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Special Issue "Simplified Sensing for Ambient Assisted Living in Smart Homes"

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

Deadline for manuscript submissions: 31 May 2022.

Special Issue Editors

Prof. Dr. Ángel García Crespo
E-Mail Website
Guest Editor
University Carlos III of Madrid, Leganés, Spain
Interests: software engineering; semantic web; software; social web; internet; web; software development; software project management; semantic technology; accesibility
Special Issues and Collections in MDPI journals
Dr. Jorge Ferraz de Abreu
E-Mail Website
Guest Editor
University of Aveiro, Aveiro, Portugal
Interests: accessibility; active ageing; human-computer interaction; interactive television; usability; user-centered design; user experience; user studies
Dr. Álvaro García-Tejedor
E-Mail Website
Guest Editor
CEIEC - Universidad Francisco de Vitoria, Pozuelo de Alarcón Madrid, Spain
Interests: evolutionary computation; AI health; accessibility
Dr. Olga Peñalba Rodríguez
E-Mail Website
Guest Editor
Universidad Francisco de Vitoria, Pozuelo de Alarcón Madrid, Spain

Special Issue Information

Dear Colleagues,

The life expectancy of the world’s population is now growing, as is the number of single-person households of elderly people, whether because they have no relatives nearby, are widowed, or because of personal preference. However, in many countries, the number of elderly people who die alone in their own homes, either from accidents or disease, has also increased (especially in the current pandemic). Considering that currently, the global population spends more time inside their homes than outside (all of this, of course, depending on the health security measures imposed in each country or city), it is necessary to expand the studies on existing and develop new systems to improve the quality of life of older people in single-person households, either through monitoring of habitual behavior, or monitoring of vital signs, reminders of routines, detection of accidents, emergency alerts, telemedicine, etc.

This Special Issue invites researchers to carry out studies and identify potential avenues for development in this topic and also promotes the use of sensors and the Internet of Things (IoT) in a non-invasive way, in order to make elderly people feel safe, but at the same time comfortable at home.

Prof. Dr. Ángel García Crespo
Dr. Jorge Ferraz de Abreu
Dr. Álvaro García-Tejedor
Dr. Olga Peñalba Rodríguez
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.

Keywords

  • Sensors and IoT for AAL and smart homes
  • Sensors and IoT for indoor and outdoor active aging
  • Sensors and IoT for rehabilitation and telemedicine and telecare in smart homes
  • Security and privacy in AAL applications
  • Machine Learning for advanced AAL applications
  • Prototypes and experiments in real scenarios
  • Social aspects of the use of AAL, considering patients, caregivers, and healthcare professionals

Published Papers (3 papers)

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Research

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Article
UWB Radio-Based Motion Detection System for Assisted Living
Sensors 2021, 21(11), 3631; https://doi.org/10.3390/s21113631 - 23 May 2021
Viewed by 524
Abstract
Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion [...] Read more.
Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers. Full article
(This article belongs to the Special Issue Simplified Sensing for Ambient Assisted Living in Smart Homes)
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Review

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Review
Review of Technology-Supported Multimodal Solutions for People with Dementia
Sensors 2021, 21(14), 4806; https://doi.org/10.3390/s21144806 - 14 Jul 2021
Viewed by 258
Abstract
The number of people living with dementia in the world is rising at an unprecedented rate, and no country will be spared. Furthermore, neither decisive treatment nor effective medicines have yet become effective. One potential alternative to this emerging challenge is utilizing supportive [...] Read more.
The number of people living with dementia in the world is rising at an unprecedented rate, and no country will be spared. Furthermore, neither decisive treatment nor effective medicines have yet become effective. One potential alternative to this emerging challenge is utilizing supportive technologies and services that not only assist people with dementia to do their daily activities safely and independently, but also reduce the overwhelming pressure on their caregivers. Thus, for this study, a systematic literature review is conducted in an attempt to gain an overview of the latest findings in this field of study and to address some commercially available supportive technologies and services that have potential application for people living with dementia. To this end, 30 potential supportive technologies and 15 active supportive services are identified from the literature and related websites. The technologies and services are classified into different classes and subclasses (according to their functionalities, capabilities, and features) aiming to facilitate their understanding and evaluation. The results of this work are aimed as a base for designing, integrating, developing, adapting, and customizing potential multimodal solutions for the specific needs of vulnerable people of our societies, such as those who suffer from different degrees of dementia. Full article
(This article belongs to the Special Issue Simplified Sensing for Ambient Assisted Living in Smart Homes)
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Review
Unobtrusive Health Monitoring in Private Spaces: The Smart Home
Sensors 2021, 21(3), 864; https://doi.org/10.3390/s21030864 - 28 Jan 2021
Cited by 5 | Viewed by 1182
Abstract
With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to [...] Read more.
With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking. Full article
(This article belongs to the Special Issue Simplified Sensing for Ambient Assisted Living in Smart Homes)
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