# Inertial and Flexible Resistive Sensor Data Fusion for Wearable Breath Recognition
## 7 Conclusions  
In conclusion, this paper introduces an innovative approach for monitoring breathing rates through the integration of flexible resistive and inertial measurement unit (IMU) sensor data. The developed sensor fusion technique effectively eliminates nonrespiratory information from flex and IMU data to extract breath-related information. The developed technique is a solution for addressing the sensitivity of flex sensors to motion-related artifacts. The effectiveness of the developed approach is validated through experiments. The experimental results showed that by applying the developed fusion technique, nonrespiratory information is effectively filtered from the flex and IMU data, resulting in a signal exclusively containing accurate breathing information. The results demonstrated that, based on the obtained signal, it is possible to accurately count breath cycles. Moreover, by segmenting the obtained signal into short time intervals and analyzing the dominant frequency in each time interval, we can successfully identify different breath types. The system can be further enhanced with wireless connectivity and by incorporating automated thresholding mechanisms by considering various body types. Advanced artificial intelligence (AI)- and machine learning-based learning algorithms can be leveraged to train and calibrate the sensor system to identify various breathing patterns based on demographics and body types. The wearable breathing sensor system presented in this paper can be used for prolonged monitoring of breathing matrices including rate, depth, and pattern of breathing in diverse applications across health, wellness, sports, and safety domains. Such breathing-related parameters can be used in applications including breathing-related sleep disorders, managing chronic respiratory conditions, performance monitoring in sports, breathing monitoring of climbers and miners, worker safety in detecting breathing hazards, health and wellness, and meditation. The ease of fitting, lower cost, higher accuracy, and small footprint make it suitable for wearable applications. Conceptualization, M.Z. and M.J.A. ; Methodology, M.Z., B., P.P., B.R.S., N.J.L. and A.R.B. ; Software, P.P. ; Validation, M.Z. ; Formal analysis, M.Z. and B.; Investigation, S.R.-G.; Resources, N.J.L. and S.A.; Data curation, B.R.S., N.J.L. and A.R.B. ; Writing--original draft, M.Z. ; Writing--review & editing, B., P.P., B.R.S., N.J.L., S.A., A.R.B., S.R.-G. and M.J.A. ; Visualization, P.P. ; Supervision, S.A., A.R.B., S.R.-G. and M.J.A. ; Project administration, M.J.A. ; Funding acquisition, A.R.B. and S.R.-G.. All authors have read and agreed to the published version of the manuscript. WE-SPARK Health Institute through an Igniting Discovery Grant and the Faculty of Engineering Innovating Sustainability Grant. This work involved human subjects in its research. Approval of all ethical and experimental procedures and protocols was granted by the University of Windsor's Research Ethics Board under Application No. REB 22-166 and performed in line with the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Breath Cycles Determined** & **Actual Number of Breath** \\  & **by Our Developed Approach** & **Cycles from Spirometer Data** \\ \hline Deep Breathing & \(\sim\)3 & 4 \\ \hline Normal Breathing & \(\sim\)11 & 11 \\ \hline Shallow Breathing & 7 & 6 \\ \hline All types of Breathing & 20 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the proposed sensor system and gold-standard spirometer data of the number of breath cycles in each breath type. Data Availability Statement: The data used in this study are available from the corresponding authors with a reasonable request. The work of SRG was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grant RGPIN-2022-04428.