A video-based fall detection using 3D sparse convolutional neural network in elderly care services

Falls in the elderly have become one of the major risks for the growing elderly population. Therefore, the application of automatic fall detection system for the elderly is particularly important. In recent years, a large number of deep learning methods (such as CNN) have been applied to such research. This paper proposed a sparse convolution method 3D Sparse Convolutions and the corresponding 3D Sparse Convolutional Neural Network (3D-SCNN), which can achieve faster convolution at the approximate accuracy, thereby reducing computational complexity while maintaining high accuracy in video analysis and fall detection task. Additionally, the preprocessing stage involves a dynamic key frame selection method, using the jitter buffers to adjust frame selection based on current network conditions and buffer state. To ensure feature continuity, overlapping cubes of selected frames are intentionally employed, with dynamic resizing to adapt to network dynamics and buffer states. Experiments are conducted on Multi-camera fall dataset and UR fall dataset, and the results show that its accuracy exceeds the three compared methods, and outperforms the traditional 3D-CNN methods in both accuracy and losses.