Xin Wang1, Xiaoyan Zheng This email address is being protected from spambots. You need JavaScript enabled to view it.1, Jianshun Liu1, Baolong Yuan1, Languang Zhao1, and Jianping Sun2

1School of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China
2School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China


Received: March 3, 2022
Accepted: June 14, 2022
Publication Date: October 4, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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This paper proposes an abnormal behavior detection using visual monitoring in rehabilitation center. The proposed method solves the problems of inaccurate and delayed detection of abnormal behavior in traditional algorithms, and provides necessary technical support for medical staff to quickly deal with patient abnormal behaviors. Firstly, the deep neural network model is adopted to detect the patient feature nodes and skeleton in the images. Then, the vector representing the position and direction of the patient limb in each frame is extracted. Finally, the angle between the trunk, legs and the ground during the fall, the aspect ratio of the body frame and the irregular motion of the wrist and ankle joins are used as discriminant features to determine the occurrence of patient fall and twitch. The experimental results show that the detection accuracy of the proposed method is 96%, and the detection speed in the real medical rehabilitation center is 25 f/s. The proposed method can monitor the patient behavior characteristics in the elderly rehabilitation center in real time, and issue an alarm timely in the case of accidental falls and twitch. Meanwhile, this paper provides a more accurate and convenient computer-aided nursing method for medical staff to monitor patient abnormal behaviors.

Keywords: Abnormal Behavior Detection, Convolutional Neural Network, Medical Care, Visual Image Processing


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