Implementation of a practical tennis training algorithm based on random characteristics for enhancing immersion

Abstract

Based on a training machine that returns balls with randomness associated with the player’s own strokes, this paper presents research results that enhance immersion while simultaneously quantifying individual performance. The system consists of four modes. In Mode 1, the area sensor determines whether the ball has been hit into the designated target zone, and the score increases if the next ball is also hit in the target zone within a set time. Building on this foundation, Mode 2 introduces a stage where the player competes against the machine. After setting a self-determined skill level in advance, the player’s score increases when that level is met; otherwise, the machine’s score increases. This mode allows players to engage in immersive play with the machine even without a partner. Mode 3 features competition against another player, where players take turns hitting the ball; if a valid return fails, the opponent’s score increases. Finally, Mode 4 introduces a system in which the player’s score increases only when the preset skill level is met and the ball is hit in the target zone under the condition of the forehand or backhand position requested by the machine; otherwise, the machine’s score increases. The player listens to the command for the required position from the machine, quickly takes the required position, and strikes the ball into the target zone. Such training through competition is expected to be highly effective for practical on-court training.