The National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
Medical Equipment Innovation Research Center, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China.
J Neuroeng Rehabil. 2024 Nov 4;21(1):196. doi: 10.1186/s12984-024-01497-5.
Physical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.
sEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.
Statistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.
This study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG's PDF features in detecting muscle fatigue among the elderly.
体育锻炼是老年人身心健康的重要方法。然而，过度运动可能会增加跌倒、严重受伤和生活质量下降的风险。因此，在社区环境中，非常需要简单有效的运动疲劳监测方法。本研究旨在探索使用表面肌电图（sEMG）特征（包括概率密度函数（PDF）的峰度和偏度）实时检测社区环境中运动引起的疲劳的可能性，以解决常用 sEMG 特征灵敏度低和计算复杂度高的问题。
在各自的社区中心，从 30 名健康的社区居住老年人的 20%最大自主收缩（MVC）任务失败收缩中记录 6 块前臂肌肉的 sEMG 信号。从 25 秒非疲劳稳定期和 25 秒疲劳数据中计算 sEMG 的 PDF 形状特征，即峰度和偏度，进行比较。进行统计检验以比较和测试这些特征的显著性。我们进一步提出了一种新的疲劳指标，通道平均峰度的时间平均峰度（TMK），以在社区环境中以相对较低的计算复杂度和足够的灵敏度检测疲劳。进行方差分析和事后分析以检查 TMK 的性能。
在非疲劳期和疲劳期之间，峰度和偏度均存在统计学上的显著差异，随着疲劳的临近，值逐渐增加。TMK 显示出在检测疲劳方面具有敏感性，随着时间的推移，计算复杂度低于样本熵。
本研究在手握运动中研究了 sEMG 信号的 PDF 形状特征，以识别社区实验中老年人的肌肉疲劳。结果表明，疲劳时峰度发生显著变化，表明 PDF 形状特征适合于社区实验中肌肉疲劳的简便检测。所提出的指标 TMK 在社区环境中具有有限的计算复杂度，在跟踪肌肉疲劳方面具有潜在的敏感性，突出了 sEMG 的 PDF 特征在检测老年人肌肉疲劳方面的潜力。
