CNN-Based Smart Sleep Posture Recognition System

Abstract

Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%.

Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera

“…These motion capture systems are generally classified into a marker system, a markerless system, and a wearable system. The most accurate and reliable way is to track the body's motion using a marker system [14][15][16] that attaches a marker to each joint or a wearable sensor, such as an accelerometer or pressure sensor [17][18][19]. Despite their high accuracy, markers or wearable sensors are inconvenient for the subject because separate equipment must be attached to the body ahead of time.…”

“…As hardware for collecting posture recognition data, three-dimensional depth cameras [51], smartphones [52,53], and inertial measurement unit sensors [17] are used. In addition, machine learning algorithms such as support vector machine (SVM) [54], CNN [19], and When the welding arc and the welder's body overlap, it is difficult to capture motion accurately because the depth hole obscures the body in the depth image captured by an RGB-D camera. To recognize the body motion or working posture, we must look for a depth hole caused by the welding arc and remove it if it is found.…”

“…As hardware for collecting posture recognition data, three-dimensional depth cameras [51], smartphones [52,53], and inertial measurement unit sensors [17] are used. In addition, machine learning algorithms such as support vector machine (SVM) [54], CNN [19], and deep belief network [55] are widely used to learn posture recognition data in terms of software.…”

A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions

“…Traditionally, measurement of sleep posture relied on videotaping with manual labeling or self-reported assessments, which could be inaccurate [ 12 , 21 ]. Tang et al [ 22 ] reviewed the nonintrusive technology applied for recognition of sleep posture, including visible light, infrared, and depth cameras [ 23 , 24 ], inertia measurement units with wireless connection [ 25 ], and radar/radio sensors [ 1 , 8 , 26 ]. Although efforts were made to utilize and integrate different sensors for better versatility, there were few attempts to recognize and classify sleeping postures accurately [ 22 ].…”

“…Tang et al [ 22 ] reviewed the nonintrusive technology applied for recognition of sleep posture, including visible light, infrared, and depth cameras [ 23 , 24 ], inertia measurement units with wireless connection [ 25 ], and radar/radio sensors [ 1 , 8 , 26 ]. Although efforts were made to utilize and integrate different sensors for better versatility, there were few attempts to recognize and classify sleeping postures accurately [ 22 ]. Body load and pressure patterns represent another area of studies dedicated to sleep posture recognition.…”