Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI

Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus(COVID-19)has impacted nearly every person across the globe either in terms of losses of life or as of lockdown.The current coronavirus(COVID-19)pandemic is a rare/special situation where people can express their feelings on Internet-based social networks.Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions.This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown.The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown.In this research,we have used a Long Short-Term Memory(LSTM)network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive,negative,or neutral emotional out bust based on their Twitter posts.The results showed that the model has 88.14%accuracy(representation of the correct prediction over the test dataset)after 10 epochs which most tweets showed had neutral polarity.The evaluation shows interesting results in positive(1),negative(–1),and neutral(0)emotions through different visualization.

A Secure and Efficient Signature Scheme for IoT in Healthcare

To provide faster access to the treatment of patients,healthcare system can be integrated with Internet of Things to provide prior and timely health services to the patient.There is a huge limitation in the sensing layer as the IoT devices here have low computational power,limited storage and less battery life.So,this huge amount of data needs to be stored on the cloud.The information and the data sensed by these devices is made accessible on the internet from where medical staff,doctors,relatives and family members can access this information.This helps in improving the treatment as well as getting faster medical assistance,tracking of routine activities and health focus of elderly people on frequent basis.However,the data transmission from IoT devices to the cloud faces many security challenges and is vulnerable to different security and privacy threats during the transmission path.The purpose of this research is to design a Certificateless Secured Signature Scheme that will provide a magnificent amount of security during the transmission of data.Certificateless signature,that removes the intricate certificate management and key escrow problem,is one of the practical methods to provide data integrity and identity authentication for the IoT.Experimental result shows that the proposed scheme performs better than the existing certificateless signature schemes in terms of computational cost,encryption and decryption time.This scheme is the best combination of high security and cost efficiency and is further suitable for the resource constrained IoT environment.

Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia.The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9,2020,named Novel Coronavirus 2019(nCoV-2019).This nCoV-2019 is now known as COVID-19.There is a big list of infections of this coronavirus which is present in the form of a big family.This virus can cause several diseases that usually develop with a serious problem.According to the World Health Organization(WHO),2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome(SARS)and Middle East Respiratory Syndrome(MERS)coronaviruses,so COVID-19 can repeatedly change its internal genome structure to extend its existence.Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against coronavirus.In this research paper,an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’complete genome.This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties.This paper identifies five main clusters of mutations with k=5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.

Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices

Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise.Moreover,it is widely utilizing for preventing injuries of athletes during a practice session and in few cases,it leads to muscle fatigue.At present,emerging technology like the internet of things(IoT)and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity.In this study,an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram(sEMG)sensor.Normally,the EMG signal is utilized to display muscle activity.Arduino controller,Wi-Fi module,and EMG sensor are utilized in developing the wearable device.The Time-frequency domain spectrum technique is employed for classifying the threemuscle fatigue conditions including meanRMS,mean frequency,etc.A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data.The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as>2V:Extensive);1–2V:Moderate,and<1V:relaxed.The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue.Moreover,the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices.The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.