Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Abstract

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation. INDEX TERMS pose-estimation, computer vision, computer aided diagnosis, gait analysis, machine learning, early neurodegenerative diseases assessment, kinematic theory of rapid human movements, sigma-lognormal

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification

“…In the future, this study will be extended to estimate other spatiotemporal parameters, such as the stride length, step length, joint angles, joint angle velocity, and acceleration, such that we can obtain greater insights on the participants’ health and classify normal and abnormal gait patterns. Although in this study we have used silhouette-based analysis [ 22 , 46 ], we will extend the work to advanced feature extraction techniques, such as pose estimation techniques [ 64 , 65 , 66 ], in the future so that the classification can be done with real-time video. Furthermore, this study was conducted using the minimum sequence length for walking speed patterns.…”

Human Gait Analysis in Neurodegenerative Diseases: A Review

“…Among kernels, the cubic one shows the best accuracy of 95.7% in classifying PD patients and control subjects, proving the important role of CoP as a discriminative feature. Higher classification accuracy has been also obtained in [75] by SVM (99.1%) with respect to RF, Ada Boost and kNN. Kinematic features computed from joint coordinates of human skeletons extracted from video captured by standard RGB cameras have been used.…”

“…Actually, the literature on the use of visionbased systems for the instrumented gait analysis of patients with neurodegenerative diseases counts few works compared to those based on wearable sensors or floor sensors. However, in the last few years, the progress in new and low-cost optical technologies together with the development of new and accurate pattern recognition approaches has led to an increase in vision-based research works [70], [71], [72], [73], [74], [75].…”

A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions—A Deep Learning Approach

“…Abnormal gait has been observed in 35% of older adults, and associated with a greater risk of institutionalization and mortality [2]. While gait evaluation is common [3], few studies have focused on the differentiation of neurological disorders, such as Parkinson's disease (PD) or multiple sclerosis (MS), using gait analysis [4], [5]. Various gait evaluations, such as motion capture during the timed 25 foot walk and timed up and go test have been explored in clinical settings to assess neurological conditions, such as MS [6], [7] and PD [8].…”