Modeling expertise in assistive navigation interfaces for blind people

Abstract

Evaluating the impact of expertise and route knowledge on task performance can guide the design of intelligent and adaptive navigation interfaces. Expertise has been relatively unexplored in the context of assistive indoor navigation interfaces for blind people. To quantify the complex relationship between the user's walking patterns, route learning, and adaptation to the interface, we conducted a study with 8 blind participants. The participants repeated a set of navigation tasks while using a smartphone-based turn-by-turn navigation guidance app. The results demonstrate the gradual evolution of user skill and knowledge throughout the route repetitions, signifcantly impacting the task completion time. In addition to the exploratory analysis, we take a step towards tailoring the navigation interface to the user's needs by proposing a personalized recurrent neural network-based behavior model for expertise level classifcation.