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An sEMG-based method to adaptively reject the effect of contraction on spectral analysis for fatigue tracking

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Kaan Gokcesu, Mert Ergeneci, Erhan Ertan, Abdallah Zaid Alkilani, Panagiotis Kosmas

Original languageEnglish
Title of host publicationISWC 2018 - Proceedings of the 2018 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450359672
Published8 Oct 2018
Event22nd International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
Duration: 8 Oct 201812 Oct 2018


Conference22nd International Symposium on Wearable Computers, ISWC 2018

King's Authors


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.

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