UbiLung: Multi-modal Passive Sensing for Lung Health Assessment

Spirometry test has been the gold standard for the measurement of a pulmonary patient’s lung function for decades. Spirometry is generally done in the hospital setting, where patients need to forcefully blow air into the spirometer’s tubes under the guidance of clinicians. Such a procedure is time-consuming, cumbersome, and extremely effort-dependent. Recent advances in ubiquitous computing investigate the feasibility of leveraging commodity devices such as smartphones to replace the standard clinical spirometry test. However, existing solutions are still demanding, usually requiring users to complete a series of tasks such as blowing towards a microphone, and could potentially introduce risks such as dizziness and shortness of breath due to the forced blowing. More importantly, the test is still dependent on the user’s effort which naturally degrades when no supervision exists. We propose UbiLung, a new method that leverages passively sensed modalities for lung function estimation. Such a method relies on the physiological correlation of the introduced passive modalities to the lung function, which consequently obviates the need for active user engagement yet can provide an accurate effort-independent measurement. We focus on sensor modalities that are feasible in passive sensing: cough and speech sound collected from microphones and blood volume pulse (BVP) signals collected via photoplethysmography (PPG) sensors. Through feature extraction and selection, our best machine learning models achieve mean absolute error of 11.1% for estimation of FVC perdicted percentage, 11.8% for FEV1 predicted percentage, and 7.4% for FEV1/FVC prediction. It significantly outperforms the baseline, with an average relative improvement of 13.9%. The generalizability of the model was further verified by an average improvement of 7.8% against baselines when applying the model directly on a completely separate and independent dataset. Moreover, we investigated important confounding factors (e.g., age, gender, and smoking behavior) and augment the results by 4.5% on average. In addition to the parameter estimation, we also trained models for a series of pulmonary disease diagnosis tasks. Our method achieves a F1-score of 0.982 on healthy v.s. diseased, 0.881 on obstructive v.s. non-obstructive, 0.854 on COPD v.s. asthma, and 0.892 on non-severe v.s. severe classification. Our technique is the first multi-modal effort-independent passive estimation of lung function, which could shed light on the passive monitoring of both pulmonary patients and general population.