TY - GEN
T1 - SleepSense
T2 - 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
AU - Zhuang, Yan
AU - Song, Chen
AU - Wang, Aosen
AU - Lin, Feng
AU - Li, Yiran
AU - Gu, Changzhan
AU - Li, Changzhi
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/10/15
Y1 - 2015/10/15
N2 - Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.
AB - Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring system for home use that can continuously detect the sleep event. Specifically, SleepSense consists of three parts: an electromagnetic probe, a robust automated radar demodulation module, and a signal processing framework for sleep event recognition, including on-bed movement, bed exit, and breathing event. We present a prototype of the SleepSense system, and perform a set of comprehensive experiments to evaluate the performance of sleep monitoring. Using a real-case evaluation, experimental results indicate that SleepSense can perform effective sleep event detection and recognition in practice.
UR - http://www.scopus.com/inward/record.url?scp=84961612874&partnerID=8YFLogxK
U2 - 10.1109/BSN.2015.7299364
DO - 10.1109/BSN.2015.7299364
M3 - Conference contribution
AN - SCOPUS:84961612874
T3 - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
BT - 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 June 2015 through 12 June 2015
ER -