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• Artificial Intelligence (AI) for Web Accessibility: Is Conformance Evaluation a Way Forward?

  Shadi Abou-Zahra, Judy Brewer, Michael Cooper · 2018 · Proceedings of the 15th International Web for All Conference (W4A 2018)

  This short paper from three W3C/WAI staff members explores the potential and limitations of applying artificial intelligence to improve web accessibility, proposing accessibility conformance evaluation as a strategic pathway to accelerate AI adoption in this domain. The authors…

  artificial intelligence · web accessibility · WCAG · automated testing · machine learning

• Accessify: An ML Powered Application to Provide Accessible Images on Web Sites

  Shivam Singh, Anurag Bhandari, Nishith Pathak · 2018 · Proceedings of the 15th International Web for All Conference (W4A 2018)

  This demonstration paper presents Accessify, a browser plugin that uses machine learning to automatically generate alternative text descriptions for all images on a website, injecting them into the page’s DOM so screen readers can access them. The system addresses the persistent…

  alternative text · image accessibility · machine learning · browser extension · computer vision

• Semantic Content Analysis Supporting Web Accessibility Evaluation

  Carlos Duarte, Inês Matos, Luís Carriço · 2018 · Proceedings of the 15th International Web for All Conference (W4A)

  This paper addresses a fundamental limitation of automated web accessibility evaluation tools: their inability to assess whether text alternatives actually describe the content they refer to. While tools can detect the presence of an alt attribute on an image, they cannot judge…

  automated testing · alternative text · semantic analysis · machine learning · image recognition

• Reliability Aware Web Accessibility Experience Metric

  Shuyi Song, Jiajun Bu, Chengchao Shen, Andreas Artmeier, Zhi Yu, Qin Zhou · 2018 · Proceedings of the 15th International Web for All Conference (W4A)

  This paper introduces RA-WAEM (Reliability Aware Web Accessibility Experience Metric), a novel approach to measuring website accessibility that incorporates actual user experience from people with disabilities while accounting for the varying reliability of different evaluators.…

  accessibility metrics · user experience · accessibility evaluation · machine learning · web accessibility

• Detecting Autism Based on Eye-Tracking Data from Web Searching Tasks

  Victoria Yaneva, Le An Ha, Sukru Eraslan, Yeliz Yesilada, Ruslan Mitkov · 2018 · Proceedings of the 15th International Web for All Conference (W4A)

  This paper investigates whether eye-tracking data collected during everyday web tasks can be used to distinguish between people with and without autism, potentially enabling a low-cost, accessible screening approach. The study collected gaze data from 15 adults with clinically…

  autism · eye tracking · machine learning · web accessibility · cognitive accessibility

• Modeling the Speed and Timing of American Sign Language to Generate Realistic Animations

  Sedeeq Al-khazraji, Larwan Berke, Sushant Kafle, Peter Yeung, Matt Huenerfauth · 2018 · Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '18)

  This paper addresses the challenge of generating realistic computer animations of American Sign Language (ASL) by automatically modeling three critical timing parameters: where prosodic pauses should be inserted, how long those pauses should last, and how the signing speed of…

  sign language · ASL · animation · machine learning · Deaf accessibility

• MANA: Designing and Validating a User-Centered Mobility Analysis System

  Boyd Anderson, Shenggao Zhu, Ke Yang, Jian Wang, Hugh Anderson, Chao Xu Tay, Vincent Y. F. Tan, Ye Wang · 2018 · Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2018)

  This paper presents MANA (Mobility ANAlytics), a wearable sensor system designed to measure gait parameters in people with Parkinson's Disease (PD) in clinical and home settings. The system addresses a significant gap in accessible health monitoring: existing gait analysis…

  Parkinson's disease · wearable sensors · gait analysis · inertial measurement units · mobility assessment

• Crowd-AI Camera Sensing in the Real World

  Anhong Guo, Anuraag Jain, Shomiron Ghose, Gierad Laput, Chris Harrison, Jeffrey P. Bigham · 2018 · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

  This paper presents Zensors++, a hybrid crowd-AI camera sensing system that allows users to point a networked camera at a scene, define a natural language question about it (such as "Is the coffee machine in use?" or "How many people are in the room?"), and receive continuous,…

  crowdsourcing · computer vision · human computation · machine learning · smart environments

