Real-Time Neural Network-Based Gait Phase Estimation Using a Robotic Hip Exoskeleton
Real-Time Neural Network-Based Gait Phase Estimation Using a Robotic Hip Exoskeleton
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DOI：
10.1109/tmrb.2019.2961749
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发表时间：
2020-02-01
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影响因子：
--
通讯作者：
Young, Aaron J.
中科院分区：
文献类型：
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作者：
Kang, Inseung;Kunapuli, Pratik;Young, Aaron J.
下肢外骨骼在步态周期中使用状态变量提供帮助，特别是步态阶段。这对于外骨骼控制器为用户提供准确的帮助至关重要。传统的方法通常利用事件标记通过计算平均步幅时间来估计步态阶段。然而，这种策略在适应动态速度方面存在局限性。我们开发了一种基于传感器融合的神经网络模型来实时估计步态相位，该模型可以适应0.6到1.1 m/s的动态速度。10名身体健全的受试者使用我们的估计器使用外骨骼行走，并提供相应的扭矩辅助。我们的最佳模型的实时估计和扭矩产生的RMSE分别低于29 ms和4%，与传统方法相比，估计误差降低了36.0% (p < 0.01)，扭矩误差降低了40.9% （p < 0.001）。我们的研究结果表明，创建一个通用的用户独立模型，并在用户特定数据上进行额外的训练，优于用户特定模型和用户独立模型。我们的研究验证了使用基于传感器融合的机器学习模型来准确估计用户的步态阶段和提高下肢外骨骼的可控性的可行性。
Lower limb exoskeletons provide assistance during the gait cycle using a state variable, one in particular is gait phase. This is crucial for the exoskeleton controller to provide the user accurate assistance. Conventional methods often utilize an event marker to estimate gait phase by computing the average stride time. However, this strategy has limitations in adapting to dynamic speeds. We developed a sensor fusion-based neural network model to estimate the gait phase in real-time that can adapt to dynamic speeds ranging from 0.6 to 1.1 m/s. Ten able-bodied subjects walked with an exoskeleton using our estimator and were provided with corresponding torque assistance. Our best performing model had RMSE below 29 ms and 4% for real-time estimation and torque generation, respectively, reducing the estimation error by 36.0% (p < 0.01) and torque error by 40.9% (p < 0.001) compared to conventional methods. Our results indicate that creating a general user-independent model and additionally training on user-specific data outperforms the user-specific model and user-independent model. Our study validates the feasibility of using a sensor fusion-based machine learning model to accurately estimate the user's gait phase and improve the controllability of a lower limb exoskeleton.