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Geometric Compression of Orientation Signals for Fast Gesture Analysis

This paper concerns itself with compression strategies for orientation signals, seen as signals evolving on the space of quaternion's. The compression techniques extend classical signal approximation strategies used in data mining, by explicitly taking into account the quotient-space properties of the quaternion space. The approximation techniques are applied to the case of human gesture recognition from cell phone-based orientation sensors. Results indicate that the proposed approach results in high recognition accuracies, with low storage requirements, with the geometric computations providing added robustness than classical vector-space computations.