Human Gait Analysis Using Machine Learning: A Review

The gait analysis is interpreted to include an overwhelming number of interrelated parameters, which, due to the high volume of data and their relationships and is difficult to implement. The integration of machine learning with biomechanics is a promising approach to simplify the evaluation. The aim of this paper is to educate readers about the key directions to implement the gait analysis with machine learning techniques. The detailed survey is based on review and implementation articles performed by numerous research scholars to detect neurological effects in gait, gait asymmetry, gait disorders, gait events, and gait activities by using supervised machine learning algorithms. This study paper also reveals the effectiveness of ML approaches for condition identification, forecasting recovery time and monitoring for clinical diagnostic instruments.