A Wearable Multisensor Patch for Breathing Pattern Recognition

In this article, a multisensor patch is presented for the purpose of detecting and recognizing the signals produced by human breathing in response to a variety of different body movements. We show that a multisensor patch consisting of an accelerometer and a pressure sensor can simultaneously measure breathing-related inertial motion and muscle stretch with a high degree of accuracy when it is attached close to the diaphragm. To construct the multisensor patch, we relied on commercially available off-the-shelf (COTS) electronic components that were relatively inexpensive. Different breathing motions were analyzed based on the accelerometer and the pressure sensor, including inhale, exhale, normal breathing, and breath hold conditions. The breathing frequency from the accelerometer and the flexible capacitive pressure sensors was found to be 0.2 Hz, and the normal breathing rate (BR) from the accelerometer and the pressure sensor was 11 breaths/min. We demonstrate that this new functional device and related approaches allow the identification of breathing patterns that are less cumbersome and tenably more reliable than conventional measures. The proposed multisensor patch holds great potential as a sensing technology in medical applications for early detection of respiratory changes, one of the most predictive and earliest vital signs for worsening health. The presented methodology can be adapted for mass production of reasonably priced noninvasive breathing pattern detection and off-line analysis.

Automated summary

This article presents a multisensor patch designed to detect and recognize signals produced by human breathing in response to different body movements. The patch, consisting of an accelerometer and a pressure sensor, accurately measures breathing-related motion and muscle stretch when placed near the diaphragm. The use of commercially available electronic components makes the patch relatively inexpensive. The proposed multisensor patch has the potential to be used in medical applications for early detection of respiratory changes and can provide more reliable and less cumbersome breathing pattern identification than traditional measures.