The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection

Authors

  • Haiguang Chen Shanghai Normal University, No.100 Haisi Road Fengxian District Shanghai, Shanghai 220000, China
  • Susheng He Shanghai Normal University, No.100 Haisi Road Fengxian District Shanghai, Shanghai 220000, China
  • Mingxing Liu Shanghai Normal University, No.100 Haisi Road Fengxian District Shanghai, Shanghai 220000, China

Keywords:

Yolov4, Openpose, Random Forest, Human joint Extraction, Fall detection

Abstract

Nowadays, the care of the elderly has become a social concern. The fall of the elderly has become one of the main factors threatening the health of the elderly. In this paper, we designed a fall detection algorithm based on human joint extraction and object detection.First,yolov4 was used to identify and detect the elderly. Then openpose was used to detect the human joint. Based on the human joint, this paper using Random Forest to classify the status of the elderly, there are three states of the elderly: falling down, lying down and other states. In the detection of a single old man, the accuracy of the model reached 99.3%, the sensitivity and specificity of the model reached 79.3% and 72.1%.

References

. 2019 World Population Outlook, The United Nations, 2019

. He J, Hu C, Wang X. A smart device enabled system for autonomous fall detection and alert[J]. International Journal of Distributed Sensor Networks, 2016, 12(2): 2308 183.

. Tamura T, Yoshimura T, Sekine M, et al. A wearable airbag to prevent fall injuries[J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(6): 910-914.

. LAI C F, CHANG S Y, CHAO H C,et al.Detection of cog- nitive injured body region using multiple triaxial accelerome- ters for elderly falling[J]. IEEE Sensors Journal, 2011, 11 ( 3) : 763-770.

. Alwan M, Rajendran P J, Kell S, et al. A smart and passive floor-vibration based fall detector for elderly[C]. Information and Communication Technologies, 2006. ICTTA'06. 2nd, 2006: 1003- 1007.

. Toreyin B U, Soyer A B, Onaran I, et al. Falling person detection using multi-sensor signal processing[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2008(1): 149304.

. Wang C-C, Chiang C-Y, Lin P-Y, et al. Development of a fall detecting system for the elderly residents[C]. Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. The 2nd International Conference on, 2008: 1359-1362.

. Wang Kun. Research on human fall detection based on deep learning features[D]. East China Normal University, 2017.

. Yan Ran. Fall detection for the elderly based on Kinect sensor [J]. Electronic Technology and Software Engineering, 2017, (22): 83-83.

. Fan Yu-yu, Li Li-pin, Dang Rui-rong based on stochastic resonance with any large frequency weak Research on signal Detection Methods 0]. Acta Instrumentation, 2013, 34(3) : 566-572.

. REDMON J, FARHADI A. YOLO9000: better, faster, stron- ger[C]/ /2017 IEEE conference on computer vision and pattern recognition. Honolulu, Hawaii: IEEE, 2017: 6517 - 6525.

. CAOZ, SIMONT, WEISE, et al. Realtime multi-person 2d pose estimation using part affinity fields[C]/ /2017 IEEE conference on computer vision and pattern recognition. Hon- olulu, Hawaii: IEEE, 2017: 1302-1310.

. CHARFI I, MITERAN J, DUBOIS J,et al. Definition and performance evaluation of a robust SVM based fall detection solution[C]/ /Eighth international conference on signal image technology and internet based systems. Naples, Italy: IEEE, 2012: 218-224.

. FAN Yaxiang, LEVINE M D, WEN Gongjian, et al. A deep neural network for real-time detection of falling humans in naturally occurring scenes[J]. Neurocomputing, 2017, 260: 43-58.

Downloads

Published

2020-12-11

How to Cite

Chen, H. ., He, S. ., & Liu, M. . (2020). The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection. International Journal of Computer (IJC), 39(1), 107–114. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1852

Issue

Section

Articles