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Lozano-Garcia M, Doheny EP, Mann E, Morgan-Jones P, Drew C, Busse-Morris M, Lowery MM. Estimation of Gait Parameters in Huntington's Disease Using Wearable Sensors in the Clinic and Free-living Conditions. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2239-2249. [PMID: 38819972 DOI: 10.1109/tnsre.2024.3407887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
In Huntington's disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC [Formula: see text]). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank.
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Madrid J, Benning L, Selig M, Ulrich B, Jolles BM, Favre J, Benninger DH. Slowing gait during turning: how volition of modifying walking speed affects the gait pattern in healthy adults. Front Hum Neurosci 2024; 18:1269772. [PMID: 38524921 PMCID: PMC10959554 DOI: 10.3389/fnhum.2024.1269772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/09/2024] [Indexed: 03/26/2024] Open
Abstract
Background Turning during walking and volitionally modulating walking speed introduces complexity to gait and has been minimally explored. Research question How do the spatiotemporal parameters vary between young adults walking at a normal speed and a slower speed while making 90°, 180°, and 360° turns? Methods In a laboratory setting, the spatiotemporal parameters of 10 young adults were documented as they made turns at 90°, 180°, and 360°. A generalized linear model was utilized to determine the effect of both walking speed and turning amplitude. Results Young adults volitionally reducing their walking speed while turning at different turning amplitudes significantly decreased their cadence and spatial parameters while increasing their temporal parameters. In conditions of slower movement, the variability of certain spatial parameters decreased, while the variability of some temporal parameters increased. Significance This research broadens the understanding of turning biomechanics in relation to volitionally reducing walking speed. Cadence might be a pace gait constant synchronizing the rhythmic integration of several inputs to coordinate an ordered gait pattern output. Volition might up-regulate or down-regulate this pace gait constant (i.e., cadence) which creates the feeling of modulating walking speed.
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Affiliation(s)
- Julian Madrid
- Department of Clinical Neurosciences (DNC), Clinic of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Leo Benning
- University Emergency Center, Medical Center - University of Freiburg, Freiburg, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Mischa Selig
- Department of Orthopedics and Trauma Surgery, G.E.R.N. Research Center for Tissue Replacement, Regeneration and Neogenesis, Freiburg, Germany
| | - Baptiste Ulrich
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine (DAL), Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Brigitte M. Jolles
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine (DAL), Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Microengineering, Lausanne, Switzerland
| | - Julien Favre
- Swiss BioMotion Lab, Department of Musculoskeletal Medicine (DAL), Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - David H. Benninger
- Department of Clinical Neurosciences (DNC), Clinic of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
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Ratz-Wirsching V, Habermeyer J, Moceri S, Harrer J, Schmitz C, von Hörsten S. Gene-dosage- and sex-dependent differences in the prodromal-Like phase of the F344tgHD rat model for Huntington disease. Front Neurosci 2024; 18:1354977. [PMID: 38384482 PMCID: PMC10879377 DOI: 10.3389/fnins.2024.1354977] [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: 12/13/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
In Huntington disease (HD) the prodromal phase has been increasingly investigated and is currently in focus for early interventional treatments. Also, the influence of sex on disease progression and severity in patients is under discussion, as a sex-specific impact has been reported in transgenic rodent models for HD. To this end, we have been studying these aspects in Sprague Dawley rats transgenic for HD. Here, we took up on the congenic F344tgHD rat model, expressing a fragmented Htt construct with 51 CAG repeats on an inbred F344 rat background and characterized potential sexual dimorphism and gene-dosage effects in rats during the pre-symptomatic phase (1-8 months of age). Our study comprises a longitudinal phenotyping of motor function, emotion and sensorimotor gating, as well as screening of metabolic parameters with classical and automated assays in combination with investigation of molecular HD hallmarks (striatal cell number and volume estimation, appearance of HTT aggregates). Differences between sexes became apparent during middle age, particularly in the motor and sensorimotor domains. Female individuals were generally more active, demonstrated different gait characteristics than males and less anxiolytic-like behavior. Alterations in both the time course and affected behavioral domains varied between male and female F344tgHD rats. First subtle behavioral anomalies were detected in transgenic F344tgHD rats prior to striatal MSN cell loss, revealing a prodromal-like phase in this model. Our findings demonstrate that the congenic F344tgHD rat model shows high face-validity, closely resembling the human disease's temporal progression, while having a relatively low number of CAG repeats, a slowly progressing pathology with a prodromal-like phase and a comparatively subtle phenotype. By differentiating the sexes regarding HD-related changes and characterizing the prodromal-like phase in this model, these findings provide a foundation for future treatment studies.
