Administration

行政

• 批准号: 10581469
• 负责人: SCOTT L DELP
• 金额: $ 18.58万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-04 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581469
• 关键词: Achievement; Address; Advisory Committees; Affect; Algorithms; Area; Behavioral Model; Big Data to Knowledge; Binding; Biomechanics; Biomedical Technology; Cellular Phone; Cerebral Palsy; Characteristics; Clinic; Clinical; Communication; Computer Models; Computer software; Consultations; Data; Degenerative polyarthritis; Development; Diagnosis; Disease; Effectiveness; Elements; Exercise; Face; Funding; Future; Gait; Geography; Goals; Image; Individual; Institution; Joints; Lead; Leadership; Locomotion; Magnetic Resonance Imaging; Measurement; Measures; Metabolic; Methodology; Modeling; Monitor; Motion; Movement; Muscle; National Institute of Biomedical Imaging and Bioengineering; Obesity; Parkinson Disease; Patients; Persons; Records; Rehabilitation Outcome; Rehabilitation therapy; Research; Research Personnel; Resources; Running; Series; Services; Structure; Technology; Testing; Time; Training; United States National Institutes of Health; Universities; Work; biomedical data science; computerized tools; cost; experience; functional improvement; health goals; improved; improved mobility; individualized medicine; innovation; insight; kinematics; machine learning model; meetings; mobile sensing; mobile sensor; multidimensional data; programs; recruit; rehabilitation research; sensor technology; simulation; skills; societal costs; technology research and development; terabyte; tool; usability; wearable sensor technology

Limited mobility due to conditions like osteoarthritis (OA), cerebral palsy, and Parkinson’s disease affects millions of individuals, at enormous personal and societal cost. Rehabilitation can dramatically improve mobility and function, but current rehabilitation practice requires in-person guidance by a skilled clinician, increasing expense and limiting access. Mobile sensing technologies are now ubiquitous and have the potential to measure patient function and guide treatment outside the clinic, but they currently fail to capture the characteristics of motion required to accurately monitor function and customize treatment. Millions of low-cost mobile sensors are generating terabytes of data that could be analyzed in combination with other data, such as images, clinical records, and video, to enable studies of unprecedented scale, but machine learning models for analyzing these large-scale, heterogeneous, time-varying data are lacking. To address these challenges, we will establish a Biomedical Technology Resource Center —The Mobilize Center. Through the leadership of an experienced scientific team, we will create and disseminate innovative tools to quantify movement biomechanics with mobile sensors. Specifically, we will: 1. Push the bounds of what we can measure via wearable sensors using models that compute muscle and joint forces and metabolic cost of locomotion. These models, based on biomechanical and machine learning models, will be disseminated via our newly created OpenSense software, which will be used by thousands of researchers to gain new insights into patient biomechanics using mobile sensors. 2. Meet the need for tools that analyze data about movement dynamics and develop machine learning models to analyze and generate insights from unstructured, high-dimensional data, including time- series (e.g., from mobile sensors), images (e.g., MRI), and video (e.g., smartphone video of a patient’s gait). 3. Provide tools needed to intervene in the real-world. We will develop algorithms to accurately quantify kinematics outside the lab for long durations using data from inertial measurement units (IMUs). We will also build behavioral models to adapt and personalize goal setting, drawing on movement records from 6 million individuals, as well as health goals and exercise for 1.7 million people. Through intensive interactions with our Collaborative Projects, we will focus on improving rehabilitation outcomes for individuals with limited mobility due to osteoarthritis, obesity, Parkinson’s disease, and cerebral palsy. The Center’s tools and services will enable researchers to revolutionize how we diagnose, monitor, and treat mobility disorders, providing tools needed to deliver precision rehabilitation at low cost and on a massive scale in the future.

由于骨关节炎(OA)、脑性瘫痪和帕金森氏症等疾病导致的行动不便 数以百万计的个人，付出了巨大的个人和社会代价。康复可以显著提高行动能力 和功能，但目前的康复实践需要熟练的临床医生亲自指导，越来越多 费用和访问限制。移动传感技术现在无处不在，并有可能 测量患者功能并指导诊所外的治疗，但他们目前未能捕捉到 准确监测功能和定制治疗所需的运动特征。数百万的低成本 移动传感器正在生成数TB的数据，这些数据可以与其他数据结合进行分析，例如 图像、临床记录和视频，以实现前所未有的规模的研究，但机器学习模型 缺乏对这些大规模、异质、时变数据的分析。 为了应对这些挑战，我们将建立一个生物医学技术资源中心-动员 中心。通过经验丰富的科学团队的领导，我们将创造和传播创新 使用移动传感器量化运动生物力学的工具。 具体来说，我们会： 1.使用计算肌肉的模型，通过可穿戴传感器推动我们可以测量的极限 以及运动的合力和代谢成本。这些模型基于生物力学和机械 学习模型，将通过我们新创建的OpenSense软件进行传播，该软件将用于 由数千名研究人员使用移动传感器获得对患者生物力学的新见解。 2.满足对分析运动动力学数据和开发机器学习工具的需求 用于分析非结构化的高维数据并生成洞察力的模型，包括时间- 系列(例如，来自移动传感器)、图像(例如，MRI)和视频(例如，患者的智能手机视频 步态)。 3.提供干预现实世界所需的工具。我们将开发算法来准确量化 使用来自惯性测量单元(IMU)的数据在实验室外进行长时间的运动学测试。我们会 还可以构建行为模型，以适应和个性化目标设置，并利用 600万人，以及170万人的健康目标和锻炼。 通过与我们的协作项目的深入互动，我们将专注于改善康复 因骨关节炎、肥胖症、帕金森病和脑部疾病导致活动受限的患者的预后 瘫痪。该中心的工具和服务将使研究人员能够彻底改变我们诊断、监控和 治疗活动障碍，提供所需的工具，以低成本和大规模的 未来的规模。