Physiotherapy Exercises Evaluation using a Combined Approach based on sEMG and Wearable Inertial Sensors

Ana Pereira, Duarte Folgado, Ricardo Cotrim, Inês Sousa

2019

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 repetitions according to a given set of common deviations.

Paper Citation

in Harvard Style

Pereira A., Folgado D., Cotrim R. and Sousa I. (2019). Physiotherapy Exercises Evaluation using a Combined Approach based on sEMG and Wearable Inertial Sensors. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 4: BIOSIGNALS; ISBN 978-989-758-353-7, SciTePress, pages 73-82. DOI: 10.5220/0007391300730082

in Bibtex Style

@conference{biosignals19,
author={Ana Pereira and Duarte Folgado and Ricardo Cotrim and Inês Sousa},
title={Physiotherapy Exercises Evaluation using a Combined Approach based on sEMG and Wearable Inertial Sensors},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 4: BIOSIGNALS},
year={2019},
pages={73-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007391300730082},
isbn={978-989-758-353-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - Volume 4: BIOSIGNALS
TI - Physiotherapy Exercises Evaluation using a Combined Approach based on sEMG and Wearable Inertial Sensors
SN - 978-989-758-353-7
AU - Pereira A.
AU - Folgado D.
AU - Cotrim R.
AU - Sousa I.
PY - 2019
SP - 73
EP - 82
DO - 10.5220/0007391300730082
PB - SciTePress