用智能手表实现羽毛球击球的精细分析，让业余选手也能获得专业级反馈。
BadminSense: Enabling Fine-Grained Badminton Stroke Evaluation on a Single Smartwatch
- 通过手表振动信号识别击球类型与质量，无需额外设备。
- 击球分类准确率达91.43%，击球质量预测误差仅0.438分。
- 适合想提升技术的业余爱好者，尤其适合日常训练自测。
评估羽毛球表现通常需要专业教练指导，但对业余选手而言难以获取。本文提出BadminSense，一种基于智能手表的可穿戴传感系统，实现精细的羽毛球表现分析。通过与经验丰富的球员访谈，明确了四项系统设计需求及三项实施洞察。我们采集了12名业余高手的击球数据集，并标注了击球类型、专家评分和球拍击打位置等细粒度标签。基于该数据集，BadminSense可分割并分类击球，预测击球质量，估算球拍击打位置，全部依赖市售智能手表的振动信号。评估结果显示，系统击球分类准确率为91.43%，平均质量评分误差为0.438，击打位置估计误差为12.9%。真实场景可用性研究进一步验证了其在日常训练中提供可靠且有意义支持的潜力。
原文摘要 · Abstract (English)
Evaluating badminton performance often requires expert coaching, which is rarely accessible for amateur players. We present BadminSense, a smartwatch-based system for fine-grained badminton performance analysis using wearable sensing. Through interviews with experienced badminton players, we identified four system design requirements with three implementation insights that guide the development of BadminSense. We then collected a badminton strokes dataset on 12 experienced badminton amateurs and annotated it with fine-grained labels, including stroke type, expert-assessed stroke rating, and shuttle impact location. Built on this dataset, BadminSense segments and classifies strokes, predicts stroke quality, and estimates shuttle impact location using vibration signal from an off-the-shelf smartwatch. Our evaluations show that BadminSense achieves a stroke classification accuracy of 91.43%, an average quality rating error of 0.438, and an average impact location estimation error of 12.9%. A real-world usability study further demonstrates BadminSense's potential to provide reliable and meaningful support for daily badminton practice.
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