Abnormal Behavior Detection For Patients In Nursing Rehabilitation Center

Xin Wang; Xiaoyan Zheng; Jianshun Liu; Baolong Yuan; Languang Zhao; Jianping Sun

doi:10.6180/jase.202307_26(7).0003

Abnormal Behavior Detection For Patients In Nursing Rehabilitation Center

Computer Science and Information Engineering

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.

Download Citation: ||https://doi.org/10.6180/jase.202307_26(7).0003

ABSTRACT

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

REFERENCES

[1] Y. WANG, L. LI, L. XIONG, J. YUAN, M. XIA, A. LIU, Y. TIAN, J. Li, and X. LI, (2019) “Quality Evaluation and Content Analysis of Existing Clinical Practice Guidelines Related to Fall Prevention in the Elderly" CHINESE JOURNAL OF NURSING 54(11): 1729–1734. DOI: 10.3761/j.issn.0254-1769.2019.11.027.
[2] V.-R. Xefteris, A. Tsanousa, G. Meditskos, S. Vrochidis, and I. Kompatsiaris, (2021) “Performance, Challenges, and Limitations in Multimodal Fall Detection Systems: A Review" IEEE Sensors Journal 21(17): 18398–18409. DOI: 10.1109/JSEN.2021.3090454.
[3] X. HUANG, J. XU, et al., (2017) “Data mining and preventive measures for 239 cases of falls in hospitalized adults" Chinese Journal of Nursing: 1087–1091.
[4] Y. Zheng, N. Bao, L. S. Xu, and X. Z. L. et al., (2014) “The Research Progress of Fall Detection System" Chinese Journal of Medical Physics 31(4): 5071–5076.
[5] A. Jisi and S. Yin, (2021) “A new feature fusion network for student behavior recognition in education" Journal of Applied Science and Engineering (Taiwan) 24(2): 133–140. DOI: 10.6180/jase.202104_24(2).0002.
[6] Z. Lei and G. Zhang, (2020) “Driver facial fatigue behavior recognition using local ensemble convolutional neural network and encoding vector" Journal of Applied Science and Engineering 23(3): 385–390. DOI:10.6180/jase.202009_23(3).0001.
[7] F. A. S. Ferreira De Sousa, C. Escriba, E. G. Avina Bravo, V. Brossa, J.-Y. Fourniols, and C. Rossi, (2022) “Wearable Pre-Impact Fall Detection System Based on 3D Accelerometer and Subject’s Height" IEEE Sensors Journal 22(2): 1738–1745. DOI: 10.1109/JSEN.2021.3131037.
[8] J. K. Lee, S. N. Robinovitch, and E. J. Park, (2015) “Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios" IEEE Transactions on Neural Systems and Rehabilitation Engineering 23(2): 258–266. DOI: 10.1109/TNSRE.2014.2357806.
[9] J. Luo, J. Tang, F. Mao, P. Zhao, and P. Wang, (2015) “Research of wearable abnormal behavior detection system for elderly based on cloud computing" Chinese Journal of Sensors and Actuators 28(8): 1108–1114. DOI: 10.3969/j.issn.1004-1699.2015.08.002.
[10] W. Ma, X.Wang, and Z.Wu, (2017) “PSO-SVM Feature Vector Fall Detection Algorithm Based on Human Postures" Chinese Journal of Sensors and Actuators 30(10): 1504–1511. DOI: 10.3969/j.issn.1004-1699.2017.10.009.
[11] R. Ramezani, Y. Xiao, and A. Naeim. “Sensing-Fi: Wi-Fi CSI and accelerometer fusion system for fall detection”. In: 2018-January. Cited by: 19. 2018, 402–405. DOI: 10.1109/BHI.2018.8333453.
[12] Y. Ogawa and K. Naito. “Fall detection scheme based on temperature distribution with IR array sensor”. In: 2020-January. Cited by: 12. 2020. DOI: 10.1109/ICCE46568.2020.9043000.
[13] J. Liu, R. Tan, G. Han, N. Sun, and S. Kwong, (2021) “Privacy-Preserving In-Home Fall Detection Using Visual Shielding Sensing and Private Information-Embedding" IEEE Transactions on Multimedia 23: 3684–3699. DOI: 10.1109/TMM.2020.3029904.
