Smart Healthcare Applications Research Articles

Retraction Note: Efficient and economical smart healthcare application based on quantum optical neural network

Retraction Note: Efficient and economical smart healthcare application based on quantum optical neural network

Securing the IoT-enabled smart healthcare system: A PUF-based resource-efficient authentication mechanism

As the Internet of Things (IoT) continues its rapid expansion, cloud computing has become integral to various smart healthcare applications. However, the proliferation of digital health services raises significant concerns regarding security and data privacy, making the protection of sensitive medical information paramount. To effectively tackle these challenges, it is crucial to establish resilient network infrastructure and data storage systems capable of defending against malicious entities and permitting access exclusively to authorized users. This requires the deployment of a robust authentication mechanism, wherein medical IoT devices, users (such as doctors or nurses), and servers undergo registration with a trusted authority. The process entails users retrieving data from the cloud server, while IoT devices collect patient data. Before granting access to data retrieval or storage, the cloud server verifies the authenticity of both the user and the IoT device, ensuring secure and authorized interactions within the system. With millions of interconnected smart medical IoT devices autonomously gathering and analyzing vital patient data, the importance of robust security measures becomes increasingly evident. Standard security protocols are fundamental in fortifying smart healthcare applications against potential threats. To confront these issues, this paper introduces a secure and resource-efficient cloud-enabled authentication mechanism. Through empirical analysis, it is demonstrated that our authentication mechanism effectively reduces computational and communication overheads, thereby improving overall system efficiency. Furthermore, both informal and formal analyses affirm the mechanism's resilience against potential cyberattacks, highlighting its effectiveness in safeguarding smart healthcare applications.

Self-Powered Wearable Pressure Sensor System for Smart Healthcare Applications

Wearable sensors mark a groundbreaking advancement in the realm of health monitoring technology. However, traditional wearable devices lack comprehensive solutions for tracking sports exercise behaviors and providing feedback on rehabilitation for those undergoing home-based recovery. To bridge this gap, a self-powered triboelectric nanogenerator (TENG)-based wearable sensing system has been developed with in-house monitoring capabilities. The wearable sensor system is composed of triboelectric pressure sensors with an electro-conductive silicone sheet and patterned Ecoflex film as the contact layers. Highly sensitive TENG sensors have been successfully embedded into a bicycle saddle and human shoe insole for real-time pressure monitoring. Experimental results demonstrate that biomechanical energy, originating from both the pelvic-saddle interactions on bicycle and human gait movements, has been successfully converted into electrical energy. The generated sensor voltage remains stable under frequency variations and external environmental conditions, such as temperature and humidity variations. Utilizing distinctive information contained in individual body mass imbalances and gait characteristics, a smart healthcare monitoring system is envisioned to extract essential biomechanical insights via artificial intelligence, facilitating a pivotal role in in-house monitoring and tracking of sports rehabilitation exercises. The as-designed low-cost, highly scalable self-powered wearable sensor system offers an accessible, user-friendly point-of-care solution for gait detection and rehabilitation monitoring for individuals with various physical impairments, with considerable potential for commercialization. Figure 1

Intelligent Health Surveillance: Cognitive IoT Enhanced by Rapid Machine Learning

Abstract: The quality of life for diabetic patients is significantly improved through continuous monitoring. Integrating various technologies such as the Internet of Things (IoT), embedded software, communication technologies, artificial intelligence, and smart devices helps reduce the financial burden on the healthcare system. Advances in communication technologies have facilitated personalized and remote healthcare. To meet the growing demand for advanced e-health applications, developing intelligent healthcare systems and increasing the number of applications connected to the network is essential. Consequently, to achieve critical needs like high bandwidth and energy efficiency, the 5G network must support smart healthcare applications. This research proposes an intelligent infrastructure for monitoring diabetes patients using machine learning methods. The architecture incorporates smart devices, sensors, and mobile phones to provide comprehensive coverage of the patient's body. Data collected from the patient is analyzed and classified using machine learning to produce a diagnosis. Several machine learning methods were tested to evaluate the proposed prediction system, and simulation results indicated that the Sequential Minimum Optimization (SMO) method provides higher classification accuracy, sensitivity, and precision than other techniques.

