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Tamir-Ostrover H, Hassin-Baer S, Fay-Karmon T, Friedman J. Quantifying Changes in Dexterity as a Result of Piano Training in People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:3318. [PMID: 38894110 PMCID: PMC11174779 DOI: 10.3390/s24113318] [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: 04/02/2024] [Revised: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024]
Abstract
People with Parkinson's disease often show deficits in dexterity, which, in turn, can lead to limitations in performing activities of daily life. Previous studies have suggested that training in playing the piano may improve or prevent a decline in dexterity in this population. In this pilot study, we tested three participants on a six-week, custom, piano-based training protocol, and quantified dexterity before and after the intervention using a sensor-enabled version of the nine-hole peg test, the box and block test, a test of finger synergies using unidimensional force sensors, and the Quantitative Digitography test using a digital piano, as well as selected relevant items from the motor parts of the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and the Parkinson's Disease Questionnaire (PDQ-39) quality of life questionnaire. The participants showed improved dexterity following the training program in several of the measures used. This pilot study proposes measures that can track changes in dexterity as a result of practice in people with Parkinson's disease and describes a potential protocol that needs to be tested in a larger cohort.
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Affiliation(s)
- Hila Tamir-Ostrover
- Department of Physical Therapy, Faculty of Medical & Health Sciences, School of Health Professions, Tel Aviv University, Tel Aviv 6997801, Israel;
| | - Sharon Hassin-Baer
- Movement Disorders Institute and Department of Neurology, Chaim Sheba Medical Center, Tel Hashomer, Ramat-Gan 5262000, Israel; (S.H.-B.); (T.F.-K.)
- Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Tsvia Fay-Karmon
- Movement Disorders Institute and Department of Neurology, Chaim Sheba Medical Center, Tel Hashomer, Ramat-Gan 5262000, Israel; (S.H.-B.); (T.F.-K.)
- Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Jason Friedman
- Department of Physical Therapy, Faculty of Medical & Health Sciences, School of Health Professions, Tel Aviv University, Tel Aviv 6997801, Israel;
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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Cisek K, Kelleher JD. Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3341-3352. [PMID: 37578924 DOI: 10.1109/tnsre.2023.3304758] [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: 08/16/2023]
Abstract
BACKGROUND There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. OBJECTIVES This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. ELIGIBILITY CRITERIA Our methodological search initially identified over 14,000 publications of the last two decades in the Web of Science and Scopus databases, which we filter, using keywords and a qualitative review, to a core corpus of 1062 documents. RESULTS We generate a 3-topic, 4-topic and 5-topic model and interpret the resulting topics as four distinct thematics in the literature, which we label as Robotics, Software, Functional and Cognitive. We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the field. These are mainly in the Cognitive thematic, especially for systems and devices for sensory loss rehabilitation, tasks of daily living performance and social participation. CONCLUSION The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic technologies, with lesser emphasis on Cognitive outcomes and combined interventions. We hope this review broadens awareness, usage and mainstream acceptance of novel technologies in rehabilitation and reintegration among clinicians, carers and patients.
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Chen X, Shao Y, Zou L, Tang S, Lai Z, Sun X, Xie F, Xie L, Luo J, Hu D. Compensatory movement detection by using near-infrared spectroscopy technology based on signal improvement method. Front Neurosci 2023; 17:1153252. [PMID: 37234262 PMCID: PMC10206030 DOI: 10.3389/fnins.2023.1153252] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Compensatory movements usually occur in stroke survivors with hemiplegia, which is detrimental to recovery. This paper proposes a compensatory movement detection method based on near-infrared spectroscopy (NIRS) technology and verifies its feasibility using a machine learning algorithm. We present a differential-based signal improvement (DBSI) method to enhance NIRS signal quality and discuss its effect on improving detection performance. Method Ten healthy subjects and six stroke survivors performed three common rehabilitation training tasks while the activation of six trunk muscles was recorded using NIRS sensors. After data preprocessing, DBSI was applied to the NIRS signals, and two time-domain features (mean and variance) were extracted. An SVM algorithm was used to test the effect of the NIRS signal on compensatory behavior detection. Results Classification results show that NIRS signals have good performance in compensatory detection, with accuracy rates of 97.76% in healthy subjects and 97.95% in stroke survivors. After using the DBSI method, the accuracy improved to 98.52% and 99.47%, respectively. Discussion Compared with other compensatory motion detection methods, our proposed method based on NIRS technology has better classification performance. The study highlights the potential of NIRS technology for improving stroke rehabilitation and warrants further investigation.
