Smart Surface Classification for Accessible Routing through Built Environment: A Crowd-sourced Approach

Abstract

In order to provide individuals with restricted mobility the opportunity to travel more efficiently, various systems have proposed modeling techniques and routing algorithms that handle accessible navigation through the built environment which is otherwise dotted with mobility barriers. Such systems use data gathered from smartphone sensors or crowd-sourcing to pinpoint the location of the barriers as well as the facilities, such as crosswalks with traffic signals or access ramps to curbs. Though the previous works have identified the type of surface and incline to be important features to determine accessibility, no extensive empirical research exists on how these parameters affect navigation. In order to address this problem, we propose to build a novel system called WheelShare, which uses machine learning to classify surfaces into accessible or otherwise and uses that knowledge to generate accessible routes for wheelchair users. We have trained our system with accelerometer and gyroscope data obtained from 26 different surfaces found frequently in indoor and outdoor environments across Europe and USA. More data is collected by the system through crowd-sourcing based contribution from interested users. Our evaluation shows that WheelShare can achieve an accuracy of up to 96% in identifying surfaces in one of the 5 different accessibility classes. Overall, WheelShare is a novel, scalable and data-centric approach to objectively identify the accessible features of a surface and can generate end-to-end routes for wheelchair users using frequently updated crowd-sourced information.