Automated Taekwondo Kick Classification Using SVM and IMU Sensor on Arduino Nano 33 BLE

Since practically all activities were impeded by the COVID-19 epidemic, they were conducted as autonomously as possible at home, or what is known as Work from Home (WFH). Taekwondo activities are among those that cannot be performed as WFH. The COVID-19 epidemic disrupted regular Taekwondo training, necessitating autonomous practice at home. However, without a trainer's presence, technical errors in Taekwondo kicks could occur. The research presents an automated system utilizing an IMU sensor and SVM for Taekwondo kick classification, empowering athletes to improve their movements independently. Type of kicks that can be classified are Eolgol Ap Chagi, Momtong Ap Chagi, Eolgol Dollyo Chagi, Momtong Dollyo Chagi, and Dwi Chagi. Because it is a basic kick that must be mastered by Taekwondo Athletes. When using tools, taekwondo athletes can move more quickly, thanks to direct implementation on compact devices. Consequently, a simple machine-learning model with the fewest input characteristics is required. On the Arduino Nano 33 BLE, the LSM9DS IMU sensor was used to collect the data. The dataset goes through a cleansing procedure before being labelled and trained, which comes before pre-processing. Three options that may have been employed are SVM, RBF, and DT. In this study, the micromlgen library will be used. Consequently, this work employs a Support Vector Machine (SVM) methodology. Mean, median, max, min, and variance are the five features used in the pre-processing technique. The median and variance properties are used to get an accuracy of 99.35%. The experimental findings demonstrate that the SVM algorithm successfully categorizes the different kinds of taekwondo kicks. The developed technology serves as a valuable tool for Taekwondo athletes, providing a means to enhance their skills through self-guided practice during situations like the COVID-19 pandemic.