preprints.org > engineering > control and systems engineering > doi: 10.20944/preprints201806.0449.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 June 2018 / Approved: 27 June 2018 / Online: 27 June 2018 (15:01:06 CEST)

This paper presents a system that merges computer vision and surface electromyography techniques to carry out grasping tasks. To perform this, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. The system has been tested with several subjects to check its performance showing a grasping accuracy of around 95% of the attempted grasps which increases by around a 9% the grasping accuracy of previous experiments in which electromyographic control was not implemented.

surface electromyography; computer vision; grasping; assistive robotics

Engineering, Control and Systems Engineering

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