Predicting Transitions in Oxygen Saturation Using Phone Sensors

Introduction: Widespread availability of mobile devices is revolutionizing health monitoring. Smartphones are ubiquitous, but it is unknown what vital signs can be monitored with medical quality. Oxygen saturation is a standard measure of health status. We have shown phone sensors can accurately measure walking patterns. Subjects and Methods: Twenty cardiopulmonary patients performed 6-min walk tests in pulmonary rehabilitation at a regional hospital. They wore pulse oximeters and carried smartphones running our MoveSense software, which continuously recorded saturation and motion. Continuous saturation defined categories corresponding to status levels, including transitions. Continuous motion was used to compute spatiotemporal gait parameters from sensor data. Our existing gait model was then trained with these data and used to predict transitions in oxygen saturation. For walking variation, 10-s windows are units for classifying into status categories. Results: Oxygen saturation clustered into three categories, corresponding to pulmonary function Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 and GOLD 2, with a Transition category where saturation varied around the mean rather than remaining steady with low standard deviation. This category indicates patients who are not clinically stable. The gait model predicted status during each measured window of free walking, with 100% accuracy for the 20 subjects, based on majority voting. Conclusions: Continuous recording of oxygen saturation can predict cardiopulmonary status, including patients in transition between status levels. Gait models using phone sensors can accurately predict these saturation categories from walking motion. This suggests medical devices for predicting clinical stability from passive monitoring using carried smartphones.