Development of a respiratory inductive plethysmography module supporting multiple sensors for wearable systems

Abstract

In this paper, we present an RIP module with the features of supporting multiple inductive sensors, no variable frequency LC oscillator, low power consumption, and automatic gain adjustment for each channel. Based on the method of inductance measurement without using a variable frequency LC oscillator, we further integrate pulse amplitude modulation and time division multiplexing scheme into a module to support multiple RIP sensors. All inductive sensors are excited by a high-frequency electric current periodically and momentarily, and the inductance of each sensor is measured during the time when the electric current is fed to it. To improve the amplitude response of the RIP sensors, we optimize the sensing unit with a matching capacitor parallel with each RIP sensor forming a frequency selection filter. Performance tests on the linearity of the output with cross-sectional area and the accuracy of respiratory volume estimation demonstrate good linearity and accurate lung volume estimation. Power consumption of this new RIP module with two sensors is very low. The performance of respiration measurement during movement is also evaluated. This RIP module is especially desirable for wearable systems with multiple RIP sensors for long-term respiration monitoring.

Figure 1.

Block diagram of how PAM works in the RIP module.

Figure 2.

Description of how TDM works in the RIP module.

Figure 3.

The structure of a parallel LC sensing unit and the impedance-frequency relationship of the transducer with variable inductance. (a) A parallel LC sensing unit; (b) Transfer function of the LC sensing unit.

Figure 4.

System block diagram of the RIP module.

Figure 5.

Illustration of the self-made apparatus for RIP performance test.

Figure 6.

Relationship between RIP output voltage and coil cross-sectional area. Regression line is based on linear polynomial least-square fit.

Figure 7.

The weighted sum of RC and AB band signals during normal breathing and simulated airway obstruction.

Figure 8.

RC and AB respiration and accelerometer reference signal during standing and running. (a) respiration during standing; (b) respiration during running.

Figure 9.

Output of adaptive filtering using least mean square (LMS) algorithm with accelerometer signal as reference.

Figure 10.

RIP band signals at the level of the xiphoid and ECG during normal breathing and simulated breath holding.