A study on machine learning-based prediction of cerebellar ataxia using gait analysis and accelerometer data

In the neurological field, predicting Cerebellar Ataxia (CA) is based on analyzing gait values of human actions. Analyzing Gait (AoG) can potentially guide effective treatment strategies. This study aimed to create a machine-learning model for predicting AoG using gait patterns indicative of pre-AoG conditions. During the execution of designed walking tasks to provokeAoG, accelerometers were attached to the lower back of 21 subjects as they performed 12 different walking positions to collect acceleration impulses. The participants engaged in walking exercises for one minute at 12 different walking speeds on a split-belt treadmill, ranging from 0.6 to 1.7 m/s in 0.1 m/s increments. The speed sequence was randomized and concealed from the subjects to minimize fatigue effects. Prior research studies have surveyed machine-learning algorithms such as support vector machine (SVM) and k-nearest neighbors (KNN). These algorithms demonstrate strong performance, especially when the dataset is trivial, and the classification is binary. SVM, KNN, decision trees, and XGBoost algorithms were utilized in the proposed study on the CA dataset. Our findings revealed that the AdaBoost algorithm, with its high precision, offers a more precise categorisation of the severity of CA disease, instilling confidence in the study's findings.