面向睡眠与冥想追踪的基于Soli的非接触式心率检测
Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking
AI总结：
本研究针对传统可穿戴心率检测在睡眠、冥想场景的不便问题，提出基于微型Soli雷达芯片的非接触式心率检测方法，经多数据集验证精度优异，为相关场景心率监测提供新方案。
摘要：
心率（HR）是可用于监测健康与体能的关键生理信号。传统心率测量方法需要可穿戴设备，存在不便或不适感，在睡眠和冥想场景下尤为突出。采用微波雷达的非接触式心率检测方法是颇具前景的替代方案，但现有文献中的方法通常使用高增益天线，且要求传感器正对用户胸部或背部，难以集成到便携设备中，不适用于睡眠与冥想追踪场景。本研究提出一种新型非接触式心率检测方法，采用嵌入便携设备（Google Nest Hub）的微型化Soli雷达芯片，该芯片尺寸为6.5 mm×5 mm×0.9 mm，可轻松集成到各类设备中。所提方法利用先进的信号处理与机器学习技术从雷达信号中提取心率。研究在包含62名用户、共498小时的睡眠数据集，以及包含114名用户、共1131分钟的冥想数据集上验证了该方法。在睡眠数据集上，该方法的平均绝对误差（MAE）为1.69 bpm，平均绝对百分比误差（MAPE）为2.67%；在冥想数据集上，MAE为1.05 bpm，MAPE为1.56%；两个数据集的召回率分别为88.53%和98.16%。本研究首次将非接触式心率检测技术应用于睡眠与冥想追踪，为睡眠和冥想期间的心率监测提供了可替代可穿戴设备的 promising 方案。
英文摘要：
Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, the existing approaches in the literature usually use high-gain antennas and require the sensor to face the user's chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a portable device (Google Nest Hub). The chip has a $6.5 \mbox{ mm} \times 5 \mbox{ mm} \times 0.9 \mbox{ mm}$ dimension and can be easily integrated into various devices. The proposed approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 hours) and a meditation dataset (114 users, 1131 minutes). The approach achieves a mean absolute error (MAE) of $1.69$ bpm and a mean absolute percentage error (MAPE) of $2.67\%$ on the sleep dataset. On the meditation dataset, the approach achieves an MAE of $1.05$ bpm and a MAPE of $1.56\%$. The recall rates for the two datasets are $88.53\%$ and $98.16\%$, respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
