Real-time pinch force estimation by surface electromyography using an artificial neural network

Medical Engineering and Physics

Volumn 32, Issue 5, 2010, Pages 429-436

  Choi, Changmok ^a   Kwon, Suncheol ^a   Park, Wonil ^a   Lee, Hae dong ^b   Kim, Jung ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Yonsei University   (South Korea)

Author keywords

Artificial neural network (ANN); Pinch force estimation; Surface electromyography (SEMG)

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BIOMECHANICAL STUDIES; CLINICAL APPLICATION; ENVIRONMENTAL SURFACES; ERGONOMIC DESIGN; ESTIMATION METHODS; ESTIMATION RESULTS; FISHER DISCRIMINANT ANALYSIS; FORCE ESTIMATION; FORCE TRANSDUCERS; HAPTIC SENSE; MUSCLE FORCE; OVERFITTING; REAL TIME; ROOT MEAN SQUARED ERRORS; SPORTS ACTIVITY; SURFACE ELECTROMYOGRAPHY; TRAINING TIME;

BIOMECHANICS; DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; ESTIMATION; FISHER INFORMATION MATRIX; LEARNING ALGORITHMS;

NEURAL NETWORKS;

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; DISCRIMINANT ANALYSIS; ELECTROMYOGRAPHY; FEMALE; FORCE; FORCE TRANSDUCER; GRIP STRENGTH; HUMAN; HUMAN EXPERIMENT; MALE; NORMAL HUMAN; PERFORMANCE MEASUREMENT SYSTEM; PINCH STRENGTH; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SIGNAL DETECTION; SYSTEM ANALYSIS; SYSTEMATIC ERROR; TACTILE STIMULATION;

ALGORITHMS; ELECTROMYOGRAPHY; FEMALE; FINGERS; HUMANS; MALE; MODELS, BIOLOGICAL; MUSCLE CONTRACTION; MUSCLE, SKELETAL; NEURAL NETWORKS (COMPUTER); STRESS, MECHANICAL; YOUNG ADULT;

EID: 77952878508     PISSN: 13504533     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.medengphy.2010.04.004     Document Type: Article

References (45)

