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

通过自动定位兼容运动跟踪贴纸和人工智能来改善人类健康的远程运动机能学

• 批准号: 10751952
• 负责人: Nolen Keeys
• 金额: $ 4.77万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-09 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10751952
• 关键词: Address; Adhesives; Adopted; Adoption; Affect; Algorithmic Analysis; Algorithms; Anatomy; Android; Articular Range of Motion; Artificial Intelligence; Body Regions; Calibration; Classification; Clinical; Confusion; Data; Data Reporting; Devices; Dimensions; Disease; Exercise; Future; Goals; Health; Hospitals; Human; Kinesiology; Lasers; Location; Machine Learning; Master of Science; Measurement; Methods; Modeling; Motion; Optics; Outcome; Persons; Physical therapy exercises; Principal Component Analysis; Publications; Radial; Rehabilitation therapy; Reporting; Research; Resources; Rotation; Skeleton; Skin; Stress; System; Techniques; Testing; Thinness; Time; Training; Visualization; Work; arm; cantilever; classification algorithm; clinical application; compliance behavior; design; fabrication; flexibility; health care service utilization; health care settings; improved; innovation; insight; kinematics; machine learning algorithm; manufacture; programs; supervised learning; usability; wireless

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.

项目摘要/摘要 车身安装的惯性测量单元（IMU）启用人体运动跟踪和运动学分析 除了医院或实验室的传统医疗保健环境之外。这是研究如何研究的强大平台 身体状况和疾病会影响日常活动的运动学。这些身体的临床采用 固定的IMU可以导致新的治疗方法和对医疗资源的更有效利用[1] - [3]。然而，身体固定的IMU的笨重和不舒服的形式因素损害了患者的依从性 并限制临床采用[4]，[5]。此外，卸下/替换的身体无法安装的IMU 身体没有破坏大多数运动学模型和活动分类算法，就会创造可能性 在无监督的使用或延长磨损期间有重大误差[3]。具有可穿戴能力的挑战 以及可用性干扰能够充分实现主体安装的IMU运动系统的潜力的能力 临床应用。为了满足这一需求，我将应用并评估快速制造技术， 由我的实验室开发，以制造完全柔性，可扩展和柔软的IMU贴纸[6]。我还将调查 使用人工智能/机器学习来检测身体安装的解剖位置位置 IMU通过常见的物理治疗练习。为此，我将利用自然运动学 身体的约束，例如旋转轴，运动范围和角速度，它们是独特的 人体的特定区域，通过监督学习对IMU的位置进行分类[7]。我已经 制造了一个不引人注目的身体安装的网络，并开发了一种使用的方法 全球参考的四元素以实时跟踪，可视化和分析人类运动。我有经验 确定该系统的准确性与其他基于IMU的运动跟踪系统相媲美，I 已经开始收集常见物理治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-neart邻居（KNN）开发了更合规的IMU贴纸 经过运动跟踪数据训练的分类器（使用主组件分析未经改变或分解 （PCA））将检测通过常见的随机放置身体安装贴纸的解剖位置 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成果 通过运动学，IMU和AI的未来。