Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618
Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive 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,… More >
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