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Special Issue "From Sensors to Ambient Intelligence for Health and Social Care"

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

Deadline for manuscript submissions: 20 December 2018

Special Issue Editors

Guest Editor
Prof. Ciprian Dobre

Computer Science, University Politehnica of Bucharest, Bucharest, Romania
Website | E-Mail
Phone: 0040745174359
Interests: Ambient-Assisted Living, well-being and exploration of Active Ageing constructs, IoT technologies, wireless and mobile future networks, crowd sensing, crowd analytics
Guest Editor
Prof. Susanna Spinsante

Department of Information Engineering, Marche Polytechnic University, Ancona, Italy
Website | E-Mail
Interests: sensors and measurements signal processing for Ambient-Assisted Living, depth-based human action recognition

Special Issue Information

Dear Colleagues,

The increase in medical expenses due to societal issues like demographic ageing puts strong pressure on the sustainability of health and social care systems, on labour participation, and on quality of life for older people or for persons with disabilities. The Special Issue targets dissemination of solutions targeting the science and technology integrating sensors and biosensors with processing and actuating capabilities, leading to the creation of ambient intelligence, in which data is used for the benefit of the older person, allowing her to live safely, comfortably, and healthily at home. It aims to promote the dissemination of solutions for provision of AAL/IoT/sensor-based infrastructures and services for independent or more autonomous living, via the seamless integration of info-communication technologies within homes and residences. Such solutions aim, fundamentally, to increase quality of life and autonomy for older adults and persons with disabilities, or support an Active Aging lifestyle, maintaining one’s home (the preferable living environment) for as long as possible, thereby not causing disruption in the web of social and family interactions.

The particular feature of this direction is that the sensory-data has to be analysed in relation to models coming from not only Ambient-Assisted Living, but from social sciences, psychology, or medical disciplines. Most efforts towards the realization of ambient-assisted living systems are based on developing pervasive platforms that integrate sensory readings and use Ambient Intelligence to construct a safe environment. The missing interaction of multiple stakeholders that need to collaborate for the provision of environments supporting a multitude of care services (actuators), as well as barriers to innovation in the markets concerned, the government, and the health and care sector, are innovations that have not yet taken place on a relevant scale.

Many fundamental issues remain open. Most of the current efforts still do not fully express the power of human beings, and the importance of the integration of sensor data with a model that is capable of accurately describing the power of social connections and societal activities. Additionally, effective solutions require appropriate ICT algorithms, Internet of Things, and Smart Objects architectures and platforms, having in view the advance of science in this area and the development of new and innovative connected solutions (particularly in the area of pervasive and mobile systems). The Special Issue provides, in this sense, a platform for the dissemination of research efforts and the presentation of advances that explicitly aim to address these challenges.

Prof. Ciprian Dobre
Prof. Susanna Spinsante
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 monthly 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 1800 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

  • Biomedical and environmental home monitoring
  • Ambient Intelligence
  • Health and Social Care
  • Internet of Things and Smart Objects for Ambient-Assisted Living

Published Papers (2 papers)

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Research

Open AccessArticle An Intelligent System for Monitoring Skin Diseases
Sensors 2018, 18(8), 2552; https://doi.org/10.3390/s18082552
Received: 9 July 2018 / Revised: 27 July 2018 / Accepted: 2 August 2018 / Published: 4 August 2018
Cited by 2 | PDF Full-text (9034 KB) | HTML Full-text | XML Full-text
Abstract
The practical increase of interest in intelligent technologies has caused a rapid development of all activities in terms of sensors and automatic mechanisms for smart operations. The implementations concentrate on technologies which avoid unnecessary actions on user side while examining health conditions. One
[...] Read more.
The practical increase of interest in intelligent technologies has caused a rapid development of all activities in terms of sensors and automatic mechanisms for smart operations. The implementations concentrate on technologies which avoid unnecessary actions on user side while examining health conditions. One of important aspects is the constant inspection of the skin health due to possible diseases such as melanomas that can develop under excessive influence of the sunlight. Smart homes can be equipped with a variety of motion sensors and cameras which can be used to detect and identify possible disease development. In this work, we present a smart home system which is using in-built sensors and proposed artificial intelligence methods to diagnose the skin health condition of the residents of the house. The proposed solution has been tested and discussed due to potential use in practice. Full article
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
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Open AccessArticle A Globally Generalized Emotion Recognition System Involving Different Physiological Signals
Sensors 2018, 18(6), 1905; https://doi.org/10.3390/s18061905
Received: 13 May 2018 / Revised: 4 June 2018 / Accepted: 7 June 2018 / Published: 11 June 2018
PDF Full-text (447 KB) | HTML Full-text | XML Full-text
Abstract
Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which
[...] Read more.
Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions. In this paper, we explore a novel robust subject-independent human emotion recognition system, which consists of two major models. The first one is an automatic feature calibration model and the second one is a classification model based on Cellular Neural Networks (CNN). The proposed system produces state-of-the-art results with an accuracy rate between 80% and 89% when using the same elicitation materials and physiological sensors brands for both training and testing and an accuracy rate of 71.05% when the elicitation materials and physiological sensors brands used in training are different from those used in training. Here, the following physiological signals are involved: ECG (Electrocardiogram), EDA (Electrodermal activity) and ST (Skin-Temperature). Full article
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
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