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

拟议的研究侧重于利用日益增长的健身空间来支持运动、治疗和康复中的形式、性能和损伤预防。监测身体动力学并提供实时反馈的能力在健身训练和伤害预防中起着重要作用。由于缺乏了解网络领域中与人体运动相关的关键因素的健身训练模型，该项目旨在利用健身监测的网络物理组成部分来跟踪和鼓励力量训练期间的适当运动。该框架将人体动力学提取为形式和性能度量，可以帮助应用程序开发人员设计基于健身和运动的新型应用程序。该项目将创造可移植到健身、治疗和康复领域的知识，并将在卫生界产生广泛影响。例如，这个项目的发现可能有助于为乳腺癌和其他术后康复患者设计一种训练方案。该项目包括三个关键推动力，旨在将网络物理系统的进步带入健康监测领域。首先，探索和理解生物力学中的基本指标，这些指标构成衡量纵向性能变化的基本工具。第二，开发随机模型，真实地捕捉物理运动过程中各种属性的依赖关系，以分析用户进度。第三，设计一个让用户参与的反馈系统，引导他们改进形式和表现。这项研究的核心贡献是监控和创建模型，以了解与用户运动有关的因素，这些因素会影响他们随着时间的推移而取得进展。获得细粒度运动的实时反馈将改变人们进行有针对性的身体运动的方式，降低他们的受伤风险，并增强用户在任何健身环境中的体验。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.