Publication: Latent Factors Limiting the Performance of sEMG-Interfaces
Loading...
Official URL
Full text at PDC
Publication Date
2018-04-06
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Citations
Exportar
Abstract
Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.
Description
Unesco subjects
Keywords
Citation
1. Basmajian, J.V.; De Luca, C.J. Muscles Alive: Their Functions Revealed by Electromyography; Williams & Wilkins: Baltimore, MD, USA, 1985.
2. Winter, D.A. Electromyogram recording, processing, and normalization: Procedures and considerations. J. Hum. Muscle Perform 1991, 1, 5–15.
3. Bishop, M.D.; Pathare, N. Considerations for the use of surface electromyography. Phys. Theor. Korea 2004, 11, 61–69.
4. Pullman, S.L.; Goodin, D.S.; Marquinez, A.I.; Tabbal, S.; Rubin, M. Clinical utility of surface EMG. Report of the therapeutics and technology assessment subcommittee of the American Academy of Neurology. Neurology 2000, 55, 171–177. [CrossRef] [PubMed]
5. Wakeling, J.M. Spectral properties of the surface EMG can characterize motor unit recruitment strategies. J. Appl. Physiol. 2008, 105, 1676–1677. [PubMed]
6. Gopura, R.A.R.C.; Kiguchi, K.; Li, Y. SUEFUL-7: A 7DOF upper-limb exoskeleton robot with muscle-model-oriented EMG-based control. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, 10–15 October 2009; pp. 1126–1131.
7. Kiguchi, K.; Hayashi, Y. An EMG-based control for an upper-limb power-assist exoskeleton robot. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2012, 42, 1064–1071. [CrossRef] [PubMed]
8. MyoTM Gesture Control Armband—Wearable Technology by Thalmic Labs. Available online: www.myo.com (accessed on 26 May 2016).
9. Lobov, S.A.; Mironov, V.I.; Kastalskiy, I.A.; Kazantsev, V.B. A spiking neural network in sEMG feature extraction. Sensors 2015, 15, 27894–27904. [CrossRef] [PubMed]
10. Chowdhury, A.; Ramadas, R.; Karmakar, S. Muscle computer interface: A review. In ICoRD’13, Lect. Notes Mechan. Eng.; Chakrabarti, A., Prakash, R.V., Eds.; Springer: New Delhi, India, 2013; pp. 411–421.
11. Roche, A.D.; Rehbaum, H.; Farina, D.; Aszmann, O.C. Prosthetic myoelectric control strategies: A clinical perspective. Curr. Surg. Rep. 2014, 2, 44. [CrossRef]
12. Hahne, J.M.; Biessmann, F.; Jiang, N.; Rehbaum, H.; Farina, D.; Meinecke, F.C.; Muller, K.-R.; Parra, L.C. Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22, 269–279. [CrossRef] [PubMed]
13. Gordon, K.E.; Kinnard, C.R.; Ferris, D.P. Locomotor adaptation to a soleus EMG-controlled antagonistic exoskeleton. J. Neurophysiol. 2013, 109, 1804–1814. [CrossRef] [PubMed]
14. Mironov, V.I.; Lobov, S.A.; Kastalskiy, I.A.; Kazantsev, V.B. Myoelectric control system of lower limb exoskeleton for re-training motion deficiencies. Lect. Notes Comput. Sci. 2015, 9492, 428–435.
