Respiratory Signal Processing and Analysis Using Flexible Capacitive Sensor Data

ABSTRACT Assessment of respiratory volumes is crucial for the long‐term management of chronic respiratory diseases. However, standard methods such as spirometry require active patient cooperation and are unsuitable for regular monitoring. This study introduces capacitive pressure sensors integrated with a signal processing algorithm for respiratory assessment/monitoring. Two sensor variants using poly(glycerol sebacate) (PGS) substrates are presented: CS1, featuring a porous structure, and CS2, incorporating a pyramidal surface pattern. Both sensors measure thoracic expansion through capacitance changes. Signals are preprocessed and statistically validated against a commercial airflow transducer in 38 healthy adult participants. Although CS1 exhibits higher sensitivity (0.09 kPa ^{− 1} ) than CS2 (0.015 kPa ^{− 1} ), both sensors demonstrate strong correlation (mean > 0.91) with the reference device across volunteers. Measurement accuracy is confirmed by low mean absolute errors across respiratory cycles: 0.122 L (95% confidence interval (CI) ± 0.027 L) for CS1, and 0.100 L (95% CI ± 0.018 L) for CS2. These results demonstrate that the developed capacitive sensors and signal processing algorithm effectively capture thoracic volume changes, showing potential for non‐invasive and continuous respiratory monitoring.