Abstract

Info

Title:: Medical care oriented visual surveillance of patient falls in the hospital

Author(s):: SUN Ying1; ZHANG Yinlong2; WANG Xin3; ZENG Ziming4; 1. Department of Critical Care Medicine, the First Hospital of China Medical University, Shenyang 110001, China 2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3. School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China 4. School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen 518055, China

Keywords:: fall detection video surveillance medical care deep convolutional neural network abnormal behavior analysis

Abstract:: Objective To propose a visual surveillance algorithm for the detection of patient falls in the hospital, thereby solving the problem that hospital patients cannot be rescued in time when they fall accidentally, and providing the essential technical support for the medical staff to quickly deal with the abnormal conditions such as patient falls. Methods The positions of human joints (such as shoulder, elbow, wrist, hip, knee, etc.) in the image were detected based on deep convolutional neural network model, and the human skeleton was extracted using part affinity fields. Finally, the angles between the trunk or the leg and the ground were calculated as features to distinguish whether there were patients in the monitoring area who fall accidentally. Results The experimental results show that the processing speed of the proposed algorithm in the actual hospital surveillance environment was as high as 25 frames per second, and that the detection accuracy was up to 96%. Conclusion The proposed method can accurately extract the behavior characteristics of hospital patients in real time, and issue an alarm for accidental falls, providing a more accurate and convenient computer-assisted medical care method for medical staff to monitor abnormal behaviors such as patient falls.