Abstract
We present a novel gesture recognition system for the application of continuous gestures in mobile devices. We explain how meaningful gesture data can be extracted from the inertial measurement unit of a mobile phone and introduce a segmentation scheme to distinguish between different gesture classes. The continuous sequences are fed into an Echo State Network, which learns sequential data fast and with good performance. We evaluated our system on crucial network parameters and on our established metric to compute the number of successfully recognized gestures and the number of misclassifications. On a total of ten gesture classes, our framework achieved an average accuracy of 85%.
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Tietz, S., Jirak, D., Wermter, S. (2019). A Reservoir Computing Framework for Continuous Gesture Recognition. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_1
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DOI: https://doi.org/10.1007/978-3-030-30493-5_1
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