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Towards Recommending Accessibility Features on Mobile Devices

Abstract:

Numerous accessibility features have been developed to increase who and how people can access computing devices. Increasingly, these features are included as part of popular platforms, e.g., Apple iOS, Google Android, and Microsoft Windows. Despite their potential to improve the computing experience, many users are unaware of these features and do not know which combination of them could benefit them. In this work, we first quantified this problem by surveying 100 participants online (including 25 older adults) about their knowledge of accessibility and features that they could benefit from, showing very low awareness. We developed four prototypes spanning numerous accessibility categories (e.g., vision, hearing, motor), that embody signals and detection strategies applicable to accessibility recommendation in general. Preliminary results from a study with 20 older adults show that proactive recommendation is a promising approach for better pairing users with accessibility features they could benefit from.