An Adaptation of Fitts' Law for Performance Evaluation and Optimization of Augmented Reality (AR) Interfaces

There is growing widespread adoption of augmented reality in tech-driven industries andsectors of society, such as medicine, gaming, flight simulation, education, interior design and modelling,entertainment, construction, tourism, repair and maintenance, public safety, agriculture, and quantumcomputing. However, ensuring smooth and intuitive interactions with augmented objects is challenging,requiring practical performance evaluation and optimization models to assess and improve users’ experiences with AR-enhanced systems. In this paper, we apply Fitts’ Law to model and predict interaction taskdifficulty with objects distributed across four spatial quadrants. We use genetic optimization algorithms tofine-tune Fitts’ Law parameters, achieving a model that significantly enhances predictive accuracy. Ouroptimized model demonstrates an approximately 40% reduction in interaction task difficulty across allquadrants, leading to a more ergonomic and intuitive user interface. This study contributes to the HumanComputer Interaction (HCI) field by offering a refined metric for evaluating and optimizing AR interfacesand addressing the unique challenges of three-dimensional interaction environments. Therefore, we proposea practical framework for the performance evaluations and optimization of augmented reality and other userinterfaces.