Analyzing the Shortcomings in Smart Healthcare for Remote Home Care-A Case Study of the Taiwan Market.

In Taiwan, remote healthcare initially focused on telemedicine, with systematic development starting in 2007 through the "Remote Care Pilot Program" and subsequent initiatives. Significant advancements came with the "Remote Health Care Services Development Plan" in 2010, which integratedArtificial intelligence and Information and communications technologies, enhancing smart healthcare in home care. This study investigated strategic gaps in smart healthcare applications for remote home care using a mixed-methods approach, particularly the Analytic Hierarchy Process (AHP). This study identified and ranked strategic gaps, including "legal regulations", "economic factors", "user behavior habits", "policy and culture", and "environment and technology". Findings, based on in-depth interviews with 6 experts and 16 AHP questionnaire samples, highlight "legal regulations" and "user behavior habits" as critical areas needing attention. Addressing these gaps can improve user acceptance and the effectiveness of smart healthcare applications, providing valuable insights for future research and practice in making remote home care more comprehensive and efficient.

A biocompatible triboelectric nanogenerator-based edible electronic skin for morse code transmitters and smart healthcare applications

Skin-inspired electronic devices have gained immense interest due to their application in prosthetics, healthcare, robotics, and humanmachine interactions. Mimicking human skin and incorporating multifunctional properties such as stretchability, flexibility, biocompatibility, biodegradability, and paving the way for self-powered electronic skin (e-skin) is highly desirable. The present work reports the fabrication of biocompatible triboelectric nanogenerator (TENG)-based edible electronic skins made of a biocompatible chitosan film. The fabricated chitosan film was engineered using plasticizers to enhance its flexibility and mechanical strength. To make the entire device biocompatible, edible gold leaf, which is used as a decorative layer in the sweets of the Indian subcontinent, has been used as an electrode. Both the chitosan film and the edible gold electrode were studied for their biocompatibility by immersion in acetic acid and H2O2, where the films were completely dissolved in 1 h and 50 h, respectively. The e-skins were used as the active layer in the triboelectric nanogenerator by working under both vertical contact separation and single electrode modes of triboelectrification. Furthermore, the fabricated device has been used for real-time applications in self-powered sleep monitoring systems as well as for wearable Morse code transmitters. The electrical response and performance in real-time applications prove that the developed e-skin-based device is a promising candidate for future wearable applications in health care, humanmachine interactions and haptics.

Editorial AI Driven Internet of Medical Things for Smart Healthcare Applications: Challenges and Future Trends

Editorial AI Driven Internet of Medical Things for Smart Healthcare Applications: Challenges and Future Trends

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications.

Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients' health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Improving the latency for 5G/B5G based smart healthcare connectivity in rural area

Smart hospitals are poised to greatly enhance life quality by offering persistent health monitoring capabilities. Remote healthcare and surgery, which are highly dependent on low latency, have seen a transformative improvement with the advent of 5G technology. This has facilitated a new breed of healthcare services, including monitoring and remote surgical procedures. The enhanced features of 5G, such as Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC), have enabled the development of advanced healthcare systems. These systems reduce the need for direct patient contact in hospitals, which is especially pertinent as 5G becomes more widespread. This research presents novel hybrid detection algorithms, specifically QR decomposition with M-algorithm maximum likelihood-minimum mean square error (QRM-MLD-MMSE) and QRM-MLD-ZF (zero forcing), for use in Massive MIMO (M-MIMO) technology. These methods aim to decrease the latency in MIMO-based Non-Orthogonal Multiple Access (NOMA) waveforms while ensuring optimal bit error rate (BER) performance. We conducted simulations to evaluate parameters like BER and power spectral density (PSD) over Rician and Rayleigh channels using both the proposed hybrid and standard algorithms. The study concludes that our hybrid algorithms significantly enhance BER and PSD with lower complexity, marking a substantial improvement in 5G communication for smart healthcare applications.