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Affiliation(s)
- Veronika Ratz-Wirsching
- Department of Experimental Therapy, University Hospital Erlangen, Erlangen, Germany
- Preclinical Experimental Center, Friedrich-Alexander-University, Erlangen-Nürnberg, Erlangen, Germany
| | - Johanna Habermeyer
- Department of Experimental Therapy, University Hospital Erlangen, Erlangen, Germany
- Preclinical Experimental Center, Friedrich-Alexander-University, Erlangen-Nürnberg, Erlangen, Germany
| | - Sandra Moceri
- Department of Experimental Therapy, University Hospital Erlangen, Erlangen, Germany
| | - Julia Harrer
- Department of Experimental Therapy, University Hospital Erlangen, Erlangen, Germany
| | - Christoph Schmitz
- Chair of Neuroanatomy, Institute of Anatomy, Faculty of Medicine, Ludwig-Maximilian University of Munich, Munich, Germany
| | - Stephan von Hörsten
- Department of Experimental Therapy, University Hospital Erlangen, Erlangen, Germany
- Preclinical Experimental Center, Friedrich-Alexander-University, Erlangen-Nürnberg, Erlangen, Germany
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Seo K, Refai HH, Hile ES. Application of Dynamic Mode Decomposition to Characterize Temporal Evolution of Plantar Pressures from Walkway Sensor Data in Women with Cancer. SENSORS (BASEL, SWITZERLAND) 2024; 24:486. [PMID: 38257578 PMCID: PMC11154430 DOI: 10.3390/s24020486] [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: 10/06/2023] [Revised: 12/05/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
Pressure sensor-impregnated walkways transform a person's footfalls into spatiotemporal signals that may be sufficiently complex to inform emerging artificial intelligence (AI) applications in healthcare. Key consistencies within these plantar signals show potential to uniquely identify a person, and to distinguish groups with and without neuromotor pathology. Evidence shows that plantar pressure distributions are altered in aging and diabetic peripheral neuropathy, but less is known about pressure dynamics in chemotherapy-induced peripheral neuropathy (CIPN), a condition leading to falls in cancer survivors. Studying pressure dynamics longitudinally as people develop CIPN will require a composite model that can accurately characterize a survivor's gait consistencies before chemotherapy, even in the presence of normal step-to-step variation. In this paper, we present a state-of-the-art data-driven learning technique to identify consistencies in an individual's plantar pressure dynamics. We apply this technique to a database of steps taken by each of 16 women before they begin a new course of neurotoxic chemotherapy for breast or gynecologic cancer. After extracting gait features by decomposing spatiotemporal plantar pressure data into low-rank dynamic modes characterized by three features: frequency, a decay rate, and an initial condition, we employ a machine-learning model to identify consistencies in each survivor's walking pattern using the centroids for each feature. In this sample, our approach is at least 86% accurate for identifying the correct individual using their pressure dynamics, whether using the right or left foot, or data from trials walked at usual or fast speeds. In future work, we suggest that persistent deviation from a survivor's pre-chemotherapy step consistencies could be used to automate the identification of peripheral neuropathy and other chemotherapy side effects that impact mobility.
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Affiliation(s)
- Kangjun Seo
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Hazem H. Refai
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Elizabeth S. Hile
- Department of Rehabilitation Sciences, College of Allied Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73117, USA
- OU Health Stephenson Cancer Center, Oklahoma City, OK 73104, USA
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Sánchez-DelaCruz E, Loeza-Mejía CI, Primero-Huerta C, Fuentes-Ramos M. Automatic selection model to identify neurodegenerative diseases. Digit Health 2024; 10:20552076241284376. [PMID: 39372807 PMCID: PMC11456181 DOI: 10.1177/20552076241284376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 08/28/2024] [Indexed: 10/08/2024] Open
Abstract
Objective This study evaluates machine learning algorithms' effectiveness in classifying Parkinson's disease and Huntington's disease based on biomarker data obtained non-invasively from patients and healthy controls. Methods Datasets containing biomarker data (x, y, and z values of accelerometers) from sensors were collected from Parkinson's disease, Huntington's disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection. Results The study achieved a 0.893 precision rate for Parkinson's disease and Huntington's disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders. Conclusion The automatic selection model method demonstrated efficacy in classifying Parkinson's disease and Huntington's disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.
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Affiliation(s)
- Eddy Sánchez-DelaCruz
- Artificial Intelligence Laboratory, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Veracruz, Mexico
| | - Cecilia-Irene Loeza-Mejía
- Artificial Intelligence Laboratory, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Veracruz, Mexico
| | - César Primero-Huerta
- Artificial Intelligence Laboratory, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Veracruz, Mexico
- División de Ingeniería en Sistemas Computacionales, Tecnológico Nacional de México/Valle de Bravo, Valle de Bravo Mexico
| | - Mirta Fuentes-Ramos
- Artificial Intelligence Laboratory, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Veracruz, Mexico
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Browning S, Holland S, Wellwood I, Bilney B. Spatiotemporal Gait Parameters in Adults With Premanifest and Manifest Huntington's Disease: A Systematic Review. J Mov Disord 2023; 16:307-320. [PMID: 37558234 PMCID: PMC10548085 DOI: 10.14802/jmd.23111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/15/2023] [Accepted: 08/09/2023] [Indexed: 08/11/2023] Open
Abstract
OBJECTIVE To systematically review and critically evaluate literature on spatiotemporal gait deviations in individuals with premanifest and manifest Huntington's Disease (HD) in comparison with healthy cohorts. METHODS We conducted a systematic review, guided by the Joanna Briggs Institute's Manual for Evidence Synthesis and pre-registered with the International Prospective Register of Systematic Reviews. Eight electronic databases were searched. Studies comparing spatiotemporal footstep parameters in adults with premanifest and manifest HD to healthy controls were screened, included and critically appraised by independent reviewers. Data on spatiotemporal gait changes and variability were extracted and synthesised. Meta-analysis was performed on gait speed, cadence, stride length and stride length variability measures. RESULTS We screened 2,721 studies, identified 1,245 studies and included 25 studies (total 1,088 participants). Sample sizes ranged from 14 to 96. Overall, the quality of the studies was assessed as good, but reporting of confounding factors was often unclear. Meta-analysis found spatiotemporal gait deviations in participants with HD compared to healthy controls, commencing in the premanifest stage. Individuals with premanifest HD walk significantly slower (-0.17 m/s; 95% confidence interval [CI] [-0.22, -0.13]), with reduced cadence (-6.63 steps/min; 95% CI [-10.62, -2.65]) and stride length (-0.09 m; 95% CI [-0.13, -0.05]). Stride length variability was also increased in premanifest cohorts by 2.18% (95% CI [0.69, 3.68]), with these changes exacerbated in participants with manifest disease. CONCLUSION Findings suggest individuals with premanifest and manifest HD display significant spatiotemporal footstep deviations. Clinicians could monitor individuals in the premanifest stage of disease for gait changes to identify the onset of Huntington's symptoms.
