Classification Models for Pulmonary Function using Motion Analysis from Phone Sensors

Qian Cheng, Joshua Juen, Shashi Bellam, Nicholas Fulara, Deanna Close, Jonathan C. Silverstein, Bruce Schatz

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)401-410
Number of pages10
JournalAMIA Annual Symposium Proceedings
StatePublished - 2016


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

ASJC Scopus subject areas

  • Medicine(all)


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