Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Ambient Intelligence in Healthcare

Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (25 May 2024) | Viewed by 15735

Share This Special Issue

Special Issue Editors

Dr. Antonio Coronato

E-Mail Website
Guest Editor

Centro di Ricerche sulle Tecnologie ICT per la Salute ed il Benessere, Università Giustino Fortunato, 82100 Benevento, Italy
Interests: ambient intelligence; artificial intelligence; reinforcement learning; self-learning; healthcare intelligent services

Dr. Muddasar Naeem

E-Mail Website
Guest Editor

Artificial Intelligence & Robotics Laboratory, Giustino Fortunato University, 82100 Benevento, Italy
Interests: application of AI methods; reinforcement learning in communication and healthcare
Special Issues, Collections and Topics in MDPI journals

Dr. Mitja Luštrek

E-Mail Website
Guest Editor

Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Interests: ambient intelligence; interpretation of sensor data; application of AI in healthcare; machine learning; decision support
Special Issues, Collections and Topics in MDPI journals

Dr. ‪Vito Janko‬

E-Mail Website
Guest Editor

Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Interests: Ambient Intelligence; Energy-efficient context recognition; Machine learning; COVID-19 modelling

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence, machine learning, robotics and sensors (ranging from Internet of Things (IoT)-enabled contactless sensors to wearables) have given rise to a new generation of ambient intelligence (AmI) applications.

With the aging of the population, healthcare is becoming increasingly important, and thus approaches to ensure a high quality of healthcare that is also affordable and accesible are urgently sought. This Special Issue is intended to report on applications in the healthcare domain that benefit from the integration of AmI technologies in a domestic environment, employing sensors to obtain a more complete picture of patients—or citizens before they become patients—and offering better healthcare services. In this context, we are envisaging contributions covering one or more of the following topics:

Intelligent monitoring of vital signs at home and on the go;
Behavior analysis of patients, seniors and others;
Identification and prevention of critical condition;
Health-promoting interventions triggered by sensors;
Ambient assisted living and active assisted living;
Smart sensors and sensing devices;
Wireless sensor networks (WSNs) for patient monitoring and healthy aging;
Methodologies and tools for the rapid integration of WSNs, IoT and AI in health monitoring;
Monitoring systems within the medical devices contest;
AmI security and privacy models for healthcare applications.

Dr. Antonio Coronato
Dr. Muddasar Naeem
Dr. Mitja Luštrek
Dr. ‪Vito Janko‬
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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

19 pages, 3924 KiB

Open AccessArticle

A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data

by Bhekumuzi M. Mathunjwa, Yu-Fen Chen, Tzung-Cheng Tsai and Yeh-Liang Hsu

Sensors 2024, 24(11), 3662; https://doi.org/10.3390/s24113662 - 5 Jun 2024

Cited by 1 | Viewed by 1859

Abstract

Background: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. Scope and Methodology: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents’ daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people’s care. Full article

(This article belongs to the Special Issue Ambient Intelligence in Healthcare)

► Show Figures

Figure 1

26 pages, 1356 KiB

Open AccessArticle

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

by Khushboo Munir, Fabrizio Frezza and Antonello Rizzi

Sensors 2022, 22(21), 8201; https://doi.org/10.3390/s22218201 - 26 Oct 2022

Cited by 27 | Viewed by 4478

Abstract

Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients’ brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by

7.5 %

in dice score,

23.91 %

insensitivity, and

7.09 %

in specificity. Depth-wise separable MI-Unet showed a performance increase by

10.83 %

in dice score,

2.97 %

in sensitivity, and

12.72 %

in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of

9.71 %

in dice score,

3.56 %

in sensitivity, and

12.6 %

in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by

15.45 %

in dice score,

20.56 %

in sensitivity, and

12.22 %

in specificity. Full article

(This article belongs to the Special Issue Ambient Intelligence in Healthcare)

► Show Figures

Figure 1

Review

Jump to: Research

41 pages, 915 KiB

Open AccessReview

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

by Muddasar Naeem, Giuseppe De Pietro and Antonio Coronato

Sensors 2022, 22(1), 309; https://doi.org/10.3390/s22010309 - 31 Dec 2021

Cited by 31 | Viewed by 8049

Abstract

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented. Full article

(This article belongs to the Special Issue Ambient Intelligence in Healthcare)

► Show Figures

Journal Menu

Journal Browser

Ambient Intelligence in Healthcare

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (3 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI