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Finger curvature movement recognition interface technique using SEMG signals

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Until recently, keyboard has been used as the primary input method for machinery operation system. But in recent years, numerous methods related to direct input interface have been developed. One of them is to measure the surface electric potential that generates on the skin surface during muscle contraction. Based of this fact, hand finger operation can also be recognized with the help of the surface muscle electric potential. The purpose of this study is to identify the hand finger operation using surface electromyogram (SEMG) during crookedness state of the finger. Design/methodology/approach: Two electrodes (Ag-AgCl electrode) were sticked randomly on the forearm muscles and the intensity of EMG signals at different muscles were measured for each crooked finger. Then depending on the intensity of the obtained electric potentials, a position was located and considered to have participated most actively during the crookedness state of that finger. Thus five locations on the forearm muscles were identified for five different fingers. Moreover, four different types of crookedness states were considered for each finger. Findings: In this experimental study, the electric current that generates on the skin during muscle activity was measured for different hand finger operations. As a result, it is found that there is a specified position related the maximum intensity of EMG signals for each finger. Practical implications: This paper cleared that the amount of crookedness of each finger can also be recognized with the help of surface EMG. It could be used as a machine interface technology in the field of welfare equipments, robot hand operation, virtual reality, etc. Originality/value: The objective of this research project was to develop the method of recognizing the hand finger operation and their crookedness states from surface electromyogram (SEMG).
Rocznik
Strony
43--46
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
autor
autor
autor
autor
autor
autor
  • Department of Gerontechnology, National Institute for Longevity Sciences, NCGG, 36-3 Gengo, Morioka, Obu, 474-8522 Aichi, Japan, yito@nils.go.jp
Bibliografia
  • [1] R.H. Jebsen, N. Taylor, R.B. Trieschman, M.J. Trotter, L.A. Howard, An objective and standardized test of hand function, Archives of Physical Medicine and Rehabilitation 50 (1969) 313-319.
  • [2] M.A. Maier, M.C Hepp-Reymond, EMG Activation Patterns during force production in precision grip. I. Contributions of 15 Finger Muscles to Isometric Force, Experimental Brain Research 103 (1995) 108-122.
  • [3] W.J. Weiss, M. Flanders, Muscular and postural synergies of the human sand, Journal of Neurophysiology 92 (2004) 523-535.
  • [4] M.W. Jiang, R.C. Wang, J.Z. Wang, D.W. Jin, A Method of recognizing finger motion using wavelet transform of Surface EMG Signal, Proceedings of the 27th Annual International Conference of IEEE Engineering in Medicine and Biology Society 27/3, 2005, 2672-2674.
  • [5] K. Choi, Y. Koike, H. Hirose, T. Iijima, Prediction of four degrees of freedom arm movement using EMG signal, Proceedings of the 27th Annual International Conference of IEEE Engineering in Medicine and Biology Society 27/6 (2005) 5820-5823.
  • [6] H. Choi, J.H. Jeong, S.H. Hwang, W.H. Cho, Feature evaluation and pattern recognition of lower limb muscle EMG during postural balance control, Key Engineering Materials 326-328 (2006) 867-870.
  • [7] J.U. Chu, I. Moon, M.S. Mun, A Real-Time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand, IEEE Transaction on Biomedical Engineering 53/11 (2006) 2232-2239.
  • [8] M.C. Hammond, S.S. Fitts, G.H. Kraft, P.B. Nutter, M.J Trotter, L.M. Robinson, Co-contraction in the Hemiparetic Forearm: Quantitative EMG Evaluation, Archives of Physical Medicine and Rehabilitation 69 (1988) 348-351.
  • [9] S.L. Kilbreath, S.C. Gandevia, Limited independent flexion of the thumb and fingers in human subjects, The Journal o Physiology 479 (1994) 487-497.
  • [10] M.H. Schieber, Muscular production of individuated finger movements, The roles of extrinsic finger muscles, Th. Journal of Neuroscience 15 (1995) 284-297.
  • [11] C.G. Burgar, F.J. Valero-Cuevas V.R. Hentz, Fine-wire electromyographic recording during force generation: application to index finger, American Journal of Physical Medicine and Rehabilitation 76 (1997) 732-740.
  • [12] C.R. Rose, D.A. Keen, G.F. Koshland, A.J. Fuglevand, Coordination of multiple muscles in the elaboration of individual finger movements, Society for Neuroscience Abstract 25 (1999) 1149.
  • [13] C.K. Hager-Ross, M.H. Schieber, Quantifying the independence of human finger movements: Comparisons of digits, hands, and movement frequencies, The Journal of Neuroscience 20 (2000) 8542-8550.
  • [14] K.T. Reilly, M.H. Schieber, Incomplete functional subdivisions of the human multitendoned finger muscle flexor digitorum profundus: An electromyographic study, Journal of Neurophysiology 90 (2003) 2560-2570.
  • [15] B.H. Kim, B.J. Yi, I.H. Suh, S.R. Oh, Y.S. Hong, Biomimetic compliance control of robot hand by considering structures of human finger, Proceedings of International Conference on Robotics and Automation (2000) 3879-3886.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS5-0019-0082
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