Citation

Chen Z, Li G, Gao C, Tan Y, Liu J, Zhao J, Ling Y, Yu X, Ren K, Chen S. Front. Hum. Neurosci. 2021; 15: e636414.

Copyright

(Copyright © 2021, Frontiers Research Foundation)

DOI

10.3389/fnhum.2021.636414

PMID

33867959

Abstract

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.

Language: en

Keywords

Parkinson’s disease; freezing of gait; fog prediction; optimization; orthogonal experimental design