SBIR Phase I: Brave Virtual Worlds Human Movement Artificial Intelligence (AI) Engine and Biofeedback Loop

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力旨在开发一种结合可穿戴传感器，实时数据捕获和沉浸式虚拟现实可视化的生物反馈系统。该系统有可能提高对关节角度和运动模式的理解，从而能够深入分析人体运动。这项技术的目标是运动训练和物理治疗行业的一个重要市场机会，价值2亿美元。这个SBIR第一阶段项目通过系统的研究方法解决智力价值。该项目涉及设计和完善生物反馈系统，进行广泛的数据收集，并实施先进的实时分析算法。该项目的目标包括开发一个用户友好的界面，优化传感器的准确性，并创建一个无缝的生物反馈系统，以改善训练和康复。预期的技术成果包括设计和开发机器学习层，以分类和识别运动的关键组成部分，以便创建上下文数据库进行进一步分析，以确定运动效率并突出运动模式，以及开发矫正练习。反馈层使用来自机器学习层的上下文来生成对导致突出显示的运动模式的原因的进一步见解。稍后将纳入建议改进的纠正练习。此外，将前两层纳入实时部分的实时层将用于向最终用户提供即时反馈，从而提供封闭的生物反馈回路。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project aims to develop a biofeedback system that combines wearable sensors, real-time data capture, and immersive virtual reality visualization. This system has the potential to enhance the understanding of joint angles and movement patterns enabling in-depth analysis of human movement. The proposed technology targets a significant market opportunity in the athletic training and physical therapy industry, which is valued at $200 million. This SBIR Phase I project addresses intellectual merit through a systematic research approach. The project involves designing and refining the biofeedback system, conducting extensive data collection, and implementing advanced algorithms for real-time analysis. The project's goals include developing a user-friendly interface, optimizing sensor accuracy, and creating a seamless biofeedback system for improved training and rehabilitation. Anticipated technical results include the design and development of a machine learning layer to classify and identify key components of movements in order to create a contextual database for further analysis to determine movement efficiency and highlight movement patterns as well as the development of a corrective exercise/feedback layer using the context from the machine learning layer to generate further insights into what is causing highlighted movement patterns This second later will incorporate corrective exercises that are suggested for improvement. Additionally, a real-time layer incorporating the first two layers into the real-time portion will be used for immediate feedback to the end-user, thus providing a closed bio-feedback loop.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.