CRII: SCH: Applying Motor Control Theories for Ambulatory Monitoring of 3D Upper-Limb Movement

三维（3D）上肢姿势和运动在社区环境中的准确监测在康复中具有至关重要的意义。这项工作的目的是开发一个系统，可以提供一个客观的评估个性化的治疗。具体而言，在具有长恢复时间的运动障碍中，例如中风或创伤性脑损伤，使用微创感测连续监测运动一直是支持长期坚持的目标。腕戴式惯性传感器由于其即时性、普遍性和持续使用的接受性而成为最常用的可穿戴传感器。然而，开发基于单个腕戴式设备的上肢运动的精确理解是具有挑战性的。来自这些设备的数据往往会随着时间的推移而漂移;也就是说，当数据被整合以估计运动时，传感器测量中的小误差会迅速增长。本项目旨在建立一个系统，通过专门解决漂移问题，允许使用单个腕戴式惯性传感器精确监测上肢运动。拟议的努力将通过利用人类中枢神经系统（CNS）介导的上肢自主运动的独特运动学特性，推进上肢运动动态监测的最新技术以及肌肉骨骼结构的物理特性。该项目从关于肢体运动的独特运动学特征的先验知识开始，这将消除对第二积分和许多漂移误差的需要。该模型将提供独特的机会，开发一种新的计算算法，精确测量上肢运动和运动学。本研究将解决以下科学挑战：1）开发数学模型和计算算法来估计动态变化的身体方向，2）建立新的机器学习框架，通过利用运动控制理论来估计传感单元的3D位置轨迹，而无需双重积分，以及3）开发基于人体肌肉骨骼特性的估计肢体关节的最可能的运动学轮廓的顺序算法。该项目的成功将导致在自由生活环境中精确手势监测的重大突破，打开一扇通往以前未开发的数据集的新大门，并通过对运动障碍中运动功能的非侵入性监测来潜在地开发个性化疾病管理。该项目还将包括可穿戴计算、信号处理、数据科学和智能健康领域的跨学科研究和本科生/研究生培训的整合。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Accurate monitoring of three dimensional (3D) upper limb posture and motion in a community setting has been of paramount importance in rehabilitation. This work is aimed at developing a system that can provide an objective assessment of individually-tailored therapeutic treatments. Specifically, in movement disorders with a long recovery time, such as stroke or traumatic brain injury, continuous monitoring of movement using a minimally-invasive sensing has been a goal to support long-term adherence. A wrist-worn inertial sensor has been the most commonly used wearable sensor due to its immediacy, ubiquity, and acceptance for sustained use. However, developing a precise understanding of upper limb movements based on a single wrist-worn device is challenging. Data from these devices tends to drift over time; that is, a small error in the sensor measurements grows rapidly as the data are integrated to estimate the movement. This project aims to establish a system that allows accurate monitoring of upper limb movement using a single wrist-worn inertial sensor by specifically addressing the issue of drift.The proposed effort will advance the state-of-the-art in ambulatory monitoring of upper limb motion by exploiting the unique kinematic properties of voluntary upper limb movements mediated by the human central nervous system (CNS) and the physical properties of the musculoskeletal structure. The project starts from prior knowledge regarding the unique kinematic characteristics of limb motion that would eliminate the need for the second integration and many of the drift errors. This model will provide unique opportunities to develop a novel computational algorithms for precise measurement of upper limb motion and kinematics. This study will address the following scientific challenges: 1) development of mathematical models and computational algorithms to estimate the dynamically changing body direction, 2) establishment of a new machine learning framework to estimate the 3D position trajectory of the sensing unit without double integration by leveraging the motor control theories, and 3) development of a sequential algorithm to estimate the most likely kinematic profiles of limb joints based on the human musculoskeletal properties. The success of this project will lead to a major breakthrough in precise gesture monitoring in the free-living setting, opening a new door leading to previously unexplored datasets and potentially new development of personalized disease management via unobtrusive monitoring of motor functions in movement disorders. This project will also embrace the integration of interdisciplinary research and undergraduate/graduate training among the areas of wearable computing, signal processing, data science, and smart health.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.