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• WLA4ND: a Wearable Dataset of Learning Activities for Young Adults with Neurodiversity to Provide Support in Education

  Hui Zheng, Pattiya Mahapasuthanon, Yujing Chen, Huzefa Rangwala, Anya S Evmenova, Vivian Genaro Motti · 2021 · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '21)

  This paper introduces WLA4ND (Wearable Learning Activities for Neurodiversity), the first wearable sensor dataset of learning activities collected from young adults with neurodiversity. While existing wearable sensor datasets focus on fitness, daily living, and locomotion…

  neurodiversity · wearable technology · machine learning · activity recognition · inclusive education

• Sharing Practices for Datasets Related to Accessibility and Aging

  Rie Kamikubo, Utkarsh Dwivedi, Hernisa Kacorri · 2021 · The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2021)

  This paper presents a systematic review of 137 accessibility datasets collected from people with disabilities and older adults over a 35-year period (1984-2020). The authors undertook an extensive two-year search process using a multilayer strategy: open searches on search…

  datasets · machine learning · data sharing · privacy · ethics

• Fluent: An AI Augmented Writing Tool for People who Stutter

  Bhavya Ghai, Klaus Mueller · 2021 · The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2021)

  This paper presents Fluent, a novel AI-powered writing tool designed to help people who stutter (PWS) prepare scripts and written content that they can deliver more fluently. Over 70 million people worldwide stutter, and a common coping strategy is word substitution — replacing…

  stuttering · speech disorders · machine learning · active learning · natural language processing

• Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors

  Lida Theodorou, Daniela Massiceti, Luisa Zintgraf, Simone Stumpf, Cecily Morrison, Edward Cutrell, Matthew Tobias Harris, Katja Hofmann · 2021 · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '21)

  This paper presents a disability-first approach to constructing a machine learning dataset for teachable object recognition, developed through the ORBIT (Objects that Recognize Blind Individuals in Their environment) project. The authors argue that while AI for accessibility is…

  disability-first design · dataset creation · teachable object recognition · blind and low vision · machine learning

• Activity Recognition in Older Adults with Training Data from Younger Adults: Preliminary Results on in Vivo Smartwatch Sensor Data

  Sabahat Fatima · 2021 · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '21)

  This extended abstract investigates a critical age-related bias in wearable activity recognition: models trained on data from younger adults perform significantly worse when applied to older adults. The study is motivated by the growing potential of smartwatch-based…

  older adults · activity recognition · machine learning · wearable technology · smartwatch

• The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective

  Danielle Bragg, Oscar Koller, Mary Bellard, Larwan Berke, Patrick Boudreault, Annelies Braffort, Naomi Caselli, Matt Huenerfauth, Hernisa Kacorri, Tessa Verhoef, Christian Vogler, Meredith Ringel Morris · 2021 · ACM Transactions on Accessible Computing

  This interdisciplinary paper examines the ethical landscape of AI datasets used for sign language recognition, generation, and translation technologies. Drawing on expertise from deaf community members, sign language linguists, and AI researchers, the authors apply the FATE…

  sign language · AI datasets · deaf community · FATE framework · machine learning

• How Could Equality and Data Protection Law Shape AI Fairness for People with Disabilities?

  Reuben Binns, Reuben Kirkham · 2021 · ACM Transactions on Accessible Computing

  This interdisciplinary paper examines how UK equality law and EU data protection law (GDPR) intersect with AI fairness for people with disabilities (PWD). The authors argue that AI fairness for PWD requires a fundamentally different approach than for other protected…

  AI fairness · disability discrimination · data protection · GDPR · equality law

• Screen Parsing: Towards Reverse Engineering of UI Models from Screenshots

  Jason Wu, Xiaoyi Zhang, Jeff Nichols, Jeffrey P. Bigham · 2021 · The 34th Annual ACM Symposium on User Interface Software and Technology (UIST)

  This paper introduces screen parsing, the task of predicting UI elements and their hierarchical relationships from a screenshot alone. While prior work could detect individual UI elements on a screen (element detection), those approaches produced flat lists of elements with no…

  screen readers · mobile accessibility · computer vision · UI semantics · machine learning

• Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixels

  Xiaoyi Zhang, Lilian de Greef, Amanda Swearngin, Samuel White, Kyle Murray, Lisa Yu, Qi Shan, Jeffrey Nichols, Jason Wu, Chris Fleizach, Aaron Everitt, Jeffrey P. Bigham · 2021 · CHI Conference on Human Factors in Computing Systems

  This paper from Apple introduces Screen Recognition, a system that automatically generates accessibility metadata for mobile apps by analyzing their visual pixels, enabling screen readers to work with apps that lack proper developer-provided accessibility information. The…

  mobile accessibility · screen readers · machine learning · object detection · VoiceOver

• Fairness of AI for People with Disabilities: Problem Analysis and Interdisciplinary Collaboration

  Jason J. G. White · 2020 · SIGACCESS Accessibility and Computing

  This paper provides a philosophical analysis of the fairness challenges that machine learning-based AI poses for people with disabilities, arguing that these challenges demand unprecedented interdisciplinary collaboration across applied ethics, human rights law, disability…

  AI fairness · algorithmic bias · disability · social justice · ethics

• Screening Risk of Dyslexia Through a Web-Game Using Language-Independent Content and Machine Learning

  Maria Rauschenberger, Ricardo Baeza-Yates, Luz Rello · 2020 · Proceedings of the 17th International Web for All Conference (W4A)

  This paper presents MusVis, a web-based game designed to screen for dyslexia risk using language-independent content and machine learning, enabling potential early detection even in pre-readers who have not yet developed literacy skills. Dyslexia affects 5-15% of the world…

  dyslexia · machine learning · screening · serious games · gamification

• Autism Detection Based on Eye Movement Sequences on the Web: A Scanpath Trend Analysis Approach

  Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva, Simon Harper · 2020 · Proceedings of the 17th International Web for All Conference (W4A)

  This paper investigates whether sequential eye-movement data — the order in which people look at elements on web pages — can be used to detect autism, improving upon the authors' previous non-sequential approach that achieved 75% accuracy but was unstable across different web…

  autism · eye tracking · machine learning · web accessibility · scanpath analysis

• Supporting the Design of Data Visualisation for the Visually Impaired through Reinforcement Learning

  Dalal Aljasem · 2020 · Proceedings of the 17th International Web for All Conference (W4A)

  This doctoral consortium paper presents a research programme aimed at making data visualizations more accessible to people with partial vision loss, specifically those with peripheral vision damage (tunnel vision from conditions like glaucoma) or central vision loss (from…

  data visualization · visual impairment · reinforcement learning · visual search · machine learning

• Exploring Collection of Sign Language Datasets: Privacy, Participation, and Model Performance

  Danielle Bragg, Oscar Koller, Naomi Caselli, William Thies · 2020 · Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2020)

  This paper tackles a fundamental tension in building machine learning systems for marginalized communities: the need for large training datasets versus the privacy risks of collecting data from small, identifiable populations. The authors focus on sign language video collection,…

  sign language · privacy · machine learning · data collection · Deaf culture

• A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

  Tong Wu, Yang Gu, Yiqiang Chen, Jiwei Wang, Siyu Zhang · 2020 · Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS)

  This paper addresses fall detection for older adults, which the WHO identifies as the second leading cause of accidental injury death worldwide. Roughly 28-35% of people over 65 experience falls each year, with the rate increasing to 32-42% for those over 70. The authors propose…

  fall detection · machine learning · wearable technology · aging · health monitoring

• SoundWatch: Exploring Smartwatch-based Deep Learning Approaches to Support Sound Awareness for Deaf and Hard of Hearing Users

  Dhruv Jain, Hung Ngo, Pratyush Patel, Steven Goodman, Leah Findlater, Jon Froehlich · 2020 · Proceedings of the 22nd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS '20)

  This paper presents SoundWatch, a smartwatch-based sound awareness system that uses deep learning to classify environmental sounds in real time and provide visual and haptic notifications to deaf and hard of hearing (DHH) users. The research addresses the finding from prior…

  deaf accessibility · hard of hearing · sound awareness · deep learning · wearable technology

• Computer Vision-based Methodology to Support AAC

  Rúbia Eliza de Oliveira Schultz Ascari, Roberto Pereira, Luciano Silva · 2020 · ACM Transactions on Accessible Computing

  This paper presents a methodology for supporting augmentative and alternative communication (AAC) through personalized gestural interaction using computer vision and machine learning. The authors developed the PGCA (Personal Gesture Communication Assistant) system, which enables…

