Authors:
Ana Pereira
1
;
Duarte Folgado
1
;
Ricardo Cotrim
2
and
Inês Sousa
1
Affiliations:
1
Associaç ão Fraunhofer Portugal Research, Rua Alfredo Allen 455/461, Porto and Portugal
;
2
Plux Wireless Biosignals S. A., Avenida 5 Outubro 70, 1050-59, Lisboa and Portugal
Keyword(s):
Physiotherapy, Inertial Sensors, Electromyography, Body Area Networks, Automatic Segmentation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Computing Systems
;
Wearable Sensors and Systems
Abstract:
The efficacy of home-based physiotherapy depends on the correct and systematic execution of prescribed exercises. Biofeedback systems enable to accurately track exercise execution and prevent patients from unconsciously introduce incorrect postures or improper muscular loads on the prescribed exercises. This is often achieved using inertial and surface electromyography (sEMG) sensors, as they can be used to monitor human motion variables and muscular activation. In this work, we propose to use machine learning techniques to automatically assess if a given exercise was properly executed. We present two major contributions: (1) a novel sEMG segmentation algorithm based on a syntactic approach and (2) a feature extraction and classification pipeline. The proposed methodology was applied to a controlled laboratory trial, for a set of 3 different exercises often prescribe by physiotherapists. The findings of this study support it is possible to automatically segment and classify exercise r
epetitions according to a given set of common deviations.
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