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In order to improve the monitoring effect of human-computer interactive medical equipment on human body in the condition of sedentary and bedridden all the year round, based on the establishment of human body posture recognition system using wireless Body area network (WBAN), a corresponding improved artificial neural network is designed to fuse with WBAN system, and is applied to human-computer interactive medical equipment. The results show that in the HiEve dataset, the method starts to converge at 20 iterations, and the Loss function value is 0.0112. In the recognition verification of different patient postures, the accuracy of human-machine interaction medical device recognition under this method is significantly higher than 90%, and the shortest time is only 23.16 seconds. It has high recognition accuracy and efficiency, providing a more reliable technical reference for human posture recognition and related medical device applications.