Abstract
Extracting applicable features from continuous gesture is uneasy since it shows up as a nonlinear dynamic system with a spatial–temporal pattern. This paper introduces a continuous gesture recognition framework that analyzes, models, and classifies the nonlinear dynamics of gestures based on chaotic theory. In this system, the trajectories of finger joints are captured as the discrete observations of nonlinear dynamic system, which defines the feature matrix of gestures by reconstructing a phase space through employing a delay-embedding scheme, the properties of the reconstructed phase space are captured in terms of dynamic and metric invariants that include Lyapunov exponent, correlation integral, and fractal dimension. Finally, we extract a feature matrix for training several classifiers with relatively few samples and get best accuracy of around 96.6% to prove our assumption that the nonlinear dynamics of continuous gesture can be approximated by a particular type of dynamical system for classification.
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Hou, W., Feng, G. (2020). Nonlinear Dynamical System Analysis for Continuous Gesture Recognition. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_120
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DOI: https://doi.org/10.1007/978-981-13-6504-1_120
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