Literature Reviews

Reviewed research papers, articles, and publications relevant to digital accessibility.

• 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…

  • fall detection
  • machine learning
  • wearable technology
  • aging
  • health monitoring
  • cloud computing
  • ensemble 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 si…

  • sign language
  • privacy
  • machine learning
  • data collection
  • Deaf culture
  • computer vision
  • sign language recognition

• 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 cen…

  • data visualization
  • visual impairment
  • reinforcement learning
  • visual search
  • machine learning
  • eye tracking
  • accessible design

• 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 uns…

  • autism
  • eye tracking
  • machine learning
  • web accessibility
  • scanpath analysis
  • screening
  • neurodevelopmental disorders

• 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 aff…

  • dyslexia
  • machine learning
  • screening
  • serious games
  • gamification
  • auditory perception
  • visual perception
  • early intervention
  • language-independent

• 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 …

  • older adults
  • activity recognition
  • machine learning
  • wearable technology
  • smartwatch
  • self-tracking
  • aging
  • sedentary behaviour
  • dataset bias

• 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 w…

  • disability-first design
  • dataset creation
  • teachable object recognition
  • blind and low vision
  • machine learning
  • data collection
  • participatory research
  • accessible mobile applications

• 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 w…

  • stuttering
  • speech disorders
  • machine learning
  • active learning
  • natural language processing
  • writing tools
  • assistive technology
  • word substitution

• 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: …

  • datasets
  • machine learning
  • data sharing
  • privacy
  • ethics
  • bias mitigation
  • AI
  • disability data
  • aging
  • systematic review

• 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 …

  • neurodiversity
  • wearable technology
  • machine learning
  • activity recognition
  • inclusive education
  • datasets
  • smartwatch
  • ADHD
  • autism
  • intellectual disability
  • Down syndrome