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Sensors 2016, 16(5), 605; doi:10.3390/s16050605

Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors

1
Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
2
School of Information Technology, Carleton University, Ottawa, ON K1S 5B6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Fabrizio Lamberti, Andrea Sanna and Jon Rokne
Received: 1 January 2016 / Revised: 19 April 2016 / Accepted: 21 April 2016 / Published: 28 April 2016
(This article belongs to the Special Issue Sensors for Entertainment)
View Full-Text   |   Download PDF [3214 KB, uploaded 28 April 2016]   |  

Abstract

This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios and evaluate the results. Our study indicates that near perfect classification accuracies are achievable for small gestures and that the speed of classification is sufficient to allow interactivity. However, such accuracies are more difficult to obtain when a participant does not participate in training, indicating that more work needs to be done in this area to create a system that can be used by the general population. View Full-Text
Keywords: gesture recognition; wearable sensors; quaternions; pattern analysis; machine learning; support vector machines; artificial neural networks gesture recognition; wearable sensors; quaternions; pattern analysis; machine learning; support vector machines; artificial neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Alavi, S.; Arsenault, D.; Whitehead, A. Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors. Sensors 2016, 16, 605.

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