Phase registration improves classification and clustering of cycles based on self-organizing maps

Abstract

Self-Organizing Maps (SOMs), also known as Self-Organizing Feature Maps, have been used to reduce the complexity of joint kinematic and kinetic data in order to cluster, classify and visualize cyclic motion data. In this paper we describe the results after training SOMs with preprocessed data based on phase registration by dynamic time warping. For validation, we recorded acceleration data of human locomotion varying the treadmill slope, activity (i.e., walking, jogging, running), and whether or not 1.5 kg weights were attached to the ankles. The topological quality of the SOMs after training improved when the phase registration was applied. Furthermore, test (i.e., combination of treadmill slope and type of gait) and subject classification improved, in particular for walking data, when the phase registration was applied for each individual activity. Activity classification improved when the phase registration was calculated from all cycles of our experiments together.