ArticleFrontiers in human neuroscience2021
Prediction of Freezing of Gait in Parkinson's Disease Using a Random Forest Model Based on an Orthogonal Experimental Design: A Pilot Study.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Lessons Learned From a Delayed-Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease.Annals of clinical and translational neurology · 2026Trial
- Use of AR/VR for Treatment of Freezing of Gait (FoG) in Parkinson's Disease (PD).Journal of clinical medicine · 2026Review
- Scoping review of AI-driven wearable technologies for rehabilitation and functional assessment in Parkinson's disease: a protocol.BMJ open · 2026Article
- Bayesian Regression Analysis for Dependent Data with an Elliptical Shape.Entropy (Basel, Switzerland) · 2024Article
- Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease.Sensors (Basel, Switzerland) · 2023Article
- Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes.JMIR diabetes · 2023Article
- Triggers for freezing of gait in individuals with Parkinson's disease: a systematic review.Frontiers in neurology · 2023Review
- Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe 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.
methodsA 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.
resultsThe 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.
conclusionThe 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.
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