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In this paper, an implementation of a virtual reality based application for drumkit simulation is presented. The system tracks user motion through the use of a Kinect camera sensor, and recognizes and detects user-generated drum-hitting gestures in real-time. In order to compensate the effects of latency in the sensing stage and provide real-time interaction, the system uses a gesture detection model to predict user movements. The paper discusses the use of two different machine learning based solutions to this problem: the first one is based on the analysis of velocity and acceleration peaks, the other solution is based on Wiener filtering. This gesture detector was tested and integrated into a full implementation of a drumkit simulator, capable of discriminating up to 3, 5 or 7 different drum sounds. An experiment with 14 participants was conducted to assess the system's viability and impact on user experience and satisfaction.

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