Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects.

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion of the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field. We conclude that CNN-based approaches are suitable for effective and accurate human activity recognition system applications despite challenges including availability of data regarding composite or group activities, high computational resource requirements, data privacy concerns, and edge computing limitations. For widespread adaptation, future research should be focused on more efficient edge computing techniques, datasets incorporating contextual information with activities, more explainable methodologies, and more robust systems.

EEG Channel Correlation Based Model for Emotion Recognition.

Emotion recognition using Artificial Intelligence (AI) is a fundamental prerequisite to improve Human-Computer Interaction (HCI). Recognizing emotion from Electroencephalogram (EEG) has been globally accepted in many applications such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. Nevertheless, due to having too low amplitude variation related to time on EEG signal, the proper recognition of emotion from this signal has become too challenging. Usually, considerable effort is required to identify the proper feature or feature set for an effective feature-based emotion recognition system. To extenuate the manual human effort of feature extraction, we proposed a deep machine-learning-based model with Convolutional Neural Network (CNN). At first, the one-dimensional EEG data were converted to Pearson's Correlation Coefficient (PCC) featured images of channel correlation of EEG sub-bands. Then the images were fed into the CNN model to recognize emotion. Two protocols were conducted, namely, protocol-1 to identify two levels and protocol-2 to recognize three levels of valence and arousal that demonstrate emotion. We investigated that only the upper triangular portion of the PCC featured images reduced the computational complexity and size of memory without hampering the model accuracy. The maximum accuracy of 78.22% on valence and 74.92% on arousal were obtained using the internationally authorized DEAP dataset.

Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic.

Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.

Scalable Telehealth Services to Combat Novel Coronavirus (COVID-19) Pandemic.

An ongoing pandemic, the novel coronavirus disease 2019 (COVID-19) is threatening the nations of the world regardless of health infrastructure conditions. In the age of digital electronic information and telecommunication technology, scalable telehealth services are gaining immense importance by helping to maintain social distances while providing necessary healthcare services. This paper aims to review the various types of scalable telehealth services used to support patients infected by COVID-19 and other diseases during this pandemic. Recently published research papers collected from various sources such as Google Scholar, ResearchGate, PubMed, Scopus, and IEEE Xplore databases using the terms "Telehealth", "Coronavirus", "Scalable" and "COVID-19" are reviewed. The input data and relevant reports for the analysis and assessment of the various aspects of telehealth technology in the COVID-19 pandemic are taken from official websites. We described the available telehealth systems based on their communication media such as mobile networks, social media, and software based models throughout the review. A comparative analysis among the reviewed systems along with necessary challenges and possible future directions are also drawn for the proper selection of affordable technologies. The usage of scalable telehealth systems improves the quality of the healthcare system and also reduces the infection rate while keeping both patients and doctors safe during the pandemic.

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19).

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers.

Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers' outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.

During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.

Development of Smart Healthcare Monitoring System in IoT Environment.

Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient's basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO2 sensor. The error percentage of the developed scheme is within a certain limit (< 5%) for each case. The condition of the patients is conveyed via a portal to medical staff, where they can process and analyze the current situation of the patients. The developed prototype is well suited for healthcare monitoring that is proved by the effectiveness of the system.

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients' Recovery.

Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

Breathing Aid Devices to Support Novel Coronavirus (COVID-19)Infected Patients.

Novel coronavirus (COVID-19), an ongoing pandemic, is threatening the whole population all over the world including the nations having high or low resource health infrastructure. The number of infection as well as death cases are increasing day by day, and outperforming all the records of previously found infectious diseases. This pandemic is imposing specific pressures on the medical system almost the whole globe. The respiration problem is the main complication that a COVID-19 infected patient faced generally. It is a matter of hope that the recent deployment of small-scale technologies like 3D printer, microcontroller, ventilator, Continuous Positive Airway Pressure (CPAP) are mostly used to resolve the problem associated with medical equipment's for breathing. This paper aims to overview the existing technologies which are frequently used to support the infected patients for respiration. We described the most recent developed breathing aid devices such as oxygen therapy devices, ventilator, and CPAP throughout the review. A comparative analysis among the developed devices with necessary challenges and possible future directions are also outlined for the proper selection of affordable technologies. It is expected that this paper would be of great help to the experts who would like to contribute in this area.

Wearable Technology to Assist the Patients Infected with Novel Coronavirus (COVID-19).

Wearable technology plays a significant role in our daily life as well as in the healthcare industry. The recent coronavirus pandemic has taken the world's healthcare systems by surprise. Although trials of possible vaccines are underway, it would take a long time before the vaccines are permitted for public use. Most of the government efforts are currently geared towards preventing the spread of the coronavirus and predicting probable hot zones. The essential and healthcare workers are the most vulnerable towards coronavirus infections due to their required proximity to potential coronavirus patients. Wearable technology can potentially assist in these regards by providing real-time remote monitoring, symptoms prediction, contact tracing, etc. The goal of this paper is to discuss the different existing wearable monitoring devices (respiration rate, heart rate, temperature, and oxygen saturation) and respiratory support systems (ventilators, CPAP devices, and oxygen therapy) which are frequently used to assist the coronavirus affected people. The devices are described based on the services they provide, their working procedures as well as comparative analysis of their merits and demerits with cost. A comparative discussion with probable future trends is also drawn to select the best technology for COVID-19 infected patients. It is envisaged that wearable technology is only capable of providing initial treatment that can reduce the spread of this pandemic.

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images.

Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.