Accelerometer-based estimation of respiratory rate using principal component analysis and autocorrelation

Objective. Respiratory rate (RR) is an important vital sign but is often neglected. Multiple technologies exist for RR monitoring but are either expensive or impractical. Tri-axial accelerometry represents a minimally intrusive solution for continuous RR monitoring, however, the method has not been validated in a wide RR range. Therefore, the aim of this study was to investigate the agreement between RR estimation from a tri-axial accelerometer and a reference method in a wide RR range. Approach. Twenty-five healthy participants were recruited. For accelerometer RR estimation, the accelerometer was placed on the abdomen for optimal breathing movement detection. The acquired accelerometry data were processed using a lowpass filter, principal component analysis (PCA), and autocorrelation. The subjects were instructed to breathe at slow, normal, and fast paces in segments of 60 s. A flow meter was used as reference. Furthermore, the PCA-autocorrelation method was compared with a similar single axis method. Main results. The PCA-autocorrelation method resulted in a bias of 0.0 breaths per minute (bpm) and limits of agreement (LOA) = [-1.9; 1.9 bpm] compared to the reference. Overall, 99% of the RRs estimated by the PCA-autocorrelation method were within +/- 2 bpm of the reference. A Pearson correlation indicated a very strong correlation with r = 0.99 ( p<0.001). The single axis method resulted in a bias of 3.7 bpm, LOA = [-14.9; 22.3 bpm], and r = 0.44 ( p<0.001). Significance. The results indicate a strong agreement between the PCA-autocorrelation method and the reference. Furthermore, the PCA-autocorrelation method outperformed the single axis method.