Research Article
A Deep Learning Model for Exercise-Based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor
@INPROCEEDINGS{10.1007/978-3-030-70569-5_7, author={Ghanashyama Prabhu and Noel E. O’Connor and Kieran Moran}, title={A Deep Learning Model for Exercise-Based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor}, proceedings={Wireless Mobile Communication and Healthcare. 9th EAI International Conference, MobiHealth 2020, Virtual Event, November 19, 2020, Proceedings}, proceedings_a={MOBIHEALTH}, year={2021}, month={7}, keywords={INSIGHT-LME dataset CNN Wearable sensor Exercise-based rehabilitation Multi-channel time-series}, doi={10.1007/978-3-030-70569-5_7} }- Ghanashyama Prabhu
Noel E. O’Connor
Kieran Moran
Year: 2021
A Deep Learning Model for Exercise-Based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor
MOBIHEALTH
Springer
DOI: 10.1007/978-3-030-70569-5_7
Abstract
The ability to accurately and automatically recognize and count the repetitions of exercises using a single sensor is essential for technology-assisted exercise-based rehabilitation. In this paper, we present a single deep learning architecture to undertake both of these tasks based on multi-channel time-series data. The models are constructed and tested using the INSIGHT-LME [] exercise dataset which consists of ten local muscular endurance (LME) exercises. For exercise recognition, we achieved an overall F1-score measure of 96% and for repetition counting, we were correct within an error of ±1 repetitions in 88% of the observed exercise sets. To the best of our knowledge, our approach of using the same deep learning model for both tasks using raw time-series sensor data information is novel.
