Generative Artificial Intelligence’s Utility for Accessibility

With the recent rapid rise in Generative Artificial Intelligence (GAI) tools, it is imperative that we understand their impact on people with disabilities, both positive and negative. However, although we know that AI in general poses both risks and opportunities for people with disabilities, little is known specifically about GAI in particular.

To address this, we conducted a three-month autoethnography of our use of GAI to meet personal and professional needs as a team of researchers with and without disabilities. Our findings demonstrate a wide variety of potential accessibility-related uses for GAI while also highlighting concerns around verifiability, training data, ableism, and false promises.

Glazko, K. S., Yamagami, M., Desai, A., Mack, K. A., Potluri, V., Xu, X., & Mankoff, J. An Autoethnographic Case Study of Generative Artificial Intelligence’s Utility for Accessibility. ASSETS 2023.

News: Can AI help boost accessibility? These researchers tested it for themselves

Presentation (starts at about 20mins)

How Do People with Limited Movement Personalize Upper-Body Gestures?

Personalized upper-body gestures that can enable input from diverse body parts (e.g., head, neck, shoulders, arms, hands, and fingers), and match the abilities of each user, might make gesture systems more accessible for people with upper-body motor disabilities. Static gesture sets that make ability assumptions about the user (e.g., touch thumb and index finger together in midair) may not be accessible. In our work, we characterize the personalized gesture sets designed by 25 participants with upper-body motor disabilities. We found that the personalized gesture sets that participants designed were specific to their abilities and needs. Six participants mentioned that their inspiration for designing the gestures was based on “how I would do [the gesture] with the abilities that I have”. We suggest three considerations when designing accessible upper-body gesture interfaces: 

1) Track the whole upper body. Our participants used their whole upper-body to perform the gestures, and some switched back and forth from the left to the right hand to combat fatigue.

2) Use sensing mechanisms that are agnostic to the location and orientation of the body. About half of our participants kept their hand on or barely took their hand off of the armrest to decrease arm movement and fatigue.

3) Use sensors that can sense muscle activations without movement. Our participants activated their muscles but did not visibly move in 10% of the personalized gestures.   

Our work highlights the need for personalized upper-body gesture interfaces supported by multimodal biosignal sensors (e.g., accelerometers, sensors that can sense muscle activity like EMG). 

Physical Therapy Accessibility for People with Disabilities and/or Chronic Conditions

Many individuals with disabilities and/or chronic conditions experience symptoms that may require intermittent or on-going medical care. However, healthcare is often overlooked as an area where accessibility needs to be addressed to improve physical and digital interactions between patients and healthcare providers. We discuss the challenges faced by individuals with disabilities and chronic conditions in accessing physical therapy and how technology can help improve access. We interviewed 15 people and found both social (e.g. financial constraints, lack of accessible transportation) and physiological (e.g. chronic pain) barriers to accessing physical therapy. Our study suggests that technology interventions that are adaptable, support movement tracking, and community building may support access to physical therapy.  Rethinking access to physical therapy for people with disabilities or chronic conditions from a lens that includes social and physiological barriers presents opportunities to integrate accessibility and adaptability into physical therapy technology.

“I’m Just Overwhelmed”: Investigating Physical Therapy Accessibility and Technology Interventions for People with Disabilities and/or Chronic Conditions. Momona Yamagami, Kelly Mack, Jennifer Mankoff, and Katherine M. Steele. ACM Transactions on Accessible Computing 15, no. 4 (2022): 1-22.