Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges

摘要

Human gait provides a way of locomotion by combined efforts of the brain, nerves, and muscles. Conventionally, the human gait has been considered subjectively through visual observations but now with advanced technology, human gait analysis can be done objectively and empirically for the better quality of life. In this paper, the literature of the past survey on gait analysis has been discussed. This is followed by discussion on gait analysis methods. Vision-based human motion analysis has the potential to provide an inexpensive, non-obtrusive solution for the estimation of body poses. Data parameters for gait analysis have been discussed followed by preprocessing steps. Then the implemented machine learning techniques have been discussed in detail. The objective of this survey paper is to present a comprehensive analysis of contemporary gait analysis. This paper presents a framework (parameters, techniques, available database, machine learning techniques, etc.) for researchers in identifying the infertile areas of gait analysis. The authors expect that the overview presented in this paper will help advance the research in the field of gait analysis. Introduction to basic taxonomies of human gait is presented. Applications in clinical diagnosis, geriatric care, sports, biometrics, rehabilitation, and industrial area are summarized separately. Available machine learning techniques are also presented with available datasets for gait analysis. Future prospective in gait analysis are discussed in the end.