I-Corps: Vision analysis system using inferred three-dimensional data to analyze and correct a user’s pose in relation to 3D space

这个i-Corps项目的更广泛的影响/商业潜力是康复技术的开发，重点是加强家庭锻炼养生法，以实现精确的行动能力恢复。目前，对可获得和一致的物理治疗支持的需求日益增长，而目前的工具导致依从性差，最终恢复效果差。这项拟议的技术提供了对人体的分析，通过音频/视觉反馈和矫正指导，鼓励在诊所和家里接受物理治疗的患者康复。该技术旨在为护理人员提供即时纠正和同步进度，而姿势分析和实时指导系统则在锻炼过程中提供信心。其目标是通过改善获得个性化康复的机会，潜在地减少医疗保健差距和有知识的、可获得的护理成本，促进更好的健康结果和提高生活质量。这个i-Corps项目是基于物理康复软件工具的开发，该工具解决了物理治疗中患者独立进行的练习。目前，临床医生受到家庭锻炼工具的限制，这些工具没有可定制的功能。所提出的视觉分析系统使用推断的三维数据来分析和校正用户相对于3D空间的姿势。这项技术包括一种机器学习(ML)算法，可以动态推断人体姿势的洞察力，并根据需要提供纠正行动。此外，拟议工具采用深度学习方法，通过从成果中吸取经验教训和确定可持续恢复的引人入胜的技术，继续改进这种方法。其目标是在家中提供高精度的支持，以补充身体恢复并直接影响行动和治疗目标。建议的技术提供实时指导、纠正指导和集成的进度跟踪，这可能会显著提高基于家庭的练习的有效性。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 broader impact/commercial potential of this I-Corps project is the development of rehabilitative technology, focusing on augmenting home-based exercise regimens for precise mobility recovery. Currently, there is a growing need for accessible and consistent physical therapy support while current tools lead to poor adherence and ultimately poor recovery outcomes. The proposed technology provides an analysis of the human body to encourage recovery for physical therapy patients both in-clinic and at home through audio/visual feedback and corrective coaching. The technology is designed to provide instantaneous corrections and synchronized progress with care providers, while the pose analysis and real-time guidance system provides confidence during exercise sessions. The goal is to facilitate better health outcomes and improved quality of life by improving access to personalized rehabilitation, potentially reducing healthcare disparities and cost of knowledgeable, accessible care. This I-Corps project is based on the development of a software tool for physical rehabilitation, that addresses independently performed exercises for patients in physical therapy. Currently, clinicians are limited by home exercise tools that do not have customizable features. The proposed vision analysis system uses inferred three-dimensional data to analyze and correct a user’s pose in relation to 3D space. The technology includes a machine learning (ML) algorithm to dynamically extrapolate human pose insights and offers corrective action as needed. In addition, the proposed tool leverages a deep-learning approach that continues to improve through learning from outcomes and identifying engaging techniques for continued recovery. The goal is to provide high-precision support at-home that complements physical recovery and directly impacts mobility and therapy objectives. The proposed technology provides real-time guidance, corrective coaching, and integrated progress tracking, which may significantly improve the effectiveness of home-based exercises.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.