Technology Training and Dissemination

技术培训与传播

• 批准号: 10581472
• 负责人: SCOTT L DELP
• 金额: $ 33.39万
• 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/10581472
• 关键词: Academia; Academy; Address; Affect; Algorithms; Ambulatory Monitoring; American; Area; Award; Awareness; Behavioral Model; Binding; Biomechanics; Biomedical Computing; Biomedical Engineering; Biomedical Technology; Cellular Phone; Cerebral Palsy; Characteristics; Clinic; Clinical; Clinics and Hospitals; Collaborations; Communication; Communities; Computer software; Congresses; Data; Data Science; Data Set; Degenerative polyarthritis; Diagnosis; Disease; Documentation; Educational workshop; Event; Exercise; Exposure to; Fostering; Future; Gait; Goals; Health; Image; Individual; Industry; Infrastructure; Institution; International; Intervention; Joints; Journals; Leadership; Locomotion; Machine Learning; Magnetic Resonance Imaging; Manufacturer; Measurement; Measures; Metabolic; Modeling; Monitor; Motion; Movement; Muscle; Newsletter; Obesity; Orthopedics; Parkinson Disease; Participant; Patients; Persons; Physical Medicine; Physical activity; Property; Publishing; Records; Rehabilitation Outcome; Rehabilitation therapy; Research; Research Personnel; Research Project Grants; Resources; Rotation; Running; Series; Services; Software Tools; Surgeon; Time; Training; Training Programs; Travel; United States National Institutes of Health; Video Recording; Woman; biomedical scientist; community center; coral; cost; experience; functional improvement; handheld mobile device; health goals; improved; improved mobility; individualized medicine; innovation; insight; interest; kinematics; live stream; mHealth; machine learning model; member; mobile computing; mobile sensing; mobile sensor; multidimensional data; new technology; online resource; program dissemination; programs; rehabilitation research; rehabilitation science; sensor technology; skills; social media; societal costs; success; symposium; technology training; terabyte; tool; user-friendly; wearable sensor technology; web site

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.

骨关节炎 (OA)、脑瘫和帕金森病等疾病导致的活动受限会影响 康复可以极大地改善数百万人的流动性，但需要付出巨大的个人和社会成本。 和功能，但目前的康复实践需要熟练临床医生的亲自指导，这增加了 移动传感技术现在无处不在，并且有潜力。 在诊所外测量患者功能和治疗指南，但目前无法捕捉到 精确监测功能和定制治疗所需的运动特征数以百万计的低成本。 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——The Mobilize 通过经验丰富的科学团队的领导，我们将创造和传播创新成果。 使用移动传感器量化运动生物力学的工具。 具体来说，我们将： 1. 使用计算肌肉的模型突破我们可以通过可穿戴传感器测量的范围 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. 满足对分析运动动态数据和开发机器学习的工具的需求 用于分析非结构化、高维数据（包括时间数据）并生成见解的模型 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准确量化的算法。 我们将使用惯性测量单元 (IMU) 的数据在实验室外进行长时间的运动学分析。 还建立行为模型来适应和个性化目标设定，利用来自的运动记录 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深入互动，我们将专注于改善康复 因骨关节炎、肥胖、帕金森病和脑血管病而行动受限的人的结果 该中心的工具和服务将使研究人员能够彻底改变我们的诊断、监测和治疗方式。 治疗行动障碍，提供以低成本和大规模进行精准康复所需的工具 未来规模化。