Investigation of Fatigue Using Different EMG Features

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Science

Background:

• Rehabilitative exercise enhances neuro-plasticity through repetitive training for upper limb impairments.
• Human-robot interaction (HRI) can be improved by incorporating physiological signals.
• Electromyogram (EMG) signals offer insights into muscle activity and fatigue.

Purpose of the Study:

• To investigate the utility of EMG signals in a human-robot interaction loop for monitoring muscle fatigue.
• To analyze changes in EMG signal features during fatiguing upper limb exercise.
• To identify the most reliable EMG feature for indicating muscle fatigue.

Main Methods:

• Twenty healthy participants performed biceps curl exercises with varying resistance (100%, 80%, 60% of maximum voluntary contraction).
• EMG data were collected from Biceps, Triceps, and Brachioradialis muscles throughout the exercise phases.
• Various EMG features, including average power, amplitude, mean frequency, and median frequency, were analyzed to assess muscle fatigue.

Main Results:

• Muscle fatigue led to increased average EMG power (94%) and amplitude (91%), and decreased mean (80%) and median frequency (57%), particularly in the Biceps muscle.
• Integrated EMG showed a continuous rise across all tested muscles, most notably in the Biceps.
• EMG average power emerged as the most consistent and reliable indicator of muscle fatigue.

Conclusions:

• EMG signal analysis is a viable method for detecting and quantifying muscle fatigue during rehabilitative exercises.
• Average EMG power is a robust feature for real-time fatigue monitoring in HRI applications.
• Findings support the integration of EMG in adaptive robotic systems for personalized rehabilitation programs.