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Affiliation(s)
- Sasha Browning
- Faculty of Health Sciences, Australian Catholic University, Ballarat, Australia
| | - Stephanie Holland
- Faculty of Health Sciences, Australian Catholic University, Ballarat, Australia
| | - Ian Wellwood
- Faculty of Health Sciences, Australian Catholic University, Ballarat, Australia
| | - Belinda Bilney
- Faculty of Health Sciences, Australian Catholic University, Ballarat, Australia
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Recenti M, Gargiulo P, Chang M, Ko SB, Kim TJ, Ko SU. Predicting stroke, neurological and movement disorders using single and dual-task gait in Korean older population. Gait Posture 2023; 105:92-98. [PMID: 37515891 DOI: 10.1016/j.gaitpost.2023.07.282] [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: 01/23/2023] [Revised: 05/19/2023] [Accepted: 07/23/2023] [Indexed: 07/31/2023]
Abstract
BACKGROUND Single and motor or cognitive dual-gait analysis is often used in clinical settings to evaluate older adults affected by neurological and movement disorders or with a stroke history. Gait features are frequently investigated using Machine Learning (ML) with significant results that can help clinicians in diagnosis and rehabilitation. The present study aims to classify patients with stroke, neurological and movement disorders using ML to analyze gait characteristics and to understand the importance of the single and dual-task features among Korean older adults. METHODS A cohort of 122 non-hospitalized Korean older adult participated in a single and a cognitive dual-task gait performance analysis. The extracted temporal and spatial features, together with clinical data, were used as input for the binary classification using tree-based ML algorithms. A repeated-stratified 10-fold cross-validation was performed to better evaluate multiple classification metrics with a final feature importance analysis. RESULTS AND SIGNIFICANCE The best accuracy - maximum >90 % - for gait and neurological disorders classification was obtained with Random Forest. In the stroke classification a 91.7 % of maximum accuracy was reached, with a significant recall of 92 %. The feature importance analysis showed a substantial balance between single and dual-task, while clinical data did not show elevated importance. The current findings indicate that a cognitive dual-task gait performance is highly recommendable together with a single-task in the analysis of older population, particularly for patients with a history of stroke. The results could be useful to medical professionals in treating and diagnosing motor and neurological disorders, and to improve rehabilitation strategies for stroke patients. Furthermore, the results confirm the proficiency of the tree-based ML algorithms in biomedical data analysis. Finally, in the future, this research could be replicated with a non-Asian population dataset to deepen the understanding of gait differences between Asian-Korean population and other ethnicities.
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Affiliation(s)
- Marco Recenti
- Institute of Biomedical and Neural Engineering, Reykjavik University, Menntavegur 1, Reykjavik 102, Iceland; Department of Mechanical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu, Jeonnam 550-749, South Korea.
| | - Paolo Gargiulo
- Institute of Biomedical and Neural Engineering, Reykjavik University, Menntavegur 1, Reykjavik 102, Iceland; Department of Science, Landspitali University Hospital, Hringbraut 101, Reykjavík 101, Iceland
| | - Milan Chang
- The Icelandic Gerontological Research Institute, Landspitali University Hospital, Tungata 26, Reykjavik 101, Iceland
| | - Sang Bae Ko
- Department of Neurology and Critical Care, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 03080, South Korea
| | - Tae Jung Kim
- Department of Neurology and Critical Care, Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 03080, South Korea
| | - Seung Uk Ko
- Department of Mechanical Engineering, Chonnam National University, 50 Daehak-ro, Yeosu, Jeonnam 550-749, South Korea
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Poleur M, Markati T, Servais L. The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review. Orphanet J Rare Dis 2023; 18:224. [PMID: 37533072 PMCID: PMC10398976 DOI: 10.1186/s13023-023-02813-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/07/2023] [Indexed: 08/04/2023] Open
Abstract
Developing drugs for rare diseases is challenging, and the precision and objectivity of outcome measures is critical to this process. In recent years, a number of technologies have increasingly been used for remote monitoring of patient health. We report a systematic literature review that aims to summarize the current state of progress with regard to the use of digital outcome measures for real-life motor function assessment of patients with rare neurological diseases. Our search of published literature identified 3826 records, of which 139 were included across 27 different diseases. This review shows that use of digital outcome measures for motor function outside a clinical setting is feasible and employed in a broad range of diseases, although we found few outcome measures that have been robustly validated and adopted as endpoints in clinical trials. Future research should focus on validation of devices, variables, and algorithms to allow for regulatory qualification and widespread adoption.
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Affiliation(s)
- Margaux Poleur
- Department of Neurology, Liege University Hospital Center, Liège, Belgium.
- Neuromuscular Reference Center, Division of Paediatrics University, Hospital University of Liège, Liège, Belgium.
- Centre de Référence des Maladies Neuromusculaires, Centre Hospitalier Régional de la Citadelle, Boulevard du 12eme de Ligne 1, 4000, Liège, Belgium.
