Statistical Evaluation of Multimodal Interfaces: Exploring User Preferences for Combined Input Methods

چکیده

Multimodal interfaces, which integrate input methods such as voice, touch, and gesture, are increasingly used to enhance user interaction. However, understanding user preferences and optimizing the combination of input modalities remains a challenge. This paper presents a comprehensive statistical analysis of user preferences for different input method combinations, focusing on how these combinations affect task performance, efficiency, and overall satisfaction. A study was conducted with participants performing tasks that required various levels of complexity. Multivariate analysis of variance (MANOVA) was applied to investigate the effects of combined input modalities on task completion time, error rates, and user satisfaction. Regression analysis was used to identify factors influencing user preference for specific input combinations, including demographic and behavioral variables. The results demonstrate clear preferences for certain combinations of input methods depending on task type and complexity, offering key insights into the design of more adaptive and efficient multimodal systems. These findings provide a statistical foundation for improving the integration of multimodal inputs, leading to enhanced user experiences and more effective system designs.