Wireless Body Area Network for Preventing Self-Inoculation Transmission of Respiratory Viral Diseases.

This paper proposes an idea of Wireless Body Area Networks (WBANs) based on Bluetooth Low-Energy (BLE) standards to recognize and alarm a gesture of touching the face, and in effect, to prevent self-inoculation of respiratory viral diseases, such as COVID-19 or influenza A, B, or C. The proposed network comprises wireless modules placed in bracelets and a necklace. It relies on the received signal strength indicator (RSSI) measurements between the bracelet and necklace modules. The measured signal is cleared of noise using the exponential moving average (EMA). Next, we use a classification algorithm based on a Least-Squares Support Vector Machine (LSSVM) in order to detect facial touches. When the results of the classification indicate that the hand is moving toward the face, an alarm is sent through the neck module and the vibrator embedded in the wrist module is switched on. Based on the performed tests, it can be concluded that the proposed solution is characterized by high accuracy and reliability. It should be useful, especially for individuals who are regularly exposed to the risk of respiratory infections.

A Hybrid Multi-Criteria Approach to the Vendor Selection Problem for Sensor-Based Medical Devices.

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions.

Laser-induced graphene (LIG)-driven medical sensors for health monitoring and diseases diagnosis.

Laser-induced graphene (LIG) is a class of three-dimensional (3D) porous carbon nanomaterial. It can be prepared by direct laser writing on some polymer materials in the air. Because of its features of simplicity, fast production, and excellent physicochemical properties, it was widely used in medical sensing devices. This minireview gives an overview of the characteristics of LIG and LIG-driven sensors. Various methods for preparing graphene were compared and discussed. The applications of the LIG in biochemical sensors for ions, small molecules, microRNA, protein, and cell detection were highlighted. LIG-based physical physiological sensors and wearable electronics for medical applications were also included. Finally, our insights into current challenges and prospects for LIG-based medical sensing devices were presented.

New Internet of Medical Things for Home-Based Treatment of Anorectal Disorders.

Home-based healthcare provides a viable and cost-effective method of delivery for resource- and labour-intensive therapies, such as rehabilitation therapies, including anorectal biofeedback. However, existing systems for home anorectal biofeedback are not able to monitor patient compliance or assess the quality of exercises performed, and as a result have yet to see wide spread clinical adoption. In this paper, we propose a new Internet of Medical Things (IoMT) system to provide home-based biofeedback therapy, facilitating remote monitoring by the physician. We discuss our user-centric design process and the proposed architecture, including a new sensing probe, mobile app, and cloud-based web application. A case study involving biofeedback training exercises was performed. Data from the IoMT was compared against the clinical standard, high-definition anorectal manometry. We demonstrated the feasibility of our proposed IoMT in providing anorectal pressure profiles equivalent to clinical manometry and its application for home-based anorectal biofeedback therapy.

An Analysis of Respiration with the Smart Sensor SENSIRIB in Patients Undergoing Thoracic Surgery.

The paper examines the problem of respiration monitoring with easily wearable instrumentation by using a smart device that is properly designed and implemented with small and light components. The practical implementation is presented both in practical aspects and from experimental results by following a properly defined method with a medical-like protocol and specific procedure of testing. The results of a statistically significant campaign of experimental tests are reported with the characteristic data from the angles and acceleration components of a sensed rib both to validate the smart device and the procedure for respiration monitoring.

In-Vivo Microsystems: A Review.

In-vivo sensors yield valuable medical information by measuring directly on the living tissue of a patient. These devices can be surface or implant devices. Electrical activity in the body, from organs or muscles can be measured using surface electrodes. For short term internal devices, catheters are used. These include cardiac catheter (in blood vessels) and bladder catheters. Due to the size and shape of the catheters, silicon devices provided an excellent solution for sensors. Since many cardiac catheters are disposable, the high volume has led to lower prices of the silicon sensors. Many catheters use a single sensor, but silicon offers the opportunity to have multi sensors in a single catheter, while maintaining small size. The cardiac catheter is usually inserted for a maximum of 72 h. Some devices may be used for a short-to-medium period to monitor parameters after an operation or injury (1-4 weeks). Increasingly, sensing, and actuating, devices are being applied to longer term implants for monitoring a range of parameters for chronic conditions. Devices for longer term implantation presented additional challenges due to the harshness of the environment and the stricter regulations for biocompatibility and safety. This paper will examine the three main areas of application for in-vivo devices: surface devices and short/medium-term and long-term implants. The issues of biocompatibility and safety will be discussed.

