Bridging Coaching Knowledge and AI Feedback to Enhance Motor Learning in Basketball Shooting Mechanics Through a Knowledge-Based SOP Framework

Research Background and Issues

• Identified Problems or Challenges
  The authors highlighted several key challenges faced by basketball beginners when practicing shooting techniques independently, including:

  1. Lack of training quality assurance in unsupervised scenarios.
  2. Difficulty for learners to understand whether their movements meet the coach's standards, leading to reduced self-training efficiency.
  3. Current automated feedback tools, such as video modeling and augmented reality systems, generally lack real-time feedback and comprehensive analysis of complex 3D movements.

• Significance
  The study emphasizes the limited guidance resources for basketball shooting mechanics learning. Such guidance often relies heavily on coaches, but the scarcity of coaching resources frequently results in suboptimal training outcomes. Advanced tools are insufficient in facilitating progress and lack support for personalized needs. This could have profound impacts on the long-term development of beginners' athletic skills.

• Research Motivation and Related Work
  Driven by the inadequacies of existing tools, this study is theoretically supported by the needs of beginners and the experiences of coaches to address the aforementioned issues. Additionally, the research evaluates the effectiveness and limitations of related technologies (e.g., augmented reality, video modeling) in basketball training. By integrating human expertise and real-time feedback, the study aims to fill critical gaps in automated feedback systems.

Solution

• Proposed Solution
  The authors proposed a knowledge-based "Standard Operating Procedure (SOP)" framework to provide personalized real-time feedback. This approach combines coaching expertise with AI measurement to decompose movement tasks step by step, helping basketball beginners improve their posture and movements during training.

• Innovations

  1. Proposed a system design method that combines coaching guidance with AI technology, integrating structured feedback with real-time video.
  2. Introduced a step-by-step SOP framework to establish clear and easily understandable guidance benchmarks for complex motor skills.
  3. Developed a dynamic visual user interface that allows coaches to dynamically adjust and evaluate learners' performance in real-time.

• Implementation Steps and Key Technologies

  1. Step-by-Step Task Decomposition: Based on expert coaching recommendations, an SOP framework was designed to cover key movements in basketball shooting mechanics, enabling task decomposition and process standardization.
  2. AI Real-Time Feedback: Using video recording and pose detection technologies (e.g., MoveNet and OpenPose), the system provides real-time prompts. The "Wizard Hat Method" was used to simulate AI feedback and manually verify posture standards.
  3. User Interface Design: An interactive interface displays analysis results for each training step and provides step-by-step guidance. The interface supports multi-angle video comparisons.

Research Outcomes

• Specific Outcomes

  1. Experiments demonstrated that SOP-based feedback significantly improved beginners' posture calibration abilities.
  2. Learners were able to more clearly identify movement errors, set explicit training goals, and consciously make adjustments.
  3. Quantitative results showed that the experimental group receiving SOP feedback achieved an average improvement of 48% in posture accuracy, significantly outperforming the control group's 29%.

• Advantages Over Existing Solutions
  Compared to traditional video modeling systems, the AI-SOP system provides actionable real-time feedback, better addressing posture calibration issues in complex 3D movements. Additionally, the system emphasizes personalized feedback, overcoming limitations of existing technologies that fail to account for individual body differences.

• Experimental and Evaluation Results

  • Quantitative Evaluation:
    • The SOP feedback group showed significant improvement in posture accuracy scores, demonstrating a more consistent progress trend.
    • Users rated the system higher in satisfaction, usability, and ease of use compared to the control group.
  • Qualitative Analysis:
    • Learners with SOP feedback exhibited precise recognition of specific posture errors and systematically set goals.
    • When setting training objectives, SOP users demonstrated greater clarity in prioritizing areas for improvement.

• Limitations and Future Directions

  1. Limitations:
     • The study duration was relatively short, and the system's effectiveness in long-term training remains unassessed.
     • Current feedback relies on manual scoring by coaches, introducing potential subjectivity risks.
     • The study only examined a single shooting distance, limiting repeatability.
  2. Future Directions:
     • Develop more automated and objective AI technologies, such as training large language models to generate precise feedback.
     • Extend the research to other motor skills (e.g., baseball batting, badminton swinging).
     • Analyze the impact of long-term training on shooting accuracy and overall athletic performance.

Conclusion

This study proposed an innovative SOP framework that combines coaching expertise with AI feedback, offering a novel method to help basketball beginners optimize their shooting movements. Experimental validation demonstrated the system's effectiveness in improving posture calibration, enhancing motor self-awareness, boosting confidence, and increasing training efficiency. The research not only addresses gaps in current automated sports feedback systems but also provides design insights for future interactive sports training technologies.