Abstract
In the background of global aging, more attention should be paid to the elders’ health and the equality of their life. Nowadays falls became one of greatest danger for old people. Almost 62% of injury-related hospitalizations for the old are the result of it. In this paper, we propose a new method to detect fall based on judging human’s moving posture from the video. It consists of three main parts, detecting the moving object, extracting the feature and recognizing the pattern of behavior. To improve the precision and increase the speed of the detection, we adopt two layers codebook background modeling and codebook fragmentation training. Two level SVM method to recognize the behavior: In the first level of the SVM classifier, we distinguish the standing posture and other posture by the feature of moving object, such as the ratio of the major and minor axis of the ellipse. In the second level of the SVM classifier, angle of the ellipse and head moving trajectory to judge the falls and squat. The experimental results indicate that our system can detect fall effectively.
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Zhu L, Zhou P, Pan A, et al. A Survey of Fall Detection Algorithm for Elderly Health Monitoring[C]// IEEE Fifth International Conference on Big Data and Cloud Computing. IEEE, 2015:270-274.
Mubashir M, Shao L, Seed L. A survey on fall detection: Principles and approaches[J]. Neurocomputing, 2013, 100(2):144-152.
Dai J, Bai X, Yang Z, et al. PerFallD: A Pervasive Fall Detection System Using Mobile Phones[C]// Eigth IEEE International Conference on Pervasive Computing and Communications, PERCOM 2010, March 29 - April 2, 2010, Mannheim, Germany, Workshop Proceedings. 2010:292-297.
Bourke A K, Lyons G M. A threshold-based fall-detection algorithm using a bi –axial gyroscope sensor [J]. Medical Engineering and Physics, 2008, 30(1): 84-90.
Schuman Sr. R J, Collins W F. Indicator apparatus for healthcare communication system: EP, US8384526[P]. 2013.
Zigel Y, Litvak D, Gannot I. A method for automatic fall detection of elderly people using floor vibrations and sound–proof of concept on human mimicking doll falls.[J]. IEEE Transactions on Biomedical Engineering, 2010, 56(12):2858-2867.
Mubashir M, Shao L, Seed L. A survey on fall detection: Principles and approaches[J]. Neurocomputing, 2013, 100(2):144-152.
Awaida S M, Mahmoud S A. Automatic Check Digits Recognition for Arabic Using Multi-Scale Features, HMM and SVM Classifiers[J]. British Journal of Mathematics & Computer Science, 2014, 4(17):2521-2535.
Zhuo K Y. Recognition of automobile types based on improved RBF neural network[J]. Journal of Computer Applications, 2011.
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Zhao, K., Jia, K., Liu, P. (2017). Fall Detection Algorithm Based on Human Posture Recognition. In: Pan, JS., Tsai, PW., Huang, HC. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-319-50212-0_15
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DOI: https://doi.org/10.1007/978-3-319-50212-0_15
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