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Telemed J E Health. 2017 Nov;23(11):913-919. doi: 10.1089/tmj.2017.0008. Epub 2017 Mar 16.

Predicting Pulmonary Function from Phone Sensors.

Author information

1
1 Department of Computer Science, University of Illinois at Urbana-Champaign , Urbana, Illinois.
2
2 Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign , Urbana, Illinois.
3
3 Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign , Urbana, Illinois.
4
4 Department of Medicine, NorthShore University HealthSystem , Evanston, Illinois.
5
5 Department of Respiratory Therapy, NorthShore University HealthSystem , Evanston, Illinois.
6
6 Center for Biomedical Research Informatics, NorthShore University HealthSystem , Evanston, Illinois.
7
7 Department of Medical Information Science, University of Illinois at Urbana-Champaign , Urbana, Illinois.

Abstract

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.

KEYWORDS:

chronic disease assessment; health monitoring; machine learning; mobile phones; predictive modeling; pulmonary function; telemedicine

PMID:
28300524
PMCID:
PMC5684658
DOI:
10.1089/tmj.2017.0008
[Indexed for MEDLINE]
Free PMC Article

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