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Affiliation(s)
- Xiang Chen
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - YinJin Shao
- Department of Rehabilitation Medicine, Ganzhou People's Hospital, Ganzhou, China
| | - LinFeng Zou
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - SiMin Tang
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhiwei Lai
- Ganzhou Hospital of Traditional Chinese Medicine, Ganzhou, China
| | - XiaoBo Sun
- Ganzhou Hospital of Traditional Chinese Medicine, Ganzhou, China
| | - FaWen Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Longhan Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Jun Luo
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Dongxia Hu
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Xie P, Lin C, Cai S, Xie L. Learning-Based Compensation-Corrective Control Strategy for Upper Limb Rehabilitation Robots. Int J Soc Robot 2022. [DOI: 10.1007/s12369-022-00943-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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de-la-Fuente-Robles YM, Ricoy-Cano AJ, Albín-Rodríguez AP, López-Ruiz JL, Espinilla-Estévez M. Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus. SENSORS (BASEL, SWITZERLAND) 2022; 22:8599. [PMID: 36433195 PMCID: PMC9696945 DOI: 10.3390/s22228599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/30/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.
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Wang X, Fu Y, Ye B, Babineau J, Ding Y, Mihailidis A. Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review. J Med Internet Res 2022; 24:e34307. [PMID: 35699982 PMCID: PMC9237771 DOI: 10.2196/34307] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/25/2022] [Accepted: 04/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Upper extremity (UE) impairment affects up to 80% of stroke survivors and accounts for most of the rehabilitation after discharge from the hospital release. Compensation, commonly used by stroke survivors during UE rehabilitation, is applied to adapt to the loss of motor function and may impede the rehabilitation process in the long term and lead to new orthopedic problems. Intensive monitoring of compensatory movements is critical for improving the functional outcomes during rehabilitation. Objective This review analyzes how technology-based methods have been applied to assess and detect compensation during stroke UE rehabilitation. Methods We conducted a wide database search. All studies were independently screened by 2 reviewers (XW and YF), with a third reviewer (BY) involved in resolving discrepancies. The final included studies were rated according to their level of clinical evidence based on their correlation with clinical scales (with the same tasks or the same evaluation criteria). One reviewer (XW) extracted data on publication, demographic information, compensation types, sensors used for compensation assessment, compensation measurements, and statistical or artificial intelligence methods. Accuracy was checked by another reviewer (YF). Four research questions were presented. For each question, the data were synthesized and tabulated, and a descriptive summary of the findings was provided. The data were synthesized and tabulated based on each research question. Results A total of 72 studies were included in this review. In all, 2 types of compensation were identified: disuse of the affected upper limb and awkward use of the affected upper limb to adjust for limited strength, mobility, and motor control. Various models and quantitative measurements have been proposed to characterize compensation. Body-worn technology (25/72, 35% studies) was the most used sensor technology to assess compensation, followed by marker-based motion capture system (24/72, 33% studies) and marker-free vision sensor technology (16/72, 22% studies). Most studies (56/72, 78% studies) used statistical methods for compensation assessment, whereas heterogeneous machine learning algorithms (15/72, 21% studies) were also applied for automatic detection of compensatory movements and postures. Conclusions This systematic review provides insights for future research on technology-based compensation assessment and detection in stroke UE rehabilitation. Technology-based compensation assessment and detection have the capacity to augment rehabilitation independent of the constant care of therapists. The drawbacks of each sensor in compensation assessment and detection are discussed, and future research could focus on methods to overcome these disadvantages. It is advised that open data together with multilabel classification algorithms or deep learning algorithms could benefit from automatic real time compensation detection. It is also recommended that technology-based compensation predictions be explored.
