Healthcare Sensor Research Articles

PSO-based DV-Hop Localization for Healthcare Sensor Nodes

Locating specific sensor nodes can be a difficult process in wireless sensor networks, making it one of the most difficult aspects of these networks. It is helpful in pinpointing the precise location where something took place. As a result of the fact that this is a one-time process, nodes are often only localised once for the course of the network entire existence. On the other hand, a landslip or some other catastrophe that takes place in the real world can lead the nodes of the network to become distributed across it. When something comparable does place, it is absolutely necessary to be aware of the locations of these sensor nodes. In this paper, we suggest a different localization technique, one that is able to function with sensor nodes that have been moved. The system that has been proposed takes into account DV-Hop localization methods that are based on particle swarm optimization (PSO). We also take into consideration the electromagnetic abnormality model as an illustration of how the strategy that was described could be implemented in an anisotropic network. When compared to the conventional PSO DV-Hop structure, the simulations’ findings indicate that our innovative localization framework significantly reduces the amount of time, inaccuracy, and power consumption required.

Blockchain Enabled Anonymous Privacy-Preserving Authentication Scheme for Internet of Health Things.

The Internet of Health Things (IoHT) has emerged as an attractive networking paradigm in wireless communications, integrated devices and embedded system technologies. In the IoHT, real-time health data are collected through smart healthcare sensors and, in recent years, the IoHT has started to have an important role in the Internet of Things technology. Although the IoHT provides comfort in health monitoring, it also imposes security challenges in maintaining patient data confidentiality and privacy. To overcome such security issues, in this paper, a novel blockchain-based privacy-preserving authentication scheme is proposed as an approach for achieving efficient authentication of the patient without the involvement of a trusted entity. Moreover, a secure handover authentication mechanism that ensures avoiding the patient re-authentication in multi-doctor communication scenarios and revoking the possible malicious misbehavior of medical professionals in the IoHT communication with the patient is developed. The performance of the proposed authentication and handover scheme is analyzed concerning the existing state-of-the-art authentication schemes. The results of the performance analyses reveal that the proposed authentication scheme is resistant to different types of security attacks. Moreover, the results of analyses show that the proposed authentication scheme outperforms similar state-of-the-art authentication schemes in terms of having lower computational, communication and storage costs. Therefore, the novel authentication and handover scheme has proven practical applicability and represents a valuable contribution to improving the security of communication in IoHT networks.

Nanoparticle‐Based Strain Gauges: Anisotropic Response Characteristics, Multidirectional Strain Sensing, and Novel Approaches to Healthcare Applications

AbstractDirectional strain sensing is essential for advanced sensor applications in the field of human‐machine interfaces and healthcare. Here, the angle dependent anisotropic strain sensitivity caused by charge carriers percolating through cross‐linked gold nanoparticle (GNP) networks is studied and these versatile materials are used for the fabrication of wearable triaxial pulse and gesture sensors. More specifically, the anisotropic response of 1,9‐nonanedithiol cross‐linked GNP films is separated into geometric and piezoresistive contributions by fitting the measured data with an analytic model. Hereby, piezoresistive coefficients of g11 ∼ 32 and g12 ∼ 21 are extracted, indicating a slightly anisotropic response behavior of the GNP‐based material. To use the material for healthcare applications, arrangements of three GNP transducers are patterned lithographically and fully embedded into a highly flexible silicone polymer (Dragon Skin 30). The new encapsulation method ensures good and robust electrical contacts and enables facile handling and protection from external influences. A facile read‐out with wireless data transmission using off‐the‐shelf electrical components underlines the great potential of these devices for applications as skin‐wearable healthcare sensors.

