IEEE Trans Neural Syst Rehabil Eng. 2021;29:273-281. doi: 10.1109/TNSRE.2020.3045425. Epub 2021 Mar 1.
Individualized reference gait patterns for lower limb rehabilitation robots can greatly improve the effectiveness of rehabilitation. However, previous methods can only generate customized gait patterns at several fixed discrete walking speeds and generating gaits at continuously varying speeds and stride lengths remains unsolved. This work proposes an individualized gait pattern generation method based on a recurrent neural network (RNN), which is proficient in series modeling. We collected the largest gait data set of this kind, which consists of 4,425 gait patterns from 137 subjects. Using this data set, we trained an RNN to create a function mapping from body parameters and gait parameters to a gait pattern. The experimental results indicate that our model is able to generate gait patterns at continuously varying walking speeds and stride lengths while also reducing the errors in the ankle, knee, and hip measurements by 12.83%, 20.95%, and 28.25%, respectively, compared to previous state-of-the-art method.
个性化参考步态模式可显著提高下肢康复机器人的康复效果。然而，之前的方法只能在几个固定离散的步行速度下生成定制的步态模式，而连续变化的速度和步长的步态生成仍然没有得到解决。本工作提出了一种基于循环神经网络（RNN）的个性化步态生成方法，该方法擅长序列建模。我们收集了最大的步态数据集，其中包括来自 137 个个体的 4425 个步态模式。使用这个数据集，我们训练了一个 RNN，以创建一个从身体参数和步态参数映射到步态模式的函数。实验结果表明，与之前的最先进方法相比，我们的模型能够生成连续变化的步行速度和步长的步态模式，同时将踝关节、膝关节和髋关节测量的误差分别降低了 12.83%、20.95%和 28.25%。
