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Applied Sciences
Special Issues
Ambient Assisted Living and Cognitive Assistants
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Ambient Assisted Living and Cognitive Assistants

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 7521

Special Issue Editors

Prof. Dr. Paulo Novais

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Guest Editor
Algoritmi Center, Campus of Gualtar, 4710-057 Braga, Portugal
Interests: ambient intelligence; ambient assisted living; conflict resolution; behavioral analysis; intelligent tutors
Special Issues, Collections and Topics in MDPI journals
Dr. Angelo Costa

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Guest Editor
Centro ALGORITMI / Department of Informatics, University of Minho, Braga, Portugal
Interests: multi-agent systems; ambient intelligence; social robotics; mobile computing; machine learning; and privacy and data protection
Prof. Dr. Vicente Julian

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Guest Editor
GTI-IA Research Group, Deputy Director of Research in the Department of Computer Systems and Computation at Universitat Politècnica de València, Valencia, Spain
Interests: multi-agent systems; agreement technologies; Ambient Intelligence; affective computing; intelligent transport systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Recent studies have shown that people who stay in their homes have a higher quality of life, often better than those who are institutionalized. Another important social factor is the high cost of institutional care, which a great number of families are unable to pay, thus being the only option to keep their elderly relatives at home. To respond to these issues, the services and care available in the institutions must be replicated in the family environment. These new challenges require new solutions, technological ones.

Ambient Assisted Living (AAL), and more recently, Cognitive Assistants (CAs) aim to provide technological advancements with the aim of assisting people with physical or cognitive disabilities. Their operational areas include complex platforms that include sensors, actuators, monitoring processes and decision support systems. These two domains provide a two-pronged approach: the AAL focuses on enriching the home environment and the CAs focuses on directly assisting the people on that environment.

Together, they include technologies such as personalized smart assistants, multi-agent systems, robotics, electronic health applications, and more, with the goal of providing elderly and disabled people the tools that best fit them using personalization methods.

Although these areas have been providing solutions to help the users, the customization element has not been widely addressed and it is necessary to have custom systems that can effectively respond to users' expectations. Thus, integrated human-computer interaction solutions are needed, which can offer a more natural way of interacting with users and supporting them effectively.

This special edition aims to present research papers in the interdisciplinary fields of ambient assisted living, cognitive assistants and cognitively inspired systems. To this end, we invite high-quality innovations and contributions that demonstrate advances in these fields.

Prof. Dr. Paulo Novais
Prof. Dr. Vicente Julian
Dr. Angelo Costa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • - Agent & Multi-agent Systems for Ambient Intelligence
  • - Ambient Assisted Living
  • - Ambient Intelligence Applications
  • - Artificial Intelligence for Ambient Intelligence
  • - Cognitive Assistants
  • - Context Aware Computing
  • - Data protection and privacy
  • - Domotics (Home Automation)
  • - Evaluation of Cognitive Assistants
  • - Evolutionary Computation
  • - Ethics and Legal Issues
  • - Intelligent Systems
  • - Knowledge Discovery and Acquisition
  • - Memory Assistants
  • - Mobile Computing
  • - Robotics
  • - Spatial Cognition and Computation
  • - Ubiquitous Computing

Published Papers (2 papers)

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Research

21 pages, 1152 KiB  
Article
Measuring the Use of the Active and Assisted Living Prototype CARIMO for Home Care Service Users: Evaluation Framework and Results
by Cornelia Schneider, Birgit Trukeschitz and Harald Rieser
Appl. Sci. 2020, 10(1), 38; https://doi.org/10.3390/app10010038 - 19 Dec 2019
Cited by 8 | Viewed by 2448
Abstract
To address the challenges of aging societies, various information and communication technology (ICT)-based systems for older people have been developed in recent years. Currently, the evaluation of these so-called active and assisted living (AAL) systems usually focuses on the analyses of usability and [...] Read more.
To address the challenges of aging societies, various information and communication technology (ICT)-based systems for older people have been developed in recent years. Currently, the evaluation of these so-called active and assisted living (AAL) systems usually focuses on the analyses of usability and acceptance, while some also assess their impact. Little is known about the actual take-up of these assistive technologies. This paper presents a framework for measuring the take-up by analyzing the actual usage of AAL systems. This evaluation framework covers detailed information regarding the entire process including usage data logging, data preparation, and usage data analysis. We applied the framework on the AAL prototype CARIMO for measuring its take-up during an eight-month field trial in Austria and Italy. The framework was designed to guide systematic, comparable, and reproducible usage data evaluation in the AAL field; however, the general applicability of the framework has yet to be validated. Full article
(This article belongs to the Special Issue Ambient Assisted Living and Cognitive Assistants)
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20 pages, 7390 KiB  
Article
Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
by Haikel Alhichri, Yakoub Bazi, Naif Alajlan and Bilel Bin Jdira
Appl. Sci. 2019, 9(21), 4656; https://doi.org/10.3390/app9214656 - 01 Nov 2019
Cited by 26 | Viewed by 4452
Abstract
This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects, regardless of their position [...] Read more.
This work presents a deep learning method for scene description. (1) Background: This method is part of a larger system, called BlindSys, that assists the visually impaired in an indoor environment. The method detects the presence of certain objects, regardless of their position in the scene. This problem is also known as image multi-labeling. (2) Methods: Our proposed deep learning solution is based on a light-weight pre-trained CNN called SqueezeNet. We improved the SqueezeNet architecture by resetting the last convolutional layer to free weights, replacing its activation function from a rectified linear unit (ReLU) to a LeakyReLU, and adding a BatchNormalization layer thereafter. We also replaced the activation functions at the output layer from softmax to linear functions. These adjustments make up the main contributions in this work. (3) Results: The proposed solution is tested on four image multi-labeling datasets representing different indoor environments. It has achieved results better than state-of-the-art solutions both in terms of accuracy and processing time. (4) Conclusions: The proposed deep CNN is an effective solution for predicting the presence of objects in a scene and can be successfully used as a module within BlindSys. Full article
(This article belongs to the Special Issue Ambient Assisted Living and Cognitive Assistants)
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