AN APPLICATION OF GRAPH DIFFUSION FOR GESTURE CLASSIFICATION

dc.contributor.advisor	Mukherjee, Sayan
dc.contributor.author	Voisin, Perry Samuel
dc.date.accessioned	2020-06-09T17:45:37Z
dc.date.available	2020-06-09T17:45:37Z
dc.date.issued	2020
dc.department	Statistical Science
dc.description.abstract	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.
dc.identifier.uri	https://hdl.handle.net/10161/20804
dc.subject	Statistics
dc.subject	Artificial intelligence
dc.subject	electromyograph
dc.subject	gesture classification
dc.subject	graph diffusion
dc.subject	Graph theory
dc.subject	Persistent homology
dc.subject	preprocessing
dc.title	AN APPLICATION OF GRAPH DIFFUSION FOR GESTURE CLASSIFICATION
dc.type	Master's thesis

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