Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China.
Sensors (Basel). 2021 Oct 29;21(21):7199. doi: 10.3390/s21217199.
In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.
在当前的研究中，我们的研究小组提出了一种非对称下肢外骨骼，以使膝上截肢者能够携带重物行走。由于外骨骼截肢侧的小腿和脚缺失，膝关节和踝关节缺乏跟踪目标，因此上下楼梯时难以实现辅助行走的功能。目前，使用下肢肌电图来预测下肢关节的角度已经取得了显著的成果。然而，仅使用大腿肌电图时，预测效果较差。因此，本文引入了髋关节角度和足底压力信号，以提高预测效果，并提出了一种通过大腿肌电图、髋关节角度和足底压力信号联合预测膝关节和踝关节角度的方法。使用黄金分割法优化的广义回归神经网络来预测关节角度。最后，计算参数（最大误差、均方根误差 () 和相关系数 (γ)) 以验证预测方法的可行性。
