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In this paper we propose a classifier capable of recognizing human body static poses and body gestures in real time. The method is called the gesture description language (GDL). The proposed methodology is intuitive, easily thought and reusable for any kind of body gestures. The very heart of our approach is an automated reasoning module. It performs forward chaining reasoning (like a classic expert system) with its inference engine every time new portion of data arrives from the feature extraction library. All rules of the knowledge base are organized in GDL scripts having the form of text files that are parsed with a LALR-1 grammar. The main novelty of this paper is a complete description of our GDL script language, its validation on a large dataset (1,600 recorded movement sequences) and the presentation of its possible application. The recognition rate for examined gestures is within the range of 80.5-98.5 %. We have also implemented an application that uses our method: it is a three-dimensional desktop for visualizing 3D medical datasets that is controlled by gestures recognized by the GDL module.

The aim of this paper is to propose and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture (kinematic) analysis. To evaluate our approach, we have used motion capture recordings (MoCap) of two highly-skilled black belt karate athletes consisting of 560 recordings of various karate techniques acquired with wearable sensors. We have evaluated the quality of generated templates; we have validated the matching algorithm that calculates similarities and differences between various MoCap data; and we have examined visualizations of important differences and similarities between MoCap data. We have concluded that our algorithms works the best when we are dealing with relatively short (2–4 s) actions that might be averaged and aligned with the dynamic time warping framework. In practice, the methodology is designed to optimize the performance of some full body techniques performed in various sport disciplines, for example combat sports and martial arts. We can also use this approach to generate templates or to compare the correct performance of techniques between various top sportsmen in order to generate a knowledge base of reference MoCap videos. The motion template generated by our method can be used for action recognition purposes. We have used the DTW classifier with angle-based features to classify various karate kicks. We have performed leave-one-out action recognition for the Shorin-ryu and Oyama karate master separately. In this case, 100% actions were correctly classified. In another experiment, we used templates generated from Oyama master recordings to classify Shorin-ryu master recordings and vice versa. In this experiment, the overall recognition rate was 94.2%, which is a very good result for this type of complex action.

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