CNS Core: Small: Collaborative Research: A Motion Tracking Library for Sports Analytics using Wireless Sensors

CNS 核心：小型：协作研究：使用无线传感器进行运动分析的运动跟踪库

• 批准号: 1909568
• 负责人: Romit Roy Choudhury
• 金额: $ 25万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909568&HistoricalAwards=false
• 关键词: CNS; Core; Small; Collaborative; Research

The project explores embedding sensors in balls, racquets, and on players to track metrics of interest in sports such as location and spin trajectories of balls, bat swings, and motion of players. The amateur sporting market is global, ranging from schools to clubs to local games among neighborhood friends. Bringing technologies that are otherwise professional-grade and expensive to the masses could result in wide-scale impact in sports analytics. This technology will encourage a healthier lifestyle among young individuals by making gym, exercise and sports even more exciting and helping them improve their skills. The motion tracking libraries to be developed and released publicly as a part of the project would catalyze innovations by allowing anyone to extend the libraries into creative applications. Further, the results will be published in academic venues and integrated into graduate and undergraduate classes, tutorials and workshops. Industry collaborations will be explored to enhance the outreach. Finally, if successful, the project can offer a valuable springboard to this broad application area, with a focus on making the technologies accessible to the masses. Foundations for Internet of Things (IoT)-based sports analytics is already underway, but most of the efforts are focused towards establishing the platform - the sensing capabilities are primitive, at best. The difficult research questions, such as high-speed ball tracking, spin estimation, racquet motion analysis, and human-gesture decomposition, remain unaddressed. Unfortunately, the rich literature in wireless localization and inertial gesture recognition do not apply under the constraints for real sporting environments. For instance, Wi-Fi based localization is not designed to support centimeter-scale 3D-location at ball speeds. Inertial sensors such as accelerometers cannot measure gravitational direction, whereas gyroscopes saturate under ball rotations. This proposal will cut across and help bring together many research areas like motion sensing, wireless networking, statistical inference, circuit design, kinesiology, and sports. The proposal will enable building sports analytics systems from cheap wireless IoT sensors which can replace the very expensive vision-based systems. To this end, it will it will focus on fusion of under constrained sensor data with aerodynamic models of ball motion, patterns of bat swings, kinematic models of arm and body motions to track location, spin, and motion of balls, bats, and players. Statistical inferencing techniques would be used to estimate the parameters under noisy sensory information. Finally, an app will be developed to visualize the motion and provide feedback to players.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.

该项目探讨了在球，球拍和玩家身上嵌入传感器，以跟踪运动中感兴趣的指标，例如球，击球，蝙蝠摆动和运动员的运动轨迹。业余体育市场是全球的，从学校到俱乐部再到邻里朋友的本地游戏。带来原本是专业级且对群众昂贵的技术可能会导致体育分析的广泛影响。这项技术将通过使健身房，锻炼和运动更加令人兴奋并帮助他们提高技能来鼓励年轻人的健康生活方式。作为该项目的一部分，将开发并公开发行的运动跟踪库将通过允许任何人将库扩展到创意应用程序中来促进创新。此外，结果将在学术场所发表，并将其纳入研究生和本科课程，教程和讲习班。将探索行业合作以增强外展活动。最后，如果成功的话，该项目可以为这个广泛的应用领域提供宝贵的跳板，重点是使群众可以使用这些技术。物联网（IoT）的基础运动分析基础已经在进行中，但是大多数努力都集中在建立平台上 - 感应功能充其量是原始的。困难的研究问题，例如高速球跟踪，自旋估计，球拍运动分析和人构分解，仍然没有解决。不幸的是，无线本地化和惯性手势识别的丰富文献不适用于对实际运动环境的限制。例如，基于Wi-Fi的本地化并非旨在在球速度下支持Centimcale 3D位置。惯性传感器（例如加速度计）无法测量重力方向，而陀螺仪在球旋转下饱和。 该建议将削减并帮助整合许多研究领域，例如运动传感，无线网络，统计推断，电路设计，运动机能学和运动。该提案将从廉价的无线物联网传感器中构建体育分析系统，这些系统可以替代非常昂贵的基于视觉的系统。为此，它将集中在受约束的传感器数据下融合与球运动的空气动力学模型，蝙蝠摆动的模式，手臂和身体运动的运动模型，以跟踪球，旋转和运动，蝙蝠，蝙蝠和球员的位置，旋转和运动。统计推理技术将用于估计嘈杂的感觉信息下的参数。最后，将开发一个应用程序来可视化运动并向玩家提供反馈。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的审查标准，被认为值得通过评估来获得支持。