Wireless sEMG Sensor for Neck Muscle Activity Measurement and Posture Classification using Machine Learning

The nature of prolonged work and lifestyle have affected upper extremities, leading to neck musculoskeletal disorders (MSD). The existing wired surface electromyography (sEMG) techniques limits the dynamic muscle activity measurement. In the current study, a wireless, lightweight, cost-effective and...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 23; no. 24; p. 1
Main Authors: Dandumahanti, Bhanu Priya, Subramaniyam, Murali
Format: Journal Article
Language:English
Published: New York IEEE 15.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Classification
Electromyogram
Electromyography
Error analysis
Machine learning
Muscle function
Muscles
Neck
neck posture classification
NRF transmission
Pattern classification
Sensors
signal acquisition system
Signal to noise ratio
Support vector machines
Wireless communication
wireless EMG
Wireless sensor networks
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:The nature of prolonged work and lifestyle have affected upper extremities, leading to neck musculoskeletal disorders (MSD). The existing wired surface electromyography (sEMG) techniques limits the dynamic muscle activity measurement. In the current study, a wireless, lightweight, cost-effective and fast data-transmitting sEMG module is developed and assisted with pattern classification techniques to identify neck postural risks. The developed system transmits EMG signals with a sampling rate of 1024 Hz and a signal-to-noise ratio of 50-60 dB. When calibrated with a standard EMG system, error analysis indicates a maximum percentage of error of 1.767% for the developed system. An experimental trial was performed on 30 subjects by measuring muscle activity on two neck muscles: sternocleidomastoid (SCM) and upper trapezius descendens (TRP). A 3-min experimental trial resulted in an increase of muscle activity by 1.64% maximum voluntary contraction (MVC) at SCM and 3.87% MVC at TRP muscle. Indicating TRP muscle shows more muscle activity than the SCM muscle during flexion. Three machine learning classification algorithms were used to distinguish neutral and flexed neck postures; the support vector machine gives higher classification accuracy of 96% than other classification algorithms. The proposed system can be used to identify the fatigued muscles, which alerts the user to adjust the posture during prolonged flexed tasks.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3329383