Scalable weighted-cumulated methodology for fatigue estimation

• Published in: Research on biomedical engineering Vol. 38; no. 4; pp. 1087 - 1101
• Main Authors: de Oliveira Nascimento, Francisco Assis, de Araújo Rocha, Valdinar, do Carmo, Jake Carvalho
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2022
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Original Article; Fourier transform; Wavelet transform; Weighted-cumulated methodology; Muscle fatigue; Surface electromyography signals
• ISSN: 2446-4740; 2446-4740
• DOI: 10.1007/s42600-022-00241-z

Purpose The objective measurement of muscle fatigue through the analysis of surface electromyographic signals (S-EMG) has been the object of study in recent decades. The use of S-EMG is interesting because it allows accessing the muscular structure and function through the use of a noninvasive technique. This is a subject of interest to many areas of science such as clinical and orthopedic medicine, biomechanics, physiotherapy and rehabilitation, telemedicine, control of interfaces, intelligent prosthetics, and exoskeletons control and for expert systems to support medical diagnosis to neuromuscular diseases. Changes in the spectral signature of the S-EMG signal such as spectral shift for low frequencies and the increase in the dynamic range of the signal indicate the installation of the biological phenomenon of fatigue. For instance, classical techniques such as zero-crossing, median frequency (MDF), and mean power frequency (MPF) are able to perceive the spectral shift in S-EMG signals. On the other hand, techniques such as root mean square (RMS) values can only perceive the variation in the dynamic range of the S-EMG signal. Methods In this work, new mathematical models for the objective assessment of muscle fatigue are presented. We sought to design models for objective fatigue estimators that simultaneously perceive the spectral shift for low frequencies and the increase in the dynamic range in the S-EMG signal during the instauration of the fatigue process. The new approach is integrated to the weighted-cumulated methodology framework previously proposed. Three new objective muscle fatigue estimators were conceived: the scalable weighted-cumulated Fourier estimator, the weighted-cumulated wavelet estimator (SWCW), and the weighted-cumulated p-side lobe attenuation algorithm (p-SL). Results To evaluate the proposed tools based on the scalable weighted-cumulated methodology, we investigated two dynamic protocols with muscle fatigue production. The S-EMG signals recorded from the biceps brachii and from the vastus lateralis were used. The results obtained with the application of the new proposed objective fatigue estimators are shown. The results are discussed and compared with the weighted-cumulated models. Conclusions The scalable fatigue estimators exhibited an experimental behavior consistent with the assumptions on which they were designed. The results have been promising.