Analog Multi-Finger Fitts' Law Experiments: Modifying Indices of Difficulty Based on Distance Variation for Improved Combined Model Fit

Abstract

Effective Human-Machine Interaction (HMI) depends on accurately capturing and interpreting information from the human user. For systems relying on hand-based operation, understanding the limits of human cognitive-motor abilities is crucial to designing intuitive and efficient interfaces. This study presents an experimental setup involving four analog buttons with a minimally complex control, where human hand performance is assessed through simultaneous multi-finger Fitts' Law tasks. Initially, task difficulty was assumed to be computed by summing the individual indices of difficulty for each button, which resulted in peak throughput performance with two fingers. However, this approach did not align with Fitts' Law. By applying a weighted summation, with weights based on the variation of distances within a task, the difficulty measure better conformed to Fitts' Law, and the highest throughput was achieved with a single finger. These findings highlight the interdependence in multi-finger movement complexity and emphasize the importance of considering cognitive-motor limitations when designing HMI interfaces to optimize user performance.