Proceedings on Automation in Medical Engineering
Vol. 2 No. 1 (2023): Proc AUTOMED

Measurement technology and diagnostics, ID 739

EMG based muscle fatigue detection using autocorrelation and k-means clustering

Main Article Content

Fars Samann (Institute of Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Gießen), Thomas Schanze (Institute of Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Gießen)

صندلی اداری سرور مجازی ایران Decentralized Exchange

Abstract

The electromyogram (EMG) can be commonly used to detect muscle fatigue during exercise to prevent injury or muscle disorder. A traditional way to indicate fatigue is based on extracting features from EMG segments in time-domain or frequency-domain. In this work, muscle fatigue detection is developed by extracting three features from the autocorrelation function of EMG segments. The classification is done using a k-means clustering approach. The proposed method has also successfully classified unknown EMG segments into non-fatigue and fatigue state. The accuracy of the proposed method is evaluated in detecting the signal of transition-to-fatigue stage.

Article Details

فروشگاه اینترنتی