Dataset of Surface Electromyography and Fatigue Levels for Muscle Fatigue Analysis
Author Information
Author(s): Sara M. Cerqueira, Rita Vilas Boas, Joana Figueiredo, Christina P. Santos
Primary Institution: University of Minho
Hypothesis
This study aims to create a comprehensive dataset for analyzing muscle fatigue through surface electromyography and self-perceived fatigue levels.
Conclusion
The dataset provides valuable data for testing new fatigue detection algorithms and understanding muscle fatigue mechanisms.
Supporting Evidence
- The dataset includes 13 hours and 20 minutes of data from 13 participants.
- Participants performed 12 dynamic movements to assess muscle fatigue.
- Self-perceived fatigue was recorded on a 3-level scale.
Takeaway
The researchers collected data from 13 people to help understand how muscles get tired, which can help prevent injuries.
Methodology
Participants performed 12 upper-limb dynamic movements while their muscle activity and self-perceived fatigue levels were recorded.
Potential Biases
Potential bias due to the self-reported nature of fatigue levels and the specific participant selection criteria.
Limitations
The dataset may not represent all demographics as it only includes healthy participants from a specific academic community.
Participant Demographics
13 participants (5 females, 8 males), aged 23.92 ± 3.36 years, all right-handed.
Digital Object Identifier (DOI)
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