Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models

Prabhu, Ghanashyama and O’Connor, Noel E and Moran, Kieran (2020) Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models. Sensors, 20 (4791). ISSN 1424-3210

[img] PDF
11550.pdf - Published Version
Restricted to Registered users only

Download (3MB) | Request a copy

Abstract

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data, is an important technology to enable patients to perform exercises independently in remote settings, e.g., their own home. In this paper, we first report on a comparison of traditional approaches to exercise recognition and repetition counting (supervised ML and peak detection) with Convolutional Neural Networks (CNNs). We investigated CNN models based on the AlexNet architecture and found that the performance was better than the traditional approaches, for exercise recognition (overall F1-score of 97.18%) and repetition counting (±1 error among 90% observed sets). To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. Also, we make the INSIGHT-LME dataset publicly available to encourage further research.

Item Type: Article
Uncontrolled Keywords: exercise-based rehabilitation; local muscular endurance exercises; deep learning; AlexNet; multi-class classification; INSIGHT-LME dataset
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 23 Apr 2021 05:50
Last Modified: 23 Apr 2021 05:50
URI: http://eprints.manipal.edu/id/eprint/156518

Actions (login required)

View Item View Item