Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks
Gait Intention Prediction Using a Lower-Limb Musculoskeletal Model and Long Short-Term Memory Neural Networks
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DOI：
10.1109/tnsre.2024.3365201
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发表时间：
2024-01-01
影响因子：
4.9
通讯作者：
Ding，Ziyun
中科院分区：
文献类型：
--
作者：
Bian，Qingyao;Castellani，Marco;Ding，Ziyun
步态运动意图的预测对于实现辅助设备的直观控制和步态障碍的诊断至关重要。为了降低与使用多模态信号和信号处理相关的成本，我们提出了一种新的方法，该方法将机器学习与肌肉骨骼建模技术相结合，仅使用运动学信号来预测时间序列关节角度。此外，我们假设堆叠的长短期记忆（LSTM）神经网络架构可以执行任务，而不依赖于通常由肌电图信号提供的任何提前运动特征。光学相机和惯性测量单元（IMU）传感器被用来跟踪水平步态运动学。使用肌肉骨骼模型对关节角度进行建模。确定了完成预测任务的最佳LSTM架构。关节角度预测进行矢状面上的关节，受益于关节角度建模，使用来自光学相机和IMU传感器的信号。我们提出的方法在10 ms的预测时间内预测了即将到来的关节角度，平均均方根误差为5.3°，决定系数为0.81。此外，为了支持我们的假设，递归堆栈LSTM网络证明了它能够准确有效地预测步态中的预期运动，优于其他两种神经网络架构：前馈MLP和混合LSTM-MLP。该方法铺平了道路的成本效益，单模态控制系统的辅助设备在步态康复的发展。
The prediction of gait motion intention is essential for achieving intuitive control of assistive devices and diagnosing gait disorders. To reduce the cost associated with using multimodal signals and signal processing, we proposed a novel method that integrates machine learning with musculoskeletal modelling techniques for the prediction of time-series joint angles, using only kinematic signals. Additionally, we hypothesised that a stacked long short-term memory (LSTM) neural network architecture can perform the task without relying on any ahead-of-motion features typically provided by electromyography signals. Optical cameras and inertial measurement unit (IMU) sensors were used to track level gait kinematics. Joint angles were modelled using the musculoskeletal model. The optimal LSTM architecture in fulfilling the prediction task was determined. Joint angle predictions were performed for joints on the sagittal plane, benefiting from joint angle modelling using signals from optical cameras and IMU sensors. Our proposed method predicted the upcoming joint angles in the prediction time of 10 ms, with an averaged root mean square error of 5.3° and a coefficient of determination of 0.81. Moreover, in support of our hypothesis, the recurrent stacked LSTM network demonstrated its ability to predict intended motion accurately and efficiently in gait, outperforming two other neural network architectures: a feedforward MLP and a hybrid LSTM-MLP. The method paves the way for the development of a cost-effective, single-modal control system for assistive devices in gait rehabilitation.