Toward Real-Time Circadian Phase Estimation with Low Latency from Wearable Sensing Data
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866917454049968128 |
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| author | Xu, Mengzhu Cabrilo, Nemanja van Gilst, Merel Linnartz, Jean-Paul |
| author_facet | Xu, Mengzhu Cabrilo, Nemanja van Gilst, Merel Linnartz, Jean-Paul |
| contents | Accurate estimation of the human circadian phase plays an important role in personalized health monitoring, but most existing wearable-based approaches operate retrospectively and require full circadian cycle recordings, leading to high estimation latency and substantial data and computational burden for real-time deployment on edge devices.
In this study, we investigated whether circadian phase can be estimated in real time using only short historical windows of wearable data. We propose a low latency framework that estimates instantaneous circadian phase from past observations, with a cosinor-fitted core body temperature rhythm serving as the reference.
Data from a free-living field study involving 14 participants were used to systematically evaluate the effects of sensor modality selection, historical window length, and model class under participant-based cross-validation. The results showed that estimation accuracy improves with increasing window length but saturates at approximately 8 hours of history. Tree-based models reached a performance plateau beyond 480 minutes, whereas sequence-based models continued to benefit from longer temporal contexts. When relying solely on light exposure and physical activity, the proposed approach achieved a mean circular mean absolute error (CMAE) of 1.19 h.
These findings provide practical guidance for efficient and deployable real-time circadian phase monitoring using wearables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00910 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Real-Time Circadian Phase Estimation with Low Latency from Wearable Sensing Data Xu, Mengzhu Cabrilo, Nemanja van Gilst, Merel Linnartz, Jean-Paul Signal Processing Accurate estimation of the human circadian phase plays an important role in personalized health monitoring, but most existing wearable-based approaches operate retrospectively and require full circadian cycle recordings, leading to high estimation latency and substantial data and computational burden for real-time deployment on edge devices. In this study, we investigated whether circadian phase can be estimated in real time using only short historical windows of wearable data. We propose a low latency framework that estimates instantaneous circadian phase from past observations, with a cosinor-fitted core body temperature rhythm serving as the reference. Data from a free-living field study involving 14 participants were used to systematically evaluate the effects of sensor modality selection, historical window length, and model class under participant-based cross-validation. The results showed that estimation accuracy improves with increasing window length but saturates at approximately 8 hours of history. Tree-based models reached a performance plateau beyond 480 minutes, whereas sequence-based models continued to benefit from longer temporal contexts. When relying solely on light exposure and physical activity, the proposed approach achieved a mean circular mean absolute error (CMAE) of 1.19 h. These findings provide practical guidance for efficient and deployable real-time circadian phase monitoring using wearables. |
| title | Toward Real-Time Circadian Phase Estimation with Low Latency from Wearable Sensing Data |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.00910 |