Abstract: This study aims to establish a dataset of kicks in Kempo martial arts to categorize athletes' kick types based on their movement patterns. The real problem addressed in this research is the lack of an accurate, efficient, and portable system to automatically recognize and classify kick types in martial arts training, especially outside of controlled laboratory environments. Previous studies often relied on optical motion capture systems, which, while accurate, are expensive and impractical for real-world training settings. To overcome this limitation, this study utilizes wearable motion sensing technology and analyzes the data with an Inertial Measurement Unit (IMU) sensor. During data collection, IMU sensors are attached to athletes to monitor their movements during training or competitions. In this research, an IMU sensor type MPU6050 is used, controlled by an ESP32 microcontroller, and the data is collected via wireless communication to a data collection server. The dataset comprises gyroscope readings for angular velocity and accelerometer measurements for linear acceleration in three axes. The study evaluates three kick types: straight kicks, sidekicks, and roundhouse kicks. It employs machine learning methodologies utilizing three principal classification algorithms: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF). These algorithms were selected due to their distinct strengths in processing sensor data derived from the accelerometer and gyroscope embedded within the IMU device. The findings indicate that the SVM algorithm successfully identified and categorized kick types in Shorinji Kempo martial arts athletes with a 96.7 per cent accuracy rate when using a dataset of 70 samples and two sensors. However, when three sensors were used, the accuracy decreased to approximately 92.4 per cent. In contrast, the k-NN algorithm achieved a classification accuracy of 92.4 per cent with a dataset of 70 samples, k = 3, and three sensors. Analyzing the contributions of features to classification provides in-depth insight into the key characteristics of movement patterns for kick type recognition.

Rudy Gunawan, Suhardi, Widyawardana Adiprawita and Tommy Apriantono, “Dataset Development for Classifying Kick Types for Martial Arts Athletes Using IMU Devices” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160614

@article{Gunawan2025,
title = {Dataset Development for Classifying Kick Types for Martial Arts Athletes Using IMU Devices},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160614},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160614},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Rudy Gunawan and Suhardi and Widyawardana Adiprawita and Tommy Apriantono}
}