Action representation for Wii bowling: Classification

Abstract

We present the method for classifying kinematical data required for control of a rehabilitation robot for upper extremities. The classification to two cases (success, no-success) was analyzed by two methods: Bayes estimation and artificial neural network (ANN). The results are presented for an example being envisioned for rehabilitation: playing the Wii bowling with the specially constructed pantograph. The pantograph transforms the pointing-like movement into the appropriate motion of the WiiMote (hand held controller for Wii game); thereby, the user is playing Wii bowling with greatly simplified movement of the hand (range and speed) compared with normal play. The data analysis reduced the information to two key parameters for distinction of success vs. no-success: 1) maximal acceleration of WiiMote and 2) the acceleration of the WiiMote at the ball release time. The Bayes estimation resulted with 82% of correct classification, while the ANN reached the level of 90%.