A lightweight logisticsed chaotic S-box encryption for IoT enabled smart health care applications

<p>The Internet of Things (IoT) acts as the major enabler for realizing more intelligent devices and establishes a new dimension of communication between humans and machines using the Internet. These intelligent devices find their role in numerous domains namely health care, automation, smart homes & other people-assisted applications. Although sensor-driven gadgets have significantly improved people’s daily lives, the majority of IoT systems have been plagued by security backlogs, which results in privacy issues. Recently, Advanced Encryption Standard (AES) provides immense light of research in maintaining security and privacy among IoT health care devices. But encrypted data generated still needs to be improvised to defend against the various IoT attacks. The proposed scheme is implemented in the IoT infrastructure that consists of real-time health care sensors interfaced with the ESP8266. Extensive experimentation has been carried out to evaluate the proposed schemes and S-box tests are conducted and analyzed. Additionally, the efficacy of the suggested methods is evaluated with regard to of time and memory usage in comparison to the other encryption schemes already in use. Experimental findings demonstrate that the suggested L-DAL-SBoX has outperformed the other existing algorithms and finds its strong place in IoT security.</p>

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency

Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes.

RETRACTED ARTICLE: Efficient and economical smart healthcare application based on quantum optical neural network

RETRACTED ARTICLE: Efficient and economical smart healthcare application based on quantum optical neural network

Snake-scale stimulated robust biomimetic composite triboelectric layer for energy harvesting and smart health monitoring

Extreme frictional wear of triboelectric layers severely hinders long-term sustainability of triboelectric nanogenerators (TENGs) for efficient ambient energy harvesting. The state-of-the-art solutions necessitate sophisticated structural designs and complex packaging constraints, impeding the feasibility of TENGs for practical applications. Herein, inspired by the structures and compositions of snake scales, a biomimetic composite (BC) film is fabricated as a wear-resistant triboelectric contact layer. The successful formation of disulfide bond-facilitated cross-linkage of cysteine proteins in the keratin-based BC film promotes excellent mechanical resilience and durability, thereby solving the material abrasion challenges associated with solid-solid triboelectric layers. The BC film, with toothed microstructure, exhibits improved charge transfer, low friction, and consistent long-term stable outputs compared to commercial films. Furthermore, a self-powered BC film-based TENG is integrated into a bicycle for biomechanical energy harvesting, lightening warning signals for safer night ride with stable 300 V electrical output for at least 6 h. Moreover, a smart health monitoring system is further proposed by embedding TENG-based sensors into the bicycle saddle, accurately capturing the rider’s pelvic-saddle interactions on varying terrain slopes. By leveraging the unique biomechanical information, machine learning-assisted user identification and terrain slope data classifications are achieved with high accuracies, laying a foundation for futuristic smart healthcare applications. This work provides a strategy to reduce material abrasion by exploiting the unique characteristics of a biomimetic material, facilitating a basis for efficient environmental energy harvesting and smart sensing.

An Efficient Human Activity Recognition In-Memory Computing Architecture Development for Healthcare Monitoring.

Human activity recognition has played a crucial role in healthcare information systems due to the fast adoption of artificial intelligence (AI) and the internet of thing (IoT). Most of the existing methods are still limited by computational energy, transmission latency, and computing speed. To address these challenges, we develop an efficient human activity recognition in-memory computing architecture for healthcare monitoring. Specifically, a mechanism-oriented model of Ag/a-Carbon/Ag memristor is designed, serving as the core circuit component of the proposed in-memory computing system. Then, one-transistor-two-memristor (1T2M) crossbar array is proposed to perform high-efficiency multiply-accumulate (MAC) operation and high-density memory in the proposed scheme. To facilitate understanding of the proposed efficient human activity recognition in-memory computing design, self-attention ConvLSTM module, multi-head convolutional attention module, and recognition module are proposed. Furthermore, the proposed system is applied to perform human activity recognition, which contains eleven different human activities, including five different postural falls, and six basic daily activities. The experimental results show that the proposed system has advantages in recognition performance (≥ 0.20% accuracy, ≥ 1.10% F1-score) and time consumption (approximately 8∼10 times speed up) compared to existing methods, indicating an advancement in smart healthcare applications.

