Stable myoelectric control of a hand prosthesis using non-linear incremental learning

Frontiers in Neurorobotics

Volumn 8, Issue FEB, 2014, Pages

  Gijsberts, Arjan ^a   Bohra, Rashida ^b   Sierra González, David ^b   Werner, Alexander ^b   Nowak, Markus ^b   Caputo, Barbara ^a   Roa, Maximo A ^b   Castellini, Claudio ^b  

^a IDIAP RESEARCH INSTITUTE   (Switzerland)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Force control; Human machine interfaces; Incremental learning; Machine learning; Rehabilitation robotics; Surface electromyography

Indexed keywords

ADULT; ARM MOVEMENT; ARTICLE; BODY POSTURE; COMPARATIVE STUDY; ELECTROMYOGRAPH ELECTRODE; ELECTROMYOGRAPHY; ELECTRONIC SENSOR; FEMALE; FOREARM; GRIP STRENGTH; HAND MOVEMENT; HAND PROSTHESIS; HUMAN; HUMAN EXPERIMENT; KERNEL METHOD; LEARNING ALGORITHM; MALE; MOTION ANALYSIS SYSTEM; MYOELECTRIC CONTROL; MYOELECTRICALLY CONTROLLED PROSTHESIS; NON LINEAR INCREMENTAL LEARNING; NONLINEAR SYSTEM; NORMAL HUMAN; PERFORMANCE MEASUREMENT SYSTEM; RANGE OF MOTION; ROTATION;

EID: 84904904157     PISSN: None     EISSN: 16625218     Source Type: Journal    
DOI: 10.3389/fnbot.2014.00008     Document Type: Article

References (34)

