Evaluating Smartphone Screen Divisions for Designing BlindFriendly TouchBased Interfaces The blind people are facing numerous challenges in identifying, locating nonvisual items on touchscreen interfaces such as smartphones, tablets, etc. The occurrence of wrong touches and subsequently following an incorrect sequence of accessing objects and UI artifacts may mislead visually impaired people to perform common activities on smartphones. The need for designing usable interface for blind people to perform their common activities, such as placing a call, sending and receiving SMS and emails, etc., is already established. There are specific usability requirements that have to meet while developing usable interfaces for blind people. One of the most important aspects of usability requirements is the division of a touchscreen in such a way that should enable blind people to locate objects and nonvisual items on the screen easily and comfortably. This includes partitioning of touchscreen into two, three, four, five, eight, and nine partitions with respect to finger placement. The study aims to design usable user interfaces for blind people to perform common activities on smartphones. This paper reports on a datagathering study, which was conducted for finding the most usable design of different screen divisions. It also outlooks a usability analysis of the screen partitioning based on usability requirements including operational, orientation, the degree of understanding, consistency, semanticloss, navigational complexity, and time consumption in locating a particular object on a touchscreen. The novel contribution is a set of guidelines for designing easytouse semantically enriched user interface framework for blind people.

TARS Mobile App with Deep Fingertip Detector for the Visually Impaired. Tactile graphics with an Audio Response System (TARS) allows visually impaired people to learn maps, diagrams, and figures by synthesizing audio descriptions of tactile graphics. The current TARS is cumbersome since it relies on a computer and touch panel to detect fingertip coordinates. Here, we improve portability by implementing a TARS mobile app endowed with a camerabased deep fingertip detector. We implemented the hand keypoint detector proposed in Simon et al. 2017, and restricted our attention on the fingertips. Performance benchmarking was done over different tactile graphics types, participants and experimental conditions (i.e. darklight environment, partial occlusion). In normal conditions, accuracy is 98% on average, but in the case of a partially occluded hand, the detection accuracy of the thumb decreases. These results indicate that the proposed mobile TARS app would perform similar to the standard TARS while being more portable and intuitive.