Abstract
Nowadays, bionic systems are a real revolution and they are increasingly used by thousands of victims of amputation all over the world. Whether of lower limb or upper limb amputation, bionic systems are a concrete solution that helps people to outdo disability. People with amputated leg can anew walk and person with amputated hand can afresh hold and manipulate objects by dint of myoelectric systems. All bionic systems are built in order to be as much as possible humanoid, regarding their aspect or their functioning, that is why a myoelectric interface should be intuitive and able to analyze and decode by itself the myoelectric excitation to command prosthesis. This paper is about the design and the achievement of a Multi-DOF myoelectric interface used to command an artificial hand that we designed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
TouchBionics Active Prosthesis. http://www.touchbionics.com
Kuiken, T.: Consideration of nerve-muscle grafts to improve the control of artificial arms. Technol. Disabil. 15, 105–111 (2003)
Miller, L., Lipschutz, R., Stubblefield, K., Lock, B.A., Huang, H., Williams, T., Weir, R., Kuiken, T.: Control of a six degree of freedom prosthetic arm after targeted muscle reinnervation surgery. Arch. Phys. Med. Rehabil. 89, 2057–2065 (2008)
Kuiken, T., Dumanian, G., Lipschutz, R., Miller, L., Stubblefield, K.: The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee. Prosthet. Orthot. Int. 28, 245–253 (2004)
Kuiken, T., Li, G., Lock, B.A., Lipschutz, R., Miller, L., Stubblefield, K., Englehart, K.: Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. J. Am. Med. Assoc. 301, 619–628 (2009)
Zhou, P., Kuiken, T.: Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation. Physiol. Meas. 27, 1311 (2006)
Raspopovic, S., Capogrosso, M., Petrini, F.M., Bonizzato, M., Rigosa, J., Pino, J.D., Carpaneto, J., Controzzi, M., Boretius, T., Fernandez, E., Granata, G., Oddo, C.M., Citi, L., Ciancio, L.A., Cipriani, C., Carrozza, M.C., Jensen, E., Guglielmelli, T., Stieglitz, P.M., Rossini, S.: Restoring natural sensory feedback in real-time bidirectional hand prosthsis. SciTransl. Med. 6, 222 (2014)
Shannon, G.F.: A comparison of alternative means of providing sensory feedback on upper limb prosthesis. Med. Biol. Eng. 14, 284–294 (1976)
Kaczmarek, K.A., Webster, J.G., Rita, P.B.Y., Tompkins, W.G.: Electrotactile and vibrotactile displays for sensory substitution systems. IEEE Trans. Biomed. Eng. 38, 1–16 (1991)
Pylatiuk, C., Kargov, A., Schulz, S.: Design and evaluation of a low-cost force feedback system for myoelectric prosthetic hands. Am. Acad. Orthotists Prosthetists 18, 5–61 (2006)
Hu, X., Whang, Z., Ren, X.: Classification of surface EMG signal using relative wavelet packet energy. Comput. Methods Programs Biomed. 79, 189–195 (2005)
Johansson, R.S., Westling, G.: Responses in glabrous skin mechanoreceptors during precision grip in humans. Exp. Brain Res. 66, 128–140 (1987)
Benchabane, S.I., Saadia, N.: Achievement of a myoelectric clamp provided by an optical shifting control for upper limb amputations. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI-CCI 2015. LNCS, vol. 9141, pp. 180–188. Springer, Heidelberg (2015)
Zipp, P.: Effect of electrode geometry on the selectivity of myoelectric recordings with surface electrodes. Eur. J. Appl. Physiol. Occup. Physiol. 50, 35–40 (1986)
Hermens, H.J., Freriks, B., Disselhorst-Klug, C., Rau, G.: Development of recommendations for SEMG sensors and sensor placement procedures. J. Electromyogr. Kinesiol. 10, 361–374 (2000)
Roy, S., De Luca, G., Cheng, M., Johansson, A., Gilmore, L., De Luca, J.: Electro-mechanical stability of surface EMG sensors. Med. Bio. Eng. Comput. 45, 447–457 (2007)
Ajiboye, A.B., Weir, R.: A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control. IEEE Neural Syst. Rehabil. 13, 280–291 (2005)
Hargrove, L., Zhou, P., Englehart, K., Kuiken, T.: The effect of ECG interference on pattern recognition based myoelectric control for targeted muscle reinnervated patients. IEEE Trans. Biomed. Eng. 56, 2197–2201 (2009)
Jiang, N., Englehart, K.B., Parker, A.: Extracting simultaneously and proportionally neural control information for multiple DOF prosthesis from the surface electromyographie signal. IEEE Trans. Biomed. 56, 1070–1080 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Benchabane, S.I., Saadia, N., Ramdane-Cherif, A. (2016). Achievement of a Multi DOF Myoelectric Interface for Hand Prosthesis. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_64
Download citation
DOI: https://doi.org/10.1007/978-3-319-41000-5_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40999-3
Online ISBN: 978-3-319-41000-5
eBook Packages: Computer ScienceComputer Science (R0)