Max Ortiz-Catalan
[1] C. Lake and J. Miguelez, “Comparative analysis ofmicroprocessors in upper limb prosthetics,” J Pros-thet Orthot, vol. 15, no. 2, 2003. [2] F. Sebelius, L. Eriksson, C. Balkenius, and T. Laurell,“Myoelectric control of a computer animated hand:A new concept based on the combined use of a tree-structured artificial neural network and a data glove,”J Med Eng Technol, vol. 30, no. 1, pp. 2–10, Jan 2006. [3] F. Sebelius, B. Ros´en, and G. Lundborg, “Refinedmyoelectric control in below-elbow amputees usingartificial neural networks and a data glove,” J HandSurg, vol. 30A, no. 4, pp. 780–789, Jul 2005.e14 [4] C. Guger, W. Harkam, C. Hertnaes, andG. Pfurtscheller, “Prosthetic control by an eeg-basedbrain-computer interface (bci),” In Proc. AAATE5th European Conference for the Advancement ofAssistive Technology, 1999. [5] D. Bergantz and H. Barad, “Neural network controlof cybernetic limb prostheses,” 10th Int Conf IEEEEMBS, vol. 3, pp. 1486–1487, 1988. [6] L. Niklasson, M. Bod´en, and T. Ziemke, “Neural con-trol of a virtual prosthesis,” International Conferenceon ANNs, 1998. [7] P. Shenoy, K. Miller, B. Crawford, and R. Rao, “On-line electromyographic control of a robotic prosthe-sis,” IEEE Trans Biomed Eng, vol. 55, no. 3, Mar2008. [8] M. Oskoei and H. Hu, “Support vector machine-basedclassification scheme for myoelectric control appliedto upper limb,” IEEE Trans Biomed Eng, vol. 55,no. 8, pp. 1956–1965, August 2008. [9] Y. Huang, K. Englehart, B. Hudgins, and A. Chan, “Agaussian mixture model based classification schemefor myoelectric control of powered upper limb pros-theses,” IEEE Trans Biomed Eng, vol. 52, no. 11, pp.1801–1811, November 2005. [10] A. Ramakrishnan and R. Sastry, “Wavelet transformsfor compound nerve action potential analysis,” ProcRC IEEE-EMBS & 14th BMESI, 1995. [11] A. Chan and K. Englehart, “Continuous myoelectriccontrol for powered prostheses using hidden markovmodels,” IEEE Trans Biomed Eng, vol. 52, no. 1, pp.121–124, January 2005. [12] A. Ajiboye and R. Weir, “A heuristic fuzzy logic ap-proach to emg pattern recognition for multifunctionalprosthesis control,” IEEE Trans Neural Syst RehabilEng, vol. 13, no. 3, pp. 280–291, Sep 2005. [13] K. Englehart and B. Hudgins, “A robust, real-timecontrol scheme for multifunction myoelectric con-trol,” IEEE Trans Biomed Eng, vol. 50, no. 7, July2003. [14] L. Hargrove, K. Englehart, and B. Hudgins, “A com-parison of surface and intramuscular myoelectric sig-nal classification,” IEEE Trans Biomed Eng, vol. 54,no. 5, pp. 847–853, May 2007. [15] H. Huang, T. A. Kuiken, and R. D. Lipschutz, “Astrategy for identifying locomotion modes using sur-face electromyography,” IEEE Trans Rehabil Eng,vol. 56, no. 1, pp. 65–73, Jan 2009. [16] P. Zhou, M. M. Lowery, K. B. Englehart, H. Huang,G. Li, L. Hargrove, J. P. A. Dewald, and T. A. Kuiken,“Decoding a new neural-machine interface for controlof artificial limbs,” J Neurophysiol, vol. 98, pp. 2974–2982, 2007. [17] J. Hoffer, R. Stein, M. Haugland, T. Sinkjaer, W. Dur-fee, A. Schwartz, G. Loeb, and C. Kantor, “Neuralsignals for command control and feedback in func-tional neuromuscular stimulation: A review,” J Reha-bil Res Dev, vol. 33, no. 2, pp. 145–157, April 1996. [18] G. Saridis and T. Gootee, “Emg pattern analysisand classification for a prosthetic arm,” IEEE TransBiomed Eng, vol. BME-29, no. 6, pp. 403–412, Jun1982. [19] B. Hudgins, P. Parker, and R. Scott, “A new strategyfor multifunction myoelectric control,” IEEE TransBiomed Eng, vol. 40, no. 1, January 1993. [20] N. M. Lopez, F. di Sciascio, C. M. Soria, and M. E.Valentinuzzi, “Robust emg sensing system based ondata fusion for myoelectric control of a robotic arm,”Biomed Eng Online, vol. 8, no. 5, 2009. [21] H. Huang, P. zhou, G. Li, and T. Kuiken, “Spatialfiltering improves emg classifcation accuracy follow-ing targeted muscle reinnervation,” Ann Biomed Eng,2009. [22] J. W. Sensinger, B. A. Lock, and T. A. Kuiken, “Adap-tive pattern recognition of myoelectric signals: Ex-ploration of conceptual framework and practical algo-rithms,” IEEE Trans Neural Syst Rehabil Eng, vol. 17,no. 3, Jun 2009. [23] J. Webster, Medical Instrumentation, Application andDesign, 3rd ed. Jhon Wiley & Sons, 1998. [24] L. S¨ornmo and P. Laguna, Bioelectrical Signal Pro-cessing in Cardiac and Neurological Applications.Elsevier, Academic Press, 2005. [25] T. Farrell and R. Weir, “A comparison of the effects ofelectrode implantation and targeting on pattern classi-fication accuracy for prosthesis control,” IEEE TransBiomed Eng, vol. 55, no. 9, pp. 2198–2211, Sep 2008. [26] A. Lopez, A. Ferreira, I. dos Santos, I. Gerv´asio,S. Salomoni, and G. Araujo, “Development of a mi-crocontrolled bioinstrumentation system for activecontrol of leg prostheses,” Proc 30th Int Conf IEEEEMBS, pp. 2393–2396, 2008. [27] D. Popovic, R. Stein, K. Jovanovic, R. Dai, A. Kos-tov, and W. Armstrong, “Sensory nerve recording forclosed-loop control to restore motor functions,” IEEETrans Biomed Eng, vol. 40, no. 10, pp. 1024–1031,Oct 1993. [28] M. Wahde, Biologically Inspired Optimization Meth-ods: An Introduction. WIT press, 2008.
Important Links:
Go Back