An accurate respiratory rate estimation algorithm for a rubber fiber respiratory sensor

Abstract

Respiratory measurement is a crucial indicator for assessing health status; however, current methods for measuring respiratory rate and frequency are passive and not intuitive. This study investigates peak detection algorithms using a resistive strain sensor integrated into a garment for respiratory rate monitoring. The sensor, constructed with CNT material and a flexible rubber substrate, exhibits high conformity to the body’s contours. Designed for respiration measurement, the sensor maintains a low 6% strain for optimal sensitivity, demonstrating a 4% decrease after 800 repetitions of 10% elongation. Garment design emphasizes cohesion between the sensor and fabric, achieved through a piping technique. Respiratory measurement relies on a resistive sensor principle, where abdominal volume changes induce tension, altering resistance. Three peak detection algorithms are evaluated: the window size algorithm, low-pass filter, and FIR filter. The window size algorithm shows a 93% matching rate for normal breathing but requires manual adjustments based on breathing speed. The low-pass filter reduces noise but introduces lag, challenging peak matching. The FIR filter effectively detects peaks at increased speeds, achieving a matching rate exceeding 98%. The study concludes that the choice of algorithm depends on respiratory scenarios, with the window size algorithm suitable for regular cycles, the low-pass filter for real-time monitoring, and the FIR filter for accelerated respiratory rates. The study primarily explores static situations, indicating the need for future research on dynamic respiratory movements to enhance algorithm versatility.