Various accessibility activities are improving blind access to the increasingly indispensable WWW. These approaches use various metrics to measure the Web's accessibility. “Ease of navigation” (navigability) is one of the crucial factors for blind usability, especially for complicated webpages used in portals and online shopping sites. However, it is difficult for automatic checking tools to evaluate the navigation capabilities even for a single webpage. Navigability issues for complete Web applications are still far beyond their capabilities. This study aims at obtaining quantitative results about the current accessibility status of real world Web applications, and analyzes real users' behavior on such websites. In Study 1, an automatic analysis method for webpage navigability is introduced, and then a broad survey using this method for 30 international online shopping sites is described. The next study (Study 2) focuses on a fine-grained analysis of real users' behavior on some of these online shopping sites. We modified a voice browser to record each user's actions and the information presented to that user. We conducted user testing on existing sites with this tool. We also developed an analysis and visualization method for the recorded information. The results showed us that users strongly depend on scanning navigation instead of logical navigation. A landmark-oriented navigation model was proposed based on the results. Finally, we discuss future possibilities for improving navigability, including proposals for voice browsers.

These days, Web authors try to describe as much information as possible in one page using various types of visual effects. This information is visually fragmented into groupings. Blind users read the Web contents in tag order, but visually fragmented groupings are not accessible using tag order reading. In addition, the Web contents are designed to be visually appealing using a lot of images. This style makes nonvisual Web access harder. Therefore we decided to develop an annotation-based transcoding system to convert already-existing Web pages to be accessible, which works between a Web server and a user. It consists of two components, one for structural annotations and one for commentary annotations. Structural annotations are used to recognize visually fragmented groupings as well as to show the importance and basic role of each group. Commentary annotations are used to give users a useful description of each grouping. In this paper, we will describe our transcoding method for nonvisual Web access based on the annotations.

Continuous and accurate smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, they are still not ready for deployment in large and complex environments, like shopping malls and hospitals, where navigation assistance is needed. To achieve accurate, continuous, and real-time localization with smartphones in such environments, we present a series of key techniques enhancing a probabilistic localization algorithm. The algorithm is designed for smartphones and employs inertial sensors on a mobile device and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m 2 shopping mall which includes three multi-story buildings and a large open underground passageway. Experiments in this space validate the effect of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

Continuous, accurate, and real-time smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, localization technologies are still not ready for deployment in large and complex environments such as shopping malls and hospitals, where navigation assistance is needed most. We identify six key challenges for accurate smartphone localization related to the large-scale nature of the navigation environments and the user's mobility. To address these challenges, we present a series of techniques that enhance a probabilistic localization algorithm. The algorithm utilizes mobile device inertial sensors and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m 2 shopping mall that includes three multi-story buildings and a large open underground passageway. Experiments conducted in this environment demonstrate the effectiveness of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

Crowdsourcing for social goals (e.g., supporting public libraries or people with disabilities) is a promising area. However, little is known about how to develop active worker communities for such goals. First, we need reliable metrics for the workers' motivation. Second, the characteristics of senior crowd workers have rarely been studied, even though they often play a primary role in social-purpose work. This work introduces a four-quadrant worker motivation model for social-purpose crowdsourcing and describes a system based on that model. Then we investigate the outcomes from the system's operations for six months, which involved both young and senior workers, seeking better ways to build an active community of crowd workers. We analyzed the workers' activities based on the system logs, conducted a survey, assessed the correlations between the subjective values and actual behaviors, and then discuss the implications.