English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The respiration pattern represents the volume of air in the lungs as a function of time during human respiration process. Abnormal changes in this pattern can be signs of several diseases or conditions. There exit several respiration pattern detection methods. Among them, an easy technique relies on sensing the movements of thoracic and (or) abdominal regions. In this study, a device based on thoracic motion tracking with complementary filtering has been developed to detect the respiration pattern. The device is equipped with a motion sensor placed in a flexible belt housing a three-axis accelerometer and a three-axis gyroscope and a UART-to-USB converter providing computer connectivity. The device is operated by a microcontroller that controls the operation of the motion sensor, applies complementary filtering to the motion data acquired and transfers the results to a personal computer. The device is powered from the computer it is connected to. Experiments with using the device during continues inhaling and exhaling, deep inhaling followed by breath-hold and deep exhaling followed by breath-hold respiration activities in standing, lying and seated postures show that thoracic motion tracking with complementary filtering may provide quite well respiration pattern detections.

Design of a Respiration Pattern Detecting Device based on Thoracic Motion Tracking with Complementary Filtering