Progress of Big Data analysis in Gait Biomechanics
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    Abstract:

    Purpose: The rapidly growing interest from both academics and practitioners towards application of big data analysis in GB has prompted the need to review the up-to-date advances in order to set a new agenda. This paper responds to this call by proposing a novel research framework that provides a panoramic view of the current literature on where and how big data analysis has been applied within the GB context. Methods: Based on the scientific and technological literature related to gait biomechanics by big data analysis from 2011 to 2020 as the research object, this paper uses content analysis method to analyze and discuss from five aspects, including topic structure, analytics level, model type and analysis technology. Results: Firstly,researches on GB field mainly involve five research directions, namely intervention and rehabilitation, exercise training, prosthesis design and evaluation, understanding of etiology and diagnosis, understanding of the characteristics of human movement. Secondly, there is a detailed review of analytics level by year toward GB using big data analytsis. Then, it reviews the models and specific techniques of big data analysis in GB field. Finally, the future directions and challenges of big data analysis in the field of GB are summarized. Conclusion: On this basis, the future research of GB in big data analysis is expected. This paper presents clearly and comprehensively the current advance and development of GB in big data analysis, laying a solid foundation for gait biomechanics research, and filling the gap in the field of GB research review by big data analysis in recent years.

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History
  • Received:October 16,2020
  • Revised:February 10,2021
  • Adopted:February 18,2021
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