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Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System
Open AccessArticle

A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography

1
Department of Physics, System Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain
2
Computer Science Research Institute, University of Alicante, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2366; https://doi.org/10.3390/s18072366
Received: 27 June 2018 / Revised: 14 July 2018 / Accepted: 16 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Assistance Robotics and Biosensors)
This paper presents a system that combines computer vision and surface electromyography techniques to perform grasping tasks with a robotic hand. In order to achieve a reliable grasping action, 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 in more than a 13% the grasping accuracy of previous experiments in which electromyographic control was not implemented. View Full-Text
Keywords: surface electromyography; computer vision; grasping; assistive robotics surface electromyography; computer vision; grasping; assistive robotics
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Úbeda, A.; Zapata-Impata, B.S.; Puente, S.T.; Gil, P.; Candelas, F.; Torres, F. A Vision-Driven Collaborative Robotic Grasping System Tele-Operated by Surface Electromyography. Sensors 2018, 18, 2366.

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