Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals

Sensors

Volumn 10, Issue 12, 2010, Pages 11100-11125

  de la Rosa, Ramon ^a   Alonso, Alonso ^a   Carrera, Albano ^a   Durán, Ramon ^a   Fernández, Patricia ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

Author keywords

Biological control systems; Electromyography; Mechanomyography; Pattern classification; Real time systems; Sensors; Training

Indexed keywords

BIOLOGICAL CONTROL SYSTEMS; FRONT END ELECTRONICS; MAN-MACHINE INTERFACE; MECHANOMYOGRAPHY; MYOELECTRIC SIGNALS; REAL TIME CONSTRAINTS; SOFTWARE IMPLEMENTATION; VOLUNTARY CONTROLS;

ELECTROMYOGRAPHY; HAPTIC INTERFACES; PATTERN RECOGNITION; PERSONNEL TRAINING; REAL TIME SYSTEMS; SENSORS; TOOLS;

INTERACTIVE COMPUTER SYSTEMS;

AMPLIFIER; ARTICLE; BIOLOGICAL MODEL; COMPUTER INTERFACE; DISABLED PERSON; ELECTROMYOGRAPHY; ELECTRONICS; EVALUATION; GENETIC PROCEDURES; HUMAN; INSTRUMENTATION; KINESIOTHERAPY; MAN MACHINE INTERACTION; MECHANOTRANSDUCTION; METHODOLOGY; MOTONEURON; MUSCLE; NEUROMUSCULAR SYNAPSE; PHYSICAL EDUCATION; PHYSIOLOGY;

AMPLIFIERS, ELECTRONIC; BIOSENSING TECHNIQUES; DISABLED PERSONS; ELECTROMYOGRAPHY; ELECTRONICS; EXERCISE THERAPY; HUMANS; MAN-MACHINE SYSTEMS; MECHANOTRANSDUCTION, CELLULAR; MODELS, BIOLOGICAL; MOTOR NEURONS; MUSCLES; NEUROMUSCULAR JUNCTION; PHYSICAL EDUCATION AND TRAINING; USER-COMPUTER INTERFACE;

EID: 78650265740     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s101211100     Document Type: Article

References (45)

