CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition
(2402.19229)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.
Overview
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The CAPTURE-24 dataset provides a large-scale, annotated collection of wrist-worn accelerometer data from free-living conditions to improve human activity recognition (HAR) models.
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Data was collected using Axivity AX3 accelerometers, wearable cameras, and sleep diaries, totaling 3883 hours, with 2562 hours of annotated data from 151 participants.
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Benchmarking with various models, including hidden Markov models (HMM), displayed enhanced HAR model performance, emphasizing the importance of large, diverse datasets.
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The dataset posits challenges in activity classification granularity in real-world settings and sets forth future research directions towards more sophisticated HAR solutions.
Comprehensive Analysis of the CAPTURE-24 Dataset for Enhancing Human Activity Recognition Models with In-the-wild Data
Background & Summary
The utility of wrist-worn accelerometers in health-related research is gaining traction, driven by their potential to offer objective, high-resolution insights into individual activity patterns. While accelerometers promise transformative applications in precision medicine, digital phenotyping, and epidemiological studies, the effectiveness of human activity recognition (HAR) models is limited by the quality and scope of available data. Existing datasets, predominantly collected under controlled laboratory conditions, fail to capture the complexity and variability of real-world human activities. The CAPTURE-24 dataset emerges as a response to this challenge, providing a large-scale, annotated collection of accelerometer data gathered from participants in free-living conditions. Spanning 3883 total hours, with 2562 hours of annotated data from 151 participants, it stands as a significantly larger and more representative resource than prior datasets. This advancement promises to usher in improved HAR models adept at navigating the nuanced landscape of human activities outside controlled environments.
Data Acquisition and Annotation
The CAPTURE-24 dataset was constituted from the data collected through wrist-worn Axivity AX3 accelerometers, wearable cameras, and sleep diaries, aimed at capturing a comprehensive 24-hour profile of participant activities. The innovative use of wearable cameras and sleep diaries as indirect measurement tools allowed for the detailed annotation of accelerometer data while ensuring participant privacy. This methodological choice not only facilitated long-duration data collection in free-living conditions but also contributed to the scalability and reduced labor intensity of the data collection process. The dataset underwent rigorous processing and annotation, adhering to a strict ethical framework to manage the privacy concerns associated with wearable camera data.
Benchmarking and Model Evaluation
Benchmarking exercises on the CAPTURE-24 dataset employed various commonly used and cutting-edge models, including random forests, XGBoost, convolutional neural networks (CNN), and recurrent neural networks (RNN), alongside the introduction of hidden Markov models (HMM) for temporal dependency modeling. These exercises underscored the critical importance of large, diverse datasets for the optimal performance of deep learning models in HAR. Notably, the research exhibits that models enhanced with HMM consistently outperform their counterparts across different tasks and metrics, highlighting the value of incorporating temporal dimensions in activity recognition models.
Challenges and Future Directions
While the CAPTURE-24 dataset represents a significant leap forward, it also accentuates the inherent challenges in HAR, particularly in distinguishing between closely related activities in a free-living environment. The findings stress the complexity of real-world human activities and the consequent difficulty in achieving high granularity in activity classification. Looking ahead, the paper suggests avenues for further research, including the exploration of larger and more demographically diverse datasets, the development of multimodal monitoring techniques, and the application of advanced model architectures capable of capturing the rich temporal and contextual nuances of human activities.
Conclusion
The CAPTURE-24 dataset, with its substantial volume and real-world applicability, serves as a crucial resource for advancing HAR research. By offering a detailed, in-the-wild dataset, it addresses the critical need for comprehensive and authentic activity data to refine the accuracy and applicability of HAR models. As research continues to evolve in this field, the CAPTURE-24 dataset will likely play a pivotal role in shaping the future of activity recognition, facilitating the development of models that are both robust and sensitive to the subtlest nuances of human movement and behavior.
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