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AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications

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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: 30 April 2024 | Viewed by 23282

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Special Issue Editors

Dr. Ali Hassan Sodhro

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Guest Editor

Department of Computer Science, Kristianstad University, SE-29188 Kristianstad, Sweden
Interests: AI; smart and wearable healthcare; IoT; 5G-IoT devices authentication; edge computing and big data analytics
Special Issues, Collections and Topics in MDPI journals

Dr. Ismail Butun

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Guest Editor

School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Interests: cyber-security; IoT; IIoT; computer networks
Special Issues, Collections and Topics in MDPI journals

Dr. Muhammad Muzammal

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Guest Editor

Department of Computer Science, Bahria University Islamabad Campus, Islamabad 44000, Pakistan
Interests: smart healthcare; IoT; sensing technologies; data science; decentralised systems

Dr. Syed Sabir Hussain Bukhari

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Guest Editor

1. Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
2. School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06910, Republic of Korea
Interests: electrical machines; energy conversion systems; power quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this technological era, it is vital to design and develop automatic systems without too much complexity or significant resource consumption. With the increase of sensor-driven and Internet of Things (IoT) technologies, human-centered platforms in every corner, there is a dire need to deploy and discuss adaptive and self-driven systems for efficient and accurate healthcare monitoring and outcomes. One of the renowned models of the self-adaptive emerging trends is artificial intelligence (AI)-based, or more precisely machine-learning-based, smart systems. Such innovative technologies have not only improved our lives but also radically changed the landscape of business models for providing services with ease and convenience. Human-centered healthcare applications are widely empowered by smart-sensing- and IoT-driven technologies, which not only provide ease and comfort to users’ lives but also provide connected, reliable, efficient, and sustainable services. Most of the current human-centric platforms could be integrated with self-learning functionalities to effectively drive, monitor, and optimize the performance of human-centered systems. Furthermore, end users could be given distinct options in terms of the techniques and architectures by interconnecting human capabilities and cognitive abilities to proliferate the technological landscapes. Human skills are flexible, and suitable ingredients to recognize the importance and significant roles of the built-in capabilities for interfacing and creating close connection between human perception and technological innovations. Moreover, human features and abilities (i.e., body structures, thinking abilities, cognitive aspects, decisive nature, and mental capabilities) are some ingredients that play a key role in the IoT-based human-centered technologies.

Thus, this Special Issue focuses on the strong tie between sensing technologies, human cognitive perceptions and decision-making capabilities to develop innovative healthcare applications that can revolutionize human-centered systems in different ways. Expert researchers from both academia and industry will be invited to publish their significant and novel contributions in the relevant domain. Unpublished and well-aligned research works on the following topics, as well as those having broader scope, will be accepted.

Authors are encouraged to consider topics for their submissions as follows:

Sensing technologies for smart, pervasive, and connected healthcare.
IoT for ubiquitous, reliable, and energy-efficient healthcare applications.
AI-based human–computer interface (HCI) applications.
Machine-learning-based applications for self-adaptive systems.
QoS/QoE optimization in human-centered systems.
Reinforcement learning techniques for human healthcare.
Self-centered and adaptive healthcare platforms for ambient assisted-living.
SDN-based IoT healthcare applications.
Frameworks and architectures of self-driven technologies.
Efficient resource allocation in human-centered systems.
Brain Internet of Things.
Blockchain-enabled self-driven and adaptive applications.
Adaptive technologies for pervasive and smart systems.
Security, privacy, and trust in human-centered systems.
Electrical machines, their applications, optimization, and control.
User-centered design for subjective wellbeing.

Dr. Ali Hassan Sodhro
Dr. Ismail Butun
Dr. Muhammad Muzammal
Dr. Syed Sabir Hussain Bukhari
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.

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Research

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33 pages, 16513 KiB

Open AccessArticle

A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients

by Sana Maqbool, Imran Sarwar Bajwa, Saba Maqbool, Shabana Ramzan and Muhammad Junaid Chishty

Sensors 2023, 23(23), 9558; https://doi.org/10.3390/s23239558 - 01 Dec 2023

Cited by 3 | Viewed by 1264

Abstract

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient’s health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient’s condition accurately and sends it to the doctor’s portal. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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15 pages, 787 KiB

Open AccessArticle

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

by Abdullah A. Al-Atawi, Saleh Alyahyan, Mohammed Naif Alatawi, Tariq Sadad, Tareq Manzoor, Muhammad Farooq-i-Azam and Zeashan Hameed Khan

Sensors 2023, 23(21), 8875; https://doi.org/10.3390/s23218875 - 31 Oct 2023

Cited by 2 | Viewed by 2415

Abstract

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients’ health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person’s physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed “Stress-Track”. The device is intended to track a person’s stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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19 pages, 592 KiB

Open AccessArticle

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach

by Ahmad Raza, Mohsin Ali, Muhammad Khurram Ehsan and Ali Hassan Sodhro

Sensors 2023, 23(17), 7456; https://doi.org/10.3390/s23177456 - 27 Aug 2023

Cited by 7 | Viewed by 1352

Abstract

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (

P_{d}

) and probability of false alarm (

P_{f}

). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE). Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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84 pages, 26371 KiB

