• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >Neuromuscular Activation Based SEMG-Torque Hybrid Modeling and Optimization for Robot Assisted Neurorehabilitation
【24h】

Neuromuscular Activation Based SEMG-Torque Hybrid Modeling and Optimization for Robot Assisted Neurorehabilitation

机译：基于神经肌肉激活的SEMG-扭矩混合建模和机器人辅助神经康复的优化

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Active engagement of human nervous system in the rehabilitation training is of great importance for the neurorehabilitation and motor function recovery of nerve injury patients. To this goal, the human motion intention should be detected and recognized in real time, which can be implemented by modeling the relationships between sEMG signals and the associated joint torques. However, present sEMG-torque modeling methods, including neurornusculoskeletal and black-box modeling methods, have their own deficiencies. Therefore, a hybrid modeling method based on the neuromuscular activations and Gaussian process regression (GPR) algorithm is proposed. Firstly, the preprocessed sEMG signals are converted into neural and muscular activations by the neurornusculoskeletal modeling method. The obtained muscle activations together with the associated joint angles are then transformed into the adjacent joint torques by a GPR algorithm to avoid the complicated modeling process of the muscle contraction dynamics, musculoskeletal geometry, and musculoskeletal dynamics. Moreover, the undetermined parameters of neuromuscular activation and GPR models are calibrated simultaneously based on an optimization algorithm designed in this study. Finally, the performance of the proposed method is demonstrated by validation and comparison experiments. It can be seen from the experiment results that, a high accuracy of torque prediction can be obtained using the proposed hybrid modeling method. Meanwhile, when the difference between the test and calibration trajectories is not very big, the joint torques for the test trajectory can be predicted with a high accuracy as well.
机译：积极参与康复训练中的神经系统对于神经损伤患者的神经康复和运动功能恢复非常重要。为此，应该实时检测和识别人体运动意图，这可以通过对sEMG信号和相关联的关节扭矩之间的关系进行建模来实现。但是，当前的sEMG转矩建模方法（包括神经肌肉骨骼模型和黑匣子建模方法）有其自身的缺陷。因此，提出了一种基于神经肌肉激活和高斯过程回归（GPR）算法的混合建模方法。首先，预处理的sEMG信号通过神经肌肉骨骼建模方法转换为神经和肌肉激活。然后，通过GPR算法将获得的肌肉激活以及相关的关节角度转换为相邻的关节扭矩，以避免复杂的肌肉收缩动力学，肌肉骨骼几何和肌肉骨骼动力学建模过程。此外，基于本研究设计的优化算法，同时校准神经肌肉激活和GPR模型的不确定参数。最后，通过验证和比较实验证明了该方法的性能。从实验结果可以看出，使用所提出的混合建模方法可以获得较高的转矩预测精度。同时，当测试轨迹和校准轨迹之间的差异不是很大时，也可以高精度地预测测试轨迹的接合扭矩。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号