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项目更广泛的影响/商业潜力是开发康复技术，重点是加强以家庭为基础的锻炼方案，以实现精确的活动能力恢复。 目前，人们越来越需要可获得和一致的物理治疗支持，而目前的工具导致依从性差，最终恢复效果不佳。所提出的技术提供了对人体的分析，以通过音频/视频反馈和纠正指导来鼓励诊所内和家中的物理治疗患者的恢复。 该技术旨在提供即时校正和与护理提供者同步的进展，而姿势分析和实时指导系统在锻炼过程中提供信心。其目标是通过改善个性化康复的可及性，促进更好的健康结果和改善生活质量，从而可能减少医疗保健差距和知识渊博、可获得的护理成本。这个I-Corps项目的基础是开发一个用于物理康复的软件工具，该软件工具解决了物理治疗患者独立进行的练习。 目前，临床医生受到不具有可定制功能的家庭锻炼工具的限制。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。 该技术包括一个机器学习（ML）算法，可以动态推断人类姿势的见解，并根据需要提供纠正措施。此外，拟议的工具利用了一种深度学习方法，通过从成果中学习和确定持续恢复的参与技术来不断改进。 我们的目标是在家中提供高精度的支持，补充身体恢复，并直接影响移动性和治疗目标。 该技术提供了实时指导、纠正训练和综合进度跟踪，这可能会显著提高家庭演习的有效性。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 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.