# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models
## 5 Conclusions  
This study demonstrated that the selection of order and cutoff frequency could affect gait analysis and machine-learning model development. The choice of filter type might play a crucial role in accurately calculating gait characteristics and improving model performance. To achieve optimal classification outcomes, proper filter parameters should be selected based on the specific classifier used for model training.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15042177/s1](https://www.mdpi.com/article/10.3390/app15042177/s1)]([https://www.mdpi.com/article/10.3390/app15042177/s1](https://www.mdpi.com/article/10.3390/app15042177/s1)), Table S1: Comparison of gait characteristics during regular walking across filters with different parameters (order or cutoff). Only pairs with significant differences (\(p<0.05\)) are presented; Table S2: Comparison of gait characteristics during gait-slip across filters with different parameters (order or cutoff). Only pairs with significant differences (\(p<0.05\)) are presented; Table S3: Comparison of gait characteristics during gait-trip across filters with different parameters (order or cutoff). Only pairs with significant differences (\(p<0.05\)) are presented; Table S4: Comparison of LR model performances (AUC and overall accuracy) across filters with different parameters (order or cutoff). Only pairs with significant differences (\(p<0.05\)) are presented; Table S5: Comparison of SVC model performances (AUC and overall accuracy) across filters with different parameters (order or cutoff). Only pairs with significant differences (\(p<0.05\)) are presented; Table S6: Comparison of RFC model performances (AUC and overall accuracy) across filters with different parameters (order or cutoff). Only pairs with significant differences (\(p<0.05\)) are presented.  
**Author Contributions:** Conceptualization, S.W. and H.S.; methodology, S.W.; software, S.W.; validation, S.W., J.P., R.P. and H.S.; formal analysis, S.W.; investigation, S.W., J.P. and R.P.; resources, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, J.P., R.P. and H.S.; visualization, S.W.; supervision, S.W.; project administration, S.W.; funding acquisition, S.W. (co-investigator). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Institutes of Health, grant number R01-AG050672 awarded to [PERSON], with [PERSON] as co-investigator.  
**Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of Illinois in Chicago (protocol code: 2016-0887; 26 October 2016).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in this study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.  
**Acknowledgments:** The authors would like to acknowledge [PERSON] for providing data from the funded grant, administrative and technical support, and materials used for data collection and analysis.  
**Conflicts of Interest:** The authors declare no conflicts of interest.