An Accelerometer-based Gesture Recognition System for a Tactical Communications Application

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In modern society, computers are primarily interacted with via keyboards, touch screens, voice recognition, video analysis, and many others. For certain applications, these methods may be the most efficient interface. However, there are applications that we can conceive where a more natural interface could be convenient and connect humans and computers in a more intuitive and natural way. These applications are gesture recognition systems and range from the interpretation of sign language by a computer to virtual reality control. This Thesis proposes a gesture recognition system that primarily uses accelerometers to capture gestures from a tactical communications application. A segmentation ... continued below

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vii, 46 pages : illustrations (chiefly color)

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Tidwell, Robert S., Jr. December 2015.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 118 times , with 6 in the last month . More information about this thesis can be viewed below.

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  • Tidwell, Robert S., Jr.

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Description

In modern society, computers are primarily interacted with via keyboards, touch screens, voice recognition, video analysis, and many others. For certain applications, these methods may be the most efficient interface. However, there are applications that we can conceive where a more natural interface could be convenient and connect humans and computers in a more intuitive and natural way. These applications are gesture recognition systems and range from the interpretation of sign language by a computer to virtual reality control. This Thesis proposes a gesture recognition system that primarily uses accelerometers to capture gestures from a tactical communications application. A segmentation algorithm is developed based on the accelerometer energy to segment these gestures from an input sequence. Using signal processing and machine learning techniques, the segments are reduced to mathematical features and classified with support vector machines. Experimental results show that the system achieves an overall gesture recognition accuracy of 98.9%. Additional methods, such as non-gesture recognition/suppression, are also proposed and tested.

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vii, 46 pages : illustrations (chiefly color)

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  • December 2015

Added to The UNT Digital Library

  • March 20, 2016, 10:34 a.m.

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  • May 25, 2017, 2:44 p.m.

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Tidwell, Robert S., Jr. An Accelerometer-based Gesture Recognition System for a Tactical Communications Application, thesis, December 2015; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc822779/: accessed August 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .