Analysis of Vertical Ground Reaction Force Data in Predicting Parkinson’s Disease
Abstract
Background: Parkinson’s disease (PD) is a complex, progressive neurodegenerative disorder known to negatively impair patient gait. Therefore, with gait and vertical ground reaction force (VGRF) data, an association can be made between the data and Parkinson’s disease.
Methods: Data from 146 participants; 93 with Parkinson’s disease and 73 without Parkinson’s disease was obtained from a PhysioNet database for use in this article. A Fourier Analysis and several support vector machine learning models were computed in MATLAB to classify whether an individual had Parkinson’s disease.
Results: From the Fourier analysis, it was determined that a statistically significant difference was present between the VGRF data of individuals with and without Parkinson’s disease. Additionally, it was found that a Minimum Classification Error Optimized SVM machine learning model using Bayesian statistics was able to classify individuals with Parkinson’s disease using VGRF data at an accuracy of 67.1%, and sensitivity of 80.43%.
Conclusion: Therefore, it can be determined that vertical ground reaction force can predict Parkinson’s Disease with considerable accuracy which could be improved with an increased number of participants.
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| Issue | Vol 10 No 3 (2024) | |
| Section | Articles | |
| Keywords | ||
| Fourier Analysis Machine Learning SVM Frequency Analysis Power Spectrum Analysis Biomedical Signals | ||
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