1. Farry, K.A.; Walker, I.D.; Baranuik, R. Myoelectric teleoperation of a complex robotic hand. IEEE Trans. Robotic. Autom. 1996, 12, 775-787.
2. Kyberd, P.J.; Holland, O.E.; Chapell, P.H.; Smith, S.; Tregidgo, R.; Bagwell, P.J.; Snaith, M. MARCUS A two degree of freedom hand prosthesis with hierarchical grip control. IEEE Trans. Rehab. Eng. 1995, 3, 70-76.
3. Artemiadis, P.K.; Kyriakopoulos, K.J. EMG-based control of a robot arm using low-dimensional embeddings. IEEE Trans. Robot. 2010, 26, 393-398.
4. Li, G.; Schultz, A.E.; Kuiken, T.A. Quantifying pattern recognition-based myoelectric control of multifunctional transradial prostheses. IEEE Trans. Neural Syst. Rehabil. Eng. 2010, 18, 185-192.
5. LaBarge, E.; Fortunati, J.; Sullivan, M.; Bartels, N.; Zhang, R.; Sun, Y., Comparison between two control algorithms for EMG-based navigation. In Proceedings of the 2010 IEEE 36th Annual Northeast Bioengineering Conference, New York, NY, USA, May 2010; pp. 1-2.
6. Dupont, A.C.; Morin, E.L. A myoelectric control evaluation and trainer system. IEEE Trans. Rehab. Eng. 1994, 2, 100-107.
7. Kampas, P. Myoelektroden-Optimal eingesetz. Medizinisch-Ortohopäsdische Technik 2001, 1, 21-27.
8. Zardosthi-Kermani, M.; Wheeler, B.C.; Badie, K.; Hashemi, R.M. EMG feature evaluation for movement control of upper extremity prostheses. IEEE Trans. Rehab. Eng. 1995, 3, 324-332.
9. Englehart, K.; Hudgins, B.; Parker, P.A. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 2001, 48, 302-311.
10. Huang, Y.; Englehart, K.B.; Hudgins, B.; Chan, A.D.C. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses. IEEE Trans. Biomed. Eng. 2001, 52, 1801-1811.
11. Silva, J.; Chau, T.; Goldenberg, A., MMG-Based multisensor data fusion for prostheses control. In Proceedings of the 25th Annual International Conference of the IEEE EMBS, Cancún, Mexico, 2003; pp. 2909-2912.
12. Orizio, C.; Baratta, R.V.; Zhou, B.H.; Solomonow, M.; Veicsteinas, A. Force and surface mechanomyogram relationship in cat gastrocnemius. J. Electromyograph. Kinesiol. 2000, 33, 427-433.
13. Yang, Z.F.; Kumar, D.K.; Arjunan, S.P. Mechanomyogram for identifying muscle activity and fatigue. In Proceedings of the Annual Internationa Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, Minneapolis, MN, USA, September 2009; pp. 408-411.
14. Hu, Y.; Mak, J.N.; Luk, K.D. Effect of electrocardiographic contamination on surface electromyography assessment of back muscles. J. Electromyograph. Kinesiol. 2009, 19, 145-156.
15. Hogan, N.; Mann, R.W. Myoelectric signal processing: Optimal estimation applied to electromyography -part i: derivation of the optimal myoprocessor. IEEE Trans. Biomed. Eng. 1980, BME-27, 382-395.
16. Hogan, N.; Mann, R.W. Myoelectric signal processing: Optimal estimation applied to electromyography -part ii: experimental demonstration of optimal myoprocessor performance. IEEE Trans. Biomed. Eng. 1980, BME-27, 396-409.
17. Clancy, E.A.; Farry, K.A. Adaptative whitening of the electromyogram to improve amplitude estimation. IEEE Trans. Biomed. Eng. 2000, 47, 709-719.
18. Clancy, E.A.; Hogan, N. Probability density of the surface electromyogram and its relation to amplitude detectors. IEEE Trans. Biomed. Eng. 1999, 46, 730-739.
19. Hauschild, M.; Davoodi, R.; Loeb, G.E. A virtual reality environment for designing and fitting neural prosthesis limbs. IEEE Trans. Rehab. Eng. 2007, 15, 9-15.
20. De la Rosa, R.; Alonso, A.; de la Rosa, S.; Abásolo, D., Myo-Pong: A neuromuscular game for the UVa-Neuromuscular Training System Platform. In Virtual Rehabilitation 2008, Vancouver, BC, Canada, 2008.
21. De Luca, C.J. The use of surface electromyography in biomechanics. J. Appl. Biomech. 1997, 13, 135-163.
22. Metting Van Rijn, A.C.; Peper, A.; Grimbergen, C.A. Amplifiers for bioelectric events: A design with minimal number of parts. Med. Biol. Eng. Comput. 1994, 32, 305-310.
23. INA114 Precision Instrumentation Amplifier: Datasheet; Texas Instruments, Inc.: Dallas, TX, USA, 2003.
24. Horowitz, P.; Hill, W. The Art of Electronics; Cambridge University Press: Cambridge, UK, 1989.
25. Ross Dunseath, W.J.; Kelly, E.F. Multichannel PC-based data-acquisition system for high-resolution EEG. IEEE Trans. Biomed. Eng. 1995, 42, 1212-1217.
26. SA5.0 Thru SA170CA Transzorb Transient Voltage Suppressors: Datasheet; Vishay Intertechnology, Inc.: Malvern, PA, USA, 2002.
27. DC/DC Converter: TMA series Datasheet; Traco Electronic, AG: Zurich, Switzerland, 2000.
28. Grimbergen, C.A.; MettingVanRijn, A.C.; Kuiper, A.P.; Honsbeek, R.H.; Speijer, K.; Peper, A. DC rejection and deblocking in multichannel bioelectric recordings. In Proceedings of the 17th Annual Conference of the IEEE EMB, Montreal, QC, Canada, 1995.
29. Spinelli, E.M.; Pallàs-Areny, R.; Mayosky, M.A. AC-coupled front-end for biopotentials measurements. IEEE Trans. Biomed. Eng. 2003, 50, 391-395.
30. TL084 JFET-Input Operational Amplifiers: Datasheet; Texas Instruments, Inc.: Dallas, TX, USA, 1999.
31. LM324 Low Power Quad Op Amps: Datasheet; Philips Semiconductors: Eindhoven, The Netherlands, 1995.
32. Wolpert, S.; Laffely, A.J.; Hilton, J.A. Control of prosthetic motion using neuron-like elements. In IEEE Case Studies in Medical Instrument Design; Nagle, H.T., Tompkins, W.J., Eds.; IEEE: Piscataway, NJ, USA, 1991; pp. 139-148.
33. Oppenheim, A.V.; Willsky, A.S.; Young, I.T. Signals and Systems; Prentice-Hall International: London, UK, 1983.
34. Oppenheim, A.V.; Schafer, R.W. Discrete-Time Signal Processing; Prentice-Hall International: London, UK, 1989.
35. Mainardi, L.T.; Bianchi, A.M.; Cerutti, S. Digital biomedical signal acquisition and processing. In The Biomedical Engineering Handbook; Bronzino, J.D., Ed.; CRC Press: Boca Raton, FL, USA, 1995; pp. 828-852.
36. Devasahayam, S.R. Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling; Kluwer Academic/Plenum publishers: New York, NY, USA, 2000.
37. Soylu, A.R.; Arpinar-Avsar, P. Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction. J. Electromyograph. Kinesiol. 2010, 20, 773-776.
38. Van Valkenburg, M.E. Analog Filter Design; Harcourt Brace Jovanovich: Orlando, FL, USA, 1982.
39. PIC16F87X Datasheet: 28/40-Pin 8-Bit CMOS FLASH Microcontrollers; Microchip Technology, Inc.: Chandler, AZ, USA, 2001.
40. Burns, A.; Wellings, A. Real-time systems and programming languages: Ada 95, Real-Time Java and Real-Time POSIX, 3rd ed.; Addison Wesley: Boston, MA, USA, 2001.
41. Hudgins, B.; Parker, P.; Scott, R.N. A new strategy for multifunction myoelectric control. IEEE Trans. Biomed. Eng. 1993, 40, 82-94.
42. Fukunaga, K. Statistical Pattern Recognition; Academic Press, Inc.: San Diego, CA, USA, 1990.
43. De la Rosa, R.; Alonso, A.; de la Rosa, S.; del Val, L. The UVA-Neuromuscular training system platform. In Lecture Nores in Computer Science; Springer-Verlang: New York, NY, USA, 2009; Volume 5518, pp. 863-869.
44. Kreifeldt, J.G.; Yao, S. A signal-to-noise investigation of nonlinear electromyographic processors. IEEE Trans. Biomed. Eng. 1974, BME-21, 298-308.
45. Filligoi, G.C.; Mandarini, P. Some theoretic results on a digital EMG signal processor. IEEE Trans. Biomed. Eng. 1984, BME-31, 333-341.