What is it about?
This work explores muscle fatigue detection using surface electromyography (sEMG) signals from multiple muscle channels during prolonged load settings. Four vital temporal features, namely Root Mean Square, Waveform Length, Mean Absolute Value, and Zero Crossing, were examined across eight channels to determine the onset and progression of fatigue. The outcomes highlight the benefits of incorporating amplitude and variability-based features to provide robust real-time fatigue monitoring in Myoelectric Control Systems (MEC) and Human-Computer Interaction (HCI) applications.
Featured Image
Why is it important?
The outcomes highlight the benefits of incorporating amplitude and variability-based features to provide robust real-time fatigue monitoring in Myoelectric Control Systems (MEC) and Human-Computer Interaction (HCI) applications.
Perspectives
The outcomes highlight the benefits of incorporating amplitude and variability-based features to provide robust real-time fatigue monitoring in Myoelectric Control Systems (MEC) and Human-Computer Interaction (HCI) applications.
Dr Mahiban Lindsay N
Hindustan University
Read the Original
This page is a summary of: Muscle Fatigue Detection Using sEMG Signals for Enhanced Prosthetic Control, February 2025, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/icccit62592.2025.10928000.
You can read the full text:
Contributors
The following have contributed to this page
