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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力旨在开发一种生物反馈系统，该系统结合了可穿戴传感器、实时数据捕获和沉浸式虚拟现实可视化。该系统有潜力增强对关节角度和运动模式的理解，从而深入分析人体运动。这项拟议中的技术瞄准了价值2亿美元的运动训练和物理治疗行业的重大市场机会。这个SBIR第一阶段项目通过系统的研究方法来解决智力价值。该项目包括设计和完善生物反馈系统，进行广泛的数据收集，并实施实时分析的先进算法。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机器学习层的设计和开发，以分类和识别运动的关键组件，以便创建一个上下文数据库进行进一步分析，以确定运动效率和突出运动模式，以及使用机器学习层的上下文开发纠正练习/反馈层，以进一步了解导致突出运动模式的原因结合建议的纠正练习进行改进。此外，将前两层整合到实时部分的实时层将用于对最终用户的即时反馈，从而提供一个封闭的生物反馈回路。该奖项反映了美国国家科学基金会的法定使命，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.