Abstract
Human–computer interaction is one of the most exciting areas of research. Hand gesture recognition stands vital for develo** better human–computer interaction systems. Most of the existing approaches using camera-based or 3D depth sensors for hand gesture recognition are rather expensive and sensitive to environmental changes. In this paper, we propose a low-cost data glove embedded with MPU9250 motion sensor which overcomes the drawbacks of existing systems. In our work, the primary focus is to develop a numerical gesture recognition system deployable in any real-time application. An extensive comparison of the performance of different machine learning and neural network models is presented. An optimal network model is chosen, and details of deploying the trained model in a real-time unity game application are presented. In our experiment, the highest accuracy achieved is 98.41% with an average real-time inference delay of 2 ms.
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Bommannan, S.R., Vineeth, C., Uma Hema Sri, M., Sri Vidya, B., Vidhya, S. (2021). Real-Time Numerical Gesture Recognition Using MPU9250 Motion Sensor. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_4
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DOI: https://doi.org/10.1007/978-981-15-8443-5_4
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