sensors-logo

Journal Browser

Journal Browser

Special Issue "Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Paolo Barsocchi
E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council, 1-56124 Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient-assisted living; indoor localization
Special Issues and Collections in MDPI journals
Dr. Filippo Palumbo
E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council, 1-56124 Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient assisted living; indoor localization; pattern recognition
Special Issues and Collections in MDPI journals
Dr. Victor Hugo C. De Albuquerque
E-Mail Website
Guest Editor
Instituto Federal de Educação, Federal University of Ceará, Fortaleza, 60020-181 Fortaleza/CE, Brazil
Interests: Artificial intelligence; image data science; internet of things; pattern recognition; information security
Special Issues and Collections in MDPI journals
Dr. Akash Kumar Bhoi
E-Mail Website
Guest Editor
Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim 737136, India
Interests: Biomedical technologies; Internet of Things; artificial intelligence; biomedical signal processing; soft computing

Special Issue Information

Dear Colleagues,

The scientific and technological breakthroughs have helped in the area of the population's longevity in recent years. However, living a long and healthy life brings new challenges to governments and society. The increasing pressure on medical services for older adults is one significant consequence of longevity within any society. In this context, ambient assisted living (AAL) has a prominent role in improving scalability in healthcare services, making them reachable to older people, and keeping the user safe in their home environments. AAL can be applied as both a technical approach, related to the instruments used and how they are implemented in a system, and as an intelligent approach to data processing that models and incorporate a system architecture capable of gathering context high-level data from sensor data. Hence, artificial intelligence (AI) plays a significant role in AAL implementation. AAL systems based on artificial intelligence play an important role in healthcare systems by enhancing the overall quality of life of older people.

Healthcare solutions are in desperate need of technology for decision-making processes able to tackle typical problems of medical systems such as providing timely feedback to prevent disease transmission.

Data science analysis using AI is newly evolving, intending to empower healthcare systems and organizations to connect to harness information and convert it to usable knowledge and preferably personalized clinical decision-making. Utilizing deep learning, the implementation of AI in infectious diseases has implemented a range of improvements in the modeling of knowledge generation. Big data can be interpreted, stored, and collected in healthcare through the constantly emerging AI models, thereby allowing the understanding, rationalization, and use of data for various reasons for healthcare solutions. The hope of using AI in healthcare solutions will greatly impact the quality of disease diagnosis, prediction, and treatment, thus delivering quality care to patients across socioeconomic and geographic boundaries. During this global health emergency, the healthcare profession is pursuing technological innovations to monitoring elderly populaces from contacting or spreading infectious diseases. AI is one of those tools that can quickly monitor the rapid spreading of any disease, classify high-risk patients, and is important for real-time monitoring of elderly patients. It can also forecast mortality risk by an appropriate review of the clinicians' previous results.

This Special Issue addresses different solution strategies using Artificial Intelligence for Ambient Assisted Living (AAL) and Healthcare Solutions.

Topics of interest

  • Artificial intelligence
  • Neural networks
  • Machine learning
  • Ambient assisted living (AAL)
  • Biomedical signals
  • AI in health
  • Medical image processing
  • Ambient intelligence applications
  • Cognitive assistants
  • Smart systems
  • Connected devices-home automation
  • Connected healthcare
  • m-Health
  • User personalization and adaptation
  • Ubiquitous computing
  • Mobility and behavioral analysis
  • Physiological signal monitoring and analysis

Dr. Paolo Barsocchi
Dr. Filippo Palumbo
Dr. Victor Hugo C. De Albuquerque
Dr. Akash Kumar Bhoi
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 papers will be 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 2200 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)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning
Sensors 2021, 21(11), 3881; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113881 - 04 Jun 2021
Viewed by 355
Abstract
The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of [...] Read more.
The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy. Full article
Show Figures

Figure 1

Back to TopTop