AccessWear: Making Smartphone Applications Accessible to Blind Users
Prerna Khanna, Shirin Feiz, Jian Xu, I. V. Ramakrishnan, Shubham Jain, Xiaojun Bi, Aruna Balasubramanian
Abstract
In this paper, we present AccessWear, a system that improves the accessibility of smartphone touchscreen interactions for blind users using smartwatch gestures. Our system design is human-centered, namely, it incorporates the design goals that were learned from a formative user study with 9 blind participants. The formative study showed that blind users liked the idea of using smartwatch gestures as an alternative: 4 participants liked that when using smart-watch gestures, they did not have to bring their expensive phones out in public and 6 participants liked that smart-watch gestures can be performed with one-hand, as the other hand is usually occupied in holding a cane or a guide dog. Even though there are several advantages to smartwatch gestures, our study also shows that gestures performed by blind users have different patterns compared to sighted users, making gesture recognition more challenging. To this end, AccessWear makes two contributions. The first is a gesture recognition system that works specifically for blind users that is lightweight and does not require per-person training. The second is a near-zero-effort gesture replacement system that does not require any changes to the original application. AccessWear uses input virtualization techniques so that a given gesture can replace the touchscreen input seamlessly. We implement AccessWear on an Android smartphone and Android watch. We perform a quantitative and qualitative study with 8 blind participants. Our study shows that AccessWear can recognize gestures with a 92% accuracy and the end-to-end latency when using an alternate gesture was 53 msec on average. The qualitative study shows that when participants perform a task, consisting of a series of gestures, the system is robust, does not have perceived delays, and does not add physical or mental load on the users.
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