Wi-Breath: A WiFi-Based Contactless and Real-Time Respiration Monitoring Scheme for Remote Healthcare

被引:15
作者
Bao, Nan [1 ]
Du, Jiajun [1 ]
Wu, Chengyang [2 ]
Hong, Duo [3 ]
Chen, Junxin [1 ]
Nowak, Robert [4 ]
Lv, Zhihan [5 ]
机构
[1] Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang 110004, Peoples R China
[2] Beijing Zhixinyu Informat Consulting Co Ltd, Beijing 100010, Peoples R China
[3] China Med Univ, Hosp 1, Dept Intervent Radiol, Shenyang 110000, Peoples R China
[4] Warsaw Univ Technol, Inst Comp Sci, Artificial Intelligence Div, PL-00661 Warsaw, Poland
[5] Uppsala Univ, Fac Arts, Dept Game Design, S-75105 Uppsala, Sweden
基金
中国国家自然科学基金;
关键词
Monitoring; Wireless fidelity; Phase measurement; Antenna measurements; Biomedical monitoring; Privacy; Temperature measurement; Respiration monitoring; channel state information; signal selection; remote healthcare; OBSTRUCTIVE SLEEP-APNEA; SENSOR;
D O I
10.1109/JBHI.2022.3186152
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.
引用
收藏
页码:2276 / 2285
页数:10
相关论文
共 31 条
[31]   Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: Toward scalable and efficient real time healthcare monitoring system [J].
Salman, Omar Hussein ;
Aal-Nouman, Mohammed Imad ;
Taha, Zahraa K. .
JOURNAL OF BIOMEDICAL INFORMATICS, 2020, 112