Microcontroller-based human stress detection system using fuzzy logic

This research presents a working prototype of human stress detection device capable of measuring human stress level perhaps also for autistic children. The device records human physiological signals in order to determine the mental stress level. High stress levels may be dangerous especially for certain individuals such as autistic children who are unable to express mounting levels of stress. The autistic children can have frequent tantrums and seizure activities without any visible signs or symptoms, making this device a useful tool for parents and doctors to anticipate any harmful behaviours of autism. This research focuses on the hardware and software development of a low cost microcontroller-based stress detection system prototype. The prototype was designed using Arduino Mega platform and tested with 35 clinical patients. The data from two sensors is fed to the microcontroller using its two analog input pins and the sensor data is sent to the fuzzy logic module which is pre-programmed into the microcontroller for further processing. The output of the prototype is displayed on the LCD module connected to five digital pins of the microcontroller. In addition, three LEDs are connected to three digital pins of the microcontroller which light up in accordance with the stress levels. In order to test the developed system, an experiment was designed which requires subjects to perform mental calculations to solve arithmetic problems. The experiment involves three phases: low stress phase (P-1), medium stress phase (P-2) and high stress phase (P-3). The results showed that the prototype measures the stress levels with high degree of accuracy and efficiency. Apart from that, the results also highlighted that the stress neither depends on age nor gender

Comparative Study on the Measurement of Human Thermal Activity

Human physiological signals measurement is the necessity of today’s modern world. The physiological signals, including heart rate, skin conductance, temperature, and pupil diameter, are significant indicators of underlying problems or illnesses and aid in indicating the underlying condition non-invasively. This study highlights the importance and needs for only one physiological signal, which is the body temperature as even a minor change in temperature values has a unique effect on the body. Hence, the present study focuses on comparing two well-known temperature sensors, namely DS18B20 and LM35, which are among the top choices for many temperature-based applications. The two sensors are compared in terms of cost, accuracy, temperature range, voltage, output type, implementation, packaging and required signal conditioning circuitry. The sole purpose is to find the adequacy of only one in terms of medical applications. The temperature readings are collected for 15 seconds from 10 participants between the age of 25 – 28 years and the data is sent to a microcontroller, which is Arduino Mega board. The microcontroller board processes the data for noise and artefacts removal and displays the final temperature readings on the serial monitor of Arduino IDE. The results highlight that DS18B20 is more accurate and robust in comparison to LM35, as it has lower fluctuations in the readings and is not affected by user movements. This study will help in the future development of healthcare systems, which may track the user’s thermal changes accurately in real-time

Wearable Devices in Healthcare Services. Bibliometrix Analysis by using R Package

Purpose: The current study aims at exploring the theme – wearable devices in healthcare services from 2008 to 2021. It intends to identify the most prominent sources, authors, affiliations, countries, documents, words and trend topics. Methodology: Total 204 records have been extracted from Scopus after applying inclusion and exclusion criteria, and analysed by using biblioshiny software of R-package. Findings: Results of bibliometrix analysis show the prominent sources in ‘wearable devices & healthcare’ search are IEEE Access and Sensors (Switzerland). Moreover, Lee S. and Shen J. are found be the most productive and prolific authors, King Saud University is leading institutions in producing the articles, China, South Korea, India and USA are identified as the most productive countries and Network Security, Cryptography, Deep Learning, Healthcare Application and Healthcare System are found to be the trending topics and themes in the year 2021. Originality: This study presents the deep analytics regarding wearable devices in healthcare services and it also suggests useful future research avenues and insights for researchers and practitioners

Convolutional neural network architecture for detecting facemask and social distancing: a preventive measure for COVID19

COVID-19 is a life-threatening virus which affected people at a global level in just a matter of few months and is highly contagious. In order to reduce its spread, SOPs must be followed, such as washing hands, wearing face masks, and maintaining social distance. Hence, to aid the strict follow up of SOPs, this paper proposes a system to detect whether the people are wearing face masks and maintaining social distance or not in order to break the chain of COVID 19. The proposed system uses Deep Learning (DL) model based on Convolutional Neural Network (CNN) architecture for training the facemask detector and OpenPose 2D skeleton extraction technique for detecting social distance. A DL model based on a 7-layered CNN architecture was proposed in this research to detect masked and unmasked faces. Based on the proposed technique, 99.98% validation and 99.98% testing accuracies were achieved. In addition to that, the maintenance of social distance which is the new normal nowadays was also detected using the images obtained from the internet as currently, there is no such database available for detecting social distancing