PDF(Pubmed)
Abstract：
Accurately estimating knee joint angle during walking from surface electromyography (sEMG) signals can enable more natural control of wearable robotics like exoskeletons. However, challenges exist due to variability across individuals and sessions. This study evaluates an attention-based deep recurrent neural network combining gated recurrent units (GRUs) and an attention mechanism (AM) for knee angle estimation. Three experiments were conducted. First, the GRU-AM model was tested on four healthy adolescents, demonstrating improved estimation compared to GRU alone. A sensitivity analysis revealed that the key contributing muscles were the knee flexor and extensors, highlighting the ability of the AM to focus on the most salient inputs. Second, transfer learning was shown by pretraining the model on an open source dataset before additional training and testing on the four adolescents. Third, the model was progressively adapted over three sessions for one child with cerebral palsy (CP). The GRU-AM model demonstrated robust knee angle estimation across participants with healthy participants (mean RMSE 7 degrees) and participants with CP (RMSE 37 degrees). Further, estimation accuracy improved by 14 degrees on average across successive sessions of walking in the child with CP. These results demonstrate the feasibility of using attention-based deep networks for joint angle estimation in adolescents and clinical populations and support their further development for deployment in wearable robotics.
摘要：
从表面肌电图(sEMG)信号准确估计行走期间的膝关节角度可以实现对可穿戴机器人如外骨骼的更自然的控制。然而,由于个人和会议之间的可变性，存在挑战。这项研究评估了基于注意力的深度递归神经网络，该神经网络结合了门控递归单元（GRU）和注意力机制（AM），用于膝盖角度估计。进行了三个实验。首先,GRU-AM模型在四个健康青少年身上进行了测试，与单独的GRU相比，显示出改进的估计。敏感性分析显示，关键的贡献肌肉是膝关节屈肌和伸肌，强调AM专注于最重要输入的能力。第二,迁移学习是通过在对四个青少年进行额外训练和测试之前，在开源数据集上对模型进行预训练来显示的。第三，该模型在三个疗程中逐步适用于一名脑瘫（CP）儿童。GRU-AM模型显示了健康参与者（平均RMSE7度）和CP参与者（RMSE37度）的可靠膝关节角度估计。Further,在患有CP的儿童连续行走的过程中，估计准确性平均提高了14度。这些结果证明了在青少年和临床人群中使用基于注意力的深度网络进行关节角度估计的可行性，并支持其在可穿戴机器人技术中的进一步发展。
