A Lidar-based  computer vision system for monitoring of patients in medical scenes

Abstract

Long-term monitoring for patients can improve patient safety, help doctors diagnose and evaluate the clinical situation. Limited manpower in hospitals makes it difficult to achieve continuous and nuanced monitoring. In this paper, we classify the patient's posture as standing, sitting, lying and falling. Using a non-intrusive, privacy-compliant lidar, a medical human pose dataset is collected in the First Affiliated Hospital, Sun Yat-Sen University, and a novel computer vision-based approach is presented to continuously detect patients pose and provide timely information to health care workers. The recognition accuracy reaches 93.46% and the recognition speed reaches 42FPS on 3080Ti. Experimental results show that the algorithm performs well on the medical human pose dataset, and can effectively solve the problem of human pose recognition in medical scenes.