FenceXR：用于错误检测训练和空间锚定反馈的AR动作回放
FenceXR: AR Movement Replay for Error-Detection Training and Spatially Grounded Feedback
- University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
FenceXR通过AR系统从单目视频重建3D动作回放，训练新手检测击剑错误，并支持教练在特定关节和时刻添加注释反馈，显著提升错误检测准确率。
AI中文摘要：
识别动作中的技术错误是运动学习中一项重要的感知技能，但对于击剑等快速、复杂运动的初学者来说，培养这种技能颇具挑战。教练的注意力是稀缺的，现场示范因重复次数不同而有所差异，而视频回放则受限于录制时所用的摄像机角度。教练和高级击剑运动员在回顾录像时面临一个相关问题：他们能看到错误，却无法将反馈锚定到动作本身，只能通过语言描述，而学习者必须将这些描述映射回自己的身体。我们提出了FenceXR，一个增强现实系统，它从单目智能手机视频中重建3D动作回放，以解决这两个问题。受训者模块训练新手检测常见的弓步错误，而评审者模块则允许教练和高级击剑运动员在回放中的特定关节和时刻附加文本或语音注释，并可异步与受训者共享。在一项涉及18名新手击剑运动员的研究中，无辅助的错误检测准确率从训练前的接近随机水平（37.5%）在单次训练后提升至64.1%，访谈显示其从广泛的视觉扫描转向对特定关节及其时序的针对性检查。在一项涉及四名击剑专家的基于视频的研究中，四位专家均认为评审者模块是对其现有教练工具的有价值补充，尤其适用于难以通过标准视频传达的反馈。最后，我们讨论了设计将基于动作的训练和反馈锚定于动作本身的AR系统的启示。
英文摘要：
Recognizing technical errors in movement is a perceptual skill important to motor learning, but it is challenging for beginners in fast, complex sports like fencing to develop it. A coach's attention is scarce, live demonstrations vary from repetition to repetition, and video review is limited to whatever camera angle was used to record it. Coaches and advanced fencers reviewing a recording face a related problem. They can see an error, but have no way to anchor their feedback to the movement itself, and are left describing it in words the learner must map back onto their own body. We present FenceXR, an augmented reality system that reconstructs 3D movement replays from monocular smartphone video to address both problems. A Trainee module trains novices to detect common lunge errors while a Reviewer module lets coaches and advanced fencers attach text or voice annotations to a specific joint and moment in a replay, which can be shared asynchronously with a trainee. In a study with 18 novice fencers, unaided error-detection accuracy rose from near-chance (37.5%) before training to 64.1% after a single session, with interviews showing a shift from broad visual scanning toward targeted inspection of specific joints and their timing. In a video-based study with four fencing experts, all four viewed the Reviewer module as a valuable complement to their existing coaching tools, particularly for feedback that is difficult to convey through standard video. We end with a discussion of implications for designing AR systems that ground movement-based training and feedback in the movement itself.
