Human-Machine Interfaces (HMIs) are essential for communication between humans and machines. The study delves into the transformation of static HMIs into Adaptive User Interfaces (AUIs) that adapt according to individual user preferences and behavior. Machine learning techniques are employed to provide smart adaptations to reduce user cognitive load. The paper presents a method for generating AUIs by analyzing user interactions and contextual data. An illustrative example using Markov chains to predict the next step for users interacting with an industrial mixing machine app is provided. The study emphasizes the importance of incorporating user interactions and contextual data into the design of adaptive HMIs and acknowledges the existing challenges and potential benefits.
Publication date: 9 Nov 2023
Project Page: https://doi.org/10.1145/3612783.3612807
Paper: https://arxiv.org/pdf/2311.03806