Molecularly Imprinted Polymer Based Sensors for Medical Applications.

Sensors have been extensively used owing to multiple advantages, including exceptional sensing performance, user-friendly operation, fast response, high sensitivity and specificity, portability, and real-time analysis. In recent years, efforts in sensor realm have expanded promptly, and it has already presented a broad range of applications in the fields of medical, pharmaceutical and environmental applications, food safety, and homeland security. In particular, molecularly imprinted polymer based sensors have created a fascinating horizon for surface modification techniques by forming specific recognition cavities for template molecules in the polymeric matrix. This method ensures a broad range of versatility to imprint a variety of biomolecules with different size, three dimensional structure, physical and chemical features. In contrast to complex and time-consuming laboratory surface modification methods, molecular imprinting offers a rapid, sensitive, inexpensive, easy-to-use, and highly selective approaches for sensing, and especially for the applications of diagnosis, screening, and theranostics. Due to its physical and chemical robustness, high stability, low-cost, and reusability features, molecularly imprinted polymer based sensors have become very attractive modalities for such applications with a sensitivity of minute structural changes in the structure of biomolecules. This review aims at discussing the principle of molecular imprinting method, the integration of molecularly imprinted polymers with sensing tools, the recent advances and strategies in molecular imprinting methodologies, their applications in medical, and future outlook on this concept.

Kinetic Energy Harvesting for Wearable Medical Sensors.

The process of collecting low-level kinetic energy, which is present in all moving systems, by using energy harvesting principles, is of particular interest in wearable technology, especially in ultra-low power devices for medical applications. In fact, the replacement of batteries with innovative piezoelectric energy harvesting devices can result in mass and size reduction, favoring the miniaturization of wearable devices, as well as drastically increasing their autonomy. The aim of this work is to assess the power requirements of wearable sensors for medical applications, and address the intrinsic problem of piezoelectric kinetic energy harvesting devices that can be used to power them; namely, the narrow area of optimal operation around the eigenfrequencies of a specific device. This is achieved by using complex numerical models comprising modal, harmonic and transient analyses. In order to overcome the random nature of excitations generated by human motion, novel excitation modalities are investigated with the goal of increasing the specific power outputs. A solution embracing an optimized harvester geometry and relying on an excitation mechanism suitable for wearable medical sensors is hence proposed. The electrical circuitry required for efficient energy management is considered as well.

RF energy harvesters for wireless sensors, state of the art, future prospects and challenges: a review.

The power consumption of portable gadgets, implantable medical devices (IMDs) and wireless sensor nodes (WSNs) has reduced significantly with the ongoing progression in low-power electronics and the swift advancement in nano and microfabrication. Energy harvesting techniques that extract and convert ambient energy into electrical power have been favored to operate such low-power devices as an alternative to batteries. Due to the expanded availability of radio frequency (RF) energy residue in the surroundings, radio frequency energy harvesters (RFEHs) for low-power devices have garnered notable attention in recent times. This work establishes a review study of RFEHs developed for the utilization of low-power devices. From the modest single band to the complex multiband circuitry, the work reviews state of the art of required circuitry for RFEH that contains a receiving antenna, impedance matching circuit, and an AC-DC rectifier. Furthermore, the advantages and disadvantages associated with various circuit architectures are comprehensively discussed. Moreover, the reported receiving antenna, impedance matching circuit, and an AC-DC rectifier are also compared to draw conclusions towards their implementations in RFEHs for sensors and biomedical devices applications.

Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment.

This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework.

A wireless magnetic resonance energy transfer system for micro implantable medical sensors.

