Name
Abstract | ||
|---|---|---|
A pointing gesture has an important role in human-human interaction. From our observation, we predict a pointing target before finishing the gesture in human-human communication. This paper, thus, proposes a method for a robot to predict human pointing gesture using the minimum-jerk model. Analytically, the final position of a wrist can be obtained by detecting the first acceleration peak, which corresponds to first 21\\% of the entire gesture. We implemented and evaluated the method with a desktop size robot named Robovie-W. The result showed that our method improves naturalness and smoothness. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2658861.2658917 | HAI |
Field | DocType | Citations |
Computer vision,Computer science,Gesture,Simulation,Naturalness,Jerk,Gesture recognition,Acceleration,Artificial intelligence,Smoothness,Robot,Human–robot interaction | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ren Ohmura | 1 | 30 | 9.43 |
| Yuki Kusano | 2 | 0 | 0.34 |
| Yuta Suzuki | 3 | 0 | 0.34 |