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.

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