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Zhang X, Jin Y, Wang M, Ji C, Chen Z, Fan W, Rainer TH, Guan Q, Li Q. The impact of anxiety on gait impairments in Parkinson's disease: insights from sensor-based gait analysis. J Neuroeng Rehabil 2024; 21:68. [PMID: 38689288 PMCID: PMC11059709 DOI: 10.1186/s12984-024-01364-3] [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: 07/12/2023] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Sensor-based gait analysis provides a robust quantitative tool for assessing gait impairments and their associated factors in Parkinson's disease (PD). Anxiety is observed to interfere with gait clinically, but this has been poorly investigated. Our purpose is to utilize gait analysis to uncover the effect of anxiety on gait in patients with PD. METHODS We enrolled 38 and 106 PD patients with and without anxiety, respectively. Gait parameters were quantitively examined and compared between two groups both in single-task (ST) and dual-task (DT) walking tests. Multiple linear regression was applied to evaluate whether anxiety independently contributed to gait impairments. RESULTS During ST, PD patients with anxiety presented significantly shorter stride length, lower gait velocity, longer stride time and stance time, longer stance phase, smaller toe-off (TO) and heel-strike (HS) angles than those without anxiety. While under DT status, the differences were diminished. Multiple linear regression analysis demonstrated that anxiety was an independent factor to a serials of gait parameters, particularly ST-TO (B = -2.599, (-4.82, -0.38)), ST-HS (B = -2.532, (-4.71, -0.35)), ST-TO-CV (B = 4.627, (1.71, 7.64)), ST-HS-CV(B = 4.597, (1.66, 7.53)), ST stance phase (B = 1.4, (0.22, 2.58)), and DT stance phase (B = 1.749, (0.56, 2.94)). CONCLUSION Our study discovered that anxiety has a significant impact on gait impairments in PD patients, especially exacerbating shuffling steps and prolonging stance phase. These findings highlight the importance of addressing anxiety in PD precision therapy to achieve better treatment outcomes.
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Affiliation(s)
- Xiaodan Zhang
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
- Department of Emergency Medicine, University of Hong Kong, Hong Kong SAR, China
| | - Yulan Jin
- Department of Clinical Laboratory, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Mateng Wang
- Department of General Surgery, Yinzhou NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Chengcheng Ji
- School of Medicine, Shaoxing University, Shaoxing, Zhejiang Province, China
| | - Zhaoying Chen
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
| | - Weinv Fan
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China
| | | | - Qiongfeng Guan
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China.
| | - Qianyun Li
- Department of Neurology, Ningbo NO.2 Hospital, Ningbo, Zhejiang Province, China.
- Department of Emergency Medicine, University of Hong Kong, Hong Kong SAR, China.
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Lützow L, Teckenburg I, Koch V, Marxreiter F, Jukic J, Stallforth S, Regensburger M, Winkler J, Klucken J, Gaßner H. The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson's disease: a long-term feasibility study. J Patient Rep Outcomes 2023; 7:106. [PMID: 37902922 PMCID: PMC10616049 DOI: 10.1186/s41687-023-00631-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/28/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Exercise therapy is considered effective for the treatment of motor impairment in patients with Parkinson's disease (PD). During the COVID-19 pandemic, training sessions were cancelled and the implementation of telerehabilitation concepts became a promising solution. The aim of this controlled interventional feasibility study was to evaluate the long-term acceptance and to explore initial effectiveness of a digital, home-based, high-frequency exercise program for PD patients. Training effects were assessed using patient-reported outcome measures combined with sensor-based and clinical scores. METHODS 16 PD patients (smartphone group, SG) completed a home-based, individualized training program over 6-8 months using a smartphone app, remotely supervised by a therapist, and tailored to the patient's motor impairments and capacity. A control group (CG, n = 16) received medical treatment without participating in digital exercise training. The usability of the app was validated using System Usability Scale (SUS) and User Version of the Mobile Application Rating Scale (uMARS). Outcome measures included among others Unified Parkinson Disease Rating Scale, part III (UPDRS-III), sensor-based gait parameters derived from standardized gait tests, Parkinson's Disease Questionnaire (PDQ-39), and patient-defined motor activities of daily life (M-ADL). RESULTS Exercise frequency of 74.5% demonstrated high adherence in this cohort. The application obtained 84% in SUS and more than 3.5/5 points in each subcategory of uMARS, indicating excellent usability. The individually assessed additional benefit showed at least 6 out of 10 points (Mean = 8.2 ± 1.3). From a clinical perspective, patient-defined M-ADL improved for 10 out of 16 patients by 15.5% after the training period. The results of the UPDRS-III remained stable in the SG while worsening in the CG by 3.1 points (24%). The PDQ-39 score worsened over 6-8 months by 83% (SG) and 59% (CG) but the subsection mobility showed a smaller decline in the SG (3%) compared to the CG (77%) without reaching significance level for all outcomes. Sensor-based gait parameters remained constant in both groups. CONCLUSIONS Long-term training over 6-8 months with the app is considered feasible and acceptable, representing a cost-effective, individualized approach to complement dopaminergic treatment. This study indicates that personalized, digital, high-frequency training leads to benefits in motor sections of ADL and Quality of Life.
