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Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 31903

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Special Issue Editors

Dr. Oana Geman

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Associate Professor, Faculty of Electrical Engineering and Computer Science, "Stefan cel Mare" University of Suceava, Suceava, Romania
Interests: non-invasive measurements of biomedical signals; wireless sensors; signal processing; data mining; deep learning; intelligent systems; biomedical applications
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Arif

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Guest Editor

Department of Computer Science, Superior University, Lahore, Pakistan
Interests: artificial intelligence; big data; cloud computing; cyberspace security; data mining; image processing; medical image processing; privacy; security; e-learning
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Prof. Dr. Valentina E. Balas

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Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Ro-310025 Arad, Romania
Interests: intelligent systems; soft computing; fuzzy control; modeling and simulation; nonlinear systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Aniello Castiglione

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Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano, SA, Italy
Interests: information forensics; digital forensics; security and privacy on cloud; communication networks; applied cryptography; sustainable computing

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide a comprehensive appraisal of state-of-the-art systems employing innovative sensing schemes, which are predicted to have an important role in the successful implementation of medical applications. We therefore wish to look at complete sensor devices and systems from the perspective of their most promising medical applications, which could contribute to truly innovative pervasive healthcare solutions for prevention, diagnosis, treatment, and rehabilitation procedures. The goal of the present Special Issue is to collect contributions in the disciplines of physical sensors, computer science, and engineering, to serve as a forum for researchers in the field of sensor technologies and sensing strategies and to foster the development of real-time SHM of real-life structures. Experimental and theoretical works are both welcome, with the aim of providing a fresh account of methods to move towards the design of robust and resilient smart sensing strategies, and to extract information from the raw data acquired by pervasive sensor networks. Critical reviews and surveys of the state-of-the-art and practice are also encouraged. We are looking for the latest developments in the manufacturing and application of smart sensor devices for personalized medicine and healthcare applications. Scientific work in the development of novel manufacturing technologies, materials, sensor devices, implementation, and device demonstrations for smart sensing is welcome.

Topics of interest include but are not limited to the following:

Biosensors for healthcare;
Rehabilitation applications for healthcare;
Monitoring of psychological disorders;
Remote sensing;
Sensor signal processing;
Sensor-oriented contributions, including wireless sensor networks, and Internet of Things approaches;
Sensing in the IoT era;
Optical sensing in bioinstrumentation;
Smart sensors and WSN;
Sensors network and the Internet of Things;
Security and privacy threats in smart health and smart-sensing;
Forensic implications of smart sensors in health systems;
Deep-learning-based diagnostic analysis and processing of biomedical data;
Behavioural biometrics in health systems;
Biosensors and randomness for secure application systems;
Big data analytics, including machine learning and statistical approaches, emerging strategies for sensor fusion, AI-based data mining, and cloud/edge/fog computing for infrastructure maintenance;
Computational modeling approaches for healthcare simulation, optimization, prediction;
High-performance computing frameworks, including parallel processing and modeling;
Other topics.

Prof. Dr. Oana Geman
Dr. Muhammad Arif
Prof. Dr. Valentina Emilia Balas
Prof. Dr. Aniello Castiglione
Guest Editors

Manuscript Submission Information

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Published Papers (5 papers)

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18 pages, 3052 KiB

Open AccessArticle

A Smart Sensing and Routing Mechanism for Wireless Sensor Networks

by Li-Ling Hung

Sensors 2020, 20(19), 5720; https://0-doi-org.brum.beds.ac.uk/10.3390/s20195720 - 08 Oct 2020

Cited by 5 | Viewed by 2195

Abstract

Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on building energy-efficient transmission mechanisms to extend monitoring lifetimes, and others have focused on building additional systems to support monitoring for enhanced accuracy. Herein, we propose a distributed cooperative mechanism where neighboring sensors mutually confirm event occurrences for improved monitoring accuracy. Moreover, the mechanism transmits events in a time- and energy-efficient manner by using smart antennae to extend monitoring lifetimes. The results of the simulations reveal that monitoring lifetime is extended and time for event notifications is shortened under the proposed mechanism. The evaluations also demonstrate that the monitoring accuracy of the proposed mechanism is much higher than that of other existing mechanisms. Full article

(This article belongs to the Special Issue Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications)

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16 pages, 3334 KiB

Open AccessArticle

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

by Ejaz Khan, Muhammad Zia Ur Rehman, Fawad Ahmed, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani and Jawad Ahmad

Sensors 2022, 22(3), 1211; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031211 - 05 Feb 2022

Cited by 65 | Viewed by 16788

Abstract

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature. Full article

(This article belongs to the Special Issue Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications)

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17 pages, 1550 KiB

Open AccessArticle

Multi-Agent Systems in Fog–Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring

by Ammar Awad Mutlag, Mohd Khanapi Abd Ghani, Mazin Abed Mohammed, Abdullah Lakhan, Othman Mohd, Karrar Hameed Abdulkareem and Begonya Garcia-Zapirain

Sensors 2021, 21(20), 6923; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206923 - 19 Oct 2021

Cited by 39 | Viewed by 2952

Abstract

In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%. Full article

(This article belongs to the Special Issue Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications)

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20 pages, 2385 KiB

Open AccessArticle

Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

by Danshi Sun, Erhu Wei, Zhuoxi Ma, Chenxi Wu and Shiyi Xu

Sensors 2021, 21(6), 1995; https://0-doi-org.brum.beds.ac.uk/10.3390/s21061995 - 12 Mar 2021

Cited by 25 | Viewed by 3427

Abstract

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods. Full article

(This article belongs to the Special Issue Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications)

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19 pages, 908 KiB

Open AccessFeature PaperArticle

A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis

by Rabbia Mahum, Saeed Ur Rehman, Talha Meraj, Hafiz Tayyab Rauf, Aun Irtaza , Ahmed M. El-Sherbeeny and Mohammed A. El-Meligy

Sensors 2021, 21(18), 6189; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186189 - 15 Sep 2021

Cited by 48 | Viewed by 4417

Abstract

In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL. Full article

(This article belongs to the Special Issue Smart Sensors, Big Data Analytics and Modelling in Healthcare and Personalized Medical Applications)

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