Bangjie Sun, Kanav Sabharwal, Gyuyeon Kim, Mun Choon Chan, and Jun Han. 2023. Testing Masks and Air Filters with Your Smartphones. In The 21st ACM Conference on Embedded Networked Sensor Systems (SenSys ’23), November 12–17, 2023, Istanbul, Turkiye. ACM, New York, NY, USA, 15 pages. Acceptance Rate: 18.9% (34 of 179). [paper] [slides]

Abstract

The demand for masks and air filters with effective filtration capabilities is skyrocketing as there are many applications that require protecting users from inhaling air pollutants or hazardous particles. Unfortunately, we are witnessing a surge in the number of counterfeit and substandard filters attributed to malicious and inept manufacturers. Hence, users are left vulnerable in not knowing which products are reliable. Exacerbating the problem, there are diverse filter standards, each with a unique expression for filtration efficiencies, adding to user confusion. Moreover, the average user lacks the necessary tools, techniques, and knowledge to independently verify the filtration efficiency. Specifically, state-of-the-art solutions are lab-based machines that are extremely expensive and difficult to access for the general public. To solve this problem, we propose FilterOp, a novel smartphone-based mask and filter testing system. FilterOp is a practical solution that allows a user to estimate the filtration efficiency of a mask or a filter using only a pair of commodity smartphones. The novelty of FilterOp comes from its use of light absorption and scattering effects, observed when light propagates through the filter. We evaluate FilterOp in comprehensive real-world experiments using 256 filter instances across 27 different make-and-model products with varying filtration efficiencies. Comparing our results to those obtained with a state-of-the-art government-certified testing machine, we observe that FilterOp yields comparable results with a low mean absolute error of 2.7%, and detects substandard products with an overall accuracy of 96%.