hernández G, Sánchez-Cervantes JL. A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases. BIOSENSORS 2022; 13:72. [PMID: 36671907 PMCID: PMC9856141 DOI: 10.3390/bios13010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/24/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Laura Nely Sánchez-Morales
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico
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15
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de l'Escalopier N, Voisard C, Michaud M, Moreau A, Jung S, Tervil B, Vayatis N, Oudre L, Ricard D. Evaluation methods to assess the efficacy of equinovarus foot surgery on the gait of post-stroke hemiplegic patients: A literature review. Front Neurol 2022; 13:1042667. [DOI: 10.3389/fneur.2022.1042667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022] Open
Abstract
IntroductionThe aim of this study was to realize a systematic review of the different ways, both clinical and instrumental, used to evaluate the effects of the surgical correction of an equinovarus foot (EVF) deformity in post-stroke patients.MethodsA systematic search of full-length articles published from 1965 to June 2021 was performed in PubMed, Embase, CINAHL, Cochrane, and CIRRIE. The identified studies were analyzed to determine and to evaluate the outcomes, the clinical criteria, and the ways used to analyze the impact of surgery on gait pattern, instrumental, or not.ResultsA total of 33 studies were included. The lack of methodological quality of the studies and their heterogeneity did not allow for a valid meta-analysis. In all, 17 of the 33 studies involved exclusively stroke patients. Ten of the 33 studies (30%) evaluated only neurotomies, one study (3%) evaluated only tendon lengthening procedures, 19 studies (58%) evaluated tendon transfer procedures, and only two studies (6%) evaluated the combination of tendon and neurological procedures. Instrumental gait analysis was performed in only 11 studies (33%), and only six studies (18%) combined it with clinical and functional analyses. Clinical results show that surgical procedures are safe and effective. A wide variety of different scales have been used, most of which have already been validated in other indications.DiscussionNeuro-orthopedic surgery for post-stroke EVF is becoming better defined. However, the method of outcome assessment is not yet well established. The complexity in the evaluation of the gait of patients with EVF, and therefore the analysis of the effectiveness of the surgical management performed, requires the integration of a patient-centered functional dimension, and a reliable and reproducible quantified gait analysis, which is routinely usable clinically if possible.
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16
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Germanotta M, Iacovelli C, Aprile I. Evaluation of Gait Smoothness in Patients with Stroke Undergoing Rehabilitation: Comparison between Two Metrics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192013440. [PMID: 36294017 PMCID: PMC9603299 DOI: 10.3390/ijerph192013440] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 05/27/2023]
Abstract
The use of quantitative methods to analyze the loss in gait smoothness, an increase in movement intermittency which is a distinguishing hallmark of motor deficits in stroke patients, has gained considerable attention in recent years. In the literature, the spectral arc length (SPARC), as well as metrics based on the measurement of the jerk, such as the log dimensionless jerk (LDLJ), are currently employed to assess smoothness. However, the optimal measure for evaluating the smoothness of walking in stroke patients remains unknown. Here, we investigated the smoothness of the body's center of mass (BCoM) trajectory during gait, using an optoelectronic system, in twenty-two subacute and eight chronic patients before and after a two-month rehabilitation program. The two measures were evaluated for their discriminant validity (ability to differentiate the smoothness of the BCoM trajectory calculated on the cycle of the affected and unaffected limb, and between subacute and chronic patients), validity (correlation with clinical scales), and responsiveness to the intervention. According to our findings, the LDLJ outperformed the SPARC in terms of the examined qualities. Based on data gathered using an optoelectronic system, we recommend using the LDLJ rather than the SPARC to investigate the gait smoothness of stroke patients.
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Affiliation(s)
| | - Chiara Iacovelli
- Department of Aging, Neurological, Orthopaedic and Head-Neck Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Francesco Vito 1, 00168 Rome, Italy
- Rehabilitation and Physical Medicine Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Largo Francesco Vito 1, 00168 Rome, Italy
| | - Irene Aprile
- IRCCS Fondazione Don Carlo Gnocchi, 50143 Florence, Italy
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17
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Concurrent validity of artificial intelligence-based markerless motion capture for over-ground gait analysis: A study of spatiotemporal parameters. J Biomech 2022; 143:111278. [DOI: 10.1016/j.jbiomech.2022.111278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 06/21/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022]
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Reliability of wearable sensors-based parameters for the assessment of knee stability. PLoS One 2022; 17:e0274817. [PMID: 36137143 PMCID: PMC9499276 DOI: 10.1371/journal.pone.0274817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 09/02/2022] [Indexed: 11/26/2022] Open
Abstract
Anterior cruciate ligament (ACL) rupture represents one of the most recurrent knee injuries in soccer players. To allow a safe return to sport after ACL reconstruction, standardised and reliable procedures/criteria are needed. In this context, wearable sensors are gaining momentum as they allow obtaining objective information during sport-specific and in-the-field tasks. This paper aims at proposing a sensor-based protocol for the assessment of knee stability and at quantifying its reliability. Seventeen soccer players performed a single leg squat and a cross over hop test. Each participant was equipped with two magnetic-inertial measurement units located on the tibia and foot. Parameters related to the knee stability were obtained from linear acceleration and angular velocity signals. The intraclass correlation coefficient (ICC) and minimum detectable change (MDC) were calculated to evaluate each parameter reliability. The ICC ranged from 0.29 to 0.84 according to the considered parameter. Specifically, angular velocity-based parameters proved to be more reliable than acceleration-based counterparts, particularly in the cross over hop test (average ICC values of 0.46 and 0.63 for acceleration- and angular velocity-based parameters, respectively). An exception was represented, in the single leg squat, by parameters extracted from the acceleration trajectory on the tibial transverse plane (0.60≤ICC≤0.76), which can be considered as promising candidates for ACL injury risk assessment. Overall, greater ICC values were found for the dominant limb, with respect to the non-dominant one (average ICC: 0.64 and 0.53, respectively). Interestingly, this between-limb difference in variability was not always mirrored by LSI results. MDC values provide useful information in the perspective of applying the proposed protocol on athletes with ACL reconstruction. Thus, The outcome of this study sets the basis for the definition of reliable and objective criteria for return to sport clearance after ACL injury.
