Privacy-Protected Contactless Sleep Parameters Measurement Using a Defocused Camera

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 8; pp. 4660 - 4673
• Main Authors: Zhu, Yingen, Hong, Hong, Wang, Wenjin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2024
• Subjects: Adult; Biomedical measurement; Biomedical monitoring; Cameras; Contactless sensing; defocused camera; Female; Heart Rate - physiology; Humans; Image Processing, Computer-Assisted - methods; Male; Middle Aged; Monitoring; Movement - physiology; Optical variables measurement; Polysomnography - instrumentation; Polysomnography - methods; Privacy; privacy protection; Respiratory Rate - physiology; Sleep - physiology; Sleep apnea; sleep monitoring; Video Recording - methods; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3396397

Sleep monitoring plays a vital role in various scenarios such as hospitals and living-assisted homes, contributing to the prevention of sleep accidents as well as the assessment of sleep health. Contactless camera-based sleep monitoring is promising due to its user-friendly nature and rich visual semantics. However, the privacy concern of video cameras limits their applications in sleep monitoring. In this paper, we explored the opportunity of using a defocused camera that does not allow identification of the monitored subject when measuring sleep-related parameters, as face detection and recognition are impossible on optically blurred images. We proposed a novel privacy-protected sleep parameters measurement framework, including a physiological measurement branch and a semantic analysis branch based on ResNet-18. Four important sleep parameters are measured: heart rate (HR), respiration rate (RR), sleep posture, and movement. The results of HR, RR, and movement have strong correlations with the reference (HR: R = 0.9076; RR: R = 0.9734; Movement: R = 0.9946). The overall mean absolute errors (MAE) for HR and RR are 5.2 bpm and 1.5 bpm respectively. The measurement of HR and RR achieve reliable estimation coverage of 72.1% and 93.6%, respectively. The sleep posture detection achieves an overall accuracy of 94.5%. Experimental results show that the defocused camera is promising for sleep monitoring as it fundamentally eliminates the privacy issue while still allowing the measurement of multiple parameters that are essential for sleep health informatics.