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Affiliation(s)
- Lisa Lützow
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Isabelle Teckenburg
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Veronika Koch
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Center for Movement Disorders, Passauer Wolf, Bad Gögging, Neustadt an der Donau, Germany
| | - Jelena Jukic
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Sabine Stallforth
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Medical Valley - Digital Health Application Center GmbH, Bamberg, Germany
| | - Martin Regensburger
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Medical Valley - Digital Health Application Center GmbH, Bamberg, Germany
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health, Strassen, Luxembourg
- Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany.
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany.
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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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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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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Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi ME, Marín Valero M, Corvol JC, Eskofier B, Van Gyseghem JM, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Front Neurol 2022; 13:788427. [PMID: 35295840 PMCID: PMC8918525 DOI: 10.3389/fneur.2022.788427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/31/2022] [Indexed: 12/18/2022] Open
Abstract
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
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Affiliation(s)
- Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany
| | - Noémi Bontridder
- Centre de Recherches Information, Droit et Societe, University of Namur, Namur, Belgium
| | | | - Enrico Glaab
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | | | - Bjoern Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | - Jürgen Winkler
- Department of Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
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Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson's Disease. SENSORS 2021; 21:s21227680. [PMID: 34833755 PMCID: PMC8623101 DOI: 10.3390/s21227680] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022]
Abstract
Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.
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Picillo M, Ricciardi C, Tepedino MF, Abate F, Cuoco S, Carotenuto I, Erro R, Ricciardelli G, Russo M, Cesarelli M, Barone P, Amboni M. Gait Analysis in Progressive Supranuclear Palsy Phenotypes. Front Neurol 2021; 12:674495. [PMID: 34177779 PMCID: PMC8224759 DOI: 10.3389/fneur.2021.674495] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/29/2021] [Indexed: 11/13/2022] Open
Abstract
The objective of the present study was to describe gait parameters of progressive supranuclear palsy (PSP) phenotypes at early stage verifying the ability of gait analysis in discriminating between disease phenotypes and between the other variant syndromes of PSP (vPSP) and Parkinson's disease (PD). Nineteen PSP (10 PSP-Richardson's syndrome, five PSP-parkinsonism, and four PSP-progressive gait freezing) and nine PD patients performed gait analysis in single and dual tasks. Although phenotypes showed similar demographic and clinical variables, Richardson's syndrome presented worse cognitive functions. Gait analysis demonstrated worse parameters in Richardson's syndrome compared with the vPSP. The overall diagnostic accuracy of the statistical model during dual task was almost 90%. The correlation analysis showed a significant relationship between gait parameters and visuo-spatial, praxic, and attention abilities in PSP-Richardson's syndrome only. vPSP presented worse gait parameters than PD. Richardson's syndrome presents greater gait dynamic instability since the earliest stages than other phenotypes. Computerized gait analysis can differentiate between PSP phenotypes and between vPSP and PD.
