# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
## 4 Results and Discussion  
The fusion technique developed in this research is based on excluding information regarding body rotation from flex sensor data to achieve a signal that purely includes breath-related information. An overview of this proposed method is depicted in Figure 7. To capture information about the body's rotational motion along three axes (x, y, z), the tri-axis gyro data from the IMU are utilized. To exclude the body movement data from the flex sensor data, a simple signal processing technique was used to identify the frequency contents. Fast Fourier Transformation (FFT) transforms the signal from the time domain to the frequency domain, providing a spectrum that illustrates the frequency components present in that signal. Employing FFT allows the identification of important frequencies associated with the body's rotational motion. Once the dominant frequencies of body rotational motion are determined, they are removed from the flex sensor data through filtering. As a result, the filtered flex sensor data exclusively contain information related to breathing, making them suitable for breath monitoring.  
To assess the effectiveness of the developed approach, an experimental test involving deep breathing with motion was conducted. Figure 8 shows the flex sensor data and  
Figure 6: Accelerometer and flex sensor signals for breath holding while body motion is present (turning left and right).  
Figure 7: Proposed sensor fusion algorithm to utilize both flex sensor and accelerometer data to accurately capture breathing signals by compensating for non-breathing-related artifacts from the signals.  
gyro signals along the three axes. The Fast Fourier Transform (FFT) of these signals is shown in Figure 9. Based on the FFT of the gyro data, the region of dominant frequencies corresponding to the body motion is identified and highlighted in this figure.  
For this specific experimental test, the dominant frequencies fall within the range of 0.45 to 0.6 Hz. The FFT analysis of the flex sensor data also revealed the presence of these frequencies (Figure 9a), but they were not dominant in the spirometer data (Figure 9c). By applying the developed approach, these frequencies are removed from the flex sensor data. As shown in Figure 9b, the FFT of the filtered flex sensor data closely resembles the FFT of the spirometer data, indicating the absence of frequencies corresponding to body motion in this range. Obtaining a signal that contains only breath-related information allows us to determine the number of breaths or the breathing rate and to determine the breathing type.  
Figure 8: Time domain signal from the sensors during deep breathing with the presence of body movement. (**a**) Flex sensor data and (**b**–**d**) tri-axis gyro signals from the IMU sensor during a deep breathing test in the presence of body motion.  
Figure 9: (**a**) FFT of the flex sensor signal before filtering, (**b**) FFT of the flex sensor signal after filtering, (**c**) FFT of the spirometer signal and (**d**–**f**) FFT of the tri-axis gyro signals of the IMU sensor. Red area showing dominant frequency in gyro data.