A Near-Infrared Plasmonic Sensor Based on Wedge Fiber-Optic for Ultra-Sensitive Hg Detection

The growing number of serious diseases caused by ever-increasing heavy metal ions pollution have necessitated the use of ion sensors in healthcare and environment monitoring systems. Even with low concentrations heavy metal ions can cause significant harm, so ultra-sensitive on-site and label-free monitoring of heavy metal ions is of great importance. In this work, a simple, portable and high sensitivity optical fiber surface plasmon resonance (SPR) sensor is demonstrated for the detection of Hg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> . On the basis of the investigation of the effects of different incident angles, the optical fiber sensor adopts wedged tip probe with a 75° incident angle. The plasmonic sensor exhibits a superior refractive index bulk sensitivity of up to 13830 nm/RIU in near-infrared band, which is a further step in ultrasensitive sensing detection, compared with the traditional optical fiber SPR sensors. In addition, we validate the optical fiber sensor's performance for Hg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> sensing. By self-assembling monolayer of 1,6-Hexanedithiol (HDT) on the sensing surface, the Au-coated fiber-optic probe is modified to capture Hg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> . The lowest detection limit is found to be 0.324 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{ug}/\mathbf{mL}$</tex-math></inline-formula> for the near-infrared band and the sensitivity can reach up to 0.22 nm/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$({\mathbf{ug}/\mathbf{mL}})$</tex-math></inline-formula> . The experimental results show the excellent promise for our sensor in applications of environmental monitoring due to high sensitivity, selectivity and low-cost.

Lion Based Butterfly Optimization with Improved YOLO-v4 for Heart Disease Prediction Using IoMT

The Internet of Medical Things (IoMT) has subsequently been used in healthcare services to gather sensor data for the prediction and diagnosis of cardiac disease. Recently image processing techniques require a clear focused solution to predict diseases. The primary goal of the proposed method is to use health information and medical pictures for classifying the data and forecasting cardiac disease. It consists of two phases for categorizing the data and prediction. If the previous phase's results are practical heart problems, then there is no need for phase 2 to predict. The first phase categorized data collected from healthcare sensors attached to the patient's body. The second stage evaluated the echocardiography images for the prediction of heart disease. A Hybrid Lion-based Butterfly Optimization Algorithm (L-BOA) is used for classifying the sensor data. In the existing method, Hybrid Faster R-CNN with SE-Rest-Net-101 is used for classification. Faster R-CNN uses areas to locate the item in the picture. The proposed method uses Improved YOLO-v4. It increases the semantic knowledge of little things. An Improved YOLO-v4 with CSPDarkNet53 is used for feature extraction and classifying the echo-cardiogram pictures. Both categorization approaches were used, and the results were integrated and confirmed in the ability to forecast heart disease. The LBO-YOLO-v4 process detected regular sensor data with 97.25% accuracy and irregular sensor data with 98.87% accuracy. The proposed improved YOLO-v4 with the CSPDarkNet53 method gives better classification among echo-cardiogram pictures.

Biocompatible and breathable healthcare electronics with sensing performances and photothermal antibacterial effect for motion-detecting

With the booming development of smart wearable devices, flexible multifunctional composites with high sensitivity and well health therapy have evoked great interest for next-generation healthcare electronics. However, the weak biocompatibility, low breathability, and narrow sensing range greatly hinder the development of healthcare sensors. Herein, a porous, flexible and conductive MXene/Polydimethylsiloxane/Polydopamine/Polyurethane Sponge (MXene/PDMS/PDA/PU) nanocomposite is developed as a promising motion-detecting device with good flexibility, breathability, sensing performance, photothermal therapy and antibacterial activity. Benefiting from the porous structure and biocompatible surface, this multifunctional sensor is further fabricated into a diagnostic and therapeutic system for monitoring human body motion and performing hot therapy/antibacterial treatment in the application of sports injury site. Moreover, both the wireless smart insole and cushion are constructed to gait monitoring and sit position detecting. This multifunctional hybrid sponge not only demonstrates great potential for motion monitoring sensors but also exhibits wide potential in wearable medical assistive and therapeutic systems.

Evaluation of Unobtrusive Microwave Sensors in Healthcare 4.0-Toward the Creation of Digital-Twin Model.

