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AMIA Annu Symp Proc. 2017 Feb 10;2016:401-410. eCollection 2016.

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

Author information

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

Abstract

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.

KEYWORDS:

knowledge representation and information modeling mobile health (patients) chronic care management (clinicians)

PMID:
28269835
PMCID:
PMC5333291
[Indexed for MEDLINE]
Free PMC Article

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