EMGRIE : Ergonomic Microgesture Recognition and Interaction Evaluation, a case study

Abstract

Given the recent success of hand pose gesture recognition via wrist-worn camera based sensors, specific hand pose interaction evaluation is needed. In order to evaluate such interactions, we built EMGRIE: a quick-prototype wrist-worn vision-based gesture recognition system that applies necessary feature extraction and training data collection techniques to facilitate user customization of specific hand pose gestures. We use EMGRIE to extract differences in microgesture task times, perceived effort, and perceived command associations across users, gestures, gesture performance iterations, and various applications. This thesis gives a summary of past wrist-worn gesture recognition systems and past gestural application design research, EMGRIE system implementation details and differences, free-hand microgesture choice rational, and sample case studies concerning Google Glass application design and associated usability experimentation. Given the results of our system and application experimentation, we find that specific hand pose gesture preferences may change drastically between users and propose hypotheses concerning hand pose usability.