Elastic Shape Analysis of Movement Data

• 成果类型: Article
• 署名作者: Borgert, J. E.; Hannig, Jan; Tucker, J. Derek; Arbeeva, Liubov; Buck, Ashley N.; Golightly, Yvonne M.; Messier, Stephen P.; Nelson, Amanda E.; Marron, J. S.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; United States Department of Energy (DOE); Sandia National Laboratories; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of Nebraska System; University of Nebraska Medical Center; University of North Carolina; University of North Carolina Chapel Hill; Wake Forest University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2572778
• 发表日期: 2026-01-02
• 页码: 126-136
• 关键词: Biomechanical data; Functional Data Analysis; nonparametric methods; Shape statistics; GROUND REACTION FORCE; JOINT SPACE WIDTH; knee osteoarthritis; gait; PREVALENCE; walking; biomechanics; arthritis; disease; adults

摘要

Osteoarthritis (OA) is a highly prevalent degenerative joint disease, and the knee is the most commonly affected joint. Biomechanical factors, particularly forces exerted during walking, are often measured in modern studies of knee joint injury and OA, and understanding the relationship among biomechanics, clinical profiles, and OA has high clinical relevance. Biomechanical forces are typically represented as curves over time, but a standard practice in biomechanics research is to summarize these curves by a small number of discrete values (or landmarks). The objective of this work is to demonstrate the added value of analyzing full movement curves over conventional discrete summaries. We developed a shape-based representation of variation in full biomechanical curve data from the Intensive Diet and Exercise for Arthritis (IDEA) study (Messier et al. 2009, 2013), and demonstrated through nested model comparisons that our approach, compared to conventional discrete summaries, yields stronger associations with OA severity and OA-related clinical traits. Notably, our work is among the first to quantitatively evaluate the added value of analyzing full movement curves over conventional discrete summaries. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.