Mubarrat S T, Chowdhury S
Department of Industrial, Manufacturing, and Systems Engineering, Texas Tech University, Lubbock, TX, USA.
Comput Methods Biomech Biomed Engin. 2023 Jan;26(1):65-77. doi: 10.1080/10255842.2022.2045974. Epub 2022 Mar 2.
We developed a Convolutional LSTM (ConvLSTM) network to predict shoulder joint reaction forces using 3D shoulder kinematics data containing 30 different shoulder activities from eight human subjects. We considered simulation outcomes from the AnyBody musculoskeletal model as the baseline force dataset to validate ConvLSTM model predictions. Results showed a good correlation (>80% accuracy, r≥0.82) between ConvLSTM predicted and AnyBody estimated force values, the generalization of the developed model for novel task type (p-value=0.07 ∼ 0.33), and a better prediction accuracy for the ConvLSTM model than conventional CNN and LSTM models.
我们开发了一种卷积长短期记忆（ConvLSTM）网络，以使用包含来自八名人类受试者的30种不同肩部活动的3D肩部运动学数据来预测肩关节反应力。我们将AnyBody肌肉骨骼模型的模拟结果作为基线力数据集，以验证ConvLSTM模型的预测。结果显示，ConvLSTM预测的力值与AnyBody估计的力值之间具有良好的相关性（准确率>80%，r≥0.82），所开发模型对新任务类型具有泛化能力（p值=0.07 ∼ 0.33），并且ConvLSTM模型的预测准确率优于传统的卷积神经网络（CNN）和长短期记忆（LSTM）模型。
