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Multichannel electrotactile feedback for simultaneous and proportional myoelectric control.
Journal of Neural Engineering 2016 October
OBJECTIVE: Closing the loop in myoelectric prostheses by providing artificial somatosensory feedback to the user is an important need for prosthetic users. Previous studies investigated feedback strategies in combination with the control of one degree of freedom of simple grippers. Modern hands, however, are sophisticated multifunction systems. In this study, we assessed multichannel electrotactile feedback integrated with an advanced method for the simultaneous and proportional control of individual fingers of a dexterous hand.
APPROACH: The feedback used spatial and frequency coding to provide information on the finger positions (normalized flexion angles). A comprehensive set of conditions have been investigated in 28 able-bodied subjects, including feedback modalities (visual, electrotactile and no feedback), control tasks (fingers and grasps), systems (virtual and real hand), control methods (ideal and realistic) and range of motion (low and high). The task for the subjects was to operate the hand using closed-loop myoelectric control and generate the desired movement (e.g., selected finger or grasp at a specific level of closure).
MAIN RESULTS: The subjects could perceive the multichannel and multivariable electrotactile feedback and effectively exploit it to improve the control performance with respect to open-loop grasping. The improvement however depended on the reliability of the feedforward control, with less consistent control exhibiting performance trends that were more complex across the conditions.
SIGNIFICANCE: The results are promising for the potential application of advanced feedback to close the control loop in sophisticated prosthetic systems.
APPROACH: The feedback used spatial and frequency coding to provide information on the finger positions (normalized flexion angles). A comprehensive set of conditions have been investigated in 28 able-bodied subjects, including feedback modalities (visual, electrotactile and no feedback), control tasks (fingers and grasps), systems (virtual and real hand), control methods (ideal and realistic) and range of motion (low and high). The task for the subjects was to operate the hand using closed-loop myoelectric control and generate the desired movement (e.g., selected finger or grasp at a specific level of closure).
MAIN RESULTS: The subjects could perceive the multichannel and multivariable electrotactile feedback and effectively exploit it to improve the control performance with respect to open-loop grasping. The improvement however depended on the reliability of the feedforward control, with less consistent control exhibiting performance trends that were more complex across the conditions.
SIGNIFICANCE: The results are promising for the potential application of advanced feedback to close the control loop in sophisticated prosthetic systems.
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