ArticleNPJ digital medicine2024
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review.JMIR mental health · 2026Pooled it
- Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.Annals of medicine · 2026Review
- Temporal Step Supervision for Robust Step Counting from Wearable Accelerometers.Sensors (Basel, Switzerland) · 2026Article
- Zero-Cycle: First Continuous, Unsupervised, At-Home Monitoring of Spinal Motion as a Per-Cycle Substrate for Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Sensor Modality Versus Pretraining for Recognising Bed-Exit-Relevant Postural Transitions: A Gyroscope-Focused Feasibility Study on Public Wearable-IMU Data.Sensors (Basel, Switzerland) · 2026Article
- Large language models show high clinical safety but differences in completeness for reproductive counselling in women with inflammatory rheumatic and musculoskeletal diseases: a comparative expert evaluation.Rheumatology international · 2026Article
- A Gear-Driven Plantar Energy Harvester with Integrated Self-Sensing for Human Locomotion Recognition.Sensors (Basel, Switzerland) · 2026Article
- Robust Assessment of Free-Living Physical Behaviors and Activity Intensity Using Dual-Wearable Multitask Learning: Development and Evaluation Study From the Multicenter WEALTH Project.JMIR mHealth and uHealth · 2026Article
- Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson's disease.Lifetime data analysis · 2026Article
- Development and evaluation of wrist- and thigh-worn accelerometer algorithms using self-training machine learning models for classification of activity type and posture: towards device placement-agnostic methods in the ProPASS consortium.The international journal of behavioral nutrition and physical activity · 2026Article
- Agentic AI in global health equity for high altitude populations.NPJ digital medicine · 2026Review
- Detect and Repair: Robust Self-Supervised Wearable Sensing Under Missing Modalities.Sensors (Basel, Switzerland) · 2026Article
- Continuous assessment of daily-living gait using self-supervised learning of wrist-worn accelerometer data.NPJ digital medicine · 2026Article
- Artificial Intelligence-Enhanced Flexible Sensors for Human Motion and Posture Sensing.Sensors (Basel, Switzerland) · 2026Review
- A Real-World Dataset for detecting Handwashing in daily Life using Wrist Motion Data from Wearables.Scientific data · 2026Article
- Article
- Smart wearables for school-based physical activity promotion: K-12 physical education teachers' continuance intention and adoption pathways.Frontiers in public health · 2026Article
- Classification of daily activities using a wireless instrumented insole (WalkinSense) in a semi free-living setting.PloS one · 2026Article
- Navigating the Choice Between Wrist-Worn Research- and Consumer-Grade Wearables to Monitor Movement Behaviours: A Perspective.International journal of public health · 2026Article
- Association between wrist-worn actigraphy and the MDS-UPDRS Parkinson's disease rating scale through machine learning: an exploratory study.Frontiers in digital health · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset-a 700,000 person-days unlabelled dataset-in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5-130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.