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)
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)
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)
Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges. Although pedestrian-and bicycle-oriented maps and associated routing algorithms continue to improve, there has been a lack of work focusing on accessibility. There is currently no way for a user to determine accessible areas of a city prior to(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)
Crowdsourcing systems leverage short bursts of focused attention from many contributors to achieve a goal. By requiring people's full attention, existing crowdsourcing systems fail to leverage people's cognitive surplus in the many settings for which they may be distracted, performing or waiting to perform another task, or barely paying attention. In this(More)
SIGACCESS continues to refine its activities to meet member needs. This report highlights SIGACCESS Awards as well as the SIG's conference, publication, and other activities. 2013. Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view. SIGACCESS Paper Impact Award This award is to be(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)