Abstract

Respiratory rate is one of the most important vital signs. It affects ventilation which relates to oxygen inhalation and carbon dioxide elimination. Currently, only a handful of prototypes are available for estimating the respiratory rate under the condition that users remain completely still. This research focuses on the development of a respiratory rate monitoring system that can detect human respiratory signals using force sensitive resistors (FSRs). The FSR sensors measure the forces from respiratory motion and then signal processing techniques are employed to minimize background noise and artifacts. Respiratory data are processed by a microcontroller and transmitted via Bluetooth to a mobile device for further processing and visualization. The system performance was evaluated in three stages. Firstly, for the proof by simulation, a mean absolute error (MAE), root mean square error (RMSE), and Pearson correlation coefficient (PCC) of 0.26, 0.37 breaths per minute (bpm), and 0.9998 are achieved, respectively, even when the noise level is very high, i.e., power signal-to-noise ratio is 0.25 or −6.02 decibel. Secondly, for the test on a robot, the MAEs are 0.25, 0.53, and 0.75 bpm; the RMSEs are 0.28, 0.64, and 0.92 bpm; the PCCs are approximately 1, 0.9993, and 0.9986, respectively, under sitting, walking, and jogging conditions. The system is further deployed on 14 human subjects yielding MAEs of 0.51, 1.24, and 1.92 bpm; RMSEs of 0.65, 1.63, and 2.22 bpm; and PCCs of 0.9893, 0.9831, and 0.9655, for human sitting, walking, and jogging, respectively. In the future, this respiratory rate monitoring system could be applied to patients, elderly individuals, or the general population who experience movement or locomotion during monitoring.

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