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Deb R, An S, Bhat G, Shill H, Ogras UY. A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:5491. [PMID: 35897995 PMCID: PMC9371095 DOI: 10.3390/s22155491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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Affiliation(s)
| | - Sizhe An
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Ganapati Bhat
- School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA;
| | - Holly Shill
- Lonnie and Muhammad Ali Movement Disorder Center, Phoenix, AZ 85013, USA;
| | - Umit Y. Ogras
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
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20
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Bois A, Tervil B, Moreau A, Vienne-Jumeau A, Ricard D, Oudre L. A topological data analysis-based method for gait signals with an application to the study of multiple sclerosis. PLoS One 2022; 17:e0268475. [PMID: 35560328 PMCID: PMC9106173 DOI: 10.1371/journal.pone.0268475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/30/2022] [Indexed: 11/30/2022] Open
Abstract
In the past few years, light, affordable wearable inertial measurement units have been providing to clinicians and researchers the possibility to quantitatively study motor degeneracy by comparing gait trials from patients and/or healthy subjects. To do so, standard gait features can be used but they fail to detect subtle changes in several pathologies including multiple sclerosis. Multiple sclerosis is a demyelinating disease of the central nervous system whose symptoms include lower limb impairment, which is why gait trials are commonly used by clinicians for their patients’ follow-up. This article describes a method to compare pairs of gait signals, visualize the results and interpret them, based on topological data analysis techniques. Our method is non-parametric and requires no data other than gait signals acquired with inertial measurement units. We introduce tools from topological data analysis (sublevel sets, persistence barcodes) in a practical way to make it as accessible as possible in order to encourage its use by clinicians. We apply our method to study a cohort of patients suffering from progressive multiple sclerosis and healthy subjects. We show that it can help estimate the severity of the disease and also be used for longitudinal follow-up to detect an evolution of the disease or other phenomena such as asymmetry or outliers.
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Affiliation(s)
- Alexandre Bois
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Université de Paris, CNRS, Centre Borelli, Paris, France
- * E-mail:
| | - Brian Tervil
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Université de Paris, CNRS, Centre Borelli, Paris, France
| | - Albane Moreau
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, Clamart, France
| | - Aliénor Vienne-Jumeau
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Université de Paris, CNRS, Centre Borelli, Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, Clamart, France
| | - Damien Ricard
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Université de Paris, CNRS, Centre Borelli, Paris, France
- Service de Neurologie, Service de Santé des Armées, Hôpital d’Instruction des Armées Percy, Clamart, France
- Ecole du Val-de-Grâce, Ecole de Santé des Armées, Paris, France
| | - Laurent Oudre
- Université Paris-Saclay, ENS Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Université de Paris, CNRS, Centre Borelli, Paris, France
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21
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Temporal Synergies Detection in Gait Cyclograms Using Wearable Technology. SENSORS 2022; 22:s22072728. [PMID: 35408342 PMCID: PMC9002595 DOI: 10.3390/s22072728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/21/2022] [Accepted: 03/29/2022] [Indexed: 01/02/2023]
Abstract
The human gait can be described as the synergistic activity of all individual components of the sensory–motor system. The central nervous system (CNS) develops synergies to execute endpoint motion by coordinating muscle activity to reflect the global goals of the endpoint trajectory. This paper proposes a new method for assessing temporal dynamic synergies. Principal component analysis (PCA) has been applied on the signals acquired by wearable sensors (inertial measurement units, IMU and ground reaction force sensors, GRF mounted on feet) to detect temporal synergies in the space of two-dimensional PCA cyclograms. The temporal synergy results for different gait speeds in healthy subjects and stroke patients before and after the therapy were compared. The hypothesis of invariant temporal synergies at different gait velocities was statistically confirmed, without the need to record and analyze muscle activity. A significant difference in temporal synergies was noticed in hemiplegic gait compared to healthy gait. Finally, the proposed PCA-based cyclogram method provided the therapy follow-up information about paretic leg gait in stroke patients that was not available by observing conventional parameters, such as temporal and symmetry gait measures.
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22
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Body-Worn IMU-Based Human Hip and Knee Kinematics Estimation during Treadmill Walking. SENSORS 2022; 22:s22072544. [PMID: 35408159 PMCID: PMC9003309 DOI: 10.3390/s22072544] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 12/22/2022]
Abstract
Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated (N=12) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87∘ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70∘ relative RMSE), and hip abduction/adduction angle (4.56∘ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27∘ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking).
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Abstract
Smoothness (i.e. non-intermittency) of movement is a clinically important property of the voluntary movement with accuracy and proper speed. Resting head position and head voluntary movements are impaired in cervical dystonia. The current work aims to evaluate if the smoothness of voluntary head rotations is reduced in this disease. Twenty-six cervical dystonia patients and 26 controls completed rightward and leftward head rotations. Patients’ movements were differentiated into “towards-dystonia” (rotation accentuated the torticollis) and “away-dystonia”. Smoothness was quantified by the angular jerk and arc length of the spectrum of angular speed (i.e. SPARC, arbitrary units). Movement amplitude (mean, 95% CI) on the horizontal plane was larger in controls (63.8°, 58.3°–69.2°) than patients when moving towards-dystonia (52.8°, 46.3°–59.4°; P = 0.006). Controls’ movements (49.4°/s, 41.9–56.9°/s) were faster than movements towards-dystonia (31.6°/s, 25.2–37.9°/s; P < 0.001) and away-dystonia (29.2°/s, 22.9–35.5°/s; P < 0.001). After taking into account the different amplitude and speed, SPARC-derived (but not jerk-derived) indices showed reduced smoothness in patients rotating away-dystonia (1.48, 1.35–1.61) compared to controls (1.88, 1.72–2.03; P < 0.001). Poor smoothness is a motor disturbance independent of movement amplitude and speed in cervical dystonia. Therefore, it should be assessed when evaluating this disease, its progression, and treatments.
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24
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Giannakopoulou KM, Roussaki I, Demestichas K. Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:1799. [PMID: 35270944 PMCID: PMC8915040 DOI: 10.3390/s22051799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Parkinson's disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients' quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson's disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson's disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson's disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.
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Affiliation(s)
- Konstantina-Maria Giannakopoulou
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Ioanna Roussaki
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
| | - Konstantinos Demestichas
- School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece; (K.-M.G.); (K.D.)
- Institute of Communication and Computer Systems, 10682 Athens, Greece
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25
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Hellec J, Chorin F, Castagnetti A, Guérin O, Colson SS. Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait. SENSORS 2022; 22:s22031196. [PMID: 35161941 PMCID: PMC8846265 DOI: 10.3390/s22031196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 12/14/2022]
Abstract
The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.
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Affiliation(s)
- Justine Hellec
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
- Ellcie Healthy, 06600 Antibes, France
- Correspondence:
| | - Frédéric Chorin
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
- Université Côte d’Azur, CHU, Cimiez, Plateforme Fragilité, 06000 Nice, France
| | | | | | - Serge S. Colson
- Université Côte d’Azur, LAMHESS, EUR HEALTHY, 06205 Nice, France; (F.C.); (S.S.C.)
