MULTI-TARGET ONLINE REHABILITATION ACTION RECOGNITION BASED ON SPATIO-TEMPORAL GRAPH NETWORK WITH HIERARCHICAL RESIDUAL STRUCTURE

Spatio-temporal graph convolutional network (ST-GCN) can automatically learn the spatial and temporal characteristics of skeleton data without interference from the external complex environment. In order to solve the problems of inadequate skeleton information feature extraction and weak local information modeling in the original model, a skeleton recognition model with layered residual structure (Res2-STGCN) is proposed. The spatio-temporal convolution module with layered residual structure was combined with the original module to form a new network model. The receptive field was further expanded by changing the size of the module. Parameters such as learning rate interval were adjusted to solve the overfitting problem. Res2-STGCN was combined with detection, pose estimation and tracking algorithm to realize multi-target rehabilitation action recognition. Experiments were designed on NTU-RGB+D and self-built data sets. Compared with the benchmark algorithm STGCN, the recognition accuracy of the improved optimal model is improved by 5.61 and 6.03 percentage points respectively under the two different data partitioning standards. The average recognition accuracy of the optimized model on self-built data sets is 99.5%, showing strong robustness for the recognition of complex actions.