IoT Solutions and AI-Based Frameworks for Masked-Face and Face Recognition to Fight the COVID-19 Pandemic.

A global health emergency resulted from the COVID-19 epidemic. Image recognition techniques are a useful tool for limiting the spread of the pandemic; indeed, the World Health Organization (WHO) recommends the use of face masks in public places as a form of protection against contagion. Hence, innovative systems and algorithms were deployed to rapidly screen a large number of people with faces covered by masks. In this article, we analyze the current state of research and future directions in algorithms and systems for masked-face recognition. First, the paper discusses the importance and applications of facial and face mask recognition, introducing the main approaches. Afterward, we review the recent facial recognition frameworks and systems based on Convolution Neural Networks, deep learning, machine learning, and MobilNet techniques. In detail, we analyze and critically discuss recent scientific works and systems which employ machine learning (ML) and deep learning tools for promptly recognizing masked faces. Also, Internet of Things (IoT)-based sensors, implementing ML and DL algorithms, were described to keep track of the number of persons donning face masks and notify the proper authorities. Afterward, the main challenges and open issues that should be solved in future studies and systems are discussed. Finally, comparative analysis and discussion are reported, providing useful insights for outlining the next generation of face recognition systems.

Design modifications and evaluation of the silicone artificial finger joints.

BACKGROUND: There are many reasons that could lead to finger joint arthroplasty, and the most familiar reason is osteoarthritis. Silicone finger joint are the most commonly used implants. However, these implants might fracture with time and cause wear which will lead to chronic inflammation and synovitis for the patient and then implant failure. OBJECTIVE: The aim of this study is to improve the design of the silicone finger joint and simulate the different designs using finite element analysis (FEA) simulation. METHOD: Three different designs were drawn and FEA has been used in this study using Solidworks software. The first design is the silicone finger joint design without any modification, the second one is modified design with added ribs to the junction of distal stem and hinge and the third design was added filler material inside the body of the artificial joint. An axial force with 625 N that was applied on the upper part of the distal stem which is nearly represents the maximum value of the grip strength for normal males. RESULTS: The results showed improvement on the design in which the concentrated stress at the junction of the distal stem and hinge of the design was distributed. In addition, the Von Mises stress was stable for the modified design with added ribs and the added filler material designs after 15°. CONCLUSION: The design modification could improve the stress distribution and stability of the artificial finger joint and increase the lifetime expectancy of these implants.

Emerging Development of Auto-Charging Sensors for Respiration Monitoring.

In recent years, the development of biomedical monitoring systems, including respiration monitoring systems, has been accelerated. Wearable and implantable medical devices are becoming increasingly important in the diagnosis and management of disease and illness. Respiration can be monitored using a variety of biosensors and systems. Auto-charged sensors have a number of advantages, including low cost, ease of preparation, design flexibility, and a wide range of applications. It is possible to use the auto-charged sensors to directly convert mechanical energy from the airflow into electricity. The ability to monitor and diagnose one's own health is a major goal of auto-charged sensors and systems. Respiratory disease model output signals have not been thoroughly investigated and clearly understood. As a result, figuring out their exact interrelationship is a difficult and important research question. This review summarized recent developments in auto-charged respiratory sensors and systems in terms of their device principle, output property, detecting index, and so on. Researchers with an interest in auto-charged sensors can use the information presented here to better understand the difficulties and opportunities that lie ahead.

Validation of earlobe site as an alternative blood glucose testing approach.

BACKGROUND: Drawing blood from the fingertips for glucose testing is painful and likely to cause tissue damage over time. Earlobes are an alternative site for glucose measurement. OBJECTIVE: This work aims to validate the earlobe as an alternate test site for blood glucose testing by demonstrating valid and reliable statistically significant differences between the earlobes and standard reference sites. METHODS: Blood glucose concentrations from 50 volunteers were measured and statistically analysed from the reference sites (forearm and fingertip) and earlobe. The analysis included: 1) one-way analysis of variance (ANOVA), 2) regression analysis, 3) Bland Altman analysis, and 4) Clarke Error Grid analysis. RESULTS: The results indicated that there is no statistically significant difference between the three blood glucose-testing methods. For the forearm-earlobe and fingertip-earlobe, all measurements were grouped around the mean of 3.7 ± 1.96 SD and 2.96± 1.96 SD, respectively. Error grid analysis showed > 97% of all earlobe and references measurements fell in Zones A and B and were in the clinically acceptable level. CONCLUSIONS: The results have shown that the earlobe is a valid substitute for blood glucose measurements.

Advancement of Nanofibrous Mats and Common Useful Drug Delivery Applications.

Electrospinning enables simple and cost-effective production of polymer nanofibers from different polymer materials. Drug delivery systems are capable of achieving maximum drug treatment benefits by significantly reducing adverse complications. Electrospun nanofibers have recently attracted considerable attention owing to their distinctive properties, including flexibility and biocompatibility. The implementation of functional constituents within nanostructure fibers blends is an effective technique for the administration of a variety of drugs in animal research, broadening the nanofiber capability and reliability. The nanofibrous mesh and its various application purposes are discussed in terms of a summary of recent research, emphasizing the ease of streaming and a large number of combinations of this approach, which could lead to a breakthrough in targeted therapy.

Recent Applications of Electrospun Nanofibrous Scaffold in Tissue Engineering.

Tissue engineering is a relatively new area of research that combines medical, biological, and engineering fundamentals to create tissue-engineered constructs that regenerate, preserve, or slightly increase the functions of tissues. To create mature tissue, the extracellular matrix should be imitated by engineered structures, allow for oxygen and nutrient transmission, and release toxins during tissue repair. Numerous recent studies have been devoted to developing three-dimensional nanostructures for tissue engineering. One of the most effective of these methods is electrospinning. Numerous nanofibrous scaffolds have been constructed over the last few decades for tissue repair and restoration. The current review gives an overview of attempts to construct nanofibrous meshes as tissue-engineered scaffolds for various tissues such as bone, cartilage, cardiovascular, and skin tissues. Also, the current article addresses the recent improvements and difficulties in tissue regeneration using electrospinning.

