Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
Spaulding National Running Center, Harvard Medical School, Cambridge, MA, USA.
Comput Methods Biomech Biomed Engin. 2024 Jul;27(9):1057-1070. doi: 10.1080/10255842.2023.2224912. Epub 2023 Jul 30.
The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered 'actual data' for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU.
本研究旨在开发一种机器学习模型，使用踝关节安装的惯性测量单元 (IMU) 重建六个不同任务下踝关节和膝关节的时变运动学和动力学曲线。四名男性大学生篮球运动员重复执行了行走、慢跑、跑步、横向跨步切割、最大高度跳跃和急停跳跃等任务，共进行了 102 次运动。踝关节和膝关节的屈伸角度和力矩使用运动捕捉和逆动力学进行估计，并作为模型拟合的“实际数据”。同步加速度和角速度数据从右侧踝关节安装的 IMU 中收集。时间序列特征提取模型用于确定一组特征作为随机森林回归模型的输入，以预测踝关节和膝关节的运动学和动力学。采用五折交叉验证来验证模型的准确性，并使用统计参数映射来确定预测时间序列与实验时间序列之间的差异。随机森林回归模型对踝关节和膝关节屈伸角度和力矩的时间序列曲线进行了高精度预测（运动学：R2 范围为 0.782 至 0.962，RMSE 范围为 2.19°至 11.58°；动力学：R2 范围为 0.711 至 0.966，RMSE 范围为 0.10 Nm/kg 至 0.41 Nm/kg）。在急停跳跃和行走过程中，膝关节屈伸力矩的预测时间序列与实际时间序列存在差异。经过适当训练的基于特征的回归模型可以使用单个踝关节安装的 IMU 预测大范围任务下的膝关节和踝关节关节角度和力矩的时间序列。
