Modeling Aggressive Behaviors With Evolutionary Taxonomers

The pivotal idea of recognizing human aggressive behaviors underlines how a taxonomer models such actions to perform recognition. In this paper, we investigate both the recognition and modeling of aggressive behaviors using kinematic (3-D) and electromyographic performance data. For this purpose, the Gaussian ground-plan projection area model has been assessed as an excellent evolutionary paradigm for the multiclass action and behavior recognition problem. In fact, it has shown superior classification accuracy with and without the use of ensemble models compared with the standard Gaussian (distance and area) models and other metrics of divergence, when dedicated groups of actions (behaviors) are being modeled. Genetic Programming is being employed to construct behavior-based taxonomers with a biomechanical primitive language. The modeling process revealed a representative subset of parameters (limbs, body segments, and marker coordinates) that are selected through the evolutionary process.