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An Interface for Audio Control Using Gesture Recognition and IMU Data

Abstract:

Hand Gesture Recognition systems using electromyography sensors in conjunction with data from inertial measurement units are currently largely used for musical interfaces. However, bracelets are susceptible to displacements causing a decrease in the accuracy when they are used in such applications. In this study, a hand gesture recognition model applied to a musical interface has been tested using two different commercial armbands, Myo and GForce. Both armbands use the same pre-trained gesture recognition model and same hand gestures are recognized. We evaluate the robustness of the pre-trained model and the reached accuracy correcting the displacement of the sensors. The test performed to evaluate the system shows a classification accuracy of 94.33% and 90.70% respectively considering the same pre-trained model. The accuracy results obtained with both sensors are similar which evidences the robustness of the tested model and the importance of correcting the displacements.