Helping physical task learning by automatic adjustment of a virtual teacher's rotation angle
Mamoun Nawahdah, Tomoo Inoue · 2011
It is known that a virtual teacher-model's position and orientation influence (a) the number of errors, and (b) the accomplishment time, in physical-task learning using mixed-reality environments. This paper proposes an automatic adjustment method of the virtual teacher's rotation angle so that the learner can observe the important body motion easily. The method divides the whole task motion into fixed duration segments, seeks the most moving part of the body in each segment, and rotates the virtual teacher to show the most important part to the learner accordingly. To test the method, a generic physical task learning experiment has been conducted. The method was revealed to be effective to the motion that gradually changes the most moving part such as manufacturing and cooking tasks.