Non-Contact Heart Sound Measurement Using Independent Component Analysis

Muramatsu, Shun; Yamamoto, Masahide; Takamatsu, Seiichi; Itoh, Toshihiro

doi:10.1109/access.2022.3206467

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Thus, traditional spectral analysis techniques, such as Fourier transform (FT) and band-pass filter (BPF) 17 , fail to perform satisfactorily. Various methods have been developed to address this issue, including ensemble empirical mode decomposition (EEMD) 18 , wavelet 19 , RELAX 20 , Kalman filter 21 , independent component analysis (ICA) 22 , and ML 23 . However, the authors of these papers have primarily focused on cases where the radar sensor is directed straight at the user’s chest or back.…”

Section: Related Workmentioning

confidence: 99%

Soli-enabled noncontact heart rate detection for sleep and meditation tracking

Xu,

Lien,

et al. 2023

Sci Rep

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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 $$6.5 \text{ mm } \times 5 \text{ mm } \times 0.9 \text{ mm }$$ 6.5 mm × 5 mm × 0.9 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 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 $$2.67\%$$ 2.67 % on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of $$1.56\%$$ 1.56 % . The recall rates for the two datasets are $$88.53\%$$ 88.53 % and $$98.16\%$$ 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.

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Section: Related Workmentioning

confidence: 99%

Soli-enabled noncontact heart rate detection for sleep and meditation tracking

Xu,

Lien,

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Thus, traditional spectral analysis techniques, such as Fourier transform (FT) and band-pass filter (BPF) 11 , fail to perform satisfactorily. Various methods have been developed to address this issue, including ensemble empirical mode decomposition (EEMD) 14 , wavelet 15 , RELAX 16 , Kalman filter 17 , independent component analysis (ICA) 18 , and ML 19 . However, the authors of these papers have primarily focused on cases where the radar sensor is directed straight at the user's chest or back.…”

Section: Introductionmentioning

confidence: 99%

Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking

Lien

et al. 2023

Preprint

View full text Add to dashboard Cite

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 mm × 5 mm × 0.9 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.

show abstract

Non-Contact Heart Sound Measurement Using Independent Component Analysis

Cited by 2 publications

References 22 publications

Soli-enabled noncontact heart rate detection for sleep and meditation tracking

Soli-enabled noncontact heart rate detection for sleep and meditation tracking

Soli-enabled Noncontact Heart Rate Detection for Sleep and Meditation Tracking

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