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Affiliation(s)
- Marina Picillo
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Carlo Ricciardi
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
| | - Maria Francesca Tepedino
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Filomena Abate
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Sofia Cuoco
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Immacolata Carotenuto
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Roberto Erro
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Gianluca Ricciardelli
- Department of Medicine, Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi D'Aragona, Salerno, Italy
| | - Michela Russo
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Mario Cesarelli
- Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Telese Terme, Italy
| | - Paolo Barone
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy
| | - Marianna Amboni
- Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy.,Istituto di Diagnosi e Cura (IDC) Hermitage-Capodimonte, Naples, Italy
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Amboni M, Ricciardi C, Picillo M, De Santis C, Ricciardelli G, Abate F, Tepedino MF, D'Addio G, Cesarelli G, Volpe G, Calabrese MC, Cesarelli M, Barone P. Gait analysis may distinguish progressive supranuclear palsy and Parkinson disease since the earliest stages. Sci Rep 2021; 11:9297. [PMID: 33927317 PMCID: PMC8084977 DOI: 10.1038/s41598-021-88877-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 04/16/2021] [Indexed: 12/22/2022] Open
Abstract
Progressive supranuclear palsy (PSP) is a rare and rapidly progressing atypical parkinsonism. Albeit existing clinical criteria for PSP have good specificity and sensitivity, there is a need for biomarkers able to capture early objective disease-specific abnormalities. This study aimed to identify gait patterns specifically associated with early PSP. The study population comprised 104 consecutively enrolled participants (83 PD and 21 PSP patients). Gait was investigated using a gait analysis system during normal gait and a cognitive dual task. Univariate statistical analysis and binary logistic regression were used to compare all PD patients and all PSP patients, as well as newly diagnosed PD and early PSP patients. Gait pattern was poorer in PSP patients than in PD patients, even from early stages. PSP patients exhibited reduced velocity and increased measures of dynamic instability when compared to PD patients. Application of predictive models to gait data revealed that PD gait pattern was typified by increased cadence and longer cycle length, whereas a longer stance phase characterized PSP patients in both mid and early disease stages. The present study demonstrates that quantitative gait evaluation clearly distinguishes PSP patients from PD patients since the earliest stages of disease. First, this might candidate gait analysis as a reliable biomarker in both clinical and research setting. Furthermore, our results may offer speculative clues for conceiving early disease-specific rehabilitation strategies.
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Affiliation(s)
- Marianna Amboni
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy. .,IDC Hermitage-Capodimonte, Naples, Italy.
| | - Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.,Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Marina Picillo
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Chiara De Santis
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Gianluca Ricciardelli
- Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Filomena Abate
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Maria Francesca Tepedino
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | | | - Giuseppe Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Naples, Italy
| | - Giampiero Volpe
- Azienda Ospedaliera Universitaria OO. RR. San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Maria Consiglia Calabrese
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
| | - Mario Cesarelli
- Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
| | - Paolo Barone
- Center for Neurodegenerative Diseases (CEMAND), Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Via Salvador Allende, 43, 84081, Baronissi, SA, Italy
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10
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Thun-Hohenstein C, Klucken J. Wearables als unterstützendes Tool für den Paradigmenwechsel in der Versorgung von Parkinson Patienten. KLIN NEUROPHYSIOL 2021. [DOI: 10.1055/a-1353-9413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
ZusammenfassungTragbare Sensoren – „Wearables“ – eignen sich, Funktionsstörungen bei Parkinson Patienten zu erheben und werden zur Prävention, Prädiktion, Diagnostik und Therapieunterstützung genutzt. In der Forschung erhöhen sie die Reliabilität der erhobenen Daten und stellen bessere Studien-Endpunkte dar, als die herkömmlichen, subjektiven und wenig quantitativen Rating- und Selbstbeurteilungsskalen. Untersucht werden motorische Symptome wie Tremor, Bradykinese und Gangstörungen und auch nicht motorische Symptome. In der Home-Monitoringanwendung kann der Ist-Zustand des Patienten im realen Leben untersucht werden, die Therapie überwacht, die Adhärenz verbessert und die Compliance überprüft werden. Zusätzlich können Wearables interventionell zur Verbesserung von Symptomen eingesetzt werden wie z. B. Cueing, Gamification oder Coaching. Der Transfer von Laborbedingungen in den häuslichen Alltag ist eine medizinisch-technische Herausforderung. Optimierte Versorgungsmodelle müssen entwickelt werden und der tatsächliche Nutzen für den individuellen Patienten in weiteren Studien belegt werden.
