Comparison of Feature Selection and Supervised Methods for Classifying Gait Disorders

Abstract

Recently, systems for classifying gait disorders have been of great interest. However, quantifying the progress of these disorders has been highly dependent on a physician's judgement in classifying sick and healthy subjects. We examine the effects of gait stability analysis on gait dysfunction problems, which are impacted by the patient's dynamic balance. The dataset in this study was collected and labelled based on the opinions of physicians at Prague Hospital; it included 84 measurements of 37 patients. A keypoint detector was applied to detect the skeletal keypoints of patients. We have prepared two different datasets from the detection and tracking results. For the proposed feature selection method, we have used statistical measurements such as the x and y coordinates for each keypoint, the distance, and the angle between two selected keypoints. Using these statistical measurements, we have prepared different subgroups with different numbers of features to examine. We have also applied ten different feature selection algorithms to obtain data from different numbers of features automatically. Then, these datasets with high-level features were used to train well-known networks, such as the long short-term memory (LSTM), gated recurrent unit (GRU), and multiple layer perceptron (MLP) networks. The study results showed that the 30 features selected by the analysis of variance (ANOVA) algorithm and used to train the GRU network ranked among the best features and resulted in a classification F-score of 85%. The results also prove that the data generated by the detector method are more effective than the data generated by the tracking method due to the format of the exercises in our dataset, which were designed by physicians. Moreover, the best feature selection approaches have considerably improved the classification F-score compared to manual feature generation.