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

Abstract

There is growing widespread adoption of augmented reality in tech-driven industries and sectors of society, such as medicine, gaming, flight simulation, education, interior design and modelling, entertainment, construction, tourism, repair and maintenance, public safety, agriculture, and quantum computing. 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 task difficulty with objects distributed across four spatial quadrants. We use genetic optimization algorithms to fine-tune Fitts’ Law parameters, achieving a model that significantly enhances predictive accuracy. Our optimized model demonstrates an approximately 40% reduction in interaction task difficulty across all quadrants, leading to a more ergonomic and intuitive user interface. This study contributes to the Human-Computer Interaction (HCI) field by offering a refined metric for evaluating and optimizing AR interfaces and addressing the unique challenges of three-dimensional interaction environments. Therefore, we propose a practical framework for the performance evaluations and optimization of augmented reality and other user interfaces.

Keywords

• Augmented reality
• SLAM
• Fitts’ law
• level of difficulty
• ergonomics in AR
• user engagement