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Capture-24: Activity tracker dataset for human activity recognition
Population Size
151
People
Years
2014 - 2016
Associated BioSamples
None/not available
Geographic coverage
United Kingdom
England
...see more
Lead time
Not applicable
Summary
This dataset contains wrist-worn activity tracker data collected from 151 participants for a period of roughly 24hs in natural settings, annotated using wearable cameras and sleep diaries.
DOI for dataset
Documentation
This dataset contains Axivity AX3 wrist-worn activity tracker data that were collected from 151 participants in 2014-2016 around the Oxfordshire area. Participants were asked to wear the device in daily living for a period of roughly 24 hours, amounting to a total of almost 4,000 hours. Vicon Autograph wearable cameras and Whitehall II sleep diaries were used to obtain the ground truth activities performed during the period (e.g. sitting watching TV, walking the dog, washing dishes, sleeping), resulting in more than 2,500 hours of labelled data. Accompanying code to analyse this data is available at https://github.com/activityMonitoring/capture24. The following papers describe the data collection protocol in full: i.) Gershuny J, Harms T, Doherty A, Thomas E, Milton K, Kelly P, Foster C (2020) Testing self-report time-use diaries against objective instruments in real time. Sociological Methodology doi: 10.1177/0081175019884591; ii.) Willetts M, Hollowell S, Aslett L, Holmes C, Doherty A. (2018) Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Scientific Reports. 8(1):7961. Regarding Data Protection, the Clinical Data Set will not include any direct subject identifiers. However, it is possible that the Data Set may contain certain information that could be used in combination with other information to identify a specific individual, such as a combination of activities specific to that individual ("Personal Data"). Accordingly, in the conduct of the Analysis, users will comply with all applicable laws and regulations relating to information privacy. Further, the user agrees to preserve the confidentiality of, and not attempt to identify, individuals in the Data Set.
Dataset type
Health and disease
Dataset sub-type
Not applicable
Dataset population size
151
Keywords
wearable technology, human activity recognition, activity trackers, accelerometers, fitness trackers, machine learning
Observations
Observed Node | Disambiguating Description | Measured Value | Measured Property | Observation Date |
|---|---|---|---|---|
Persons | Number of participants in the study after quality control filtering | 151 | Count | 31 Dec 2021 |
Provenance
Purpose of dataset collection
Study
Collection source setting
Other
Patient pathway description
N/A
Image contrast
Not stated
Biological sample availability
None/not available
Structural Metadata
Details
Publishing frequency
Static
Version
1.0.0
Modified
08/10/2024
Citation Requirements
University of Oxford
Coverage
Start date
01/01/2014
End date
31/12/2016
Time lag
Not applicable
Geographic coverage
United Kingdom, England, South East, South Oxfordshire, West Oxfordshire
Minimum age range
18
Maximum age range
91
Follow-up
0 - 6 Months
Accessibility
Language
en
Controlled vocabulary
OTHER
Format
text
Data Access Request
Dataset pipeline status
Not available
Time to dataset access
Not applicable
Access request cost
N/A
Access service description
The data is hosted by the Oxford University Research Archive and access is free.
Jurisdiction
GB-ENG
Data use limitation
General research use
Data Controller
University of Oxford
Data Processor
University of Oxford