Since the global pandemic declaration by the World Health Organization (WHO) due to the spread of COVID-19, the universal wearing of facemasks has been worldwide recommended or imposed to the general population in an effort to prevent the rapid spread of SARS-CoV-2, the respiratory virus that causes COVID-19 disease. Without prejudice to the widespread evidence in favour of facemasks to reduce community transmission, there is also broad agreement on the potential adverse effects caused by their prolonged usage, mainly as a consequence of the increased breathing resistance and CO2 rebreathing. In this context, we have developed a battery-free and wearable sensing platform for gaseous CO2 real-time monitoring into the dead space volume (DSV) of a filtering facepiece FFP2 facemask. While standard facemasks simply act as air filters for the nasal and/or mouth passage, the integration of sensors to control parameters of interest can be an added value to improve their usage and effectiveness, creating a new paradigm of smart facemasks.
As depicted in Fig. 1, the proposed system consists of an opto-chemical sensor combined with a flexible, battery-less, near-field-enabled tag. Both the sensor and the conditioning circuitry have been printed onto a light and flexible polymeric substrate, conforming what it is usually named as sensing tag. The fabricated tag is wirelessly powered by the near-field communication link (NFC) of a smartphone by means of a custom-developed Android application, which is also used for data processing, alert management, and results displaying and sharing. Through performance tests during daily activity and exercise monitoring, we demonstrate the utility of the sensing system for non-invasive, wearable health monitoring and its potential applicability for preclinical research and diagnostics.
The proposed opto-chemical CO2 sensor is based on the combination of a stable inorganic phosphor, whose luminescence is modulated by an acid-base indicator through an inner filter mechanism responding to the gaseous CO2 acidity. This sensing cocktail has been optimized to reduce the interfacial tension and facilitate the permeation of CO2 gas. In brief, the process is carried out by biasing an ultraviolet (UV) light-emitting diode (LED) placed on an NFC tag, attached to the inner face of the mask, with the electromagnetic field provided by the smartphone NFC link. The energy harvested by the tag antenna is conditioned by the NFC chip, then being able to power the rest of the electronic components. When the UV LED is powered on, the CO2 sensing membrane is optically excited and the luminescence intensity is measured to determine the CO2 concentration through the red coordinate (R) of the RGB colour space. The luminescence intensity coming from the sensor is registered using a digital colour detector, , which filters out UV excitation light, and the collected data is sent to a microcontroller unit (MCU). This information is processed to obtain the value of the R coordinate, computing the CO2 concentration from the sensor calibration curve. A temperature sensor is also included in the design to correct the temperature dependence of the chemical sensor. Finally, results are displayed and further processing is carried out in the software application running in the smartphone.
The performance of the smart facemask was preliminary assessed for both short-term and long-term use in static conditions (i.e. sitting and working on a computer) as depicted in Fig. 2a and 2b, respectively. In the first case, the CO2 concentration was monitored every 30 s, while in the case of the long-term test, the facemask was worn during 2.5 hours and the measurements were taken every 15 min. It can be observed how both systems showed very similar trends beyond punctual differences. The inset graph included in Fig. 2b shows how the system allows a very clear reconstruction of the CO2 reduction during a two-minute apnoea and the subsequent CO2 increment from then on.
The performance of the developed smart facemask was also assessed during a graded cycling exercise test by monitoring the data delivered by the instrumented facemask as well as the cycling power and heart rate (HR). Fig. 2c and 2d depict the 4-period Simple Moving Average (SMA) of the calculated CO2 concentration and HR as the cycling power varies during the cycling exercise test. Both figures also include the SMA power during the exercise. This parameter is closely correlated to the HR trend (see red and orange lines). These tests showed that the concentration of CO2 in the breathing zone while wearing the facemask during the exercise increased by approximately 1.9%, which is in line with other studies found in the literature. Our results are in line with the reviewed literature, with CO2 values around 2% during low work-rate activities (Fig. 2a-b) and peak values up to ~5% during high-intensity exercise (Fig. 2c-d). Even though the conducted performance tests do not constitute a formal clinical trial, their purpose is to give an idea of the potential of the developed system in the field of wearable electronics for non-invasive health monitoring.
In summary, we have developed a smart facemask system that distinguishes from previous solutions by combining the three following main aspects simultaneously: i) Both the sensor and the electronics are integrated on flexible substrate, thus achieving better conformability and wearability; ii) It is able to determine CO2 concentration, a key parameter during the process of breathing that has been hardly ever contemplated in the current state-of-the-art of sensing facemasks; and iii) It implements wireless powering through RF energy harvesting and data transmission using NFC technology, thus making the system compatible with any NFC-enabled device without modification, including smartphones, through a custom-developed application. All these features strengthen the potential applications of the proposed device in the fields of non-invasive health monitoring, preclinical research, prognostics and diagnostics with wearable electronics.
For more details, please see the original manuscript: https://www.nature.com/articles/s41467-021-27733-3
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