[14] Y. Li, (2021) “A new physical posture recognition method based on feature complement-oriented convolutional neural network" Journal of Applied Science and Engineering (Taiwan) 24(1): 83–89. DOI: 10.6180/jase.202102_24(1).0011.
[15] Z. Xuan, (2022) “DRN-LSTM: A Deep Residual Network Based On Long Short-term Memory Network For Students Behaviour Recognition In Education" Journal of Applied Science and Engineering 26: 245–252. DOI: 10.6180/jase.202302_26(2).0010.
[16] C. Vishnu, R. Datla, D. Roy, S. Babu, and C. K. Mohan, (2021) “Human Fall Detection in Surveillance Videos Using Fall Motion Vector Modeling" IEEE Sensors Journal 21(15): 17162–17170. DOI: 10.1109/JSEN.2021.3082180.
[17] C. Li, Z. Han, Q. Ye, and J. Jiao, (2013) “Visual abnormal behavior detection based on trajectory sparse reconstruction analysis" Neurocomputing 119: 94–100. DOI: 10.1016/j.neucom.2012.03.040.
[18] K.-E. Ko and K.-B. Sim, (2018) “Deep convolutional framework for abnormal behavior detection in a smart surveillance system" Engineering Applications of Artificial Intelligence 67: 226–234. DOI: 10.1016/j.engappai.2017.10.001.
[19] Y. Chen and D. He, (2019) “Spatial-temporal Stream Anomaly Detection Based on Bayesian Fusion" Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 41(5): 1137–1144. DOI: 10.11999/JEIT180429.
[20] S. Moccia, L. Migliorelli, V. Carnielli, and E. Frontoni, (2020) “Preterm Infants’ Pose Estimation with Spatio-Temporal Features" IEEE Transactions on Biomedical Engineering 67(8): 2370–2380. DOI: 10.1109/TBME.2019.2961448.
[21] H. Zheng, J. Liu, and M. Liao, (2019) “Human Abnormal Behavior Detection and Recognition Based on Hybrid Algorithm in Indoor Video Surveillance" Computer Applications and Software 36(7): 224–230.
[22] T. Liu, J. Kong, and M. Jiang, (2019) “RGB-D Action recognition using multimodal correlative representation learning model" IEEE Sensors Journal 19(5): 1862–1872. DOI: 10.1109/JSEN.2018.2884443.
[23] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. “Realtime multi-person 2D pose estimation using part affinity fields”. In: 2017-January. Cited by: 2833; All Open Access, Green Open Access. 2017, 1302–1310. DOI: 10.1109/CVPR.2017.143.
[24] J. Jaworek-Korjakowska, P. Kleczek, and M. Gorgon. “Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning”. In: 2019-June. Cited by: 27. 2019, 2748–2756. DOI: 10.1109/CVPRW.2019.00333.
[25] G. Wang, Q. Li, L. Wang, Y. Zhang, and Z. Liu, (2020) “CMFALL: A Cascade and Parallel Multi-State Fall Detection Algorithm Using Waist-Mounted Tri-Axial Accelerometer Signals" IEEE Transactions on Consumer Electronics 66(3): 261–270. DOI: 10.1109/TCE.2020.3000338.
[26] B.-H.Wang, J. Yu, K.Wang, X.-Y. Bao, and K.-M. Mao, (2020) “Fall Detection Based on Dual-Channel Feature Integration" IEEE Access 8: 103443–103453. DOI: 10.1109/ACCESS.2020.2999503.
[27] N. Lu, Y.Wu, L. Feng, and J. Song, (2019) “Deep learning for fall detection: Three-dimensional CNN Combined with LSTM on video kinematic data" IEEE Journal of Biomedical and Health Informatics 23(1): 314–323. DOI: 10.1109/JBHI.2018.2808281.
[28] A. Abobakr, M. Hossny, and S. Nahavandi, (2018) “A skeleton-free fall detection system from depth images using random decision forest" IEEE Systems Journal 12(3): 2994–3005. DOI: 10.1109/JSYST.2017.2780260.