Paper Info
Title
An sEMG-based method to adaptively reject the effect of contraction on spectral analysis for fatigue tracking.
Abstract
Muscle fatigue detection and tracking has gained significant attention as the sports science and rehabilitation technologies developed. It is known that muscle fatigue can be evaluated through surface Electromyography (sEMG) sensors, which are portable, non-invasive and applicable for real-time systems. There are plenty of fatigue tracking algorithms, many of which uses frequency, time and time-frequency behaviors of sEMG signals. An example to most commonly used sEMG-based fatigue detection methods can be mean frequency (MNF), median frequency (MDF), zero-crossing rate (ZCR) and continuous wavelet transform (CWT). However, all of these muscle fatigue calculation methods are adversely affected by the dynamically changing sEMG contraction amplitude, since EMG spectrum also demonstrates a shift with the changing signal RMS; powerful contractions lead a shift to high frequency bounds and the opposite happens for the weak. To overcome that, we propose an adaptive algorithm, which learns the effect of contraction power on sEMG power spectral density (PSD) and subtracts that amount of frequency shift from the PSD.
Year
DOI
Venue
2018
10.1145/3267242.3267292
UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing Singapore Singapore October, 2018
Keywords
Field
DocType
sEMG Signal Processing, Muscle Fatigue Tracking, Spectral Analysis, Wearable Computing
Frequency shift,Computer vision,Computer science,Electromyography,Continuous wavelet transform,Spectral density,Artificial intelligence,Muscle fatigue,Adaptive algorithm,Spectral analysis,Amplitude
Conference
ISBN
Citations 
PageRank 
978-1-4503-5967-2
0
0.34
References 
Authors
7
5
Name
Order
Citations
PageRank
Kaan Gokcesu185.26
Mert Ergeneci250.82
Erhan Ertan350.82
Abdallah Zaid Alkilani400.34
Panagiotis Kosmas56615.02