Corvini Giovanni, Arvanitidis Michail, Falla Deborah, Conforto Silvia
Department of Industrial, Electronic and Mechanical EngineeringUniversity of Roma Tre 00154 Rome Italy.
School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental SciencesUniversity of Birmingham Birmingham B15 2TT U.K.
IEEE Open J Eng Med Biol. 2024 Aug 26;5:760-768. doi: 10.1109/OJEMB.2024.3449548. eCollection 2024.
This study introduces a novel approach to examine the temporal-spatial information derived from High-Density surface Electromyography (HD-sEMG). By integrating and adapting postural control parameters into a framework for the analysis of myoelectrical activity, new metrics to evaluate muscle fatigue progression were proposed, investigating their ability to predict endurance time. Nine subjects performed a fatiguing isometric contraction of the lumbar erector spinae. Topographical amplitude maps were generated from two HD-sEMG grids. Once identified the coordinates of the muscle activity, novel metrics for quantifying the muscle spatial distribution over time were calculated. Spatial metrics showed significant differences from beginning to end of the contraction, highlighting their ability of characterizing the neuromuscular adaptations in presence of fatigue. Additionally, linear regression models revealed strong correlations between these spatial metrics and endurance time. These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.
本研究介绍了一种新颖的方法来检查从高密度表面肌电图（HD-sEMG）获得的时空信息。通过将姿势控制参数整合并应用于肌电活动分析框架中，提出了用于评估肌肉疲劳进展的新指标，并研究了它们预测耐力时间的能力。九名受试者进行了腰部竖脊肌的疲劳等长收缩。从两个HD-sEMG网格生成地形图幅度图。一旦确定了肌肉活动的坐标，就计算出用于量化肌肉随时间的空间分布的新指标。空间指标在收缩开始到结束时显示出显著差异，突出了它们在存在疲劳时表征神经肌肉适应性的能力。此外，线性回归模型显示这些空间指标与耐力时间之间存在强相关性。这些创新指标可以表征肌肉活动的空间分布并预测任务失败的时间。
