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

机器学习用于分析膝骨关节炎的步行模式和身体活动

• 批准号: 10066579
• 负责人: Kerry Elizabeth Costello
• 金额: $ 6.94万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2021-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10066579
• 关键词: 3-Dimensional; Accelerometer; Adult; Affect; Age; Algorithms; Area Under Curve; Behavior; Biomechanics; Biomedical Engineering; Boston; Classification; Clinical; Complex; Computing Methodologies; Data; Decision Trees; Degenerative polyarthritis; Disease; Disease Progression; Educational workshop; Enrollment; Environment; Epidemiology; Exhibits; Foundations; Funding; Gait; Generations; Goals; Health; Image; Individual; Institution; International; Intervention; Investigation; Joints; Knee; Knee Osteoarthritis; Knee joint; Laboratories; Learning; Logistic Regressions; Longitudinal Studies; Machine Learning; Maps; Mentorship; Monitor; Movement; Musculoskeletal Diseases; Outcome; Pain; Pathogenesis; Patients; Pattern; Performance; Physical activity; Principal Investigator; Process; Reaction; Rehabilitation therapy; Research; Research Personnel; Resources; Rheumatology; Role; Severity of illness; Statistical Methods; Structure; Symptoms; Time; Training; United States National Institutes of Health; Universities; Walking; Work; career; career development; cohort; complex data; computer program; disability; gait rehabilitation; improved; insight; joint loading; medical specialties; meetings; neglect; neural network; novel; post-doctoral training; random forest; recurrent neural network; skills; support vector machine; vigorous intensity

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.

项目摘要/摘要 膝盖骨关节炎（OA）的人表现出改变的步行模式，引起重复的异常力 在膝关节上，导致疾病进展。现有的干预措施以减少步行过程中的膝盖负荷 没有导致膝关节症状或关节结构的有意义的变化。现有的关键限制 研究一直在使用简化指标来描述步行模式，而不是考虑步行 数量和强度（即体育活动）。这项研究的目的是全面表征 有和没有膝盖OA的人的步行模式和活动，并评估步行的关联 膝盖OA结局的2年变化。机器学习方法将用于分析地面 反应力（GRF）数据和加速度计衍生的体育活动指标 从多中心骨关节炎研究（大多数）中表征了队列（n = 2575）。机器学习方法 使用选定的功能和不可知论的功能，并利用了随着时变的所有可用信息 GRF将与体育活动指标结合使用，以对症状和结构变化进行分类。 机器学习方法的结果将与常见统计方法的结果进行比较。 这项研究将允许表征步行模式和活动之间的复杂关系， 提供有关OA疾病过程的新见解。此外，这项研究将提供宝贵的培训 将机器学习方法应用于生物力学数据，并可能将特定于患者的策略告知 优化步行模式和体育活动，用于个性化的膝盖OA管理。 首席研究人员将利用应用于OA研究的生物医学工程的事先培训 为了进一步提高她在传统和不可知论的机器学习方法中的技能，以分析 生物力学和体育活动数据。波士顿大学（BU）的赞助商兼共同提案者将提供 OA临床方面的指导，在大型队列中实施拟议的研究，赠款， 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，首席调查员将在BU上参加与机器有关的教学课程和讲习班 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BU和其他地方机构以及国家和国际会议上的网络机会。这 赞助商和机构提供了一个环境，PI将作为多元化的一部分工作和学习 OA研究人员跨康复，风湿病学，流行病学，计算跨学科团队 方法和成像专业。这个博士后培训环境将为主要研究人员做好准备 从事具有机器学习方法专业知识的独立研究人员的职业 用于研究肌肉骨骼疾病（例如骨关节炎）的生物力学。