[Article] Estimation of respiratory rate and effort from a chest-worn accelerometer using constrained and recursive principal component analysis & Accelerometer-based estimation of respiratory rate using principal component analysis and autocorrelation
Estimation of respiratory rate and effort from a chest-worn accelerometer using constrained and recursive principal component analysis
Summary: This study proposes a constrained and recursive PCA method to robustly estimate respiratory effort and respiratory rate from a chest-worn 3-axis accelerometer under realistic sleeping conditions with varying sensor positions and body postures. The accelerometer signal is projected onto a gravity-based horizontal plane, and recursive PCA combined with STFT and a quality index is applied to extract respiratory effort and estimate respiratory rate, significantly reducing estimation error compared to conventional block-wise, unconstrained PCA. The method demonstrated a clear trade-off between coverage and accuracy, achieving agreement intervals below 1.5 breaths/min for high coverage settings and below 0.2 breaths/min for low coverage settings, indicating high robustness and flexibility for clinical and wearable applications.
Accelerometer-based estimation of respiratory rate using principal component analysis and autocorrelation
Summary: This study proposed a novel PCA–autocorrelation method for estimating respiratory rate (RR) using a tri-axial accelerometer placed on the abdomen and validated it against a reference flow meter. Results from 25 healthy participants showed a very strong correlation (r = 0.99) and narrow limits of agreement (±1.9 bpm), outperforming single-axis approaches. The method demonstrates a low-cost, non-intrusive solution for continuous RR monitoring, with potential for future clinical validation.