Progress of Big Data Analysis in Gait Biomechanics / 医用生物力学

ABSTRACT

Objective To study the research progress of big data analysis in gait biomechanics. Methods Based on the scientific and technological literature related to big data analysis in gait biomechanics during the year 2011-2020 as the research object, content analysis method was used to analyze and discuss from four aspects, including topic structure, hierarchy level, model type and analysis technology. On this basis, the future research of gait biomechanics big data analysis was prospected. Results The application of big data analysis in gait biomechanics mainly involves five research directions, namely, intervention and rehabilitation, exercise training, prosthesis design and evaluation, understanding of etiology and diagnosis, understanding of human movement characteristics. Big data analysis in gait biomechanics is divided into three levels, of which descriptive analysis is the most used type, accounting for about 41%. The models and specific techniques of big data analysis in gait biomechanics field were reviewed. Topological data analysis is a promising big data exploration tool for future research. Conclusions Big data technology has great potential in gait biomechanics and clinical medicine research.