Gait biomechanics in the era of data science.
Gait biomechanics in the era of data science.
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DOI：
10.1016/j.jbiomech.2016.10.033
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发表时间：
2016-12-08
影响因子：
2.4
通讯作者：
Delp SL
中科院分区：
文献类型：
--
作者：
Ferber R;Osis ST;Hicks JL;Delp SL
数据科学已经改变了计算机视觉和经济学等领域。现代数据科学方法从大型、复杂、异构和嘈杂的数据集中提取见解的能力，开始为实验运动捕捉和生物力学建模的传统方法提供强大的补充。本文的目的是为数据科学方法如何融入我们的领域提供一个视角，以促进我们对步态生物力学的理解，并改进治疗计划程序。我们提供了数据科学方法如何应用于生物力学数据的例子。然后，我们讨论了在临床步态分析和步态生物力学研究中有效使用数据科学方法所面临的挑战，包括对新工具的需求，更好的基础设施和激励机制，以及跨生物力学和数据科学学科的教育。通过解决这些挑战，我们可以通过利用过去几十年来步态研究人员获得的丰富知识以及在世界各地的临床和研究实验室收集的大量但通常是孤立的数据，彻底改变治疗计划和生物力学研究。
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.