Abstract
With the increase of age, the elderly often fall, which seriously threatens their lives. Investigations and studies have shown that timely assistance after a fall can reduce the risk of death. So this paper proposes a fall recognition method based on the human skeleton in the video. First, the video data set is passed through the human pose estimation algorithm to obtain a new video data set containing human skeleton information and annotated. Then build a two-class model (falls and non-falls) based on the three-dimensional convolutional neural network to train and test the fall behavior model. The experimental results show that the method has high accuracy and recall rate and has good application value.
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Liu, H., Mu, J. (2022). Fall Recognition Based on Human Skeleton in Video. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_102
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DOI: https://doi.org/10.1007/978-981-19-0390-8_102
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