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Sensing and Data Analysis Techniques for Intelligent Healthcare

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 64337

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Special Issue Editors

Dr. Qammer Abbasi

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Guest Editor

James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Interests: nano communication; biomedical applications of millimeter and terahertz communication; wearable and flexible sensors; compact antenna design; RF design and radio propagation; antenna interaction with human body; implants; body centric wireless communication issues; wireless body sensor networks; non-invasive health care solutions; physical layer security for wearable/implant communication and multiple-input–multiple-output systems
Special Issues, Collections and Topics in MDPI journals

Dr. Syed Aziz Shah

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Guest Editor

Research Centre for Intelligent Healthcare, Institute of Health and Wellbeing, Coventry University, Coventry CV1 5RW, UK
Interests: machine learning for wireless sensing in healthcare application; radar technology; software defined radios; antennas and propagation; intelligent healthcare; disease monitoring; agriculture technologies; antenna interaction with human body; implants; body centric wireless communication issues; wireless body sensor networks; non-invasive health care solutions

Dr. Jawad Ahmad

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Guest Editor

School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: cryptography; blockchain; chaos theory; image encryption; IoT; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Muhammad Ali Imran

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James Watt School of Engineering, University of Glasgow, Glasgow, UK
Interests: 5G and Beyond networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in wearable and non-wearable sensing systems are experiencing a rapid transformation that is influencing our daily lives. Wearable devices, such as smart watches, fitness trackers, and head-mounted virtual reality (VR)/augmented reality (AR) devices, and non-wearable technologies, including radar, Wi-Fi, and RFID, are attracting increasing attention due to their numerous advantages. Remote healthcare and telemedicine applications are some applications of these wearable and non-wearable sensors. Wireless sensors can dramatically enhance the quality of elderly people’s lives by delivering better services. It is anticipated that people will make full use of next-generation healthcare systems that leverage wearable and non-wearable sensors for monitoring, diagnosis, and surgical procedures.

Furthermore, data analytics (DA) is transforming the digital healthcare sector in various respects. One of the biggest challenges is the development of machine-learning-based intelligent healthcare technologies that can enhance daily living, enable the monitoring of large-scale and small-scale body movements such as falls, monitor a person’s respiratory rate and heartbeat, and allow for automatic alerts when critical events are predicted to occur. Physiological markers and biomarkers play an important role in overcoming these challenges. Behavioural biometrics, including voice, hand–eye coordination, and gait, when considered as biomarkers, can be used as metrics to detect a person’s ADLs. The acquired data can be divided into two main categories: cognitive and physical.

This Special Issues invites the submission of original scientific and research articles on state-of-the-art remote patient monitoring in the intelligent healthcare sector. An additional focus of this Special Issue is articles/reviews in the area of cybersecurity in digital healthcare. The core idea is to provide academics, researchers, and industry professionals with the opportunity to showcase their current research and set future research directions.

Dr. Qammer Hussain Abbasi
Dr. Syed Aziz Shah
Dr. Jawad Ahmad
Dr. Muhammad Ali Imran
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 (10 papers)

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Research

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23 pages, 754 KiB

Open AccessArticle

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes

by Hasina Attaullah, Adeel Anjum, Tehsin Kanwal, Saif Ur Rehman Malik, Alia Asheralieva, Hassan Malik, Ahmed Zoha, Kamran Arshad and Muhammad Ali Imran

Sensors 2021, 21(14), 4933; https://doi.org/10.3390/s21144933 - 20 Jul 2021

Cited by 7 | Viewed by 2669

Abstract

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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14 pages, 1157 KiB

Open AccessArticle

Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data

by Shahab Haghayegh, Sepideh Khoshnevis, Michael H. Smolensky, Kenneth R. Diller and Richard J. Castriotta

