Towards deep clustering of human activities from wearables
Open accessAbstract
Our ability to exploit low-cost wearable sensing modalities for critical human behaviour and activity monitoring applications in health and wellness is reliant on supervised learning regimes; here, deep learning paradigms have proven extremely successful in learning activity representations from annotated data. However, the costly work of gathering and annotating sensory activity datasets is labor intensive, time consuming and not scalable to large volumes of data. While existing unsupervised remedies of deep clustering leverage network architectures and optimization objectives that are tailored for static image datasets, deep architectures to uncover cluster structures from raw sequence data captured by on-body sensors remains largely unexplored. In this paper, we develop an unsupervised end-to-end learning strategy for the fundamental problem of human activity recognition (HAR) from wearables. Through extensive experiments, including comparisons with existing methods, we show the effectiveness of our approach to jointly learn unsupervised representations for sensory data and generate cluster assignments with strong semantic correspondence to distinct human activities.
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- Authors
- Abedin, A., Motlagh, F., Shi, Q., Rezatofighi, H., & Ranasinghe, D.
- Affiliations
- The University of Adelaide (AU)
- Venue
- Proceedings of the 2020 International Symposium on Wearable Computers
- Publisher
- ACM
- Published
- 2020-09-04
- Pages
- 1-6
- OpenAlex
- W3047073692
- Cited by
- 27
- References
- 42
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 9
- FWCI
- 1.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Symposium on Wearable Computers (publishedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- human activity recognition, wearable sensors, deep clustering, unsupervised learning, on-body sensing, activity monitoring, health and wellness, raw sequence data
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