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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2503.03783 |
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| _version_ | 1866909547706187776 |
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| author | Liao, Shun Di Achille, Paolo Wu, Jiang Borac, Silviu Wang, Jonathan Liu, Xin Teasley, Eric Cai, Lawrence Yang, Yuzhe Liu, Yun McDuff, Daniel Su, Hao-Wei Winslow, Brent Pathak, Anupam Patel, Shwetak Taylor, James A. Rogers, Jameson K. Poh, Ming-Zher |
| author_facet | Liao, Shun Di Achille, Paolo Wu, Jiang Borac, Silviu Wang, Jonathan Liu, Xin Teasley, Eric Cai, Lawrence Yang, Yuzhe Liu, Yun McDuff, Daniel Su, Hao-Wei Winslow, Brent Pathak, Anupam Patel, Shwetak Taylor, James A. Rogers, Jameson K. Poh, Ming-Zher |
| contents | Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during everyday smartphone use, using facial video-based photoplethysmography. Our system was developed using 225,773 videos from 495 participants and validated on 185,970 videos from 205 participants in laboratory and free-living conditions, representing the largest validation study of its kind. Compared to reference electrocardiogram, PHRM achieved a mean absolute percentage error (MAPE) < 10% for HR measurements across three skin tone groups of light, medium and dark pigmentation; MAPE for each skin tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error < 5 bpm compared to a wearable HR tracker, and was associated with known risk factors. These results highlight the potential of smartphones to enable passive and equitable heart health monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03783 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Passive Heart Rate Monitoring During Smartphone Use in Everyday Life Liao, Shun Di Achille, Paolo Wu, Jiang Borac, Silviu Wang, Jonathan Liu, Xin Teasley, Eric Cai, Lawrence Yang, Yuzhe Liu, Yun McDuff, Daniel Su, Hao-Wei Winslow, Brent Pathak, Anupam Patel, Shwetak Taylor, James A. Rogers, Jameson K. Poh, Ming-Zher Tissues and Organs Artificial Intelligence Emerging Technologies Human-Computer Interaction Machine Learning Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during everyday smartphone use, using facial video-based photoplethysmography. Our system was developed using 225,773 videos from 495 participants and validated on 185,970 videos from 205 participants in laboratory and free-living conditions, representing the largest validation study of its kind. Compared to reference electrocardiogram, PHRM achieved a mean absolute percentage error (MAPE) < 10% for HR measurements across three skin tone groups of light, medium and dark pigmentation; MAPE for each skin tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error < 5 bpm compared to a wearable HR tracker, and was associated with known risk factors. These results highlight the potential of smartphones to enable passive and equitable heart health monitoring. |
| title | Passive Heart Rate Monitoring During Smartphone Use in Everyday Life |
| topic | Tissues and Organs Artificial Intelligence Emerging Technologies Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2503.03783 |