• Screening Dyslexia for English Using HCI Measures and Machine Learning

  Luz Rello, Enrique Romero, Maria Rauschenberger, Abdullah Ali, Kristin Williams, Jeffrey P. Bigham, Nancy Cushen White · 2018 · International Conference on Digital Health

  This paper presents a machine learning approach to screening for dyslexia in English speakers by analyzing how users interact with a linguistic computer-based game called Dytective. More than 10% of the population has dyslexia, but most are diagnosed only after failing in…

  dyslexia · machine learning · screening · serious games · early detection

• Modeling Expertise in Assistive Navigation Interfaces for Blind People

  Eshed Ohn-Bar, João Guerreiro, Dragan Ahmetovic, Kris M. Kitani, Chieko Asakawa · 2018 · Proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI)

  This short IUI paper asks a question most assistive-navigation research leaves unasked: what happens as a blind user becomes an expert on a route? Existing smartphone guidance apps deliver the same instruction set on a user's tenth trip down a corridor as on their first,…

  blind navigation · indoor navigation · turn-by-turn navigation · visual impairment · blindness

• Active Learning for Web Accessibility Evaluation

  Mengni Zhang, Can Wang, Zhi Yu, Chao Shen, Jiajun Bu · 2017 · Proceedings of the 14th International Web for All Conference (W4A)

  This paper introduces "active-prediction," a semi-supervised machine learning method that addresses a fundamental bottleneck in web accessibility evaluation: the prohibitive cost of evaluating all pages in a large website. Current practice relies on sampling methods (ad hoc,…

  accessibility evaluation · machine learning · active learning · web accessibility · automated testing

• WAEM: A Web Accessibility Evaluation Metric Based on Partial User Experience Order

  Shuyi Song, Can Wang, Liangcheng Li, Zhi Yu, Xiao Lin, Jiajun Bu · 2017 · Proceedings of the 14th International Web for All Conference (W4A)

  This paper introduces WAEM (Web Accessibility Experience Metric), a novel accessibility metric that derives checkpoint weights from actual user experience data rather than from WCAG priority levels. The authors demonstrate that existing metrics like WAB and WAQM, which weight…

  accessibility metrics · user experience · accessibility evaluation · machine learning · SVM

• A Task Assignment Strategy for Crowdsourcing-Based Web Accessibility Evaluation System

  Liangcheng Li, Can Wang, Shuyi Song, Zhi Yu, Fenqin Zhou, Jiajun Bu · 2017 · Proceedings of the 14th International Web for All Conference (W4A)

  This paper addresses a practical challenge in scaling web accessibility evaluation: how to effectively assign manual evaluation tasks to volunteer crowdsource workers with varying levels of expertise. While automated tools can check many accessibility checkpoints, they cannot…

  web accessibility evaluation · crowdsourcing · machine learning · automated testing · conformance testing

• Real-Time Depth-Camera Based Hand Tracking for ASL Recognition

  Brandon Taylor, Anind Dey, Daniel Siewiorek, Asim Smailagic · 2017 · Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS)

  This demonstration paper validates the use of a publicly available real-time hand tracking algorithm (Sphere-Mesh) for recognizing American Sign Language (ASL) handshapes using a depth camera. Sign Language Recognition (SLR) has long been a motivating goal for high-precision…

  sign language recognition · hand tracking · computer vision · depth camera · machine learning

• Speed-Accuracy Tradeoffs for Detecting Sign Language Content in Video Sharing Sites

  Frank M. Shipman, Satyakiran Duggina, Caio D.D. Monteiro, Ricardo Gutierrez-Osuna · 2017 · Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS)

  This paper addresses the problem of automatically detecting sign language content in videos on sharing platforms like YouTube and Vimeo. For many deaf and hard-of-hearing people, sign language is their primary communication medium, and they rely on online video content to stay…

  sign language · computer vision · video classification · information retrieval · deaf and hard of hearing

• "Hands On" Visual Recognition for Visually Impaired Users

  Joan Sosa-García, Francesca Odone · 2017 · ACM Transactions on Accessible Computing

  This paper presents a collaborative visual recognition system designed to help blind or visually impaired (BVI) users identify specific product instances — distinguishing between brands, models, or types of objects that feel similar when handled. While BVI individuals can often…

  visual impairment · object recognition · computer vision · assistive technology · wearable technology

• People with Visual Impairment Training Personal Object Recognizers: Feasibility and Challenges

