Communications of the ACM
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hand Gesture Recognition Using Input-Output Hidden Markov Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
On the usability of gesture interfaces in virtual reality environments
CLIHC '05 Proceedings of the 2005 Latin American conference on Human-computer interaction
International Journal of Advanced Media and Communication
Vision-Based Human-Computer System Using Hand Gestures
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
Real-Time Robotic Hand Control Using Hand Gestures
ICMLC '10 Proceedings of the 2010 Second International Conference on Machine Learning and Computing
Vision-Based Hand Gesture Recognition Using Combinational Features
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Teaching natural user interaction using OpenNI and the Microsoft Kinect sensor
Proceedings of the 2011 conference on Information technology education
Code space: touch + air gesture hybrid interactions for supporting developer meetings
Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
Empirical study of a vision-based depth-sensitive human-computer interaction system
Proceedings of the 10th asia pacific conference on Computer human interaction
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We have designed and implemented a vision-based system capable of interacting with user's natural arm and finger gestures. Using depth-based vision has reduced the effect of ambient disturbances such as noise and lighting condition. Various arm and finger gestures are designed and a system capable of detection and classification of gestures is developed and implemented. Finally the gesture recognition routine is linked to a simplified desktop for usability and human factor studies. Several factors such as precision, efficiency, ease-of-use, pleasure, fatigue, naturalness, and overall satisfaction are investigated in detail. Through different simple and complex tasks, it is concluded that finger-based inputs are superior to arm-based ones in the long run. Furthermore, it is shown that arm gestures cause more fatigue and appear less natural than finger gestures. However, factors such as time, overall satisfaction, and easiness were not affected by selecting one over the other.