2016 年 28 巻 6 号 p. 911-919
Predicting pointing gesture can be an effective way for increasing fluency and naturalness in human-robot interaction. This paper, thus, proposes a method to predict a human pointing gesture. The method predicts the final hand position based on one of the mathematical models of human hand motion called the minimum-jerk model. Analytically, the final position of the hand and its pointing gesture finishing time can be predicted by detecting the first peak of hand acceleration, which corresponds to first 21% of the entire movement. We implemented and evaluated the method using Microsoft Kinect and a desktop size robot named Robovie-W. The result showed that the estimation error was about 18cm in CEP(Circular Error Probability), and implied that the feeling of naturalness could be improved, while it improved the impression for motion of a robot.
