使用长期短期记忆模型,根据时空参数预测泰拳转动的力值
Dariusz Mosler1, Michalina Błażkiewicz2, Tomasz Góra1
11Institute of Physical Culture Sciences, Jan Długosz University in Częstochowa, Częstochowa, Poland.
Acta of bioengineering and biomechanics
|April 16, 2025
概括
长短期记忆 (LSTM) 神经网络显示出使用惯性测量单元 (IMU) 传感器数据预测泰拳拳力推力的潜力. 这种具有成本效益的方法为体育生物力学中的传统力板提供了可行的替代方案.
科学领域:
- 体育 生物力学 运动 生物力学
- 运动中的机器学习
背景情况:
- 传统的强力板是昂贵的,限制了现场研究.
- 惯性测量单元 (IMU) 传感器提供了一种便携且具有成本效益的数据收集方法.
研究的目的:
- 评估使用长短期记忆 (LSTM) 神经网络来预测泰拳道力的可行性.
- 探索IMU传感器数据作为体育生物力学中的强力板的替代方案.
主要方法:
- 收集了来自13名国际拳道联合会 (ITF) 运动员执行转的IMU数据.
- 将IMU传感器连接到腿的脚,腿部和大腿上.
- 训练有素的LSTM模型使用下肢IMU数据预测力.
主要成果:
- 在训练数据上,LSTM模型实现了高精度 (R2 0.972-0.978).
- 足背部屈曲被确定为影响力预测的关键特征.
- 验证数据集的性能有所不同,这表明需要对模型进行改进.
结论:
- 与IMU数据相结合的LSTM模型显示出预测泰拳道力有前途.
- 这种方法可以提高生物力学现场研究的可访问性.
- 为了保持一致的验证准确性,需要进一步的模型开发.
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