Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.06458 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916316515926016 |
|---|---|
| author | Xu, Luzhou Lien, Jaime Li, Haiguang Gillian, Nicholas Nongpiur, Rajeev Li, Jihan Zhang, Qian Cui, Jian Jorgensen, David Bernstein, Adam Bedal, Lauren Hayashi, Eiji Yamanaka, Jin Lee, Alex Wang, Jian Shin, D Poupyrev, Ivan Thormundsson, Trausti Pathak, Anupam Patel, Shwetak |
| author_facet | Xu, Luzhou Lien, Jaime Li, Haiguang Gillian, Nicholas Nongpiur, Rajeev Li, Jihan Zhang, Qian Cui, Jian Jorgensen, David Bernstein, Adam Bedal, Lauren Hayashi, Eiji Yamanaka, Jin Lee, Alex Wang, Jian Shin, D Poupyrev, Ivan Thormundsson, Trausti Pathak, Anupam Patel, Shwetak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_06458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking Xu, Luzhou Lien, Jaime Li, Haiguang Gillian, Nicholas Nongpiur, Rajeev Li, Jihan Zhang, Qian Cui, Jian Jorgensen, David Bernstein, Adam Bedal, Lauren Hayashi, Eiji Yamanaka, Jin Lee, Alex Wang, Jian Shin, D Poupyrev, Ivan Thormundsson, Trausti Pathak, Anupam Patel, Shwetak Signal Processing 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. |
| title | Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2407.06458 |