I-Corps: Embedded Machine Vision for Accurate Gait Analyses and Body Movement Measurements

I-Corps项目更广泛的影响/商业潜力是，通过最大限度地提高无处不在的平板电脑和2D相机的能力，以低成本的机器视觉为基础的解决方案，彻底改变传统的康复助手和身体运动评估工具，这些设备比更昂贵的3D相机具有显著的成本和可访问性优势。基于嵌入式机器视觉技术，可以开发一套平板电脑/智能手机应用程序，团队计划使用订阅模式创建相关服务。这项创新将惠及广泛的医疗保健市场，包括康复设施、外科/神经病学门诊诊所、物理治疗诊所、中风中心和个人。总的来说，这项创新将使嵌入式机器视觉技术能够为社会上的许多人群提供服务。这个I-Corps项目是基于我们在PC平台上获得的接近实时的人体关节点识别和跟踪的初步结果。原型利用了新颖的机器学习算法和高性能处理器（图形处理单元）。我们下一阶段的研究是在平板平台上通过2D摄像头生成深度图，识别和跟踪身体关节点，并为身体运动打分。挑战包括2D图像的深度生成，以及在资源受限的嵌入式平台上执行的高效模型和算法。该团队将利用嵌入式深度学习技术对现代平板电脑上的嵌入式处理器架构进行算法优化，从而使概念验证能够提供令人满意的性能。该团队还计划与一名医生和一名康复设施顾问合作，审查和完善软件功能，并验证早期原型。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 I-Corps project is to revolutionize conventional rehabilitation assistant and body movement assessment tools with low-cost machine vision based solutions by maximizing the capability of ubiquitous tablets and 2D cameras, which have significant cost and accessibility advantage over more expensive 3D cameras. A set of tablet/smartphone Apps can be developed base on the embedded machine vision technology, and the team plan to create related services using a subscription model. This innovation will benefit broad healthcare market, which includes rehab facilities, surgery/neurology outpatient clinics, physical therapy clinics, stroke centers, and individuals. Overall, the innovation will make embedded machine vision technology accessible to and serve many population groups in the society. This I-Corps project is based on preliminary results that we obtained on a PC platform to identify and track human body joint points in close to real-time. The prototype leverages novel machine learning algorithms and high performance processors (graphics processing units). Our next phase of research is to generate depth map from a 2D camera, recognize and track body joint points, and score body movements, all on a tablet platform. The challenges include depth generation with 2D images and efficient models and algorithms to execute on a resource constraint embedded platform. The team will leverage embedded deep learning techniques to optimize the algorithms towards an embedded processor architecture on modern tablets so that the proof-of-concept can deliver satisfactory performance. The team also plan to work with a medical doctor and a rehabilitation facility advisor to review and refine the software features and validate the early prototype.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.