计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 257-264.doi: 10.11896/jsjkx.231000040
汪文淼
WANG Wenmiao
摘要： 表面肌电信号(Surface Electromyography,sEMG)提前于人体动作产生,常用于预测人体行为运动意图。但由于其自身的非平稳性与时变特性,因此难以较为准确地预测人体下肢关节角度变化。文中研究人体下肢肌肉针对正常行走、上下楼梯这3种动作进行的肌肉选取,提出了一种VMD-ELMAN角度拟合算法,提高了表面肌电信号角度预测精度,增强了角度预测的实时性,为提升人与外骨骼设备人机融合度提供了有效的解决方案。实验结果表明,相比常见角度拟合算法,所提算法的时间耗时较短,在3种常见动作中,髋关节角度预测值RMSE的最高精度达0.578 9,膝关节角度预测值RMSE均在0.2以内,预测精度均优于常见模型,模型鲁棒性强。
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