UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916454120554496 |
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| author | Bian, Haoyu Guo, Bin Liu, Sicong Ding, Yasan Gao, Shanshan Yu, Zhiwen |
| author_facet | Bian, Haoyu Guo, Bin Liu, Sicong Ding, Yasan Gao, Shanshan Yu, Zhiwen |
| contents | Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients. Non-contact sensing, compared to contact-based tools, allows for natural user monitoring, potentially enabling more accurate and holistic data collection. However, in open and uncontrolled mobile environments, user movement and lighting introduce. Existing methods, such as curve-based or short-range deep learning recognition based on adjacent frames, strike the optimal balance between real-time performance and accuracy, especially under limited device resources. In this paper, we present UbiHR, a ubiquitous device-based heart rate sensing system. Key to UbiHR is a real-time long-range spatio-temporal model enabling noise-independent heart rate recognition and display on commodity mobile devices, along with a set of mechanisms for prompt and energy-efficient sampling and preprocessing. Diverse experiments and user studies involving four devices, four tasks, and 80 participants demonstrate UbiHR's superior performance, enhancing accuracy by up to 74.2\% and reducing latency by 51.2\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19279 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices Bian, Haoyu Guo, Bin Liu, Sicong Ding, Yasan Gao, Shanshan Yu, Zhiwen Signal Processing Artificial Intelligence Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients. Non-contact sensing, compared to contact-based tools, allows for natural user monitoring, potentially enabling more accurate and holistic data collection. However, in open and uncontrolled mobile environments, user movement and lighting introduce. Existing methods, such as curve-based or short-range deep learning recognition based on adjacent frames, strike the optimal balance between real-time performance and accuracy, especially under limited device resources. In this paper, we present UbiHR, a ubiquitous device-based heart rate sensing system. Key to UbiHR is a real-time long-range spatio-temporal model enabling noise-independent heart rate recognition and display on commodity mobile devices, along with a set of mechanisms for prompt and energy-efficient sampling and preprocessing. Diverse experiments and user studies involving four devices, four tasks, and 80 participants demonstrate UbiHR's superior performance, enhancing accuracy by up to 74.2\% and reducing latency by 51.2\%. |
| title | UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2410.19279 |