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Proper indoor ventilation through buildings' heating, ventilation, and airconditioning (HVAC) systems has become an increasing public health concern thatsignificantly impacts individuals' health and safety at home, work, and school.While much work has progressed in providing energy-efficient and user comfortfor HVAC systems through IoT devices and mobile-sensing approaches, ventilationis an aspect that has received lesser attention despite its importance. With amotivation to monitor airflow from building ventilation systems throughcommodity sensing devices, we present FlowSense, a machine learning-basedalgorithm to predict airflow rate from sensed audio data in indoor spaces. OurML technique can predict the state of an air vent-whether it is on or off-aswell as the rate of air flowing through active vents. By exploiting a low-passfilter to obtain low-frequency audio signals, we put together aprivacy-preserving pipeline that leverages a silence detection algorithm toonly sense for sounds of air from HVAC air vent when no human speech isdetected. We also propose the Minimum Persistent Sensing (MPS) as apost-processing algorithm to reduce interference from ambient noise, includingongoing human conversation, office machines, and traffic noises. Together,these techniques ensure user privacy and improve the robustness of FlowSense.We validate our approach yielding over 90% accuracy in predicting vent statusand 0.96 MSE in predicting airflow rate when the device is placed within 2.25meters away from an air vent. Additionally, we demonstrate how our approach asa mobile audio-sensing platform is robust to smartphone models, distance, andorientation. Finally, we evaluate FlowSense privacy-preserving pipeline througha user study and a Google Speech Recognition service, confirming that the audiosignals we used as input data are inaudible and inconstructible.

FlowSense: Monitoring Airflow in Building Ventilation Systems Using Audio Sensing