State Machine Approach for Gesture Classification

David O Cipoletta, University of Rhode Island

Abstract

There are many new developments in technology in the last decades. Even though the world is moving towards more autonomy, the human in the loop control of technology is still crucial. To improve efficiency and advance the technology further, there are new ways of human to machine interfaces (HMI) being developed: for example, eye control, voice control, brain control interface (BCI), gesture control, etc. All of these new paradigms of control face some common issues such as adoption, learning curve, and reliability. The goal for this project is to demonstrate a new state machine approach for gesture classification and demonstrate a use case for gesture classification.

Subject Area

Electrical engineering|Engineering

Recommended Citation

David O Cipoletta, "State Machine Approach for Gesture Classification" (2019). Dissertations and Master's Theses (Campus Access). Paper AAI27669227.
https://digitalcommons.uri.edu/dissertations/AAI27669227

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