No abstract

a b s t r a c tIndependent mobility involves a number of challenges for people with visual impairment or blindness. In particular, in many countries the majority of traffic lights are still not equipped with acoustic signals. Recognizing traffic lights through the analysis of images acquired by a mobile device camera is a viable solution already experimented in scientific literature. However, there is a major issue: the recognition techniques should be robust under different illumination conditions. This contribution addresses the above problem with an effective solution: besides image processing and recognition, it proposes a robust setup for image capture that makes it possible to acquire clearly visible traffic light images regardless of daylight variability due to time and weather. The proposed recognition technique that adopts this approach is reliable (full precision and high recall), robust (works in different illumination conditions) and efficient (it can run several times a second on commercial smartphones). The experimental evaluation conducted with visual impaired subjects shows that the technique is also practical in supporting road crossing.

In the last years several solutions were proposed to support people with visual impairments or blindness during road crossing. These solutions focus on computer vision techniques for recognizing pedestrian crosswalks and computing their relative position from the user. Instead, this contribution addresses a different problem; the design of an auditory interface that can effectively guide the user during road crossing. Two original auditory guiding modes based on data sonification are presented and compared with a guiding mode based on speech messages.Experimental evaluation shows that there is no guiding mode that is best suited for all test subjects. The average time to align and cross is not significantly different among the three guiding modes, and test subjects 1 arXiv:1506.07272v1 [cs.HC] 24 Jun 2015 distribute their preferences for the best guiding mode almost uniformly among the three solutions. From the experiments it also emerges that higher effort is necessary for decoding the sonified instructions if compared to the speech instructions, and that test subjects require frequent 'hints' (in the form of speech messages). Despite this, more than 2/3 of test subjects prefer one of the two guiding modes based on sonification. There are two main reasons for this: firstly, with speech messages it is harder to hear the sound of the environment, and secondly sonified messages convey information about the "quantity" of the expected movement.

In the field of assistive technology, large scale user studies are hindered by the fact that potential participants are geographically sparse and longitudinal studies are often time consuming. In this contribution, we rely on remote usage data to perform large scale and long duration behavior analysis on users of iMove, a mobile app that supports the orientation of people with visual impairments. Exploratory analysis highlights popular functions, common configuration settings, and usage patterns among iMove users. The study shows stark differences between users accessing the app through VoiceOver and other users, who tend to use the app more scarcely and sporadically. Analysis through clustering of VoiceOver iMove user interactions discovers four distinct user groups: 1) users interested in surrounding points of interest, 2) users keeping the app active for long sessions while in movement, 3) users interacting in short bursts to inquire about current location, and 4) users querying in bursts about surrounding points of interest and addresses. Our analysis provides insights into iMove's user base and can inform decisions for tailoring the app to diverse user groups, developing future improvements of the software, or guiding the design process of similar assistive tools. CCS Concepts •Human-centered computing → Accessibility design and evaluation methods; •Computing methodologies → Cluster analysis; •Social and professional topics → People with disabilities;