SpiroSense: Transforming Smartphones into Pulmonary Metrics Monitors with Ultrasonic Technology

Chronic respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma often progress insidiously, making early detection vital for effective intervention. Current gold-standard Pulmonary Function Testing (PFT) methods, such as spirometry, evaluate lung function by measuring airflow rates to detect potential obstructions. But, their cost, often several hundred dollars or more, limits their accessibility for regular at-home monitoring. In this paper, we present SpiroSense, a novel system that transforms a smartphone into a portable, low-cost, and accurate PFT device for everyday use by integrating a custom 3D-printed attachment costing just a dozen dollars. However, a critical limitation arises from the smartphone's inherent audio sampling rate (typically 48kHz), which constrains the airflow resolution to 11.9L/s when using conventional cross-correlation-based time delay estimation. This coarse resolution is insufficient to capture key pulmonary metrics, such as a Peak Expiratory Flow (PEF) of 10 L/s, with high fidelity. To address this, we propose SonicFlow, which establishes a foundational airflow rate sensing model based on ultrasonic phase features and improves the airflow rate resolution to 0.148L/s. Furthermore, airflow-induced high-frequency harmonic noise within the 3D-printed model, combined with ambient environmental noise, further complicates accurate sensing. To mitigate this, we introduce NoiseClear, an end-to-end ultrasonic signal enhancement model designed to effectively suppress noise while preserving critical airflow velocity information. We prototype SpiroSense and evaluate its performance on a cohort of 59 participants, including 29 healthy individuals and 30 patients. Experimental results show that SpiroSense achieves average estimation error of 6.44% for Forced Vital Capacity (FVC), 7.42% for Forced Expiratory Volume in one second (FEV1), and 3.01% for the FEV1/FVC ratio.