Abstract
Prosthetic care for handicapped persons requires new and reliable robotics technology. In this paper, developmental approaches for prosthetic applications are described. In addition, the challenges associated with the adaptation and control of materials for human hand prosthetics are presented. The new technology of robotics for prosthetics provides many possibilities for the detection of human intention. This is particularly true with the use of electromyogram (EMG) and mechanical actuation with multiple degrees of freedom. The EMG signal is a nonlinear wave, and has time dependency and big individual differences. The EMG signal is a nonlinear wave that has time dependency and significant differences from one individual to another. A method for how an individual adapts to the processing of EMG signals is being studied to determine and classify a human’s intention to move. A prosthetic hand with 11 degrees of freedom (DOF) was developed for this study. In order to make it light-weight, an adaptive joint mechanism was applied. The application results demonstrate the challenges for human adaptation. The f-MRI data show a process of replacement from a phantom limb image to a prosthetic hand image.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Uchida, M., Ide, H., Ninomiya, S.P.: Control of a robot arm by myoelectric potential. Journal of Robotics and Mechatronics 5(3), 259–265 (1993)
Farry, K.A., Walker, I.D., Baraniuk, R.G.: Myoelectric teleoperation of a complex robotic hand. IEEE Trans. Robotics and Automation 12(5), 775–788 (1996)
Hudgins, B., Parker, P., Scott, R.N.: New strategy for multifunction myoelectric control. IEEE Trans. Biomedical Engineering 40(1), 82–94 (1993)
Sears, H.H., Shaperman, J.: Electric wrist rotation in proportional-controlled systems. Journal of Prosthetics and Orthotics 10(4), 92–98
Dechev, N., Cleghorn, W.L., Naumann, S.: Multiple finger, passive adaptive grasp prosthetic hand. Mechanism and Machine Theory 36, 1157–1173 (2001)
Neal, M.: Coming to grips with artificial hand design. Design Engineering, 26–27,29,32,34 (March 1993)
SensorHand technical information booklet : Otto Bock Co., Ltd (2001), http://www.healthcare.ottobock.com/
Ishikawa, Y., Yu, W., Yokoi, H., Kakazu, Y.: Development of robot hands with an adjustable power transmitting mechanism. Intelligent Engineering Systems Through Neural Networks 10, 631–636 (2000)
Hirose, S., Ma, S.: Coupled tendon-driven multijoint manipulator. In: Proc. IEEE Intl. Conf. on Robotics & Automation, pp. 1268–1275 (1991)
D. Nishikawa, et al., “On-line learning based electromyogram to forearm motion classifier with motor skill evaluation,” JSME Intl. Journal Series C, Vol. 43, No. 4, pp. 906–915, 2000.
Katoh, R., et al.: Evaluation of biosignal processing method for welfare assisting devices - Evaluation of EMG information extraction processing using entropy -. Journal of Robotics and Mechatronics 14(6), 573–580 (2002)
Crinier, S.: Behavior-Based Control of a Robot Hand using Tactile Sensors.” Master’s thesis written at the Center for Autonomous Systems, Royal Inst. Tech. In: Sweden (2002)
Buttazzo, G., et al.: Robot Tactile Perception. In: Lee, C.S.G. (ed.) Sensor Based Robots: Algorithms and Architectures, Springer, Heidelberg (1992)
Bicchi, A.:Optimal Control of Robotic Grasping. In: Proc. American Control Conf. (1992)
Buttazzo, G., et al.: Robot Tactile Perception. In: Lee, C.S.G. (ed.) Sensor Based Robots: Algorithms and Architectures, Springer, Heidelberg (1992)
Kyberd, P.J., Chappell, P.H.: The Southampton hand: An intelligent myoelectric prosthesis. J. Rehabilitation Research and Development 31(4), 326–334 (1994)
17. Conductive rubber silicon sheet. Characteristic Performance CS57-7RSC(CSA)
Plettenburg, D.H.: Prosthetic control: A case for extended physiological proprioception. Univ. New Brunswick, MyoElectric Controls/Powered Prosthetics Symposium (2002)
Rios Poveda, A.: Myoelectric prosthesis with sensorial feedback. University of New Brunswick, MyoElectric Controls/Powered Prosthetics Symposium (2002)
Handa, Y., et al.: Sensory feedback on the FES. J. Biomechanism 12(1) (1988)
Yoshida, M., Sasaki, Y., Nakayama, N.: Sensory feedback for biomimetic prosthetic hand. In: BPES, The 17th living body and physiology engineering symposium (2002) (in Japanese)
Flor, H., et al.: Taub.Phantom-Limb Pain As A Perceptual Correlate Of Cortical Reorganization Following Arm Amputation. Nature 375, 482–484 (1995)
MacLachlan, M., et al.: Psychological correlates of illusory body experiences. Journal of Rehabilitation Research and Development 40(1), 59–66 (2003)
Mano, Y., et al.: Central motor reorganization after anastomosis of the musculocutaneous and intercostal nerves following cervical root avulsion. Ann. Neurol. 38, 15–20 (1995)
Naruse, K., et al.: Development of EMG-based force sensing system in virtual reality system. In: Proc. of The Ninth Intl. Conf. on Advanced Robotics, pp. 185-190 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Yokoi, H., Arieta, A.H., Katoh, R., Yu, W., Watanabe, I., Maruishi, M. (2004). Mutual Adaptation in a Prosthetics Application. In: Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds) Embodied Artificial Intelligence. Lecture Notes in Computer Science(), vol 3139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27833-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-27833-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22484-6
Online ISBN: 978-3-540-27833-7
eBook Packages: Springer Book Archive