el 15(8): e5

Research Article

Real-Time Gesture Recognition Based On Motion Quality Analysis

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  • @ARTICLE{10.4108/icst.intetain.2015.259608,
        author={C\^{e}line Jost and Igor Stankovic and Pierre De Loor and Alexis N\^{e}d\^{e}lec and Elisabetta Bevacqua},
        title={Real-Time Gesture Recognition Based On Motion Quality Analysis},
        journal={EAI Endorsed Transactions on e-Learning},
        keywords={gesture recognition, quality motion features, morphology independence},
  • Céline Jost
    Igor Stankovic
    Pierre De Loor
    Alexis Nédélec
    Elisabetta Bevacqua
    Year: 2015
    Real-Time Gesture Recognition Based On Motion Quality Analysis
    DOI: 10.4108/icst.intetain.2015.259608
Céline Jost1,*, Igor Stankovic2, Pierre De Loor1, Alexis Nédélec1, Elisabetta Bevacqua1
  • 1: UEB, Lab-STICC, ENIB, France
  • 2: University of Gothenburg, Sweden
*Contact email: jost@enib.fr


This paper presents a robust and anticipative real-time 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