As sports training becomes more data-driven, traditional dart coaching based mainly on experience and visual observation is increasingly inadequate for high-precision, goal-oriented movements. Although prior studies have highlighted the importance of release parameters, joint motion, and coordination in dart throwing, most quantitative methods still focus on local variables, single-release metrics, or static template matching. These approaches offer limited support for personalized training and often overlook useful movement variability. This paper presents a data-driven dart training assistance system. The system creates a closed-loop framework spanning motion capture, feature modeling, and personalized feedback. Dart-throwing data were collected in markerless conditions using a Kinect 2.0 depth sensor and an optical camera. Eighteen kinematic features were extracted from four biomechanical dimensions: three-link coordination, release velocity, multi-joint angular configuration, and postural stability. Two modules were developed: a personalized optimal throwing trajectory model that combines historical high-quality samples with the minimum jerk criterion, and a motion deviation diagnosis and recommendation model based on z-scores and hierarchical logic. A total of 2,396 throwing samples from professional and non-professional athletes were collected. Results show that the system generates smooth personalized reference trajectories consistent with natural human movement. Case studies indicate that it can detect poor trunk stability, abnormal elbow displacement, and imbalanced velocity control, then provide targeted recommendations. The framework shifts dart evaluation from deviation from a uniform standard to deviation from an individual's optimal control range, improving personalization and interpretability for darts training and other high-precision target sports.

翻译：随着运动训练日益数据驱动，传统的依赖经验和视觉观察的飞镖教练方法已难以满足高精度、目标导向动作的需求。尽管先前研究强调了释放参数、关节运动及协调性在飞镖投掷中的重要性，但多数定量方法仍局限于局部变量、单一释放指标或静态模板匹配。这些方法对个性化训练的支持有限，且常忽略有益的动作变异性。本文提出一种数据驱动的飞镖训练辅助系统，构建了涵盖动作捕捉、特征建模与个性化反馈的闭环框架。基于Kinect 2.0深度传感器与光学摄像头，在无标记条件下采集了飞镖投掷数据。从四个生物力学维度提取了十八项运动学特征：三连杆协调性、释放速度、多关节角构型及姿态稳定性。开发了两个模块：结合历史高质量样本与最小加加速度准则的个性化最优投掷轨迹模型，以及基于Z分数与层次逻辑的动作偏差诊断与推荐模型。共采集了职业与非职业运动员的2396次投掷样本。结果表明，该系统生成的平滑个性化参考轨迹与自然人体运动一致。案例分析显示，系统可检测躯干稳定性不足、肘部异常偏移及速度控制失衡，并提供针对性建议。该框架将飞镖评估从偏离统一标准转向偏离个体最优控制范围，提升了飞镖训练及其他高精度目标运动的个性化与可解释性。