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

项目总结/摘要 膝关节骨关节炎（OA）患者的行走模式发生改变，导致重复性的异常力量 导致疾病恶化现有的干预措施，以减少行走过程中的膝关节负荷 未导致膝关节OA症状或关节结构发生有意义的变化。现有的一个关键限制 研究一直是使用简化的度量来描述步行模式， 量和强度（即体力活动）。本研究的目标是全面表征 有和没有膝关节OA的人的步行模式和活动，并评估步行与膝关节OA的相关性。 膝关节OA结局的2年变化。机器学习方法将用于分析地面 反作用力（GRF）数据和加速度计衍生的身体活动指标，在现有的，大的，良好的， 多中心骨关节炎研究（MOST）的特征队列（n=2575）。机器学习方法 使用选定的功能和那些是不可知的，并利用所有可用的信息，从随时间变化的 GRF将与身体活动指标结合使用，以分类症状和结构变化。 机器学习方法的结果将与常见的统计方法进行比较。 这项研究将允许步行模式和活动之间的复杂关系的表征， 提供了对OA疾病过程的新见解。此外，这项研究将提供有价值的培训， 将机器学习方法应用于生物力学数据，并可以告知患者特定的策略， 优化步行模式和身体活动，以实现个性化的膝关节OA管理。 主要研究者将利用之前接受的生物医学工程应用于OA研究的培训 进一步提高她在传统和不可知的机器学习方法中的技能， 生物力学和身体活动数据。波士顿大学（BU）的赞助商和联合赞助商将提供 OA临床方面的指导，在大型队列中实施拟议研究， 和职业发展。该团队将与BU的计算生物医学合作者合作， 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。除了动手 培训，主要研究者将参加BU与机器相关的教学课程和研讨会 学习和计算机编程。培训的其他主要方面包括参与研究和 在BU和其他当地机构以及国家和国际会议上建立联系的机会。的 赞助商和机构提供了一个环境，PI将工作和学习的一部分， OA研究人员跨康复，风湿病学，流行病学，计算 方法和成像专业。这种博士后培训环境将为主要研究者做好准备 作为一名独立研究员，拥有机器学习方法的专业知识， 研究骨关节炎等肌肉骨骼疾病的生物力学。

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.