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26
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Son EJ, Kim JH, Noh HE, Kim I, Lim JA, Han SH. Comparison of Gait Parameters during Forward Walking under Different Visual Conditions Using Inertial Motion Sensors. Yonsei Med J 2022; 63:82-87. [PMID: 34913287 PMCID: PMC8688370 DOI: 10.3349/ymj.2022.63.1.82] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 01/12/2023] Open
Abstract
PURPOSE Gait evaluation in patients with dizziness is essential during both initial evaluation and vestibular rehabilitation. Inertial measurement unit (IMU)-based gait analysis systems are clinically applicable in patients with dizziness. Since dizzy patients can utilize visual inputs to compensate for vestibular deficits, it is more difficult for them to walk with their eyes closed (EC). In this study, we compared gait characteristics during forward walking with both eyes open (EO) and EC between healthy subjects and dizzy patients. MATERIALS AND METHODS Forty-nine healthy controls (mean age 37.18±10.71 years) and 23 patients with dizziness (mean age 49.25±15.16 years) were subjected to vestibular and gait analyses. Medical histories, physical examinations, and vestibular function tests ruled out possible vestibular deficits in the controls. Subjects were instructed to walk at a comfortable pace for 10 m under two conditions (EO or EC). Spatiotemporal parameters, kinematics, and simulated kinetics of each gait recording were recorded using a shoe-type IMU system and analyzed. RESULTS Although gait speeds were slower, stride lengths were smaller, and double support times were increased under the EC, compared to the EO condition, in both healthy subjects and dizzy patients, the difference was more prominent in dizzy patients. Phase coordination index values did not differ significantly in either group. Gait asymmetry (GA) increased significantly under the EC condition, compared to the EO condition, in dizzy patients. CONCLUSION GA during forward walking was greater in dizzy patients under an EC condition than under an EO condition.
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Affiliation(s)
- Eun Jin Son
- Department of Otorhinolaryngology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea.
| | - Ji Hyung Kim
- Department of Otorhinolaryngology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hye Eun Noh
- Department of Otorhinolaryngology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Inon Kim
- Department of Otorhinolaryngology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Joo Ae Lim
- Department of Orthopaedic Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seung Hwan Han
- Department of Orthopaedic Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
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27
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Servais L, Yen K, Guridi M, Lukawy J, Vissière D, Strijbos P. Stride Velocity 95th Centile: Insights into Gaining Regulatory Qualification of the First Wearable-Derived Digital Endpoint for use in Duchenne Muscular Dystrophy Trials. J Neuromuscul Dis 2021; 9:335-346. [PMID: 34958044 PMCID: PMC9028650 DOI: 10.3233/jnd-210743] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In 2019, stride velocity 95th centile (SV95C) became the first wearable-derived digital clinical outcome assessment (COA) qualified by the European Medicines Agency (EMA) for use as a secondary endpoint in trials for Duchenne muscular dystrophy. SV95C was approved via the EMA’s qualification pathway for novel methodologies for medicine development, which is a voluntary procedure for assessing the regulatory acceptability of innovative methods used in pharmaceutical research and development. SV95C is an objective, real-world digital ambulation measure of peak performance, representing the speed of the fastest strides taken by the wearer over a recording period of 180 hours. SV95C is correlated with traditional clinic-based assessments of motor function and has greater sensitivity to clinical change over 6 months than other wearable-derived stride variables, for example, median stride length or velocity. SV95C overcomes many limitations of episodic, clinic-based motor function testing, allowing the assessment of ambulation ability between clinic visits and under free-living conditions. Here we highlight considerations and challenges in developing SV95C using evidence generated by a high-performance wearable sensor. We also provide a commentary of the device’s technical capabilities, which were a determining factor in the regulatory approval of SV95C. This article aims to provide insights into the methods employed, and the challenges faced, during the regulatory approval process for researchers developing new digital tools for patients with diseases that affect motor function.
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Affiliation(s)
- Laurent Servais
- Division of Child Neurology, Centre de Références des Maladies Neuromusculaires, Department of Pediatrics, University Hospital Liège and University of Liège, Liège, Belgium.,Muscular Dystrophy UK Oxford Neuromuscular Centre, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Karl Yen
- F. Hoffmann-La Roche Ltd, Basel, Switzerland
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28
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Carvajal-Castaño HA, Lemos-Duque JD, Orozco-Arroyave JR. Effective detection of abnormal gait patterns in Parkinson's disease patients using kinematics, nonlinear, and stability gait features. Hum Mov Sci 2021; 81:102891. [PMID: 34781093 DOI: 10.1016/j.humov.2021.102891] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/24/2021] [Accepted: 10/28/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND OBJECTIVES Parkinson's disease (PD) is a neurodegenerative disease that produces movement disorders and it is the second most common neurodegenerative disease after Alzheimer's. Among other symptoms, PD affects gait patterns and produces bradykinesia, abnormal changes in posture, and shortened strides. In this study we present a comprehensive analysis of three different feature sets to model those abnormal gait patterns. The proposed approach is evaluated upon three groups of subjects: PD patients, young healthy controls (YHC), and elderly healthy controls (EHC). METHODS Three feature sets are created: (1) kinematic measures including those that allow modeling time, distance and velocity of the strides, (2) nonlinear dynamics including different measures extracted from embedded attractors resulting from the time-series of the gait signals, and (3) different stability measures extracted in the time and frequency-domains. Support Vector Machine, Random Forest and XGBoost classifiers are trained to automatically discriminate between PD patients and healthy subjects. RESULTS Among the considered feature sets, three individual measures emerge as the ones that yield accurate detection of PD and could potentially be used in clinical practice. Accuracies of up to 87.0% and 90.0% are found for the classification between PD vs. YHC and PD vs. EHC, respectively, considering individual measures. CONCLUSIONS This study contributes to a better understanding of abnormal gait patterns observed in PD patients. Particularly the introduced approach shows good results that could be potentially used in clinical practice as a tool to support the diagnosis and follow-up of the patients.
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Affiliation(s)
- H A Carvajal-Castaño
- GITA Lab, Electronics Engineering and Telecommunications Department, Faculty of Engineering, Universidad de Antioquia, Calle 70 No. 52-21, Medellin, Colombia; GIBIC Lab, Bioengineering Department, Engineering Faculty, Universidad de Antioquia, Calle 70 No. 52-21, Medellin, Colombia.
| | - J D Lemos-Duque
- GIBIC Lab, Bioengineering Department, Engineering Faculty, Universidad de Antioquia, Calle 70 No. 52-21, Medellin, Colombia
| | - J R Orozco-Arroyave
- GITA Lab, Electronics Engineering and Telecommunications Department, Faculty of Engineering, Universidad de Antioquia, Calle 70 No. 52-21, Medellin, Colombia; Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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29
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Betteridge C, Mobbs RJ, Fonseka RD, Natarajan P, Ho D, Choy WJ, Sy LW, Pell N. Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis. JOURNAL OF SPINE SURGERY 2021; 7:254-268. [PMID: 34734130 DOI: 10.21037/jss-21-16] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 06/25/2021] [Indexed: 11/06/2022]
Abstract
Background Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS. Methods We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m. Results Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure. Conclusions The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.
