In computer systems that are used for actions recognition the human movements are often represented by three-dimensional coordinates of body joints that are tracked by motion capture hardware. The motivation of our research was to propose a novel method for automatic generation of knowledge base for syntactic Gesture Description Language (GDL) classifier by analyzing unsegmented data recordings of gestures. We have proposed novel unsupervised learning approach to deal with this task. Because this process seems to be reverse engineering to GDL approach, the learning algorithm we introduce in this paper, is called Revers-GDL (R-GDL). The R-GDL machine-learning approach for full-body movements recognition is a novel method of time-varying multidimensional signals classification. The description of R-GDL and its validation is our original and never before published achievement. The evaluation of R-GDL was performed with k-fold cross validation on large dataset that contains 770 complete movements samples of 9 gym exercises performed by 14 persons and compared with results from multivariate normally continuous density hidden Markov model classifier. Depending on exercise type GDL obtained recognition rate at the level of 100% to 91%. 
Full body movements recognition – unsupervised learning approach with heuristic R-GDL method