Arm Motion Reconstruction via Feature Clustering in Joint Angle Space
We hypothesize that a set of movements can be used to reconstruct biomechanically realistic movements. Using parameters from a reaching and grasping task we create a representative three-dimensional motion. From this motion we extract features from the joint angle space. We believe that the physiological importance of these features makes them worth investigating as possible movements. Machine learning techniques are employed to cluster similar features. The clusters are then used to recursively reconstruct the motion trajectory. Even with only twenty clusters, the average trajectory reconstruction error in Cartesian space is less than 1% of the dynamic range of motion. Our ability to create and analyze realistic motions may be crucial to both future BMI experiments where a desired signal is not available and our understanding of motor control.