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Affiliation(s)
| | - Jochen Klucken
- Molekulare Neurologie, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg
- Fraunhofer IIS, Erlangen
- Medical Valley Digital Health Application Center GmbH, Bamberg
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11
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Nasri A, Ben Djebara M, Sghaier I, Mrabet S, Zidi S, Gargouri A, Kacem I, Gouider R. Atypical parkinsonian syndromes in a North African tertiary referral center. Brain Behav 2021; 11:e01924. [PMID: 33179436 PMCID: PMC7821582 DOI: 10.1002/brb3.1924] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/06/2020] [Accepted: 10/14/2020] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION Data on epidemiology of atypical parkinsonian syndromes (APS) in North African countries are limited. Our objective was to study the epidemiological features of APS in a Tunisian population. METHODS We conducted a 17-year retrospective cross-sectional descriptive study in the Department of Neurology at Razi University Hospital. We included all patients responding to consensus diagnosis criteria of APS. We recorded demographic and clinical data. Group differences were assessed with a post hoc ANOVA with a Bonferroni error correction. RESULTS We included 464 APS patients. Hospital prevalence of APS among all parkinsonism cases was 20.6%. Mean annual increase of incidence defined as newly diagnosed APS cases per year reached 38.8%/year. APS were divided into 4 etiological subgroups: dementia with Lewy bodies (DLB; 56.7%); progressive supranuclear palsy(PSP; 16.2%); multiple system atrophy (MSA; 14.6%); and finally corticobasal syndrome (CBS; 12.5%). Sex-ratio was 1.2. This male predominance was found in all subgroups except MSA (p = .013). Mean age at onset was 68.5 years, most belated in DLB (69.7 years; p < .001). Young-onset parkinsonism (<40 years) was found only in MSA subgroup (p = .031). Parkinsonism was of late onset (>70 years) in 50.7% of patients and was significantly associated with DLB subgroup (p = .013). Inaugural parkinsonism was associated with CBS and MSA (p = .0497), and gait disorders at disease onset were associated with PSP and MSA (p = .0062). Cognitive and mood disorders were more marked in DLB and most preserved in MSA. Consanguinity was more marked in CBS (p = .037), and family history of dementia and psychiatric diseases was more common in DLB. Thirty-seven families with similar cases of APS were identified. CONCLUSIONS This is the largest African epidemiological study on APS. In our population, APS were frequent and dominated by DLB. The age of onset of parkinsonism was the most decisive feature for differential diagnosis.
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Affiliation(s)
- Amina Nasri
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
- Faculty of Medicine of TunisUniversity of Tunis El ManarTunisTunisia
| | - Mouna Ben Djebara
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
- Faculty of Medicine of TunisUniversity of Tunis El ManarTunisTunisia
| | - Ikram Sghaier
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
| | - Saloua Mrabet
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
- Faculty of Medicine of TunisUniversity of Tunis El ManarTunisTunisia
| | - Sabrina Zidi
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
| | - Amina Gargouri
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
- Faculty of Medicine of TunisUniversity of Tunis El ManarTunisTunisia
| | - Imen Kacem
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
- Faculty of Medicine of TunisUniversity of Tunis El ManarTunisTunisia
| | - Riadh Gouider
- Neurology Department, LR18SP03, Clinical Investigation Center (CIC) "Neurosciences and Mental Health"Razi University HospitalTunisTunisia
- Faculty of Medicine of TunisUniversity of Tunis El ManarTunisTunisia
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12
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Sidoroff V, Raccagni C, Kaindlstorfer C, Eschlboeck S, Fanciulli A, Granata R, Eskofier B, Seppi K, Poewe W, Willeit J, Kiechl S, Mahlknecht P, Stockner H, Marini K, Schorr O, Rungger G, Klucken J, Wenning G, Gaßner H. Characterization of gait variability in multiple system atrophy and Parkinson's disease. J Neurol 2020; 268:1770-1779. [PMID: 33382439 PMCID: PMC8068710 DOI: 10.1007/s00415-020-10355-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 10/07/2020] [Accepted: 12/04/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND Gait impairment is a pivotal feature of parkinsonian syndromes and increased gait variability is associated with postural instability and a higher risk of falls. OBJECTIVES We compared gait variability at different walking velocities between and within groups of patients with Parkinson-variant multiple system atrophy, idiopathic Parkinson's disease, and a control group of older adults. METHODS Gait metrics were recorded in 11 multiple system atrophy, 12 Parkinson's disease patients, and 18 controls using sensor-based gait analysis. Gait variability was analyzed for stride, swing and stance time, stride length and gait velocity. Values were compared between and within the groups at self-paced comfortable, fast and slow walking speed. RESULTS Multiple system atrophy patients displayed higher gait variability except for stride time at all velocities compared with controls, while Parkinson's patients did not. Compared with Parkinson's disease, multiple system atrophy patients displayed higher variability of swing time, stride length and gait velocity at comfortable speed and at slow speed for swing and stance time, stride length and gait velocity (all P < 0.05). Stride time variability was significantly higher in slow compared to comfortable walking in patients with multiple system atrophy (P = 0.014). Variability parameters significantly correlated with the postural instability/gait difficulty subscore in both disease groups. Conversely, significant correlations between variability parameters and MDS-UPDRS III score was observed only for multiple system atrophy patients. CONCLUSION This analysis suggests that gait variability parameters reflect the major axial impairment and postural instability displayed by multiple system atrophy patients compared with Parkinson's disease patients and controls.
