期刊文献+

基于肌电信号的肢体刚度特性 被引量:1

Upper Limb Stiffness Based on Electromyography
下载PDF
导出
摘要 提出了一种基于肌电信号的上肢刚度仿真方法。将上肢运动简化为两自由度的关节运动,然后采集上肢在屈伸运动中肘关节处相关肌肉的肌电信号(EMG),并从中提取肌肉活跃度特征。在此基础上,利用Hill-type肌肉模型估算肌肉力进而计算肌肉刚度。最后,利用现有的人工神经网络模型计算上肢的肌肉刚度,并验证推算结果的可靠性。 A simulation method of upper-limb stiffness based on electromyography( EMG) was proposed. The upper-limb movement was simplified as the two degrees of freedom joint movement.Then,EMG at the elbow joint of the upper limbs during flexion and extension movement was collected and the characteristics of muscle activity were extracted. Based the above data, the muscle stiffness was calculated with Hill-type muscle model. Finally, the existing artificial neural network model was used to calculate the muscle stiffness of the upper limbs and the reliability of the calculated results was verified.
出处 《系统仿真技术》 2017年第2期136-140,共5页 System Simulation Technology
基金 上海市自然科学基金(15ZR1414800) 机器人技术与系统国家重点实验室开放基金(SKLRS-2014-MS-07)
关键词 肌电信号(EMG) 肌肉刚度 Hill-type肌肉模型 electromyography(EMG) muscle stiffness Hill-type muscle model
  • 相关文献

参考文献4

二级参考文献87

  • 1Goodrich M A, Schultz A C. Human-robot interaction: a survey. Foundations and Trends in Human-Computer Inter- action, 2007, 1(3): 203-275.
  • 2Nam Y, Koo B, Cichocki A, Choi S. GOM-face: GKP, EOG, and EMG-based multimodal interface with application to humanoid robot control. IEEE Transactions on BiomedicM Engineering, 2014, 61(2): 453-462.
  • 3Artemiadis P. EMG-based robot control interfaces: past present and future. Advances in Robotics ~z Automation 2012, 1(2): 1-3.
  • 4Ngeo J G, Tamei T, Shibata T. Continuous and simul- taneous estimation of finger kinematics using inputs from an EMCl-to-muscle activation model. Journal of NeuroEngi- neering and Rehabilitation, 2014, 11:122.
  • 5Chowdhury R H, Reaz M B I, Ali M A B, Bakar A A A, Chellappan K, Chang T G. Surface electromyography sig- nal processing and classification techniques. Sensors, 2013, 13(9): 12431-12466.
  • 6Ahsan M R, Ibrahimy M I, Khalifa O O. EMG signal classifi- cation for human computer interaction: a review. European Journal of Scientific Research, 2009, 33(3): 480-501.
  • 7Ison M, Artemiadis P. Multi-directional impedance control with electromyography for compliant human-robot interac- tion. In: Proceedings of the 2015 International Conference on Rehabilitation Robotics (ICORR). Singapore: IEEE, 2015. 416-421.
  • 8Farina D, Merletti R, Enolau R M. The extraction of neural strategies from the surface EMG. Journal of Applied Phys- iology, 2004, 96(4): 1486-1495.
  • 9De Luca C J. Imaging the Behavior of Motor Units by De- composition of the EMG Signal, Boston, MA, USA: Delsys Inc.. 2008.
  • 10Chu J U, Moon I, Lee Y J, Kim S K, Mun M S. A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Transactions on Mechatronics, 2007, 12(3): 282-290.

共引文献125

同被引文献10

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部