Human Respiration Detection using Acoustic Signal

This project presents the design of an audio-based highly-accurate system for human respiration monitoring, leveraging on commodity speaker and microphone widely available in home environments. The basic idea behind the audio-based method is that when a user is close to a pair of speaker and microphone, body movement during respiration causes periodic audio signal changes, which can be extracted to obtain the respiration rate. Experimental results show that our system detects respiration with the median error lower than 0.35 breaths/min, outperforming the state-of-the-art.

This work has been reported in UbiComp 2018.

Tao Gu
Tao Gu
Email: firstname dot lastname AT mq.edu.au
Phone Number: +61-2-9850-4357
Address: Room 267, 4 Research Park Drive, North Ryde, NSW, 2109, Australia

My research interests include Internet of Things, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics.