ArticleScientific data2024
CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Domain-specific physical activity levels and risk of dementia: a systematic review and meta-analysis.The Lancet. Public health · 2026Pooled it
- A Head-to-Head Comparison of Three Literature Algorithms for Physical Activity Endpoints from a Wrist Accelerometer in Free-Living Conditions.Sensors (Basel, Switzerland) · 2026Article
- Robust Assessment of Free-Living Physical Behaviors and Activity Intensity Using Dual-Wearable Multitask Learning: Development and Evaluation Study From the Multicenter WEALTH Project.JMIR mHealth and uHealth · 2026Article
- Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson's disease.Lifetime data analysis · 2026Article
- IMU50: a high-frequency multi-modal wrist-worn IMU dataset of 50 subjects over 5 continuous days for supervised and self-supervised learning in free-living conditions.Data in brief · 2026Article
- Impact of Gyroscope Integration, Sensor Placement, and Activity Granularity on Human Activity Recognition Performance.Sensors (Basel, Switzerland) · 2026Article
- Data from the UK Biobank demonstrates that increased brief, sporadic moderate-to-vigorous physical activity reduces mortality.Communications medicine · 2026Article
- Standardized Methods for Evaluating Physical and Eating Behaviors: The WEALTH Cross-Sectional Study Protocol.JMIR research protocols · 2026Article
- A comprehensive IMU dataset for evaluating sensor layouts in human activity and intensity recognition.Scientific data · 2026Article
- Machine and Deep Learning for Detection of Moderate-to-Vigorous Physical Activity From Accelerometer Data: Systematic Scoping Review.Interactive journal of medical research · 2026Review
- Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities.Scientific data · 2026Article
- Accelerometer-derived "weekend warrior" physical activity pattern and all-cause mortality in individuals with kidney stones: a prospective cohort study.Frontiers in nutrition · 2026Article
- A Feature-Augmented Transformer Model to Recognize Functional Activities From in-the-Wild Smartwatch Data.IEEE journal of biomedical and health informatics · 2026Article
- Towards Practical, Best Practice Video Annotation to Support Human Activity Recognition.Annotation of real-world data for artificial intelligence systems : 9th international workshop, ARDUOUS 2025, Bologna Italy, October 25-26, 2025, proceedings. International Workshop on Annotation of Real-World Data for Artificial Intell... · 2026Article
- From accelerometer data to activity patterns in chronic pain: methodical reasoning is key.Frontiers in sports and active living · 2026Review
- Clinical Validity of Wrist- and Trunk-Worn Sensor-Derived Data Models in Parkinson's Disease.Parkinson's disease · 2026Article
- Generation of a Free-Living Ground-Truth Validation Dataset for Wearable Measures of Physical Activity, Sedentary Behavior, Sleep, and Heart Rate in Adults (OxWEARS): Protocol for a Cross-Sectional Study.JMIR research protocols · 2025Article
- A Context-Assisted, Semi-Automated Activity Recall Interface Allowing Uncertainty.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Reducing annotation burden in physical activity research using vision language models.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.