Autocharging Techniques for Implantable Medical Applications.

Implantable devices have successfully proven their reliability and efficiency in the medical field due to their immense support in a variety of aspects concerning the monitoring of patients and treatment in many ways. Moreover, they assist the medical field in disease diagnosis and prevention. However, the devices' power sources rely on batteries, and with this reliance, comes certain complications. For example, their depletion may lead to surgical interference or leakage into the human body. Implicit studies have found ways to reduce the battery size or in some cases to eliminate its use entirely; these studies suggest the use of biocompatible harvesters that can support the device consumption by generating power. Harvesting mechanisms can be executed using a variety of biocompatible materials, namely, piezoelectric and triboelectric nanogenerators, biofuel cells, and environmental sources. As with all methods for implementing biocompatible harvesters, some of them are low in terms of power consumption and some are dependent on the device and the place of implantation. In this review, we discuss the application of harvesters into implantable devices and evaluate the different materials and methods and examine how new and improved circuits will help in assisting the generators to sustain the function of medical devices.

Methods of energy generation from the human body: a literature review.

The purpose of this study is to review available methods of utilising the human body to obtain energy during the course of daily life activities, without interference with an individual's lifestyle. The number of individuals with health issues requiring assistance from external or internal health-aiding devices is rapidly increasing. Battery life associated with these devices is currently a major limitation. Currently, medical devices that depend on batteries (i.e., implantable devices) require constant battery monitoring. Development of implantable devices with rechargeable batteries is, therefore, essential. Technologies that can capture energy from the human body can be developed, with different organs, systems, and activities having the potential to be utilised to generate energy. This energy source can act as an alternative to conventional batteries. This study provides an overview of various methods for obtaining energy from the human body. These methods are summarised, compared and analysed. The best results achieved (in terms of power output) are compared and listed.

Eye blinking-based method for detecting driver drowsiness.

Drowsy driving is a major cause of traffic accidents. Eye blinking is considered as important evidence of driver drowsiness. In this paper, a portable and low cost device for monitoring a driver's drowsiness is proposed. The proposed system consists of two main parts that detect eye blinking based on IR sensors mounted on eyewear. Depending on the reflected and absorbed IR radiation, this system detects and classifies the eye blinking into normal blinking (NB) or prolonged blinking (PB). The detected prolonged blinking is used to trigger an audio/visual alarm system which draws the driver's attention back. The system was simulated initially by LabVIEW® software. Moreover, the system was bench tested on 15 adult volunteers; eye blinking were detected and classified successfully for all subjects. The results of this research are promising and additional investigation is required to further improve the method.

A practical technique for measuring human biofluid conductivity using high gain-frequency characteristics.

Currently, the study of ion composition and performance in human biofluids plays an important role in biomedical engineering research and technology. This field may become universal for human diagnostics; it allows early detection of different diseases in humans by measuring changes in ion behaviour in human biofluids. Practical experiments were conducted to analyse the liquid composite electrolyte conductivity in an alternating electric current field. These experiments allow the contribution of separate types of ions to the overall conductivity to be estimated. The method of estimating the concentration of active ions contained in biofluids is also introduced; it illustrates the possibility of performing qualitative and quantitative analysis over a wide range of concentrations and compositions. The authors present a procedure to determine the concentration of active liquid ions based on conductivity gain-frequency characteristic curve tracing. The experimental results validate the practical use of the proposed method. The results of this research are promising, and further investigation is required to further improve the method.

Statistical approach for brain cancer classification using a region growing threshold.

In brain cancer, a biopsy as an invasive procedure is needed in order to differentiate between malignant and benign brain tumor. However, in some cases, it is difficult or harmful to perform such a procedure, to the brain. The aim of this study is to investigate a new method in maximizing the probability of brain cancer type detection without actual biopsy procedure. The proposed method combines both image and statistical analysis for tumor type detection. It employed image filtration and segmentation of the target region of interest with MRI to assure an accurate statistical interpretation of the results. Statistical analysis was based on utilizing the mean, range, box plot, and testing of hypothesis techniques to reach acceptable and accurate results in differentiating between those two types. This method was performed, examined and compared on actual patients with brain tumors. The results showed that the proposed method was quite successful in distinguishing between malignant and benign brain tumor with 95% confident that the results are correct based on statistical testing of hypothesis.

Utilizing wavelet transform and support vector machine for detection of the paradoxical splitting in the second heart sound.

Paradoxical splitting occurs when pulmonic valve (P2) closes before the aortic valve (A2). This causes second heart sound (S2) to be a single sound during inspiration and split during exhalation. Etiology delay in aortic closure: aortic stenosis, volume overload of left ventricle (LV), conduction defects in LV, and left bundle branch block (LBBB). In this article, a method was proposed in early detection of a reverse in the appearance of A2 and P2 within S2. This method is based on the time-frequency maps obtained with the continuous wavelet transform (CWT), namely, the Meyer wavelet. A number of patients with LBBB and others with fitted pacemakers were studied. The above method is combined with the support vector machine (SVM) and performance of this method is evaluated using classification accuracy (Ca), sensitivity (Se), specificity, positive, and negative predicted values. Results show that it is relatively easy to detect the reverse in A2 and P2 and the Ca and Se is 90.97 and 94.44%, respectively, for the sample of 42 patients whose data were collected from the Cardiology Department at Brighton and Sussex University Hospital in England.