[1]牛苗赫,雷飞.面向下肢外骨骼的运动意图识别算法研究[J].智能系统学报,2025,20(2):407-415.[doi:10.11992/tis.202403025]
NIU Miaohe,LEI Fei.Motion intention recognition algorithms for lower limb exoskeleton[J].CAAI Transactions on Intelligent Systems,2025,20(2):407-415.[doi:10.11992/tis.202403025]
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NIU Miaohe,LEI Fei.Motion intention recognition algorithms for lower limb exoskeleton[J].CAAI Transactions on Intelligent Systems,2025,20(2):407-415.[doi:10.11992/tis.202403025]
面向下肢外骨骼的运动意图识别算法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第2期
页码:
407-415
栏目:
学术论文—机器感知与模式识别
出版日期:
2025-03-05
- Title:
- Motion intention recognition algorithms for lower limb exoskeleton
- Keywords:
- lower limb exoskeleton; assisted walking; surface electromyography signals; human–machine movement; multi-scale; convolutional neural network; perceptual model; intention recognition
- 分类号:
- TP391.4
- 摘要:
- 随着人工智能和传感技术的快速发展，下肢外骨骼技术在助力行走和运动辅助领域展现出巨大的潜力，利用表面肌电(surface myoelectricity, sEMG)信号解码人体运动意图对实现人机运动的协调统一至关重要。然而，由于sEMG信号具有时空差异和非线性动态的特点，导致现有方法存在特征捕捉单一、识别准确率低等不足。针对上述问题，提出一种基于多尺度卷积神经网络的运动意图感知模型。该模型采用多个差异卷积块提取sEMG信号的时间与空间尺度特征，并利用多层深度网络捕捉sEMG信号的非线性动态特征。该模型针对离线肌电数据库的识别准确率达到94%，其中对全脚离地运动类别的识别准确率高达98%。在人体穿戴下肢外骨骼进行在线运动意图识别实验中，该模型的平均识别准确率超过90%，验证了其在下肢外骨骼意图识别领域的有效性。
- Abstract:
- The rapid advancement of artificial intelligence and sensing technology has highlighted the potential of lower-limb exoskeleton technology in assisted walking and motion assistance. Decoding human motion intention using surface myoelectric (sEMG) signals is essential for achieving coordination and unification of human–machine motion. However, owing to the spatiotemporal differences and nonlinear dynamics of sEMG signals, existing methods have limitations, such as single feature capture and low recognition accuracy. A motion intention perception model based on a multiscale convolutional neural network is proposed to solve these problems. The model uses multiple differential convolution blocks to extract the temporal and spatial scale features of sEMG signals. It utilizes a multi-layer deep network to capture the nonlinear dynamic features of sEMG signals. The recognition accuracy of this model for the offline EMG database is 94%, and that of the whole-foot off-ground motion category is 98%. The average recognition accuracy of this model is more than 90% in the experiment of online motion intention recognition using a lower-limb exoskeleton, which verifies its effectiveness in the field of lower limb exoskeleton intention recognition.
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- 相似文献/References:
-
[1]衣淳植,郭浩,丁振,等.下肢外骨骼研究进展及关节运动学解算综述[J].智能系统学报,2018,13(6):878.[doi:10.11992/tis.201804063]
YI Chunzhi,GUO Hao,DING Zhen,et al.Research progress of lower-limb exoskeleton and joint kinematics calculation[J].CAAI Transactions on Intelligent Systems,2018,13():878.[doi:10.11992/tis.201804063]
备注/Memo
收稿日期:2024-3-25。
作者简介:牛苗赫，硕士研究生，主要研究方向为机器学习、计算生物学与生物信息学。E-mail：949400110@qq.com;雷飞，副教授，博士，主要研究方向为计算机视觉、智能控制。主持国家试点项目1项，获批发明专利6项，发表学术论文30余篇，出版教材3部。E-mail：leifei@bjut.edu.cn。
通讯作者:雷飞. E-mail：leifei@bjut.edu.cn
更新日期/Last Update:
2025-03-05