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Affiliation(s)
- Victoria Sidoroff
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Cecilia Raccagni
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria. .,Department of Neurology, Regional General Hospital Bolzano, Lorenz Boehler Street 5, 39100, Bolzano, Italia.
| | | | - Sabine Eschlboeck
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Roberta Granata
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Björn Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Klaus Seppi
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Werner Poewe
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johann Willeit
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Stefan Kiechl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Philipp Mahlknecht
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Heike Stockner
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kathrin Marini
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Oliver Schorr
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Jochen Klucken
- Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany.,AG Digital Health Pathways, Fraunhofer Institute for Integrated Circuits, Erlangen, Germany
| | - Gregor Wenning
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Heiko Gaßner
- Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University, Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
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13
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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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14
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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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15
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Ahad MAR, Ngo TT, Antar AD, Ahmed M, Hossain T, Muramatsu D, Makihara Y, Inoue S, Yagi Y. Wearable Sensor-Based Gait Analysis for Age and Gender Estimation. SENSORS 2020; 20:s20082424. [PMID: 32344673 PMCID: PMC7219505 DOI: 10.3390/s20082424] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/20/2020] [Accepted: 04/22/2020] [Indexed: 02/05/2023]
Abstract
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams-for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.
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Affiliation(s)
- Md Atiqur Rahman Ahad
- Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan; (T.T.N.); (D.M.); (Y.M.); (Y.Y.)
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh;
- Correspondence: or
| | - Thanh Trung Ngo
- Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan; (T.T.N.); (D.M.); (Y.M.); (Y.Y.)
| | - Anindya Das Antar
- Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Masud Ahmed
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh;
| | - Tahera Hossain
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan; (T.H.); (S.I.)
| | - Daigo Muramatsu
- Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan; (T.T.N.); (D.M.); (Y.M.); (Y.Y.)
| | - Yasushi Makihara
- Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan; (T.T.N.); (D.M.); (Y.M.); (Y.Y.)
| | - Sozo Inoue
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan; (T.H.); (S.I.)
| | - Yasushi Yagi
- Department of Media Intelligent, Osaka University, Ibaraki 567-0047, Japan; (T.T.N.); (D.M.); (Y.M.); (Y.Y.)
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16
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Gaßner H, Jensen D, Marxreiter F, Kletsch A, Bohlen S, Schubert R, Muratori LM, Eskofier B, Klucken J, Winkler J, Reilmann R, Kohl Z. Gait variability as digital biomarker of disease severity in Huntington's disease. J Neurol 2020; 267:1594-1601. [PMID: 32048014 PMCID: PMC7293689 DOI: 10.1007/s00415-020-09725-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 01/20/2020] [Accepted: 01/22/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND Impaired gait plays an important role for quality of life in patients with Huntington's disease (HD). Measuring objective gait parameters in HD might provide an unbiased assessment of motor deficits in order to determine potential beneficial effects of future treatments. OBJECTIVE To objectively identify characteristic features of gait in HD patients using sensor-based gait analysis. Particularly, gait parameters were correlated to the Unified Huntington's Disease Rating Scale, total motor score (TMS), and total functional capacity (TFC). METHODS Patients with manifest HD at two German sites (n = 43) were included and clinically assessed during their annual ENROLL-HD visit. In addition, patients with HD and a cohort of age- and gender-matched controls performed a defined gait test (4 × 10 m walk). Gait patterns were recorded by inertial sensors attached to both shoes. Machine learning algorithms were applied to calculate spatio-temporal gait parameters and gait variability expressed as coefficient of variance (CV). RESULTS Stride length (- 15%) and gait velocity (- 19%) were reduced, while stride (+ 7%) and stance time (+ 2%) were increased in patients with HD. However, parameters reflecting gait variability were substantially altered in HD patients (+ 17% stride length CV up to + 41% stride time CV with largest effect size) and showed strong correlations to TMS and TFC (0.416 ≤ rSp ≤ 0.690). Objective gait variability parameters correlated with disease stage based upon TFC. CONCLUSIONS Sensor-based gait variability parameters were identified as clinically most relevant digital biomarker for gait impairment in HD. Altered gait variability represents characteristic irregularity of gait in HD and reflects disease severity.
