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*** NB: This is an old version of the dataset which has now been superseded by a new version at: https://ora.ox.ac.uk/objects/uuid:99d7c092-d865-4a19-b096-cc16440cd001 with the DOI: 10.5287/bodleian:NGx0JOMP5 (resolving to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
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Effective performance across differing sensor locations allows leveraging large-scale unlabeled datasets typically collected at the wrist to jumpstart recognition for rarer leg-based activities such as classifying freeze of gait.
“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]
Capture-24 is a wrist-worn dataset with 151 users and free-living activities.
“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]
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
Cited by 8
- 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 › Physiology › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
