Breaking Barriers: A Paradigm Shift in Technology Accessibility for Individuals with Physical Disabilities

Authors

  • Kshitij Mishra Indian Institute of Technology Patna, India
  • Manisha Burja Indian Institute of Technology Patna, India
  • Asif Ekbal Indian Institute of Technology Jodhpur, India

DOI:

https://doi.org/10.1609/aaai.v39i23.34670

Abstract

Individuals living with disabilities often face challenges in their daily lives, from managing physical tasks to coping with emotional needs. It is imperative to provide them with personalized, courteous, and empathetic support that can address their unique needs. To bridge this gap, we propose an Empathetic Disability Support System (EDiSS), designed to offer personalized support tailored with correct politeness and empathetic strategies as per individual users’ OCEAN traits, gender, and age. To train EDiSS, first, a specialized personalized disability support dialogue dataset (PDCARE) is created encompassing a wide spectrum of disabilities, such as Spinal Cord Injuries, Neurological Disorders, Orthopedic Disabilities, etc, and support areas like Physical Therapy Exercises, Pain Management, Emotional Support, etc. EDiSS employs a reinforcement learning-based dialogue model with a novel reward function. It adapts its tone and content based on the user’s persona, gender, and age to provide respectful and empathetic assistance across various aspects of daily living. Our experiments and evaluation demonstrate the effectiveness of EDiSS in improving the quality of life of individuals with disabilities, marking a significant advancement in leveraging technology to provide much-needed support and assistance in their daily challenges.

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Published

2025-04-11

How to Cite

Mishra, K., Burja, M., & Ekbal, A. (2025). Breaking Barriers: A Paradigm Shift in Technology Accessibility for Individuals with Physical Disabilities. Proceedings of the AAAI Conference on Artificial Intelligence, 39(23), 24876–24884. https://doi.org/10.1609/aaai.v39i23.34670

Issue

Section

AAAI Technical Track on Natural Language Processing II