Generación de la biomecánica del movimiento de extremidades inferiores

Abstract

Human beings, in their quest to improve the quality of life in people who suffer from gait disturbances, have developed technologies capable of identifying patterns and characteristics of different conditions that deteriorate mobility. Although, some studies explore invasive methods such as electromyography, the use of sensors and/or markers for the analysis and evaluation of pathological gaits, there is still little research that addresses methods that do not invade the body, given that in current times it is an essential approach. The purpose of this study is to develop a non-invasive system, based on vision techniques and artificial intelligence capable of generating spatio-temporal parameters of the biomechanics of movement of the lower extremities from normal or pathological gaits such as hemiparetic and paraparetic, as well as the analysis and classification of these gaits. The methodology used consists of capturing RGB images in people who perform several cycles of the normal, hemiparetic and paraparetic gaits. These images are processed by using models like OpenPose and PoseNet to estimate the pose. Then, cutting, synchronization, filtering, normalization and 2D analysis techniques are applied, as well as new approaches such as Skeleton Gait Energy Image (SGEI) to characterize the gait. Finally, through algorithms such as Convolutional Neural Network (CNN) or Support Vector Machine (SVM), the system is trained to classify the analyzed gaits. As a result, it is possible to generate the parameters of stride length, cadence, stride width, step time, gait speed, front body posture inclination and angles of the lower extremities of the human body of the 3 gaits using a non-invasive system approach, additionally experimental results show high efficiency in the classification of the gaits with a 98.57 % using OpenPose and a 98.15 % with PoseNet.