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Gesture Recognition via Capacitive Sensors

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Published on Feb 23, 2017

This project is awarded "Overall Senior Design Project Winner" at College of Engineering and Applied Science, University of Colorado Denver, Spring 2016.

Project Title: Hand Gesture Recognition in Real-­‐time via 3-D Printed Capacitive Wristband
Department: Computer Science and Engineering
Faculty Advisor(s): Prof. Tam Vu
Team Members: Cory Coellen, Michael Hunsinger, Raphael O’Flynn, Sean Kuhlman

Project Description:
Wearable devices are part of an emerging market that will fundamentally change how users interact with technology. Our device is a wireless wristband worn like a watch that communicates with host devices via Bluetooth. The wristband includes a set of integrated capacitive sensors used in conjunction with a specialized microcontroller to measure very small changes in capacitance caused by moving the hand. By collecting marker data of different gestures, we are able to use machine learning to determine a model to classify sensor readings that correspond to a particular gesture. This model can then be used during normal operation to determine the user’s current gesture in real time. Potential applications of this device including controlling a remote device such as a phone or computer.
To enable extensibility, we have created an API that developers can use to implement custom applications utilizing modern web-­‐based technologies. We will present a demo web application that utilizes the API to facilitate gesture training and recognition. Gesture recognition will be used to control an off the shelf application on the host computer.

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