Sensors 2021, 21(1), 25; https://doi.org/10.3390/s21010025 - 23 Dec 2020

Cited by 12 | Viewed by 2935

Abstract

Background: Performance of wrist actigraphy in assessing sleep not only depends on the sensor technology of the actigraph hardware but also on the attributes of the interpretative algorithm (IA). The objective of our research was to improve assessment of sleep quality, relative to existing IAs, through development of a novel IA using deep learning methods, utilizing as input activity count and heart rate variability (HRV) metrics of different window length (number of epochs of data). Methods: Simultaneously recorded polysomnography (PSG) and wrist actigraphy data of 222 participants were utilized. Classic deep learning models were applied to: (a) activity count alone (without HRV), (b) activity count + HRV (30-s window), (c) activity count + HRV (3-min window), and (d) activity count + HRV (5-min window) to ascertain the best set of inputs. A novel deep learning model (Haghayegh Algorithm, HA), founded on best set of inputs, was developed, and its sleep scoring performance was then compared with the most popular University of California San Diego (UCSD) and Actiwatch proprietary IAs. Results: Activity count combined with HRV metrics calculated per 5-min window produced highest agreement with PSG. HA showed 84.5% accuracy (5.3–6.2% higher than comparator IAs), 89.5% sensitivity (6.2% higher than UCSD IA and 6% lower than Actiwatch proprietary IA), 70.0% specificity (8.2–34.3% higher than comparator IAs), and 58.7% Kappa agreement (16–23% higher than comparator IAs) in detecting sleep epochs. HA did not differ significantly from PSG in deriving sleep parameters—sleep efficiency, total sleep time, sleep onset latency, and wake after sleep onset; moreover, bias and mean absolute error of the HA model in estimating them was less than the comparator IAs. HA showed, respectively, 40.9% and 54.0% Kappa agreement with PSG in detecting rapid and non-rapid eye movement (REM and NREM) epochs. Conclusions: The HA model simultaneously incorporating activity count and HRV metrics calculated per 5-min window demonstrates significantly better sleep scoring performance than existing popular IAs. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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16 pages, 1479 KiB

Open AccessArticle

Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor

by Lilia Aljihmani, Oussama Kerdjidj, Yibo Zhu, Ranjana K. Mehta, Madhav Erraguntla, Farzan Sasangohar and Khalid Qaraqe

Sensors 2020, 20(23), 6897; https://doi.org/10.3390/s20236897 - 03 Dec 2020

Cited by 8 | Viewed by 2279

Abstract

Fatigue is defined as “a loss of force-generating capacity” in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant’s dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier’s performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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19 pages, 1031 KiB

Open AccessArticle

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

by Sami Elzeiny and Marwa Qaraqe

Sensors 2020, 20(18), 5312; https://doi.org/10.3390/s20185312 - 17 Sep 2020

Cited by 5 | Viewed by 3089

Abstract

Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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16 pages, 10293 KiB

Open AccessArticle

GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism

by Asif Nawaz, Zhiqiu Huang, Senzhang Wang, Azeem Akbar, Hussain AlSalman and Abdu Gumaei

Sensors 2020, 20(18), 5143; https://doi.org/10.3390/s20185143 - 09 Sep 2020

Cited by 16 | Viewed by 3562

Abstract

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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22 pages, 1714 KiB

Open AccessArticle

Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review

by Abdul Ahad, Mohammad Tahir, Muhammad Aman Sheikh, Kazi Istiaque Ahmed, Amna Mughees and Abdullah Numani

