# CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition

arXiv: https://arxiv.org/abs/2402.19229 · HTML: https://mlsanity.com/paper/2402.19229/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 19 of 19)

- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Scaling laws in wearable human activity recognition](https://mlsanity.com/paper/2502.03364/) (2025)
- [Learning Transferable Sensor Models via Language-Informed Pretraining](https://mlsanity.com/paper/2603.11950/) (2026)
- [A Training Framework for Optimal and Stable Training of Polynomial Neural Networks](https://mlsanity.com/paper/2505.11589/) (2025)
- [Inertia-1: An Open Exploration of Wearable Motion Foundation Models](https://mlsanity.com/paper/2607.06617/) (2026)
- [MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition](https://mlsanity.com/paper/2406.03857/) (2024)
- [SlotFM: A Motion Foundation Model with Slot Attention for Diverse Downstream Tasks](https://mlsanity.com/paper/2509.21673/) (2025)
- [TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning](https://mlsanity.com/paper/2602.14200/) (2026)
- [BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition](https://mlsanity.com/paper/2605.08296/) (2026)
- [Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series](https://mlsanity.com/paper/2607.04245/) (2026)
- [Grounding Intelligence in Movement](https://mlsanity.com/paper/2507.02771/) (2025)
- [Understanding Human Daily Experience Through Continuous Sensing: ETRI Lifelog Dataset 2024](https://mlsanity.com/paper/2508.03698/) (2025)
- [TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding](https://mlsanity.com/paper/2608.00200/) (2026)
- [Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers](https://mlsanity.com/paper/2502.17477/) (2025)
- [Reducing Annotation Burden in Physical Activity Research Using Vision-Language Models](https://mlsanity.com/paper/2505.03374/) (2025)
- [ActiNet: An Open-Source Tool for Activity Intensity Classification of Wrist-Worn Accelerometry Using Self-Supervised Deep Learning](https://mlsanity.com/paper/2510.01712/) (2025)
- [Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation](https://mlsanity.com/paper/2604.05257/) (2026)
- [A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health](https://mlsanity.com/paper/2606.14604/) (2026)
- [Variable Input Masked Autoencoders for Wrist-Worn Accelerometry](https://mlsanity.com/paper/2610.17383/) (2026)

## Abstract (arXiv)

Existing activity tracker datasets for human activity recognition are typically obtained by having participants perform predefined activities in an enclosed environment under supervision. This results in small datasets with a limited number of activities and heterogeneity, lacking the mixed and nuanced movements normally found in free-living scenarios. As such, models trained on laboratory-style datasets may not generalise out of sample. To address this problem, we introduce a new dataset involving wrist-worn accelerometers, wearable cameras, and sleep diaries, enabling data collection for over 24 hours in a free-living setting. The result is CAPTURE-24, a large activity tracker dataset collected in the wild from 151 participants, amounting to 3883 hours of accelerometer data, of which 2562 hours are annotated. CAPTURE-24 is two to three orders of magnitude larger than existing publicly available datasets, which is critical to developing accurate human activity recognition models.