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Affiliation(s)
- Callum Betteridge
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Ralph J Mobbs
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - R Dineth Fonseka
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Pragadesh Natarajan
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Daniel Ho
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Wen Jie Choy
- Faculty of Medicine, University of New South Wales, Sydney, Australia.,NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
| | - Luke W Sy
- NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia.,School of Biomechanics, University of New South Wales, Sydney, Australia
| | - Nina Pell
- NeuroSpine Surgery Research Group, Sydney, Australia.,NeuroSpine Clinic, Prince of Wales Private Hospital, Randwick, Australia.,Wearables and Gait Assessment Group, Sydney, Australia
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Quijoux F, Nicolaï A, Chairi I, Bargiotas I, Ricard D, Yelnik A, Oudre L, Bertin‐Hugault F, Vidal P, Vayatis N, Buffat S, Audiffren J. A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open-access code. Physiol Rep 2021; 9:e15067. [PMID: 34826208 PMCID: PMC8623280 DOI: 10.14814/phy2.15067] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/10/2021] [Accepted: 09/14/2021] [Indexed: 12/15/2022] Open
Abstract
Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-s open-eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-s eyes-open recordings with a research laboratory standard force platform.
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Affiliation(s)
- Flavien Quijoux
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
- ORPEA GroupPuteauxFrance
| | - Alice Nicolaï
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
| | - Ikram Chairi
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
- Groupe MSDAUniversité Mohammed VI PolytechniqueBenguerirMaroc
| | - Ioannis Bargiotas
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
| | - Damien Ricard
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
- Service de Neurologie de l’Hôpital d’Instruction des Armées de PercySSAClamartFrance
- Ecole du Val‐de‐GrâceEcole de Santé des ArméesParisFrance
| | - Alain Yelnik
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
- PRM DepartmentGH Lariboisière F. WidalAP‐HPUniversité de ParisUMR 8257ParisFrance
| | - Laurent Oudre
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
| | | | - Pierre‐Paul Vidal
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
- Institute of Information and ControlHangzhou Dianzi UniversityZhejiangChina
| | - Nicolas Vayatis
- Centre Borelli UMR 9010/Université Paris‐SaclayENS Paris‐SaclayCNRSSSA, InsermUniversité de ParisParisFrance
| | - Stéphane Buffat
- Laboratoire d’accidentologie de biomécanique et du comportement des conducteursGIE Psa Renault GroupesNanterreFrance
| | - Julien Audiffren
- Department of NeuroscienceUniversity of FribourgFribourgSwitzerland
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Ali F, Loushin SR, Botha H, Josephs KA, Whitwell JL, Kaufman K. Laboratory based assessment of gait and balance impairment in patients with progressive supranuclear palsy. J Neurol Sci 2021; 429:118054. [PMID: 34461552 PMCID: PMC8489851 DOI: 10.1016/j.jns.2021.118054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/27/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Gait and balance abnormalities are a significant source of morbidity and mortality in progressive supranuclear palsy (PSP). Gait impairment in PSP is primarily assessed clinically on exam or with the use of rating scales. Three dimensional video based gait and balance analysis performed in a laboratory setting is a highly accurate method of motion analysis (Wren et al., 2020), however limited data is available in patients with PSP. RESEARCH QUESTION In this study we assess the objective features of postural control, kinematics, kinetic and temporal-spatial gait metrics in PSP, using three-dimensional video motion analysis in a laboratory setting compared to normal controls. METHODS Three-dimensional motion was captured using a 10-camera motion capture system, 41 body markers and ground embedded force plates in 16 patients with PSP patients and compared to motorically normal controls. RESULTS Spatiotemporal, kinematic, and kinetic gait measures effectively differentiated patients with PSP from controls. Patients had slower gait velocity, lower cadence, increased double support time and abnormal antero-posterior sway. Joint kinematics and kinetics were reduced and showed less variation among patients with PSP compared to controls which is suggestive of bradykinesia. Objective gait measures of abnormality correlated with clinical disease severity. Postural sway metrics distinguished PSP from controls and captured gait imbalance. SIGNIFICANCE Objective measures of gait and balance abnormalities in patients with PSP provide an outcome measure that can be potentially used for early disease detection, in clinical trials and to validate portable motion capture devices in the future.
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Affiliation(s)
- Farwa Ali
- Department of Neurology, Rochester, MN, United States of America.
| | - Stacy R Loushin
- Department of Orthopedic Surgery, Rochester, MN, United States of America
| | - Hugo Botha
- Department of Neurology, Rochester, MN, United States of America
| | - Keith A Josephs
- Department of Neurology, Rochester, MN, United States of America
| | - Jennifer L Whitwell
- Department of Radiology, Mayo Clinic Rochester, Rochester, MN, United States of America
| | - Kenton Kaufman
- Department of Orthopedic Surgery, Rochester, MN, United States of America
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Picerno P, Iosa M, D'Souza C, Benedetti MG, Paolucci S, Morone G. Wearable inertial sensors for human movement analysis: a five-year update. Expert Rev Med Devices 2021; 18:79-94. [PMID: 34601995 DOI: 10.1080/17434440.2021.1988849] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings. AREAS COVERED Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings. EXPERT OPINION IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
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Affiliation(s)
- Pietro Picerno
- SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "Ecampus", Novedrate, Comune, Italy
| | - Marco Iosa
- Department of Psychology, Sapienza University, Rome, Italy.,Irrcs Santa Lucia Foundation, Rome, Italy
| | - Clive D'Souza
- Center for Ergonomics, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan, USA.,Department of Rehabilitation Science and Technology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Maria Grazia Benedetti
- Physical Medicine and Rehabilitation Unit, IRCCS-Istituto Ortopedico Rizzoli, Bologna, Italy
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The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review. SENSORS 2021; 21:s21144808. [PMID: 34300546 PMCID: PMC8309920 DOI: 10.3390/s21144808] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/05/2021] [Accepted: 07/08/2021] [Indexed: 12/28/2022]
Abstract
Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.
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Albán-Cadena AC, Villalba-Meneses F, Pila-Varela KO, Moreno-Calvo A, Villalba-Meneses CP, Almeida-Galárraga DA. Wearable sensors in the diagnosis and study of Parkinson's disease symptoms: a systematic review. J Med Eng Technol 2021; 45:532-545. [PMID: 34060967 DOI: 10.1080/03091902.2021.1922528] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Nowadays, there are several diseases which affect different systems of the body, producing changes in the correct functioning of the organism and the people lifestyles. One of them is Parkinson's disease (PD), which is defined as a neurodegenerative disorder provoked by the destruction of dopaminergic neurons in the brain, resulting in a set of motor and non-motor symptoms. As this disease affects principally to ancient people, several researchers have studied different treatments and therapies for stopping neurodegeneration and diminishing symptoms, to improve the quality patients' lives. The most common therapies created for PD are based on pharmacological treatment for controlling the degeneration advance and the physical ones which do not reveal the progress of patients. For this reason, this review paper opens the possibility for using wearable motion capture systems as an option for the control and study of PD. Therefore, it aims to (1) study the different wearable systems used for capture the movements of PD patients and (2) determine which of them bring better results for monitoring and assess PD people. For the analysis, it uses papers based on experiments that prove the functioning of several motion systems in different aspects as monitoring, treatment and diagnose of the disease. As a result, it works with 30 papers which describe the factors mentioned before. Additionally, the paper uses journals and literature review about the pathology, its characteristics and the function of wearable sensors for the correct understanding of the topic.
