Tidal Volume and Instantaneous Respiration Rate Estimation using a Volumetric Surrogate Signal Acquired via a Smartphone Camera

Two parameters that a breathing status monitor should provide include tidal volume (<inline-formula> <tex-math notation="LaTeX">$V_{T}$</tex-math></inline-formula>) and respiration rate (RR). Recently, we implemented an optical monitoring approach that tracks chest wall movements directly on a smartphone. In this paper, we explore the use of such noncontact optical monitoring to obtain a volumetric surrogate signal, via analysis of intensity changes in the video channels caused by the chest wall movements during breathing, in order to provide not only average RR but also information about <inline-formula><tex-math notation="LaTeX">$V_{T}$</tex-math></inline-formula> and to track RR at each time instant (IRR). The algorithm, implemented on an Android smartphone, is used to analyze the video information from the smartphone's camera and provide in real time the chest movement signal from <inline-formula> <tex-math notation="LaTeX">$N = 15$</tex-math></inline-formula> healthy volunteers, each breathing at <inline-formula> <tex-math notation="LaTeX">$V_{T}$</tex-math></inline-formula> ranging from 300 mL to 3 L. These measurements are performed separately for each volunteer. Simultaneous recording of volume signals from a spirometer is regarded as reference. A highly linear relationship between peak-to-peak amplitude of the smartphone-acquired chest movement signal and spirometer <inline-formula><tex-math notation="LaTeX">$V_{T}$</tex-math></inline-formula> is found ( <inline-formula><tex-math notation="LaTeX">$r^{2} = 0.951 \pm 0.042$</tex-math></inline-formula>, mean ± SD). After calibration on a subject-by-subject basis, no statistically significant bias is found in terms of <inline-formula><tex-math notation="LaTeX">$V_{T}$</tex-math></inline-formula> estimation; the 95% limits of agreement are −0.348 to 0.376 L, and the root-mean-square error (RMSE) was <inline-formula> <tex-math notation="LaTeX">$0.182 \pm 0.107 \,{\rm L}$</tex-math></inline-formula>. In terms of IRR estimation, a highly linear relation between smartphone estimates and the spirometer reference was found (<inline-formula> <tex-math notation="LaTeX">$r^{2} = 0.999 \pm 0.002$</tex-math></inline-formula>). The bias, 95% limits of agreement, and RMSE are −0.024 breaths-per-minute (bpm), −0.850 to 0.802 bpm, and 0.414 <inline-formula> <tex-math notation="LaTeX">$\pm 0.178 \,{\rm bpm}$</tex-math></inline-formula>, respectively. These promising results show the feasibility of developing an inexpensive and portable breathing monitor, which could provide information about IRR as well as <inline-formula><tex-math notation="LaTeX">$V_{T}$</tex-math></inline-formula>, when calibrated on an individual basis, using smartphones. Further studies are required to enable practical implementation of the proposed approach.