Posture Action Correction Method for Sports Dance Using Improved Deep Reinforcement Learning in IoT

To help Latin dancers feel the rhythm of the sports dance, it is necessary to standardize the posture action during basic training. Therefore, it is important to study the method of correcting Latin dance posture action. The traditional Latin dance posture correction methods have some problems, such as the included angle error of head motion, the angle error of spine transformation, the error of fit between foot and ground and so on. In this paper, a Latin dance posture correction method using improved deep reinforcement learning in the Internet of things (IoT) is proposed. First, the Latin dance posture image acquisition architecture is constructed using IoT and binocular stereo vision to acquire Latin dance posture images and extract Latin dance posture features. Second, the channel attention module in the deep learning network is improved, and the Latin dance posture diagnosis model is constructed based on the action feature extraction results using the improved deep robust chemical network. Finally, the action correction coefficients are calculated according to the Latin dance posture diagnosis results to realize the Latin dance posture correction. The results showed that after the application of the proposed correction method, including angle error of head movement, the spine transformation angle error and fit between foot and ground error of the participants' motions were kept below 1 degrees, and the frame position offset was 1.3cm. It indicates that the proposed method can effectively improve the degree of Latin dance posture specification.