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Affiliation(s)
- Andrea C Albán-Cadena
- School of Biological Sciences & Engineering, Universidad Yachay Tech, Urcuquí, Ecuador
| | - Fernando Villalba-Meneses
- School of Biological Sciences & Engineering, Universidad Yachay Tech, Urcuquí, Ecuador.,University of Zaragoza, Zaragoza, Spain
| | - Kevin O Pila-Varela
- School of Biological Sciences & Engineering, Universidad Yachay Tech, Urcuquí, Ecuador
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Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases. SENSORS 2021; 21:s21103444. [PMID: 34063355 PMCID: PMC8156914 DOI: 10.3390/s21103444] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/03/2021] [Accepted: 05/12/2021] [Indexed: 12/11/2022]
Abstract
Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients' engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson's n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions.
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Garcia FDV, da Cunha MJ, Schuch CP, Schifino GP, Balbinot G, Pagnussat AS. Movement smoothness in chronic post-stroke individuals walking in an outdoor environment-A cross-sectional study using IMU sensors. PLoS One 2021; 16:e0250100. [PMID: 33886640 PMCID: PMC8061986 DOI: 10.1371/journal.pone.0250100] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 03/30/2021] [Indexed: 12/28/2022] Open
Abstract
Background Walking speed is often used in the clinic to assess the level of gait impairment following stroke. Nonetheless, post-stroke individuals may employ the same walking speed but at a distinct movement quality. The main objective of this study was to explore a novel movement quality metric, the estimation of gait smoothness by the spectral arc length (SPARC), in individuals with a chronic stroke displaying mild/moderate or severe motor impairment while walking in an outdoor environment. Also, to quantify the correlation between SPARC, gait speed, motor impairment, and lower limb spasticity focused on understanding the relationship between the movement smoothness metric and common clinical assessments. Methods Thirty-two individuals with a chronic stroke and 32 control subjects participated in this study. The 10 meters walking test (10 MWT) was performed at the self-selected speed in an outdoor environment. The 10 MWT was instrumented with an inertial measurement unit system (IMU), which afforded the extraction of trunk angular velocities (yaw, roll, and pitch) and subsequent SPARC calculation. Results Movement smoothness was not influenced by gait speed in the control group, indicating that SPARC may constitute an additional and independent metric in the gait assessment. Individuals with a chronic stroke displayed reduced smoothness in the yaw and roll angular velocities (lower SPARC) compared with the control group. Also, severely impaired participants presented greater variability in smoothness along the 10 MWT. In the stroke group, a smoother gait in the pitch angular velocity was correlated with lower limb spasticity, likely indicating adaptive use of spasticity to maintain the pendular walking mechanics. Conversely, reduced smoothness in the roll angular velocity was related to pronounced spasticity. Conclusions Individuals with a chronic stroke displayed reduced smoothness in the yaw and roll angular velocities while walking in an outdoor environment. The quantification of gait smoothness using the SPARC metric may represent an additional outcome in clinical assessments of gait in individuals with a chronic stroke.
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Affiliation(s)
- Flora do Vale Garcia
- Department of Physiotherapy, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
- Movement Analysis and Rehabilitation Laboratory, UFCSPA, Porto Alegre, Brazil
| | - Maira Jaqueline da Cunha
- Movement Analysis and Rehabilitation Laboratory, UFCSPA, Porto Alegre, Brazil
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Clarissa Pedrini Schuch
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Giulia Palermo Schifino
- Movement Analysis and Rehabilitation Laboratory, UFCSPA, Porto Alegre, Brazil
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
| | - Gustavo Balbinot
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada
| | - Aline Souza Pagnussat
- Department of Physiotherapy, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
- Movement Analysis and Rehabilitation Laboratory, UFCSPA, Porto Alegre, Brazil
- Rehabilitation Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
- Health Sciences Graduate Program, Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA), Porto Alegre, Brazil
- * E-mail:
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Motti Ader LG, Greene BR, McManus K, Caulfield B. Reliability of inertial sensor based spatiotemporal gait parameters for short walking bouts in community dwelling older adults. Gait Posture 2021; 85:1-6. [PMID: 33497966 DOI: 10.1016/j.gaitpost.2021.01.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 01/06/2021] [Accepted: 01/11/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND When performing quantitative analysis of gait in older adults we need to strike a balance between capturing sufficient data for reliable measurement and avoiding issues such as fatigue. The optimal bout duration is that which contains sufficient gait cycles to enable a reliable and representative estimate of gait performance. RESEARCH QUESTION How does the number of gait cycles in a walking bout influence reliability of spatiotemporal gait parameters measured using body-worn inertial sensors in a cohort of community dwelling older adults? METHODS One hundred and fifteen (115) community dwelling older adults executed three 30-metre walk trials in a single measurement session. Bilateral gait data were collected using two inertial sensors attached to each participant's right and left shank, and gait events detected from the medio-lateral angular velocity signal. The number of gait cycles selected from each walking trial was varied from 3 to 16. Intraclass correlation coefficients (ICC(2,k)) were calculated to evaluate the reliability of each spatiotemporal gait parameter according to the number of gait cycles included in the analysis. RESULTS The specified algorithm and the clipping procedure for extracting short bouts of gait data seem appropriate for assessing older adults, providing reliable spatiotemporal measures from three gait cycles (three strides per leg) and good reliability for most parameters describing gait variability and gait asymmetry after six gait cycles (six strides per leg). SIGNIFICANCE A combination of using bilateral sensor data and adaptive thresholds for gait event detection enable reliable measures of spatiotemporal gait parameters over short walking bouts (minimum six gait cycles) in community dwelling older adults. This opens new possibilities in the use of wearable sensors in gait assessment based on short walking tasks. We recommend the number of gait cycles should be reported along with the calculated measures as reference values.
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Affiliation(s)
- Lilian Genaro Motti Ader
- CeADAR - Centre for Applied Data Analytics, NexusUCD, Block 9/10 Belfield Office Park, Clonskeagh, Dublin, D04 V2N9, Ireland; Kinesis Health Technologies Ltd., NexusUCD, Block 9/10 Belfield Office Park, Clonskeagh, Dublin, D04 V2N9, Ireland; School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin, D04, Ireland.
| | - Barry R Greene
- Kinesis Health Technologies Ltd., NexusUCD, Block 9/10 Belfield Office Park, Clonskeagh, Dublin, D04 V2N9, Ireland.
| | - Killian McManus
- Kinesis Health Technologies Ltd., NexusUCD, Block 9/10 Belfield Office Park, Clonskeagh, Dublin, D04 V2N9, Ireland; School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin, D04, Ireland; Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin, D04, Ireland.
| | - Brian Caulfield
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin, D04, Ireland; Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin, D04, Ireland.
