Hemiparesis is one of the most frequent post-stroke conditions, which causes muscle weakness and/or inability to move one side of the body. Physical rehabilitation is the main treatment for hemiparesis recovery, and physiotherapists agree that using the impaired arm in the activities of daily living (ADLs) is crucial for a complete recovery. Currently, rehabilitation is assessed through diaries and self-questionnaires, which are subjective and do not tell the real condition of the patients throughout the day. Assistive devices can objectively evaluate the functional improvement of the impaired arm monitoring its activity. This work aimed to identify the purposeful arm movements during patient's ADLs. We consider arm's swing while walking as a non-purposeful movement. Firstly, the event-based approach was applied to separate movement and non-movement segments. Secondly, movement segments were used to detect the change-point (events) and their locations in time series signal. Two machine learning classifiers, Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained using 10-fold cross validation for the classification of purposeful and non-purposeful movements. Data from 10 healthy and 12 post-strokes volunteers from Institute Guttmann (Barcelona) were collected using the SensHand device. The volunteers, wearing one SensHand on each wrist, performed the following activities: resting, eating, pouring water, drinking, brushing, folding towel, grasp towel, grasp brush, grasp glass, continuous and walking. Developing a model based on the healthy subjects, the overall classification accuracy obtained from SVM classifier and ANN was 81.21% and 97.06% respectively. Similarly, with post-stroke subjects obtained accuracy with the SVM and ANN was 84.18% and 99.74% respectively. Considering the whole dataset, SVM and ANN obtained maximum accuracy equal to 86.21% and 99.91% respectively. In conclusion, our work showed promising results for the classification of purposeful and non-purposeful movements.

Assessment of Purposeful Movements for Post-Stroke Patients in Activites of Daily Living with Wearable Sensor Device / Butt, AH; Zambrana, C; Idelsohn-Zielonka, S; Claramunt-Molet, M; Ugartemendia-Etxarri, A; Rovini, E; Moschetti, A; Molleja, C; Martin, C; Salleras, EO; Cavallo, F. - ELETTRONICO. - (2019), pp. 69-76. ((Intervento presentato al convegno 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology tenutosi a IT [10.1109/CIBCB.2019.8791470].

Assessment of Purposeful Movements for Post-Stroke Patients in Activites of Daily Living with Wearable Sensor Device

Rovini, E;Cavallo, F
2019

Abstract

Hemiparesis is one of the most frequent post-stroke conditions, which causes muscle weakness and/or inability to move one side of the body. Physical rehabilitation is the main treatment for hemiparesis recovery, and physiotherapists agree that using the impaired arm in the activities of daily living (ADLs) is crucial for a complete recovery. Currently, rehabilitation is assessed through diaries and self-questionnaires, which are subjective and do not tell the real condition of the patients throughout the day. Assistive devices can objectively evaluate the functional improvement of the impaired arm monitoring its activity. This work aimed to identify the purposeful arm movements during patient's ADLs. We consider arm's swing while walking as a non-purposeful movement. Firstly, the event-based approach was applied to separate movement and non-movement segments. Secondly, movement segments were used to detect the change-point (events) and their locations in time series signal. Two machine learning classifiers, Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained using 10-fold cross validation for the classification of purposeful and non-purposeful movements. Data from 10 healthy and 12 post-strokes volunteers from Institute Guttmann (Barcelona) were collected using the SensHand device. The volunteers, wearing one SensHand on each wrist, performed the following activities: resting, eating, pouring water, drinking, brushing, folding towel, grasp towel, grasp brush, grasp glass, continuous and walking. Developing a model based on the healthy subjects, the overall classification accuracy obtained from SVM classifier and ANN was 81.21% and 97.06% respectively. Similarly, with post-stroke subjects obtained accuracy with the SVM and ANN was 84.18% and 99.74% respectively. Considering the whole dataset, SVM and ANN obtained maximum accuracy equal to 86.21% and 99.91% respectively. In conclusion, our work showed promising results for the classification of purposeful and non-purposeful movements.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
IT
Goal 3: Good health and well-being
Butt, AH; Zambrana, C; Idelsohn-Zielonka, S; Claramunt-Molet, M; Ugartemendia-Etxarri, A; Rovini, E; Moschetti, A; Molleja, C; Martin, C; Salleras, EO; Cavallo, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1213617
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