An Overview of Respiratory Airflow Estimation Techniques: Acoustic vs NonAcoustic The primary objective of this analysis to provide an overview of the respiratory airflow estimation techniques, including both acoustic and nonacoustic approaches. This paper also highlights and addresses the applications where the breathing flow estimation was found to be crucial. The detailed summary of both acoustic and nonacoustic techniques were presented. The nonacoustic methods require airflow measurement devices or sensors whereas the acoustic methods only involve processing of breath sound signals to estimate airflow. A brief discussion of the advantages and disadvantages of the acoustic and nonacoustic approaches of respiratory flow estimation were included in this article. The literature discloses that current advances in computing technology provide a platform to integrate the signal processing methods and machine learning algorithms to estimate airflow noninvasively. The future studies to be extended after estimation the respiratory flow also were addressed in the paper.

RGBthermal Imaging System Collaborated with Marker Tracking for Remote Breathing Rate Measurement The pixel variation signal extracted from the nasal region of RGBthermal images can be used to achieve breathing rate (BR) measurement. However, this method fails when the nasal region is not detected in complicated motion scenarios. In this paper, we develop an RGBthermal imaging system collaborated with marker sticker to achieve unobtrusive and accurate BR measurement. Pixel variation signal of Regions of interest (ROI) is extracted from the thermal video and chest movement signal is extracted from the RGB video with the assistance of marker stickers. Subsequently, a custommade timedomain signal processing approach is developed for determining BR. We further propose a method of splicing computation to measure the BR after separate processing of signal segments. We construct an RGBthermal video dataset with different head and body movements to evaluate the effectiveness of the proposed algorithm. After linear regression analysis, the determination coefficient (R2) of 0.905 has been observed for the estimated and reference BRs, indicating the feasibility of our proposed method in complex motion scenarios.