Description: Multimodal Human-Machine Interaction Models are approaches that aim to enhance communication and interaction between users and machines by utilizing multiple modes of input and output. These modalities can include text, voice, gestures, images, and other types of sensory data. The central idea is that by combining different forms of interaction, a richer and more natural experience can be created for the user, facilitating the understanding and use of complex systems. These models are based on the premise that humans do not limit themselves to a single communication channel; in everyday life, we use a combination of words, facial expressions, and gestures to convey information. Therefore, by replicating this capability in machines, the goal is to make interaction more intuitive and efficient. The main characteristics of these models include the ability to process and integrate data from different sources, as well as adapting to user preferences and contexts. The relevance of Multimodal Human-Machine Interaction Models lies in their potential to improve the accessibility and usability of emerging technologies, such as virtual assistants, augmented reality systems, and smart devices, enabling a smoother and more effective interaction.
