This thesis discusses three projects that revolve around the central concept of the control of artificial hands. The first part of the thesis discusses the design of a museum exhibit for the South Florida Science Center that allows the public to control an i-limb Revolution prosthetic hand using electromyograph (EMG) sensors. A custom armature was designed to house the EMG sensors that are used to control the prosthesis. The top arm of the armature utilized a double rocker design for a greater range of motion which allows the display to accommodate arm sizes ranging from small children to large adults. This display became open to the public in March of 2019. The second part of the thesis describes a new concept for a simultaneous multi-object grasp using the Shadow hand robotic hand. This grasp is tested in an experiment that involves grasp and transportation tasks. This experiment also aims to analyze the benefit of soft robotic haptic feedback armband during the grasp and transportation tasks when a simulated break threshold is imposed on the objects. The usefulness of the haptic feedback was further tested with a guess the object task where the subjects had to determine which object was in the hand based solely off the armband. The new grasp synergy was deemed a success as all subjects were able to use the control method effectively with very little initial training. It was also found that the haptic feedback greatly aided in the successfully completing the transportation tasks. The human subjects were asked to rate the haptic feedback after each task, the overall rating for the helpfulness of the haptic feedback was rated as 4.6 out of 5. The final part of the thesis discusses an approach at gaining additional control signals for a dexterous artificial hand using a brain computer interface. This project seeks to investigate three neuromarkers for control which are: mu, xi and alpha. During analysis, the mu rhythm was not seen in our subject but alpha and xi were. Using deep learning approaches at classification, we were able to classify alpha and xi with at least a 90 percent accuracy. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_41938 |
| Contributors | Ingicco, Joseph (author), Engeberg, Erik (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 144 p., application/pdf |
| Rights | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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