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  • Title: CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition.
    Author: Chan S, Hang Y, Tong C, Acquah A, Schonfeldt A, Gershuny J, Doherty A.
    Journal: Sci Data; 2024 Oct 16; 11(1):1135. PubMed ID: 39414802.
    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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