Respiration volume has been widely used as an important indication for diagnosis and treatment of pulmonary diseases and other health care related issues such as critically ill patients neonatal ventilation, post-operative monitoring and various others. Most of existing technologies for respiration volume monitoring require physical contact with the human body. While wireless-based approaches have also been discussed in the literature, there are still limitations in terms of estimation accuracy and time efficiency preventing these approaches from being realized in practice. In this paper, we present an automated, wireless-based, vision-supervised system, called WiKiSpiro, for monitoring an individual’s respiration volume. In particular, we present a system design encompassing a wireless device, motion tracking and control system, and a set of methods to accurately derive breathing volume from the reflected signal and to address challenges caused by body movement and posture changes. We present our preliminary results of WikiSpiro, and identify possible challenges for future research and development.
Phuc Nguyen, Shane Transue, Min-Hyung Choi, Ann C. Halbower, and Tam Vu, "WiKiSpiro: Non-contact Respiration volume Monitoring during Sleep", in the 8th Annual S3 Workshop (in conjunction with: ACM MobiCom 2016), New York, October, 2016. (Best Paper Award)
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