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Affiliation(s)
- Xiaoyi Wang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Fu
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Bing Ye
- KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada
| | - Jessica Babineau
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Yong Ding
- Department of Rehabilitation Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Alex Mihailidis
- KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.,Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada
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Tanioka R, Ito H, Takase K, Kai Y, Sugawara K, Tanioka T, Locsin R, Tomotake M. Usefulness of 2D Video Analysis for Evaluation of Shoulder Range of Motion during Upper Limb Exercise in Patients with Psychiatric Disorders. THE JOURNAL OF MEDICAL INVESTIGATION 2022; 69:70-79. [PMID: 35466149 DOI: 10.2152/jmi.69.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Aging and its associated problems related to movement impacts the care of people with psychiatric disorders. This study sought to clarify the usefulness of 2D video analysis for evaluating shoulder range of motion (ROM) during upper limb exercises in patients with psychiatric disorders. Subjects (N=54) were patients with psychiatric disorders categorized as the following:having either a high or low activities of daily living (ADL) score using the Barthel Index;experiencing shoulder ROM limitation, and whether or not compensatory movements were exhibited. Compensatory movement was also considered in patients with Parkinsonism, cerebrovascular disease, and cognitive dysfunction. Shoulder joint ROM was measured using a goniometer and active ROM was captured using ImageJ. No significant difference between passive ROM measured by a goniometer and active ROM measured by ImageJ considering disease groups, ADL level, and shoulder ROM limitation was found. Factoring in compensatory movements, however, significant differences were found between passive and active ROM:existence compensatory movement group, left side (z=-2.30, p=0.02);nonexistence compensatory movement group, right side (z=-2.63, p<0.001). Image-evaluating devices help assess ROM in patients with psychiatric disorders, enhancing the development of physical rehabilitation programs to regain critical ADL, sustaining self-care capabilities. J. Med. Invest. 69 : 70-79, February, 2022.
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Affiliation(s)
- Ryuichi Tanioka
- Graduate School of Health Sciences, Lifelong Health and Medical Science, Tokushima University, Tokushima, Japan
| | - Hirokazu Ito
- Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Kensaku Takase
- Department of Rehabilitation, Anan Medical Center, Tokushima, Japan
| | - Yoshihiro Kai
- Department of Mechanical Engineering, Tokai University, Kanagawa, Japan
| | - Kenichi Sugawara
- Department of Physical Therapy, Kanagawa University of Human Service, Kanagawa, Japan
| | - Tetsuya Tanioka
- Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Rozzano Locsin
- Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Masahito Tomotake
- Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan
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Lo Presti D, Zaltieri M, Bravi M, Morrone M, Caponero MA, Schena E, Sterzi S, Massaroni C. A Wearable System Composed of FBG-Based Soft Sensors for Trunk Compensatory Movements Detection in Post-Stroke Hemiplegic Patients. SENSORS 2022; 22:s22041386. [PMID: 35214287 PMCID: PMC8963020 DOI: 10.3390/s22041386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/06/2022] [Accepted: 02/09/2022] [Indexed: 11/16/2022]
Abstract
In this study, a novel wearable system for the identification of compensatory trunk movements (CTMs) in post-stroke hemiplegic patients is presented. The device is composed of seven soft sensing elements (SSEs) based on fiber Bragg grating (FBG) technology. Each SSE consists of a single FBG encapsulated into a flexible matrix to enhance the sensor’s robustness and improve its compliance with the human body. The FBG’s small size, light weight, multiplexing capability, and biocompatibility make the proposed wearable system suitable for multi-point measurements without any movement restriction. Firstly, its manufacturing process is presented, together with the SSEs’ mechanical characterization to strain. Results of the metrological characterization showed a linear response of each SSE in the operating range. Then, the feasibility assessment of the proposed system is described. In particular, the device’s capability of detecting CTMs was assessed on 10 healthy volunteers and eight hemiplegic patients while performing three tasks which are representative of typical everyday life actions. The wearable system showed good potential in detecting CTMs. This promising result may foster the use of the proposed device on post-stroke patients, aiming at assessing the proper course of the rehabilitation process both in clinical and domestic settings. Moreover, its use may aid in defining tailored strategies to improve post-stoke patients’ motor recovery and quality of life.
