Deep Learning for Quantified Gait Analysis: A Systematic Literature Review

Abstract

Over the past few years, there has been notable advancement in the field of Quantified Gait Analysis (QGA), thanks to machine learning techniques. QGA and gait prediction are areas where Deep learning (DL) techniques are gaining popularity. There has been a significant amount of attention from the scientific community on the application of gait analysis in various fields. Based on our understanding, there is a noticeable absence of a comprehensive review and current understanding of gait analysis utilizing DL and Multi-task learning (MTL) models. Therefore, this paper provides a comprehensive assessment of the current application of DL algorithms for QGA. The study takes a systematic approach to explore this topic in depth. We conducted a thorough search of three databases, namely Web of Science, IEEEXplore, and Scopus, to identify relevant papers published from 1989 to October 2023. A total of 55 papers were considered eligible and included in this review. Approximately 46% of the studies that were identified utilized classification models to categorize gait phases and locomotion modes. Additionally, a significant portion of the studies (45%) utilized regression models to estimate and predict various kinematic and kinetic parameters, including joint angles, trajectories, moments, and torques. Interestingly, a notable 9% of the studies employed the use of MTL techniques in the realm of DL for gait analysis. We have also provided information on the most commonly utilized datasets for QGA.

Keywords

• Deep learning
• gait rehabilitation
• joint angles
• multi-task learning
• quantified gait analysis
• systematic literature review