Abstract
Falling is one of the biggest public health issues that can cause many serious long-term repercussions for patients and their families. In this paper, we propose an appropriate model for fall detection using graph convolutional networks. Recently, most problems related to human action recognition, including fall detection, can be handled by applying the spatio-temporal graph convolutional model using 2D or 3D skeletal data. We take advantage of the transfer learning technique from the NTU RGB + D consisting of 60 daily actions to extract features for fall detection tasks efficiently. Besides, to highlight critical frames in the original sequence, we suggest using a temporal attention module consisting of two parts: (1) an average global pooling, and (2) two fully connected layers. We conduct the test on two datasets, leading to a 3.12% increase in the TST dataset and a 2.67% improvement in the FallFree dataset. Notably, concerning the FallFree dataset, the model’s accuracy is up to 100%.
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Mai, C.T.H., Dung, D.T.P., Le Anh Duc, P., Hung, P.D. (2023). Skeleton-Based Fall Detection Using Computer Vision. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2023. Lecture Notes in Computer Science, vol 14166. Springer, Cham. https://doi.org/10.1007/978-3-031-43815-8_15
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DOI: https://doi.org/10.1007/978-3-031-43815-8_15
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