Neurological disease prediction using impaired gait analysis for foot position in cerebellar ataxia by ensemble approach

Abstract

In neurological field, Cerebellar Ataxia (CA) prediction is done with Gait values of human actions. The Analysis of Gait (AoG) may lead the good treatment. The goal of this work was to develop a machine-learning-based model for predicting AoG using the poor gait patterns that occur before AoG. While executing designed AoG-provoking walking tasks, an accelerometer was connected to the lower back of 21 subjects with 12 different walking positions to gather acceleration impulses. The exercise was walking for one minute at each of 12 varied walking speeds on a split-belt treadmill in the range [0.6, 1.7] m/s in 0.1 m/s increments. To reduce the effects of weariness, the speed sequence was randomized and kept a secret from the subjects. Machine-learning algorithms like support vector machine (SVM) and k-nearest neighbours (KNN) have been tested in existing research studies. These algorithms perform well when the amount of data is little and the classification is binary. SVM, KNN, decision trees, and XGBoost algorithms have all been used in the proposed study on the CA data set. We discovered that the AdaBoost algorithm provides a more accurate categorization of the severity of CA disease.