Triple Parallel LSTM Networks for Classifying the Gait Disorders Using Kinect Camera and Robot Platform During the Clinical Examination

Abstract

This paper presents a new methodology for the data processing and classification method for gait disorders, which is observed with a Kinect camera. The study of gait and motion stability in gait disorders is one of the most interesting research areas in the field. The patient and the physician must monitor the progress of the rehabilitation process before and after surgery to obtain an objective view of the rehabilitation process. In this study, the patient is scanned with the Kinect camera placed on a mobile robotic platform. For feature extraction and feature analysis, the exercises (three walking exercises) frames are collected and saved in data folders. This study uses 84 measurements of 37 patients with complex observations based on the physician's opinion in a clinical setting to address classification problems. In the analysis of gait disorders, motion data play an essential role. Furthermore, it reduces the selection of helpful body features for assessing gait disorders. The proposed system uses a key-point detector that computes body landmarks and classifies gait disorders using triple-parallel long short-term memory (LSTM) networks. The present study demonstrates the success of the method in classification evaluation when combined with the state-of-the-art pose estimation method. Around 81 percent accuracy was achieved for given sets of individuals using velocity-based, angle-based, and position-based features.