Vision and Sensing of Human Motion for Athletes and Artists

运动员和艺术家的人体运动视觉和感知

• 批准号: 217173-2013
• 负责人: Boyd, Jeffrey
• 金额: $ 1.09万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546537
• 关键词: Vision; Sensing; Human; Motion; Athletes

Biometrics typically refer to the application of pattern recognition methods to the measurement of humans for security purposes. We take a broader view, by applying a variety of signal processing and computer vision techniques to the measurement and analysis of human motion, for applications in athletics and clinical treatment of gait disorders. The prototypical application is an athlete running on a treadmill, but experiencing pain. We know that if the athlete runs differently, we can eliminate the pain, but gaits are not easily altered. Using athletics such as this as a case study, we seek to develop algorithms and systems that measure and analyze human motion to produce feedback to that person in real-time. A working hypothesis is that properly designed feedback will be an effective way to correct a gait. We have demonstrated the concept in previous work with a system designed to correct the technique of a speed skater who had lost his ability to do cross overs. We focus on audio feedback because it leaves the eyes free for the athlete's safety, and because we know that people can coordinate their movements with sound. The proposed research builds on this previous success by exploring the use of various sensors to measure locomotion (computer vision, motion capture, and sensors mounted on the athlete), measuring in a variety of settings (speed skating oval, treadmill, running track, sidewalk), developing signal processing algorithms for synchronization with the various sensors and motions, examining a variety of modes of locomotion (walking, running, skating), and developing and testing algorithms and systems that target a variety of desired outcomes (athletic performance, clinical treatment).

生物识别通常是指应用模式识别方法来测量人类的安全目的。我们采取更广泛的观点，通过应用各种信号处理和计算机视觉技术的测量和分析人体运动，在田径和临床治疗步态障碍的应用。 原型应用程序是一个在跑步机上跑步的运动员，但是他感到疼痛。 我们知道，如果运动员以不同的方式跑步，我们可以消除疼痛，但步态不容易改变。 以这样的体育运动为案例研究，我们寻求开发算法和系统来测量和分析人体运动，以实时向该人提供反馈。 一个工作假设是，适当设计的反馈将是一种有效的方式来纠正步态。 我们已经证明了这个概念在以前的工作与系统设计，以纠正技术的速度滑冰运动员谁失去了他的能力，做交叉。 我们专注于音频反馈，因为它让眼睛自由运动员的安全，因为我们知道，人们可以协调他们的运动与声音。 拟议的研究建立在这一先前的成功，探索使用各种传感器来测量运动（计算机视觉、动作捕捉和安装在运动员身上的传感器），在各种设置下进行测量（速度滑冰椭圆形，跑步机，跑道，人行道），开发与各种传感器和运动同步的信号处理算法，检查各种运动模式（步行、跑步、滑冰），以及开发和测试针对各种期望结果（运动表现、临床治疗）的算法和系统。