Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The assessment of local muscle fatigue during dynamic exercises is viewed as essential, since people tend to maximise effective muscle training and minimise harmful muscle injuries. This study aims to investigate how surface electromyography (sEMG) and subjective metrics together can play a role in dynamic muscle fatigue prediction during exercises to promote the design of health and fitness technology. 20 healthy male participants were recruited in the experiment, and sEMG and self-reported data were collected. Features in temporal and spatial domain were extracted from sEMG data, such as RMS (root mean square) and FInsm5 (spectral parameter proposed by Dimitrov). Our results showed that some sEMG features indicated directional changes with the increase of dynamic muscle fatigue. Spearman correlation analysis indicated that Borg ratings had strong correlations with RMS and FInsm5 (spectral parameter proposed by Dimitrov) slopes. Then this paper discusses how to use sEMG and Borg data to evaluate muscle fatigue during exercises. A framework is proposed based on the joint analysis of spectra and amplitudes across RMS and FInsm5 slopes. This paper further discusses how to design health and fitness technology for the benefits and the limitations of the study.

Original publication

DOI

10.1007/978-3-031-05900-1_15

Type

Conference paper

Publication Date

01/01/2022

Volume

13322 LNCS

Pages

223 - 237