TY - GEN
T1 - Monitoring a person’s heart rate and respiratory rate on a shared bed using geophones
AU - Jia, Zhenhua
AU - Xu, Chenren
AU - Bonde, Amelie
AU - Wang, Jingxian
AU - Li, Sugang
AU - Zhang, Yanyong
AU - Howard, Richard E.
AU - Zhang, Pei
N1 - Funding Information:
We are grateful to the SenSys reviewers for their constructive critique, and our shepherd, Dr. Wen Hu, for his valuable comments, all of which have helped us greatly improve this paper. This work was supported in part by the U.S. National Science Foundation (NSF) under grant CNS-1404118, CNS-1423020, CNS-1149611 and CMMI-1653550, Intel, Pennsylvania Infrastructure Technology Alliance (PITA), Google, Science and Technology Innovation Project of Foshan City, China under Grant No. 2015IT100095 and Science and Technology Planning Project of Guangdong Province, China under Grant No. 2016B010108002.
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person’s heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person’s respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal. We have developed a prototype bed to evaluate the proposed algorithms. A total of 86 subjects participated in our study, and we collected 5084 geophone samples, totaling 56 hours of data. We show that our technique is accurate – its breathing rate estimation error for a single person is 0.38 breaths per minute (median error is 0.22 breaths per minute), heart rate estimation error when two persons share a bed is 1.90 beats per minute (median error is 0.72 beats per minute), and breathing rate estimation error when two persons share a bed is 2.62 breaths per minute (median error is 1.95 breaths per minute). By varying sleeping posture and mattress type, we show that our system can work in many different scenarios.
AB - Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person’s heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person’s respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal. We have developed a prototype bed to evaluate the proposed algorithms. A total of 86 subjects participated in our study, and we collected 5084 geophone samples, totaling 56 hours of data. We show that our technique is accurate – its breathing rate estimation error for a single person is 0.38 breaths per minute (median error is 0.22 breaths per minute), heart rate estimation error when two persons share a bed is 1.90 beats per minute (median error is 0.72 beats per minute), and breathing rate estimation error when two persons share a bed is 2.62 breaths per minute (median error is 1.95 breaths per minute). By varying sleeping posture and mattress type, we show that our system can work in many different scenarios.
KW - Amplitude Modulation
KW - Blind Source Separation
KW - Geophone
KW - Time-frequency Masking
KW - Unobtrusive Sensing
KW - Vital Signs
UR - http://www.scopus.com/inward/record.url?scp=85052021798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052021798&partnerID=8YFLogxK
U2 - 10.1145/3131672.3131679
DO - 10.1145/3131672.3131679
M3 - Conference contribution
AN - SCOPUS:85052021798
T3 - SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
BT - SenSys 2017 - Proceedings of the 15th ACM Conference on Embedded Networked Sensor Systems
A2 - Eskicioglu, Rasit
PB - Association for Computing Machinery, Inc
T2 - 15th ACM Conference on Embedded Networked Sensor Systems, SenSys 2017
Y2 - 6 November 2017 through 8 November 2017
ER -