The purpose of this study was to check the position classification prediction rate based on the movement data of field hockey players using the random forest algorithm. In order to achieve the purpose of this study, movement data were collected using wearable devices in 15 practice matches. The collected information was then analyzed using the Random Forest algorithm, one of the ensemble techniques, with Python, a high-level, general-purpose programming language. As a result of this study, first, the position classification prediction rate was 52.4±3.3% when data measured by GPS sensors were used. Second, when using the data measured by an inertial measurement unit (IMU) sensor, the position classification prediction rate was 50.8±2.4%. Third, when both Global Positioning System (GPS) and IMU data were used, the position classification prediction rate was 55.6±2.0%. As a result of the study, it showed that the prediction rate was the highest when both GPS and IMU data were used.

This paper presents a GPS-based method for outdoor robots to track humans. This new method can overcome the crucial problems of conventional techniques in complex environments with obstacles or sloped terrain that do not allow detecting the locations of humans out of the robot"s line of sight. The robot determines the position of the human with respect to GPS data and forms the trajectory of the human’s movement. This trajectory is then smoothed in real time to reduce sudden changes in the path and improve the tracking performance. We also propose an autonomous trajectory tracking method for the robot to avoid obstacles while effectively tracking the human trajectories. This method allows the robot to follow the human even in an environment with many robots and humans simultaneously present because the robot can always distinguish the human it should follow. The experiments demonstrate that robots can effectively follow the human while avoiding obstacles in complex environments.