# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Data Extraction and Categorization
#### 2.4.6 Findings  
Regarding the compiled results, they can be categorized based on the effectiveness of sensor fusion methods and data signal analysis techniques, the performance of classifiers for detecting BP, the reliability in identifying signal events, the optimal number of sensors and setup, the impact of changing position and body dynamics on data acquisition, and the challenges and limitations encountered in the studies. In the realm of sensor fusion methods and data signal analysis techniques, several noteworthy findings emerged from various studies. [PERSON] et al. [66] highlighted the superiority of [PERSON]'s algorithm over Kalman-based methods, even at low sampling rates, due to its accuracy and low computational load along with its ability to compensate for magnetic distortion and gyroscope bias drift. Additionally, they found that the Principal Component Analysis (PCA) fusion method exhibited optimal performance by selecting the most suitable quaternion component based on Power Spectral Density (PSD) analysis. [PERSON] et al. [62] emphasized that fusion methods outperformed single accelerometers, reducing error rates from 11.9% to 7.3%. They achieved this improvement by employing a Kalman filter to mitigate gyro sensor drift, enabling precise respiration tracking during physical activity. [PERSON] et al. [51] conducted a window size analysis, revealing that larger window sizes yielded better accelerometer-spirometer correlation by eliminating unwanted rib cage motion. Smaller window sizes resulted in increased standard deviation, impacting Mean Square Error (MSE) distributions. The choice of window size depended on the characteristics of breathing disorders. In the evaluation of classifiers' performance in detecting BP, several studies offered valuable insights. [PERSON] et al. [63] demonstrated that Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) outperformed generic Support Vector Machines (SVMs) with an F1 score of 71% compared with 65%. LSTM-RNNs also exhibited smaller differences in Apnea-Hypopnea Index (AHI) and achieved high accuracy in severity classification. [PERSON] et al. [48] introduced an approach that effectively recognized breathing movements compared with other methods employing reflective markers or kinematics. [PERSON] et al. [52] reported impressive results with SVM and two sensors, achieving 97.50% accuracy using all features for breathing dysfunction classification, even surpassing 99% accuracy in distinguishing healthy individuals. [PERSON] et al. [55] and [PERSON] et al. [64] both presented classification results, including 95% accuracy in detecting Central Sleep Apnea and 86% accuracy in detecting Obstructive Sleep Apnea, with F1 scores ranging from 52% to 95% for various events in multi-event classification. [PERSON] et al. [56] leveraged Neural Networks to achieve classification accuracies ranging from 61% to 97% across different breathing conditions, while [PERSON] et al. [57] reported classification results aligning with clinical standards, achieving a 70.95% accuracy in detecting disrupted respiratory events and a mean sensitivity of 76.05% while using a simple setup. In assessing the reliability of identifying signal events related to BP, several studies provided valuable findings. [PERSON] et al. [63] highlighted the challenges in identifying Hypopnea (HYP) events while successfully identifying Normal Obstructive Respiratory (NOR) and Obstructive Sleep Apnea (OSA) events, achieving an accuracy of 92.3% in distinguishing abnormal events. [PERSON] et al. [67] emphasized the importance of multisensor patches as they improved event detection reliability by complementing each other's flaws, utilizing various sensors like accelerometers and pressure sensors. [PERSON] et al. [69] showcased the capability of detecting breath rates and identifying apnea events using accelerometer data and audio features, calculating the AHI and achieving variance values of 0.5 for heart rate and 0.87 for respiratory rate. [PERSON] et al.