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Computational Intelligence for Qualitative Coaching Diagnostics: Automated Assessment of Tennis Swings to Improve Performance and Safety

机译:用于定性教练诊断的计算智能:自动评估网球挥杆以提高性能和安全性

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Coaching technology, wearables, and exergames can provide quantitative feedback based on measured activity, but there is little evidence of effective qualitative feedback to aid technique improvement. To achieve personalized qualitative feedback, we demonstrated a proof-of-concept prototype combining kinesiology and computational intelligence that could help improving tennis swing technique utilizing three-dimensional (3D) tennis motion data acquired from multicamera video. Expert data labeling relied on virtual 3D stick figure replay. Diverse assessment criteria for novice to those with intermediate skill levels and configurable coaching scenarios matched with a variety of tennis swings (22 backhands and 21 forehands), including good technique and common errors. A set of selected coaching rules (CRs) was transferred to adaptive assessment modules able to learn from data, evolve their internal structures, and produce autonomous personalized feedback, including verbal cues over virtual camera 3D replay and an end-of-session progress report. The prototype demonstrated autonomous assessment on future data based on learning from prior examples, aligned with skill level, flexible coaching scenarios, and CRs. The generated intuitive diagnostic feedback consisted of elements of safety and performance for tennis swing technique, where each swing sample was compared with the expert. For safety aspects of the relative swing width, the prototype showed improved assessment (from 81% to 91%) when taking into account occluded parts of the pelvis. This study has shown proof of concept for personalized qualitative feedback. The next generation of augmented coaching and exergaming systems will be able to help improve end user's sport discipline-specific techniques. By learning from small expert-labeled data sets, such systems will be able to adapt and provide personalized intuitive autonomous assessment and diagnostic feedback aligned with a specified coaching program and context requirements.
机译:教练技术,可穿戴设备和运动游戏可以根据测得的活动提供定量反馈,但是很少有有效定性反馈有助于改善技术的证据。为了获得个性化的定性反馈,我们展示了结合运动学和计算智能的概念验证原型,可以利用从多摄像机视频中获取的三维(3D)网球运动数据来帮助改进网球挥拍技术。专家数据标签依赖于虚拟3D简笔画回放。对于中级水平和可配置教练情景的初学者来说,多样化的评估标准与各种网球挥杆动作(22个反手和21个正手)相匹配,包括熟练的技术和常见的错误。一组选定的指导规则(CR)已转移到自适应评估模块,这些模块可以从数据中学习,发展其内部结构并产生自主的个性化反馈,包括通过虚拟相机3D回放的口头提示和会话结束进度报告。该原型展示了基于对先前示例的学习,对技能水平,灵活的教练场景和CR的一致评估,可以对未来数据进行自主评估。生成的直观诊断反馈由网球挥杆技术的安全性和性能组成,每个挥杆样本都与专家进行了比较。对于相对摆幅的安全性,当考虑到骨盆的闭塞部分时,原型显示出改进的评​​估(从81%到91%)。这项研究显示了个性化定性反馈的概念证明。下一代增强型教练和锻炼系统将能够帮助改善最终用户针对运动学科的技术。通过从专家标记的小型数据集中学习,这样的系统将能够适应并提供符合指定教练程序和上下文要求的个性化直观自治评估和诊断反馈。

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