E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health"

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 15 April 2017

Special Issue Editors

Guest Editor
Dr. Ioannis Kompatsiaris

Centre for Research & Technology Hellas, Information Technologies Institute 6th km, Charilaou-Thermi Rd, P.O. Box 60361, GR 57001 Thermi, Thessaloniki, Greece
Website | E-Mail
Phone: +30 2311 257774
Interests: semantic multimedia analysis; indexing and retrieval; web 2.0 content analysis; knowledge structures; reasoning and personalization for multimedia applications; eHealth and environmental applications
Guest Editor
Dr. Thanos G. Stavropoulos

Centre for Research & Technology Hellas, Information Technologies Institute 6th km, Charilaou-Thermi Rd, P.O. Box 60361, GR 57001 Thermi, Thessaloniki, Greece
E-Mail
Phone: +30 2311 257738
Interests: intelligent autonomous systems; semantic web service matching and composition; ambient intelligence in environmental applications and ambient assisted living; middleware and wireless sensor networks in the internet of things
Guest Editor
Dr. Antonis Bikakis

Department of Information Studies, University College London, Gower Street, London WC1E 6BT, UK
Website | E-Mail
Phone: +44 20 7679 2477
Interests: knowledge representation; logic-based reasoning; argumentation; semantic web; ambient intelligence

Special Issue Information

Dear Colleagues,

This Special Issue of Sensors, entitled “Sensors in Ambient Assisted Living, Ubiquitous and Mobile Health” focuses on the use of sensors in Ambient Intelligence, as well as in pervasive and mobile technologies for healthcare. As we transition from the world of personal computing, powerful, compact devices are distributed across the user’s environment, enabling the contextualized enrichment of business processes with the ability to sense, process and combine data, and turning our living and working environments into intelligent spaces. This interconnection of devices, machines and “things” enables the dynamic generation, analysis and communication of multiple data types, leading to an increase in the operational efficiency and effectiveness of existing business. Although some open platforms address the integration of devices and services to deliver intelligent solutions, several challenges still need to be overcome for the deployment of Ambient Intelligence technologies relevant to the heterogeneity of hardware, communication protocols, interfaces, context-awareness, knowledge representation and interpretation.

Such technologies promise breakthroughs, particularly in the field of healthcare. The ever-ageing population, together with the prevalence of chronic, hard-to-treat diseases, such as dementia, call for eHealth solutions that reduce cost, increase Quality of Life and retain an active role in society by postponing hospitalization.

This Special Issue aims to explore emerging research topics of interest concerning all aspects of integrating sensors and the Internet of Things (IoT) at the heart of Ambient Assisted Living, as well as pervasive and mobile health. The main objective is to stimulate original, unpublished research addressing the integration of sensors; mobile, wearable and biomedical devices; IoT platform convergence and dissemination; and the context-aware and real-time acquisition, storage, mining and interpretation of data for building healthcare applications.

Specifically, its scope includes:

  • Integration of communication protocols, sensor platforms and IoT for health
  • Sensors in ubiquitous, pervasive and mobile healthcare solutions
  • Sensors for Ambient Assisted Living
  • Sensor interoperability, context-awareness, representation and reasoning with sensor data in eHealth
  • Biomedical devices, clinical, preventive, early-diagnosis, patient monitoring and public health applications of sensors
  • Acquisition, transmission, representation, storage, management and mining of healthcare information
  • Sensor-enabled activity recognition, physiological monitoring and biometrics
  • Wearable, ambient, fitness and lifestyle sensing for healthcare
  • Smart homes, buildings and cities for health and well-being
  • Sensor applications in clinical trials, short or long term hospitalization

Dr. Ioannis Kompatsiaris
Dr. Thanos G. Stavropoulos
Dr. Antonis Bikakis
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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).

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle Development of Portable Digital Radiography System with a Device for Monitoring X-ray Source-Detector Angle and Its Application in Chest Imaging
Sensors 2017, 17(3), 531; doi:10.3390/s17030531
Received: 15 December 2016 / Revised: 28 February 2017 / Accepted: 2 March 2017 / Published: 7 March 2017
PDF Full-text (7719 KB) | HTML Full-text | XML Full-text
Abstract
This study developed a device measuring the X-ray source-detector angle (SDA) and evaluated the imaging performance for diagnosing chest images. The SDA device consisted of Arduino, an accelerometer and gyro sensor, and a Bluetooth module. The SDA values were compared with the values
[...] Read more.
This study developed a device measuring the X-ray source-detector angle (SDA) and evaluated the imaging performance for diagnosing chest images. The SDA device consisted of Arduino, an accelerometer and gyro sensor, and a Bluetooth module. The SDA values were compared with the values of a digital angle meter. The performance of the portable digital radiography (PDR) was evaluated using the signal-to-noise (SNR), contrast-to-noise ratio (CNR), spatial resolution, distortion and entrance surface dose (ESD). According to different angle degrees, five anatomical landmarks were assessed using a five-point scale. The mean SNR and CNR were 182.47 and 141.43. The spatial resolution and ESD were 3.17 lp/mm (157 μm) and 0.266 mGy. The angle values of the SDA device were not significantly difference as compared to those of the digital angle meter. In chest imaging, the SNR and CNR values were not significantly different according to the different angle degrees. The visibility scores of the border of the heart, the fifth rib and the scapula showed significant differences according to different angles (p < 0.05), whereas the scores of the clavicle and first rib were not significant. It is noticeable that the increase in the SDA degree was consistent with the increases of the distortion and visibility score. The proposed PDR with a SDA device would be useful for application in the clinical radiography setting according to the standard radiography guidelines. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
Figures

Figure 1

Open AccessArticle Dynamic Context-Aware Event Recognition Based on Markov Logic Networks
Sensors 2017, 17(3), 491; doi:10.3390/s17030491
Received: 6 January 2017 / Revised: 23 February 2017 / Accepted: 25 February 2017 / Published: 2 March 2017
PDF Full-text (4971 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations,
[...] Read more.
Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
Figures

Figure 1

Open AccessArticle Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors
Sensors 2017, 17(1), 187; doi:10.3390/s17010187
Received: 4 November 2016 / Revised: 3 January 2017 / Accepted: 16 January 2017 / Published: 19 January 2017
PDF Full-text (3976 KB) | HTML Full-text | XML Full-text
Abstract
The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality
[...] Read more.
The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring. Full article
(This article belongs to the Special Issue Sensors for Ambient Assisted Living, Ubiquitous and Mobile Health)
Figures

Figure 1

Journal Contact

MDPI AG
Sensors Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Special Issue Edit a special issue Review for Sensors
Back to Top