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Recent Advances in Active Assisted Living: Challenges in Architectures, Applications and Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 762

Special Issue Editors


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Guest Editor
Computer Science Department, School of Technology and Management, Computer Science and Communications Research Centre, Polytechnic of Leiria, Campus 2, Morro do Lena-Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal
Interests: Internet of Things; SMART IoT Ecosystems; Internet of Unmanned Vehicles; Industry 4.0; next-generation networks and services and ambient assisted living
Special Issues, Collections and Topics in MDPI journals

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Co-Guest Editor
Computer Science Department, School of Technology and Management, Computer Science and Communications Research Centre, Polytechnic of Leiria, Campus 2, Morro do Lena-Alto do Vieiro, Apartado 4163, 2411-901 Leiria, Portugal
Interests: wireless sensor networks; IoT-based sensor networks; smart and intelligent detection; sensor network reliability; sensor network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The demographic shift towards an elderly population has presented new challenges to families and caregivers, as well as health services and governments, from both a health and social point of view.

Active assisted living (AAL) is a research area that focuses on information and communications technology (ICT) solutions to support and assist elders in living safer, more active, and healthier lives, while increasing their quality of life. Here “active” refers to more than keeping the elderly individual physically active. It means enabling the individual’s continuous connection, among others, in social, cultural, and economic spheres while decreasing loneliness and social isolation.

The research community, governments, and health services are now engaged in using emerging technologies and systems as long-term solutions to offer health and social care services in the future. However, the development of AAL solutions is still presented with various issues and challenges, from social to technical, preventing their full deployment.

This Special Issue aims to share recent advances and major trends towards the innovation of AAL systems. We invite researchers who are working on AAL to submit original and unpublished manuscripts addressing topics including (but not limited to) the following:

  • Quality of life;
  • Assistive technologies;
  • Independent living;
  • Active and assisted living;
  • Ambient assisted living;
  • Activities of daily living;
  • Education and leisure;
  • Health and safety;
  • Secure and safe services;
  • Human interaction;
  • Social robots;
  • Social recognition;
  • Elderly-centric products, services, and applications;
  • Future architectures and frameworks for AAL;
  • Social solutions in AAL environments;
  • Standards and protocols for AAL;
  • Social engagement and participation;
  • Social sensing;
  • Crowdsensing;
  • Smart governance;
  • Smart mobility;
  • Digitalization of health systems and social services;
  • Public institutions and private sector informatization;
  • E-government, e-governance and e-democracy;
  • Smart homes environments and infrastructures;
  • Protypes and experiments in real-world;
  • Device-free human activity systems;
  • Internet of Things (IoT) for AAL;
  • Blockchain applied to AAL;
  • Cloud and edge computing for AAL;
  • Digital twins applied to AAL;
  • Smart environments in the context of AAL;
  • Big data analytics for AAL;
  • Machine learning and artificial intelligence for AAL;
  • Edge and cloud computing for AAL;
  • Architectures and future designs of AAL models;
  • Services and applications for AAL;
  • Devices, sensors and communication networks for AAL;
  • Standards and protocols for AAL;
  • AAL sensors and actuators.

Prof. Dr. António Manuel De Jesus Pereira
Dr. Luís Frazão
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. Sensors 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 2600 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.

Published Papers (1 paper)

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29 pages, 616 KiB  
Systematic Review
Systematic Review of Emotion Detection with Computer Vision and Deep Learning
by Rafael Pereira, Carla Mendes, José Ribeiro, Roberto Ribeiro, Rolando Miragaia, Nuno Rodrigues, Nuno Costa and António Pereira
Sensors 2024, 24(11), 3484; https://doi.org/10.3390/s24113484 - 28 May 2024
Viewed by 292
Abstract
Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of [...] Read more.
Emotion recognition has become increasingly important in the field of Deep Learning (DL) and computer vision due to its broad applicability by using human–computer interaction (HCI) in areas such as psychology, healthcare, and entertainment. In this paper, we conduct a systematic review of facial and pose emotion recognition using DL and computer vision, analyzing and evaluating 77 papers from different sources under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our review covers several topics, including the scope and purpose of the studies, the methods employed, and the used datasets. The scope of this work is to conduct a systematic review of facial and pose emotion recognition using DL methods and computer vision. The studies were categorized based on a proposed taxonomy that describes the type of expressions used for emotion detection, the testing environment, the currently relevant DL methods, and the datasets used. The taxonomy of methods in our review includes Convolutional Neural Network (CNN), Faster Region-based Convolutional Neural Network (R-CNN), Vision Transformer (ViT), and “Other NNs”, which are the most commonly used models in the analyzed studies, indicating their trendiness in the field. Hybrid and augmented models are not explicitly categorized within this taxonomy, but they are still important to the field. This review offers an understanding of state-of-the-art computer vision algorithms and datasets for emotion recognition through facial expressions and body poses, allowing researchers to understand its fundamental components and trends. Full article
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