Analyze: Machine Learning for Mobility Data

分析：移动数据的机器学习

• 批准号: 10581471
• 负责人: Christopher Re
• 金额: $ 37.23万
• 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/10581471
• 关键词: Academia; Accelerometer; Address; Affect; Algorithms; Behavioral Model; Binding; Biomechanics; Biomedical Research; Biomedical Technology; Cartilage; Cellular Phone; Cerebral Palsy; Characteristics; Classification; Clinic; Clinical; Complex; Computer software; Data; Data Set; Degenerative polyarthritis; Democracy; Devices; Diagnosis; Disease; Elements; Exercise; Explosion; Familiarity; Foundations; Freezing; Future; Gait; Goals; Image; Individual; Industry; Joints; Knee; Knowledge; Label; Language; Leadership; Locomotion; Machine Learning; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Metabolic; Methods; Modeling; Modernization; Monitor; Motion; Movement; Muscle; Obesity; Parkinson Disease; Pathologic; Pathology; Patients; Persons; Physical activity; Population; Process; Pythons; Records; Rehabilitation Outcome; Rehabilitation therapy; Research; Research Personnel; Resources; Roentgen Rays; Series; Services; Technology; Text; Time; Training; Work; automated segmentation; biomedical scientist; clinical practice; coral; cost; deep learning; design; evidence based guidelines; experience; functional improvement; gene interaction; health goals; heterogenous data; imaging Segmentation; improved; improved mobility; individualized medicine; innovation; insight; instrument; kinematics; large datasets; learning strategy; machine learning model; mobile sensing; mobile sensor; model building; multidimensional data; multimodality; neural; open source; scripting interface; sensor technology; skills; societal costs; success; terabyte; time use; tool; treatment planning; 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）、脑瘫和帕金森病等疾病导致的活动受限， 数以百万计的人，以巨大的个人和社会成本。康复治疗可以显著改善行动能力 和功能，但目前的康复实践需要由熟练的临床医生亲自指导， 费用和限制访问。移动的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 精确监测功能和定制治疗所需的运动特性。数百万低成本 移动的传感器正在生成数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、异构、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的 用移动的传感器量化运动生物力学的工具。 具体而言，我们将： 1.通过使用计算肌肉的模型的可穿戴传感器， 以及运动的合力和代谢成本。这些模型，基于生物力学和机器 学习模型，将通过我们新创建的OpenSense软件传播，该软件将用于 成千上万的研究人员使用移动的传感器来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的工具的需求 模型来分析非结构化的高维数据并从中生成见解，包括时间- 系列（例如，来自移动的传感器），图像（例如，MRI）和视频（例如，病人的智能手机视频 步态）。 3.提供干预现实世界所需的工具。我们将开发算法来精确地量化 使用来自惯性测量单元（伊穆斯）的数据，在实验室外长时间测量运动学。我们将 我还建立行为模型，以适应和个性化的目标设定，借鉴运动记录， 600万人，以及170万人的健康目标和锻炼。 通过与我们的合作项目的密切互动，我们将专注于改善康复 由于骨关节炎、肥胖、帕金森病和脑血管病导致活动受限的个体的结局 麻痹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，监测和治疗疾病的方式。 治疗行动障碍，提供所需的工具，以低成本和大规模的 未来的规模。