# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 4 Conclusions  
This SLR has provided comprehensive insights into the field of detecting and monitoring BP using sensor technologies. Key findings across various aspects, including sensor fusion methods, classifier performance, the reliability of signal event identification, optimal sensor setups, and the impact of changing body dynamics, have been synthesized.  
Sensor fusion methods and data signal analysis techniques have shown promising results, with [PERSON]'s algorithm emerging as a standout method due to its accuracy, low computational load, and ability to compensate for distortions [66]. Fusion methods have generally outperformed single accelerometers, improving accuracy and reducing error rates [62].  
Classifiers, particularly Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) and Support Vector Machines (SVMs), have demonstrated high accuracy in detecting breathing disorders, with potential for enhancing diagnostic accuracy and treatment efficacy [52; 63].  
Reliability in identifying signal events related to BP has been confirmed, with multisensor patches and strong correlations between sensor signals enhancing event detection [67; 69]. However, challenges such as motion artifacts and sensor placement issues persist [61].  
Optimal sensor setups have been explored, suggesting effective configurations for detecting breathing patterns [48; 68]. However, increasing the number of sensors beyond a certain point may not significantly enhance estimation quality, and trade-offs between accuracy, power usage, and cost must be considered [58; 65].  
The impact of changing body dynamics on data acquisition has been investigated, highlighting the influence of body position on signal reliability and the challenges of motion artifacts [49; 67]. Nevertheless, certain setups have shown reliability, even during movement [61].  
In conclusion, this SLR shed light on the methods, challenges, and potential future developments in using inertial sensors to assess adult human BP. The field holds promise, but addressing challenges related to sensor placement, motion artifacts, and algorithm development is crucial for its continued advancement. Future research should focus on expanding the application of these sensors to clinical settings and a broader range of populations, ultimately improving our understanding and management of respiratory health.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a17060223/s1](https://www.mdpi.com/article/10.3390/a17060223/s1)]([https://www.mdpi.com/article/10.3390/a17060223/s1](https://www.mdpi.com/article/10.3390/a17060223/s1)), Table S1: Search strategy, string definition, and results; Table S2: Comparative assessment between the characteristics of the studies using inertial sensors for adult human BP assessment; Table S3: Reference device and applied data processing methods in the studies using inertial sensors for adult human BP assessment; Table S4: Acronyms/Abbreviations/Initiisms. Reference [70] is cited in Supplementary Materials.  
Conceptualization, R.M., F.R., S.C. and N.C.; methodology, R.M., S.C. and N.C.; validation, F.R., S.C. and N.C.; formal analysis, R.M., F.R., S.C. and N.C.; investigation, R.M.; resources, R.M., S.C. and N.C.; data curation, R.M.; writing--original draft preparation, R.M.; writing--review and editing, R.M., F.R., S.C. and N.C.; visualization, R.M.; supervision, F.R., S.C. and N.C.; project administration, S.C. and N.C.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FCT--Fundacao para a Ciencia e Tecnologia--under the individual research grant 2022.09627.BD awarded to [PERSON], and by FCT--Fundacao para a Ciencia e Tecnologia--within the R&D Units Project Scope: UIDB/00319/2020.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.