Applied Bionics and Biomechanics · 2005 · 19 citations · 5 references
EngineeringBiomedical EngineeringElectrical EngineeringMotor ControlRobot ControlRoboticsBrain-computer InterfacesElectromyogram AnalysisHuman-machine InterfacesMuscle Signal ProcessingNeuromuscular ControlUpper Limb BiomechanicsElectromyographyElectromyography Signal AcquisitionElectromyography Signal InterpretationElectromyography-based Human-computer InteractionJoint Torque ComputationJoint Torque EstimationReal-time ProcessingWrist Biomechanics
The aim of this paper is to design a human–interface system, using EMG signals elicited by various wrist movements, to control a robot. EMG signals are normalized and based on joint torque. A three‐layer neural network is used to estimate posture of the wrist and forearm from EMG signals. After training the neural network and obtaining appropriate weights, the subject was able to control the robot in real time using wrist and forearm movements.
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