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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23414 |
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| _version_ | 1866911029505556480 |
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| author | Poh, Ming-Zher Wang, Jonathan Hsu, Jonathan Cai, Lawrence Teasley, Eric Taylor, James A. Rogers, Jameson K. Pathak, Anupam Patel, Shwetak |
| author_facet | Poh, Ming-Zher Wang, Jonathan Hsu, Jonathan Cai, Lawrence Teasley, Eric Taylor, James A. Rogers, Jameson K. Pathak, Anupam Patel, Shwetak |
| contents | Smartphone-based heart rate (HR) monitoring apps using finger-over-camera photoplethysmography (PPG) face significant challenges in performance evaluation and device compatibility due to device variability and fragmentation. Manual testing is impractical, and standardized methods are lacking. This paper presents a novel, high-throughput bench-testing platform to address this critical need. We designed a system comprising a test rig capable of holding 12 smartphones for parallel testing, a method for generating synthetic PPG test videos with controllable HR and signal quality, and a host machine for coordinating video playback and data logging. The system achieved a mean absolute percentage error (MAPE) of 0.11% +/- 0.001% between input and measured HR, and a correlation coefficient of 0.92 +/- 0.008 between input and measured PPG signals using a clinically-validated smartphone-based HR app. Bench-testing results of 20 different smartphone models correctly classified all the devices as meeting the ANSI/CTA accuracy standards for HR monitors (MAPE <10%) when compared to a prospective clinical study with 80 participants, demonstrating high positive predictive value. This platform offers a scalable solution for pre-deployment testing of smartphone HR apps to improve app performance, ensure device compatibility, and advance the field of mobile health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A High-Throughput Platform to Bench Test Smartphone-Based Heart Rate Measurements Derived From Video Poh, Ming-Zher Wang, Jonathan Hsu, Jonathan Cai, Lawrence Teasley, Eric Taylor, James A. Rogers, Jameson K. Pathak, Anupam Patel, Shwetak Computer Vision and Pattern Recognition Smartphone-based heart rate (HR) monitoring apps using finger-over-camera photoplethysmography (PPG) face significant challenges in performance evaluation and device compatibility due to device variability and fragmentation. Manual testing is impractical, and standardized methods are lacking. This paper presents a novel, high-throughput bench-testing platform to address this critical need. We designed a system comprising a test rig capable of holding 12 smartphones for parallel testing, a method for generating synthetic PPG test videos with controllable HR and signal quality, and a host machine for coordinating video playback and data logging. The system achieved a mean absolute percentage error (MAPE) of 0.11% +/- 0.001% between input and measured HR, and a correlation coefficient of 0.92 +/- 0.008 between input and measured PPG signals using a clinically-validated smartphone-based HR app. Bench-testing results of 20 different smartphone models correctly classified all the devices as meeting the ANSI/CTA accuracy standards for HR monitors (MAPE <10%) when compared to a prospective clinical study with 80 participants, demonstrating high positive predictive value. This platform offers a scalable solution for pre-deployment testing of smartphone HR apps to improve app performance, ensure device compatibility, and advance the field of mobile health. |
| title | A High-Throughput Platform to Bench Test Smartphone-Based Heart Rate Measurements Derived From Video |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.23414 |