Impact of Load Variation on Joint Angle Estimation From Surface EMG Signals
Impact of Load Variation on Joint Angle Estimation From Surface EMG Signals
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DOI：
10.1109/tnsre.2015.2502663
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发表时间：
2016-12-01
影响因子：
4.9
通讯作者：
Cang, Shuang
中科院分区：
文献类型：
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作者：
Tang, Zhichuan;Yu, Hongnian;Cang, Shuang
许多研究使用表面肌电图（sEMG）信号来估计关节角度，用于控制上肢外骨骼和假肢。然而，一些实际因素仍然影响其临床适用性。这些因素之一是日常使用过程中的负载变化。本文证明了负载变化可以对肘角估计的性能产生实质性影响。在我们的实验测试中，这种影响导致平均RMSE（均方根误差）从7.86增加到20.44。因此，我们提出了三种方法来解决这个问题：1）将来自所有负载的训练数据汇集在一起，以形成训练模型的汇集训练数据; 2）添加测量的负载值（力传感器）作为额外的输入;以及3）开发基于负载和sEMG的两步混合估计方法。实验进行了五个主题，以探讨所提出的三种方法的可行性。结果表明，使用方法一，方法二和方法三的平均RMSE分别从20.44降低到13.54，10.47和8.48。研究表明：1）所提出的方法可以提高关节角度估计的性能和稳定性; 2）传感器融合（表面肌电信号传感器和力传感器）是解决负载变化不利影响的有效方法。
Many studies use surface electromyogram (sEMG) signals to estimate the joint angle, for control of upper-limb exoskeletons and prostheses. However, several practical factors still affect its clinical applicability. One of these factors is the load variation during daily use. This paper demonstrates that the load variation can have a substantial impact on performance of elbow angle estimation. This impact leads an increase in mean RMSE (Root-Mean-Square Error) from 7.86 to 20.44 in our experimental test. Therefore, we propose three methods to address this issue: 1) pooling the training data from all loads together to form the pooled training data for the training model; 2) adding the measured load value (force sensor) as an additional input; and 3) developing a two-step hybrid estimation approach based on load and sEMG. Experiments are conducted with five subjects to investigate the feasibility of the proposed three methods. The results show that the mean RMSE is reduced from 20.44 to 13.54 using method one, 10.47 using method two, and 8.48 using method three, respectively. Our study indicates that 1) the proposed methods can improve performance and stability on joint angle estimation and 2) sensor fusion (sEMG sensor and force sensor) is an efficient way to resolve the adverse effect of load variation.