SportSense-Fatigue: EEG, ECG, and IMU Signals for Mental Fatigue Prediction

A dataset containing physiological and motion signals for modeling mental fatigue. It includes EEG, ECG, and IMU data, likely collected from participants during physical or cognitive tasks. The dataset is intended for use in federated learning models, suggesting a structure designed for distributed training across multiple clients.

Use Cases

• Train federated learning models for mental fatigue prediction based on the described multimodal sensor signals.
• Analyze correlations between physiological signals (EEG/ECG) and motion (IMU) for fatigue state classification.
• Benchmark privacy-preserving ML algorithms using the federated-ready structure implied by the description.
• Develop sensor fusion techniques for wearable health monitoring based on the combined EEG, ECG, and IMU data.

Strengths

• Contains three distinct signal types (EEG, ECG, IMU), enabling multimodal analysis.
• Designed for federated learning, suggesting a structure suitable for distributed and privacy-sensitive model training.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Kaggle

Collection Method

Likely collected from participants via wearable sensors during tasks inducing mental fatigue.

Time Range

null

Freshness

Last update date is unknown; freshness unverified.

Geography

null