15. Peerdeman, B.; Boere, D.; Witteveen, H.J.B.; Hermens, H.; Stramigioli, S.; Rietman, J.S.; Veltnik, P.H.; Misra, S. Myoelectric forearm prostheses: State of the art from a user-centered perspective. J. Rehabil. Res. Dev. 2011, 48, 719–738. [CrossRef] [PubMed]
16. Chu, J.U.; Moon, I.; Mun, M.S. A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand. IEEE Trans. Biomed. Eng. 2006, 53, 2232–2239. [PubMed]
17. Chan, B.S.; Sia, C.L.; Wong, F.; Chin, R.; Dargham, J.A.; Siang, Y.S. Analysis of surface electromyography for on-off control. Adv. Mater. Res. 2013, 701, 435–439. [CrossRef]
18. Englehart, K.; Hudgins, B. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 2003, 50, 848–854. [CrossRef] [PubMed]
19. Farina, D.; Fevotte, C.; Doncarli, C.; Merletti, R. Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals. IEEE Trans. Biomed. Eng. 2004, 51, 1555–1567. [CrossRef] [PubMed]
20. Huang, Y.; Englehart, K.B.; Hudgins, B.; Chan, A.D. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans. Biomed. Eng. 2005, 52, 1801–1811. [CrossRef] [PubMed]
21. MacIsaac, D.T.; Parker, P.A.; Englehart, K.B.; Rogers, D.R. Fatigue estimation with a multivariable myoelectric mapping function. IEEE Trans. Biomed. Eng. 2006, 53, 694–700. [CrossRef] [PubMed]
22. Kiguchi, K.; Imada, Y.; Liyanage, M. EMG-based neuro-fuzzy control of a 4DOF upper-limb power-assist exoskeleton. In Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22–26 August 2007; pp. 3040–3043.
23. Shenoy, P.; Miller, K.J.; Crawford, B.; Rao, R.P. Online electromyographic control of a robotic prosthesis. IEEE Trans. Biomed. Eng. 2008, 55, 1128–1135. [CrossRef] [PubMed]
24. Lorrain, T.; Jiang, N.; Farina, D. Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses. J. Neuroeng. Rehabil. 2011, 8, 25. [CrossRef] [PubMed]
25. Fougner, A.; Stavdahl, O.; Kyberd, P.J.; Losier, Y.G.; Parker, P.A. Control of upper limb prostheses: Terminology and proportional myoelectric control—A review. IEEE Trans. Neural Syst. Rehabil. Eng. 2012, 20, 663–667. [CrossRef] [PubMed]
26. Wurth, S.M.; Hargrove, L.J. A real-time comparison between direct control, sequential pattern recognition control and simultaneous pattern recognition control using a Fitts’ law style assessment procedure. J. Neuroeng. Rehabil. 2014, 11, 91. [CrossRef] [PubMed]
27. Earley, E.J.; Hargrove, L.J.; Kuiken, T.A. Dual window pattern recognition classifier for improved partial-hand prosthesis control. Front. Neurosci. 2016, 10, 58. [CrossRef] [PubMed]
28. Hahne, J.M.; Farina, D.; Jiang, N.; Liebetanz, D. A novel percutaneous electrode implant for improving robustness in advanced myoelectric control. Front. Neurosci. 2016, 10, 114. [CrossRef] [PubMed]
29. Jiang, N.; Vest-Nielsen, J.L.; Muceli, S.; Farina, D. EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees. J. Neuroeng. Rehabil. 2012, 9, 92. [CrossRef] [PubMed]
30. Hargrove, L.; Englehart, K.; Hudgins, B. A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans. Biomed. Eng. 2007, 54, 847–853. [CrossRef] [PubMed]
31. Farina, D.; Merletti, R.; Indino, B.; Graven-Nielsen, T. Surface EMG crosstalk evaluated from experimental recordings and simulated signals. Reflections on crosstalk interpretation, quantification and reduction. Methods Arch. 2004, 43, 30–35.
32. Mann, P.S. Introductory Statistics; John Wiley and Sons: Hoboken, NJ, USA, 2006.
33. Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning internal representations by error propagation. In Parallel Distributed Processing; California Univ.: San Diego, CA, USA, 1985; pp. 318–362.
