TY - GEN
T1 - A Deep Learning Model for Exercise-Based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor
AU - Prabhu, Ghanashyama
AU - O’Connor, Noel E.
AU - Moran, Kieran
N1 - Funding Information:
Supported by Insight SFI Research Centre for Data Analytics.
Publisher Copyright:
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2021
Y1 - 2021
N2 - 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 [1] 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.
AB - 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 [1] 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.
UR - https://www.scopus.com/pages/publications/85104472638
UR - https://www.scopus.com/inward/citedby.url?scp=85104472638&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-70569-5_7
DO - 10.1007/978-3-030-70569-5_7
M3 - Conference contribution
AN - SCOPUS:85104472638
SN - 9783030705688
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 104
EP - 115
BT - Wireless Mobile Communication and Healthcare - 9th EAI International Conference, MobiHealth 2020, Proceedings
A2 - Ye, Juan
A2 - O’Grady, Michael J.
A2 - Civitarese, Gabriele
A2 - Yordanova, Kristina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th EAI International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2020
Y2 - 19 November 2020 through 19 November 2020
ER -