Motion classification using IMU for human-robot interaction

This paper proposed the method for identifying motion of the robot using signals from an Inertial Measurement Unit (IMU). When human interacted with the robot, the motion of the robot needed to be classified in order to create the suitable emotions of the robot during the human-robot interaction process. The output data from the IMU were classified by applying heuristic conditions to the high and low frequency data. Wavelet transform was added in the pre-processing step for finding the starting point of the motion state. We found that most of our tested actions could be correctly classified using our proposed method with 100% accuracy.