Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK.
Sensors (Basel). 2023 Dec 29;24(1):211. doi: 10.3390/s24010211.
Conventional trajectory planning for lower limb assistive devices usually relies on a finite-state strategy, which pre-defines fixed trajectory types for specific gait events and activities. The advancement of deep learning enables walking assistive devices to better adapt to varied terrains for diverse users by learning movement patterns from gait data. Using a self-attention mechanism, a temporal deep learning model is developed in this study to continuously generate lower limb joint angle trajectories for an ankle and knee across various activities. Additional analyses, including using Fast Fourier Transform and paired -tests, are conducted to demonstrate the benefits of the proposed attention model architecture over the existing methods. Transfer learning has also been performed to prove the importance of data diversity. Under a 10-fold leave-one-out testing scheme, the observed attention model errors are 11.50% (±2.37%) and 9.31% (±1.56%) NRMSE for ankle and knee angle estimation, respectively, which are small in comparison to other studies. Statistical analysis using the paired -test reveals that the proposed attention model appears superior to the baseline model in terms of reduced prediction error. The attention model also produces smoother outputs, which is crucial for safety and comfort. Transfer learning has been shown to effectively reduce model errors and noise, showing the importance of including diverse datasets. The suggested joint angle trajectory generator has the potential to seamlessly switch between different locomotion tasks, thereby mitigating the problem of detecting activity transitions encountered by the traditional finite-state strategy. This data-driven trajectory generation method can also reduce the burden on personalization, as traditional devices rely on prosthetists to experimentally tune many parameters for individuals with diverse gait patterns.
传统的下肢辅助设备轨迹规划通常依赖于有限状态策略，该策略为特定步态事件和活动预先定义固定的轨迹类型。深度学习的进步使行走辅助设备能够通过从步态数据中学习运动模式，更好地适应不同用户的不同地形。本研究使用自注意力机制开发了一个时间深度学习模型，用于在各种活动中连续生成踝关节和膝关节的下肢关节角度轨迹。此外，还进行了包括快速傅里叶变换和配对检验在内的额外分析，以证明所提出的注意力模型架构相对于现有方法的优势。还进行了迁移学习以证明数据多样性的重要性。在 10 折交叉验证方案下，观察到的注意力模型误差分别为脚踝和膝盖角度估计的 11.50%（±2.37%）和 9.31%（±1.56%）NRMSE，与其他研究相比很小。使用配对检验的统计分析表明，所提出的注意力模型在降低预测误差方面优于基线模型。注意力模型还产生更平滑的输出，这对于安全性和舒适性至关重要。迁移学习已被证明可以有效地减少模型误差和噪声，表明包含多样化数据集的重要性。建议的关节角度轨迹生成器有可能在不同的运动任务之间无缝切换，从而减轻传统有限状态策略在检测活动转换时遇到的问题。这种基于数据的轨迹生成方法还可以减轻个性化的负担，因为传统设备依赖于假肢专家根据个人的步态模式来实验性地调整许多参数。