The prevalence of chronic diseases and the rapid rise in the aging population are some of the major challenges in our society. The utilization of the latest and unique technologies to provide fast, accurate, and economical ways to collect and process data is inevitable. Industry 4.0 (I4.0) is a trend toward automation and data exchange. The utilization of the same concept of I4.0 in healthcare is termed Healthcare 4.0 (H4.0). Digital Twin (DT) technology is an exciting and open research field in healthcare. DT can provide better healthcare in terms of improved patient monitoring, better disease diagnosis, the detection of falls in stroke patients, and the analysis of abnormalities in breathing patterns, and it is suitable for pre- and post-surgery routines to reduce surgery complications and improve recovery. Accurate data collection is not only important in medical diagnoses and procedures but also in the creation of healthcare DT models. Health-related data acquisition by unobtrusive microwave sensing is considered a cornerstone of health informatics. This paper presents the 3D modeling and analysis of unobtrusive microwave sensors in a digital care-home model. The sensor is studied for its performance and data-collection capability with regards to patients in care-home environments.

An electrical-mechanical cohesive zone model combining viscoelasticity and fatigue damage for soft adhesive layer in wearable sensor

The wearable biomedical devices are promising technologies in the field of human healthcare as the sensors are attached to human skin to monitor in real time physiological signal. The signal drift caused by the damage of adhesive layer between the sensor electrode and human skin is an important factor that affects the detection accuracy of the sensor. Considering the viscoelasticity and conductivity of the adhesive layer, a rate-dependent electrical–mechanical cohesive zone model (CZM) was developed to simulate the electrical signal degradation of wearable sensor electrode during cyclic loading condition. In the proposed CZM, the electrical response of the adhesive layer is related to its mechanical behavior and damage state, and the rate-dependent mechanical behavior of the adhesive layer is described by the Maxwell model. In addition, the fatigue damage evolution is also rate-dependent as the threshold of damage initiation and fracture are defined as the function of loading velocity. The proposed CZM was implemented in ABAQUS by the UEL subroutine and applied to simulate the damage and electrical signal degradation of a wearable electrocardiograph (ECG) sensor electrode in working condition. The results demonstrated that the proposed CZM is effective to characterize the rate-dependent fatigue damage behavior and electrical signal degradation behavior of the ECG electrode during cyclic loadings. It is a promising tool for predicting the electrical signal drift of wearable healthcare sensors and other similar components caused by the damage of adhesive layer, which is important for the accurate calibration of the sensors in long-term service.

A stretchable and zigzag structured hydrogel for highly sensitive strain sensors

• We proposed a zigzag structured hydrogel with high sensitivity. • The GF of the zigzag structured hydrogel can reach 2.89 at the strain of 100%. • The assembled strain sensor successfully detects various human motions. • The zigzag structured strategy is widely applicable on conductive hydrogels. Hydrogel always suffers from poor sensitivity which limits their applications in the flexible devices. To address that, we designed zigzag structures onto the rectangular hydrogel which are commonly employed in recent years. The special designed structure tremendously increased the travelling distance of the mobile ions in the conductive hydrogel during the deformation of the zigzags, which lead to significant change in the resistance of hydrogel upon stretching. Therefore, the structured hydrogel achieved outstanding sensitive performance with the gauge factor (GF) of 2.89, which was almost two times higher than that of the original one. Therefore, we proposed a feasible way to adjust the sensitivity of the hydrogel, which holds great potential to be used for flexible strain sensors in healthcare, e-skin and artificial intelligence devices.

Structural Flexibility in Triboelectric Nanogenerators: A Review on the Adaptive Design for Self-Powered Systems.

There is an increasing need for structural flexibility in self-powered wearable electronics and other Internet of Things (IoT), where adaptable triboelectric nanogenerators (TENGs) play a key role in realizing the true potential of IoT by endowing the latter with self-sustainability. Thus, in this review, the topic was restricted to the adaptive design of TENGs with structural flexibility that aims to promote the sustainable operation of various smart electronics. This review begins with an emphatical discussion of the concept of flexible electronics and TENGs, and continues with the introduction of TENG-based self-powered intelligent systems while placing the emphasis on self-powered flexible intelligent devices. Self-powered healthcare sensors, e-skins, and other intelligent wearable electronics with enhanced intelligence and efficiency in practical applications due to the integration with TENGs are illustrated, along with an emphasis on the design strategy of structural flexibility of TENGs and the associated integration schemes. This review aims to cover recent achievements in the field of self-powered systems, and provides information on how flexibility or adaptability in TENGs can be adopted, their types, and why they are required in promoting advanced IoT applications with sustainability and intelligence algorithms.

