Study on Quality Evaluation Method of Action Sets Based on Space-time Pyramid Model under Joint Constraints of Time-space Sequences
This paper describes the unmarked motion capture technology based on multi-eye vision,greatly improves the accuracy of 3D reconstruction of human motion and continuous attitude measurement and quantitative analysis,and proposes a set of motion quality evaluation method based on spatio-temporal pyramid network model.Specifically,this paper presents for the first time the joint constraints of action space and action sequence time based on multi-eye vision,uses the Levenberg-Marquardt method for multi-coordinate fusion calculations,and uses the unmarked motion capture method under visible light vision.The accuracy of gesture recognition has been improved from the centimeter level to the millimeter level,at the same time,for continuous action gesture sequences,a space-time pyramid network modeling method based on spatial hierarchical structure and multi-time scale features and a set of action quality evaluation suitable for multi-type sets of actions are proposed.Deep learning method,and achieved overall better results than existing rehabilitation evaluation methods on KIMORE and UI-PRMD datasets.
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