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.
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Acknowledgments
The authors gratefully acknowledge the financial support provided by Unidad de Innovacion y Tecnologia (UITEC) - Universidad de las Americas (UDLA) for the development of the research.
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Vimos, V.H., Valdivieso Caraguay, Á.L., Barona López, L.I., Pozo Espín, D., Benalcázar, M.E. (2022). An Interface for Audio Control Using Gesture Recognition and IMU Data. In: Botto-Tobar, M., S. Gómez, O., Rosero Miranda, R., Díaz Cadena, A., Montes León, S., Luna-Encalada, W. (eds) Trends in Artificial Intelligence and Computer Engineering. ICAETT 2021. Lecture Notes in Networks and Systems, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-96147-3_14
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