1. Schlesinger G. Der mechanische aufbau der kunstlichen Glieder, in Ersatzglieder und Arbeitshilfen 1919, Springer Verlag, Berlin.
2. Freivalds A. Biomechanics of the upper limbs: mechanics, modelling and musculoskeletal injuries 2004, CRC Press, Boca Raton, FL.
3. Cutkosky M.R. On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Trans Robot Automat 1989, 5:269.
4. Taylor C.L., Schwarz R.J. The anatomy and mechanics of the human hand. Artif Limbs 1955, 2:22-35.
5. DiDomenico A., Nussbaum M.A. Estimation of forces exerted by the fingers using standardised surface electromyography from the forearm. Ergonomics 2008, 51:858-871.
6. Keir P.J., Mogk J.P. The development and validation of equations to predict grip force in the workplace: contributions of muscle activity and posture. Ergonomics 2005, 48:1243-1259.
7. Disselhorst-Klug C., Schmitz-Rode T., Rau G. Surface electromyography and muscle force: limits in sEMG-force relationship and new approaches for applications. Clin Biomech 2009, 24:225-235.
8. Mobasser F., Eklund J.M., Hashtrudi-Zaad K. Estimation of elbow-induced wrist force with EMG signals using fast orthogonal search. IEEE Trans Biomed Eng 2007, 54:683-693.
9. Cavallaro E.E., Rosen J., Perry J.C., Burns S. Real-time myoprocessors for a neural controlled powered exoskeleton arm. IEEE Trans Biomed Eng 2006, 53:2387-2396.
10. Wang L., Buchanan T.S. Prediction of joint moments using a neural network model of muscle activations from EMG signals. IEEE Trans Neural Syst Rehabil Eng 2002, 10:30-37.
11. Koike Y., Kawato M. Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biol Cybern 1995, 73:291-300.
12. Luh J.J., Chang G.C., Cheng C.K., Lai J.S., Kuo T.S. Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model. J Electromyogr Kinesiol 1999, 9:173-183.
13. Lloyd D.G., Besier T.F. An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. J Biomech 2003, 36:765-776.
14. Fleischer C., Hommel G. A human-exoskeleton interface utilizing electromyography. IEEE Trans Robot 2008, 24:872-882.
15. Bogey R.A., Perry J., Gitter A.J. An EMG-to-force processing approach for determining ankle muscle forces during normal human gait. IEEE Trans Neural Syst Rehabil Eng 2005, 13:302-310.
16. Mesin L., Gervasio R. Detection volume of simulated electrode systems for recording sphincter muscle electromyogram. Med Eng Phys 2008, 30:896-904.
17. Strambi S.K., Rossi B., De Michele G., Sello S. Effect of medication in Parkinson's disease: a wavelet analysis of EMG signals. Med Eng Phys 2004, 26:279-290.
18. Tabernig C.B., Acevedo R.C. M-wave elimination from surface electromyogram of electrically stimulated muscles using singular value decomposition: preliminary results. Med Eng Phys 2008, 30:800-803.
19. Sinkjaer T., Haugland M., Inmann A., Hansen M., Nielsen K.D. Biopotentials as command and feedback signals in functional electrical stimulation systems. Med Eng Phys 2003, 25:29-40.
20. Reddy N.P., Gupta V. Toward direct biocontrol using surface EMG signals: control of finger and wrist joint models. Med Eng Phys 2007, 29:398-403.
21. Xie H.B., Zheng Y.P., Guo J.Y., Chen X., Shi J. Estimation of wrist angle from sonomyography using support vector machine and artificial neural network models. Med Eng Phys 2009, 31:384-391.
22. Yu H.-L., Chase R.A., Strauch B. Atlas of hand anatomy and clinical implications 2004, Mosby, St. Louis.
23. d'Avella A., Saltiel P., Bizzi E. Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci 2003, 6:300-308.
24. Li J., Wang Z.J., Eng J.J., McKeown M.J. Bayesian network modeling for discovering " dependent synergies" among muscles in reaching movements. IEEE Trans Biomed Eng 2008, 55:298-310.
25. Martin B.J., Armstrong T.J., Foulke J.A., Natarajan S., Klinenberg E., Serina E., et al. Keyboard reaction force and finger flexor electromyograms during computer keyboard work. Hum Factors 1996, 38:654-664.
26. De Luca C.J., Merletti R. Surface myoelectric signal cross-talk among muscles of the leg. Electroencephalogr Clin Neurophysiol 1988, 69:568-575.
27. Chu 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 T Mech 2007, 12:282-290.
28. Barniv Y., Aguilar M., Hasanbelliu E., Branch H., Aeronaut N., Adm S., et al. Using EMG to anticipate head motion for virtual-environment applications. IEEE Trans Biomed Eng 2005, 52:1078-1093.
29. Haykin S.S. Neural networks: a comprehensive foundation 1999, vol. xxi. Prentice Hall, Upper Saddle River, NJ, 842 pp. 2nd ed.
30. Clancy E.A., Farina D., Filligoi G. Single-channel techniques for information extraction from the surface emg signal. Electromyography: physiology, engineering, and noninvasive applications 2004, vol. xxii:133-168. IEEE/JohnWiley & Sons, Hoboken, NJ.
31. St-Amant Y., Rancourt D., Clancy E.A. Influence of smoothing window length on electromyogram amplitude estimates. IEEE Trans Biomed Eng 1998, 45:795-800.
32. Hermens H.J., Freriks B., Disselhorst-Klug C., Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 2000, 10:361-374.
33. Maier M.A., Hepp-Reymond M.C. EMG activation patterns during force production in precision grip I. Contribution of 15 finger muscles to isometric force. Exp Brain Res 1995, 103:108-122.
34. Duda R.O., Hart P.E., Stork D.G. Pattern classification 2001, vol. xx. Wiley, New York, 654 pp. 2nd ed.
35. Hushaba R.N., Al-Jumaily A., Al-Ani A. Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control. Pattern Recogn Lett 2009, 30:699-707.
36. Mogk J.P., Keir P.J. The effects of posture on forearm muscle loading during gripping. Ergonomics 2003, 46:956-975.
37. Hastie T., Tibshirani R., Friedman J.H. The elements of statistical learning: data mining, inference, and prediction 2001, vol. xvi. Springer, New York, 533 pp.
38. Danion F., Li S., Zatsiorsky V.M., Latash M.L. Relations between surface EMG of extrinsic flexors and individual finger forces support the notion of muscle compartments. Eur J Appl Physiol 2002, 88:185-188.
39. Lawrence J.H., De Luca C.J. Myoelectric signal versus force relationship in different human muscles. J Appl Physiol 1983, 54:1653-1659.
40. Lee K.Y., Shin K.Y., Kim H.S., Mun J.H. Estimating muscle fatigue of the biceps brachii using high to low band ratio in EMG during isotonic exercise. Int J Precis Eng Man 2009, 10:147-153.
41. Rosen J., Fuchs M.B., Arcan M. Performances of Hill-type and neural network muscle models-toward a myosignal-based exoskeleton. Comput Biomed Res 1999, 32:415-439.
42. Mali U., Goljar N., Munih M. Application of haptic interface for finger exercise. IEEE Trans Neural Syst Rehabil Eng 2006, 14:352-360.
43. Watts P.B., Jensen R.L., Gannon E., Kobeinia R., Maynard J., Sansom J. Forearm EMG during rock climbing differs from EMG during handgrip dynamometry. Int J Exerc Sci 2008, 1:4-13.
44. Vigouroux L., Quaine F., Labarre-Vila A., Moutet F. Estimation of finger muscle tendon tensions and pulley forces during specific sport-climbing grip techniques. J Biomech 2006, 39:2583-2592.
45. Kong Y.K., Lowe B.D. Optimal cylindrical handle diameter for grip force tasks. Int J Ind Ergonom 2005, 35:495-507.