Novel Metrics for High-Density sEMG Analysis in the Time-Space Domain During Sustained Isometric Contractions

Area of Science:

• Biomechanics
• Neuroscience
• Physiology

Background:

• Muscle fatigue assessment is crucial for understanding neuromuscular adaptation.
• High-Density surface Electromyography (HD-sEMG) offers detailed myoelectrical activity mapping.
• Existing methods for fatigue analysis may not fully capture spatial-temporal muscle behavior.

Purpose of the Study:

• To introduce novel metrics for analyzing temporal-spatial information from HD-sEMG.
• To evaluate the ability of these new metrics to assess muscle fatigue progression.
• To investigate the predictive capability of these metrics for endurance time.

Main Methods:

• Nine subjects performed fatiguing isometric contractions of the lumbar erector spinae.
• Topographical amplitude maps were generated using two HD-sEMG grids.
• Novel spatial metrics were calculated to quantify muscle activity distribution over time.

Main Results:

• Significant differences in spatial metrics were observed from the beginning to the end of contractions.
• These spatial metrics demonstrated the ability to characterize neuromuscular adaptations during fatigue.
• Linear regression models showed strong correlations between spatial metrics and endurance time.

Conclusions:

• Innovative spatial metrics can effectively characterize muscle activity distribution.
• These metrics show promise in predicting the time to task failure.
• The findings suggest a new approach for fatigue assessment using HD-sEMG.