Monitoring Passenger's Breathing - A Feasibility Study

Type: Semester Thesis
Student: Basem Dokhan
Advisor: Cornelia Setz, Bert Arnrich
Project: SEAT

SEAT (Smart tEchnologies for stress-free Air Travel) aims at making long distance flights healthier and more comfortable. Passenger’s comfort is measured by embedding innovative sensors in the seat to detect the passenger’s psychological and physiological state in real time.The breathing signal is one of the vital signs which gives a good insight into the passenger’s emotional state. Traditional measurement techniques used for clinical studies are not applicable in the SEAT environment, because they are either obstructive or they require unsuitable contacting procedures.

In this project, two customized novel techniques to measure the breathing signal in this inherently constrained environment are investigated, two signal processing schemes to estimate the respiration rate are applied, a prototype for one method is demonstrated, and a simulation model for the other method is realized. We conclude with an early comparison between the two methods.

In the first method, a textile resistive sensor is incorporated in the seatbelt (see Fig.1 left). During the breathing cycle, the sensor is periodically stretched and it changes its resistance accordingly. To deduce the breathing rate, two peak detection algorithms were tested. Firstly, the QRS detection algorithm described in [1] is used (Fig.2 left). Secondly, a shift-and-compare (SAC) algorithm is evaluated. In this algorithm a smoothing moving average is applied. The resulting signal is then shifted and a dc is added. Crossings of the new and the smoothed version of the signal determine the peaks. Both peak detectors performed accurately (100%) in the motion-free signal. In the case of motion artifacts, the SAC method clearly outperformed the QRS detector (90% compared to 20%).

The second method employs a coil embedded in the seatback to induce eddy current in the torso which exhibits conductivity change Δσ during the breathing cycle. The EM Simulation results (Fig.2 right) showed the change of the magnetic field on an axially symmetric eccentric cylinders model as the lungs volume change during breathing cycle.

[1] Willis. J. Tompkins (1993) Biomedical Digital Signal Processing, Printice-Hall.