# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
## 3 Sensor Data Fusion  
The main challenge addressed in this research is the isolation of breathing-related motion from non-breathing-related body movement through sensor data fusion. The inertial sensor, when attached to the diaphragmatic area, will respond and provide signals corresponding to both breathing- and non-breathing-related body movement. Similarly, the flex sensor, when attached to the skin near the diaphragmatic area, will respond to breathing-related muscle stretch as well as muscle stretch due to body twists or another non-breathing-related signal from muscle movement. The challenge is to isolate the non-breathing-related signals from both sensor signals, which requires a proper sensor fusion technique. To understand the importance of sensor data fusion and challenges in the sensor signal due to non-breathing-related sensor responses, and to determine the appropriate method for performing the fusion, it is necessary to evaluate the performance of the IMU and flex sensors individually. For this purpose, the ability of both sensors to measure breathing signals was studied first. Flex sensors can capture muscle stretching and relaxation at the location to which they are attached. Moreover, IMU sensors can capture breathing-related motion as well as various body movements, such as rolling, bending, walking, and running. To detect abdominal movement using the IMU sensor, accelerometer data in the normal direction were utilized. Figure 2: Breathing sensor system including IMU and flex sensors. Figure 3 shows the accelerometer data in the normal direction and the flex sensor data during normal (Figure 3a) and shallow breathing (Figure 3b). Notably, there was no body movement during these breathing tests. This shows that without the body movement, both sensors can capture the breathing signal. Therefore, after data processing, it is possible to find the breathing rate and type from these data without sensor fusion when the participant is completely stationary and there is no body movement. After applying a low-pass filter to address the high noise-to-signal ratio of the accelerometer data, the filtered accelerometer signals for normal and shallow breathing are depicted in Figure 4. After filtering, it is demonstrated that both signals provide consistent information, and monitoring breathing is possible based on the measurements of each sensor when the participant is stationary and in the absence of body motion. To assess the performance of these sensor signals in the presence of body movement, a normal breathing test was conducted while the participant's body was gently turning to the left and right. As depicted in Figure 5, the results highlight the significant impact of body motion on the accelerometer data, whereas the flex sensor data exhibit only minor variation. Consequently, the flex sensor is more robust to body motion than the IMU sensor. It is evident that if the accelerometer and flex sensor data are fused, the fusion does not yield additional information compared to using only the flex sensor data in the presence of body motion. This observation leads to the question of whether relying solely on a flex sensor is adequate for breath monitoring. To address this issue, a test involving breath holding during body motion was conducted. Figure 6 shows a breathing test that includes two intervals of breath-holding while the participant turns to the left and right. During these intervals, it was anticipated that the flex sensor data would remain nearly constant. However, due to muscle motion during turns to the right and left, the flex sensor data exhibited cyclic behavior corresponding to the body's motion. This cyclical pattern introduces the risk of false alarms when detecting breathing. Consequently, relying solely on a flex sensor is insufficient. Therefore, flex sensor data must be integrated with other sensory information. Figure 3: Raw data of accelerometer and flex signals for (**a**) normal breathing, (**b**) shallow breathing.