Design Strategies for Strain‐Insensitive Wearable Healthcare Sensors and Perspective Based on the Seebeck Coefficient

AbstractLarge healthcare markets have been created in highly developed economies to improve the quality of life. Wearable healthcare sensors are attracting considerable interest because of their 24 h real‐time monitoring capability, which make them useful in the detection of potential diseases. To guide the diagnosis, these sensors are designed to monitor various physical (e.g., pressure, temperature, strain, touch, bioelectricity, etc...) or chemical (e.g., glucose, oxygen, bacteria, viruses, proteins, etc...) quantities. In order to be comfortable to wear for a longer period of time, the sensors must be made with good stretchability to conformably deform with human organs. However, high stretchability always brings the problem that the measurement is very often polluted by the deformation of the substrate, making the data unreliable. According to each the sensor mechanism, multiple strain‐insensitive design strategies compatible with large deformations of the human body are discussed and the performance of these strategies are comprehensively analyzed. Then, how the intrinsic strain insensitivity of the Seebeck coefficient of nanomaterial percolation networks can define an alternative promising strategy is demostrated. Finally, the outlooks for future research and challenges in realizing strain‐insensitive sensors by applying the Seebeck effect are reported.

Time dependency: an efficient biometric-based authentication for secure communication in wireless healthcare sensor networks

Time dependency: an efficient biometric-based authentication for secure communication in wireless healthcare sensor networks

Food‐Based Capacitive Sensors Using a Dynamic Permittivity Change with Hydrogels Responsive to Hydrogen Peroxide

AbstractRecent advances in ingestible electronics have elucidated the chemical composition of the gastrointestinal tract. Most devices are typically assembled from nondegradable materials, which require surgery for removal in cases of retention. Thus, edible electronic components based on naturally derived materials have been developed. However, a disease‐specific digestible sensor is difficult to construct due to the limited ingredients available. Here, a food‐based capacitive sensor using large dielectric constant changes with excellent selectivity is proposed. The sensor uses a combination of an interlayer of an enzyme‐containing degradable hydrogel, an insulator of food‐based slippery liquid‐infused porous surfaces, and Au/gelatin electrodes. In the enzymatic reaction, generated micro‐bubbles change the permittivity of the capacitor dynamically. Compared to most approaches for capacitive sensors using swelling/shrinking of the interlayer, this simple method excels in a large permittivity change with selectivity associated with the enzymatic reaction. This sensor is potentially applicable to various gaseous reactions, such as redox reactions, weak acid release reactions, and thermal decomposition. The capacitive component works in the contact‐free device as the resonator or sensor. Therefore, this study would open the way to human‐friendly material‐based wireless chemical sensors in healthcare or environmental domains.

Wearable Fiber-Based Supercapacitors Enabled by Additive-Free Aqueous MXene Inks for Self-Powering Healthcare Sensors

Wearable Fiber-Based Supercapacitors Enabled by Additive-Free Aqueous MXene Inks for Self-Powering Healthcare Sensors

Application of Internet of Things and Sensors in Healthcare.

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic.

Wearable Sensors for Healthcare: Fabrication to Application.

This paper presents a substantial review of the deployment of wearable sensors for healthcare applications. Wearable sensors hold a pivotal position in the microelectronics industry due to their role in monitoring physiological movements and signals. Sensors designed and developed using a wide range of fabrication techniques have been integrated with communication modules for transceiving signals. This paper highlights the entire chronology of wearable sensors in the biomedical sector, starting from their fabrication in a controlled environment to their integration with signal-conditioning circuits for application purposes. It also highlights sensing products that are currently available on the market for a comparative study of their performances. The conjugation of the sensing prototypes with the Internet of Things (IoT) for forming fully functioning sensorized systems is also shown here. Finally, some of the challenges existing within the current wearable systems are shown, along with possible remedies.

