Real-time gesture recognition based on motion quality analysis

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Abstract

This paper presents a robust and anticipative realtime gesture recognition and its motion quality analysis module. By utilizing a motion capture device, the system recognizes gestures performed by a human, where the recognition process is based on skeleton analysis and motion features computation. Gestures are collected from a single person. Skeleton joints are used to compute features which are stored in a reference database, and Principal Component Analysis (PCA) is computed to select the most important features, useful in discriminating gestures. During real-time recognition, using distance measures, real-time selected features are compared to the reference database to find the most similar gesture. Our evaluation results show that: i) recognition delay is similar to human recognition delay, ii) our module can recognize several gestures performed by different people and is morphology-independent, and iii) recognition rate is high: all gestures are recognized during gesture stroke. Results also show performance limits.

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Jost, C., De Loor, P., Nedelec, L., Bevacqua, E., & Stankovic, I. (2015). Real-time gesture recognition based on motion quality analysis. In Proceedings of the 2015 7th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2015 (pp. 47–56). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.4108/icst.intetain.2015.259608

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