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Affiliation(s)
- Daniela Lo Presti
- Research Unit of Measurements and Biomedical Instrumentation, Center for Integrated Research, Università Campus Bio-Medico di Roma, 00128 Roma, Italy; (D.L.P.); (M.Z.); (E.S.); (C.M.)
| | - Martina Zaltieri
- Research Unit of Measurements and Biomedical Instrumentation, Center for Integrated Research, Università Campus Bio-Medico di Roma, 00128 Roma, Italy; (D.L.P.); (M.Z.); (E.S.); (C.M.)
| | - Marco Bravi
- Unit of Physical Medicine, Campus Bio-Medico di Roma, Rehabilitation of Policlinico Universitario, 00128 Roma, Italy; (M.B.); (M.M.)
| | - Michelangelo Morrone
- Unit of Physical Medicine, Campus Bio-Medico di Roma, Rehabilitation of Policlinico Universitario, 00128 Roma, Italy; (M.B.); (M.M.)
| | | | - Emiliano Schena
- Research Unit of Measurements and Biomedical Instrumentation, Center for Integrated Research, Università Campus Bio-Medico di Roma, 00128 Roma, Italy; (D.L.P.); (M.Z.); (E.S.); (C.M.)
| | - Silvia Sterzi
- Unit of Physical Medicine, Campus Bio-Medico di Roma, Rehabilitation of Policlinico Universitario, 00128 Roma, Italy; (M.B.); (M.M.)
- Correspondence:
| | - Carlo Massaroni
- Research Unit of Measurements and Biomedical Instrumentation, Center for Integrated Research, Università Campus Bio-Medico di Roma, 00128 Roma, Italy; (D.L.P.); (M.Z.); (E.S.); (C.M.)
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Sellmann A, Wagner D, Holtz L, Eschweiler J, Diers C, Williams S, Disselhorst-Klug C. Detection of Typical Compensatory Movements during Autonomously Performed Exercises Preventing Low Back Pain (LBP). SENSORS (BASEL, SWITZERLAND) 2021; 22:111. [PMID: 35009660 PMCID: PMC8747326 DOI: 10.3390/s22010111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/15/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
With the growing number of people seeking medical advice due to low back pain (LBP), individualised physiotherapeutic rehabilitation is becoming increasingly relevant. Thirty volunteers were asked to perform three typical LBP rehabilitation exercises (Prone-Rocking, Bird-Dog and Rowing) in two categories: clinically prescribed exercise (CPE) and typical compensatory movement (TCM). Three inertial sensors were used to detect the movement of the back during exercise performance and thus generate a dataset that is used to develop an algorithm that detects typical compensatory movements in autonomously performed LBP exercises. The best feature combinations out of 50 derived features displaying the highest capacity to differentiate between CPE and TCM in each exercise were determined. For classifying exercise movements as CPE or TCM, a binary decision tree was trained with the best performing features. The results showed that the trained classifier is able to distinguish CPE from TCM in Bird-Dog, Prone-Rocking and Rowing with up to 97.7% (Head Sensor, one feature), 98.9% (Upper back Sensor, one feature) and 80.5% (Upper back Sensor, two features) using only one sensor. Thus, as a proof-of-concept, the introduced classification models can be used to detect typical compensatory movements in autonomously performed LBP exercises.
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Affiliation(s)
- Asaad Sellmann
- Department of Rehabilitation and Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, 52074 Aachen, Germany; (D.W.); (L.H.); (S.W.); (C.D.-K.)
| | - Désirée Wagner
- Department of Rehabilitation and Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, 52074 Aachen, Germany; (D.W.); (L.H.); (S.W.); (C.D.-K.)
| | - Lucas Holtz
- Department of Rehabilitation and Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, 52074 Aachen, Germany; (D.W.); (L.H.); (S.W.); (C.D.-K.)
| | - Jörg Eschweiler
- Department of Orthopaedics, Trauma and Reconstructive Surgery, RWTH Aachen University Clinic, 52074 Aachen, Germany;
| | | | - Sybele Williams
- Department of Rehabilitation and Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, 52074 Aachen, Germany; (D.W.); (L.H.); (S.W.); (C.D.-K.)
| | - Catherine Disselhorst-Klug
- Department of Rehabilitation and Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, 52074 Aachen, Germany; (D.W.); (L.H.); (S.W.); (C.D.-K.)
