Motion Capture Technology-Based Gesture Detection Method for Folk Dance Movements

Abstract

In China, folk dancing is a relatively new regional tradition that differs considerably from region to region. With recent advancements in these areas, it is worthwhile to investigate the idea of using 3D digital technology and human motion sensing technology in folk dance. In keeping with this, we use the human signal confirmation technology, the following technologies, and human body identification to watch and gather dance motions in this piece. This work integrates complex needs and stores data using his AAM model for 3D digital modelling. Last but not least, this study intends to combine the few-shots learning technique with the folk-dance learning strategy. In order to improve the teaching strategy described in this study, additional informative survey test experiments, computational information survey correlation studies, and objectively consistent computational test experiments are planned. Using information from motion-captured human skeletons, we evaluate the efficacy of characterization strategies for identifying various dance styles. The objective is to use data on body joints gathered by a Kinect sensor to particularly identify trademark motions for each dance performed. Six different folk dances and their arrangements are represented in the datasets used. Different posture ID procedures are employed using fleeting imperatives, geographical data, and element space circulations to produce a suitable preparation dataset. The collected data is assessed and examined.