Tai Chi Movement Recognition Method Based on Deep Learning Algorithm - Liu, Lihua; Qing, MA; Chen, Si; Li, Zhifang (Mathematical problems in engineering, 2022)
The current action recognition method has good effect when applied to static recognition, but, when applied to dynamic action sequence recognition, the temporal and spatial feature segmentation is too dependent on sample template, resulting in low recognition accuracy. To address the inadequacies of standard movement detection techniques in the application of comparable domains, a deep learning algorithm is utilised to recognise Tai Chi Chuan motions. For Tai Chi Chuan movement human body skeleton framework, add image depth parameter is added, and OpenPose model is utilised to estimate joint point coordinates. The ST-GCN deep learning model was created to recognise Tai Chi Chuan motions by extracting movement features from the spatiotemporal trajectory of human joints during Tai Chi Chuan movements. Instance test results show that rate of using the deep learning algorithm of gesture recognition is 89.22%, with significantly lower error detection rate, which is good for Tai chi chuan movement recognition effect.