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Updated: Nov 8, 2025
Applying the RatWalker System for Gait Analysis in a Genetic Rat Model of Parkinson's Disease
Published on: January 18, 2021
Prediction of Freezing of Gait in Parkinson's Disease Using a Random Forest Model Based on an Orthogonal Experimental
Zhonelue Chen1, Gen Li2, Chao Gao2
1Gyenno Science Co., Ltd., Shenzhen, China.
Purpose:
The purpose of this study was to introduce an orthogonal experimental design (OED) to improve the efficiency of building and optimizing models for freezing of gait (FOG) prediction.
Methods:
A random forest (RF) model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG (preFOG). An OED was introduced to optimize the feature extraction parameters.
Results:
The main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time (MPT) were 27%, 68%, and 2.99 s, respectively.
Conclusion:
The OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model.
