Kotaro Hara

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Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city <i>a priori</i>. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and(More)
Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (<i>e.g</i>., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information <i>a priori</i> via computational tools or services. In this paper, we introduce and evaluate a(More)
We explore the feasibility of using crowd workers from Amazon Mechanical Turk to identify and rank sidewalk accessibility issues from a manually curated database of 100 Google Street View images. We examine the effect of three different interactive labeling interfaces <i>(Point, Rectangle, and Outline)</i> on task accuracy and duration. We close the paper(More)
Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for an expected shelter, bench, or newspaper bin). However, there are currently few, if any, methods to determine this information <i>a priori</i> via computational tools or services. In this article, we introduce and(More)
Building on recent prior work that combines Google Street View (GSV) and crowdsourcing to remotely collect information on physical world accessibility, we present the first 'smart' system, Tohme, that combines machine learning, computer vision (CV), and custom crowd interfaces to find curb ramps remotely in GSV scenes. Tohme consists of two workflows, a(More)
In our previous research, we examined whether minimally trained crowd workers could find, categorize, and assess sidewalk accessibility problems using Google Street View (GSV) images. This poster paper presents a first step towards combining automated methods (e.g., machine vision-based curb ramp detectors) in concert with human computation to improve the(More)
Language barrier is the primary challenge for effective cross-lingual conversations. Spoken language translation (SLT) is perceived as a cost-effective alternative to less affordable human interpreters, but little research has studied how people interact with such technology. Using a prototype translator application, we performed a formative evaluation to(More)
In this paper, we investigate how people with mobility impairments assess and evaluate accessibility in the built environment and the role of current and emerging location-based technologies therein. We conducted a three-part formative study with 20 mobility impaired participants: a semi-structured interview (Part 1), a participatory design activity (Part(More)
Improvements in image understanding technologies are making it possible for computers to pass traditional CAPTCHA tests with high probability. This suggests the need for new kinds of tasks that are easy to accomplish for humans but remain difficult for computers. In this paper, we introduce Fluency CAPTCHA (FluTCHA), a novel method to distinguish humans(More)
Poorly maintained sidewalks pose considerable accessibility challenges for mobility impaired persons; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, I introduce four threads of research that I will conduct for my Ph.D. thesis aimed at creating new methods and tools to provide(More)