34. Huang, H.; Zhou, P.; Li, G.; Kuiken, T.A. An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2008, 16, 37–45. [CrossRef] [PubMed]
35. Lobov, S.A.; Mironov, V.I.; Kastalskiy, I.A.; Kazantsev, V.B. Combined use of command-proportional control of external robotic devices based on electromyography signals. Mod. Technol. Med. 2015, 7, 30–38. [CrossRef]
36. Lobov, S.; Krilova, N.; Kastalskiy, I.; Kazantsev, V.; Makarov, V.A. Human-computer interface based on electromyography command-proportional control. In Proceedings of the 4th International Congress on Neurotechnology, Electronics and Informatics, Porto, Portugal, 7–8 November 2016; pp. 57–64.
37. Sammut, C.; Webb, G.I. Encyclopedia of Machine Learning; Springer: New York, NY, USA, 2010; pp. 81–89.
38. Moussaid, M.; Helbing, D.; Theraulaz, G. How simple rules determine pedestrian behaviour and crowd disasters. Proc. Natl. Acad. Sci. USA 2011, 108, 6884–6888. [CrossRef] [PubMed]
39. Villacorta-Atienza, J.A.; Calvo, C.; Makarov, V.A. Prediction-for-CompAction: Navigation in social environments using generalized cognitive maps. Biol. Cybern. 2015, 109, 307–320. [CrossRef] [PubMed]
40. Calvo, C.; Villacorta-Atienza, J.A.; Mironov, V.I.; Gallego, V.; Makarov, V.A. Waves in isotropic totalistic cellular automata: Application to real-time robot navigation. Adv. Complex Syst. 2016, 19, 1650012. [CrossRef]
41. Makarov, V.A.; Makarova, J.; Herreras, O. Disentanglement of local field potential sources by independent component analysis. J. Comput. Neurosci. 2010, 29, 445–457. [CrossRef] [PubMed]
42. Herreras, O.; Makarova, J.; Makarov, V.A. New uses of LFPs: Pathway-specific threads obtained through spatial discrimination. Neuroscience 2015, 310, 486–503. [CrossRef] [PubMed]
43. Benito, N.; Martin-Vazquez, G.; Makarova, J.; Makarov, V.A.; Herreras, O. The right hippocampus leads the bilateral integration of gamma-parsed lateralized information. eLife 2016, 5, e16658. [CrossRef] [PubMed]
44. Kurenkov, A.L.; Kuzenkova, L.M.; Bursagova, B.I.; Petrova, S.A.; Klochkova, O.A.; Nikitin, S.S.; Artemenko, A.R.; Mamadyarov, A.M. An electromyographic study on the development of optimal tactics of botulinum toxin type a injections in children with spastic forms of cerebral palsy. Zhurnal Nevrologii i Psihiatrii
2013, 113, 53–60.
45. Matsubara, T.; Morimoto, J. Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface. IEEE Trans. Biomed. Eng. 2013, 60, 2205–2213. [CrossRef] [PubMed]
46. Tyukin, I.; Gorban, A.N.; Calvo, C.; Makarova, J.; Makarov, V.A. High-dimensional brain: A tool for encoding and rapid learning of memories by single neurons. Bull. Math. Biol. 2018. [CrossRef] [PubMed]
47. Khalil, S.F.; Mohktar, M.S.; Ibrahim, F. The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Sensors 2014, 14, 10895–10928. [CrossRef] [PubMed]
48. Cherapkina, L. The neurofeedback successfulness of sportsmen. J. Hum. Sport Exerc. 2012, 7, S116–S127. [CrossRef]
49. Bianco, V.; Berchicci, M.; Perri, R.L.; Quinzi, F.; Di Russo, F. Exercise-related cognitive effects on sensory-motor control in athletes and drummers compared to non-athletes and other musicians. Neuroscience 2017, 360, 39–47. [CrossRef] [PubMed]
50. Liburkina, S.P.; Vasilyev, A.N.; Yakovlev, L.V.; Gordleeva, S.Y.; Kaplan, A.Y. Motor imagery based brain computer interface with vibrotactile interaction. Zhurnal vysshey nervnoy deyatel’nosti im. I.P. Pavlova 2017, 67,
414–429.