CPS: Small: Performance Monitoring Cyber-Physical System for Emerging Fitness Spaces

CPS：小型：新兴健身空间的性能监控网络物理系统

基本信息

批准号：
2110193
负责人：
Shubham Jain
金额：
$ 50万
依托单位：
SUNY at Stony Brook
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110193&HistoricalAwards=false
关键词：
CPS Small Performance Monitoring Cyber

项目摘要

The proposed research focuses on harnessing the growing fitness spaces in support of form, performance and injury prevention in exercise, therapy, and rehabilitation. The ability to monitor body dynamics and to provide real-time feedback is instrumental in fitness training and injury prevention. Motivated by the lack of fitness training models that understand the key factors related to human motion in the cyber realm, this project aims to leverage the cyber-physical components of fitness monitoring to track and encourage proper movement during strength training. The proposed framework distills body dynamics into form and performance measures that can assist application developers with the design of novel fitness and movement-based applications. This project will create knowledge that can be ported across the fitness, therapy and rehabilitation domains and will have a broad impact in the health community. For example, the findings of this project can potentially help devise a training regimen for breast cancer and other post-surgery rehabilitation patients.This project includes three key thrusts that aim to bring the advancements in cyber-physical systems to the realm of fitness monitoring. First, exploring and understanding fundamental metrics in biomechanics, which form the foundational tools to measure longitudinal performance changes. Second, developing stochastic models to faithfully capture the dependencies of various attributes during physical motion for analyzing user progress. Third, designing a feedback system that engages the user, leading them to improved form and performance. The core contribution of this research is in monitoring and creating models to understand factors pertaining to users' motion that affect their progress over time. Access to real-time feedback on fine-grained motion will transform the way people perform targeted physical movements, reducing their injury risks and enhancing users' experiences in any fitness environment.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.

拟议的研究重点是利用运动，治疗和康复中的形式，绩效和预防伤害的增长空间。监测身体动力并提供实时反馈的能力在健身训练和预防伤害方面具有重要作用。由于缺乏了解与网络领域中人类运动相关的关键因素的缺乏健身训练模型的激励，该项目旨在利用健身监测的网络物理组件，以跟踪和鼓励在力量训练期间进行适当的运动。提出的框架将身体动态提炼成形式和性能指标，可以帮助应用程序开发人员设计新颖的健身和基于运动的应用。该项目将创造知识，这些知识可以在健身，治疗和康复领域中移植，并将对卫生社区产生广泛的影响。例如，该项目的发现可能有助于设计针对乳腺癌和其他手术后康复患者的培训方案。该项目包括三个关键推力，旨在将网络物理系统的进步带入健身监测领域。首先，探索和理解生物力学中的基本指标，这些指标构成了衡量纵向绩效变化的基础工具。其次，开发随机模型以忠实地捕获物理运动期间各种属性的依赖性，以分析用户进度。第三，设计一个吸引用户的反馈系统，使他们提高了形式和性能。这项研究的核心贡献在于监视和创建模型，以了解与用户运动有关的因素，这些因素随着时间的推移影响其进度。获得对细粒运动的实时反馈将改变人们执行有针对性的身体运动，降低伤害风险并在任何健身环境中增强用户的经历的方式。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来评估的。