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.

翻译：评估羽毛球运动表现通常需要专业教练指导，而业余玩家难以获得此类资源。我们提出BadminSense——一种基于可穿戴感知的智能手表羽毛球击球性能分析系统。通过与经验丰富的羽毛球选手访谈，我们确定了四项系统设计需求及三项实现洞见，用以指导BadminSense开发。随后，我们收集了12名资深羽毛球爱好者的击球数据集，并标注了击球类型、专家评分击球等级及球拍击球位置等细粒度标签。基于该数据集，BadminSense利用商用智能手表的振动信号实现击球分割与分类、击球质量预测及击球点位置估计。评估表明：BadminSense的击球分类准确率达91.43%，质量评分平均误差为0.438，击球点位置估计平均误差为12.9%。实际可用性研究进一步验证了BadminSense为日常羽毛球训练提供可靠有效支持的潜力。