Related Experiment Video
Updated: Jun 18, 2026
Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test
Published on: July 27, 2015
Detection of chaos in human fatigue mechanomyogarphy signals
Hong-Bo Xie1, Yong-Ping Zheng, Guo Jing-Yi
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, P.R.China.
Abstract:
We undertake the study of the chaotic nature of mechanomygraphy (MMG) signal by recourse to the recent developments in the field of nonlinear dynamics. The MMG signals were measured from biceps brachii muscle of 5 subjects during fatigue of isometric contraction at 80% maximal voluntary contraction (MVC) level. Deterministic chaotic character was detected in all data by using the Volterra-Wiener-Korenberg model and noise titration approach. The noise limit (NL), which is a power indicator of chaos of fatigue MMG signals, is 22.2000 + or - 8.7293. Furthermore, we studied the nonlinear dynamic features of MMG signals by computing their correlation dimension D(2), which is 3.3524 + or - 0.3645 across all the subjects. These results indicate that MMG is a high-dimensional chaotic signal and support the use of the theory of nonlinear dynamics for the analysis and modeling the MMG signals.
