Hankelet-based action classification for motor intention recognition

For the recognition of actions, we use two off-the-shelf classifiers, nearest neighbor (NN) and support vector machines (SVM), in cross-subject validation. We present results using either the joint angles or the raw sensor data showing a net improvement of the Hankelet-based approach against a baseline method. In addition, we compare results on action recognition using joint angles provided by trakSTAR, a high-accuracy motion tracking unit, demonstrating - somewhat surprisingly - that best results (in terms of average recognition accuracy over different actions) are provided by raw inertial data, paving the way towards a wider usage of our method in the field of active prosthetics, in particular, and motor intention recognition, in general.