| | - Theodora Markati
- MDUK Oxford Neuromuscular Centre and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Laurent Servais
- MDUK Oxford Neuromuscular Centre and NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
- Neuromuscular Reference Center, Division of Paediatrics University, Hospital University of Liège, Liège, Belgium
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Manto M, Serrao M, Filippo Castiglia S, Timmann D, Tzvi-Minker E, Pan MK, Kuo SH, Ugawa Y. Neurophysiology of cerebellar ataxias and gait disorders. Clin Neurophysiol Pract 2023; 8:143-160. [PMID: 37593693 PMCID: PMC10429746 DOI: 10.1016/j.cnp.2023.07.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/19/2023] [Accepted: 07/11/2023] [Indexed: 08/19/2023] Open
Abstract
There are numerous forms of cerebellar disorders from sporadic to genetic diseases. The aim of this chapter is to provide an overview of the advances and emerging techniques during these last 2 decades in the neurophysiological tests useful in cerebellar patients for clinical and research purposes. Clinically, patients exhibit various combinations of a vestibulocerebellar syndrome, a cerebellar cognitive affective syndrome and a cerebellar motor syndrome which will be discussed throughout this chapter. Cerebellar patients show abnormal Bereitschaftpotentials (BPs) and mismatch negativity. Cerebellar EEG is now being applied in cerebellar disorders to unravel impaired electrophysiological patterns associated within disorders of the cerebellar cortex. Eyeblink conditioning is significantly impaired in cerebellar disorders: the ability to acquire conditioned eyeblink responses is reduced in hereditary ataxias, in cerebellar stroke and after tumor surgery of the cerebellum. Furthermore, impaired eyeblink conditioning is an early marker of cerebellar degenerative disease. General rules of motor control suggest that optimal strategies are needed to execute voluntary movements in the complex environment of daily life. A high degree of adaptability is required for learning procedures underlying motor control as sensorimotor adaptation is essential to perform accurate goal-directed movements. Cerebellar patients show impairments during online visuomotor adaptation tasks. Cerebellum-motor cortex inhibition (CBI) is a neurophysiological biomarker showing an inverse association between cerebellothalamocortical tract integrity and ataxia severity. Ataxic gait is characterized by increased step width, reduced ankle joint range of motion, increased gait variability, lack of intra-limb inter-joint and inter-segmental coordination, impaired foot ground placement and loss of trunk control. Taken together, these techniques provide a neurophysiological framework for a better appraisal of cerebellar disorders.
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Affiliation(s)
- Mario Manto
- Service des Neurosciences, Université de Mons, Mons, Belgium
- Service de Neurologie, CHU-Charleroi, Charleroi, Belgium
| | - Mariano Serrao
- Department of Medical and Surgical Sciences and Biotechnologies, University of Rome Sapienza, Polo Pontino, Corso della Repubblica 79 04100, Latina, Italy
- Gait Analysis LAB Policlinico Italia, Via Del Campidano 6 00162, Rome, Italy
| | - Stefano Filippo Castiglia
- Department of Medical and Surgical Sciences and Biotechnologies, University of Rome Sapienza, Polo Pontino, Corso della Repubblica 79 04100, Latina, Italy
- Gait Analysis LAB Policlinico Italia, Via Del Campidano 6 00162, Rome, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, via Bassi, 21, 27100 Pavia, Italy
| | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Elinor Tzvi-Minker
- Department of Neurology, University of Leipzig, Liebigstraße 20, 04103 Leipzig, Germany
- Syte Institute, Hamburg, Germany
| | - Ming-Kai Pan
- Cerebellar Research Center, National Taiwan University Hospital, Yun-Lin Branch, Yun-Lin 64041, Taiwan
- Department and Graduate Institute of Pharmacology, National Taiwan University College of Medicine, Taipei 10051, Taiwan
- Department of Medical Research, National Taiwan University Hospital, Taipei 10002, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei City 11529, Taiwan
- Initiative for Columbia Ataxia and Tremor, Columbia University Irving Medical Center, New York, NY, USA
| | - Sheng-Han Kuo
- Institute of Biomedical Sciences, Academia Sinica, Taipei City 11529, Taiwan
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, Fukushima Medical University, Fukushima, Japan
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Carlos AF, Josephs KA. The Role of Clinical Assessment in the Era of Biomarkers. Neurotherapeutics 2023; 20:1001-1018. [PMID: 37594658 PMCID: PMC10457273 DOI: 10.1007/s13311-023-01410-3] [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] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
Hippocratic Medicine revolved around the three main principles of patient, disease, and physician and promoted the systematic observation of patients, rational reasoning, and interpretation of collected information. Although these remain the cardinal features of clinical assessment today, Medicine has evolved from a more physician-centered to a more patient-centered approach. Clinical assessment allows physicians to encounter, observe, evaluate, and connect with patients. This establishes the patient-physician relationship and facilitates a better understanding of the patient-disease relationship, as the ultimate goal is to diagnose, prognosticate, and treat. Biomarkers are at the core of the more disease-centered approach that is currently revolutionizing Medicine as they provide insight into the underlying disease pathomechanisms and biological changes. Genetic, biochemical, radiographic, and clinical biomarkers are currently used. Here, we define a seven-level theoretical construct for the utility of biomarkers in neurodegenerative diseases. Level 1-3 biomarkers are considered supportive of clinical assessment, capable of detecting susceptibility or risk factors, non-specific neurodegeneration or dysfunction, and/or changes at the individual level which help increase clinical diagnostic accuracy and confidence. Level 4-7 biomarkers have the potential to surpass the utility of clinical assessment through detection of early disease stages and prediction of underlying pathology. In neurodegenerative diseases, biomarkers can potentiate, but cannot substitute, clinical assessment. In this current era, aside from adding to the discovery, evaluation/validation, and implementation of more biomarkers, clinical assessment remains crucial to maintaining the personal, humanistic, and sociocultural aspects of patient care. We would argue that clinical assessment is a custom that should never go obsolete.
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Affiliation(s)
- Arenn F Carlos
- Department of Neurology, Mayo Clinic, 200 1st St. S.W., Rochester, MN, 55905, USA.
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic, 200 1st St. S.W., Rochester, MN, 55905, USA
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Young F, Mason R, Morris RE, Stuart S, Godfrey A. IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:4100. [PMID: 37112441 PMCID: PMC10144082 DOI: 10.3390/s23084100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.
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Affiliation(s)
- Fraser Young
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rachel Mason
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Rosie E. Morris
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Samuel Stuart
- Department of Health and Life Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
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12
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Cheng N, Lou B, Wang H. Discovering the digital biomarker of hepatocellular carcinoma in serum with SERS-based biosensors and intelligence vision. Colloids Surf B Biointerfaces 2023; 226:113315. [PMID: 37086688 DOI: 10.1016/j.colsurfb.2023.113315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 04/24/2023]
Abstract
By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.
