This thesis explores the field of hand exoskeleton robotic systems with slip detection and its applications. It presents the design and control of the intelligent sensing and force- feedback exoskeleton robotic (iSAFER) 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 thesis 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 mechanical and electrical improvements to the previous design, the sensing and force- feedback exoskeleton robotic (SAFER) glove, are described while details of the controller design and the proposed assistive and rehabilitative applications are explained. Experimental results confirming the validity of the proposed system are also presented. In closing, this thesis concludes with topics for future exploration. / Master of Science / Exoskeletons are robotic systems that have rigid external covering, such as links, joints, and/or soft artificial tendons or muscles, for the desired body part to provide support and/or protection. These are typically used to enhance power and strength, provide rehabilitation and assistance, and teleoperate other robots from a distance. While the US Army developed exoskeletons for strengthening purposes, another potential purpose of exoskeletons, which is serving medical needs, such as rehabilitation, attracted a lot of attention.
Among numerous illnesses and injuries that may lead to impaired hand functionality, the U.S. Department of Health and Human Services estimated that approximately 470,000 people survive strokes every year in the United States and require continuous rehabilitation to recover their motor functions. Though medical professionals believe that the intensity and duration of rehabilitation is the key for maximizing the rate of recovery, it is often limited due to many reasons, such as cost or difficulty in attending rehabilitation sessions. To augment the availability and quality of rehabilitation, the study of exoskeletons has earned popularity. Beyond the capability of providing simple movements, such as passive rehabilitation, many scientists researched to provide active rehabilitation, which involves active participation from the patients. Furthermore, detecting the patient’s intention to activate the rehabilitation glove became a topic of interest, and many types of sensors were utilized in research.
This thesis explores the design and control of the intelligent sensing and force- feedback exoskeleton robotic (iSAFER) glove, which detects the user’s intentions to activate the system through motion amplification. The iSAFER glove performs soft initial grasp until the fingers touch an object. After the object is gently grabbed and lifted, the grasp is autonomously adjusted through slip detection until there is no more slip. To facilitate this idea, a low cost force sensor was created and leveraged to improve the grasping control of the exoskeleton. The mechanical and electrical improvements to the previous design, the sensing and force-feedback exoskeleton robotic (SAFER) glove, are described while details of the controller design and the proposed assistive and rehabilitative applications are explained. Experimental results confirming the validity of the proposed system are also presented. In closing, this thesis concludes with topics for future exploration.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/83924 |
| Date | January 2018 |
| Creators | Lee, Brielle |
| Contributors | Mechanical Engineering, Ben-Tzvi, Pinhas, Wicks, Alfred L., Asbeck, Alan T. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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