F-2-5 サブリミナルキャリブレーションによる機械操作熟達支援 : 合成ニューラルネットワークによる操作入力特性解析(F-2 メカニカルシステムとその知能化-機械と人の視点を中心に,口頭発表:メカニカルシステムとその知能化)

抄録

This paper addresses a human skill evaluation technique with human input command decision characteristics during a mobile object operation. The characteristics is how long past of tracking error information is utilized to decide the operation command. This evaluation is achieved by a novel human model with multiple neural networks which have different time series of input signals. Then, by the prediction errors from the neural networks, which time series is dominant to decide operation command can be estimated. In this paper, target tracking tasks in 2D CG environment are experimented. A main goal of the task is to operate the mobile target by joystick operation to keep approaching a reference target behaving randomly. Finally, by analysis for distribution the neural networks prediction errors, the operator's skill is evaluated and verify the technique with conventional average of tracking errors during the task.