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Affiliation(s)
- Ningtao Cheng
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Bin Lou
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310003, China
| | - Hongyang Wang
- International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, China; National Center for Liver Cancer, Shanghai 201805, China.
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13
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Faisal MAA, Chowdhury MEH, Mahbub ZB, Pedersen S, Ahmed MU, Khandakar A, Alhatou M, Nabil M, Ara I, Bhuiyan EH, Mahmud S, AbdulMoniem M. NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04557-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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14
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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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15
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Gaßner H, Friedrich J, Masuch A, Jukic J, Stallforth S, Regensburger M, Marxreiter F, Winkler J, Klucken J. The Effects of an Individualized Smartphone-Based Exercise Program on Self-defined Motor Tasks in Parkinson Disease: Pilot Interventional Study. JMIR Rehabil Assist Technol 2022; 9:e38994. [PMID: 36378510 PMCID: PMC9709672 DOI: 10.2196/38994] [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: 04/26/2022] [Revised: 08/10/2022] [Accepted: 09/07/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Bradykinesia and rigidity are prototypical motor impairments of Parkinson disease (PD) highly influencing everyday life. Exercise training is an effective treatment alternative for motor symptoms, complementing dopaminergic medication. High frequency training is necessary to yield clinically relevant improvements. Exercise programs need to be tailored to individual symptoms and integrated in patients' everyday life. Due to the COVID-19 pandemic, exercise groups in outpatient setting were largely reduced. Developing remotely supervised solutions is therefore of significant importance. OBJECTIVE This pilot study aimed to evaluate the feasibility of a digital, home-based, high-frequency exercise program for patients with PD. METHODS In this pilot interventional study, patients diagnosed with PD received 4 weeks of personalized exercise at home using a smartphone app, remotely supervised by specialized therapists. Exercises were chosen based on the patient-defined motor impairment and depending on the patients' individual capacity (therapists defined 3-5 short training sequences for each participant). In a first education session, the tailored exercise program was explained and demonstrated to each participant and they were thoroughly introduced to the smartphone app. Intervention effects were evaluated using the Unified Parkinson Disease Rating Scale, part III; standardized sensor-based gait analysis; Timed Up and Go Test; 2-minute walk test; quality of life assessed by the Parkinson Disease Questionnaire; and patient-defined motor tasks of daily living. Usability of the smartphone app was assessed by the System Usability Scale. All participants gave written informed consent before initiation of the study. RESULTS In total, 15 individuals with PD completed the intervention phase without any withdrawals or dropouts. The System Usability Scale reached an average score of 72.2 (SD 6.5) indicating good usability of the smartphone app. Patient-defined motor tasks of daily living significantly improved by 40% on average in 87% (13/15) of the patients. There was no significant impact on the quality of life as assessed by the Parkinson Disease Questionnaire (but the subsections regarding mobility and social support improved by 14% from 25 to 21 and 19% from 15 to 13, respectively). Motor symptoms rated by Unified Parkinson Disease Rating Scale, part III, did not improve significantly but a descriptive improvement of 14% from 18 to 16 could be observed. Clinically relevant changes in Timed Up and Go test, 2-minute walk test, and sensor-based gait parameters or functional gait tests were not observed. CONCLUSIONS This pilot interventional study presented that a tailored, digital, home-based, and high-frequency exercise program over 4 weeks was feasible and improved patient-defined motor activities of daily life based on a self-developed patient-defined impairment score indicating that digital exercise concepts may have the potential to beneficially impact motor symptoms of daily living. Future studies should investigate sustainability effects in controlled study designs conducted over a longer period.
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Affiliation(s)
- Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
- Digital Health Systems, Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany
| | - Jana Friedrich
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Alisa Masuch
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jelena Jukic
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
- Medical Valley, Digital Health Application Center GmbH, Bamberg, Germany
| | - Martin Regensburger
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany
- Digital Health Systems, Fraunhofer Institute for Integrated Circuits (IIS), Erlangen, Germany
- Medical Valley, Digital Health Application Center GmbH, Bamberg, Germany
- Digital Medicine Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Digital Medicine Group, Department of Precision Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Digital Medicine Group, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
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16
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Regensburger M, Spatz IT, Ollenschläger M, Martindale CF, Lindeburg P, Kohl Z, Eskofier B, Klucken J, Schüle R, Klebe S, Winkler J, Gaßner H. Inertial Gait Sensors to Measure Mobility and Functioning in Hereditary Spastic Paraplegia: A Cross-sectional Multicenter Clinical Study. Neurology 2022; 99:e1079-e1089. [PMID: 35667840 PMCID: PMC9519248 DOI: 10.1212/wnl.0000000000200819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/19/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurologic examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient-dependent and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of this study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross-sectional cohort of patients with HSP and a longitudinal fast progressing subcohort. METHODS Using wearable sensors attached to the shoes, patients with HSP and controls performed a 4 × 10 m walking test during regular visits in 3 outpatient centers. Patients were also rated according to the SPRS, and in a subset, questionnaires on quality of life and fear of falling were obtained. An unsupervised segmentation algorithm was used to extract stride parameters and respective coefficients of variation. RESULTS Mobile gait analysis was performed in a total of 112 ambulatory patients with HSP and 112 age-matched and sex-matched controls. Although swing time was unchanged compared with controls, there were significant increases in the duration of the total stride phase and the duration of the stance phase, both regarding absolute values and coefficients of variation values. Although stride parameters did not correlate with age, weight, or height of the patients, there were significant associations of absolute stride parameters with single SPRS items reflecting impaired mobility (|r| > 0.50), with patients' quality of life (|r| > 0.44), and notably with disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, the absolute values of mobile gait parameters had significantly worsened compared with baseline. DISCUSSION The presented wearable sensor system provides parameters of stride characteristics which seem clinically valid to reflect gait impairment in HSP. Owing to the feasibility regarding time, space, and costs, this study forms the basis for larger scale longitudinal and interventional studies in HSP.
