Applications and Trends on Artificial Intelligence-Based Assistive Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 4842

Special Issue Editors

Department of Computer Architecture and Technology, Universidad de Sevilla, 41004 Sevilla, Spain
Interests: robotics; eAccessibility; eHealth; neuromorphic engineering; engineering education
Special Issues, Collections and Topics in MDPI journals
Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: biomedical signal processing; clinical engineering; image processing; big data; m-health
Special Issues, Collections and Topics in MDPI journals
Department of Computer Architecture and Technology, University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), 48049 Bilbao, Spain
Interests: human–computer interaction; accessibility and design for all; ubiquitous computing for all; user modeling and user adapted interaction

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has recently been applied to various research domains, including computer vision, natural language processing, voice recognition, etc., with encouraging results. These efforts demonstrated that the knowledge and strategies developed in the AI field can be effectively applied to the development of products and services to improve the quality of life of people with disabilities. Some AI-based assistive products are even starting to appear in the market.

However, the potential of these technologies to help people with disabilities in their daily tasks goes far beyond what it is currently available, since most of these efforts are still in the research stage.

The main aim of this Special Issue is to present novel approaches to Assistive Technology efficiently focusing on the usefulness for the final and (possible) intermediate users. Both theoretical and experimental studies for AI-enabled Assistive Technologies, frameworks, platforms, and protocols are encouraged. Furthermore, high-quality review and survey papers are also welcomed.

The papers considered for possible publication may focus on but not necessarily be limited to the following areas:

  • AI-enabled vision assistance for people with visual impairments;
  • Machine learning approaches for dysarthric speech processing;
  • AI based alternative and augmentative communication tools;
  • AI-based tools for people with hearing related disabilities;
  • Personal Assistants for people with cognitive disabilities;
  • AI-based assistive environments;
  • Intelligent mobility aids;
  • Novel applications and case studies in intelligent Assistive Technology.

Prof. Dr. Anton Civit
Prof. Dr. Panagiotis Bamidis
Prof. Dr. Julio Abascal
Guest Editors

Manuscript Submission Information

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Keywords

  • AI-enabled vision assistance for people with visual impairments
  • Machine learning approaches for dysarthric speech processing
  • AI based alternative and augmentative communication tools
  • AI-based tools for people with hearing related disabilities
  • Personal Assistants for people with cognitive disabilities
  • AI-based assistive environments
  • Intelligent mobility aids
  • Novel applications and case studies in intelligent Assistive Technology

Published Papers (2 papers)

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Research

15 pages, 4680 KiB  
Article
Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method
Electronics 2021, 10(16), 1993; https://doi.org/10.3390/electronics10161993 - 18 Aug 2021
Cited by 1 | Viewed by 1951
Abstract
Microscopic laser engraving surface defect classification plays an important role in the industrial quality inspection field. The key challenges of accurate surface defect classification are the complete description of the defect and the correct distinction into categories in the feature space. Traditional classification [...] Read more.
Microscopic laser engraving surface defect classification plays an important role in the industrial quality inspection field. The key challenges of accurate surface defect classification are the complete description of the defect and the correct distinction into categories in the feature space. Traditional classification methods focus on the terms of feature extraction and independent classification; therefore, feed handcrafted features may result in useful feature loss. In recent years, convolutional neural networks (CNNs) have achieved excellent results in image classification tasks with the development of deep learning. Deep convolutional networks integrate feature extraction and classification into self-learning, but require large datasets. The training datasets for microscopic laser engraving image classification are small; therefore, we used pre-trained CNN models and applied two fine-tuning strategies. Transfer learning proved to perform well even on small future datasets. The proposed method was evaluated on the datasets consisting of 1986 laser engraving images captured by a metallographic microscope and annotated by experienced staff. Because handcrafted features were not used, our method is more robust and achieves better results than traditional classification methods. Under five-fold-validation, the average accuracy of the best model based on DenseNet121 is 96.72%. Full article
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22 pages, 730 KiB  
Article
Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks
Electronics 2020, 9(11), 1843; https://doi.org/10.3390/electronics9111843 - 03 Nov 2020
Cited by 8 | Viewed by 1933
Abstract
Non-verbal communication is essential in the communication process. This means that its lack can cause misinterpretations of the message that the sender tries to transmit to the receiver. With the rise of video calls, it seems that this problem has been partially solved. [...] Read more.
Non-verbal communication is essential in the communication process. This means that its lack can cause misinterpretations of the message that the sender tries to transmit to the receiver. With the rise of video calls, it seems that this problem has been partially solved. However, people with cognitive disorders such as those with some kind of Autism Spectrum Disorder (ASD) are unable to interpret non-verbal communication neither live nor by video call. This work analyzes the relationship between some physiological measures (EEG, ECG, and GSR) and the affective state of the user. To do that, some public datasets are evaluated and used for a multiple Deep Learning (DL) system. Each physiological signal is pre-processed using a feature extraction process after a frequency study with the Discrete Wavelet Transform (DWT), and those coefficients are used as inputs for a single DL classifier focused on that signal. These multiple classifiers (one for each signal) are evaluated independently and their outputs are combined in order to optimize the results and obtain additional information about the most reliable signals for classifying the affective states into three levels: low, middle, and high. The full system is carefully detailed and tested, obtaining promising results (more than 95% accuracy) that demonstrate its viability. Full article
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