Machine learning for analysis of walking patterns and physical activity in knee osteoarthritis

项目摘要/摘要 膝骨性关节炎(OA)患者表现出行走方式的改变，导致重复的、异常的力量 在膝关节上，导致疾病进展。减少步行过程中膝关节负荷的现有干预措施 未导致膝骨性关节炎症状或关节结构的有意义的变化。现有的一个关键限制 研究一直使用简化的度量来描述步行模式，而不考虑步行 数量和强度(即体力活动)。这项研究的目标是全面描述 膝骨性关节炎患者和非膝骨性关节炎患者的步行模式和活动，并评估步行的相关性 膝关节骨关节炎结果的两年变化。将使用机器学习方法来分析地面 现有的大型井中的反作用力(GRF)数据和加速度计派生的体力活动指标 来自多中心骨关节炎研究(MOST)的特征队列(n=2575)。机器学习方法 使用选定的特征和那些不可知的特征，并利用来自时变的所有可用信息 GRFS将与体力活动指标结合使用，对症状性和结构性变化进行分类。 机器学习方法的结果将与常见统计方法的结果进行比较。 这项研究将能够描述行走模式和活动之间的复杂关系， 提供了对骨性关节炎疾病过程的新见解。此外，这项研究将在以下方面提供有价值的培训 将机器学习方法应用于生物力学数据，并可能为患者提供特定的策略 优化步行模式和体力活动，以实现个性化的膝盖骨关节炎管理。 首席研究员将利用先前在生物医学工程应用于开放存取研究方面的培训。 进一步提高她在传统的和不可知的机器学习方法方面的技能，以分析 生物力学和体力活动数据。波士顿大学(BU)的赞助商和共同赞助商将提供 在骨性关节炎的临床方面的指导，在大规模队列中实施拟议的研究， 和职业发展。该团队将与波士顿大学计算生物医学的一名合作者合作 在机器学习方面的专业知识，以实现该项目的科学和培训目标。除了亲身实践之外 培训期间，首席调查员将参加BU与机器有关的教学课程和研讨会 学习和计算机编程。培训的其他关键方面包括参与研究和 在波士顿大学和其他地方机构以及国家和国际会议上建立联系的机会。这个 赞助商和该机构提供了一个环境，在那里，国际和平协会将作为多样化和 由康复、风湿病、流行病学、计算机等领域的OA研究人员组成的跨学科团队 方法和影像特点。这种博士后培训环境将为首席研究员做好准备 作为一名独立研究员，在机器学习方法方面具有专业知识，适用于 生物力学用于研究肌肉骨骼疾病，如骨关节炎。

Project Summary/Abstract Individuals with knee osteoarthritis (OA) exhibit altered walking patterns that cause repetitive, abnormal forces on the knee joint, leading to disease progression. Existing interventions to reduce knee loading during walking have not resulted in meaningful change in knee OA symptoms or joint structure. A key limitation of existing research has been the use of simplified metrics to describe walking patterns and not accounting for walking amount and intensity (i.e. physical activity). The goal of this research is to comprehensively characterize walking patterns and activity in people with and without knee OA and to assess the associations of walking with 2-year change in knee OA outcomes. Machine learning approaches will be used to analyze ground reaction force (GRF) data and accelerometer-derived physical activity metrics in an existing, large, well- characterized cohort (n=2575) from the Multicenter Osteoarthritis Study (MOST). Machine learning approaches that use selected features and those that are agnostic and utilize all available information from time-varying GRFs will be used in combination with physical activity metrics to classify symptomatic and structural change. The results from machine learning approaches will be compared to those of common statistical approaches. This research will allow for characterization of the complex relationships between walking patterns and activity, providing novel insights into OA disease processes. Further, this research will provide valuable training in applying machine learning approaches to biomechanics data and may inform patient-specific strategies to optimize walking patterns and physical activity for personalized knee OA management. The principal investigator will leverage prior training in biomedical engineering applied to OA research to further advance her skills in traditional and agnostic machine learning approaches for analyses of biomechanics and physical activity data. The sponsor and co-sponsor at Boston University (BU) will provide mentorship in clinical aspects of OA, implementation of the proposed studies in a large cohort, grantsmanship, and career development. The team will work with a collaborator in computational biomedicine at BU with expertise in machine learning to achieve the scientific and training goals of this project. In addition to hands-on training, the principal investigator will enroll in didactic coursework and workshops at BU related to machine learning and computer programming. Other key aspects of training include participation in research and networking opportunities at BU and other local Institutions, as well as national and international meetings. The sponsors and the Institution provide an environment where the PI will work and learn as a part of a diverse and interdisciplinary team of OA researchers across rehabilitation, rheumatology, epidemiology, computational methods, and imaging specialties. This postdoctoral training environment will prepare the principal investigator for a career as an independent researcher with expertise in machine learning approaches as applied to biomechanics for the study of musculoskeletal diseases such as osteoarthritis.