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체압 분포 영상 초고해상도 보정을 이용한 욕창 예방을 위한 환자 자세 추정
김진규(Jin-Gyu Kim),심명훈(Myounghoon Shim),배은경(Eunkyung Bae),문영진(Youngjin Moon),최재순(Jaesoon Choi) 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.5
In this study, to improve the prediction of pressure ulcer spots, we have developed super-resolution (SR) techniques to reconstruct a high-resolution (HR) pressure image from a low-resolution (LR) body pressure image to overcome the limitations of sensor resolution. We implemented a super-resolution generative adversarial network (SRGAN) to reconstruct pressure images and a convolution neural network (CNN) to predict posture. To evaluate the similarity between the original pressure image and the 4× rescaled LR body pressure image restored using SR technology, we used image quality assessment (IQA) technology, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). The reconstructed pressure images were classified into four patient postures (supine, right side, left side, and others) with 98.37% accuracy showing the feasibility of practical implementation.