Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model  

作　　者：Awais Khan Chomyong Kim Jung-Yeon Kim Ahsan Aziz Yunyoung Nam 

机构地区：[1]Department of ICT Convergence,Soonchunhyang University,Asan,31538,Korea [2]ICT Convergence Research Center,Soonchunhyang University,Asan,31538,Korea [3]Emotional and Intelligent Child Care Convergence Research Center,Soonchunhyang University,Asan,31538,Korea

出　　处：《Computer Modeling in Engineering & Sciences》2024年第8期1729-1755,共27页工程与科学中的计算机建模（英文）

基　　金：supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI);funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:H12C1831);Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea Government(MOTIE)(P0012724,HRD Program for Industrial Innovation);the National Research Foundation of Korea(NRF)Grant funded by the Korea Government(MSIT)(No.RS-2023-00218176);the Soonchunhyang University Research Fund.

摘　　要：Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

关 键 词：Human sleep posture VGG16 deep learning ResNet50 FUSION machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]