This dissertation focuses on improving the capabilities of an assistive robotic exoskeleton glove designed for patients with Brachial Plexus Injuries (BPI). The aim of this research is to develop a force control method, an automatic force planning method, and a Human-Machine Interface (HMI) to refine the grasping functionalities of the exoskeleton glove, thus helping rehabilitation and independent living for individuals with BPI. The exoskeleton glove is a useful tool in post-surgery therapy for patients with BPI, as it helps counteract hand muscle atrophy by allowing controlled and assisted hand movements. This study introduces an assistive exoskeleton glove with rigid side-mounted linkages driven by Series Elastic Actuators (SEAs) to perform five different types of grasps. In the aspect of force control, data-driven SEA fingertip force prediction methods were developed to assist force control with the Linear Series Elastic Actuators (LSEAs). This data-driven force prediction method can provide precise prediction of SEA fingertip force taking into account the deformation and friction force on the exoskeleton glove. In the aspect of force planning, a slip-grasp force planning method with hybrid slip detection is implemented. This method incorporates a vision-based approach to estimate object properties to refine grasp force predictions, thus mimicking human grasping processes and reducing the trial-and-error iterations required for the slip- grasp method, increasing the grasp success rate from 71.9% to 87.5%. In terms of HMI, the Configurable Voice Activation and Speaker Verification (CVASV) system was developed to control the proposed exoskeleton glove, which was then complemented by an innovative one-shot learning-based alternative, which proved to be more effective than CVASV in terms of training time and connectivity requirements. Clinical trials were conducted successfully in patients with BPI, demonstrating the effectiveness of the exoskeleton glove. / Doctor of Philosophy / This dissertation focuses on improving the capabilities of a robotic exoskeleton glove designed to assist individuals with Brachial Plexus Injuries (BPI). The goal is to enhance the glove's ability to grasp and manipulate objects, which can help in the recovery process and enable patients with BPI to live more independently. The exoskeleton glove is a tool for patients with BPI to used after surgery to prevent the muscles of the hand from weakening due to lack of use. This research introduces an exoskeleton glove that utilizes special mechanisms to perform various types of grasp. The study has three main components. First, it focuses on ensuring that the glove can accurately control its grip strength. This is achieved through a special method that takes into account factors such as how the materials in the glove change when it moves and the amount of friction present. Second, the study works on a method for planning how much force the glove should use to hold objects without letting them slip. This method combines a camera-based object and material detection to estimate the weight and size of the target object, making the glove better at holding things without dropping them. The third part involves designing how people can instruct the glove what to do. The command can be sent to the robot by voice. This study proposed a new method that quickly learns how you talk and recognizes your voice. The exoskeleton glove was tested on patients with BPI and the results showed that it is successful in helping them. This study enhances assistive technology, especially in the field of assistive exoskeleton glove, making it more effective and beneficial for individuals with hand disabilities.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/116502 |
| Date | 18 October 2023 |
| Creators | Guo, Yunfei |
| Contributors | Electrical and Computer Engineering, Ben-Tzvi, Pinhas, Southward, Steve C., Gerdes, Ryan M., Jones, Creed Farris, Plassmann, Paul E. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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