# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
###### Abstract
Ref. [30] showed that placing piezoresistive sensors on the upper thorax mitigates abdominal motion during daily activities and improves performance. In [29], a wearable device employing a piezoresistive transducer was utilized to monitor the breathing rate. The algorithm used to calculate the breathing rate operates based on the number of zero crossings within a specific time window. In [25], a neural network trained on data collected from ten users successfully predicts respiration patterns. In [24], a wearable device utilized six piezoresistive sensors to detect the motion of the pulmonary rib cage, abdominal rib cage, and abdomen for breath monitoring. Additionally, other types of wearable breathing sensors attached to the diaphragmatic area or around the mouth that utilize piezoelectric, triboelectric, resistive, and capacitive methods showed promising results in detecting breathing [31; 32]. In the realm of wearable devices for breath monitoring, IMUs can be mounted around the diaphragmatic area and can be employed to detect inhalation- and exhalation-related motions [13]. IMU sensors include accelerometers and gyroscopes that can measure accelerations and angular velocities [33]. IMU sensors are typically positioned on the chest or abdomen because they can directly measure respiratory-related motion activity [34; 35]. However, the overall non-respiratory body movement not related to breathing can significantly impact the recorded IMU data, thus restricting the applicability of these sensors and requiring additional algorithms/filtering to distinguish non-breathing related signals. Several investigations have explored the application of inertial measurement units (IMUs) in breath monitoring. In [4], a nine-axis IMU along with a MEMS microphone was applied to calculate the breathing rate and coughing frequency. In [36], an accelerometer was used to collect chest movement data. After filtering the raw data, wavelet analysis with multiscale peak detection was applied to determine the number of breath cycles. Sensor data fusion by combining data from multiple sensors plays a crucial role in enhancing the accuracy and reliability of breath detection systems. Several research investigations have been conducted to examine the utilization of multiple sensors for this purpose [37, 38, 39, 40, 41]. The main disadvantage of IMUs and pressure or resistive sensors is their sensitivity to responding to body movement that does not correspond to breathing. This sensitivity can be mitigated by developing hybrid systems that include more than one of these sensors and operate based on fusing the sensors' data. In [16], a comprehensive examination of the utilization of wearable piezoresistive and inertial sensors for monitoring respiration rates was provided. The authors explored the advancements, challenges, and potential applications of these sensor technologies, offering valuable insights into the evolving landscape of wearable health monitoring devices. In [42], the authors introduced an innovative data fusion algorithm designed to eliminate motion artifacts from respiratory signals obtained using four piezoresistive textile sensors and one IMU sensor. Piezoresistive sensors primarily capture breathing activity, while IMU sensors are highly influenced by physical activity. Independent component analysis (ICA) was subsequently used to blindly separate various sources of activities and derive distinct independent components. By comparing the PSDs of the identified independent components and the IMU signal, researchers have reconstructed the signals from IMUs and piezoresistive sensors, removing the effects of physical activity. In [43], three six-axis IMU sensors were employed for breath monitoring. Two of them are positioned on the chest and abdomen, while the third is strategically placed where it is not affected by breathing. This third IMU sensor is utilized to filter out motions not related to breathing from the first two IMU sensors.