CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition
Year: 2024
Type: data-paper
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.
Source: Scientific Data
Institutions: University of Oxford, University College London
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,290
Funders: GlaxoSmithKline, Wellcome Trust, UK Research and Innovation, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, Novo Nordisk, Samsung, Engineering and Physical Sciences Research Council, Economic and Social Research Council, H2020 European Research Council, FP7 Ideas: European Research Council
Awards: Using wearable sensors to transform the understanding of cardiovascular disease: Prediction, mechanisms, and patient relevant endpoints., MOA: High Efficiency Deep Learning for Embedded and Mobile Platforms (Full EPSRC Fellowship Submission), Social Change and Everyday Life, Research Excellence (round 3), G3478219543, G3716259689, IRC Next Steps Plus : OPERA - Opportunistic Passive Radar for Non-Cooperative Contextual Sensing, G5651301161, G6082034655, Re-thinking Efficiency in Deep Learning under Accelerators and commodity and processors, G7701755039, G8099590239, G8302630852, G8500213005, Collecting New Time Use Resources (CNTUR), G974806393
OpenAlex ID: W4403473621