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Quijoux F, Bertin-Hugault F, Zawieja P, Lefèvre M, Vidal PP, Ricard D. Postadychute-AG, Detection, and Prevention of the Risk of Falling Among Elderly People in Nursing Homes: Protocol of a Multicentre and Prospective Intervention Study. Front Digit Health 2021; 2:604552. [PMID: 34713067 PMCID: PMC8521935 DOI: 10.3389/fdgth.2020.604552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/15/2020] [Indexed: 12/02/2022] Open
Abstract
Introduction: While falls among the elderly is a public health issue, because of the social, medical, and economic burden they represent, the tools to predict falls are limited. Posturography has been developed to distinguish fallers from non-fallers, however, there is too little data to show how predictions change as older adults' physical abilities improve. The Postadychute-AG clinical trial aims to evaluate the evolution of posturographic parameters in relation to the improvement of balance through adapted physical activity (APA) programs. Methods: In this prospective, multicentre clinical trial, institutionalized seniors over 65 years of age will be followed for a period of 6 months through computer-assisted posturography and automatic gait analysis. During the entire duration of the follow-up, they will benefit from a monthly measurement of their postural and locomotion capacities through a recording of their static balance and gait thanks to a software developed for this purpose. The data gathered will be correlated with the daily record of falls in the institution. Static and dynamic balance measurements aim to extract biomechanical markers and compare them with functional assessments of motor skills (Berg Balance Scale and Mini Motor Test), expecting their superiority in predicting the number of falls. Participants will be followed for 3 months without APA and 3 months with APA in homogeneous group exercises. An analysis of variance will evaluate the variability of monthly measures of balance in order to record the minimum clinically detectable change (MDC) as participants improve their physical condition through APA. Discussion: Previous studies have stated the MDC through repeated measurements of balance but, to our knowledge, none appear to have implemented monthly measurements of balance and gait. Combined with a reliable measure of the number of falls per person, motor capacities and other precipitating factors, this study aims to provide biomechanical markers predictive of fall risk with their sensitivity to improvement in clinical status over the medium term. This trial could provide the basis for posturographic and gait variable values for these elderly people and provide a solution to distinguish those most at risk to be implemented in current practice in nursing homes. Trial Registration: ID-RCB 2017-A02545-48. Protocol Version: Version 4.2 dated January 8, 2020.
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Affiliation(s)
- Flavien Quijoux
- Centre Borelli UMR 9010/Université Paris-Saclay, ENS Paris-Saclay, CNRS, SSA, Université de Paris, Inserm, Paris, France
- ORPEA Group, Puteaux, France
| | | | | | | | - Pierre-Paul Vidal
- Centre Borelli UMR 9010/Université Paris-Saclay, ENS Paris-Saclay, CNRS, SSA, Université de Paris, Inserm, Paris, France
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China
| | - Damien Ricard
- Centre Borelli UMR 9010/Université Paris-Saclay, ENS Paris-Saclay, CNRS, SSA, Université de Paris, Inserm, Paris, France
- Service de Neurologie de l'Hôpital d'Instruction des Armées de Percy, Service de Santé des Armées, Clamart, France
- Ecole du Val-de-Grâce, Ecole de Santé des Armées, Paris, France
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MejiaCruz Y, Franco J, Hainline G, Fritz S, Jiang Z, Caicedo JM, Davis B, Hirth V. Walking speed measurement technology: A review. CURRENT GERIATRICS REPORTS 2021; 10:32-41. [PMID: 33816062 DOI: 10.1007/s13670-020-00349-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Purpose of review This article presents an overview of the main technologies used to estimate gait parameters, focusing on walking speed (WS). Recent findings New wearable and environmental technologies to estimate WS have been developed in the last five years. Wearable technologies refer to sensors attached to parts of the patient's body that capture the kinematics during walking. Alternatively, environmental technologies capture walking patterns using external instrumentation. In this review, wearable and external technologies have been included.From the different works reviewed, external technologies face the challenge of implementation outside controlled facilities; an advantage that wearable technologies have, but have not been fully explored. Additionally, systems that can track WS changes in daily activities, especially at-home assessments, have not been developed. Summary Walking speed is a gait parameter that can provide insight into an individual's health status. Image-based, walkways, wearable, and floor-vibrations technologies are the most current used technologies for estimating WS. In this paper, research from the last five years that explore each technology's capabilities on WS estimation and an evaluation of their technical and clinical aspects is presented.
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Affiliation(s)
- Yohanna MejiaCruz
- San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132
| | - Jean Franco
- University of South Carolina, 300 Main St, Columbia SC, 29201
| | - Garret Hainline
- University of South Carolina, 300 Main St, Columbia SC, 29201
| | - Stacy Fritz
- University of South Carolina, 300 Main St, Columbia SC, 29201
| | - Zhaoshuo Jiang
- San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132
| | - Juan M Caicedo
- University of South Carolina, 300 Main St, Columbia SC, 29201
| | - Benjamin Davis
- Advanced Smart Systems and Evaluation Technologies (ASSET), LLC, 1400 Laurel Street, Suite 1B, Columbia, South Carolina 29201
| | - Victor Hirth
- Geriatric Health and Wellness, LTD, One Still Hopes Drive, West Columbia, SC 29169
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40
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Melendez-Calderon A, Shirota C, Balasubramanian S. Estimating Movement Smoothness From Inertial Measurement Units. Front Bioeng Biotechnol 2021; 8:558771. [PMID: 33520949 PMCID: PMC7841375 DOI: 10.3389/fbioe.2020.558771] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/09/2020] [Indexed: 12/11/2022] Open
Abstract
Inertial measurement units (IMUs) are increasingly used to estimate movement quality and quantity to the infer the nature of motor behavior. The current literature contains several attempts to estimate movement smoothness using data from IMUs, many of which assume that the translational and rotational kinematics measured by IMUs can be directly used with the smoothness measures spectral arc length (SPARC) and log dimensionless jerk (LDLJ-V). However, there has been no investigation of the validity of these approaches. In this paper, we systematically evaluate the use of these measures on the kinematics measured by IMUs. We show that: (a) SPARC and LDLJ-V are valid measures of smoothness only when used with velocity; (b) SPARC and LDLJ-V applied on translational velocity reconstructed from IMU is highly error prone due to drift caused by integration of reconstruction errors; (c) SPARC can be applied directly on rotational velocities measured by a gyroscope, but LDLJ-V can be error prone. For discrete translational movements, we propose a modified version of the LDLJ-V measure, which can be applied to acceleration data (LDLJ-A). We evaluate the performance of these measures using simulated and experimental data. We demonstrate that the accuracy of LDLJ-A depends on the time profile of IMU orientation reconstruction error. Finally, we provide recommendations for how to appropriately apply these measures in practice under different scenarios, and highlight various factors to be aware of when performing smoothness analysis using IMU data.
