3D-Printed Face Mask with Integrated Sensors as Protective and Monitoring Tool

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system's fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population's aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask's breathing zone in order to use the patient's breath as a fingerprint for various diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Design and Development Integrated Sensor System for Measuring Body Temperature and Heart Rate

Heart rate and body temperature are some of the important components of a person's main vital signs that need to be monitored regularly and periodically. The detection system technology continues to develop which allows a person to detect his own condition, to avoid exposure to COVID-19. However, the tools that are developing in the market are quite expensive and sometimes complex in operation because they are external products, so that it becomes a difficulty in itself. For this reason, it is important to design a detection device with sensor components that exist in the country and with a simple design so that it is easy to operate and inexpensive. In this paper, utilizing pulse sensors and AD8232 sensors to detect heart rate and MLX90614 sensors to measure body temperature, then NodeMCU ESP8266 to process sensor signals received and will be forwarded to the Display (LCD) to display the results carry out the design and development of an integrated sensor system. From the research results, the accuracy of the MLX90614 temperature sensor is very good with the achievement of 99.24% and the pulse sensor with the achievement of 98.86%. For the test results on each sample obtained accuracy values of 98.4% and 99% for the temperature sensor, and 92.3% and 92.2% for the pulse sensor, respectively. From these results, it is very clear that the sensor design deserves to be promoted as a quality product. © 2022 IEEE.

A Carbon Nanotube-Functional Polymer Composite Film for Low-Power Indoor CO₂ Monitoring

Indoor air quality (IAQ) has been a growing concern in recent years, only to be expedited by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is commonly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents a polymer composite-based chemiresistive CO2 sensor that leverages branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. This polymer blend was incorporated with single wall carbon nanotubes (CNT), which serve as the charge carriers. Prototype sensors were assessed in a bench-top environmental test chamber which varied temperature (22–26 °C), relative humidity level (20–80%), CO2 concentration (400–20,000 ppm), as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as a low-power alternative to current commercially available technologies for indoor CO2 monitoring.

Remote monitoring of Covid-19 patients using IoT and AI

The covid-19 pandemic has brought changes in various sectors like healthcare, business, education and economy. Due to the large spread of covid-19 in a lot of countries there is shortage of hospital beds, oxygen supply and healthcare workers. So, the pandemic generated need to use smart pioneering technologies like Artificial Intelligence and Internet of Things to monitor patient in an effective way. In this research paper a prototype is proposed based on IoT and AI for monitoring home quarantine covid-19 patients. Wearable IoT devices automatically collect information like oxygen level, temperature of body, etc. with the help of integrated sensors. Coughing is one of the most noticeable symptoms of people infected with covid-19. Frequency of cough is detected using Tensor flow library of Deep learning model. This prototype is a way to make IoT sensors smarter enough to detect coughs with the help of a trained model. Coughing dataset is collected and labelled manually. Dataset is self-created and categorized into cough and noise. Cough detection is based on MFCC features using DNN and CNN. The use of these technologies can bring a quick transition in healthcare to avoid risks caused with the life of human beings. © 2022 IEEE.