在普通手机上实现低功耗长距离无接触心率监测
UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices
- 设计实时长时序模型，抗干扰能力强
- 准确率提升74.2%，延迟降低51.2%
- 适合慢性病患者和高压力人群使用
普遍存在的设备上进行无接触心率感知对高压人群和慢病患者至关重要。相比接触式设备，非接触感知可实现自然状态下的用户监测，有助于更准确、全面的数据采集。然而，在开放且不受控的移动环境中，用户运动和光照变化会引入干扰。现有方法如基于曲线或短时程深度学习识别相邻帧，能在实时性与准确性间取得较好平衡，尤其适用于资源受限设备。本文提出UbiHR，一种基于通用移动设备的心率感知系统。其核心是一个实时长时序时空模型，支持噪声无关的心率识别与显示，并配备快速、节能的采样与预处理机制。在四种设备、四项任务及80名参与者上的实验表明，UbiHR显著提升性能，准确率最高提升74.2%，延迟降低51.2%。
原文摘要 · Abstract (English)
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\%.
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