Abstract
Nowadays, the safety of the elderly living alone has drawn more and more attention in China. In view of the early warning of the fall detection and the application of the Internet of Things, the fall detection system based on the wearable device and the environmental sensor has entered the market, but there are some disadvantages, such as high invasion, low precision, poor robustness and large environmental impact. This paper presents a fall detection system based on depth learning and image processing in cloud environment, which does not rely on wearable devices and sensors. The high-frequency images taken by the camera are transmitted to the server which detects the key points of the human body through the Deepcut neural network model. The output data of the human body key points detection map is input into the deep neural network to judge the fall through the softmax function and the prepared model which was trained by using the training data of the key points distributed in all kinds of human bodies prepared in advance. The relatives will also be informed through relevant communication means. The experimental tests show that the proposed method can effectively detect falls in different state of the fall and the human body in various forms.
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Acknowledgments
This work is financially supported by the National Natural Science Foundation of P. R. China (Nos. 61572260, 61572261, 61672296, 61602261), Natural Science Foundation of Jiangsu Province (No. BK20140888), Jiangsu Natural Science Foundation for Excellent Young Scholars (BK20160089), Scientific & Technological Support Project of Jiangsu Province (Nos. BE2015702, BE2016185, BE2016777), China Postdoctoral Science Foundation (No. 2014M561696), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1401005B), Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX17_0798) and NUPT STITP (No. SZDG2018014).
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Shen, L., Zhang, Q., Cao, G., Xu, H. (2019). Fall Detection System Based on Deep Learning and Image Processing in Cloud Environment. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_53
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DOI: https://doi.org/10.1007/978-3-319-93659-8_53
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