Ferber Reed, Osis Sean T, Hicks Jennifer L, Delp Scott L
Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada; Faculty of Nursing, University of Calgary, Calgary, Alberta, Canada; Running Injury Clinic, Calgary, Alberta, Canada.
Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada; Running Injury Clinic, Calgary, Alberta, Canada.
J Biomech. 2016 Dec 8;49(16):3759-3761. doi: 10.1016/j.jbiomech.2016.10.033. Epub 2016 Oct 27.
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.
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、异构和有噪声的数据集中提取见解的能力，开始为传统的实验性运动捕捉和生物力学建模方法提供有力补充。本文的目的是探讨如何将数据科学方法融入我们这个领域，以增进我们对步态生物力学的理解，并改进治疗计划程序。我们给出了数据科学方法应用于生物力学数据的示例。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仍然存在的挑战，包括对新工具的需求、更好的数据共享基础设施和激励措施，以及生物力学和数据科学跨学科教育。通过应对这些挑战，我们可以利用步态研究人员在过去几十年中积累的丰富知识，以及世界各地临床和研究实验室收集的大量但往往分散的数据，彻底改变治疗计划和生物力学研究。