  Hernisa Kacorri, Kris M. Kitani, Jeffrey P. Bigham, Chieko Asakawa · 2017 · CHI Conference on Human Factors in Computing Systems

  This paper explores whether people with visual impairments can train their own personalized object recognition systems using a smartphone camera and a small number of example photos. The authors address a fundamental limitation of existing object recognition tools for blind…

  object recognition · computer vision · blindness · transfer learning · personalization

• Using data from social media websites to inspire the design of assistive technology

  Xing Yu · 2016 · Proceedings of the 13th International Web for All Conference (W4A)

  This doctoral consortium paper proposes using social media data as a low-cost, scalable method to inform the design of assistive technology, addressing limitations of traditional user research approaches. The author argues that designing assistive technology faces unique…

  assistive technology · social media · natural language processing · machine learning · prosthetics

• Computer vision-based analysis of web page structure for assistive interfaces

  Michael Cormier · 2016 · Proceedings of the 13th International Web for All Conference (W4A)

  This doctoral consortium paper proposes a novel approach to understanding web page structure by analyzing rendered page images using computer vision, rather than relying on the DOM tree or source code as most existing web page segmentation methods do. The author argues that the…

  computer vision · web page segmentation · screen reader accessibility · cognitive accessibility · machine learning

• Supporting the selection of web content modality based on user interactions logs

  Fabiano Marcon de Moraes, Vagner Figueredo de Santana, Juliana Cristina Braga · 2016 · Proceedings of the 13th International Web for All Conference (W4A)

  This paper explores using machine learning to automatically detect whether a web user is employing assistive technology based solely on their interaction patterns during a single pageview, without requiring explicit profile configuration or customization. Grounded in Universal…

  universal design · machine learning · personalization · assistive technology · user interaction

• Using Web Interaction to Monitor Parkinson's Disease Progression through Behavioural Inferences on the Web

  Julio Vega · 2016 · Proceedings of the 13th International Web for All Conference (W4A)

  This doctoral consortium paper from the University of Manchester proposes a novel approach to monitoring Parkinson's Disease (PD) progression using passive smartphone data collection and web interaction analysis, replacing the intrusive wearable devices and scripted evaluation…

  health monitoring · Parkinson's disease · smartphones · machine learning · digital health

• Laying a Foundation for the Graphical Course Map

  Linda DuHadway, Thomas C. Henderson · 2016 · Proceedings of the 13th International Web for All Conference (W4A)

  This paper from the University of Utah presents ENABLE, a system that transforms traditional linear, text-based learning management system (LMS) course presentations into interactive graphical course maps. The authors argue that current LMS platforms like Canvas impose two…

  education accessibility · personalized learning · data visualization · learning management systems · machine learning

• Supporting Orientation of People with Visual Impairment: Analysis of Large Scale Usage Data

  Hernisa Kacorri, Sergio Mascetti, Andrea Gerino, Dragan Ahmetovic, Hironobu Takagi, Chieko Asakawa · 2016 · Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '16)

  This paper analyzes large-scale remote usage data from iMove, an iOS GPS-based orientation app for people with visual impairments, to understand how users interact with assistive navigation technology in real-world conditions. Traditional assistive technology user studies are…

  visual impairment · blindness · navigation · orientation and mobility · mobile accessibility

• A Personalizable Mobile Sound Detector App Design for Deaf and Hard-of-Hearing Users

  Danielle Bragg, Nicholas Huynh, Richard E. Ladner · 2016 · Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '16)

  This paper presents the design and evaluation of a personalizable mobile phone app that detects sounds of interest to deaf and hard-of-hearing (DHH) users by learning from training examples recorded by the user themselves. Unlike existing commercial sound detection products —…

  deaf and hard of hearing · mobile accessibility · machine learning · sound detection · personalization

• Sign Transition Modeling and a Scalable Solution to Continuous Sign Language Recognition for Real-World Applications

  Kehuang Li, Zhengyu Zhou, Chin-Hui Lee · 2016 · ACM Transactions on Accessible Computing

  This paper presents a scalable framework for continuous sign language recognition (SLR) designed to work in real-world conditions using affordable hardware. The researchers address a fundamental challenge in SLR: modeling the transitions between signs. Unlike spoken language…

  sign language recognition · hidden Markov models · machine learning · deaf and hard of hearing · wearable technology