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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570107
• 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). A key part of our methodology is to engage with artists and musicians. While artists and musicians can use our technological advances to further their aesthetic goals, our scientific work receives a reciprocal benefit as they contribute to the design of audio and visual feedback that is effective in obtaining the desired response from an athlete.

生物识别技术通常是指出于安全目的将模式识别方法应用于人体测量。我们的视野更广阔，将各种信号处理和计算机视觉技术应用于人体运动的测量和分析，以应用于体育运动和步态障碍的临床治疗。 原型应用程序是一名运动员在跑步机上跑步，但感到疼痛。 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，但步态却不容易改变。 以此类运动作为案例研究，我们寻求开发算法和系统来测量和分析人体运动，以便实时向该人提供反馈。 一个可行的假设是，正确设计的反馈将是纠正步态的有效方法。 我们在之前的工作中已经通过一个系统演示了这个概念，该系统旨在纠正失去交叉能力的速滑运动员的技术。 我们专注于音频反馈，因为它可以让运动员的眼睛自由下来，以确保运动员的安全，而且我们知道人们可以通过声音来协调他们的动作。 拟议的研究建立在先前成功的基础上，探索使用各种传感器来测量运动（计算机视觉、运动捕捉和安装在运动员身上的传感器），在各种设置（速滑椭圆形、跑步机、跑道、人行道）中进行测量，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），并开发 测试针对各种期望结果（运动表现、临床治疗）的算法和系统。 我们方法论的一个关键部分是与艺术家和音乐家互动。 虽然艺术家和音乐家可以利用我们的技术进步来实现他们的审美目标，但我们的科学工作也获得了互惠互利，因为他们有助于音频和视觉反馈的设计，从而有效地获得运动员所需的反应。