Estimation of respiration rate from three-dimensional acceleration data based on body sensor network.
Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Guan-Zheng
GZ
Liu
Yan-Wei
YW
Guo
Qing-Song
QS
Zhu
Bang-Yu
BY
Huang
Lei
L
Wang
D000328:Adult; D000465:Algorithms; D004867:Equipment Design; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D056152:Respiratory Rate; D012890:Sleep / Q000502:physiology; D016138:Walking / Q000502:physiology