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NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks. SENSORS 2021; 21:s21134482. [PMID: 34208996 PMCID: PMC8271972 DOI: 10.3390/s21134482] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/20/2021] [Accepted: 06/24/2021] [Indexed: 11/16/2022]
Abstract
A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl-Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.
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Chen Y, Ma K, Yang L, Yu S, Cai S, Xie L. Trunk compensation electromyography features purification and classification model using generative adversarial network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Compensatory Trunk Movements in Naturalistic Reaching and Manipulation Tasks in Chronic Stroke Survivors. J Appl Biomech 2021; 37:215-223. [PMID: 33631718 DOI: 10.1123/jab.2020-0090] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 11/12/2020] [Accepted: 12/09/2020] [Indexed: 11/18/2022]
Abstract
Impairment of arm movements poststroke often results in the use of compensatory trunk movements to complete motor tasks. These compensatory movements have been mostly observed in tightly controlled conditions, with very few studies examining them in more naturalistic settings. In this study, the authors quantified the presence of compensatory movements during a set of continuous reaching and manipulation tasks performed with both the paretic and nonparetic arm (in 9 chronic stroke survivors) or the dominant arm (in 20 neurologically unimpaired control participants). Kinematic data were collected using motion capture to assess trunk and elbow movement. The authors found that trunk displacement and rotation were significantly higher when using the paretic versus nonparetic arm (P = .03). In contrast, elbow angular displacement was significantly lower in the paretic versus nonparetic arm (P = .01). The reaching tasks required significantly higher trunk compensation and elbow movement than the manipulation tasks. These results reflect increased reliance on compensatory trunk movements poststroke, even in everyday functional tasks, which may be a target for home rehabilitation programs. This study provides a novel contribution to the rehabilitation literature by examining the presence of compensatory movements in naturalistic reaching and manipulation tasks.
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Zucchi B, Mangone M, Agostini F, Paoloni M, Petriello L, Bernetti A, Santilli V, Villani C. Movement Analysis with Inertial Measurement Unit Sensor After Surgical Treatment for Distal Radius Fractures. Biores Open Access 2020; 9:151-161. [PMID: 32461820 PMCID: PMC7247043 DOI: 10.1089/biores.2019.0035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2020] [Indexed: 01/01/2023] Open
Abstract
Inertial measurement unit (IMU) has recently been used to evaluate a movement of a body segment to provide accurate information of movement's characteristics. IMU systems have been validated to successfully measure joint angle during upper limb range of motion (ROM). The study aimed to retrospectively evaluate, using an IMU, the ROM recovery of the wrist after surgical treatment for distal-radius fractures with Kirschner wire fixation (KWF) or with volar plate fixation (VPF) and screws. To assess pain in the wrist joint, muscle-fatigue (MF), and functional difficulties in activities of daily living, we evaluated the patients through patient-related wrist evaluation questionnaire (PRWE) scale, disability of the arm, shoulder and hand (DASH) scale, Hand Grip Strength (HGS), and surface electromyography (EMG). We used a single IMU composed of three-axis gyroscope, a three-axis accelerometer, and a magnetometer. We calculated the value of ROM as a percentage with respect to the unaffected wrist. We also recorded surface-EMG signals over biceps brachialis, flexor carpi radialis (FCR), extensor carpi radialis (ECR), and pronator teres muscles. Forty patients were recruited for our study. Ulnar deviation (UD) was significantly higher for VPF than for KWF (p = 0.017); supination was significantly higher for VPF than for KWF (p = 0.031). The percentage of decay of the median frequency of FCR of volar plate was significantly higher than KWF. The HGS of KWF was significantly higher than VPF. In literature, there were no significant differences between the two types of treatment at long-term follow-up. Our results demonstrate a superior efficacy of VPF in terms of ROM improvement in UD and supination, but for these patients, muscle fatigue is greater than the KWF group. Based on the data available, VPF is similar to KWF for the treatment of distal radius fractures. The IMU sensor could be used in the future to evaluate ROM after surgery during patient's rehabilitation and to compare the effects with stratified analysis regarding age and fracture type, paralleled with cost-effectiveness analysis.
