ArticleScientific reports2023
Soli-enabled noncontact heart rate detection for sleep and meditation tracking.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating Assistive Technologies and Smart Home Systems in Neuromuscular Disorders: A Systematic Narrative Review of Clinical Outcomes and Occupational Therapy Perspectives.Occupational therapy international · 2026Pooled it
- Personalizing ecological momentary intervention for substance use disorders through data-driven decision rules.Frontiers in psychiatry · 2026Review
- mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Atrial Fibrillation.IEEE journal of translational engineering in health and medicine · 2026Article
- Radar Multiple Bin Selection for Breathing and Heart Rate Monitoring in Acute Stroke Patients in a Clinical Setting.Sensors (Basel, Switzerland) · 2025Article
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20 authors.
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Abstract
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 [Formula: see text] 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 h) and a meditation dataset (114 users, 1131 min). The approach achieves a mean absolute error (MAE) of 1.69 bpm and a mean absolute percentage error (MAPE) of [Formula: see text] on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of [Formula: see text]. The recall rates for the two datasets are [Formula: see text] and [Formula: see text], 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.
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