  AAC · augmentative and alternative communication · computer vision · machine learning · gesture recognition

• Automated Class Discovery and One-Shot Interactions for Acoustic Activity Recognition

  Jason Wu, Chris Harrison, Jeffrey P. Bigham, Gierad Laput · 2020 · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20)

  This paper presents Listen Learner, an end-to-end system for acoustic activity recognition that automatically discovers and learns to classify environmental sounds with minimal user effort. Traditional approaches to sound recognition face a tradeoff: custom models trained in a…

  acoustic activity recognition · smart home · machine learning · Internet of Things · context awareness

• A Computer Anxiety Model for Elderly Users Interacting with the Web

  Thiago Donizetti dos Santos, Vagner Figueredo de Santana · 2019 · Proceedings of the 16th International Web for All Conference (W4A)

  This paper presents a model for automatically detecting Computer Anxiety (CA) levels in older adults through analysis of their interaction logs while browsing the web. Computer Anxiety — defined as negative emotions and cognition processes evoked during actual or imagined…

  computer anxiety · older adults · aging · interaction logging · machine learning

• Automatic Identification of Widgets and their Subcomponents Based on a Classification Pipeline for DOM Mutation Records

  Eduardo Henrique Rizo, Renata Pontin de Mattos Fortes, Humberto Lidio Antonelli, Willian Massami Watanabe · 2019 · Proceedings of the 16th International Web for All Conference (W4A)

  This paper presents a machine learning pipeline for automatically classifying web widgets (specifically dropdown menus) and their subcomponents by analyzing DOM mutation records — the dynamic changes that occur in a web page's HTML structure when users interact with it or visual…

  WAI-ARIA · machine learning · web accessibility · automated testing · widgets

• Revisiting Blind Photography in the Context of Teachable Object Recognizers

  Kyungjun Lee, Jonggi Hong, Simone Pimento, Ebrima Jarjue, Hernisa Kacorri · 2019 · Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS)

  This paper introduces a real-time audio-haptic feedback system to help people with visual impairments frame objects in their smartphone camera when training teachable object recognizers. The challenge is that teachable recognizers — which let users train personalized models to…

  blind photography · teachable object recognizer · computer vision · deep learning · visual impairment

• Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery

  Galen Weld, Esther Jang, Anthony Li, Aileen Zeng, Kurtis Heimerl, Jon E. Froehlich · 2019 · Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2019)

  This paper presents the first application of deep learning to automatically assess sidewalk accessibility from Google Street View (GSV) panoramas, addressing four types of accessibility problems: curb ramps, missing curb ramps, sidewalk obstructions, and surface problems.…

  computer vision · deep learning · sidewalk accessibility · curb ramps · crowdsourcing

• An Intelligent Decision Support System for Stroke Rehabilitation Assessment

  Min Hun Lee · 2019 · Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2019)

  This student research abstract presents an interactive multimodal machine learning approach for automatically assessing upper-limb stroke rehabilitation exercises and supporting therapist decision-making. Physical rehabilitation is critical for people recovering from stroke to…

  stroke rehabilitation · machine learning · human-AI interaction · decision support · motor disability

• App Usage Predicts Cognitive Ability in Older Adults

  Mitchell L. Gordon, Leon Gatys, Carlos Guestrin, Jeffrey P. Bigham, Andrew Trister, Kayur Patel · 2019 · CHI Conference on Human Factors in Computing Systems

  This paper investigates how older adults use smartphones differently from younger adults and whether those differences can be explained by cognitive function. The researchers collected three months of iPhone usage data from 84 healthy older adults (aged 61-76), logging 494,641…

  older adults · cognitive decline · smartphone usage · mobile accessibility · digital biomarkers

• Drop-Down Menu Widget Identification Using HTML Structure Changes Classification

  Humberto Lidio Antonelli, Rodrigo Augusto Igawa, Renata Pontin De Mattos Fortes, Eduardo Henrique Rizo, Willian Massami Watanabe · 2018 · ACM Transactions on Accessible Computing (TACCESS)

  This paper addresses a critical gap in web accessibility: the widespread failure of interactive widgets in rich internet applications (RIAs) to implement WAI-ARIA markup, rendering them inaccessible to screen reader users and other assistive technology users. The authors propose…

  ARIA · web accessibility · machine learning · widgets · automated testing