Algorithm Design for Sleep Monitoring System Based on Mattress

In order to help health care provider monitor patient status such as respiration rate, body movement and apnea, we designed a low-cost sleep monitoring mattress based on pressure sensors in this paper. In particular, some mattresses have been used in nursing homes so that they can master the old people’s sleep condition at a minimal cost. Experimental results show that the respiration rate compared to medical equipment with the difference between one or less reached 92%. Body movement monitoring had a 95% accuracy rate. As for apnea, the accuracy of monitoring results was close to 90%.

[1]  Feng Zhao,et al.  Interference alignment and game-theoretic power allocation in MIMO Heterogeneous Sensor Networks communications , 2016, Signal Process..

[2]  Patrick Levy,et al.  Adaptive Servo-Ventilation for Central Sleep Apnea in Systolic Heart Failure. , 2015, The New England journal of medicine.

[3]  M. Johns,et al.  A new method for measuring daytime sleepiness: the Epworth sleepiness scale. , 1991, Sleep.

[4]  Feng Zhao,et al.  Joint Beamforming and Power Allocation for Cognitive MIMO Systems Under Imperfect CSI Based on Game Theory , 2013, Wireless Personal Communications.

[5]  N B Kavey,et al.  Sleeping position and sleep apnea syndrome. , 1985, American journal of otolaryngology.

[6]  Feng Zhao,et al.  Group buying spectrum auction algorithm for fractional frequency reuse cognitive cellular systems , 2017, Ad Hoc Networks.

[7]  Raanan Arens,et al.  A randomized trial of adenotonsillectomy for childhood sleep apnea. , 2013, The New England journal of medicine.

[8]  J. Piccirillo,et al.  The efficacy of surgical modifications of the upper airway in adults with obstructive sleep apnea syndrome. , 1996, Sleep.

[9]  T. Young,et al.  Association between asthma and risk of developing obstructive sleep apnea. , 2015, Journal of the American Medical Association (JAMA).

[10]  Feng Zhao,et al.  Outage performance of relay-assisted primary and secondary transmissions in cognitive relay networks , 2014, EURASIP Journal on Wireless Communications and Networking.

[11]  J. Kocher,et al.  CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model , 2013, Nucleic acids research.

[12]  Feng Zhao,et al.  Optimal Time Allocation for Wireless Information and Power Transfer in Wireless Powered Communication Systems , 2016, IEEE Transactions on Vehicular Technology.

[13]  Shwetak N. Patel,et al.  DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar , 2015, UbiComp.

[14]  Shyamnath Gollakota,et al.  Contactless Sleep Apnea Detection on Smartphones , 2015, GetMobile Mob. Comput. Commun..

[15]  Enzo Pasquale Scilingo,et al.  Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis , 2014, IEEE Journal of Biomedical and Health Informatics.

[16]  Linda S de Vries,et al.  Comparison between simultaneously recorded amplitude integrated electroencephalogram (cerebral function monitor) and standard electroencephalogram in neonates. , 2002, Pediatrics.