1. Artemiadis, P. K., and Kyriakopoulos, K. J. (2011). A switching regime model for the emg-based control of a robot arm. IEEE Trans. Syst. Man Cybern. Part B Cybern. 41, 53-63. doi: 10.1109/TSMCB.2010.2045120
2. Battye, C. K., Nightengale, A., and Whillis, J. (1955). The use of myo-electric current in the operation of prostheses. J. Bone Joint Surg. B 37, 506-510.
3. Bottomley, A. H. (1965). Myoelectric control of powered prostheses. J. Bone Joint Surg. B 47, 411-415.
4. Castellini, C., Gruppioni, E., Davalli, A., and Sandini, G. (2009). Fine detection of grasp force and posture by amputees via surface electromyography. J. Physiol. (Paris) 103, 255-262. doi: 10.1016/j.jphysparis.2009.08.008
5. Castellini, C., and Passig, G. (2011). "Ultrasound image features of the wrist are linearly related to finger positions," in Proceedings of IROS - International Conference on Intelligent Robots and Systems (San Francisco, CA), 2108-2114. doi: 10.1109/IROS.2011.6048503
6. Castellini, C., Passig, G., and Zarka, E. (2012). Using ultrasound images of the forearm to predict finger positions. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 788-797. doi: 10.1109/TNSRE.2012.2207916
7. Castellini, C., and van der Smagt, P. (2009). Surface EMG in advanced hand prosthetics. Biol. Cybern. 100, 35-47. doi: 10.1007/s00422-008-0278-1
8. Chen, X., Zhang, D., and Zhu, X. (2013). Application of a self-enhancing classification method to electromyography pattern recognition for multifunctional prosthesis control. J. NeuroEng. Rehabil. 10:44. doi: 10.1186/1743-0003-10-44
9. Criswell, E. (2010). Cram's Introduction to Surface Electromyography. Sudbury, MA: Jones & Bartlett Learning.
10. Diers, M., Christmann, C., Koeppe, C., Ruf, M., and Flor, H. (2010). Mirrored, imagined and executed movements differentially activate sensorimotor cortex in amputees with and without phantom limb pain. PAIN 149, 296-304. doi: 10.1016/j.pain.2010.02.020
11. Droniou, A., Ivaldi, S., Stalph, P., Butz, M., and Sigaud, O. (2012). "Learning velocity kinematics: experimental comparison of on-line regression algorithms," in Proceedings of Robotica 2012, (Milan), 7-13.
12. Englehart, K., and Hudgins, B. (2003). A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 50, 848-854. doi: 10.1109/TBME.2003.813539
13. Finley, F. R., and Wirta, R. W. (1967). Myocoder studies of multiple myopotential response. Arch. Phys. Med. Rehabil. 48, 598-601.
14. Fougner, A., Scheme, E., Chan, A. D. C., Englehart, K., and Stavdahl, O. (2011). Resolving the limb position effect in myoelectric pattern recognition. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 644-651. doi: 10.1109/TNSRE.2011.2163529
15. Fougner, A., Stavdahl, Ø., Kyberd, P. J., Losier, Y. G., and Parker, P. A. (2012). Control of upper limb prostheses: terminology and proportional myoelectric control - a review. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 663-677. doi: 10.1109/TNSRE.2012.2196711
16. Gijsberts, A., Atzori, M., Castellini, C., Müller, H., and Caputo, B. (2014). The movement error rate for evaluation of machine learning methods for semg-based hand movement classification. IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2014.2303394. (in press).
17. Gijsberts, A., and Metta, G. (2011). "Incremental learning of robot dynamics using random features," in IEEE International Conference on Robotics and Automation (Shanghai), 951-956. doi: 10.1109/ICRA.2011.5980191
18. Hoerl, A. E., and Kennard, R. W. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55-67. doi: 10.1080/00401706.1970.10488634
19. Jiang, N., Dosen, S., Muller, K., and Farina, D. (2012). Myoelectric control of artificial limbs: is there a need to change focus? [in the spotlight]. IEEE Signal Process. Mag. 29, 152-150. doi: 10.1109/MSP.2012.2203480
20. Kato, R., Yokoi, H., and Arai, T. (2006). "Real-time learning method for adaptable motion-discrimination using surface emg signal," in IEEE/RSJ International Conference on Intelligent Robots and Systems (Beijing), 2127-2132. doi: 10.1109/IROS.2006.282492
21. Nishikawa, D., Yu, W., Maruishi, M., Watanabe, I., Yokoi, H., Mano, Y., et al. (2000). On-line learning based electromyogram to forearm motion classifier with motor skill evaluation. JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf. 43, 906-915. doi: 10.1299/jsmec.43.906
22. Ott, C., Albu-Schaeffer, A., Kugi, A., and Hirzinger, G. (2008). On the passivity based impedance control of flexible joint robots. IEEE Trans. Robot. 24, 416-429. doi: 10.1109/TRO.2008.915438
23. Ott, C., Eiberger, O., Roa, M., and Albu-Schaeffer, A. (2012). Hardware and control concept for an experimental bipedal robot with joint torque sensors. J. Robot. Soc. Jpn. 30, 378-382. doi: 10.7210/jrsj.30.378
24. Peerdeman, B., Boere, D., Witteveen, H., In 't Veld, R. H., Hermens, H., Stramigioli, S., et al. (2011). Myoelectric forearm prostheses: State of the art from a user-centered perspective. J. Rehabil. Res. Dev. 48, 719-738. doi: 10.1682/JRRD.2010.08.0161
25. Pilarski, P., Dawson, M., Degris, T., Fahimi, F., Carey, J., and Sutton, R. (2011). Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning. 2011 IEEE Int. Conf. Rehabil. Robot. 2011:5975338. doi: 10.1109/ICORR.2011.5975338
26. Rahimi, A., and Recht, B. (2008a). Random features for large-scale kernel machines. Adv. Neural Inf. Process. Syst. 20, 1177-1184. Available online at: http://books.nips.cc/papers/files/nips20/NIPS2007_0833.pdf
27. Rahimi, A., and Recht, B. (2008b). "Uniform approximation of functions with random bases," in Allerton Conference on Communication Control and Computing (Allerton08) (Urbana-Champaign, IL), 555-561. doi: 10.1109/ALLERTON.2008.4797607
28. Rifkin, R., Yeo, G., and Poggio, T. (2003). "Regularized least squares classification," in Advances in Learning Theory: Methods, Model and Applications, Vol. 190 of NATO Science Series III: Computer and Systems Sciences Chapter 7, eds J. Suykens, G. Horváth, S. Basu, C. Micchelli, and J. Vandewalle (Amsterdam: IOS Press), 131-154.
29. Saunders, C., Gammerman, A., and Vovk, V. (1998). "Ridge regression learning algorithm in dual variables," in ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning (San Francisco, CA), 515-521.
30. Sensinger, J., Lock, B., and Kuiken, T. (2009). Adaptive pattern recognition of myoelectric signals: exploration of conceptual framework and practical algorithms. IEEE Trans. Neural Syst. Rehabil. Eng. 17, 270-278. doi: 10.1109/TNSRE.2009.2023282
31. Sierra González, D., and Castellini, C. (2013). A realistic implementation of ultrasound imaging as a human-machine interface for upper-limb amputees. Front. Neurorobot. 7:17. doi: 10.3389/fnbot.2013.00017
32. Smith, L., Hargrove, L., Lock, B., and Kuiken, T. (2011). Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay. IEEE Trans. Neural Syst. Rehabil. Eng. 19, 186-192. doi: 10.1109/TNSRE.2010.2100828
33. Wagstaff, K. (2012). "Machine learning that matters," in Proceedings of the 29th International Conference on Machine Learning (ICML-12), eds J. Langford and J. Pineau (New York, NY: Omni Press), 529-536.
34. Winkel, J., and Jørgensen, K. (1991). Significance of skin temperature changes in surface electromyography. Eur. J. Appl. Physiol. Occup. Physiol. 63, 345-348. doi: 10.1007/BF00364460