Self-attention transformer unit-based deep learning framework for skin lesions classification in smart healthcare

Rising mortality rates in recent years have elevated melanoma to the ranks of the world’s most lethal cancers. Dermoscopy images (DIs) have been used in smart healthcare applications to determine medical features using deep transfer learning (DTL). DI-related lesions are widespread, have local features, and are associated with uncertainty. There are three components to our bi-branch parallel model: (1) the Transformer module (TM), (2) the self-attention unit (SAU), and (3) a convolutional neural network (CNN). With CNN and TM able to extract local and global features, respectively, a novel model has been developed to fuse global and local features using cross-fusion to generate fine-grained features. Parallel systems between the branches are merged using a feature-fusion architecture, resulting in a pattern that identifies the characteristics of a variety of lesions. Moreover, this paper proposes an optimized and lightweight CNN architecture version (optResNet-18) that discriminates skin cancer lesions with high accuracy. To verify the proposed method, the procedure evaluated the accuracy for the ISIC-2019 and the PH2 datasets as 97.48 and 96.87%, respectively, a significant difference over traditional CNN networks (e.g., ResNet-50 and ResNet-101) and the TM. The proposed model outperforms state-of-the-art performance metrics such as AUC, F1-score, specificity, precision, and recall. The proposed method can also be used as a generalizable model to diagnose different lesions in DIs with smart healthcare applications by combining DTL and medical imaging. With the proposed e-Health platform, skin diseases can be detected in real-time, which is crucial to speedy and reliable diagnostics.

Liquid Metal Functionalization Innovations in Wearables and Soft Robotics for Smart Healthcare Applications

AbstractSmart healthcare solutions are crucial for early diagnosis, real‐time health monitoring, and personalized therapy. Essential to these advancements are flexible wearable devices and medical robotics, which incorporate a range of elements, including soft sensors, therapeutic modules, human‐machine interfaces, actuators, and systems for communication, computation, and power. Liquid metal‐based materials are rapidly emerging as one of the key elements for these innovations. This review will delve into the latest breakthroughs in liquid metal (LM) functionalization innovation and the applications in healthcare‐related wearables and soft robotics. Firstly, techniques to modify LM are explored, emphasizing strategies to enhance properties such as electromechanical, thermal, electromagnetic, biochemical, self‐healing, and magnetic capabilities. Next, the authors shed light on contemporary wearable sensors, therapeutic on‐skin devices, and other flexible device innovations. They finally conclude by highlighting the challenges and charting the path forward for LM‐driven healthcare advances.

A Novel Approach for the Detection of Tuberculosis and Pneumonia Using Chest X-Ray Images for Smart Healthcare Applications

A Novel Approach for the Detection of Tuberculosis and Pneumonia Using Chest X-Ray Images for Smart Healthcare Applications

An efficient disease prediction framework based on optimized machine learning models for a smart healthcare application

An efficient disease prediction framework based on optimized machine learning models for a smart healthcare application

Medical image super-resolution for smart healthcare applications: A comprehensive survey

The digital transformation in healthcare, propelled by the integration of deep learning models and the Internet of Things (IoT), is creating unprecedented opportunities for improving patient care. However, the utilization of low-resolution images, often generated by IoT devices, introduces biases in the deep learning models, thereby affecting the overall clinical decision-making process. While super-resolution techniques have been extensively employed to transform low-resolution images into high-resolution counterparts, the challenge of achieving highly accurate image restoration remains unresolved. This is especially critical in the medical imaging domain, where even minor inaccuracies can lead to significant biases in model training and, consequently, impact clinical outcomes. Although existing surveys have explored various super-resolution methods and their applications across different fields, a comprehensive review emphasizing the accuracy of image restoration in medical imaging and its subsequent influence on deep learning models is notably lacking. This survey seeks to bridge this gap by offering a systematic review of current state-of-the-art models, highlighting the limitations of existing surveys, and underscoring open questions that merit further research. Specifically, we delve into the intricacies of medical image restoration, identify research gaps and unmet challenges in achieving optimal restoration of medical images, and emphasize the crucial role of developing more precise and resilient super-resolution methods to enhance the quality of medical images and, consequently, the performance of deep learning models in healthcare applications. Ultimately, this survey fosters a deeper comprehension of the prevailing challenges and unresolved questions in the field, thus setting the stage for future research efforts focused on refining medical image restoration and, subsequently, boosting the efficacy of deep learning models in healthcare.