A Comprehensive Dataset of Surface Electromyography and Self-Perceived Fatigue Levels for Muscle Fatigue Analysis

Area of Science:

• Biomedical Engineering
• Sports Science
• Occupational Health

Background:

• Muscle fatigue is a significant risk factor for athletic and occupational injuries.
• Understanding the biochemical processes of muscle fatigue is crucial for injury prevention.
• Existing datasets may not fully capture the complexity of muscle fatigue during dynamic movements.

Purpose of the Study:

• To present a novel, comprehensive dataset for muscle fatigue analysis.
• To facilitate the development and testing of advanced fatigue detection algorithms.
• To support research into the underlying mechanisms of muscle fatigue.

Main Methods:

• Collected surface electromyography (sEMG) data from upper limbs.
• Recorded participants' self-perceived fatigue levels.
• Included 13 hours of data from 13 participants performing 12 dynamic upper-limb movements (uni-articular and complex).

Main Results:

• A novel dataset comprising 13h 20min of sEMG and self-reported fatigue data was created.
• The dataset covers 12 distinct upper-limb dynamic movements.
• The data provides a rich resource for analyzing muscle fatigue patterns.

Conclusions:

• The presented dataset is a valuable resource for advancing muscle fatigue research.
• It can be used to test and refine new algorithms for fatigue detection.
• Further analysis of this dataset may elucidate the mechanisms of muscle fatigue.