The standard camera- and force plate-based set-up for motion analysis suffers from the disadvantage of being limited to laboratory settings. Since adaptive algorithms are able to learn the connection between known inputs and outputs and generalise this knowledge to unknown data, these algorithms can be used to leverage motion analysis outside the laboratory. In most biomechanical applications, feedforward neural networks are used, although these networks can only work on time normalised data, while recurrent neural networks can be used for real time applications. Therefore, this study compares the performance of these two kinds of neural networks on the prediction of ground reaction force and joint moments of the lower limbs during gait based on joint angles determined by optical motion capture as input data. The accuracy of both networks when generalising to new data was assessed using the normalised root-mean-squared error, the root-mean-squared error and the correlation coefficient as evaluation metrics. Both neural networks demonstrated a high performance and good capabilities to generalise to new data. The mean prediction accuracy over all parameters applying a feedforward network was higher (r = 0.963) than using a recurrent long short-term memory network (r = 0.935).

用于运动分析的基于标准相机和测力台的设置存在仅限于实验室设置的缺点。由于自适应算法能够学习已知输入和输出之间的联系并将这些知识推广到未知数据，因此这些算法可用于在实验室外利用运动分析。在大多数生物力学应用中，使用前馈神经网络，尽管这些网络只能处理时间归一化数据，而循环神经网络可用于实时应用。因此，本研究基于光学运动捕捉确定的关节角度作为输入数据，比较了这两种神经网络在步态过程中预测地面反作用力和下肢关节力矩的性能。使用归一化均方根误差、均方根误差和相关系数作为评估指标来评估两个网络在推广到新数据时的准确性。两个神经网络都表现出高性能和良好的泛化新数据的能力。应用前馈网络的所有参数的平均预测精度更高（r = 0.963) 而不是使用循环长短期记忆网络 ( r = 0.935)。