## 5 Conclusions  
The present study revealed that gait function parameters (stride length, stance duration CoV, and LR CoV) and sensory parameters/principal components (corrected contrast sensitivity score at a 3 m distance, corrected visual ability, SRW and SRS better and average scores, SRW and SRS total number of mistakes, LOS time, postural testing time, and the olfactory discrimination score) have a significant correlation with cognitive performance. Additionally, regular management of ocular health was positively correlated, whereas history of neurovascular disease was negatively correlated with cognitive performance. By utilizing the years of education, LOS time, regular visual exams, testing time principal component, SRS better ear score, and olfactory discrimination score, an elastic net regression model was able to approximate the K-MoCA score, with an RMSE of 2 points in both training and testing datasets. These results showcase the use of motor and sensory parameters in approximating the cognitive score, which is a method that can be used in clinical settings to alert physicians to the possibility of early cognitive deterioration of the patient. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14052098/s1](https://www.mdpi.com/article/10.3390/app14052098/s1)]([https://www.mdpi.com/article/10.3390/app14052098/s1](https://www.mdpi.com/article/10.3390/app14052098/s1)), Dataset S1: dataset.csv; Table S1: Principal component analysis of gait data: rotated component matrix; Table S2: Principal component analysis of gait data: total variance explained; Table S3: Principal component analysis of visual data: rotated component matrix; Table S4: Principal component analysis of vision data: total variance explained; Table S5: Principal component analysis of auditory data: rotated component matrix; Table S6: Principal component analysis of auditory data: total variance explained; Table S7: Principal component analysis of postural stability data: rotated component matrix; Table S8: Principal component analysis of postural stability data: total variance explained; Table S9: Principal component analysis of olfactory data: rotated component matrix; Table S10: Principal component analysis of olfactory data: total variance explained; Figure S1: Correlation between K-MoCA score and age; Figure S2: K-MoCA score depending on (a) neurovascular pathology, (b) regular ophthalmological testing, and (c) alcohol consumption; Figure S3: Correlation between K-MoCA score and stride length; Figure S4: Correlation between K-MoCA score and stance duration CoV; Figure S5: Correlation between K-MoCA score and LR CoV; Figure S6: Correlation between K-MoCA score and corrected contrast sensitivity at 3 m distance; Figure S7: Correlation between K-MoCA score and corrected vision principal component; Figure S8: Correlation between K-MoCA score and SRW better; Figure S9: Correlation between K-MoCA score and SRW average; Figure S10: Correlation between K-MoCA score and SRW total number of mistakes; Figure S11: Correlation between K-MoCA score and SRS average; Figure S12: Correlation between K-MoCA score and SRS total number of mistakes; Figure S13: Correlation between K-MoCA score and sentence recognition principal component. **Author Contributions:** Conceptualization, E.K., K.K. and D.K. ; data curation, K.K. ; formal analysis, E.K., S.L. and K.K. ; funding acquisition, K.K. and D.K. ; investigation, E.K., S.L. and K.K. ; methodology, E.K., S.L. and K.K. ; project administration, K.K. and D.K. ; resources, K.K. ; supervision, D.K. ; visualization, E.K. ; writing--original draft, E.K. ; writing--review and editing, E.K., S.L., K.K. and D.K. All authors have read and agreed to the published version of the manuscript.