The DOI matches its DataCite record.
Capture-24: Activity tracker dataset for human activity recognition
Open accessAbstract
*** NB: This is an old version of the dataset which has now been superseded by a new version at: https://ora.ox.ac.uk/objects/uuid:99d7c092-d865-4a19-b096-cc16440cd001 with the DOI: 10.5287/bodleian:NGx0JOMP5 (resolving to https://doi.org/10.5287/bodleian:NGx0JOMP5) *** ------------------------------------------------------------------ Data and methods descriptions can be found in the following: Testing Self-Report Time-Use Diaries against Objective Instruments in Real Time https://journals.sagepub.com/doi/full/10.1177/0081175019884591 Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants https://www.nature.com/articles/s41598-018-26174-1
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In the citing paper
“Effective performance across differing sensor locations allows for leveraging large scale unlabeled datasetswhich are typically collected at the wrist (such as Capture-24 [4,18,81], Fenland [46,51], and Biobank [14]), to jumpstart recognition for rarer leg-based activities such as classifying freeze of gait.”
From Assessing the State of Self-Supervised Human Activity Recognition Using Wearables (2022) · cites this paper as [4]
In the citing paper
“Capture-24 [4, 18, 81] Wrist 151 -Free living”
From Assessing the State of Self-Supervised Human Activity Recognition Using Wearables (2022) · cites this paper as [4]
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HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?
Ji, S., Zheng, X., & Wu, C. (2024) · 2024 IEEE International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things (FMSys) · doi:10.1109/fmsys62467.2024.00011 · Cited by 114
Assessing the State of Self-Supervised Human Activity Recognition Using Wearables
Haresamudram, H., Essa, I., & Plötz, T. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550299 · Cited by 107
Large Language Models Are Zero-Shot Recognizers for Activities of Daily Living
Civitarese, G., Fiori, M., Choudhary, P., & Bettini, C. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3725856 · Cited by 53
Logacjov, A., & Bach, K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107478 · Cited by 35
Comparing self-supervised learning techniques for wearable human activity recognition
Ek, S., Presotto, R., Civitarese, G., Portet, F., Lalanda, P., & Bettini, C. (2025) · CCF Transactions on Pervasive Computing and Interaction · doi:10.1007/s42486-024-00182-9 · Cited by 15
“To the best of our knowledge, this is the first adaptation of MAE to the HAR domain that is closely aligned with the original implementation.”
Influential
Metadata
- Authors
- Chan Chang, S., & Doherty, A.
- Affiliations
- University of Oxford
- University of Oxford (GB)
- ORCID
- Doherty, Aiden
- Venue
- University of Oxford
- Publisher
- University of Oxford
- Published
- 2021
- OpenAlex
- W3194552713
- Cited by
- 8
- References
- 0
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 2
- Type
- dataset
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Oxford University Research Archive (ORA) (University of Oxford) (cc-by)
- Funders
- The British Heart Foundation Centre of Research Excellence
- University of Oxford Advanced Research Computing
- Oxford Biomedical Research Centre
- Novo Nordisk
- Li Ka Shing Foundation
- Health Data Research UK
- National Institute for Health Research
- The Alan Turing Institute
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- human activity recognition, wearable activity trackers, wearable sensors, objective activity measurement, physical activity, sleep phenotypes, UK Biobank, sensor data
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- CoChat. (2026). Citation check: Capture-24: Activity tracker dataset for human activity recognition. https://app.cochat.ai/papers/10.5287/bodleian:nzvqj2ojw
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- Chan Chang, S., & Doherty, A. (2021). Capture-24: Activity tracker dataset for human activity recognition. University of Oxford. https://doi.org/10.5287/bodleian%3Anzvqj2ojw
