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Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review
by
de Freitas, Maurício Pasetto
, Piai, Vinícius Aquino
, Farias, Ricardo Heffel
, de Moraes Rossetto, Anubis Graciela
, Leithardt, Valderi Reis Quietinho
, Fernandes, Anita M. R.
in
Adaptive technology
/ AIoT
/ Artificial Intelligence
/ assistive technology
/ Computers
/ Decision making
/ Deep learning
/ Design and construction
/ Disability
/ Handicapped assistance devices
/ Humans
/ Internet of Things
/ Labor market
/ Literature reviews
/ Machine Learning
/ Memory
/ Neural networks
/ People with disabilities
/ Persons with Disabilities
/ Quality of life
/ Research methodology
/ Self-Help Devices
/ Self-help devices for the disabled
/ Sign language
/ Society
/ Software engineering
/ Systematic Review
/ Technology
/ Telemedicine
2022
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Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review
by
de Freitas, Maurício Pasetto
, Piai, Vinícius Aquino
, Farias, Ricardo Heffel
, de Moraes Rossetto, Anubis Graciela
, Leithardt, Valderi Reis Quietinho
, Fernandes, Anita M. R.
in
Adaptive technology
/ AIoT
/ Artificial Intelligence
/ assistive technology
/ Computers
/ Decision making
/ Deep learning
/ Design and construction
/ Disability
/ Handicapped assistance devices
/ Humans
/ Internet of Things
/ Labor market
/ Literature reviews
/ Machine Learning
/ Memory
/ Neural networks
/ People with disabilities
/ Persons with Disabilities
/ Quality of life
/ Research methodology
/ Self-Help Devices
/ Self-help devices for the disabled
/ Sign language
/ Society
/ Software engineering
/ Systematic Review
/ Technology
/ Telemedicine
2022
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Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review
by
de Freitas, Maurício Pasetto
, Piai, Vinícius Aquino
, Farias, Ricardo Heffel
, de Moraes Rossetto, Anubis Graciela
, Leithardt, Valderi Reis Quietinho
, Fernandes, Anita M. R.
in
Adaptive technology
/ AIoT
/ Artificial Intelligence
/ assistive technology
/ Computers
/ Decision making
/ Deep learning
/ Design and construction
/ Disability
/ Handicapped assistance devices
/ Humans
/ Internet of Things
/ Labor market
/ Literature reviews
/ Machine Learning
/ Memory
/ Neural networks
/ People with disabilities
/ Persons with Disabilities
/ Quality of life
/ Research methodology
/ Self-Help Devices
/ Self-help devices for the disabled
/ Sign language
/ Society
/ Software engineering
/ Systematic Review
/ Technology
/ Telemedicine
2022
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Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review
Journal Article
Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review
2022
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Overview
According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.
Publisher
MDPI AG,MDPI
Subject
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