# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
###### Abstract
For this purpose, quaternions are computed to determine the changes in the orientation of the chest and abdomen with respect to the orientational position, considering the third sensor as the reference sensor. The first main component is then derived through Principal Component Analysis of the quaternions. Subsequently, breathing information is obtained by analyzing the power spectral density of the main component. IMU with a capacitive pressure sensor was used to reduce the error from the IMU sensor and, when positioned near the diaphragm, the multi-sensor system showed the capability to capture breathing patterns [44]. In contrast to the capacitive sensor which requires active components in this paper, we used a flexible resistive sensor which does not require active electronic components. In addition to utilizing different sensors, several studies have investigated the fusion of sensor data through the application of machine learning techniques to increase breathing pattern identification capability [45, 46]. In [47], a deep learning approach was devised, leveraging data from both inertial measurement unit (IMU) sensors and respiratory sound audio captured by earbuds to count exercise repetitions. Wearable breathing sensors and human activity recognition technologies benefit from the extensive research and development that is being reported using various methods for multi-sensor data fusion. A vast body of literature explores wearable sensor networks and fusion algorithms, employing diverse sensor data fusion approaches across different levels of sensor integration [48, 49, 50, 51]. Previous reviews, such as those reported in [45, 48, 49, 50, 51], thoroughly examine and compare the advantages and disadvantages of various traditional, advanced, and emerging sensor fusion techniques. Although machine learning and AI techniques are powerful tools for fusing sensor data, they require access to a large amount of data and computation, and their performance highly depends on the quality of the data. Therefore, researchers are also seeking simple but effective data fusion techniques. To address this issue, in this paper, we develop a simple fusion technique aimed at isolating breathing information from sensory data, effectively mitigating the impact of body movement due to non-breathing activities. Sensor fusion is a technique used for human activity detection to combine information from multiple sensors to improve the reliability and accuracy of activity detection. In this work, we used information from IMU and flexible sensors to improve the performance and accuracy of breathing pattern detection. This work led to the creation of a wearable multisensory patch employing an accelerometer and a flex sensor for accurate motion and muscle stretching capture during both stationary and body movement conditions. Here, we used IMU sensor data to remove nonbreathing-related artifacts from flex sensor data to achieve accurate breathing detection during body motion. Through the analysis of various breathing motions, the sensing system demonstrated high accuracy, providing a more reliable method for identifying breathing types with promising applications in the early detection of respiratory changes. In the present work, we introduce, for the first time, integrating resistive flex sensor and inertial measurement unit (IMU) sensor data to detect breathing rates. This method leverages IMU sensor data to extract insights into body motion resulting from physical activities. Subsequently, a filtering process is applied to the flex sensor data to eliminate motion-related artifacts, ensuring that the filtered signal exclusively conveys information pertaining to breathing. The main contributions of this paper are as follows:  
* A sensor data fusion technique was developed to eliminate non-respiratory information from flex sensor data through the integration of IMU sensor data, thereby attaining a signal that exclusively contains breathing-related information.