Open AccessArticle

A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

by Samuel Mcmurray and Ali Hassan Sodhro

Sensors 2023, 23(7), 3470; https://doi.org/10.3390/s23073470 - 26 Mar 2023

Cited by 5 | Viewed by 2441

Abstract

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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19 pages, 6189 KiB

Open AccessArticle

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

by Ch. Anwar ul Hassan, Jawaid Iqbal, Rizwana Irfan, Saddam Hussain, Abeer D. Algarni, Syed Sabir Hussain Bukhari, Nazik Alturki and Syed Sajid Ullah

Sensors 2022, 22(19), 7227; https://doi.org/10.3390/s22197227 - 23 Sep 2022

Cited by 24 | Viewed by 3926

Abstract

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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19 pages, 1418 KiB

Open AccessArticle

Blockchain Socket Factories with RMI-Enabled Framework for Fine-Grained Healthcare Applications

by Saleem Ahmed, Abdullah Lakhan, Orawit Thinnukool and Pattaraporn Khuwuthyakorn

Sensors 2022, 22(15), 5833; https://doi.org/10.3390/s22155833 - 04 Aug 2022

Cited by 3 | Viewed by 1812

Abstract

The usage of digital and intelligent healthcare applications on mobile devices has grown progressively. These applications are generally distributed and access remote healthcare services on the user’s applications from different hospital sources. These applications are designed based on client–server architecture and different paradigms such as socket, remote procedure call, and remote method invocation (RMI). However, these existing paradigms do not offer a security mechanism for healthcare applications in distributed mobile-fog-cloud networks. This paper devises a blockchain-socket-RMI-based framework for fine-grained healthcare applications in the mobile-fog-cloud network. This study introduces a new open healthcare framework for applied research purposes and has blockchain-socket-RMI abstraction level classes for healthcare applications. The goal is to meet the security and deadline requirements of fine-grained healthcare tasks and minimize execution and data validation costs during processing applications in the system. This study introduces a partial proof of validation (PPoV) scheme that converts the workload into the hash and validates it among mobile, fog, and cloud nodes during offloading, execution, and storing data in the secure form. Simulation discussions illustrate that the proposed blockchain-socket-RMI minimizes the processing and blockchain costs and meets the security and deadline requirements of fine-grained healthcare tasks of applications as compared to existing frameworks in work. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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16 pages, 1819 KiB

Open AccessArticle

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

by Abdullah Lakhan, Tor Morten Groenli, Arnab Majumdar, Pattaraporn Khuwuthyakorn, Fida Hussain Khoso and Orawit Thinnukool

Sensors 2022, 22(12), 4346; https://doi.org/10.3390/s22124346 - 08 Jun 2022

Cited by 9 | Viewed by 2567

Abstract

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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18 pages, 2827 KiB

Open AccessArticle

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

by Abdullah Lakhan, Ali Hassan Sodhro, Arnab Majumdar, Pattaraporn Khuwuthyakorn and Orawit Thinnukool

Sensors 2022, 22(6), 2379; https://doi.org/10.3390/s22062379 - 19 Mar 2022

Cited by 17 | Viewed by 2102

Abstract

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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18 pages, 1676 KiB

Open AccessArticle

Towards Cognitive Authentication for Smart Healthcare Applications

by Ali Hassan Sodhro, Charlotte Sennersten and Awais Ahmad

Sensors 2022, 22(6), 2101; https://doi.org/10.3390/s22062101 - 09 Mar 2022

Cited by 21 | Viewed by 2772

Abstract

Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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Review

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18 pages, 1036 KiB

Open AccessReview

Stepping Forward: A Scoping Systematic Literature Review on the Health Outcomes of Smart Sensor Technologies for Diabetic Foot Ulcers

by Ioulietta Lazarou, Vasiliki Fiska, Lampros Mpaltadoros, Dimitris Tsaopoulos, Thanos G. Stavropoulos, Spiros Nikolopoulos, George E. Dafoulas, Zoe Dailiana, Alexandra Bargiota and Ioannis Kompatsiaris

Sensors 2024, 24(6), 2009; https://doi.org/10.3390/s24062009 - 21 Mar 2024

Viewed by 1023

Abstract

Diabetic foot ulcers (DFUs) pose a significant challenge in diabetes care, demanding advanced approaches for effective prevention and management. Smart insoles using sensor technology have emerged as promising tools to address the challenges associated with DFU and neuropathy. By recognizing the pivotal role of smart insoles in successful prevention and healthcare management, this scoping review aims to present a comprehensive overview of the existing evidence regarding DFU studies related to smart insoles, offloading sensors, and actuator technologies. This systematic review identified and critically evaluated 11 key studies exploring both sensor technologies and offloading devices in the context of DFU care through searches in CINAHL, MEDLINE, and ScienceDirect databases. Predominantly, smart insoles, mobile applications, and wearable technologies were frequently utilized for interventions and patient monitoring in diabetic foot care. Patients emphasized the importance of these technologies in facilitating care management. The pivotal role of offloading devices is underscored by the majority of the studies exhibiting increased efficient monitoring, prevention, prognosis, healing rate, and patient adherence. The findings indicate that, overall, smart insoles and digital technologies are perceived as acceptable, feasible, and beneficial in meeting the specific needs of DFU patients. By acknowledging the promising outcomes, the present scoping review suggests smart technologies can potentially redefine DFU management by emphasizing accessibility, efficacy, and patient centricity. Full article

(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)

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