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Multisensory Big Data Analytics for Enhanced Living Environments
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Multisensory Big Data Analytics for Enhanced Living Environments

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

Deadline for manuscript submissions: closed (20 November 2016) | Viewed by 30120

Special Issue Editors

Prof. Dr. M. Shamim Hossain

E-Mail Website
Guest Editor
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia
Interests: social media; big data; cloud for healthcare; smart health; ambient assisted living, sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Athanasios V. Vasilakos

grade E-Mail Website
Guest Editor
Center for AI Research, University of Agder, Grimstad, Norway
Interests: security & privacy; cryptography; cybersecurity; cryptocurrency protocols; Internet of Things; cloud computing; big data; machine learning; biocomputing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, multisensory big data analytics has played a crucial role for Enhanced Living Environments (ELE). Enriched with several sensing capabilities and communication interfaces, ELE complements the classic scenario with sensor network environments, aiming to build smart environment for providing noteworthy improvement to quality of life for the elderly or, people with special needs. The current ELE or elderly care systems produce a massive amount of structured, semi-structure and unstructured sensory data, as well as medical images from the connected sensors, smart phones, and smart things. However, due to the uncertainty, unpredictability (e.g., data volume, velocity and heterogeneity or variety), massiveness and multimodality of this multisensory data, there is a major challenge for capturing or recording, storage, searching, correlating, transferring, sharing, and analysis of the huge amounts of data in ELE. The potential of multisensory big ELE data analytics is immense, as it can solve challenges that cannot be solved using traditional techniques.

This Special Issue aims to report high-quality research on recent advances in various aspects of multisensory Enhanced Living big data analytics, and, more specifically, the state-of-the-art approaches, methodologies, and systems in the design, development, deployment and innovative use of multisensory Enhanced Living big data analytics. Authors are encouraged to submit complete, unpublished papers on the following topics:

  • Novel approaches and techniques for co-processing of cloud-based big data from enhanced living environment (ELE)
  • Sensor-driven big data analytics for enhanced living environment
  • Sensors, devices and systems design for handling big ELE data analytics
  • Services localization and sensing for elderly living
  • Multimedia big data for remote therapy management
  • Security and privacy issue of multimedia big data for in-home therapy management
  • Multimedia (audio, video, image) big data processing for enhanced living environment
  • Multisensory fall detection in ELE
  • Cyber physical big data analytics for remote therapy management in ELE
  • Gesture-based natural user interfaces for elderly living or enhanced living or assisted living
  • Open platforms and system architectures to support ambient assisted living environments
  • Convergence of cloud computing, and IoT or CPS for big ELE data in social networks
  • Novel models, frameworks, techniques, and algorithms for big ELE data analytics in sensor networks

Dr. M. Shamim Hossain
Prof. Dr. Athanasios V. Vasilakos
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.

Published Papers (5 papers)

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Research

1867 KiB  
Article
On the Prediction of Flickr Image Popularity by Analyzing Heterogeneous Social Sensory Data
by Samah Aloufi, Shiai Zhu and Abdulmotaleb El Saddik
Sensors 2017, 17(3), 631; https://doi.org/10.3390/s17030631 - 19 Mar 2017
Cited by 21 | Viewed by 5285
Abstract
The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a [...] Read more.
The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
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10171 KiB  
Article
Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints
by Ryan Wen Liu, Lin Shi, Simon Chun Ho Yu, Naixue Xiong and Defeng Wang
Sensors 2017, 17(3), 509; https://doi.org/10.3390/s17030509 - 03 Mar 2017
Cited by 11 | Viewed by 5143
Abstract
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly [...] Read more.
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
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11219 KiB  
Article
Towards Building a Computer Aided Education System for Special Students Using Wearable Sensor Technologies
by Raja Majid Mehmood and Hyo Jong Lee
Sensors 2017, 17(2), 317; https://doi.org/10.3390/s17020317 - 08 Feb 2017
Cited by 24 | Viewed by 6891
Abstract
Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on [...] Read more.
Human computer interaction is a growing field in terms of helping people in their daily life to improve their living. Especially, people with some disability may need an interface which is more appropriate and compatible with their needs. Our research is focused on similar kinds of problems, such as students with some mental disorder or mood disruption problems. To improve their learning process, an intelligent emotion recognition system is essential which has an ability to recognize the current emotional state of the brain. Nowadays, in special schools, instructors are commonly use some conventional methods for managing special students for educational purposes. In this paper, we proposed a novel computer aided method for instructors at special schools where they can teach special students with the support of our system using wearable technologies. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
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3597 KiB  
Article
Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix
by Ghulam Muhammad, Mohammed F. Alhamid, M. Shamim Hossain, Ahmad S. Almogren and Athanasios V. Vasilakos
Sensors 2017, 17(2), 267; https://doi.org/10.3390/s17020267 - 29 Jan 2017
Cited by 35 | Viewed by 4843
Abstract
A large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). [...] Read more.
A large number of the population around the world suffers from various disabilities. Disabilities affect not only children but also adults of different professions. Smart technology can assist the disabled population and lead to a comfortable life in an enhanced living environment (ELE). In this paper, we propose an effective voice pathology assessment system that works in a smart home framework. The proposed system takes input from various sensors, and processes the acquired voice signals and electroglottography (EGG) signals. Co-occurrence matrices in different directions and neighborhoods from the spectrograms of these signals were obtained. Several features such as energy, entropy, contrast, and homogeneity from these matrices were calculated and fed into a Gaussian mixture model-based classifier. Experiments were performed with a publicly available database, namely, the Saarbrucken voice database. The results demonstrate the feasibility of the proposed system in light of its high accuracy and speed. The proposed system can be extended to assess other disabilities in an ELE. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
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4106 KiB  
Article
An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments
by Hao Chen, Xiaoyun Xie, Wanneng Shu and Naixue Xiong
Sensors 2016, 16(10), 1706; https://doi.org/10.3390/s16101706 - 15 Oct 2016
Cited by 9 | Viewed by 7183
Abstract
With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order [...] Read more.
With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates. Full article
(This article belongs to the Special Issue Multisensory Big Data Analytics for Enhanced Living Environments)
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