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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.09173 |
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| _version_ | 1866913107471761408 |
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| author | Cao, Peng Yang, Zhijian Liu, Tennison Wang, Jonathan Wu, Jiang Proszewska, Magdalena Pillai, Arvind Gao, Mingwu Farjadian, Amir Cai, Lawrence Blanchard, Emily McDuff, Daniel Rudrapatna, Pramod Thompson, Matthew Pathak, Anupam Malhotra, Mark Patel, Shwetak Katabi, Dina Di Achille, Paolo Poh, Ming-Zher |
| author_facet | Cao, Peng Yang, Zhijian Liu, Tennison Wang, Jonathan Wu, Jiang Proszewska, Magdalena Pillai, Arvind Gao, Mingwu Farjadian, Amir Cai, Lawrence Blanchard, Emily McDuff, Daniel Rudrapatna, Pramod Thompson, Matthew Pathak, Anupam Malhotra, Mark Patel, Shwetak Katabi, Dina Di Achille, Paolo Poh, Ming-Zher |
| contents | Wearable sensors enable the continuous acquisition of high-resolution physiological waveforms, such as photoplethysmography and accelerometry, under free-living conditions. However, inferring health-related phenotypes from these signals presents significant challenges due to high sampling frequencies, multimodal dependencies, and extreme sequence lengths (e.g., weeks of recordings), compounded by a scarcity of ground-truth labels. To address these challenges, existing self-supervised learning (SSL) methodologies typically follow two paradigms: (1) learning rich morphological representations from short waveform segments while collapsing longitudinal dynamics through simple aggregation, or (2) modeling behavioral patterns from coarse, hand-crafted features (e.g. heart rate, step counts) spanning longer horizons but foregoing subtle, predictive signatures in raw waveforms. To bridge this gap, we propose WavesFM, a foundation model utilizing a two-stage SSL framework for longitudinal physiological data. Specifically, we decompose the learning problem into two stages: first, a segment-level encoder is pretrained to extract local embeddings from short waveforms; subsequently, a temporal encoder is trained to model the sequence of these embeddings across a multi-day horizon. This hierarchical approach overcomes the computational complexity of high-resolution, long-sequence data, allowing the overall model to capture both local signal semantics and the complex circadian and inter-day variations governing physiological dynamics. Pretrained on over 6.8M hours (N=324k individuals) of recordings for the first stage and 5.3M hours (N=10k) for the second stage, WavesFM demonstrates superior performance across 58 diverse tasks spanning demographics, lifestyle, health conditions, and medications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_09173 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms Cao, Peng Yang, Zhijian Liu, Tennison Wang, Jonathan Wu, Jiang Proszewska, Magdalena Pillai, Arvind Gao, Mingwu Farjadian, Amir Cai, Lawrence Blanchard, Emily McDuff, Daniel Rudrapatna, Pramod Thompson, Matthew Pathak, Anupam Malhotra, Mark Patel, Shwetak Katabi, Dina Di Achille, Paolo Poh, Ming-Zher Machine Learning Artificial Intelligence Wearable sensors enable the continuous acquisition of high-resolution physiological waveforms, such as photoplethysmography and accelerometry, under free-living conditions. However, inferring health-related phenotypes from these signals presents significant challenges due to high sampling frequencies, multimodal dependencies, and extreme sequence lengths (e.g., weeks of recordings), compounded by a scarcity of ground-truth labels. To address these challenges, existing self-supervised learning (SSL) methodologies typically follow two paradigms: (1) learning rich morphological representations from short waveform segments while collapsing longitudinal dynamics through simple aggregation, or (2) modeling behavioral patterns from coarse, hand-crafted features (e.g. heart rate, step counts) spanning longer horizons but foregoing subtle, predictive signatures in raw waveforms. To bridge this gap, we propose WavesFM, a foundation model utilizing a two-stage SSL framework for longitudinal physiological data. Specifically, we decompose the learning problem into two stages: first, a segment-level encoder is pretrained to extract local embeddings from short waveforms; subsequently, a temporal encoder is trained to model the sequence of these embeddings across a multi-day horizon. This hierarchical approach overcomes the computational complexity of high-resolution, long-sequence data, allowing the overall model to capture both local signal semantics and the complex circadian and inter-day variations governing physiological dynamics. Pretrained on over 6.8M hours (N=324k individuals) of recordings for the first stage and 5.3M hours (N=10k) for the second stage, WavesFM demonstrates superior performance across 58 diverse tasks spanning demographics, lifestyle, health conditions, and medications. |
| title | WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.09173 |