Anzeige
Erschienen in:
2020 | OriginalPaper | Buchkapitel
Nonlinear Dynamical System Analysis for Continuous Gesture Recognition
verfasst von : Wenjun Hou, Guangyu Feng
Erschienen in: Communications, Signal Processing, and Systems
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.