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CAPTURE-24

  • Modality: Wrist-worn Axivity AX3 accelerometer (100 Hz) + wearable camera + sleep diary
  • Primary Tasks: Free-living activity recognition, wearable HAR benchmarking, sedentary behavior analysis
  • Scale: 151 participants, 3883 hours total (2562 annotated), 200+ activity labels mapped to Compendium of Physical Activities
  • License: Creative Commons Attribution 4.0 (CC BY 4.0)
  • Access: https://github.com/OxWearables/capture24

Summary

CAPTURE-24 is the largest annotated wrist-worn accelerometer dataset collected in free-living conditions, 2-3 orders of magnitude larger than prior wearable HAR datasets. Participants from the Oxford area wore an Axivity AX3 on the wrist alongside a body-worn camera that provided ground-truth annotations. The combination of scale, ecological validity, and fine-grained labeling (200+ activities) makes it a critical resource for training and evaluating models intended for population-level physical activity epidemiology.

Reference Paper

  • Matthew Willetts et al. "CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition." Scientific Data, 2024. PDF

Benchmarks & Baselines

  • Random Forest (hand-crafted features) - Balanced accuracy ~75% on Willetts split; Willetts et al., 2024.
  • SSL-pretrained CNN - Balanced accuracy ~80% (5-class sleep/sedentary/light/moderate/vigorous); Oxford Wearables Group baselines.
  • Official evaluation uses per-participant held-out splits; results reported on both 200+ fine-grained and coarser activity groupings.

Tooling & Ecosystem

  • Official OxWearables/capture24 repository with data loading and preprocessing scripts.
  • actipy library for raw accelerometer signal processing and feature extraction.
  • ssl-wearables provides self-supervised pre-training recipes compatible with CAPTURE-24.

Known Challenges

  • Wearable camera annotations can miss activities performed in low-light or private settings; sleep diary supplements these gaps.
  • Label granularity varies across participants depending on camera image quality and annotator agreement.
  • Class distribution is heavily skewed toward sedentary and sleep; class-weighted evaluation is recommended.
  • Raw data is large (~several GB); plan storage accordingly for full 100 Hz signals.

Cite

@article{willetts2024capture24,
  title   = {CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition},
  author  = {Willetts, Matthew and Sherlock, Aidan and Sherwood, Owen and others},
  journal = {Scientific Data},
  year    = {2024}
}