Data-Driven Prediction of Freezing of Gait Events From Stepping Data.

ABSTRACT: Freezing of gait (FoG) is typically a symptom of advanced Parkinson's disease (PD) that negatively influences the quality of life and is often resistant to pharmacological interventions. Novel treatment options that make use of auditory or sensory cues might be optimized by prediction of freezing events. These predictions might help to trigger external sensory cues-shown to improve walking performance-when behavior is changed in a manner indicative of an impending freeze (i.e., when the user needs it the most), rather than delivering cue information continuously. A data-driven approach is proposed for predicting freezing events using Random Forrest (RF), Neural Network (NN), and Naive Bayes (NB) classifiers. Vertical forces, sampled at 100 Hz from a force platform were collected from 9 PD

SUBMITTER: Parakkal Unni M 

PROVIDER: S-EPMC8757792 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature