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SCH: Multimodal,Task-Aware Movement Assessment and Control: Clinic to Home

SCH：多模式、任务感知运动评估和控制：诊所到家庭

基本信息

批准号：
10448378
负责人：
Louis N Awad
金额：
$ 28.36万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-30 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10448378
关键词：
Address Adult Algorithms Awareness Caregivers Clinic Clinical Communities Data Detection Development Devices Diagnosis Distant Emerging Technologies Environment Evaluation Event Foundations Frail Elderly Functional disorder Goals Healthcare Systems Home Hospitalization Hospitals Impairment Independent Living Individual Institution Instruction Intervention Laboratories Learning Module Life Lower Extremity Machine Learning Measurement Modality Modeling Monitor Motion Movement Muscle Patients Persons Physical activity Physiological Population Protocols documentation Robot Robotics Self-Help Devices Standardization System Techniques Technology Testing Time Training Vision Walking Work accurate diagnosis aging in place base cost frailty functional decline functional electrical stimulation functional status human model loss of function multimodality neuroprosthesis next generation novel prevent recruit response sensor technology development tool wearable sensor technology

项目摘要

We propose to develop a novel, distributed sensor platform that continuously assesses movement in the background of one's life with the goal of helping people age in place and avoid expensive and lengthy hospitalizations. On the one hand, the platform will combine measurements from a heterogeneous and complementary set of inertial, physiological , and vision sensors with state-of-the-art techniques from robotics and machine learning, together with clinically informed dynamic models of human motion. On the other hand, the platform will use these data to target the prompt detection of the mobility deficits that often precipitate the onset of frailty, with the goal of facilitating personalized caregiver alerts if a decline in functional status is detected. Moreover, the platform will provide context-aware control inputs to facilitate unconstrained use of powered assistive technologies in the home. This project has three main thrusts: assessment, control, and home intervention. In the assessment component, our work will extend well-proven techniques of multi-modal sensor fusion for mapping and localization of robots to home-based movement monitoring and intervention. The novelty of this work lies in the tight integration of machine learning modules for real-time activity recognition and movement dysfunction diagnosis. In the control component, our work will push the boundaries of what is possible with current powered assistive devices by developing novel control mechanisms that take advantage of the new capabilities provided by the estimation component (e.g., adapting control to changes in activities and environmental contexts). In the home intervention component, we will collect data that will refine the sensing and control algorithms and involve caregivers in alerts. A patient-in-the-loop development approach will be utilized where domain-informed protocols will generate the data necessary to train and evaluate our system, both in the clinic and in the home. By enabling timely detection of movement dysfunction and facilitating unconstrained use of powered assistive technologies, this foundational technology has paradigm-disrupting potential to prevent the onset of frailty and alter the treatment options for frail individuals. In parallel, the estimation component of the system could be used in clinical settings to automate and standardize time-intensive and highly subjective functional movement assessments, allowing more accurate diagnoses while freeing clinicians for other important tasks. RELEVANCE (See instructions): Frail older adults constitute the sickest, most expensive, and fastest growing segment of the US population. Home-based technologies that facilitate aging in place and reduce high-cost, hospital- and institution-based interventions are desperately needed. Our proposed distributed sensor platform has the potential to address this need by enabling the timely detection of the mobility deficits that often precipitate the onset of frailty and proactive caregiver and technological interventions that can delay, or prevent, mobility loss.

我们建议开发一种新型的分布式传感器平台，它可以连续评估一个人的生活背景，目的是帮助人们在适当的地方变老，避免昂贵和漫长住院治疗。一方面，该平台将结合来自不同类型和一套互补的惯性、生理和视觉传感器，采用机器人最先进的技术和机器学习，以及临床上了解到的人体运动的动态模型。另一方面，该平台将使用这些数据来及时检测经常导致开始虚弱，目标是促进个性化的照顾者警报，如果功能状态下降检测到。此外，该平台将提供上下文感知控制输入，以促进不受限制地使用为家中的辅助技术提供动力。该项目有三个主要推动力：评估、控制和家庭干预。在评估中组件，我们的工作将扩展多模式传感器融合的成熟技术，用于映射和将机器人本地化到以家庭为基础的运动监测和干预。这项工作的新颖性在于机器学习模块的紧密集成，可实现实时活动识别和运动障碍诊断。在控制组件中，我们的工作将推动当前可能实现的极限通过开发新的控制机制为辅助设备提供动力，该机制利用新的估计组件提供的能力(例如，使控制适应活动的变化和环境背景)。在家庭干预组件中，我们将收集数据以改进感知和控制算法，并让照顾者参与警报。循环中的患者开发方法将是在域通知协议将生成训练和评估我们的系统所需的数据的情况下使用，无论是在诊所还是在家里。通过及时检测运动功能障碍并促进不受限制地使用电动辅助器技术，这种基础性技术具有颠覆范式的潜力，可以防止脆弱性的出现并改变对虚弱个体的治疗选择。同时，系统的估计部分可以在临床环境中用于自动化和标准化时间密集型和高度主观的功能运动评估，允许更准确的诊断，同时将临床医生从其他重要任务中解放出来。相关性(请参阅说明)：虚弱的老年人构成了美国人口中病情最重、费用最高、增长最快的部分。基于家庭的技术，可促进就地老龄化并减少基于医院和机构的高成本我们迫切需要进行干预。我们建议的分布式传感器平台有可能解决这一需求的方法是能够及时检测到经常导致脆弱而主动的照顾者和技术干预，可以延缓或防止行动不便。