Toward Personalized Darts Training: A Data-Driven Framework Based on Skeleton-Based Biomechanical Analysis and Motion Modeling

2026-04-01

AI Summary

This study addresses the lack of data-driven modeling and precise feedback on individual movement variability in traditional dart-throwing training. The authors propose a closed-loop, data-driven framework that integrates markerless motion capture (using Kinect 2.0 and optical cameras), biomechanical feature extraction (encompassing 18 kinematic metrics across four categories), and personalized modeling. Leveraging the minimum-jerk principle, the system generates individualized optimal throwing trajectories consistent with natural motor control. A hierarchical logical diagnostic model identifies specific deviations—such as trunk instability or abnormal elbow displacement—and delivers interpretable, personalized corrective suggestions. Validated over 2,396 throws, the approach shifts performance evaluation from rigid normative templates to individualized optimal control ranges, significantly enhancing training precision and adaptability.

Abstract

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.

Problem

• personalized training
• dart throwing
• movement variability
• biomechanical analysis
• data-driven sports coaching

Innovation

• personalized training
• skeleton-based biomechanics
• motion modeling
• minimum jerk trajectory
• z-score deviation diagnosis