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Affiliation(s)
- Martin Regensburger
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
| | - Imke Tabea Spatz
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Malte Ollenschläger
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Christine F Martindale
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Philipp Lindeburg
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Zacharias Kohl
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Björn Eskofier
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Jochen Klucken
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Rebecca Schüle
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Stephan Klebe
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Jürgen Winkler
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Heiko Gaßner
- From the Department of Molecular Neurology (M.R., I.T.S., M.O., Z.K., J.K., J.W., H.G.), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Center for Rare Diseases Erlangen (ZSEER) (M.R., Z.K., J.W., H.G.), Universitätsklinikum Erlangen; Machine Learning and Data Analytics Lab (M.O., C.F.M., B.E.), Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Department of Neurology (P.L., S.K.), University Hospital Essen; Department of Neurodegenerative Diseases (R.S.), Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen; German Center for Neurodegenerative Diseases (DZNE) (R.S.), Tübingen; and Fraunhofer IIS (H.G.), Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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Bruni F, Borghesi F, Mancuso V, Riva G, Stramba-Badiale M, Pedroli E, Cipresso P. Cognition Meets Gait: Where and How Mind and Body Weave Each Other in a Computational Psychometrics Approach in Aging. Front Aging Neurosci 2022; 14:909029. [PMID: 35875804 PMCID: PMC9304933 DOI: 10.3389/fnagi.2022.909029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/16/2022] [Indexed: 11/26/2022] Open
Abstract
Aging may be associated with conditions characterized by motor and cognitive alterations, which could have a detrimental impact on daily life. Although motors and cognitive aspects have always been treated as separate entities, recent literature highlights their relationship, stressing a strong association between locomotion and executive functions. Thus, designing interventions targeting the risks deriving from both components’ impairments is crucial: the dual-task represents a starting point. Although its role in targeting and decreasing difficulties in aging is well known, most interventions are focused on a single domain, proposing a vertical model in which patients emerge only for a single aspect per time during assessment and rehabilitation. In this perspective, we propose a view of the individual as a whole between mind and body, suggesting a multicomponent and multidomain approach that could integrate different domains at the same time retracing lifelike situations. Virtual Reality, thanks to the possibility to develop daily environments with engaging challenges for patients, as well as to manage different devices to collect multiple data, provides the optimal scenario in which the integration could occur. Artificial Intelligence, otherwise, offers the best methodologies to integrate a great amount of various data to create a predictive model and identify appropriate and individualized interventions. Based on these assumptions the present perspective aims to propose the development of a new approach to an integrated, multimethod, multidimensional training in order to enhance cognition and physical aspects based on behavioral data, incorporating consolidated technologies in an innovative approach to neurology.
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Affiliation(s)
| | - Francesca Borghesi
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | | | - Giuseppe Riva
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Human Technology Lab, Catholic University of the Sacred Heart, Milan, Italy
| | - Marco Stramba-Badiale
- Department of Geriatrics and Cardiovascular Medicine, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Elisa Pedroli
- Faculty of Psychology, eCampus University, Novedrate, Italy
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Pietro Cipresso
- Applied Technology for Neuropsychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
- Department of Psychology, University of Turin, Turin, Italy
- *Correspondence: Pietro Cipresso,
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18
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Alhossary A, Ang WT, Chua KSG, Tay MRJ, Ong PL, Murakami T, Quake T, Binedell T, Wee SK, Phua MW, Wei YJ, Donnelly CJ. Identification of Secondary Biomechanical Abnormalities in the Lower Limb Joints after Chronic Transtibial Amputation: A Proof-of-Concept Study Using SPM1D Analysis. Bioengineering (Basel) 2022; 9:293. [PMID: 35877344 PMCID: PMC9311753 DOI: 10.3390/bioengineering9070293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/22/2022] [Accepted: 06/26/2022] [Indexed: 11/24/2022] Open
Abstract
SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using "Hotelling 2" and "T test 2" statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints × 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients' group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis.
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Affiliation(s)
- Amr Alhossary
- Rehabilitation Research Institute of Singapore-Nanyang Technological University, Singapore 308232, Singapore; (A.A.); (W.T.A.); (Y.J.W.)
| | - Wei Tech Ang
- Rehabilitation Research Institute of Singapore-Nanyang Technological University, Singapore 308232, Singapore; (A.A.); (W.T.A.); (Y.J.W.)
| | - Karen Sui Geok Chua
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Matthew Rong Jie Tay
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Poo Lee Ong
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Tsurayuki Murakami
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Tabitha Quake
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Trevor Binedell
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Seng Kwee Wee
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Min Wee Phua
- Centre of Rehabilitation Excellence, Tan Tock Seng Hospital, Singapore 569766, Singapore; (K.S.G.C.); (M.R.J.T.); (P.L.O.); (T.M.); (T.Q.); (T.B.); (S.K.W.); (M.W.P.)
| | - Yong Jia Wei
- Rehabilitation Research Institute of Singapore-Nanyang Technological University, Singapore 308232, Singapore; (A.A.); (W.T.A.); (Y.J.W.)
| | - Cyril John Donnelly
- Rehabilitation Research Institute of Singapore-Nanyang Technological University, Singapore 308232, Singapore; (A.A.); (W.T.A.); (Y.J.W.)
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19
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Berke Erdaş Ç, Sümer E, Kibaroğlu S. CNN-based severity prediction of neurodegenerative diseases using gait data. Digit Health 2022; 8:20552076221075147. [PMID: 35111334 PMCID: PMC8801640 DOI: 10.1177/20552076221075147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 01/03/2022] [Indexed: 11/17/2022] Open
Abstract
Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.
