Motion classification and features recognition of a traditional Chinese sport (Baduanjin) using sampled-based methods
Document Type
Article
Publication Date
8-1-2021
Abstract
This study recognized the motions and assessed the motion accuracy of a traditional Chinese sport (Baduanjin), using the data from the inertial sensor measurement system (IMU) and sampled-based methods. Fifty-three participants were recruited in two batches to participate in the study. Motion data of participants practicing Baduanjin were captured by IMU. By extracting features from motion data and benchmarking with the teacher's assessment of motion accuracy, this study verifies the effectiveness of assessment on different classifiers for motion accuracy of Baduanjin. Moreover, based on the extracted features, the effectiveness of Baduanjin motion recognition on different classifiers was verified. The k-Nearest Neighbor (k-NN), as a classifier, has advantages in accuracy (more than 85%) and a short average processing time (0.008 s) during assessment. In terms of recognizing motions, the classifier One-dimensional Convolutional Neural Network (1D-CNN) has the highest accuracy among all verified classifiers (99.74%). The results show, using the extracted features of the motion data captained by IMU, that selecting an appropriate classifier can effectively recognize the motions and, hence, assess the motion accuracy of Baduanjin.
Keywords
Motion analysis, Motion accuracy, Inertial sensor measurement systems, Baduanjin
Divisions
mechanical,1234
Funders
Neijiang Normal University (YLZY201912-1/-11),University of Malaya Impact Oriented Interdisciplinary Research Grant Programmer, IIRG (IIRG001A-19IISS)
Publication Title
Applied Sciences
Volume
11
Issue
16
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND