In the field of assistive technology, large scale user studies are hindered by the fact that potential participants are ge ographically 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 ori entation 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. Analy sis through clustering of VoiceOver iMove user interactions discovers four distinct user groups: 1) users interested in sur rounding 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.
Large-scale analysis of iMove usage data from 4,055 users of the system shows how users can be clustered in different user groups based on their interest in different types of spatial information REF .
Supporting Orientation of People with Visual Impairment: Analysis of Large Scale Usage Data
ASSETS '16
Computer Science