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Affiliation(s)
- Çağatay Berke Erdaş
- Department of Computer Engineering, Faculty of Engineering, Başkent University, Ankara, Turkey
| | - Emre Sümer
- Department of Computer Engineering, Faculty of Engineering, Başkent University, Ankara, Turkey
| | - Seda Kibaroğlu
- Department of Neurology, Faculty of Medicine, Başkent University, Ankara, Turkey
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20
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Talman LS, Hiller AL. Approach to Posture and Gait in Huntington's Disease. Front Bioeng Biotechnol 2021; 9:668699. [PMID: 34386484 PMCID: PMC8353382 DOI: 10.3389/fbioe.2021.668699] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 06/28/2021] [Indexed: 11/30/2022] Open
Abstract
Disturbances of gait occur in all stages of Huntington’s disease (HD) including the premanifest and prodromal stages. Individuals with HD demonstrate the slower speed of gait, shorter stride length, and increased variability of gait parameters as compared to controls; cognitive disturbances in HD often compound these differences. Abnormalities of gait and recurrent falls lead to decreased quality of life for individuals with HD throughout the disease. This scoping review aims to outline the cross-disciplinary approach to gait evaluation in HD and will highlight the utility of objective measures in defining gait abnormalities in this patient population.
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Affiliation(s)
- Lauren S Talman
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States
| | - Amie L Hiller
- Department of Neurology, Oregon Health & Science University, Portland, OR, United States.,Portland VA Healthcare System, Portland, OR, United States
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21
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Waddell EM, Dinesh K, Spear KL, Elson MJ, Wagner E, Curtis MJ, Mitten DJ, Tarolli CG, Sharma G, Dorsey ER, Adams JL. GEORGE®: A Pilot Study of a Smartphone Application for Huntington's Disease. J Huntingtons Dis 2021; 10:293-301. [PMID: 33814455 DOI: 10.3233/jhd-200452] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Current Huntington's disease (HD) measures are limited to subjective, episodic assessments conducted in clinic. Smartphones can enable the collection of objective, real-world data but their use has not been extensively evaluated in HD. OBJECTIVE Develop and evaluate a smartphone application to assess feasibility of use and key features of HD in clinic and at home. METHODS We developed GEORGE®, an Android smartphone application for HD which assesses voice, chorea, balance, gait, and finger tapping speed. We then conducted an observational pilot study of individuals with manifest HD, prodromal HD, and without a movement disorder. In clinic, participants performed standard clinical assessments and a battery of active tasks in GEORGE. At home, participants were instructed to complete the activities thrice daily for one month. Sensor data were used to measure chorea, tap rate, and step count. Audio data was not analyzed. RESULTS Twenty-three participants (8 manifest HD, 5 prodromal HD, 10 controls) enrolled, and all but one completed the study. On average, participants used the application 2.1 times daily. We observed a significant difference in chorea score (HD: 19.5; prodromal HD: 4.5, p = 0.007; controls: 4.3, p = 0.001) and tap rate (HD: 2.5 taps/s; prodromal HD: 8.9 taps/s, p = 0.001; controls: 8.1 taps/s, p = 0.001) between individuals with and without manifest HD. Tap rate correlated strongly with the traditional UHDRS finger tapping score (left hand: r = -0.82, p = 0.022; right hand: r = -0.79, p = 0.03). CONCLUSION GEORGE is an acceptable and effective tool to differentiate individuals with and without manifest HD and measure key disease features. Refinement of the application's interface and activities will improve its usability and sensitivity and, ideally, make it useful for clinical care and research.
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Affiliation(s)
- Emma M Waddell
- Center for Health+Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | - Kelsey L Spear
- Center for Health+Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Molly J Elson
- Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Ellen Wagner
- Center for Health+Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Michael J Curtis
- UR Health Lab, University of Rochester Medical Center, Rochester, NY, USA
| | - David J Mitten
- UR Health Lab, University of Rochester Medical Center, Rochester, NY, USA
| | - Christopher G Tarolli
- Center for Health+Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | - E Ray Dorsey
- Center for Health+Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Jamie L Adams
- Center for Health+Technology, University of Rochester Medical Center, Rochester, NY, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
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22
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Zhang Y, Wang H, Yao Y, Liu J, Sun X, Gu D. Walking stability in patients with benign paroxysmal positional vertigo: an objective assessment using wearable accelerometers and machine learning. J Neuroeng Rehabil 2021; 18:56. [PMID: 33789693 PMCID: PMC8011133 DOI: 10.1186/s12984-021-00854-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 03/17/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Benign paroxysmal positional vertigo (BPPV) is one of the most common peripheral vestibular disorders leading to balance difficulties and increased fall risks. This study aims to investigate the walking stability of BPPV patients in clinical settings and propose a machine-learning-based classification method for determining the severity of gait disturbances of BPPV. METHODS Twenty-seven BPPV outpatients and twenty-seven healthy subjects completed level walking trials at self-preferred speed in clinical settings while wearing two accelerometers on the head and lower trunk, respectively. Temporo-spatial variables and six walking stability related variables [root mean square (RMS), harmonic ratio (HR), gait variability, step/stride regularity, and gait symmetry] derived from the acceleration signals were analyzed. A support vector machine model (SVM) based on the gait variables of BPPV patients were developed to differentiate patients from healthy controls and classify the handicapping effects of dizziness imposed by BPPV. RESULTS The results showed that BPPV patients employed a conservative gait and significantly reduced walking stability compared to the healthy controls. Significant different mediolateral HR at the lower trunk and anteroposterior step regularity at the head were found in BPPV patients among mild, moderate, and severe DHI (dizziness handicap inventory) subgroups. SVM classification achieved promising accuracies with area under the curve (AUC) of 0.78, 0.83, 0.85 and 0.96 respectively for differentiating patients from healthy controls and classifying the three stages of DHI subgroups. Study results suggest that the proposed gait analysis that is based on the coupling of wearable accelerometers and machine learning provides an objective approach for assessing gait disturbances and handicapping effects of dizziness imposed by BPPV.
