Nina Sakhnini

CS Ph.D. student, University of Illinois at Chicago
UI Engineer, Caterpillar

nsakhn2 [AT]

Human Interference with personal pollution monitoring wearables

Human Interference with Low-Cost PM2.5 Sensors

Abstract. When assessing personal pollution monitoring, physical characteristics of the PM2.5 measured in a human’s personal cloud vary per source. Some studies have shown variation in toxicity of PM2.5 based on its source. Measuring PM2.5 near the human body is prone to PM2.5 resulting from the body’s emission, such as skin and hair emissions as well as bio-aerosol emissions of breath. Also, clothing and textile contribute to the PM2.5 in the personal cloud. Studies have shown that human body emissions increase with the increase of physical activity. Studying human interference with low-cost PM2.5 sensors informs design guidelines for a personal pollution exposure monitoring wearable, particularly, where to wear the sensor so that the measured PM2.5 is least interfered with. Moreover, studying the patterns of different human interference situations can help in predicting the situation or the activity the user is currently taking in order to infer more accurate personal pollution exposure monitoring. We studied different situations of human interference with low-cost PM2.5 sensors using the prototype we created and supported with ground truth measurements using SidePak. We explored human interference using the two tools in an exploratory pilot study of which results, in addition to wearability and comfort, we made our decisions for our human interference experiments.