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

Autor: Chan S; Big Data Institute, University of Oxford, Oxford, UK.; Nuffield Department of Population Health, University of Oxford, Oxford, UK., Hang Y; Big Data Institute, University of Oxford, Oxford, UK.; Nuffield Department of Population Health, University of Oxford, Oxford, UK., Tong C; Department of Computer Science, University of Oxford, Oxford, UK., Acquah A; Big Data Institute, University of Oxford, Oxford, UK.; Nuffield Department of Population Health, University of Oxford, Oxford, UK.; Department of Engineering Science, University of Oxford, Oxford, UK., Schonfeldt A; Big Data Institute, University of Oxford, Oxford, UK.; Nuffield Department of Population Health, University of Oxford, Oxford, UK., Gershuny J; Social Research Institute, University College London, London, UK., Doherty A; Big Data Institute, University of Oxford, Oxford, UK. aiden.doherty@ndph.ox.ac.uk.; Nuffield Department of Population Health, University of Oxford, Oxford, UK. aiden.doherty@ndph.ox.ac.uk.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Oct 16; Vol. 11 (1), pp. 1135. Date of Electronic Publication: 2024 Oct 16.
DOI: 10.1038/s41597-024-03960-3
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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