Electromyography Signals from Wrestlers for Movement Analysis

Electromyography (EMG) signals were recorded from eight muscles per athlete during various wrestling movements. The dataset, containing 59,200 data points, is designed for stable and repeatable signal acquisition with labeled movement data. It supports supervised learning for analyzing muscle activity in sports science and biomedical engineering.

Use Cases

• Movement classification based on labeled EMG signals from eight muscles.
• Pattern recognition of muscle activation sequences during wrestling techniques.
• Analysis of muscle activity and fatigue for sports science applications.
• Training supervised learning models for biosignal interpretation.

Strengths

• Includes labeled movement data, enabling supervised learning approaches.
• Protocol designed for stable and repeatable signal acquisition, improving reliability.
• Data recorded from eight distinct muscles, providing multi-channel physiological detail.

Limitations

• Column names and exact row count are not specified, limiting precise structural understanding.
• Dataset size is given as a single number (59200), which may represent total data points rather than a conventional row count.
• Geographic origin of the athletes and specific time range of data collection are not provided.

Provenance

Source

Kudratjon Zohirov

Collection Method

Electromyography (EMG) signals recorded from athletes practicing wrestling.

Time Range

null

Freshness

Last updated March 21, 2026.

Geography

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