Based on the magnetic resonance coupling principle, in this paper a wireless energy transfer system is designed and implemented for the power supply of micro-implantable medical sensors. The entire system is composed of the in vitro part, including the energy transmitting circuit and resonant transmitter coils, and in vivo part, including the micro resonant receiver coils and signal shaping chip which includes the rectifier module and LDO voltage regulator module. Transmitter and receiver coils are wound by Litz wire, and the diameter of the receiver coils is just 1.9 cm. The energy transfer efficiency of the four-coil system is greatly improved compared to the conventional two-coil system. When the distance between the transmitter coils and the receiver coils is 1.5 cm, the transfer efficiency is 85% at the frequency of 742 kHz. The power transfer efficiency can be optimized by adding magnetic enhanced resonators. The receiving voltage signal is converted to a stable output voltage of 3.3 V and a current of 10 mA at the distance of 2 cm. In addition, the output current varies with changes in the distance. The whole implanted part is packaged with PDMS of excellent biocompatibility and the volume of it is about 1 cm(3).

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique.

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the estimation of intrusion detection.

Remote diagnostic and detection of coronavirus disease (COVID-19) system based on intelligent healthcare and internet of things.

In this paper, we will propose a novel system for remote detecting COVID-19 patients based on artificial intelligence technology and internet of things (IoT) in order to stop the virus spreading at an early stage. In this work, we will focus on connecting several sensors to work together as a system that can discover people infected with the Coronavirus remotely, this will reduce the spread of the disease. The proposed system consists of several devices called smart medical sensors such as: pulse, thermal monitoring, and blood sensors. The system is working sequentially starting by pulse sensor and end by blood sensor including an algorithm to manage the data given from sensors. The pulse sensor is devoted to acquire a high quality data using a smartphone equipped by a mobile dermatoscope with 20× magnification. The processing is used RGB color system to perform moving window to segment regions of interest (ROIs) as inputs of the heart rate estimation algorithm. The heart rate (HR) estimation is then given by computing the dominant frequency by identifying the most prominent peak of the discrete Fourier transform (DFT) technique. The thermal monitoring is used for fever detection using a smart camera that can provide an optimum solution for fever detection. The infrared sensor can quickly measure surface temperature without making any contact with a person's skin. A blood sensor is used to measure percentages of white, red blood (WBCs, RBCs) volume and platelets non-invasively using the bioimpedance analysis and independent component analysis (ICA). The proposed sensor consists of two electrodes which can be used to send the current to the earlobe and measure the produced voltage. A mathematical model was modified to describe the impedance of earlobe in different frequencies (i.e., low, medium, and high). The COMSOL model is used to simulate blood electrical properties and frequencies to measure WBCs, RBCs and Platelets volume. These devices are collected to work automatically without user interaction for remote checking the coronavirus patients. The proposed system is experimented by six examples to prove its applicability and efficiency.

Stretchable and Sensitive Silver Nanowire-Hydrogel Strain Sensors for Proprioceptive Actuation.

Safer human-robot interactions mandate the adoption of proprioceptive actuation. Strain sensors can detect the deformation of tools and devices in unstructured and capricious environments. However, such sensor integration in surgical/clinical settings is challenging due to confined spaces, structural complexity, and performance losses of tools and devices. Herein, we report a highly stretchable skin-like strain sensor based on a silver nanowire (AgNW) layer and hydrogel substrate. Our facile fabrication method utilizes thermal annealing to modulate the gauge factor (GF) by forming multidimensional wrinkles and a layered conductive network. The developed AgNW-hydrogel (AGel) sensors sustain and exhibit a strain-sensitive profile (max. GF = ∼70) with high stretchability (200%). Due to its conformability, the sensor demonstrates efficacy in integration and motion monitoring with minimal mechanical constraints. We provide contextual cognizance of tooltip during a transoral procedure by incorporating AGel sensors and showing the fabrication methodology's versatility by developing a hybrid self-sensing actuator with real-time performance feedback.

An investigation on power loss of an out-to-in body wireless radio frequency link.

