Imperial College London
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Professor Pantelis Georgiou

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Biomedical Electronics
 
 
 
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Contact

 

+44 (0)20 7594 6326pantelis Website

 
 
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Location

 

902Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Moniri:2020:10.1109/TBME.2020.3012783,
author = {Moniri, A and Terracina, D and Rodriguez-Manzano, J and Strutton, P and Georgiou, P},
doi = {10.1109/TBME.2020.3012783},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {718--727},
title = {Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning},
url = {http://dx.doi.org/10.1109/TBME.2020.3012783},
volume = {68},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Moniri,A
AU  - Terracina,D
AU  - Rodriguez-Manzano,J
AU  - Strutton,P
AU  - Georgiou,P
DO  - 10.1109/TBME.2020.3012783
EP  - 727
PY  - 2020///
SN  - 0018-9294
SP  - 718
TI  - Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning
T2  - IEEE Transactions on Biomedical Engineering
UR  - http://dx.doi.org/10.1109/TBME.2020.3012783
UR  - https://ieeexplore.ieee.org/document/9152074
UR  - http://hdl.handle.net/10044/1/81191
VL  - 68
ER  -
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