Zhang Jiajin, Guo Rong, Zhu Yan, Che Yonglin, Zeng Yucheng, Yu Lin, Yang Ziqiong, Yang Jianke
College of Big Data, Yunnan Agricultural University, Kunming 650201, China.
Center for Sports Intelligence Innovation and Application, Yunnan Agricultural University, Kunming 650201, China.
Sensors (Basel). 2025 Jun 13;25(12):3709. doi: 10.3390/s25123709.
In recent years, advances in artificial intelligence, machine vision, and the Internet of Things have significantly impacted sports analytics, particularly basketball, where accurate measurement and analysis of player performance have become increasingly important. This study proposes a real-time goal state recognition system based on inertial measurement unit (IMU) sensors, focusing on four shooting scenarios: rebounds, swishes, other shots, and misses. By installing IMU sensors around the basketball net, the system captures real-time data on acceleration, angular velocity, and angular changes to comprehensively analyze the fluency and success rate of shooting execution, utilizing five deep learning models-convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), CNN-LSTM, and CNN-LSTM-Attention-to classify shot types. Experimental results indicate that the CNN-LSTM-Attention model outperformed other models with an accuracy of 87.79% in identifying goal states. This result represents a commanding level of real-time goal state recognition, demonstrating the robustness and efficiency of the model in complex sports environments. This high accuracy not only supports the application of the system in skill analysis and sports performance evaluation but also lays a solid foundation for the development of intelligent basketball training equipment, providing an efficient and practical solution for athletes and coaches.
近年来，人工智能、机器视觉和物联网的发展对体育分析产生了重大影响，尤其是篮球领域，球员表现的精确测量和分析变得越来越重要。本研究提出了一种基于惯性测量单元（IMU）传感器的实时进球状态识别系统，重点关注四种投篮场景：篮板球、空心球、其他投篮和未命中。通过在篮球网周围安装IMU传感器，该系统捕获加速度、角速度和角度变化的实时数据，以全面分析投篮执行的流畅性和成功率，并利用卷积神经网络（CNN）、循环神经网络（RNN）、长短期记忆网络（LSTM）、CNN-LSTM和CNN-LSTM-Attention这五种深度学习模型对投篮类型进行分类。实验结果表明，CNN-LSTM-Attention模型在识别进球状态方面优于其他模型，准确率达到87.79%。这一结果代表了实时进球状态识别的领先水平，证明了该模型在复杂体育环境中的鲁棒性和效率。这种高精度不仅支持该系统在技能分析和运动表现评估中的应用，也为智能篮球训练设备的开发奠定了坚实基础，为运动员和教练提供了一种高效实用的解决方案。
