Ontological Model for Operator Hand Gesture Recognition
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Abstract
The article presents an ontological model for operator hand gesture recognition based on hand skeletonization using the MediaPipe Hands library and an original method for cognitive transformation of finger states from skeleton keypoints into binary code with its subsequent conversion into a digital control signal. Improvement in the technical and methodological characteristics of the system is achieved through explicit definition of cause-and-effect relationships between objects and processes within the ontology, as well as the application of clear binary encoding rules. The mathematical model of the system includes the following stages: video frame preprocessing, hand skeletonization, coordinate analysis of keypoints, binary encoding of finger states, and generation of the final decimal control signal. The developed gesture recognition interface ensures an accuracy of 98% under normal conditions and no less than 94% under noisy and low-light conditions (50 lux or less), with an average response time of 0.5 seconds, outperforming baseline methods based on widely used libraries such as OpenCV and TensorFlow.
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References
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