Dan Stieper Karbing's research works

Figure 1. The experimental setup. 1: Camera. 2: NI-DAQ. 3: Accelerometer. 4: Flow meter. 5: iWorx Recorder. The participant was lying in a supine position on an examination couch with a 30 • incline. A noseclip covered the participant's nose to ensure breathing occurred through the flow meter.

Figure 2. The signal processing steps for one participant. First, raw accelerometer data was recorded from the x, y, and z axes. Next, a lowpass filter was applied to all three axes. The filtered data was then fused using PCA by projecting it onto the first principal component. Finally, finding peaks within the autocorrelation was used to estimate the RR.

Figure 3. Data snippet from a single participant recorded simultaneously from the accelerometer and flow meter during a normal breathing pace segment. (a) Lowpass-filtered accelerometer data, with x-axis in blue, y-axis in red, and z-axis in yellow. (b) Accelerometer data after projection onto the first principal component. (c) Reference flow meter data.

Figure 4. Left scatterplot: RR estimates from the PCA-autocorrelation method and flow meter. Right scatterplot: RR estimates from the z-axis method and flow meter. Each point represents the RR estimated in a single segment.

Figure 5. Bland-Altman plots of the difference between the RR estimates from the PCA-autocorrelation method and the flow meter. The red dashed lines represent the LOAs. The bias is represented by the black line. The blue points each represent compared RRs for one segment.

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

March 2025

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Physiological Measurement

Mads Christian Frederiksen Hostrup

Anne Sofie Nielsen

Freja Emborg Sørensen

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Dan Stieper Karbing

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 seconds. A flow meter was used as reference. Main results. Furthermore, the PCA-autocorrelation method was compared with a similar single axis method. 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 PCAautocorrelation 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.

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