Remote Kinesiology for Improving Human Health with Auto-locating Compliant Motion Tracking Stickers and Artificial Intelligence

项目总结/摘要 身体安装的惯性测量单元（伊穆斯）能够实现人体运动跟踪和运动学分析 超越了医院或实验室的传统医疗环境。这是一个强大的平台， 身体状况和疾病影响日常活动的运动学。临床采用这些机构 安装的伊穆斯可以带来新的治疗方法和更有效地利用医疗资源[1]- [3]。然而，安装在身体上的伊穆斯的庞大和不舒适的形状因素损害了患者的依从性 并限制临床应用[4]，[5]。此外，身体安装的伊穆斯不能在 在不破坏大多数运动学模型和活动分类算法的情况下， 在无监督使用或长时间佩戴期间出现重大错误[3]。这些挑战与可穿戴性 和可用性干扰了充分实现身体安装的IMU运动系统的潜力， 临床应用。为了满足这一需求，我将应用和评估快速制造技术， 由我的实验室开发，以制造一个完全灵活，可扩展和柔软的IMU贴纸[6]。我也会调查 使用人工智能/机器学习来检测身体安装的解剖放置位置 伊穆斯通过共同的物理治疗演习。为了做到这一点，我将利用自然的运动学 身体的约束，如旋转轴，运动范围和角速度，这是唯一的 身体的特定区域，用监督学习对IMU位置进行分类[7]。我已经 制造了一个网络的不显眼的身体安装伊穆斯贴纸，并开发了一种方法，使用 全局引用的四元数来实时跟踪、可视化和分析人体运动。我根据经验 确定该系统的精度与其他基于IMU的运动跟踪系统相当，并且我 已经开始收集常见物理治疗练习的运动跟踪数据。我假设 使用快速制造方法和K-最近邻（KNN）开发的更柔顺的IMU贴纸 用运动跟踪数据（未改变的或使用主成分分析分解的）训练的分类器 （PCA））将通过共同的检测随机放置的身体安装的IMU贴纸的解剖位置。 物理治疗练习这项工作的结果将使我能够改善人们的健康状况， 通过运动学、伊穆斯和人工智能来改变未来。

PROJECT SUMMARY/ABSTRACT Body mounted inertial measurement units (IMUs) enable human motion tracking and kinematic analysis beyond the traditional healthcare setting of a hospital or lab. This is a powerful platform for studying how physical conditions and diseases affect the kinematics of daily activities. Clinical adoption of these body mounted IMUs can lead to new treatment methods and more efficient utilization of healthcare resources [1]– [3]. Bulky and uncomfortable form factors of body mounted IMUs, however, compromise patient adherence and limit clinical adoption [4], [5]. Furthermore, the inability of body mounted IMUs to be removed/replaced on the body without corrupting most kinematic models and activity classification algorithms, creates the possibility of significant error during unsupervised use or extended durations of wear [3]. Such challenges with wearability and usability interfere with the ability to fully realize the potential of body mounted IMU motion systems for clinical applications. In an effort to address this need, I will apply and evaluate the rapid fabrication techniques, developed by my lab, to manufacture a fully flexible, extensible, and soft IMU sticker [6]. I will also investigate the use of artificial intelligence/machine learning to detect the anatomical placement location of body mounted IMUs through common physical therapy exercises. To accomplish this, I will leverage the natural kinematic constraints of the body such as axes of rotation, range of motion, and angular velocity, which are unique to specific regions of the body, to classify the IMU locations with supervised learning [7]. I have already manufactured a network of unobtrusive body mounted IMUs stickers and developed an approach of using globally referenced quaternions to track, visualize, and analyze human motion in real-time. I have empirically determined that the accuracy of this system is comparable to other IMU-based motion tracking systems, and I have already begun collecting motion tracking data of common physical therapy exercises. I hypothesize that a more compliant IMU sticker developed using rapid fabrication methods and a K-nearest neighbor (KNN) classifier trained with motion tracking data (unaltered or decomposed using Principal Component Analysis (PCA)) will detect the anatomical location of randomly placed body mounted IMU stickers through common physical therapy exercises. The result of this work will allow me to improve the health outcomes of people in the future through kinematics, IMUs, and AI.