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Respiratory Signal Processing and Analysis Using Flexible Capacitive Sensor Data
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DOI：10.1002/admt.202502118.png)
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Abstract
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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 R 2 $R^{2}\;$ > 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.
Keywords:
capacitive sensors
respiratory monitoring
signal processing
statistical analysis
wearable devices
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