IoT-Based Humanoid Software for Identification and Diagnosis of Covid-19 Suspects

Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT

Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized.

IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm

New potential for healthcare has been made possible by the development of the Internet of Medical Things (IoMT) with deep learning. This is applied for a broad range of applications. Normal medical devices together with sensors can gather important data when connected to the Internet, and deep learning uses this data to reveal symptoms and patterns and activate remote care. In recent years, the COVID-19 pandemic caused more mortality. Millions of people have been affected by this virus, and the number of infections is continually rising daily. To detect COVID-19, researchers attempt to utilize medical imaging and deep learning-based methods. Several methodologies were suggested utilizing chest X-ray (CXR) images for COVID-19 diagnosis. But these methodologies do not provide satisfactory accuracy. To overcome these drawbacks, a recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm (RERNN-GEO) is proposed in this paper. The intention of this work is to provide IoT-based deep learning method for the premature identification of COVID-19. This paradigm can be able to ease the workload of radiologists and medical specialists and also help with pandemic control. RERNN-GEO is a deep learning-based method; this is utilized in chest X-ray (CXR) images for COVID-19 diagnosis. Here, the Gray-Level Co-Occurrence Matrix (GLCM) window adaptive algorithm is used for extracting features to enable accurate diagnosis. By utilizing this algorithm, the proposed method attains better accuracy (33.84%, 28.93%, and 33.03%) and lower execution time (11.06%, 33.26%, and 23.33%) compared with the existing methods. This method can be capable of helping the clinician/radiologist to validate the initial assessment related to COVID-19.

Hybrid deep learning model based smart IOT based monitoring system for Covid-19

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices

Worldwide COVID-19 is a highly infectious and rapidly spreading disease in almost all age groups. The Computed Tomography (CT) scans of lungs are found to be accurate for the timely diagnosis of COVID-19 infection. In the proposed work, a deep learning-based P-shot N-ways Siamese network along with prototypical nearest neighbor classifiers is implemented for the classification of COVID-19 infection from lung CT scan slices. For this, a Siamese network with an identical sub-network (weight sharing) is used for image classification with a limited dataset for each class. The feature vectors are obtained from the pre-trained sub-networks having weight sharing. The performance of the proposed methodology is evaluated on the benchmark MosMed dataset having categories zero (healthy control) and numerous COVID-19 infections. The proposed methodology is evaluated on (a) chest CT scans provided by medical hospitals in Moscow, Russia for 1110 patients, and (b) case study of low-dose CT scans of 42 patients provided by Avtaran healthcare in India. The deep learning-based Siamese network (15-shot 5-ways) obtained an accuracy of 98.07%, the sensitivity of 95.66%, specificity of 98.83%, and F1-score of 95.10%. The proposed work outperforms the COVID-19 infection severity classification with limited scans availability for numerous infection categories.

Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction

Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.

Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques

Pandemic novel Coronavirus (Covid-19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid-19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid-19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid-19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)-based meta-analysis to predict the trend of epidemic Covid-19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time-series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid-19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid-19 observed symptoms, a list of Top-20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid-19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction.

Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems.

On Development of MySignals based prototype for application in health vitals monitoring

India's health infrastructure is under pressure since the daily COVID-19 cases have crossed the milestone of 4 Lakhs cases per day which surpass the previous years' peak. Patients with mild symptoms have been advised for home treatment since most of the hospitals are running out of bed. In this situation delivering healthcare to people has become revolutionizing due to the rapid advancement of embedded systems, communication, and informatics technologies. Integration of different health sensors, handheld devices, and internet can be a great potential for significant improvement of the quality of remote healthcare. This paper discusses the use of MySignals HW shield which is a hardware development platform for medical devices to build e-health monitoring system. Wearable health monitoring system prototype has been developed in this work. To conduct experiments, health vitals such as body temperature, ECG, oxygen saturation level, and pulse rate from 5 volunteers have been measured, collected, and stored in a cloud database using the system prototype. To evaluate the performance of the prototype, transmission delay has been recorded in both wired (Ethernet) and wireless (Wi-Fi) communication modes. It is observed that it takes 2.71 ms and 5.18 ms of time to collect and store the health vitals to the cloud database in wired and wireless mode respectively. Comparing the collected health vitals with the normal range of health vitals, no abnormality is found in all volunteer's health. Finally, a framework for contactless monitoring of COVID-affected patients is proposed. Contactless monitoring of health vitals can reduce the chance of community spread.

Progress in robotics for combating infectious diseases

The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.

Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.