Gait biomechanics in the era of data science. Issue 16 (8th December 2016)

• Record Type: Journal Article
• Authors: Ferber, Reed; Osis, Sean T.; Hicks, Jennifer L.; Delp, Scott L.
• Is Part Of: Journal of biomechanics. Volume 49:Issue 16(2016)
• Page Start: 3759
• Page End: 3761
• Publication Date: 2016-12-08
• Subjects: Biomechanics -- Gait -- Data science -- Machine learning; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2016.10.033
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Data science has transformed fields such as computer vision and economics. The ability of modern data science methods to extract insights from large, complex, heterogeneous, and noisy datasets is beginning to provide a powerful complement to the traditional approaches of experimental motion capture and biomechanical modeling. The purpose of this article is to provide a perspective on how data science methods can be incorporated into our field to advance our understanding of gait biomechanics and improve treatment planning procedures. We provide examples of how data science approaches have been applied to biomechanical data. We then discuss the challenges that remain for effectively using data science approaches in clinical gait analysis and gait biomechanics research, including the need for new tools, better infrastructure and incentives for sharing data, and education across the disciplines of biomechanics and data science. By addressing these challenges, we can revolutionize treatment planning and biomechanics research by capitalizing on the wealth of knowledge gained by gait researchers over the past decades and the vast, but often siloed, data that are collected in clinical and research laboratories around the world.