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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29527


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9999869
Bioarm, a Prostheses without Surgery
Abstract:
Robotics provides answers to amputees. The most expensive solutions surgically connect the prosthesis to nerve endings. There are also several types of non-invasive technologies that recover nerve messages passing through the muscles. After analyzing these messages, myoelectric prostheses perform the desired movement. The main goal is to avoid all surgeries, which can be heavy and offer cheaper alternatives. For an amputee, we use valid muscles to recover the electrical signal involved in a muscle movement. EMG sensors placed on the muscle allows us to measure a potential difference, which our program transforms into control for a robotic arm with two degrees of freedom. We have shown the feasibility of non-invasive prostheses with two degrees of freedom. Signal analysis and an increase in degrees of freedom is still being improved.
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References:

[1] Gnahoua Zoabli, Institut de génie Biomédical, Faculté de médecine de Montréal, Thèse : Imagerie des muscles du membre supérieur et du dos, décembre 2005.
[2] M. Atieh, P. Y. Glorennec, C. Nasr: Commande myoélectrique d’une prothèse de main communicante et « intelligente », 4th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications, March 25-29, 2007.
[3] Pascale Thousalin-Chretien, Université de Strasbourg, Ecole Doctoral des Sciences de la Vie et de la Santé, Thèse: Etude des liens entres les systèmes visuel et proprioceptif : approche électrophysiologique et comportementale chez le sujet sain et le patient amputé du membre supérieur. Journal of Cognitive Neuroscience, 2009, vol. 21, n° 11, Pages 2207-2216.
[4] T. A. Kuiken, G. A. Dumanian, R.D. Lipschutz, L.A. Miller, K.A. Stubbllefield: The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee, Prosthetics and Orthotics International, 2004.28.245-253.
[5] Todd A. Kuiken, Guanglin Li, Blair A. Lock, Robert D. Lipschutz, Laura A. Miller, Kathy A. Stubblefield, Kevin B. Englehart, :Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms, American Medical Association, 2009.
[6] Lionel Rousseau, Université de Paris Est, Thèse : Développement de nouvelles matrices de microélectrodes pour l’analyse et la compréhension du système nerveux central, directeur de Thèse Prof; Gaëlle LISSORGUES, 13 janvier 2010.
[7] Muhammad Zahak Jamal: Signal Acquisition Using Surface EMG and Circuit Design Considerations for Robotic Prosthesis, National University of Sciences and Technology, Pakistan. Book: “Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges”, October 17, 2012.
[8] Erik Scheme, MSc, PEng; Kevin Englehart, PhD, PEng : Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use, Journal of Rehabilitation Research & Development, Volume 48 Number 6,Pages 643 — 660, 2011.
[9] Bart Peerdeman, MSc; Daphne Boere, MSc; Heidi Witteveen, MSc; Rianne Huis in `t Veld, PhD; Hermie Hermens, PhD; Stefano Stramigioli, PhD; HansRietman, MD, PhD; Peter Veltink, PhD; Sarthak Misra, PhD, Myoelectric forearm prostheses: State of the art from a user-centered perspective, Journal of Rehabilitation Research & Development, Volume 48 Number 6, Pages 719 — 738, 2011.
[10] Hanneke Bouwsema, MSc; Peter J. Kyberd, PhD; Wendy Hill, BScOT; Corry K. van der Sluis, MD, PhD; Raoul M. Bongers, PhD: Determining skill level in myoelectric prosthesis use with multiple outcome measures, Journal of Rehabilitation Research & Development, Volume 49, Number 9, 2012, Pages 1331—1348.
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[13] Martin Lotze, Herta Flor, Wolfgang Grodd, Wolfgang Larbig and Niels Birbaumer: Phantom movements and pain An fMRI study in upper limb amputees, Brain ,2001 - Oxford Univ Press.
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