Context-based decision system for human-machine interaction applications

In this paper we present a decision process to auto-adapt and improve human-machine interaction, simplifying the integration of algorithms and functionalities. The decision process is part of an innovative approach that integrates contextual information to orchestrate behaviours of an interactive system (i.e. perception and actuation features involved during interaction). Classical approaches focus on designing and implementing algorithms that take into account several environment features (e.g. light, pose, etc.) to adapt its performance obtaining accurate results. An advantage of these approaches is to concentrate complexity in one algorithm leading to simple system architectures. In the other hand, a disadvantage of such approaches is their limitation to adapt to conditions under different scenarios, which typically requires manual adjustments to compensate changes of environment features. Our hypothesis is that, we can improve the overall performance of human-machine interaction process if a decision process is introduced, which is responsible for selecting the most adequate actions/algorithms, with maximum performance, that achieve a certain goal under a given context. The results from exploratory simulations validate the proposed approach to be more effective in attaining specific goals in the interaction process, resorting to algorithms with low complexity.