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.

Predicting Violence-Induced Stress in an Arabic Social Media Forum

Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over time.Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and chil-dren.In this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media.We searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on Facebook.We applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social media.We extracted a set of tex-tual,spatial,and interaction attributes from various features.Therefore,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress detection.Experiments depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in F1-measure.Also,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.

Revealing the Invisible： A New Approach for Enhancing Industrial Safety, Reliability and Remaining Life Assessment

Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, an innovative technique for early detection of potential failure and condition monitoring is urgently required by many engineers. This document describes a novel approach to improve industrial equipment safety, reliability and life cycle management. A new field portable instrument called the ＂IMS （indicator of mechanical stresses）＂ utilizes magneto-anisotropic （＂cross＂） transducers to measure anisotropy of magnetic properties in ferromagnetic material. Mechanical stresses including residual stresses in Ferro-magnetic parts, are ＂not visible＂ to most traditional NDT （non-destructive testing） methods; for example, radiography and ultrasonic inspection. Stress build-up can be the first indicator that something is faulty with a structure. This can be the result of a manufacturing defect; or as assets age and fatigue, stress loads can become unevenly distributed throughout the metal. We outline the evaluation of IMS as a fast screening tool to provide structural condition or deterioration feedback in novel applications for pipelines, petrochemical refinery, cranes, and municipal infrastructure.

Prospect and research progress of detecting dynamic change in crustal stress by bedrock temperature

A new method of detecting stress change by temperature(DSCT),has been recently proposed on the basis of the experimental results in laboratory,and verified by field observation.In this paper,at first,physical background is concisely introduced,and experimental researches are followed.Then,the key techniques are reviewed,and the main results on in-situ observations are also given in detail.At last,we emphasize on the prospects of this method for being investigated further.The potential prospect includes six contents:(1)to observe the tidal force and its secondary fluid thermal effect;(2)to study temperature response to change in direction of the stress change;(3)to carry out practical engineering application;(4)to analyze the strong earthquake risk,based on bedrock temperature observation;(5)to conduct in situ experiment on DSCT;(6)to explain quantitatively the satellite thermal infrared anomaly.In short,considering that the dynamic change of the crustal stress is a key parameter of earthquake forecasting or engineering application,the method of DSCT has important practical significance for earthquake risk or engineering applications.