BACKGROUND: Implantable medical sensors for monitoring and transmitting physiological signals like blood glucose, blood oxygen, electrocardiogram, and endoscopic video present a new way for health care and disease prevention. Nevertheless, the signals transmitted by implantable sensors undergo significant attenuation as they propagate through various biological tissue layers. OBJECTIVE: This paper mainly aims to investigate the power loss of an out-to-in body wireless radio frequency link at 2.45 GHz. METHODS: Two simulation models including the single-layer human tissue model and three-layer human tissue model were established, applying the finite element method (FEM). Two experiments using physiological saline and excised porcine tissue were conducted to measure the power loss of a wireless radio frequency link at 2.45 GHz. Various communication distances and implantation depths were investigated in our study. RESULTS: The results from our measurements show that each 2 cm increase in implantation depth will result in an additional power loss of about 10 dB. The largest difference in values obtained from the measurements and the simulations is within 4 dB, which indicates that the experiments are in good agreement with the simulations. CONCLUSIONS: These results are significant for the estimate of how electromagnetic energy changes after propagating through human tissues, which can be used as a reference for the link budget of transceivers or other implantable medical devices.

mHealth4Afrika: Supporting Standards Based Integration of Medical Sensor Data.

mHealth4Afrika has introduced the use of CE approved medical sensors at the point of care in primary healthcare facilities in Africa as part of an integrated platform supporting primary health care services. This paper shares insights into the standards-based architecture and HL7 FHIR service developed to support data transfer from sensors with proprietary standards to populate the mHealth4Afrika electronic patient record via custom Android and Windows applications. The current iteration is being validated in healthcare facilities in Ethiopia, Kenya, Malawi and South Africa.

Electronics and orthopaedic surgery.

Over the past few decades, sensors have been gaining a lot of popularity in the medical field. These sensors have helped shift the paradigm in medicine from having things done manually to digitalizing them. In the medical field, sensors have been manufactured in different forms and shapes including wearable and implantable wireless devices. With the aid of these sensors, healthcare professionals hope to revolutionize the system in a cost-effective way. In fact, this is already evident in most healthcare systems with the use of sensors for blood pressure, oxygen saturation, and arrhythmias on a daily basis. Also, more sophisticated sensors have made way into the medical field with a feedback loop, such as insulin pumps. On the other hand, similar technologies have been introduced in the orthopaedics world in the past decade. In this paper we summarize some of the sensors used in the medical field in general, and in orthopaedics in particular.

Authenticating Health Activity Data Using Distributed Ledger Technologies.

The on-demand digital healthcare ecosystem is on the near horizon. It has the potential to extract a wealth of information from "big data" collected at the population level, to enhance preventive and precision medicine at the patient level. This may improve efficiency and quality while decreasing cost of healthcare delivered by professionals. However, there are still security and privacy issues that need to be addressed before algorithms, data, and models can be mobilized safely at scale. In this paper we discuss how distributed ledger technologies can play a key role in advancing electronic health, by ensuring authenticity and integrity of data generated by wearable and embedded devices. We demonstrate how the Masked Authenticated Messaging extension module of the IOTA protocol can be used to securely share, store, and retrieve encrypted activity data using a tamper-proof distributed ledger.

Data Integrity-Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis.

BACKGROUND: Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks. OBJECTIVE: This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks. METHODS: We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved. RESULTS: Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified. CONCLUSIONS: We developed a data integrity-based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects.

Medical Sensors for the Diagnosis and Management of Disease: The Physician Perspective.

The objective of this paper is to assist developers of medical sensors to better formulate the clinically relevant design criteria and required performance characteristics of their novel sensor based on an understanding of how these devices will be used by physicians. Sensor technologies play a central role in medicine, and the most critical aspect of the sensor's clinical utility relates to these design decisions. Clinically, sensors are used by health care providers to make both diagnostic and management decisions, and the sensors that aid in these decisions are evaluated by certain clinical, as well as analytical, criteria. Failure to adequately address these end-user requirements can lead to the development of sensors without clinical utility.