Abstract
The paper presents a dynamic gesture recognizer, that assumes that the gesture can be described by Kendon Gesture model. The gesture recognizer has four modules. The first module performs the feature extaction, using the skeleton representation of the body person provided by NITE library of Kinect. The second module, formed by Learning Vector Quantization, has the task of individuating the initial and the final handposes of the gesture, i.e., when the gesture starts and terminates. The third unit performs the dimensionality reduction. The last module, formed by a discrete Hidden Markov, perfoms the gesture classification. The proposed recognizer compares favourably, in terms of accuracy, most of existing dynamic gesture recognizers.
The research was entirely developed when Domenico De Felice, as M. Sc. student in Applied Computer Science, was at the Department of Science and Technology, University of Naples Parthenope.
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Recent trackers, prefers to measure orientation by quaternions, instead of usual Euler angles, since Euler angle representation can be affected by the gymbel lock.
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Acknowledgements
FIrstly, the authors wish to thank the anonymous reviewers for their valuable comments.
Part of the work, was developed by Domenico De Felice during his M. Sc. thesis in Computer Science at University of Naples Parthenope, with the supervision of Francesco Camastra.
This research was funded by Sostegno alla ricerca individuale per il triennio 2015–17 project of University of Naples Parthenope.
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De Felice, D., Camastra, F. (2019). Kendon Model-Based Gesture Recognition Using Hidden Markov Model and Learning Vector Quantization. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Quantifying and Processing Biomedical and Behavioral Signals. WIRN 2017 2017. Smart Innovation, Systems and Technologies, vol 103. Springer, Cham. https://doi.org/10.1007/978-3-319-95095-2_16
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