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使用人工智能从动作捕捉数据中预测自由阿基里斯肌应变.

Zhengliang Xia, Daniel Devaprakash, Bradley M Cornish

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |July 17, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一个人工智能工作流程,用运动捕捉数据来估计跑步期间的阿基里斯肌应变. 人工智能模型准确地预测了应变,使运动员和研究人员能够更轻松地进行现场评估.

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    科学领域:

    • 生物力学 生物力学
    • 人工智能的人工智能
    • 运动科学 运动科学 运动科学

    背景情况:

    • 阿基里斯肌 (AT) 的机械特性在适当的应力下得到改善.
    • 估计自由AT菌株通常需要耗时的神经肌肉骨 (NMSK) 建模和实验室数据.
    • 实践应用需要在现场进行AT应变评估.

    研究的目的:

    • 开发和验证人工智能 (AI) 工作流程,用于预测运行时使用运动捕捉数据预测自由阿基里斯肌应变.
    • 为了比较两个人工智能工作流程的性能:直接应变预测 (仅LSTM) 与力介导应变预测 (LSTM+).
    • 评估输入特征对现场应用中的预测准确性的影响.

    主要方法:

    • 开发了两种人工智能工作流程 (仅LSTM,LSTM+) 使用合成的移动捕获关键点数据与添加噪音.
    • 从经过验证的NMSK模型中对自由AT菌株估计进行训练和评估的AI模型.
    • 研究了关键点位置,速度,加速度和参与者的高度/质量对应变预测的影响.

    主要成果:

    • 在预测自由AT应变方面,LSTM+工作流显著优于LSTM唯一的工作流 (p < 0.001).
    • 通过使用关键点位置/速度和参与者的身高/质量来实现最佳预测.
    • 实现的平均时间序列RMSE为1.72±0.95%应变 (r2=0.92±0.10) 和峰值应变RMSE为2.20% (r2=0.54).

    结论:

    • 一个人工智能工作流程可以准确地预测在从低保真姿势估计数据中运行时的自由阿基里斯肌应变.
    • 预测力然后应变的LSTM+方法在AT应变估计方面优越.
    • 这种人工智能驱动的方法有助于在现场对阿基里斯肌负荷进行可行的评估.