Human Fall Detection Using Spatial Temporal Graph Convolutional Networks. | ||
| IJCI. International Journal of Computers and Information | ||
| Article 8, Volume 10, Issue 2, September 2023, Pages 80-98 PDF (1.13 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijci.2023.204529.1105 | ||
| Authors | ||
| Hadeer Atef Abdo* 1; Khalid Amin2; Ahmed Mahmoud Hamad3 | ||
| 1Information technology, Menofia University's faculty of computers and information | ||
| 2Information Technology dept., Faculty of Computers and Information, Menoufia University, Egypt | ||
| 3Information Tech. Dep., faculty of computers and information, Menoufia University. | ||
| Abstract | ||
| Falls are a serious issue in society and have become a major topic in the healthcare domain. Because of the rapidly increasing number of elderly people, falling can cause serious consequences for the elderly, especially if the fallen person is unable to get up. Early detection of falls and reducing waiting time help in saving the lives of the elderly. The increasing number of cameras in our daily environment coupled with the presence of a smart environment makes the vision-based system the optimal solution for fall detection tasks. A vision-based system using Convolution Neural Networks (CNN) to detect a fall event in different scenes with different background models is proposed in this paper. For privacy concerns and to avoid complex background problems, we use skeleton data as an input to the network. A pre-trained Spatial Temporal Graph Convolutional Networks (ST-GCN) model is used for the fall event classification task. ST-GCN classifies the extracted spatial and temporal features from skeleton data of a detected human as falling or non-falling. To evaluate the proposed system, three public datasets (FDD, URFD, and MCF) that have different environmental issues are used. The experimental results prove the efficiency and the robustness of the proposed system in complex situations. The proposed system achieves high performance rates compared to several state-of-the-art systems, with an overall accuracy of 98.6% | ||
| Keywords | ||
| Fall detection; Deep Learning; Skeleton Data | ||
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