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Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.

, , , , , , and . npj Digit. Medicine, (2024)

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Self-supervised Learning for Human Activity Recognition Using 700, 000 Person-days of Wearable Data., , , , , and . CoRR, (2022)Self-supervised learning for human activity recognition using 700,000 person-days of wearable data., , , , , , and . npj Digit. Medicine, (2024)Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test., , , , , , , , , and 1 other author(s). IEEE J. Biomed. Health Informatics, 25 (3): 838-849 (2021)Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis., , , , , , , , , and 3 other author(s). npj Digit. Medicine, (2024)Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones., , , , , , , and . IEEE J. Biomed. Health Informatics, 27 (7): 3633-3644 (July 2023)Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling., , and . Healthcare AI and COVID-19 Workshop, volume 184 of Proceedings of Machine Learning Research, page 41-53. PMLR, (2022)Synthesizing Mixed-type Electronic Health Records using Diffusion Models., , , , , and . CoRR, (2023)Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones., , , and . CoRR, (2021)2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries., , , , , and . Sensors, 23 (18): 7705 (September 2023)