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Affiliation(s)
- Benedetta Zucchi
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Massimiliano Mangone
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Francesco Agostini
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Marco Paoloni
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Luisa Petriello
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Andrea Bernetti
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Valter Santilli
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
| | - Ciro Villani
- Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University, Rome, Italy
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Cai S, Li G, Su E, Wei X, Huang S, Ma K, Zheng H, Xie L. Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy. IEEE J Biomed Health Inform 2020; 24:2630-2638. [PMID: 31902785 DOI: 10.1109/jbhi.2019.2963365] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVES Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the real-time monitoring of compensation in patients with stroke by using pressure distribution data and machine learning algorithms. Whether trunk compensation can be reduced by combining the online detection of compensation and haptic feedback of a rehabilitation robot was also investigated. METHODS Six patients with stroke did three forms of reaching movements while pressure distribution data were recorded as Dataset1. A support vector machine (SVM) classifier was trained with features extracted from Dataset1. Then, two other patients with stroke performed reaching tasks, and the SVM classifier trained by Dataset1 was employed to classify the compensatory patterns online. Based on the real-time monitoring of compensation, a rehabilitation robot provided an assistive force to patients with stroke to reduce compensations. RESULTS Good classification performance (F1 score > 0.95) was obtained in both offline and online compensation analysis using the SVM classifier and pressure distribution data of patients with stroke. Based on the real-time detection of compensatory patterns, the angles of trunk rotation, trunk lean-forward and trunk-scapula elevation decreased by 46.95%, 32.35% and 23.75%, respectively. CONCLUSION High classification accuracies verified the feasibility of detecting compensation in patients with stroke based on pressure distribution data. Since the validity and reliability of the online detection of compensation has been verified, this classifier can be incorporated into a rehabilitation robot to reduce trunk compensations in patients with stroke.
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Ma K, Chen Y, Zhang X, Zheng H, Yu S, Cai S, Xie L. sEMG-Based Trunk Compensation Detection in Rehabilitation Training. Front Neurosci 2019; 13:1250. [PMID: 31824250 PMCID: PMC6881307 DOI: 10.3389/fnins.2019.01250] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 11/05/2019] [Indexed: 11/21/2022] Open
Abstract
Stroke patients often use trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, which results in a reduced rehabilitation training effect. Detecting trunk compensations can improve the effect of rehabilitation training. This study investigates the feasibility of a surface electromyography-based trunk compensation detection (sEMG-bTCD) method. Five healthy subjects and nine stroke subjects with cognitive and comprehension skills were recruited to participate in the experiments. The sEMG signals from nine superficial trunk muscles were collected during three rehabilitation training tasks (reach-forward-back, reach-side-to-side, and reach-up-to-down motions) without compensation and with three common trunk compensations [lean-forward (LF), trunk rotation (TR), and shoulder elevation (SE)]. Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value (MAV), waveform length, and the fourth order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent TCD performance was achieved in healthy participants by using support vector machine (SVM) classifier (LF: accuracy = 94.0%, AUC = 0.97, F1 = 0.94; TR: accuracy = 95.8%, AUC = 0.99, F1 = 0.96; SE: accuracy = 100.0%, AUC = 1.00, F1 = 1.00). By using SVM classifier, TCD performance in stroke participants was also obtained (LF: accuracy = 74.8%, AUC = 0.90, F1 = 0.73; TR: accuracy = 67.1%, AUC = 0.85, F1 = 0.71; SE: accuracy = 91.3%, AUC = 0.98, F1 = 0.90). Compared with the methods based on cameras or inertial sensors, better detection performance was obtained in both healthy and stroke participants. The results demonstrated the feasibility of the sEMG-bTCD method, and it helps to prompt the stroke patients to correct their incorrect posture, thereby improving the effectiveness of rehabilitation training.
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Affiliation(s)
- Ke Ma
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Yan Chen
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Xiaoya Zhang
- Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Haiqing Zheng
- Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Song Yu
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Siqi Cai
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Longhan Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
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