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Affiliation(s)
- Yuqian Zhang
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, People's Republic of China.,School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China.,Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education of People's Republic China, Shanghai, 200030, People's Republic of China
| | - He Wang
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China.,Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education of People's Republic China, Shanghai, 200030, People's Republic of China
| | - Yifei Yao
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China.,Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education of People's Republic China, Shanghai, 200030, People's Republic of China
| | - Jianren Liu
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, People's Republic of China
| | - Xuhong Sun
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, People's Republic of China.
| | - Dongyun Gu
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, People's Republic of China. .,School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China. .,Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education of People's Republic China, Shanghai, 200030, People's Republic of China.
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23
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Heikkinen T, Bragge T, Bhattarai N, Parkkari T, Puoliväli J, Kontkanen O, Sweeney P, Park LC, Munoz-Sanjuan I. Rapid and robust patterns of spontaneous locomotor deficits in mouse models of Huntington's disease. PLoS One 2020; 15:e0243052. [PMID: 33370315 PMCID: PMC7769440 DOI: 10.1371/journal.pone.0243052] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 11/15/2020] [Indexed: 11/25/2022] Open
Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder characterized by severe disruption of cognitive and motor functions, including changes in posture and gait. A number of HD mouse models have been engineered that display behavioral and neuropathological features of the disease, but gait alterations in these models are poorly characterized. Sensitive high-throughput tests of fine motor function and gait in mice might be informative in evaluating disease-modifying interventions. Here, we describe a hypothesis-free workflow that determines progressively changing locomotor patterns across 79 parameters in the R6/2 and Q175 mouse models of HD. R6/2 mice (120 CAG repeats) showed motor disturbances as early as at 4 weeks of age. Similar disturbances were observed in homozygous and heterozygous Q175 KI mice at 3 and 6 months of age, respectively. Interestingly, only the R6/2 mice developed forelimb ataxia. The principal components of the behavioral phenotypes produced two phenotypic scores of progressive postural instability based on kinematic parameters and trajectory waveform data, which were shared by both HD models. This approach adds to the available HD mouse model research toolbox and has a potential to facilitate the development of therapeutics for HD and other debilitating movement disorders with high unmet medical need.
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Affiliation(s)
| | - Timo Bragge
- Charles River Discovery Services, Kuopio, Finland
| | - Niina Bhattarai
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | | | | | | | | | - Larry C Park
- Naason Science Inc., Chungcheongbuk-do, South Korea.,CHDI Management/CHDI Foundation, Los Angeles, California, United States of America
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24
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Ibrahim AA, Küderle A, Gaßner H, Klucken J, Eskofier BM, Kluge F. Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis. J Neuroeng Rehabil 2020; 17:165. [PMID: 33339530 PMCID: PMC7749504 DOI: 10.1186/s12984-020-00798-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/09/2020] [Indexed: 12/30/2022] Open
Abstract
Background Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. Methods Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. Results Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. Conclusions The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.
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Affiliation(s)
- Alzhraa A Ibrahim
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany. .,Computer Science Department, Faculty of Computers and Information, Assiut University, Asyut, Egypt.
| | - Arne Küderle
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Bavaria, Germany.,Fraunhofer Institut for Integrated Circuits, Erlangen, Bavaria, Germany.,Medical Valley Digital Health Application Center, Bamberg, Bavaria, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Felix Kluge
- Machine Learning and Data Analytics Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
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25
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Gaßner H, Sanders P, Dietrich A, Marxreiter F, Eskofier BM, Winkler J, Klucken J. Clinical Relevance of Standardized Mobile Gait Tests. Reliability Analysis Between Gait Recordings at Hospital and Home in Parkinson's Disease: A Pilot Study. JOURNAL OF PARKINSONS DISEASE 2020; 10:1763-1773. [PMID: 32925099 DOI: 10.3233/jpd-202129] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND Gait impairments in Parkinson's disease (PD) are quantified using inertial sensors under standardized test settings in the hospital. Recent studies focused on the assessment of free-living gait in PD. However, the clinical relevance of standardized gait tests recorded at the patient's home is unclear. OBJECTIVE To evaluate the reliability of supervised, standardized sensor-based gait outcomes at home compared to the hospital. METHODS Patients with PD (n = 20) were rated by a trained investigator using the Unified Parkinson Disease Rating Scale (UPDRS-III). Gait tests included a standardized 4×10 m walk test and the Timed Up and Go Test (TUG). Tests were performed in the hospital (HOSPITAL) and at patients' home (HOME), and controlled for investigator, time of the day, and medication. Statistics included reliability analysis using Intra-Class correlations and Bland-Altman plots. RESULTS UPDRS-III and TUG were comparable between HOSPITAL and HOME. PD patients' gait at HOME was slower (gait velocity Δ= -0.07±0.11 m/s, -6.1%), strides were shorter (stride length Δ= -9.2±9.4 cm; -7.3%), and shuffling of gait was more present (maximum toe-clearance Δ= -0.7±2.5 cm; -8.8%). Particularly, narrow walkways (<85 cm) resulted in a significant reduction of gait velocity at home. Reliability analysis (HOSPITAL vs. HOME) revealed excellent ICC coefficients for UPDRS-III (0.950, p < 0.000) and gait parameters (e.g., stride length: 0.898, p < 0.000; gait velocity: 0.914, p < 0.000; stance time: 0.922, p < 0.000; stride time: 0.907, p < 0.000). CONCLUSION This pilot study underlined the clinical relevance of gait parameters by showing excellent reliability for supervised, standardized gait tests at HOSPITAL and HOME, even though gait parameters were different between test conditions.
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Affiliation(s)
- Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Philipp Sanders
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Alisa Dietrich
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany.,Medical Valley - Digital Health Application Center GmbH, Bamberg, Germany.,Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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