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Affiliation(s)
- Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Dennis Jensen
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - F Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Anja Kletsch
- George-Huntington Institute (GHI) GmbH, Münster, Germany
| | - Stefan Bohlen
- George-Huntington Institute (GHI) GmbH, Münster, Germany
| | - Robin Schubert
- George-Huntington Institute (GHI) GmbH, Münster, Germany
| | - Lisa M Muratori
- George-Huntington Institute (GHI) GmbH, Münster, Germany
- Rehabilitation Research and Movement Performance Laboratory (RRAMP Lab), Stony Brook University, Stony Brook, NY, USA
| | - Bjoern Eskofier
- Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
- Medical Valley-Digital Health Application Center GmbH, Bamberg, Germany
- Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
| | - Jürgen Winkler
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany
| | - Ralf Reilmann
- George-Huntington Institute (GHI) GmbH, Münster, Germany
- Department of Radiology, University of Muenster, Muenster, Germany
- Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Zacharias Kohl
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany.
- Center for Rare Diseases Erlangen, University Hospital Erlangen, Erlangen, Germany.
- Department of Neurology, University of Regensburg, Regensburg, Germany.
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Aholt K, Eskofier BM, Martindale CF, Kuderle A, Gassner H, Gladow T, Rojo J, Villanueva-Mascato S, Klucken J, Arredondo Waldmeyer MT. A Mobile Solution for Rhythmic Auditory Stimulation Gait Training. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:309-312. [PMID: 31945903 DOI: 10.1109/embc.2019.8857143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Recent studies showed that Parkinson's disease (PD) patients improved their gait parameters while walking with rhythmic auditory stimulation (RAS). They achieved a longer stride length, a reduced stride time variability and a higher walking speed. Combining RAS with mobile gait analysis would allow continuous monitoring of RAS effects and gait in natural environments. This paper proposes a mobile solution for home-based assessment of RAS by combining RAS gait training and a mobile system for data acquisition. Existing datasets were used to investigate the cadence of PD patients and to propose suitable frequencies for RAS gait training. The cadence calculation was implemented using a peak detection algorithm, which uses the time difference between two mid-swing events as stride time values. We validated our system as a whole using a cohort of 13 PD patients who performed RAS gait training. The algorithms were also validated against the eGaIT system, a state-of-the-art system, and achieved a mean F1 score for detected strides of 97.57 % ± 0.86 % and a mean absolute error for the cadence of 0.16 spm ± 0.09 spm. This study lays the ground work for further clinical studies investigating the effectiveness of mobile RAS within a home environment.
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18
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Haji Ghassemi N, Hannink J, Roth N, Gaßner H, Marxreiter F, Klucken J, Eskofier BM. Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson's Disease. SENSORS 2019; 19:s19143103. [PMID: 31337067 PMCID: PMC6679564 DOI: 10.3390/s19143103] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 07/09/2019] [Accepted: 07/09/2019] [Indexed: 01/08/2023]
Abstract
Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson’s disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians’ gait assessment and to monitor patients in their daily environment.
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Affiliation(s)
- Nooshin Haji Ghassemi
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Strasse 2b, D-91052 Erlangen, Germany.
| | - Julius Hannink
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Strasse 2b, D-91052 Erlangen, Germany
| | - Nils Roth
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Strasse 2b, D-91052 Erlangen, Germany
| | - Heiko Gaßner
- Department of Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, D-91054 Erlangen, Germany
| | - Franz Marxreiter
- Department of Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, D-91054 Erlangen, Germany
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, D-91054 Erlangen, Germany
| | - Björn M Eskofier
- Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Strasse 2b, D-91052 Erlangen, Germany
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