SmartWheels: Detecting urban features for wheelchair usersxe2x80x99 navigation Abstract   People with mobility impairments have heterogeneous needs and abilities while moving in an urban environment and hence they require personalized navigation instructions. Providing these instructions requires the knowledge of urban features like curb ramps, steps or other obstacles along the way. Since these urban features are not available from maps and change in time, crowdsourcing this information from endusers is a scalable and promising solution. However, it is inconvenient for wheelchair users to input data while on the move. Hence, an automatic crowdsourcing mechanism is needed.  In this contribution we present SmartWheels, a solution to detect urban features by analyzing inertial sensors data produced by wheelchair movements. Activity recognition techniques are used to process the sensors data stream. SmartWheels is evaluated on data collected from 17 real wheelchair users navigating in a controlled environment (10 users) and inthewild (7 users). Experimental results show that SmartWheels is a viable solution to detect urban features, in particular by applying specific strategies based on the confidence assigned to predictions by the classifier.

Augmented Reality Controlled Smart Wheelchair Using Dynamic Signifiers for Affordance Representation The design of augmented reality interfaces for people with mobility impairments is a novel area with great potential, as well as multiple outstanding research challenges. In this paper we present an augmented reality user interface for controlling a smart wheelchair with a headmounted display to provide assistance for mobility restricted people. Our motivation is to reduce the cognitive requirements needed to control a smart wheelchair. A key element of our platform is the ability to control the smart wheelchair using the concepts of affordances and signifiers. In addition to the technical details of our platform, we present a baseline study by evaluating our platform through usertrials of ablebodied individuals and two different affordances: 1) Door Go Through and 2) People Approach. To present these affordances to the user, we evaluated fixed symbol based signifiers versus our novel dynamic signifiers in terms of ease to understand the suggested actions and its relation with the objects. Our results show a clear preference for dynamic signifiers. In addition, we show that the task load reported by participants is lower when controlling the smart wheelchair with our augmented reality user interface compared to using the joystick, which is consistent with their qualitative answers.