Self-supervised transfer learning of physiological representations from free-living wearable data
Open accessAbstract
Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in free-living conditions. To date, research has primarily focused on the purely supervised task of human activity recognition, demonstrating limited success in inferring high-level health outcomes from low-level signals. Here, we present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. With a deep neural network, we set HR responses as the supervisory signal for the activity data, leveraging their underlying physiological relationship. In addition, we propose a custom quantile loss function that accounts for the long-tailed HR distribution present in the general population.
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The learned embeddings showed strong performance at inferring physiologically meaningful variables ranging from anthropometrics to fitness, outperforming unimodal autoencoders and common biomarkers.
“The value captured by the learned embeddings was showcased through strong performance at inferring physiologically meaningful variables ranging from anthropometrics to fitness, outperforming unimodal autoencoders and common biomarkers.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [144]
The model maps activity data to future HR responses and can be used as a feature extractor for wearable data.
“The model maps activity data to future HR responses and can be used as a feature extractor for wearable data.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [144]
We recently developed a self-supervised model which exploits the multi-modal data of modern wearables to learn meaningful representations which generalize to several outcomes with transfer learning.
“Further, we recently developed a self-supervised model which exploits the multi-modal data of modern wearables to learn meaningful representations which generalize to several outcomes with transfer learning [144].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [144]
The embeddings achieved an AUC of 0.68 for cardio-fitness prediction and an AUC of 0.80 for physical activity energy expenditure.
“For instance, the embeddings achieved an AUC of 0.68 for cardio-fitness prediction and an AUC of 0.80 for physical activity energy expenditure.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [144]
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Influential
- Authors
- Spathis, D., Perez-Pozuelo, I., Brage, S., Wareham, N. J., & Mascolo, C.
- Affiliations
- University of Cambridge (GB)
- University of Cambridge, UK
- Venue
- Proceedings of the Conference on Health, Inference, and Learning
- Publisher
- ACM
- Published
- 2021-04-08
- Pages
- 69-78
- OpenAlex
- W3110080536
- Cited by
- 40
- References
- 64
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 6 · 2023: 11 · 2022: 8 · 2021: 4
- FWCI
- 2.37
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Health, Inference, and Learning (CHIL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Apollo (cc-by)
- Funders
- EPSRC (Grant DTP (EP/N509620/1), iCase fellowship (17100053))
- Jesus College, University of Cambridge (Embiricos Trust Scholarship)
- GlaxoSmithKline
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- self-supervised representation learning, transfer learning, wearable devices, smartwatches, physical activity monitoring, human activity recognition, heart rate, physiological signals, long-tailed distributions
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