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

该项目探索将传感器嵌入球、球拍和球员身上，以跟踪运动中感兴趣的指标，如球的位置和旋转轨迹、球棒摆动和球员的运动。业余体育市场是全球性的，从学校到俱乐部，再到邻里朋友之间的当地比赛。将专业级和昂贵的技术带给大众可能会对体育分析产生广泛的影响。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，鼓励年轻人更健康的生活方式。作为该项目的一部分，将开发和公开发布的运动跟踪库将通过允许任何人将库扩展到创造性应用中来促进创新。此外，研究结果将在学术场所发表，并纳入研究生和本科生课程，教程和研讨会。将探索行业合作，以加强外联。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一个有价值的跳板，重点是让大众可以使用这些技术。基于物联网（IoT）的体育分析的基础已经在进行中，但大部分工作都集中在建立平台上-传感功能充其量是原始的。困难的研究问题，如高速球跟踪，旋转估计，球拍运动分析，和人体姿态分解，仍然没有得到解决。不幸的是，无线定位和惯性手势识别方面的丰富文献并不适用于真实的体育环境的限制。例如，基于Wi-Fi的定位并不被设计为支持以球速度的厘米级3D定位。惯性传感器如加速度计不能测量重力方向，而陀螺仪在球旋转下饱和。 该提案将跨越并有助于汇集许多研究领域，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。该提案将使廉价的无线物联网传感器能够构建体育分析系统，从而取代非常昂贵的基于视觉的系统。为此，它将专注于将受约束的传感器数据与球运动的空气动力学模型、球棒摆动模式、手臂和身体运动的运动学模型融合，以跟踪球、球棒和球员的位置、旋转和运动。统计推断技术将被用来估计噪声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的法定使命，并通过使用基金会的智力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 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.