Sensors 2020, 20(14), 4047; https://doi.org/10.3390/s20144047 - 21 Jul 2020

Cited by 134 | Viewed by 14802

Abstract

Smart health-care is undergoing rapid transformation from the conventional specialist and hospital-focused style to a distributed patient-focused manner. Several technological developments have encouraged this rapid revolution of health-care vertical. Currently, 4G and other communication standards are used in health-care for smart health-care services and applications. These technologies are crucial for the evolution of future smart health-care services. With the growth in the health-care industry, several applications are expected to produce a massive amount of data in different format and size. Such immense and diverse data needs special treatment concerning the end-to-end delay, bandwidth, latency and other attributes. It is difficult for current communication technologies to fulfil the requirements of highly dynamic and time-sensitive health care applications of the future. Therefore, the 5G networks are being designed and developed to tackle the diverse communication needs of health-care applications in Internet of Things (IoT). 5G assisted smart health-care networks are an amalgamation of IoT devices that require improved network performance and enhanced cellular coverage. Current connectivity solutions for IoT face challenges, such as the support for a massive number of devices, standardisation, energy-efficiency, device density, and security. In this paper, we present a comprehensive review of 5G assisted smart health-care solutions in IoT. We present a structure for smart health-care in 5G by categorizing and classifying existing literature. We also present key requirements for successful deployment of smart health-care systems for certain scenarios in 5G. Finally, we discuss several open issues and research challenges in 5G smart health-care solutions in IoT. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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20 pages, 3284 KiB

Open AccessArticle

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

by Muhammad Diyan, Bhagya Nathali Silva and Kijun Han

Sensors 2020, 20(12), 3450; https://doi.org/10.3390/s20123450 - 18 Jun 2020

Cited by 19 | Viewed by 3272

Abstract

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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20 pages, 619 KiB

Open AccessArticle

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

by William Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H. Abbasi and Muhammad Ali Imran

Sensors 2020, 20(9), 2653; https://doi.org/10.3390/s20092653 - 06 May 2020

Cited by 111 | Viewed by 6882

Abstract

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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Review

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19 pages, 462 KiB

Open AccessEditor’s ChoiceReview

A Review of the State of the Art in Non-Contact Sensing for COVID-19

by William Taylor, Qammer H. Abbasi, Kia Dashtipour, Shuja Ansari, Syed Aziz Shah, Arslan Khalid and Muhammad Ali Imran

Sensors 2020, 20(19), 5665; https://doi.org/10.3390/s20195665 - 03 Oct 2020

Cited by 70 | Viewed by 8651

Abstract

COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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20 pages, 376 KiB

Open AccessReview

A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis

by Hichem Mrabet, Sana Belguith, Adeeb Alhomoud and Abderrazak Jemai

Sensors 2020, 20(13), 3625; https://doi.org/10.3390/s20133625 - 28 Jun 2020

Cited by 168 | Viewed by 14553

Abstract

The Internet of Things (IoT) is leading today’s digital transformation. Relying on a combination of technologies, protocols, and devices such as wireless sensors and newly developed wearable and implanted sensors, IoT is changing every aspect of daily life, especially recent applications in digital healthcare. IoT incorporates various kinds of hardware, communication protocols, and services. This IoT diversity can be viewed as a double-edged sword that provides comfort to users but can lead also to a large number of security threats and attacks. In this survey paper, a new compacted and optimized architecture for IoT is proposed based on five layers. Likewise, we propose a new classification of security threats and attacks based on new IoT architecture. The IoT architecture involves a physical perception layer, a network and protocol layer, a transport layer, an application layer, and a data and cloud services layer. First, the physical sensing layer incorporates the basic hardware used by IoT. Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions. Transport protocols are exhibited and the security threats against them are discussed while providing common solutions. Then, the application layer involves application protocols and lightweight encryption algorithms for IoT. Finally, in the data and cloud services layer, the main important security features of IoT cloud platforms are addressed, involving confidentiality, integrity, authorization, authentication, and encryption protocols. The paper is concluded by presenting the open research issues and future directions towards securing IoT, including the lack of standardized lightweight encryption algorithms, the use of machine-learning algorithms to enhance security and the related challenges, the use of Blockchain to address security challenges in IoT, and the implications of IoT deployment in 5G and beyond. Full article

(This article belongs to the Special Issue Sensing and Data Analysis Techniques for Intelligent Healthcare)

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