Tennis Posture Classification and Recognition Based on an Improved KNN

Motion recognition is widely used in somatosensory games, rehabilitation training and robot motion learning. Tennis training can identify and classify the captured actions, and improve the performance of computer-aided tennis teaching timely and accurately. Traditional image based or video based human posture capture recognition are easily affected by complex background environments, different lighting conditions and other factors in practical applications. In this paper, Microsoft Kinect is used as the sensor device to capture the data information of the tennis player. Firstly, Kinect's depth sensing technology is used to obtain human skeleton data. Secondly, in order to improve the efficiency of classification, this paper reduces the dimension of data by extracting the feature value of human skeletons. Thirdly, a kind of KNN algorithm which defines the dimension weight is proposed to implement the movement classification, compared with the 94% accuracy of the algorithm, the accuracy of the KNN algorithm is 92.4%, the accuracy of the decision tree algorithm is 92.81%, and the accuracy of the CNN algorithm The accuracy is 89.97%. The evaluation method of tennis action is defined to provide guidance for users. By comparing the difference between the user's postures and the standard postures in the joint positions and angles which is prone to get error, this method can correct the user's postures and build up the function of movements guidance. From the teaching effect of tennis aficionados and general tennis players, this method is more practical and targeted than the traditional tennis graphics and video teaching.