System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
A Haptic Teleoperation Approach Based on Contact Force Control
International Journal of Robotics Research
An exoskeletal robot for human elbow motion support-sensor fusion,adaptation, and control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Mutual information analysis with ordinal pattern for EMG based hand motion recognition
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
Tele-impedance: Teleoperation with impedance regulation using a body-machine interface
International Journal of Robotics Research
Speed control of an omnidirectional walker by forearm pressures
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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Human-robot control interfaces have received increased attention during the past decades. With the introduction of robots in everyday life, especially in providing services to people with special needs (i.e., elderly, people with impairments, or people with disabilities), there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the user's skin, making the user's upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real time an anthropomorphic robot arm in 3-D space, using upper limb motion estimates based only on EMG recordings. Moreover, the proposed interface is robust to EMG changes with respect to time, mainly caused by muscle fatigue or adjustments of contraction level. The efficiency of the method is assessed through real-time experiments, including random arm motions in the 3-D space with variable hand speed profiles.