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- Publisher Website: 10.1109/JIOT.2021.3075167
- Scopus: eid_2-s2.0-85104581104
- WOS: WOS:000714714400042
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Article: mmHRV: Contactless Heart Rate Variability Monitoring using Millimeter-Wave Radio
Title | mmHRV: Contactless Heart Rate Variability Monitoring using Millimeter-Wave Radio |
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Authors | |
Keywords | RevFirstHeart Rate Variability (HRV) wireless sensing RF signals Estimation Chirp heartbeat estimation Heart beat millimeter-wave radio. Interference Heart rate variability Monitoring |
Issue Date | 2021 |
Citation | IEEE Internet of Things Journal, 2021 How to Cite? |
Abstract | Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. To alleviate the user burden and explore the usability for long-term health monitoring, non-contact methods for HRV monitoring have drawn tremendous attention. In this paper, we present mmHRV, the first contact-free multi-user HRV monitoring system using commercial millimeter-Wave (mmWave) radio. The design of mmHRV consists of two key components. First, we develop a calibration-free target detector to identify each user’s location. Second, a heartbeat signal extractor is devised, which can optimize the decomposition of the phase of the channel information modulated by the chest movement, and thus estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the Inter-Beat Intervals (IBIs) can be further derived for evaluating the HRV metrics of each target. We evaluate the system performance and the impact of different settings including the distance between human and the device, user orientation, incidental angle and blockage. Experimental results show that mmHRV can measure the HRV accurately with a median IBI estimation error of 28ms (w.r.t. 96.16% accuracy). In addition, the Root-Mean-Square-Error (RMSE) measured in the Non-Line-of-Sight (NLOS) scenarios is 31.71ms based on the experiments with 11 participants. The performance of the multi-user scenario is slightly degraded compared with the single-user case, however, the median error of the 3-user case is within 52ms for all 3 tested locations. |
Persistent Identifier | http://hdl.handle.net/10722/303773 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wang, Fengyu | - |
dc.contributor.author | Zeng, Xiaolu | - |
dc.contributor.author | Wu, Chenshu | - |
dc.contributor.author | Wang, Beibei | - |
dc.contributor.author | Liu, K. J.R. | - |
dc.date.accessioned | 2021-09-15T08:25:59Z | - |
dc.date.available | 2021-09-15T08:25:59Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | IEEE Internet of Things Journal, 2021 | - |
dc.identifier.uri | http://hdl.handle.net/10722/303773 | - |
dc.description.abstract | Heart Rate Variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. To alleviate the user burden and explore the usability for long-term health monitoring, non-contact methods for HRV monitoring have drawn tremendous attention. In this paper, we present mmHRV, the first contact-free multi-user HRV monitoring system using commercial millimeter-Wave (mmWave) radio. The design of mmHRV consists of two key components. First, we develop a calibration-free target detector to identify each user’s location. Second, a heartbeat signal extractor is devised, which can optimize the decomposition of the phase of the channel information modulated by the chest movement, and thus estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the Inter-Beat Intervals (IBIs) can be further derived for evaluating the HRV metrics of each target. We evaluate the system performance and the impact of different settings including the distance between human and the device, user orientation, incidental angle and blockage. Experimental results show that mmHRV can measure the HRV accurately with a median IBI estimation error of 28ms (w.r.t. 96.16% accuracy). In addition, the Root-Mean-Square-Error (RMSE) measured in the Non-Line-of-Sight (NLOS) scenarios is 31.71ms based on the experiments with 11 participants. The performance of the multi-user scenario is slightly degraded compared with the single-user case, however, the median error of the 3-user case is within 52ms for all 3 tested locations. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Internet of Things Journal | - |
dc.subject | RevFirstHeart Rate Variability (HRV) | - |
dc.subject | wireless sensing | - |
dc.subject | RF signals | - |
dc.subject | Estimation | - |
dc.subject | Chirp | - |
dc.subject | heartbeat estimation | - |
dc.subject | Heart beat | - |
dc.subject | millimeter-wave radio. | - |
dc.subject | Interference | - |
dc.subject | Heart rate variability | - |
dc.subject | Monitoring | - |
dc.title | mmHRV: Contactless Heart Rate Variability Monitoring using Millimeter-Wave Radio | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JIOT.2021.3075167 | - |
dc.identifier.scopus | eid_2-s2.0-85104581104 | - |
dc.identifier.eissn | 2327-4662 | - |
dc.identifier.isi | WOS:000714714400042 | - |