Verification of embedding conditions for FBG sensor into textile product for the development of wearable healthcare sensor.

To develop wearable healthcare sensors that use fiber Bragg grating (FBG) sensors, a stretch textile product with an embedded FBG sensor is required. The FBG sensor, which is an optical fiber, was embedded into a textile product following a wavy pattern by using a warp knitting machine. When an optical fiber is embedded in a textile product, the effect of the cycle length of wavy pattern and the number of cycles on the optical loss is verified. The shorter the cycle length of the wavy pattern of the optical fiber, and more increase in the number of cycles, the longer the textile product in which the optical fiber is embedded can expand and contract. However, when the cycle length of the wave pattern is 30 mm (shortest), large in optical loss, the pulse wave signal cannot be measured. If the cycle length of the wavy pattern is 50 mm or more, small in optical loss, the pulse wave signal is measured. Compared with a straight pattern embedding FBG sensor, the amplitude value of the pulse wave signal measured with a cycle length of 50 mm is large, therefore the sensor sensitivity in this state is greater. This result is consistent with the measurement sensitivity depending on the angle of installation with respect to the direction of the artery. With a cycle length of wavy pattern of 50 mm and 4 cycles, a stretch textile product with an embedded FBG sensor can be fabricated. Pulse wave signals are measured with this textile product, and the development of wearable healthcare sensors is expected.

A Micro Neural Network for Healthcare Sensor Data Stream Classification in Sustainable and Smart Cities.

A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities.

A Facile, Fabric Compatible, and Flexible Borophene Nanocomposites for Self-Powered Smart Assistive and Wound Healing Applications.

Smart fabrics that can harvest ambient energy and provide diverse sensing functionality via triboelectric effects have evoked great interest for next‐generation healthcare electronics. Herein, a novel borophene/ecoflex nanocomposite is developed as a promising triboelectric material with tailorability, durability, mechanical stability, and flexibility. The addition of borophene nanosheets enables the borophene/ecoflex nanocomposite to exhibit tunable surface triboelectricity investigated by Kelvin probe force microscopy. The borophene/ecoflex nanocomposite is further fabricated into a fabric‐based triboelectric nanogenerator (B‐TENG) for mechanical energy harvesting, medical assistive system, and wound healing applications. The durability of B‐TENG provides consistent output performance even after severe deformation treatments, such as folding, stretching, twisting, and washing procedures. Moreover, the B‐TENG is integrated into a smart keyboard configuration combined with a robotic system to perform an upper‐limb medical assistive interface. Furthermore, the B‐TENG is also applied as an active gait phase sensing system for instantaneous lower‐limb gait phase visualization. Most importantly, the B‐TENG can be regarded as a self‐powered in vitro electrical stimulation device to conduct continuous wound monitoring and therapy. The as‐designed B‐TENG not only demonstrates great potential for multifunctional self‐powered healthcare sensors, but also for the promising advancements toward wearable medical assistive and therapeutic systems.

Wearable Near-Field Communication Sensors for Healthcare: Materials, Fabrication and Application.

The wearable device industry is on the rise, with technology applications ranging from wireless communication technologies to the Internet of Things. However, most of the wearable sensors currently on the market are expensive, rigid and bulky, leading to poor data accuracy and uncomfortable wearing experiences. Near-field communication sensors are low-cost, easy-to-manufacture wireless communication technologies that are widely used in many fields, especially in the field of wearable electronic devices. The integration of wireless communication devices and sensors exhibits tremendous potential for these wearable applications by endowing sensors with new features of wireless signal transferring and conferring radio frequency identification or near-field communication devices with a sensing function. Likewise, the development of new materials and intensive research promotes the next generation of ultra-light and soft wearable devices for healthcare. This review begins with an introduction to the different components of near-field communication, with particular emphasis on the antenna design part of near-field communication. We summarize recent advances in different wearable areas of near-field communication sensors, including structural design, material selection, and the state of the art of scenario-based development. The challenges and opportunities relating to wearable near-field communication sensors for healthcare are also discussed.