Model adaptation with least-squares SVM for adaptive hand prosthetics
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Development of an anthropomorphic prosthetic hand for man-machine interaction
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Empirical Copula driven hand motion recognition via surface electromyography based templates
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Adaptive multi sensor based nonlinear identification of skeletal muscle force
WSEAS TRANSACTIONS on SYSTEMS
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
On-line frequency adaptation and movement imitation for rhythmic robotic tasks
International Journal of Robotics Research
Tele-impedance: Teleoperation with impedance regulation using a body-machine interface
International Journal of Robotics Research
Local Hand Control for Tezpur University Bionic Hand Grasping
Proceedings of Conference on Advances In Robotics
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One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible.