CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition
CoRR(2024)
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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