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FatigueSense

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drowsiness-detection
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metadata 
license: mit
language:
  - en
tags:
  - computer-vision
  - pose-estimation
  - drowsiness-detection
  - driver-monitoring
pretty_name: FatigueSense Training Data

FatigueSense Training Data

Domain-specific recordings and derived training artifacts for the FatigueSense fatigue-detection pipeline. This dataset supports YOLO11n-pose retraining (upper-body keypoints) and BiGRU temporal model training (1 Hz feature windows).

Contents

Path Used by Description
pose/ model_architecture.train_yolo_pose YOLO-pose images + labels (5 kpts: nose, ears, shoulders). Pseudo-labeled with yolo11n-pose.pt.
pose/dataset.yaml Ultralytics training Portable config (path: .); 70/15/15 train/val/test by video (no subject leakage).
temporal/features/ model_architecture.train_temporal_model Stage 2 Parquets — one file per video, 17-dim per-second features.
temporal/raw_probs/ Reproducibility Stage 1 per-frame eye/mouth probs + pose keypoints.
videos/ Optional 25 raw source clips (upload with --include-videos).
manifest.json — Split stems, feature schema, file counts (auto-generated on upload).

Provenance

  1. Videos — local videos/ directory (25 clips, driver/desk POV).
  2. Pose — scripts/pose/extract_frames.py → scripts/pose/pseudo_label.py → data/pose/.
  3. Temporal — scripts/temporal/extract_probs.py → scripts/temporal/aggregate_features.py → data/temporal/.

Temporal labels are bootstrap heuristics over the same features (PERCLOS, yawn rate, blink duration, posture), not human focus scores. Replace when annotated labels exist.

Feature schema (17 dimensions)

perclos, blink_rate_bpm, mean_blink_duration, eye_closure_variance, yawn_rate_per_min, mean_yawn_duration, mouth_open_ratio, mean_p_eye, mean_p_mouth, head_pitch, head_roll, shoulder_tilt, head_size_ratio, head_motion_energy, head_drift_y, posture_drift, kpt_visibility.

See fatigue_pipeline/constants.py for definitions.

Download 

from huggingface_hub import snapshot_download

root = snapshot_download("Jlords32/FatigueSense", repo_type="dataset")
# pose:     {root}/pose
# temporal: {root}/temporal/features

Citation

If you use this data, cite the FatigueSense project repository.