Abstract: Human action recognition provides the basic support for human-computer cooperation. Robots can enhance the flexibility and production efficiency of manufacturing system by recognizing and understanding the operator’s action. To resolve the problem of human motion recognition, the original 3D skeleton data was smoothed and denoised to conform to the smooth rule of human joint-point motion based on 3D skeleton data. The fusion feature composed of static and dynamic features was constructed to represent human action. The key frame extraction model was introduced to extract the key frames in human action sequences to reduce the computing load. A Bi-LSTM neural network model based on LSTM neural network was established to classify human actions, and the attention mechanism and Dropout were utilized to classify and recognize human actions, with the main parameters of the neural network optimized by the orthogonal test method. Finally, the open data set was employed for the action recognition experiment. The results show that the proposed model algorithm has a high recognition rate for human actions. 

Key words:  , action recognition, fusion features, LSTM neural network, attention mechanism, Dropout