## 4 Discussion  
When students learn the motions of Baduanjin, it would be advantageous to know the accuracy of the motions in real time, so that they can make the correction immediately. However, due to the high student-teacher ratio, students are unable to know the accuracy of motions during the teaching and learning process. On the other hand, given the unique motion characteristics of Baduanjin, it is difficult to assess the accuracy of students' motions without an experienced teacher. Therefore, the study aimed to use IMU to obtain motion data to assess the accuracy of students' motions of Baduanjin. For assessing motion accuracy, some studies have transformed it into comparing the assessed motions with the standard motions [8,49]. For example, [PERSON] et al. [8] used one sequence-based method to assess motion accuracy for another traditional Chinese martial art, namely Tai Chi. They applied DTW to calculate the difference of the motion data between students and teachers to assess the motion accuracy of Tai Chi, and the correlation of assessment to teachers was about 80%. However, in teaching traditional Chinese sports, such as Baduanjin, the motion accuracy is assessed by using the grade scoring method, which means that assessing the motion accuracy of Baduanjin is one classifier problem. In this study, on the extracted time-domain features of the motion data, the assessment results of teachers were used as the motion classification criteria to train the classifiers. From the study results, it can be seen that some classifiers can have the capability to be close to a teacher's assessment. Among the selected classifiers, \(k\)-NN has the best accuracy of classification, whether it is trained by using scores from Teacher A or Teacher B as the standard. The lowest accuracy of assessing the eight motions of Baduanjin on \(k\)-NN reached 86%. However, \(k\)-NN does not achieve the highest accuracy entirely when assessing the eight motions of Baduanjin. The accuracy of SVM in Motion-7 is higher than that of \(k\)-NN. Nevertheless, the problem of SVM is that the processing time of SVM is much longer than that of \(k\)-NN. The processing time of \(k\)-NN is the least of the seven selected classifiers, i.e., only 00.8 s. Therefore, the current study results show that, when assessing the motion accuracy of Baduanjin, \(k\)-NN as a classifier is the best choice in terms of accuracy and processing time. Traditional Chinese martial arts, including Baduanjin, require that the sequence of motions must be followed. The recognition of motions helps students find out whether there are errors in the sequence of motions when they are learning and practicing Baduanjin. The recognition result of the study shows that 1D-CNN has the highest accuracy to recognize the motions (99.74); however, this classifier has problems classifying Motion-1 and Motion-3. Another classifier, SVM, is slightly different from 1D-CNN on the accuracy (99.47%), but the average processing time (0.914 s) of SVM is much less than 1D-CNN. The shortest processing time is \(k\)-NN, and the accuracy also reached 97.63%. Therefore, if only considering the accuracy of recognizing motion, 1D-CNN is the best classifier. However, if the accuracy and processing time are taken into account, SVM is better than 1D-CNN to meet the needs of practical application. From the results, the accuracy of two commonly used ANN, namely BPNN and RBFNN, in motion recognition and motion assessment is not recommended. For example, the accuracy of RBPNN and BPNN in motion recognition is only 86.97% and 75.53%, respectively. Besides, BPNN also has the disadvantage that the processing time is much longer than other verified classifiers. The low accuracy of the two ANN classifiers may be caused by the insufficient sample size, which is one of the limitations of the study. Moreover, we used the type of sample-based method for assessing motions and recognizing motions, and there is another type of sequence-based method which is not involved in this study [17].