The use of wearable sensors for real-time monitoring of exercise-related measures has been extensively studied in recent years (e.g., performance enhancement, optimizing athlete's training, and preventing injuries). Surface electromyography (sEMG), which measures muscle activity, is a widely researched technology in exercise monitoring. However, due to their cumbersome nature, traditional sEMG electrodes are limited. In particular, facial EMG (fEMG) studies in physical training have been limited, with some scarce evidence suggesting that fEMG may be used to monitor exercise-related measurements. Altogether, sEMG recordings from facial muscles in the context of exercise have been examined relatively inadequately. In this feasibility study, we assessed the ability of a new wearable sEMG technology to measure facial muscle activity during exercise. Six young, healthy, and recreationally active participants (5 females), performed an incremental cycling exercise test until exhaustion, while facial sEMG and vastus lateralis (VL) EMG were measured. Facial sEMG signals from both natural expressions and voluntary smiles were successfully recorded. Stable recordings and high-resolution facial muscle activity mapping were achieved during different exercise intensities until exhaustion. Strong correlations were found between VL and multiple facial muscles' activity during voluntary smiles during exercise, with statistically significant coefficients ranging from 0.80 to 0.95 (p<0.05). This study demonstrates the feasibility of monitoring facial muscle activity during exercise, with potential implications for sports medicine and exercise physiology, particularly in monitoring exercise intensity and fatigue.
Copyright: © 2024 Ibrahim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.