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

项目摘要/摘要 车载惯性测量单元(IMU)能够实现人体运动跟踪和运动学分析 超越医院或实验室的传统医疗保健环境。这是一个研究如何 身体状况和疾病影响日常活动的运动学。这些机构的临床应用 安装IMU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IMU笨重和不舒服的形状因素会影响患者的坚持 并限制临床采用[4]、[5]。此外，车身安装的IMU无法拆卸/更换 在不破坏大多数运动学模型和活动分类算法的情况下，身体创造了这样的可能性 在无人监督下使用或延长磨损时间期间出现重大错误[3]。这样的耐磨性挑战 和可用性干扰了充分实现车载IMU运动系统的潜力的能力 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，以制造完全灵活、可扩展和柔软的IMU贴纸[6]。我也会调查 利用人工智能/机器学习检测人体安放位置 Imus通过常见的理疗练习。为了做到这一点，我将利用自然运动学 物体的约束，如旋转轴、运动范围和角速度，这些都是 身体的特定区域，通过有监督的学习对IMU位置进行分类。我已经去过了 制作了一个不显眼的安装在身上的Imus贴纸网络，并开发了一种使用 全局参考四元数，可实时跟踪、可视化和分析人体运动。我从经验上看 确定该系统的精度可与其他基于IMU的运动跟踪系统相媲美，并且I 已经开始收集常见理疗练习的运动跟踪数据。我假设一个 使用快速制造方法和K近邻(KNN)开发出更符合要求的IMU贴纸 使用运动跟踪数据(未更改或使用主成分分析分解)训练的分类器 (PCA))将检测随机放置的身体安装IMU贴纸的解剖位置 理疗练习。这项工作的结果将使我能够改善 通过运动学、IMUS和人工智能来展望未来。

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.