Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning
File(s)
Accepted version
Author(s)
Moniri, Ahmad
Terracina, Dan
Rodriguez-Manzano, Jesus
Strutton, Paul
Georgiou, Pantelakis
Type
Journal Article
Abstract
Objective: Several features of the surface electromyography (sEMG) signal are related to muscle activity and fatigue. However, the time-evolution of these features are non-stationary and vary between subjects. The aim of this study is to investigate the use of adaptive algorithms to forecast sMEG feature of the trunk muscles. Methods: Shallow models and a deep convolutional neural network (CNN) were used to simultaneously learn and forecast 5 common sEMG features in real-time to provide tailored predictions. This was investigated for: up to a 25 second horizon; for 14 different muscles in the trunk; across 13 healthy subjects; while they were performing various exercises. Results: The CNN was able to forecast 25 seconds ahead of time, with 6.88% mean absolute percentage error and 3.72% standard deviation of absolute percentage error, across all the features. Moreover, the CNN outperforms the best shallow model in terms of a figure of merit combining accuracy and precision by at least 30% for all the 5 features. Conclusion: Even though the sEMG features are non-stationary and vary between subjects, adaptive learning and forecasting, especially using CNNs, can provide accurate and precise forecasts across a range of physical activities. Significance: The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially prevent low back pain. Additionally, the explicit realtime forecasting of sEMG features provides a general model which can be applied to many applications of muscle activity monitoring, which helps practitioners and physiotherapists improve therapy.
Date Issued
2020-07-29
Date Acceptance
2020-07-22
Citation
IEEE Transactions on Biomedical Engineering, 2020, 68 (2), pp.718-727
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers
Start Page
718
End Page
727
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
68
Issue
2
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9152074
Subjects
0801 Artificial Intelligence and Image Processing
0903 Biomedical Engineering
0906 Electrical and Electronic Engineering
Biomedical Engineering
Publication Status
Published
Date Publish Online
2020-07-29