Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Abstract

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (−300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant’s biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

What are the best indicators of myoelectric manifestation of fatigue?

“…Multiscale entropy was therefore introduced to better detect the presence of complexity in time series and overcome the limitations of approximate and sample entropy [57]. Although multiscale entropy was shown more sensitive to muscle fatigue than median frequency [118], our results evidenced that multiscale entropy had significantly smaller VIP values than median frequency and changed for a moderate proportion of the participants (35%-45%) for the anterior and medial deltoids between RPT initiation and termination. Consequently, fuzzy entropy, as previously discussed, is the only entropy-based indicator that may be efficient to assess MMF.…”

Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables’ Data from the Crowd

“…We use the 4.5 kg weight dumbbell in our work because of three reasons. The first reason is that several previous works have used medium-weight dumbbells ranging between 3.5 kg and 5.5 kg to study muscular strength and fatigue [51][52][53]. The second reason is that medium-weight dumbbells are often reported as the most commonly used dumbbells across gym-goers [54].…”

“…In contrast, heavyweight dumbbells lead to shorter recording sessions with fewer data entries, which do not capture kinetic changes clearly throughout the exercise because participants reach fatigue quickly. Although we use a 4.5 kg weight dumbbell as recommended by previous studies, we believe having dumbbell weights will provide us with more information and different patterns of biceps muscle fatigue [51][52][53].…”

The Refined Composite Downsampling Permutation Entropy Is a Relevant Tool in the Muscle Fatigue Study Using sEMG Signals

“…It is therefore interesting to evaluate more thoroughly and precisely the level of sEMG complexity by means of complexity-based measures. The entropy-based complexity measures have proven to be efficient tools for addressing complexity of such signals [ 14 , 15 , 16 , 17 ]. For example, in the recently published paper [ 17 ], the authors argued that the modified sample entropy measure appeared to be more sensitive to muscle fatigue and could yield more consistent results compared to the classical measures.…”

“…The entropy-based complexity measures have proven to be efficient tools for addressing complexity of such signals [ 14 , 15 , 16 , 17 ]. For example, in the recently published paper [ 17 ], the authors argued that the modified sample entropy measure appeared to be more sensitive to muscle fatigue and could yield more consistent results compared to the classical measures. The Permutation Entropy (PE) is also particularly a suitable tool for capturing useful information from the ordinal patterns of sEMG time series [ 15 , 18 ].…”