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

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 nuan...

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Bibliographic Details
Published inarXiv.org
Main Authors Chan, Shing, Yuan, Hang, Tong, Catherine, Acquah, Aidan, Schonfeldt, Abram, Gershuny, Jonathan, Doherty, Aiden
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.02.2024
Subjects
Accelerometers
Data collection
Datasets
Heterogeneity
Human activity recognition
Human motion
Tracking devices
Wrist
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Summary: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.
ISSN:2331-8422