AN APPLICATION OF GRAPH DIFFUSION FOR GESTURE CLASSIFICATION

Reliable and widely available robotic prostheses have long been a dream of science fiction writers and researchers alike. The problem of sufficiently generalizable gesture recognition algorithms and technology remains a barrier to these ambitions despite numerous advances in computer science, engineering, and machine learning. Often the failure of a particular algorithm to generalize to the population at large is due to superficial characteristics of subjects in the training data set. These superficial characteristics are captured and integrated into the signal intended to capture the gesture being performed. This work applies methods developed in computer vision

and graph theory to the problem of identifying pertinent features in a set of time series modalities.