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A Deep Learning Model for Exercise-Based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor

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Wireless Mobile Communication and Healthcare (MobiHealth 2020)

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 [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.

Supported by Insight SFI Research Centre for Data Analytics.

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Notes

  1. 1.

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  2. 2.

    https://www.shimmersensing.com/products/shimmer-9dof-calibration.

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Acknowledgement

This publication has emanated from research supported by INSIGHT SFI Research Centre for Data Analytics and Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin, Ireland

    Ghanashyama Prabhu, Noel E. O’Connor & Kieran Moran

  2. School of Electronic Engineering, Dublin City University, Dublin, Ireland

    Ghanashyama Prabhu & Noel E. O’Connor

  3. School of Health and Human Performance, Dublin City University, Dublin, Ireland

    Kieran Moran

  4. Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

    Ghanashyama Prabhu

Authors
  1. Ghanashyama Prabhu
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  2. Noel E. O’Connor
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  3. Kieran Moran
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Corresponding author

Correspondence to Ghanashyama Prabhu .

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Editors and Affiliations

  1. School of Computer Science, University of St Andrews, St Andrews, UK

    Juan Ye

  2. School of Computer Science and Information Technology, University College Dublin, Dublin, Dublin, Ireland

    Michael J. O'Grady

  3. Department of Computer Science, University of Milan, Milan, Italy

    Gabriele Civitarese

  4. Faculty of Computer Science and Electrical Engineering, University of Rostock, Rostock, Germany

    Kristina Yordanova

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Prabhu, G., O’Connor, N.E., Moran, K. (2021). A Deep Learning Model for Exercise-Based Rehabilitation Using Multi-channel Time-Series Data from a Single Wearable Sensor. In: Ye, J., O'Grady, M.J., Civitarese, G., Yordanova, K. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-70569-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-70569-5_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-70569-5

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