A Novel Feature of Smart Mask: Monitoring and Control System of Respiratory Conditions Using IoT for High-Activity-Person During New Normal Era

During the Covid-19 period, respitory rate monitoring is the main way to know respiratory conditions. However, the use of masks can cause stuffiness for the wearer. Therefore, a smart mask that respiratory rate monitoring was designed based on the difference in the temperature of the breathing, which was read by the LM35 temperature sensor and displayed in the blynk application on the Smartphone. The system is also equipped with a buzzer that will give a warning when breathing conditions are abnormal. In addition to the monitoring system, there is also a humidity control that is the of an exhaust fan actuator to maintain the humidity inside the mask that always adjusts to the setpoint. From the sensor performance test, it was found that the mean error of the LM35 temperature sensors was 3.14%, the DHT11 temperature sensors was 2.7% and the mean error of the DHT11 humidity sensor was 4%. In the respiratory rate measurement prototype, the mean error was 1.87%, and the accuracy of 98.1 %. Meanwhile, testing the fan response to the 84% RH setpoint, the Error Steady State (ess) was 0% and the Maximum Overshoot (Mp) was 1.19%, which means it is still below the 2% allowed tolerance value. Thus, the control system to be in a steady state. © 2021 IEEE.

Smart Mask for COVID-19 E-wear

COVID-19 has been one of the most disastrous pandemics of the decade which has impacted several human lives and global economy. It is really difficult to control this kind of problem in a developing country like India where we have huge population. It is also difficult to maintain social distancing in such places. So, we have come up with an idea for the people to self-monitor and protect themselves from the spread of the virus. This paper aims to implement a smart mask which uses digital technology, for detection, prevention and precaution from this deadly virus. This virus is bound to spread in the event of interaction with other people, and we use technology to detect and prevent the virus by forming a personalized mask, which is capable of monitoring your vitals and indicating if the patient is infected by the virus. We are integrating the heart rate pulse sensor, infrared thermometer—MLX90614, respiratory sensor and Dht11 sensor which are used to monitor the inner temperature and humidity present inside the mask. This sensor is mounted on the mask which helps us to monitor the person who is wearing it 24/7. The overall equipment is less bulky with all the required features, rechargeable and replaceable filter. The vitals are monitored through personalized cloud using ThinkSpeak and mobile-based monitoring with alert system. The mask has an OLED display for real-time monitoring. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning-Based Smart Mask for Social Distancing

The COVID-19 is having a significant impact on our everyday lives. One of the measures put in place to slow the spread of the disease is social distancing. Because of the abrupt implementation of these laws, it become impossible for a person to follow this form of lifestyle. So we have developed a device that can assist both normal and visually disabled people in maintaining physical distance from other people by using deep learning to identify an individual and verify if the other person is six feet away from the user so that social distance can be sustained, and virus spread can be reduced. Our architecture includes a Webcam and a Raspberry Pi 4 model B to run an advanced image processing algorithm SSD MobileNet for human detection. SSD MobileNet is implemented to determine if the other individual is physically close to the user. If an individual does not adhere to social distancing, an automated reading aid is activated, and an audio warning is sent to the user. Since the whole setup is light and portable, it can be conveniently installed with any mask. The job is evaluated in real time. The results show that the proposed device improves mobility, convenience, and ease of navigation for both normal and visually disabled people in this pandemic situation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Charge-Switchable Zwitterionic Peptide for Rapid Detection of SARS-CoV-2 Main Protease.

The transmission of SARS-CoV-2 coronavirus has led to the COVID-19 pandemic. Nucleic acid testing while specific has limitations for mass surveillance. One alternative is the main protease (Mpro ) due to its functional importance in mediating the viral life cycle. Here, we describe a combination of modular substrate and gold colloids to detect Mpro via visual readout. The strategy involves zwitterionic peptide that carries opposite charges at the C-/N-terminus to exploit the specific recognition by Mpro . Autolytic cleavage releases a positively charged moiety that assembles the nanoparticles with rapid color changes (t<10 min). We determine a limit of detection for Mpro in breath condensate matrices <10 nM. We further assayed ten COVID-negative subjects and found no false-positive result. In the light of simplicity, our test for viral protease is not limited to an equipped laboratory, but also is amenable to integrating as portable point-of-care devices including those on face-coverings.

Smart Face Mask with an Integrated Heat Flux Sensor for Fast and Remote People's Healthcare Monitoring.

The COVID-19 pandemic has highlighted a large amount of challenges to address. To combat the spread of the virus, several safety measures, such as wearing face masks, have been taken. Temperature controls at the entrance of public places to prevent the entry of virus carriers have been shown to be inefficient and inaccurate. This paper presents a smart mask that allows to monitor body temperature and breathing rate. Body temperature is measured by a non-invasive dual-heat-flux system, consisting of four sensors separated from each other with an insulating material. Breathing rate is obtained from the temperature changes within the mask, measured with a thermistor located near the nose. The system communicates by means of long-range (LoRa) backscattering, leading to a reduction in average power consumption. It is designed to establish the relative location of the smart mask from the signal received at two LoRa receivers installed inside and outside an access door. Low-cost LoRa transceivers with WiFi capabilities are used in the prototype to collect information and upload it to a server. Accuracy in body temperature measurements is consistent with measurements made with a thermistor located in the armpit. The system allows checking the correct placement of the mask based on the recorded temperatures and the breathing rate measurements. Besides, episodes of cough can be detected by sudden changes in thermistor temperature.