Figure 4 from A Conceptual Model for Inclusive Technology: Advancing Disability Inclusion through Artificial Intelligence | Semantic Scholar (2024)

This paper aims to analyse the risks and opportunities associated with adopting digital replicas of humans (HDTs) within the Metaverse and the challenges related to managing digital identities in this context by examining the current technological landscape.

This comprehensive review provides insights into the latest trends in biomimetic prosthetics, focusing on leveraging knowledge from natural biomechanics, sensory feedback mechanisms, and control systems to closely mimic biological appendages.

ViT models are showing good results in identifying facial features associated with ASD, leading toward early diagnostics, and results show the ViT model’s capability in distinguishing the faces of autistic and non-autistic children.

A systematic comparison of the performance of various transfer learning approaches, including feature extraction and fine-tuning, across a spectrum of popular pretrained models, such as visual geometry group (VGG), Inception, ResNet, EfficientNet, and DenseNet is presented.

An approach for sign language identification for the Malayalam language using advanced deep learning and computer vision techniques is proposed and results show that the identification accuracy is comparable to other sign language identification systems.

An AI perspective on future assistive technologies and ethical concerns arising from the use of such cutting-edge communication and learning technologies for children with disabilities is summarized.

A review of the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs, and summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education.

Here it is described how to apply advanced brain-inspired methods for inference and prediction, the evolving fuzzy neural network (EFuNN) paradigm and the spiking neural network [SNN] paradigm, and the system requirements to develop a wearable electronic prosthesis for functional rehabilitation.

Application of Artificial Intelligence and robotics technology has a huge impact in achieving independent mobility and enhances the quality of life in Persons with Disabilities (PwDs).

It is asserted that, by doing so, it is possible to ensure greater precision regarding dementia care while simultaneously enhancing personalization by incorporating digital twins to support better dementia care.

The better performance of the proposed methodology is a step forward to achieve intuitive control of BCI-based exoskeletons, and the proposed system showed better performance as compared to using all channels for classification.

It is argued that the multiplicity of different kinds and intensities of people’s disabilities and the fluid, contextual ways in which they manifest point to the limits of algorithmic fairness initiatives, and that fairness alone is insufficient to secure accessible, inclusive AI.

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