Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Abstract

Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method.

Smart Environments and Social Robots for Age-Friendly Integrated Care Services

“…Visual context (e.g., keep track of daily living activities performed by the older adult, locating the older adults in house) [43][44][45][46] Acoustic sensors Fall-detection [48][49][50] Ambient sensors (temperature, appliances, toilet)…”

“…Piezoelectric film sensors can be embedded in the bed mattress to monitor the sleep quality, based on the variation of the heart and respiration rate and the binary actigram [42]. Microsoft Kinect camera can be employed to detect the human shape and the body movements and a sensor tag that provides information about the sleep environment such as the temperature and humidity [43], while Near-Infrared camera can be used to analyze the sleep behavior, based on the collected videos/images [44].…”

Sensing and Signal Processing in Smart Healthcare

“…Two of the papers aimed at the detection of the presence of a human being at specific locations either using pre-placed sensors [2], or using Bluetooth signals (assuming that the person being monitored carries a Bluetooth device) [8]. The remaining three papers aimed at direct human activity detection using inertial sensors [9], time of flight sensors [9], near-infrared camera [10], and Microsoft Kinect sensor [11].…”

“…In [10], Wang et al reported a non-intrusive video-based sleep monitoring system. They addressed some major technical challenges in detecting human sleep poses with infrared images.…”

Sleep Apnea Detection with Polysomnography and Depth Sensors

“…In general, the use of image and depth sensors [1,7,[9][10][11][12] allow sleep monitoring and 3D modeling of the chest and abdomen volume changes during breaths as an alternative to conventional spirometry with the use of red-green-blue (RGB) cameras [13,14], infrared cameras [15,16], or Doppler multi-radar systems [17] in addition to ultrasonic and biomotion sensors [18][19][20][21]. The paper provides motivation for further studies of methods related to big data processing problems, dimensionality reduction, principal component analysis, and parallel signal processing to increase the speed, accuracy, and reliability of the results.…”