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

在社区环境中，准确监测上肢的三维姿势和运动对康复至关重要。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方法。具体来说，对于需要长时间恢复的运动障碍，如中风或创伤性脑损伤，使用微创传感技术对运动进行持续监测已成为支持长期坚持治疗的目标。由于其即时性、普及性和可持续性，腕式惯性传感器已成为最常用的可穿戴传感器。然而，基于单个腕戴设备开发对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，传感器测量中的一个小误差会随着数据的集成而迅速增长，以估计运动。该项目旨在通过专门解决漂移问题，建立一个能够使用单个腕式惯性传感器精确监测上肢运动的系统。通过利用人类中枢神经系统（CNS）介导的自主上肢运动的独特运动学特性和肌肉骨骼结构的物理特性，提出的努力将推进上肢运动动态监测的最新技术。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，这将消除对第二次积分和许多漂移误差的需要。该模型将为开发一种新的计算算法提供独特的机会，用于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)建立数学模型和计算算法来估计动态变化的身体方向；2)利用运动控制理论建立新的机器学习框架来估计传感单元的三维位置轨迹，而不需要双重积分；3)开发一种基于人体肌肉骨骼特性的序列算法来估计肢体关节最可能的运动学轮廓。该项目的成功将导致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不显眼监测，潜在地开发个性化疾病管理的新发展。该项目还将整合跨学科研究和本科生/研究生培训，涵盖可穿戴计算、信号处理、数据科学和智能健康等领域。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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.