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Affiliation(s)
- Alejandro Melendez-Calderon
- Cereneo Advanced Rehabilitation Institute (CARINg), Vitznau, Switzerland
- Biomedical Engineering Group, School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD, Australia
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Camila Shirota
- The Hopkins Centre, Menzies Health Institute Queensland, Griffith University, Nathan, QLD, Australia
- Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Neurology, University of Zurich, Zurich, Switzerland
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Betteridge CMW, Natarajan P, Fonseka RD, Ho D, Mobbs R, Choy WJ. Objective falls-risk prediction using wearable technologies amongst patients with and without neurogenic gait alterations: a narrative review of clinical feasibility. Mhealth 2021; 7:61. [PMID: 34805392 PMCID: PMC8572751 DOI: 10.21037/mhealth-21-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 06/12/2021] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES The present narrative review aims to collate the literature regarding the current use of wearable gait measurement devices for falls-risk assessment in neurological and non-neurological populations. Thereby, this review seeks to determine the extent to which the aforementioned barriers inhibit clinical use. BACKGROUND Falls contribute a significant disease burden in most western countries, resulting in increased morbidity and mortality with substantial therapeutic costs. The recent development of gait analysis sensor technologies has enabled quantitative measurement of several gait features related to falls risk. However, three main barriers to implementation exist: accurately measuring gait-features associated with falls, differentiating between fallers and non-fallers using these gait features, and the accuracy of falls predictive algorithms developed using these gait measurements. METHODS Searches of Medline, PubMed, Embase and Scopus were screened to identify 46 articles relevant to the present study. Studies performing gait assessment using any wearable gait assessment device and analysing correlation with the occurrence of falls during a retrospective or prospective study period were included. Risk of Bias was assessed using the Centre for Evidence Based Medicine (CEBM) Criteria. CONCLUSIONS Falls prediction algorithms based entirely, or in-part, on gait data have shown comparable or greater success of predicting falls than existing stratification scoring systems such as the 10-meter walk test or timed-up-and-go. However, data is lacking regarding their accuracy in neurological patient populations. Inertial measurement units (IMU) have displayed competency in obtaining and interpreting gait metrics relevant to falls risk. They have the potential to enhance the accuracy and efficiency of falls risk assessment in inpatient and outpatient setting.
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Affiliation(s)
- Callum M. W. Betteridge
- Department of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia
- NeuroSpine Surgery Research Group, Sydney, Australia
- Wearables and Gait Assessment Group, Sydney, Australia
| | - Pragadesh Natarajan
- Department of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia
- NeuroSpine Surgery Research Group, Sydney, Australia
- Wearables and Gait Assessment Group, Sydney, Australia
| | - R. Dineth Fonseka
- Department of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia
- NeuroSpine Surgery Research Group, Sydney, Australia
- Wearables and Gait Assessment Group, Sydney, Australia
| | - Daniel Ho
- Department of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia
- NeuroSpine Surgery Research Group, Sydney, Australia
- Wearables and Gait Assessment Group, Sydney, Australia
| | - Ralph Mobbs
- Department of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia
- NeuroSpine Surgery Research Group, Sydney, Australia
- Wearables and Gait Assessment Group, Sydney, Australia
| | - Wen Jie Choy
- Department of Medicine, University of New South Wales, Sydney, Australia
- NeuroSpineClinic, Suite 7 Level 7, Prince of Wales Private Hospital, Randwick, Australia
- NeuroSpine Surgery Research Group, Sydney, Australia
- Wearables and Gait Assessment Group, Sydney, Australia
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McGrath T, Stirling L. Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6887. [PMID: 33276492 PMCID: PMC7729748 DOI: 10.3390/s20236887] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 11/17/2020] [Accepted: 11/28/2020] [Indexed: 11/17/2022]
Abstract
Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints.
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Affiliation(s)
- Timothy McGrath
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Leia Stirling
- Industrial and Operations Engineering, Robotics Institute, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI 48109, USA;
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43
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Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review. SENSORS 2020; 20:s20226627. [PMID: 33228056 PMCID: PMC7699399 DOI: 10.3390/s20226627] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/12/2020] [Accepted: 11/17/2020] [Indexed: 12/11/2022]
Abstract
The occurrence of peripheral neuropathy (PNP) is often observed in Parkinson’s disease (PD) patients with a prevalence up to 55%, leading to more prominent functional deficits. Motor assessment with mobile health technologies allows high sensitivity and accuracy and is widely adopted in PD, but scarcely used for PNP assessments. This review provides a comprehensive overview of the methodologies and the most relevant features to investigate PNP and PD motor deficits with wearables. Because of the lack of studies investigating motor impairments in this specific subset of PNP-PD patients, Pubmed, Scopus, and Web of Science electronic databases were used to summarize the state of the art on PNP motor assessment with wearable technology and compare it with the existing evidence on PD. A total of 24 papers on PNP and 13 on PD were selected for data extraction: The main characteristics were described, highlighting major findings, clinical applications, and the most relevant features. The information from both groups (PNP and PD) was merged for defining future directions for the assessment of PNP-PD patients with wearable technology. We established suggestions on the assessment protocol aiming at accurate patient monitoring, targeting personalized treatments and strategies to prevent falls and to investigate PD and PNP motor characteristics.
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Rast FM, Labruyère R. Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. J Neuroeng Rehabil 2020; 17:148. [PMID: 33148315 PMCID: PMC7640711 DOI: 10.1186/s12984-020-00779-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 10/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties. METHODS A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review. RESULTS Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported. CONCLUSION This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.
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Affiliation(s)
- Fabian Marcel Rast
- Swiss Children's Rehab, University Children's Hospital Zurich, Mühlebergstrasse 104, 8910, Affoltern am Albis, Switzerland. .,Children's Research Center, University Children's Hospital of Zurich, University of Zurich, Zurich, Switzerland. .,Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| | - Rob Labruyère
- Swiss Children's Rehab, University Children's Hospital Zurich, Mühlebergstrasse 104, 8910, Affoltern am Albis, Switzerland.,Children's Research Center, University Children's Hospital of Zurich, University of Zurich, Zurich, Switzerland
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45
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Sunarya U, Sun Hariyani Y, Cho T, Roh J, Hyeong J, Sohn I, Kim S, Park C. Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns. SENSORS 2020; 20:s20216253. [PMID: 33147794 PMCID: PMC7662266 DOI: 10.3390/s20216253] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 10/25/2020] [Accepted: 10/28/2020] [Indexed: 12/30/2022]
Abstract
Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities.
