Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs
Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs
复制标题
通过拮抗肌和主动肌对的表面肌电图预测膝关节力矩
DOI：
10.1109/access.2019.2923747
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhang, Qian
中科院分区：
文献类型：
--
作者：
Li, Yurong;Zhang, Qianhui;Zhang, Qian
在下LIMB康复设备中，使用表面肌电图信号对膝关节的预测是运动意图识别的重要方法。为了提高人类计算机相互作用的控制能力并降低膝关节预测模型的复杂性，本文介绍了基于人工神经网络的膝关节矩的预测模型，膝关节角度，膝关节，膝关节，膝关节关节角速度和一对来自膝关节拮抗和激动性肌肉的表面肌电图信号被选为输入。两个公共数据库，包括偏瘫患者和健康人的步行数据，用于测试肌肉对选择对非均衡收缩下膝关节矩预测的影响。还测试了模型对速度和个体的依赖性。相关系数和平均绝对误差用作性能指标。结果表明，提出的模型可以很好地预测膝关节矩。在速度和受试者的差异中，肌肉对的选择对膝关节矩的预测没有显着影响。与先前的研究相比，提出的模型简化了测量参数和信号处理过程，减少了实际应用中使用的传感器数量，从而提高了下LIMB运动的安全性和流畅性。
In lower-limb rehabilitation equipment, the prediction of the knee joint moment using surface electromyography signals is an important method of motion intention recognition. To improve the viability of control by human-computer interactions and to reduce the complexity of the knee joint moment prediction model, this paper presents a prediction model for knee joint moment based on artificial neural networks, in which the knee joint angle, the knee joint angular velocity, and a pair of surface electromyography signals from the antagonistic and agonistic muscles of the knee joint are selected as inputs. Two public databases that include the walking data of hemiplegic patients and healthy people are used to test the effect of muscle pair selection on knee joint moment prediction under non-isometric contraction. The dependence of the model on speed and the individual is also tested. The correlation coefficient and the mean absolute error are used as performance indicators. The results demonstrate that the proposed model can predict the knee joint moment well. Across the difference of speeds and subjects, the choice of muscle pair has no significant effect on the prediction of the knee joint moment. Compared with previous research, the proposed model simplifies the measurement parameters and the signal processing process, reducing the number of sensors used in practical applications, which increases the safety and the fluency of the lower-limb movement.