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

• 批准号: 10188391
• 负责人: Louis N Awad
• 金额: $ 28.94万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188391
• 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; 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

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，该平台可以连续评估 一个人的生活背景，以帮助人们的年龄到位，避免昂贵和漫长的目标 住院治疗一方面，该平台将联合收割机从异构和 惯性、生理和视觉传感器与机器人技术的最新技术互补 和机器学习，以及临床上知情的人体运动动态模型。另一方面，在一项研究中， 该平台将使用这些数据，以及时检测经常导致 虚弱的发作，目的是如果功能状态下降， 检测到此外，该平台将提供上下文感知控制输入，以促进不受约束地使用 在家里使用动力辅助技术。 该项目有三个主要目标：评估、控制和家庭干预。评估中 组件，我们的工作将扩展行之有效的多模态传感器融合技术， 将机器人本地化到基于家庭的移动监测和干预。这项工作的新奇在于 机器学习模块的紧密集成，用于实时活动识别和运动功能障碍 诊断.在控制组件中，我们的工作将推动当前可能的边界 电动辅助设备，开发新的控制机制，利用新的 由估计组件提供的能力（例如，使控制适应活动的变化， 环境背景）。在家庭干预部分，我们将收集数据， 和控制算法，并让护理人员参与警报。患者在回路开发方法将是 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。 通过能够及时检测运动功能障碍并促进动力辅助器具的不受约束的使用， 技术，这种基础技术具有颠覆范式的潜力，可以防止脆弱的发生 并改变虚弱个体的治疗选择。并行地，系统的估计组件可以 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，同时释放临床医生的其他重要任务。 相关性（参见说明）： 虚弱的老年人构成了美国人口中最多病、最昂贵和增长最快的部分。 以家庭为基础的技术，促进就地老龄化，减少高成本，医院和机构为基础的 迫切需要采取干预措施。我们提出的分布式传感器平台有可能 通过及时检测经常会加速发病的流动性不足来满足这一需求 脆弱和积极主动的护理人员和技术干预，可以延迟或防止行动能力丧失。