Abstract
Sleep apnea syndrome (SAS) is a high incidence respiratory disorder disease with potential mortality risk. Thus, researchers continuously present diverse techniques to monitor sleeping posture and respiratory rate (RR) which are closely correlated to the occurrence of SAS. Current techniques can implement high detection accuracy in both sleeping posture and RR, nevertheless, some undesired attributes still remain, such as high device cost, personal privacy leakage, and heavy burden on hardware. To satisfy the expectations, in this paper, we develop an integrated sleep monitoring system, based on flexible piezoresistive architectures and machine learning algorithms, in a low-cost and privacy protection manner. The experimental results successfully demonstrate the feasibility of the proposed technique, by showing a high sleeping posture classification accuracy of 98.1% and RR estimation accuracy of 97.5%, indicating a strong potential in practical utilization for SAS patients.
Published Version
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