Wearable Graphene-based smart face mask for Real-Time human respiration monitoring

Abstract

After the pandemic of SARS-CoV-2, the use of face-masks is considered the most effective way to prevent the spread of virus-containing respiratory fluid. As the virus targets the lungs directly, causing shortness of breath, continuous respiratory monitoring is crucial for evaluating health status. Therefore, the need for a smart face mask (SFM) capable of wirelessly monitoring human respiration in real-time has gained enormous attention. However, some challenges in developing these devices should be solved to make practical use of them possible. One key issue is to design a wearable SFM that is biocompatible and has fast responsivity for non-invasive and real-time tracking of respiration signals. Herein, we present a cost-effective and straightforward solution to produce innovative SFMs by depositing graphene-based coatings over commercial surgical masks. In particular, graphene nanoplatelets (GNPs) are integrated into a polycaprolactone (PCL) polymeric matrix. The resulting SFMs are characterized morphologically, and their electrical, electromechanical, and sensing properties are fully assessed. The proposed SFM exhibits remarkable durability (greater than1000 cycles) and excellent fast response time (∼42 ms), providing simultaneously normal and abnormal breath signals with clear differentiation. Finally, a developed mobile application monitors the mask wearer's breathing pattern wirelessly and provides alerts without compromising user-friendliness and comfort.

Keywords: Graphene and Advanced materials; Piezoresistive sensor; Real-time monitoring; Respiration monitoring; Smart face mask; Wearable sensing device.

Graphical abstract

Fig. 1

Proof-of-concept (a); PCL/GNP composite fabrication route (b) and sketch of the steps to spray the PCL-GNP solution over the external surface of a commercial mask (c).

Fig. 2

Graphene-coated strip-line (l = 5 cm, w = 0.5 cm) acting as strain sensor and cast over the external layer of a mask cut into a rectangular shape with dimensions a = 5 cm, b = 0.5 cm and attached on a plexiglass beam (c = 15 cm, and d = 3 cm) (a). Three-point bend test setup at the laboratory (b); Schematic illustration of artificial respiration test setup (c); smart face mask with left, center, and right graphene-coated strip-lines (d), illustration of the position of the sensor on a three-layer surgical mask (e).

Fig. 3

SEM images, (a,b,c) pure mask, (d,e,f) coated mask, (g) respective schematic illustration of GNPs and polymer bonded to the fibers under no deformation, (h) during inhalation and exhalation.

Fig. 4

Relative resistance variation ΔR/R₀ under the applied strain for a graphene-coated face mask (a) sample 1, (b) sample 2, (c) sample 3, and (d) respected gauge factors. Measured average response time (e) and result of reproducibility test when the samples are subjected to long-term use (f).

Fig. 5

Relative resistance change ΔR/R0 versus time of artificial respiration test on the graphene-coated surgical mask with three different locations waveforms of (a) Left sensor (LS), (b) Right sensor (RS), (c) Centre sensor (CS), (d) relative average, (e) highlight of the center sensor with second breath cycle, (e) respiration waveform of the single breath cycle highlighted in (f) is magnified.

Fig. 6

Illustration of real-time respiration test with smart face mask under wearable application test (a), resistance changes (R)versus time, signals acquired from the volunteer (b), relative resistance change versus temperature change due to breathing inside and outside of the mask (c), relative resistance change versus humidity change due to breathing inside and outside of the mask (d).