Abstract: Falling behavior will bring serious injury to the elderly, especially to the elderly living alone. How to correctly identify the falling behavior and issue a warning is an important factor for reducing the injury. A fall detection algorithm is proposed, which is based on TSSI (tree structure skeleton image) and learnable STB-CNN (spatio-temporal block convolutional neural network). Firstly, human joint point is extracted by the three-dimensional pose estimation algorithm, and the corresponding skeleton sequence can be obtained. Secondly, the skeleton sequence is encoded into TSSI by the algorithm based DFS (depth first search) method. Finally, a learnable STB-CNN is proposed to classify TSSI and detect the fall behavior, which consists of spatio-temporal difference module, learnable spatio-temporal framework and spatio-temporal multi-branch convolution module,. Experiments are carried out on the public datasets UR FALL Detection Datasets and the simulation datasets. Experimental results are shown that our fall detection algorithm is more accurate than other related algorithms, especially the accuracy to 98.6% and 98.3% respectively.

Key words: fall detection, tree structure skeleton image (TSSI), learnable spatio-temporal block convolution neural network, pose estimation