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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: Intelligent Object Grasping With Sensor Fusion for Rehabilitation and Assistive Applications.
    Author: Lee BJB, Williams A, Ben-Tzvi P.
    Journal: IEEE Trans Neural Syst Rehabil Eng; 2018 Aug; 26(8):1556-1565. PubMed ID: 29994121.
    Abstract:
    This paper presents the design and control of the intelligent sensing and force-feedback exoskeleton robotic glove to create a system capable of intelligent object grasping initiated by detection of the user's intentions through motion amplification. Using a combination of sensory feedback streams from the glove, the system has the ability to identify and prevent object slippage, as well as adapting grip geometry to the object properties. The slip detection algorithm provides updated inputs to the force controller to prevent an object from being dropped, while only requiring minimal input from a user who may have varying degrees of functionality in their injured hand. This paper proposes the use of a high dynamic range, low cost conductive elastomer sensor coupled with a negative force derivative trigger that can be leveraged in order to create a controller that can intelligently respond to slip conditions through state machine architecture, and improve the grasping robustness of the exoskeleton. The improvements to the previous design are described while the details of the controller design and the proposed assistive and rehabilitative applications are explained. Experimental results confirming the validity of the proposed system are presented. Finally, this paper concludes with topics for future exploration.
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