Abstract
Fall detection is a major challenge in the field of public healthcare, especially for the elderly. And reliable surveillance is critical to mitigate the incidence rate of falls. In this paper, we propose a multistage architecture to obtain the human pose estimation. The proposed network architecture contains two branches. The first branch is the confidence maps of joint points; the second branch proposes a bi-directional graph structure information model (BGSIM) to encode the rich contextual information. Then we define a linear function to determine whether the people (especially the elderly) fall or have tendency to fall. We test the system in a simulated environment, such as a bathroom, a kitchen, and a hallway. Meanwhile, we also give some prediction results from real scenes.
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Wang, J., Zhou, B., Peng, Z., Sun, J., Zhang, Y. (2019). Fall Detection Using a Multistage Deep Convolutional Network Architecture. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_19
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DOI: https://doi.org/10.1007/978-981-13-6837-0_19
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