Taijiquan Auxiliary Training and Scoring Based on Motion Capture Technology and DTW Algorithm

Introduction

Taijiquan is one of China's intangible cultural heritages, which is not only a kind of dialing profound Chinese martial arts but also an internal and external cultivation, rigid and flexible sports program, which has the functions of competitive confrontation, physical fitness, and cultivation of sentiment, and promotes the physical and mental health of human beings.

The Dynamic Time Warping (DTW) algorithm is a method for comparing the similarity of two time series (Chong & Polap, 2022). It is widely used in the fields of time series analysis, pattern recognition, and data mining, especially when dealing with time series with large variations in length or local alignment problems. The main idea of the DTW algorithm is to compute the similarity between two time series by pitting them against each other by establishing the best correspondence between different time points of the time series (Lin et al., 2022). This pairing process allows some local deformations that allow the similar parts of the sequences to be aligned without the need for perfectly equal correspondences. Therefore, the DTW algorithm is particularly well suited to deal with transformations, such as warping, scaling, and translation of time series (Li, 2022).

The teaching process of Taijiquan in China's colleges and universities is based on traditional collective and small-group teaching, supplemented by student-stratified-assisted teaching (Pashin et al., 2019). The traditional mode of learning Taijiquan movements is time-consuming. It is not easy for students to find out the mistakes and negligence in their movements during the learning period, and it is difficult to self-evaluate. Therefore, in the face of the problems highlighted in the traditional Taijiquan teaching mode, the teaching of Taijiquan is in urgent need of a training assistance model that incorporates computer network technology to help students learn. In this context, research on a Taijiquan training assistance system based on motion capture technology and a DTW algorithm was carried out.

Since the development of computers in 1946, human-computer interaction (HCI) technology has influenced the development of computer technology. HCI is the study of the development of human and computer technology. Human-computer interaction is the study of the process of information exchange between humans and computers and is studied for the aesthetics and user-friendliness of the system, which is nowadays applied in all aspects of life (Zhu et al., 2018).

The way of human-computer interaction belongs to the natural user interface; we can communicate with the computer through three-dimensional (3D) virtual reality, emotional computing, multichannel interaction, intelligent user interface, and other somatosensory technologies to realize human-computer interaction with human core convenient to operate and more interesting. Therefore, integrated intelligence has become an important feature of a Chinese Taijiquan-assisted training system (Hilfiker et al., 2018).

In this study, we will take Taijiquan as an example and combine the DTW algorithm with motion capture technology to design a Taijiquan-assisted training model, aiming to make learners' Taijiquan training more intelligent, data-driven, and visualized, and to bring more standardized and reliable suggestions for learners' daily taijiquan training.

Using the local search algorithm based on a DTW factor, three characteristic parameters related to the influence index of Taijiquan athletes, and an auxiliary training system are selected, and an auxiliary training system based on the characteristic parameters of Taijiquan movement capture and training is proposed. Through the study of daily training, gait analysis and testing, and physical consumption, the hierarchical framework and index relationship of the whole Taijiquan auxiliary training system is defined clearly. The auxiliary system is evaluated from many aspects. In order to establish an intelligent Taijiquan auxiliary training system, a multiangle exploration is carried out, and then a DTW algorithm is used to analyze the data of athletes’ auxiliary training results.