Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Indoor Positioning Technology for Monitoring Older Adults in E-health Applications
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Indoor Positioning Technology for Monitoring Older Adults in E-health Applications

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 10085

Special Issue Editor

Dr. Óscar Belmonte Fernández

E-Mail Website
Guest Editor
Computer Languages and Systems Department, Jaume I University, Castellón de la Plana 12071, Spain
Interests: machine learning; indoor positioning and navigation; sensor networks; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a result of the rise in life expectancy, the world population is ageing, which is poised to become one of the most significant social transformations of the twenty-first century. For older adults, it is of great importance to maintain their independence and autonomy while remaining at their own homes.

E-Health technology has proven to be a useful tool for remote monitoring and intervention to give care to older adults, while providing valuable tools to their caregivers and health practitioners to be aware of their current health status. Indoor positioning technologies (Wi-Fi, BLE, and sound/ultrasound) are able to provide positional information of older adults at home that can be used for continuous behavior monitoring for detecting potential health issues such as falls, cognitive decline, and adherence to medical prescriptions, to cite just a few. Machine learning algorithms are well suited for creating such models to tackle the high uncertainty and variability in the data used to model older adults’ behavior.

The aim of this Special Issue is to contribute to the state-of-the-art research concerning indoor positioning technologies for monitoring older adults in E-Health applications.

Keywords: E-Health; gerontechnology; indoor positioning; machine learning; remote monitoring; behavior modelling; continuous monitoring

Dr. Óscar Belmonte Fernández
Guest Editor

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.

Keywords

  • E-Health
  • gerontechnology
  • indoor positioning
  • machine learning
  • remote monitoring
  • behavior modelling
  • continuous monitoring

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 7998 KiB  
Article
Design of a Millimeter-Wave Radar Remote Monitoring System for the Elderly Living Alone Using WIFI Communication
by Kai Guo, Chang Liu, Shasha Zhao, Jingxin Lu, Senhao Zhang and Hongbo Yang
Sensors 2021, 21(23), 7893; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237893 - 26 Nov 2021
Cited by 5 | Viewed by 3504
Abstract
In response to the current demand for the remote monitoring of older people living alone, a non-contact human vital signs monitoring system based on millimeter wave radar has gradually become the object of research. This paper mainly carried out research regarding the detection [...] Read more.
In response to the current demand for the remote monitoring of older people living alone, a non-contact human vital signs monitoring system based on millimeter wave radar has gradually become the object of research. This paper mainly carried out research regarding the detection method to obtain human breathing and heartbeat signals using a frequency modulated continuous wave system. We completed a portable millimeter-wave radar module for wireless communication. The radar module was a small size and had a WIFI communication interface, so we only needed to provide a power cord for the radar module. The breathing and heartbeat signals were detected and separated by FIR digital filter and the wavelet transform method. By building a cloud computing framework, we realized remote and senseless monitoring of the vital signs for older people living alone. Experiments were also carried out to compare the performance difference between the system and the common contact detection system. The experimental results showed that the life parameter detection system based on the millimeter wave sensor has strong real-time performance and accuracy. Full article
(This article belongs to the Special Issue Indoor Positioning Technology for Monitoring Older Adults in E-health Applications)
►▼ Show Figures

Figure 1

25 pages, 1971 KiB  
Article
A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting
by Óscar Belmonte-Fernández, Emilio Sansano-Sansano, Antonio Caballer-Miedes, Raúl Montoliu, Rubén García-Vidal and Arturo Gascó-Compte
Sensors 2021, 21(7), 2392; https://0-doi-org.brum.beds.ac.uk/10.3390/s21072392 - 30 Mar 2021
Cited by 6 | Viewed by 3077
Abstract
Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented [...] Read more.
Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed. Full article
(This article belongs to the Special Issue Indoor Positioning Technology for Monitoring Older Adults in E-health Applications)
►▼ Show Figures

Figure 1

20 pages, 2226 KiB  
Article
Indoor Positioning System Using Dynamic Model Estimation
by Yuri Assayag, Horácio Oliveira, Eduardo Souto, Raimundo Barreto and Richard Pazzi
Sensors 2020, 20(24), 7003; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247003 - 08 Dec 2020
Cited by 11 | Viewed by 2735
Abstract
Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using [...] Read more.
Indoor Positioning Systems (IPSs) are used to locate mobile devices in indoor environments. Model-based IPSs have the advantage of not having an exhausting training and signal characterization of the environment, as required by the fingerprint technique. However, most model-based IPSs are done using fixed model parameters, treating the whole scenario as having a uniform signal propagation. This might work for most small scale experiments, but not for larger scenarios. In this paper, we propose PoDME (Positioning using Dynamic Model Estimation), a model-based IPS that uses dynamic parameters that are estimated based on the location the signal was sent. More specifically, we use the set of anchor nodes that received the signal sent by the mobile node and their signal strengths, to estimate the best local values for the log-distance model parameters. Also, since our solution depends highly on the selected anchor nodes to use on the position computation, we propose a novel method for choosing the three best anchor nodes. Our method is based on several data analysis executed on a large-scale, Bluetooth-based, real-world experiment and it chooses not only the nearest anchor but also the ones that benefit our least-square-based position computation. Our solution achieves a position estimation error of 3 m, which is 17% better than a fixed-parameters model from the literature. Full article
(This article belongs to the Special Issue Indoor Positioning Technology for Monitoring Older Adults in E-health Applications)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop