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.
It is said that human postural sway in quietly-standing position involves individual differences. From this viewpoint, some contributions have been made for person identification problem. However, current researches on person identification problem based on postural sway data have the following two problems: (1) the most target behavior is the postural sway after completely stepping on a stabilometer, (2) the effect of carrying weight for person identification accuracy is unclear. Therefore in this study we measure postural sway data while stepping on and off a stabilometer as well as standing quietly. In addition, we analyze the effects of shouldering a backpack for person identification accuracy. The results of experiments with 10 human subjects data show that carrying a 2kg backpack affects the identification accuracy, but the postural sway data in shouldering a backpack has a possibility to identify persons. Also, we show some extracted features in stepping on and off intervals have good effect to identify persons.