Paper Info
Title
Quaternion based gesture recognition using worn inertial sensors in a motion tracking system
Abstract
Wearable wireless devices and ubiquitous computing are expected to grow significantly in the upcoming years. Standard inputs such as a mouse and keyboard are not well suited for these more on-the-go style systems. Gestures are seen as an effective alternative to these classical input styles. In this paper we examine two recognition gesture algorithms that use an inertial sensor worn on the forearm. The recognition algorithms use the sensor's quaternion orientation in either a Hidden Markov Model or Markov Chain based approach. A set of six gestures were selected to fit within the context of the active game. Despite the fact that the Hidden Markov Model is one of the most commonly used methods for gesture recognition, our results found that the Markov Chain algorithm outperformed the Hidden Markov Model. The Markov Chain algorithm obtained an average accuracy of 95%, while also having a faster computation time, making it better suited for real time applications.
Year
DOI
Venue
2014
10.1109/GEM.2014.7048108
Games Media Entertainment
Keywords
DocType
Citations 
computer games,gesture recognition,haptic interfaces,hidden markov models,inertial systems,markov chain,hidden markov model,motion tracking system,quaternion based gesture recognition,sensor quaternion orientation,worn inertial sensors,active gaming,wearable computing,worn sensors,accuracy,games,quaternions
Conference
3
PageRank 
References 
Authors
0.44
7
2
Name
Order
Citations
PageRank
Dennis L. Arsenault150.83
Anthony Whitehead214320.84