HEALing LB3P: Profiling Biomechanical, Biological and Behavioral phenotypes

HEALing LB3P：分析生物力学、生物和行为表型

• 批准号: 10415626
• 负责人: Gwendolyn A Sowa
• 金额: $ 13.41万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415626
• 关键词: 3-Dimensional; Acceleration; Administrative Supplement; Algorithms; Back; Biological; Biomechanics; Characteristics; Chronic low back pain; Clinical; Clinical assessments; Complex; Computer Vision Systems; Data; Development; E-learning; Goals; Hip Joint; Hip region structure; Human; Intervention; Lateral; Lifting; Machine Learning; Measurement; Measures; Methods; Modernization; Motion; Movement; Musculoskeletal Diseases; Outcome; Pain; Participant; Patients; Performance; Phase; Phenotype; Physical activity; Process; Public Health; Research; Rotation; System; Thigh structure; Training; Vertebral column; Work; base; behavioral phenotyping; experience; healing; improved; insight; interest; kinematics; multimodality; optimal treatments; pain patient; predictive modeling; sensor; socioeconomics; tool; wearable sensor technology

Project Summary Identifying the optimal treatment for chronic low back pain (CLBP), the most prevalent of painful musculoskeletal disorders, on a patient-specific basis is an important and unresolved challenge. Tailoring interventions according to patient movement characteristics may improve clinical outcomes. Multi-modal studies are underway to characterize CLBP patients and to provide insight into the phenotypes associated with the experience of CLBP in relation to direct targeted and improved treatments. Comprehensive assessment of lumbar spine movement CLBP patients may be an important facet of such treatments such that patient-specific spine biomechanics may be included in predictive models to improve the ability to characterize them. To that end, the purpose of this administrative supplement is to explore additional clinical tools for characterizing lumbopelvic kinematics during functional tasks and daily activities. Specifically, this work aims to develop computer vision methods with which to characterize motions of the lumbar spine, thereby providing an accurate and unobtrusive clinical tool that can be used to supplement or replace currently considered tools such as wearable sensors, handheld sensors, and complex marker-based motion capture systems. This project will use video, marker-based 3D video motion capture data, and wearable inertial measurement data to create and validate single-camera computer-vision algorithms that can be used to compute known clinical metrics. During clinical assessments of patients in the Pitt LB3P Biomechanics Core study, participants are asked to perform functional tasks (e.g., repeated flexion/extension, axial rotation, lateral bending, lifting, chair rises) while wearing inertial measurement units (IMUs) attached to the upper back at T1, low back at L1 and L5, and thigh. The functional performance exams are also recorded by video. The standard metrics determined from these trials will include maximum and minimum lumbar spine and hip ROM, angular velocity at mid-excursion, maximum rotation acceleration, and phase angles for lumbar and hip joint rotation. The goal of this supplemental proposal is to use collected data to develop and train computer vision algorithms (so-called markerless motion capture) to quantify the metrics of interest. Use of video is much simpler for clinicians as it avoids the setup process required by wearable sensors. Recent developments in markerless motion capture have enabled simple camera systems to provide quantitative information about human motions but few studies have assessed computer vision for use in the clinical setting. The research plan in this proposal involves the use of existing data to train and validate computer vision algorithms, and then to further investigate their use in the clinical setting with single camera video to determine the extent to which video-based markerless motion capture may be clinically useful in the assessment of CLBP patients.

项目摘要 确定慢性腰痛（CLBP）的最佳治疗方法，CLBP是最常见的肌肉骨骼疼痛 在患者特异性的基础上治疗疾病是一个重要的和未解决的挑战。根据需要定制干预措施 患者的运动特征可以改善临床结果。多模式研究正在进行中， 描述CLBP患者的特征，并深入了解与CLBP经历相关的表型 与直接靶向和改进的治疗有关。腰椎运动的综合评估 CLBP患者可能是这种治疗的重要方面，使得患者特异性脊柱生物力学可以 包括在预测模型中，以提高对它们进行表征的能力。为此目的， 行政补充是探索额外的临床工具，用于表征腰盆运动学， 功能任务和日常活动。具体来说，这项工作旨在开发计算机视觉方法， 以表征腰椎的运动，从而提供一种精确且不显眼的临床工具， 用于补充或替代目前考虑的工具，如可穿戴传感器，手持传感器， 复杂的基于标记的动作捕捉系统。 该项目将使用视频、基于标记的3D视频运动捕捉数据和可穿戴惯性测量数据 创建和验证单相机计算机视觉算法，可用于计算已知的临床 指标.在Pitt LB 3 P生物力学核心研究中对患者进行临床评估期间，参与者 被要求执行功能任务（例如，反复屈曲/伸展、轴向旋转、侧弯、提升、椅子 上升），同时佩戴惯性测量单元（伊穆斯），该惯性测量单元在T1处连接到上背部，在L1和L5处连接到下背部， 和大腿。功能性能检查也通过视频记录。标准度量由 这些试验将包括最大和最小腰椎和髋关节ROM，中间偏移角速度， 最大旋转加速度以及腰椎和髋关节旋转的相位角。本次增补的目的 一个建议是使用收集的数据来开发和训练计算机视觉算法（所谓的无标记运动 捕获）以量化感兴趣的度量。视频的使用对于临床医生来说要简单得多，因为它避免了设置 可穿戴传感器所需的过程。无标记运动捕捉的最新发展使得简单的 相机系统提供有关人体运动的定量信息，但很少有研究评估 计算机视觉在临床上的应用。本提案中的研究计划涉及使用现有的 数据来训练和验证计算机视觉算法，然后进一步研究它们在临床中的应用。 使用单个摄像机视频来确定基于视频的无标记运动捕捉可以 临床上可用于CLBP患者的评估。