About this Research Topic
According to the WHO (2022), more than 1.3 billion people, or 1 in 6 people worldwide experience significant disabilities. Various assistive technologies (ATs) have been created to help individuals with physical, sensory, or cognitive impairments so that they can live and work more effectively and independently in all aspects of their lives. Intelligent ATs, which use artificial intelligence (AI) and machine learning (ML) techniques, have been woven into smart homes, prompting systems, chatbots, augmentative communication devices, smart wheelchairs, and brain-computer interfaces.
In a broader sense, emerging sensory/actuation technologies including intelligent human-computer interaction (HCI), augmented reality (AR), new sensors and multimodal stimulators, provide new ways for people with disabilities to interact with others and the world. However, the existing ATs have many untackled challenges, including limited capabilities, accessibility, and/or accuracy, and more seriously, a lack of customization when the situation or abilities change for individuals.
AI, HCI and ML cover a spectrum of current exciting research and industrial innovations that provide more efficient, effective, and automated algorithms to deal with large-scale data and multiple sensory interfaces in a wide variety of disciplines: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, health informatics, medical image analysis, recommender systems, fraud detection, etc. Advances in machine learning and HCI provide opportunities to transform the landscape of existing ATs. For example, ML-powered gesture-based prediction provides better speed and precision in analysing and deciphering complex communication, expressions, and visual behaviours. Intelligent HCI can be utilized to adapt to an individual’s situation and ability changes by integrating health data. We envision that AI/ML/HCI will be key in all sorts of intelligent AT applications. There are still challenges and barriers in designing and using AI-powered ATs for various impairments. For example: (1) How to collect large and diverse data sets of people with disabilities (PWDs) to train machine learning models considering the privacy issues and individual uniqueness of them? (2) How to make AI-powered ATs more accurate and adaptable for children with multiple developmental impairments to match their developing abilities? (3) How to design AI-powered ATs that continuously assist individuals with changing security and privacy concerns? (4) Regarding usability, how to develop ATs that have minimum requirements
Original research papers are invited to submit to this special issue, with topics that include, but are not limited to: - Novel AI-powered ATs applications - Novel AI/ML algorithms for ATs - Data sets for AI-based ATs - Human subject studies to identify specific requirements - Security and usability of ATs
In a broader sense, emerging sensory/actuation technologies including intelligent human-computer interaction (HCI), augmented reality (AR), new sensors and multimodal stimulators, provide new ways for people with disabilities to interact with others and the world. However, the existing ATs have many untackled challenges, including limited capabilities, accessibility, and/or accuracy, and more seriously, a lack of customization when the situation or abilities change for individuals.
AI, HCI and ML cover a spectrum of current exciting research and industrial innovations that provide more efficient, effective, and automated algorithms to deal with large-scale data and multiple sensory interfaces in a wide variety of disciplines: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, health informatics, medical image analysis, recommender systems, fraud detection, etc. Advances in machine learning and HCI provide opportunities to transform the landscape of existing ATs. For example, ML-powered gesture-based prediction provides better speed and precision in analysing and deciphering complex communication, expressions, and visual behaviours. Intelligent HCI can be utilized to adapt to an individual’s situation and ability changes by integrating health data. We envision that AI/ML/HCI will be key in all sorts of intelligent AT applications. There are still challenges and barriers in designing and using AI-powered ATs for various impairments. For example: (1) How to collect large and diverse data sets of people with disabilities (PWDs) to train machine learning models considering the privacy issues and individual uniqueness of them? (2) How to make AI-powered ATs more accurate and adaptable for children with multiple developmental impairments to match their developing abilities? (3) How to design AI-powered ATs that continuously assist individuals with changing security and privacy concerns? (4) Regarding usability, how to develop ATs that have minimum requirements
Original research papers are invited to submit to this special issue, with topics that include, but are not limited to: - Novel AI-powered ATs applications - Novel AI/ML algorithms for ATs - Data sets for AI-based ATs - Human subject studies to identify specific requirements - Security and usability of ATs
Keywords: Assistive technologies, Intelligent, Privacy and Security, Usability, Accessibility
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