Novel Metrics for High-Density sEMG Analysis in the Time–Space Domain During Sustained Isometric Contractions - Giovanni Corvini; Michail Arvanitidis; Deborah Falla; Silvia Conforto (IEEE Open Journal of Engineering in Medicine and Biology, 2024)
Goal: 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. Methods: 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. Results: 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. Conclusions: These innovative metrics can characterize the spatial distribution of muscle activity and predict the time of task failure.