Predicting Pulmonary Function from Phone Sensors.

INTRODUCTION: Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns. Here we show that improved classification models can accurately predict pulmonary function, with sole inputs being motion sensors from carried phones. SUBJECTS AND METHODS: Twenty-five cardiopulmonary patients performed 6-minute walk tests in pulmonary rehabilitation at a regional hospital. They carried smartphones running custom software recording phone motion. Each patient's pulmonary function was measured by spirometry. A universal model, based on support vector machine, then computed the category of function with input from signal processing features and patient demographic features. RESULTS: All but a few of every 10-second interval for every patient was correctly predicted. The trained model perfectly computed the GOLD (Global Initiative for Chronic Obstructive Lung Disease) level 1/2/3, which is a standard classification of pulmonary function. Each level was determined to have a characteristic motion, which could be recognized from the sensor features. In addition, longitudinal changes were detected for 10 patients with multiple walk tests, except for cases with clinical instability. CONCLUSIONS: These results are encouraging toward clinical validation of passive monitors running continuously in the background, for patients in homes during daily activities. Initial testing indicates the same high accuracy as with active monitors, for patients in hospitals during walk tests. We expect patients can simply carry their phones during everyday living, while models support automatic prediction of pulmonary function for health monitoring.

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.

Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary Patients.

Smartphones are ubiquitous now, but it is still unclear what physiological functions they can monitor at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown that predictive models can accurately classify cardiopulmonary conditions from healthy status, as well as different severity levels within cardiopulmonary disease, the GOLD stages. Here we propose several universal models to monitor cardiopulmonary conditions, including DPClass, a novel learning approach we designed. We carefully prepare motion dataset covering status from GOLD 0 (healthy), GOLD 1 (mild), GOLD 2 (moderate), all the way to GOLD 3 (severe). Sixty-six subjects participate in this study. After de-identification, their walking data are applied to train the predictive models. The RBF-SVM model yields the highest accuracy while the DPClass model provides better interpretation of the model mechanisms. We not only provide promising solutions to monitor health status by simply carrying a smartphone, but also demonstrate how demographics influences predictive models of cardiopulmonary disease.

Classification Models for Pulmonary Function using Motion Analysis from Phone Sensors.

Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns. Here we show that improved classification models can accurately measure pulmonary function, with sole inputs being sensor data from carried phones. Twenty-four cardiopulmonary patients performed six minute walk tests in pulmonary rehabilitation at a regional hospital. They carried smartphones running custom software recording phone motion. For every patient, every ten-second interval was correctly computed. The trained model perfectly computed the GOLD level 1/2/3, which is a standard categorization of pulmonary function as measured by spirometry. These results are encouraging towards field trials with passive monitors always running in the background. We expect patients can simply carry their phones during daily living, while supporting automatic computation ofpulmonary function for health monitoring.

A natural walking monitor for pulmonary patients using mobile phones.

Mobile devices have the potential to continuously monitor health by collecting movement data including walking speed during natural walking. Natural walking is walking without artificial speed constraints present in both treadmill and nurse-assisted walking. Fitness trackers have become popular which record steps taken and distance, typically using a fixed stride length. While useful for everyday purposes, medical monitoring requires precise accuracy and testing on real patients with a scientifically valid measure. Walking speed is closely linked to morbidity in patients and widely used for medical assessment via measured walking. The 6-min walk test (6MWT) is a standard assessment for chronic obstructive pulmonary disease and congestive heart failure. Current generation smartphone hardware contains similar sensor chips as in medical devices and popular fitness devices. We developed a middleware software, MoveSense, which runs on standalone smartphones while providing comparable readings to medical accelerometers. We evaluate six machine learning methods to obtain gait speed during natural walking training models to predict natural walking speed and distance during a 6MWT with 28 pulmonary patients and ten subjects without pulmonary condition. We also compare our model's accuracy to popular fitness devices. Our universally trained support vector machine models produce 6MWT distance with 3.23% error during a controlled 6MWT and 11.2% during natural free walking. Furthermore, our model attains 7.9% error when tested on five subjects for distance estimation compared to the 50-400% error seen in fitness devices during natural walking.

Health monitors for chronic disease by gait analysis with mobile phones.

We have developed GaitTrack, a phone application to detect health status while the smartphone is carried normally. GaitTrack software monitors walking patterns, using only accelerometers embedded in phones to record spatiotemporal motion, without the need for sensors external to the phone. Our software transforms smartphones into health monitors, using eight parameters of phone motion transformed into body motion by the gait model. GaitTrack is designed to detect health status while the smartphone is carried during normal activities, namely, free-living walking. The current method for assessing free-living walking is medical accelerometers, so we present evidence that mobile phones running our software are more accurate. We then show our gait model is more accurate than medical pedometers for counting steps of patients with chronic disease. Our gait model was evaluated in a pilot study involving 30 patients with chronic lung disease. The six-minute walk test (6 MWT) is a major assessment for chronic heart and lung disease, including congestive heart failure and especially chronic obstructive pulmonary disease (COPD), affecting millions of persons. The 6 MWT consists of walking back and forth along a measured distance for 6 minutes. The gait model using linear regression performed with 94.13% accuracy in measuring walk distance, compared with the established standard of direct observation. We also evaluated a different statistical model using the same gait parameters to predict health status through lung function. This gait model has high accuracy when applied to demographic cohorts, for example, 89.22% accuracy testing the cohort of 12 female patients with ages 50-64 years.