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IEEE Sensors Journal ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-2088063


Mask wearing has become critical for preventing the aerosolization and inhalation of virus-laden particles during the ongoing COVID-19 global pandemic. However, facial masks with effective filtration are either not readily accessible (e.g., N95) or have reduced filtration efficiency due to air gaps between the mask and wearer (e.g., cloth masks). We have developed a novel combination of a mask and shield named Mask And Shield Integrated (MASI) that provides nearly the same levels of protection as an N95 mask by addressing these issues. Magnetic latches reduce gaps between the mask and wearer, while a novel fin structure on the shield provides protection against floating particles. A series of experiments was performed to study MASI’s efficacy in both eliminating mask gaps and also providing N95-like filtration efficiency. MASI was found to solve both problems, thus providing a low-cost mask solution that can be applied to a broad range of environments to prevent inhalation of small air-borne particles. IEEE

IEEE Sensors Journal ; 2021.
Article in English | Scopus | ID: covidwho-1574574


This paper describes a wearable, open-source wrist temperature monitoring system that enables the reliable identification of slowly-varying skin temperature patterns that may be indicative of infections. The hardware platform uses a Bluetooth Low Energy (BLE) wireless interface and includes three skin temperature sensors, a thermally-isolated ambient temperature sensor, an inertial measurement unit (IMU), and a Galvanic skin response (GSR) sensor. A template-matching algorithm is used to detect weak but long-lived anomalous temperature patterns that deviate from the normal circadian rhythm are thus may be driven by infections. Experimental and simulation results confirm that small temperature anomalies (peak value <0.4°C) extending over 2-3 weeks can be detected with a total error rate <10%. IEEE

2021 IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2021 ; 2021-August:676-679, 2021.
Article in English | Scopus | ID: covidwho-1447889


The rapid spread of COVID-19 prompted many to take precautions to control the transmission of the virus. The most crucial of these is face coverings, such as face masks, as the virus is an airborne pathogen capable of transmission via respiratory droplets. While there are developments in smart mask technologies, they only provide passive protection through filtration and internal decontamination against the virus. This paper describes a new approach to smart mask technology that actively monitors for airborne pathogens using a PM sensor and reduces the wearer's exposure to them by an active mitigation strategy. This strategy involves using a mist spray to increase the pathogen's particle size, which reduces their ability to linger in the air. This system can be controlled wirelessly via a mobile application, which also displays the monitoring data. Deployment areas of this smart mask include classrooms, hospitals, and workspaces. Our experimental results demonstrate that increasing droplet sizes reduces the settling time for pathogens. Our future work includes a more precise detection of pathogens and improvements in deploying mitigation using fine-grained analysis about the aerosol and artificial intelligence. © 2021 IEEE.

Ieee Consumer Electronics Magazine ; 10(2):72-79, 2021.
Article in English | Web of Science | ID: covidwho-1129419


Face masks provide effective, easy-to-use, and low-cost protection against airborne pathogens or infectious agents, including SARS-CoV-2. Existing masks are all passive in nature, i.e., simply act as air filters for the nasal passage and/or mouth. This article presents a new "active mask" paradigm, in which the wearable device is equipped with smart sensors and actuators to both detect the presence of airborne pathogens in real time and take appropriate action to mitigate the threat. The proposed approach is based on a closed-loop control system that senses airborne particles of different sizes near the mask and then makes intelligent decisions to reduce their concentrations. In the current implementation, an onboard controller determines ambient air quality via a commercial particulate matter sensor, and if necessary activates a piezoelectric actuator that generates a mist spray to load these particles, thus causing them to fall to the ground. The system communicates with the user via a smart phone application that provides various alerts, including the need to recharge and/or decontaminate the mask prior to reuse. The application also enables a user to override the onboard control system and manually control the mist generator if necessary. Experimental results from a functional prototype demonstrate significant reduction in airborne PM counts near the mask when the active protection system is enabled.