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Affiliation(s)
- Unang Sunarya
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (U.S.); (Y.S.H.)
- School of Applied Science, Telkom University, Bandung 40257, Indonesia
| | - Yuli Sun Hariyani
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (U.S.); (Y.S.H.)
- School of Applied Science, Telkom University, Bandung 40257, Indonesia
| | - Taeheum Cho
- Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Korea;
| | - Jongryun Roh
- Human Convergence Technology R&D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea; (J.R.); (J.H.)
| | - Joonho Hyeong
- Human Convergence Technology R&D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea; (J.R.); (J.H.)
| | - Illsoo Sohn
- Department of Computer Science and Engineering Seoul National University of Science and Technology, Seoul 01811, Korea;
| | - Sayup Kim
- Human Convergence Technology R&D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea; (J.R.); (J.H.)
- Correspondence: (S.K.); (C.P.); Tel.: +82-2-940-8251 (C.P.)
| | - Cheolsoo Park
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (U.S.); (Y.S.H.)
- Correspondence: (S.K.); (C.P.); Tel.: +82-2-940-8251 (C.P.)
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Trentzsch K, Weidemann ML, Torp C, Inojosa H, Scholz M, Haase R, Schriefer D, Akgün K, Ziemssen T. The Dresden Protocol for Multidimensional Walking Assessment (DMWA) in Clinical Practice. Front Neurosci 2020; 14:582046. [PMID: 33192268 PMCID: PMC7649388 DOI: 10.3389/fnins.2020.582046] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/06/2020] [Indexed: 12/14/2022] Open
Abstract
Walking impairments represent one of the most debilitating symptom areas for people with multiple sclerosis (MS). It is important to detect even slightest walking impairments in order to start and optimize necessary interventions in time to counteract further progression of the disability. For this reason, a regular monitoring through gait analysis is highly necessary. At advanced stages of MS with significant walking impairment, this assessment is also necessary to optimize symptomatic treatment, choose the most suitable walking aid and plan individualized rehabilitation. In clinical practice, walking impairment is only assessed at higher levels of the disease using e.g., the Expanded Disability Status Scale (EDSS). In contrast to the EDSS, standardized functional tests such as walking speed, walking endurance and balance as well as walking quality and gait-related patient-reported outcomes allow a more holistic and sensitive assessment of walking impairment. In recent years, the MS Center Dresden has established a standardized monitoring procedure for the routine multidimensional assessment of gait and balance disorders. In the following protocol, we present the techniques and procedures for the analysis of gait and balance of people with MS at the MS Center Dresden. Patients are assessed with a multidimensional gait analysis at least once a year. This enables long-term monitoring of walking impairment, which allows early active intervention regarding further progression of disease and improves the current standard clinical practice.
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Affiliation(s)
- Katrin Trentzsch
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Marie Luise Weidemann
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Charlotte Torp
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Hernan Inojosa
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Maria Scholz
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Rocco Haase
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Dirk Schriefer
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katja Akgün
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Tjalf Ziemssen
- Department of Neurology, MS Center Dresden, Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
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Werner C, Schneider S, Gassert R, Curt A, Demko L. Complementing Clinical Gait Assessments of Spinal Cord Injured Individuals using Wearable Movement Sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3142-3145. [PMID: 33018671 DOI: 10.1109/embc44109.2020.9175703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Today's standard clinical practice to assess the walking ability of patients with neurological disorders during rehabilitation is based on simple gait tests such as the six-minute walking test (6MWT). Since the outcome of these tests is the average walking speed only, the aim of this work was to show that the application of movement sensors during a standardized walking test for the population of spinal cord injured (SCI) patients provides additional information on gait quality not directly described by the average speed. Hence, gait features that are related to quantitative and qualitative aspects of gait were extracted from the ankle sensor recordings of 29 SCI subjects and 19 healthy controls performing the 6MWT. The subjects were clustered into groups based on these gait features, and six gait features were selected to demonstrate the key differences between the clusters. The correlation of these features to the outcome of the 6MWT is discussed with their implications on gait quality.
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48
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The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review. SENSORS 2020; 20:s20195625. [PMID: 33019633 PMCID: PMC7583905 DOI: 10.3390/s20195625] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 09/26/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022]
Abstract
This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.
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Motti Ader LG, Greene BR, McManus K, Tubridy N, Caulfield B. Short Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis. BIOSENSORS 2020; 10:E128. [PMID: 32962269 PMCID: PMC7558375 DOI: 10.3390/bios10090128] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 11/17/2022]
Abstract
Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gait data collected in non-controlled settings are often fragmented and may not provide enough information for reliable measures. This paper evaluates a novel approach to (1) determine the effects of the length of the walking task on the reliability of calculated measures and (2) identify digital biomarkers for gait assessments from fragmented data. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS range 0 to 4.5) executed two trials, walking 20 m each, with inertial sensors attached to their right and left shanks. Gait events were identified from the medio-lateral angular velocity, and short bouts of gait data were extracted from each trial, with lengths varying from 3 to 9 gait cycles. Intraclass correlation coefficients (ICCs) evaluate the degree of agreement between the two trials of each participant, according to the number of gait cycles included in the analysis. Results show that short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using fragmented data (e.g., wearable devices, community assessments). Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, should be further explored as digital biomarkers to support the monitoring of symptoms of persons with neurological diseases.
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Affiliation(s)
- Lilian Genaro Motti Ader
- CeADAR—Centre for Applied Data Analytics, University College Dublin, Dublin D04 V2N9, Ireland
- Kinesis Health Technologies Ltd., Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland; (B.R.G.); (K.M.)
- School of Public Health, Physiotherapy and Sport Sciences, University College Dublin, Dublin D04 V1W8, Ireland;
| | - Barry R. Greene
- Kinesis Health Technologies Ltd., Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland; (B.R.G.); (K.M.)
| | - Killian McManus
- Kinesis Health Technologies Ltd., Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland; (B.R.G.); (K.M.)
- Insight Centre for Data Analytics, University College Dublin, Dublin D04 V1W8, Ireland
| | - Niall Tubridy
- Department of Neurology, St. Vincent’s University Hospital, Dublin D04 T6F4, Ireland;
| | - Brian Caulfield
- School of Public Health, Physiotherapy and Sport Sciences, University College Dublin, Dublin D04 V1W8, Ireland;
- Insight Centre for Data Analytics, University College Dublin, Dublin D04 V1W8, Ireland
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50
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Monje MHG, Foffani G, Obeso J, Sánchez-Ferro Á. New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease. Annu Rev Biomed Eng 2020; 21:111-143. [PMID: 31167102 DOI: 10.1146/annurev-bioeng-062117-121036] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.
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Affiliation(s)
- Mariana H G Monje
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Hospital Nacional de Parapléjicos, Servicio de Salud de Castilla La Mancha, 45071 Toledo, Spain
| | - José Obeso
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain
| | - Álvaro Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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