TY - JOUR
T1 - Respiration Monitoring in High-Dynamic Environments via Combining Multiple WiFi Channels Based on Wire Direct Connection Between RX/TX
AU - Qiu, Jiefan
AU - Zheng, Pand
AU - Chi, Kaikai
AU - Xu, Ruiji
AU - Liu, Jiajia
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2023/1/15
Y1 - 2023/1/15
N2 - As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.
AB - As one widely applied wireless technique, WiFi has the potential to execute noncontact monitoring of vital signs based on channel state information (CSI). However, due to the dynamic of the surrounding environment, the bandwidth of the WiFi channel is not enough to identify the respiration-induced path from other movement-induced paths and this seriously limits the accuracy of respiration rate detection. In this article, we propose ExRadio, a system that can monitor respiration in high-dynamic environments via combining multiple WiFi channels. Specifically, the receiver synchronously switches the channels with the transmitter and samples CSI at multiple channels, and then the CSI data are combined and regarded as CSI data of one extended-bandwidth channel. However, the hardware-related noises from multiple channels are also accumulated. Eliminating these noises causes too heavy computation overhead to be afforded by the embedded devices and affects the real-time performance of respiration monitoring. To address this problem, we propose an effective approach that employs the ratio of CSI readings from the wireless channel and wire direct connection channel to shorten the time of eliminating the hardware-related noise. We deploy the ExRadio in commercial off-the-shelf embedded devices and conduct a series of experiments. The experimental results demonstrate that reducing the execution time is beneficial to respiration rate detection under high-dynamic environments, and the overall detection error of ExRadio is less than 0.5 bpm even when multiple persons who are 1.5-m away from the monitored person are fast walking.
KW - Channel combining
KW - channel state information (CSI)
KW - respiration monitoring
KW - wire direct connection (WDC)
UR - http://www.scopus.com/inward/record.url?scp=85139399976&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2022.3209172
DO - 10.1109/JIOT.2022.3209172
M3 - 文章
AN - SCOPUS:85139399976
SN - 2327-4662
VL - 10
SP - 1558
EP - 1573
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
ER -