Automated Non-Contact Detection of Head and Body Positions During Sleep

Abstract

Obstructive sleep apnea (OSA) is a respiratory disorder characterized by interruption to breathing during sleep. Usually, the OSA is more severe in the supine sleeping position. Recent studies also demonstrated that the head position may play an important role in the pathophysiology of the OSA. Therefore, monitoring the sleeping body and the head position has high clinical importance to optimize the treatment of the OSA. In this paper, three machine learning approaches were used to detect the head position during sleep in infrared images. In the first two methods, supervised classifiers were trained to estimate the head position based on different feature sets extracted from infrared images. In the third method, three different convolutional neural network (CNN) structures (ResNet50, MobileNet, and Darknet19) were trained to detect the head position during sleep. To detect the body position, the same CNN architectures were trained on infrared images. Overnight sleeping data (sleep duration = 5±1 h) from 50 participants (age: 53 ± 15 years, BMI: 29 ± 6 kg/m2, and 30 men/20 women) with various levels of OSA severity as measured by the apnea-hypopnea index (AHI = 25 ± 29 events/h and OSA severity: 12 normal, 13 mild, 11 moderate, and 14 severe) were collected for this paper. The models were trained on the data collected in one laboratory room from half of the participants and tested on the data from the other half collected in a different laboratory room. The best performing model (Darknet19) correctly estimated the lateral versus supine head position with 92% accuracy and 94% F1-Score and the lateral versus supine body position with 87% accuracy and 87% F1-Score. INDEX TERMS Computer vision, machine learning, position detection, sleep apnea, non-contact monitoring. The associate editor coordinating the review of this manuscript and approving it for publication was Thomas Penzel.

An Inclusive Survey of Contactless Wireless Sensing: A Technology Used for Remotely Monitoring Vital Signs Has the Potential to Combating COVID-19

“…According to authors, results indicate that the longer it took the patient to fall asleep the higher the pain rating was the next day. 14) Authors in [38] used infrared images to monitor the head position of patients suffering from obstructive sleep apnea (OSA). The authors use three machine learning algorithm to process the images.…”

“…To reduce the costs and the dimensions of the system, discrete components were preferred. In particular, the chosen components were HMC587LC4B [38] for the VCO, HMC392ALC4 [39] for both the PA and the LNA, ZN2PD2-63-S+ for the power divider [40], and HMC557A [41] for the mixer. The transmission and the reception of the signal were controlled and synchronized by the NI DAQ USB 6361 [42].…”

“…The next stage of the algorithm was the peak detection subroutine which marked local maximums of the continuous probabilities output, imposing established constraints on the minimal distance between the consecutive peaks based on the known physiological range in rest 40-120 beats/min [38,39] and their prominence (detection amplitude). When the duration of the detected inter-pulse interval (IPI) was twice as large as the previously detected IPIs (within the established constraints), a beat was interpolated as having occurred at the point in time that was the arithmetic mean between the occurrences of the current and previously detected heartbeats.…”

“…This study has also shown that contantless sensors have great potential to revolutionise human vital sign detection and monitoring. The study has demonstrated that contactless sensing technology can be used to limit contact between patients, [37] Surgery recovery (endometriosis) FMCW Not specified [38] Head position FMCW Not specified BodyCompass [41] Sleep position FMCW 5.4 GHz -7.2 GHz Emirald [39] Pacing FMCW Not specified [42] PD FMCW Not specified [40] Human Figure FMCW 5.46 -7.24 [34] Respiratory rate and heartbeat FMCW 5.8 [35] Respiration rate FMCW 5.8 [17] Human activity (sitting standing) CSI 2.4…”

Automated Detection of Movements During Sleep Using a 3D Time-of-Flight Camera: Design and Experimental Evaluation

“…The contactless approach is comfortable for the patient [15] and preserves natural sleep behavior. Infrared videos of sleep environments can be automatically analyzed by image processing software [16]- [19]. However, these works evaluated their systems only with a small number of healthy volunteers and/or did not allow for the usage of a blanket.…”