Recognition of TaeKwonDo kicking techniques based on accelerometer sensors

Abstract

TaeKwonDo (TKD) is a worldwide sport in both competitive among athletes and physical exercise among the public scenarios. Measuring TKD kicks have been studied a lot in a laboratory setting but rarely in a free-living situation. Machine learning algorithm combined with accelerometer data was used to study some martial art styles, e.g., Chinese KungFu but little in TKD. The purpose of this study was to discover a method to recognize different kicking techniques in TKD. A total of 20 participants (35 % male) were recruited to perform front kick, roundhouse kick, side kick and back kick 6 times on each side with three accelerometers wore on waist, right ankle and left ankle. SVM and decision tree were used to analyze data and classify kicking movements. The usage of different combination of accelerometers were also compared. The result showed that using accelerometers on waist and both ankles, on waist and only right ankle, on only waist and combined with SVM model could have at least 0.96 accuracy of classification, while decision tree had the accuracies around 0.8. It was concluded that using SVM model on only waist data is the optimal choice because of the high accuracy and less accelerometers used.