Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems

Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics

Volumn , Issue , 2012, Pages 1287-1292

  Liarokapis, Minas V ^a   Artemiadis, Panagiotis K ^b   Katsiaris, Pantelis T ^a   Kyriakopoulos, Kostas J ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^b Arizona State University   (United States)

Author keywords

ElectroMyoGraphy (EMG); EMG Based Tele operation; Learning Scheme; Model Switching; Random Forests; Robotic Arm Hand System

Indexed keywords

ARM-HAND SYSTEM; LEARNING SCHEMES; MODEL SWITCHING; RANDOM FORESTS; TELE-OPERATIONS;

DECISION TREES; KINEMATICS; ROBOTIC ARMS;

ELECTROMYOGRAPHY;

EID: 84867429656     PISSN: 21551774     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BioRob.2012.6290724     Document Type: Conference Paper

References (29)

1. F. E. Zajac, Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control, in CRC Critical Reviews in Biomedical Engineering, J. R. Bourne, Ed. Boca Raton, FL: CRC Press, 1986, pp. 359-411.
2. O. Fukuda, T. Tsuji, M. Kaneko, and A. Otsuka, A human-assisting manipulator teleoperated by EMG signals and arm motions, IEEE Trans. Robot. Autom., vol. 19, no. 2, pp. 210-222, Apr. 2003.
3. J. Zhao, Z. Xie, L. Jiang, H. Cai, H. Liu, and G. Hirzinger, Levenbergmarquardt based neural network control for a five-fingered prosthetic hand, in Proc. IEEE Int. Conf. Robot. Autom., 2005, pp. 4482-4487.
4. S. Bitzer and P. van der Smagt, Learning EMG control of a robotic hand: Towards active prostheses, in Proc. IEEE Int. Conf. Robot. Autom., 2006, pp. 2819-2823.
5. M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, Control of multifunctional prosthetic hands by processing the electromyographic signal, Crit. Rev. Biomed. Eng., vol. 30, no. 4-6, pp. 459-485, 2002.
6. D. Nishikawa, W. Yu, H. Yokoi, and Y. Kakazu, EMG prosthetic hand controller using real-time learning method, in Proc. IEEE Int. Conf. Syst., Man, Cybern., 1999, pp. 153-158.
7. S. Maier and P. van der Smagt, "Surface EMG suffices to classify the motion of each finger independently", Proceedings of MOVIC 2008, 9th International Conference on Motion and Vibration Control, 2008.
8. K. Takahashi, T. Nakauke, and M. Hashimoto, Remarks on hands-free manipulation using bio-potential signals, in Proc. ISIC/IEEE Int. Conf. Syst., Man Cybern., 2007, pp. 2965-2970.
9. A. V. Hill, The heat of shortening and the dynamic constants of muscle, Proc. R. Soc. Lond. B, Biol., vol. 126, no. 843, pp. 136195, Oct. 1938.
10. E. Cavallaro, J. Rosen, J. C. Perry, S. Burns, and B. Hannaford, Hill-Based Model as a Myoprocessor for a Neural Controlled Powered Exoskeleton Arm-Parameters Optimization, in Proc. IEEE Int. Conf. Robot. Autom., 2005, pp. 4514-4519.
11. P. K. Artemiadis and K. J. Kyriakopoulos, Teleoperation of a robot manipulator using EMG signals and a position tracker, in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2005, pp. 1003-1008.
12. J. Potvin, R. Norman, and S. McGill, Mechanically corrected EMG for the continuous estimation of erector spine muscle loading during repetitive lifting, Eur. J. Appl. Physiol., vol. 74, no. 1/2, pp. 119-132, Aug. 1996.
13. D. G. Lloyd and T. F. Besier, An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo, J. Biomechanics, vol. 36, no. 6, pp. 765-776, Jun. 2003.
14. R. J. Smith, F. Tenore, D. Huberdeau, R. Etienne-Cummings, and N. V. Thakor, Continuous decoding of finger position from surface EMG signals for the control of powered prostheses, in Proc. Annu. Int. IEEE EMBS Conf., 2008, pp. 197-200.
15. Y. Koike and M. Kawato, Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model, Biol. Cybern., v. 73, no. 4, p. 291-300, 1995.
16. W. Ryu, B. Han, and J. Kim, Continuous position control of 1 DOF manipulator using EMG signals, in Proc. Int. Conf. Convergence Hybrid Inf. Technol., 2008, pp. 870-874.
17. P. K. Artemiadis, K. J. Kyriakopoulos, "A switching regime model for the EMG-based control of a robot arm", IEEE Transactions on Systems, Man and Cybernetics, vol. 41(1), pp. 53-63, 2011.
18. P. K. Artemiadis and K. J. Kyriakopoulos, EMG-based teleoperation of a robot arm using low-dimensional representation Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2007, Pp. 489-495.
19. P. K. Artemiadis and K. J. Kyriakopoulos, "EMG-based Control of a Robot Arm using Low-Dimensional Embeddings", IEEE Transactions on Robotics, vol. 26(2), pp. 393-398, 2010.
20. J. Vogel, C. Castellini, P. van der Smagt, "EMG-Based Teleoperation and Manipulation with the DLR LWR-III", Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011.
21. R. Diankov and J. Kuffner, "OpenRAVE: A Planning Architecture for Autonomous Robotics," Robotics Institute, Carnegie Mellon University, Tech. Rep. CMU-RI-TR-08-34, 2008.
22. Rosen Diankov, "Automated Construction of Robotic Manipulation Programs", PhD Thesis, Carnegie Mellon University, Robotics Institute, 2010.
23. P. K. Artemiadis, P. T. Katsiaris and K. J. Kyriakopoulos, "A biomimetic approach to inverse kinematics for a redundant robot arm", Autonomous Robots, vol. 29(3), pp. 293-308, 2010.
24. J. R. Cram and G. S. Kasman, Introduction to Surface Electromyography. Gaithersburg, MD: Aspen, 1998.
25. Kim, Dae-Hyeong et al., "Epidermal Electronics", Science, v. 333, 6044, pages 838-843, 2011.
26. M.V. Liarokapis, P. K. Artemiadis, P. T. Katsiaris, Kostas J. Kyriakopoulos and Elias S. Manolakos, "Learning Human Reach-to-Grasp Strategies: Towards EMG-based Control of Robotic Arm-Hand Systems", IEEE International Conference on Robotics and Automation (ICRA), 2012.
27. A. Daffertshofer, C. J. C. Lamoth, O. G. Meijer, and P. J. Beek, PCA in studying coordination and variability: A tutorial, Clin. Biomech., vol. 19, no. 4, pp. 415-428, May 2004.
28. T. Ho, "Random Decision Forest", 3rd Int'l Conf. on Document Analysis and Recognition. pp. 278-282, 1995.
29. L. Breiman, "Random Forests", Machine Learning, vol. 45 (1): 532. doi:10.1023/A:1010933404324, 2001.