A Posteriori Processing Optimization Method for Gesture Interaction in Aseptic Operation Room

Abstract

Gesture recognition plays an important role in the applications of human-centered smart technologies. This paper designs a gesture recognition approach in a special human-computer interaction environment. Generally, in an aseptic operation room, doctors cannot access the patient’s image data directly, and they have to let the nurse operate the system under their instructions. After the nurses finish their task, they also need to re-dress the surgical gown, disinfect their hands, then they can go back to the operating room. If the doctor could access the image directly by gestures during the operation, they would be able to save a lot of time and work more efficiently. Although some template matching algorithms, such as dynamic time warping (DTW), have been widely used in gesture recognition, those traditional methods are not accurate enough, and they are unable to identify continuous repetitive gestures. This paper proposes an improved DTW method based on posteriori processing, which achieved some key operations, including parameter regulations, invalid area determination, and static gesture processing. This method will address the problem of continuous repetitive gesture recognition, with higher accuracy. The experimental results show that the method can improve the accuracy of recognition by over 40%, and it can also perform real-time dynamic gesture recognition tasks effectively.