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.

This thesis explores the field of robotic hand exoskeletons and their applications. These systems have emerged in popularity over the years, due to their potentials to advance the medical field as assistive and rehabilitation devices, and the field of virtual reality as haptic gloves. Although much progress has been made, hand exoskeletons are faced with several design challenges that are hard to overcome without having some tradeoffs. These challenges include: (1) the size and weight of the system, which can affect both the comfort of wearing it and its portability, (2) the ability to impose natural joint angle relationships among the user's fingers and thumb during grasping motions, (3) safety in terms of limiting the range of motions produce by the system to that of the natural human hand and ensuring the mechanical design does not cause harm or injury to the user during usage, (4) designing a device that is user friendly to use, and (5) the ability to effectively perform grasping motions and provide sensory feedback for the system to be applicable in various application fields. In order to address these common issues of today's state-of-the-art hand exoskeleton systems, this thesis proposes a mechanism design for a novel hand exoskeleton and presents the integration of several prototypes. The proposed hand exoskeleton is designed to assist the user with grasping motions while maintaining a natural coupling relationship among the finger and thumb joints to resemble that of a normal human hand. The mechanism offers the advantage of being small-size and lightweight, making it ideal for prolong usage. Several applications are discussed to highlight the proposed hand exoskeleton functionalities in processing sensory information, such as position and interactive forces. / MS / Hand exoskeletons are wearable devices that are designed to augment, reinforce, and/or restore hand performances and movements among the fingers and thumb. These hand exoskeleton systems have emerged in popularity within the medical field, where they serve as rehabilitation devices or assistive gloves, and within the field of virtual reality as haptic devices. Throughout the years, many hand exoskeleton designs have been proposed and even developed further into commercial products. Unfortunately, there still exist many design challenges for making an efficient and feasible hand exoskeleton without experiencing major tradeoffs. Some of the common challenges include designing a hand exoskeleton that is small in size, lightweight, and able to achieving natural grasping motions efficiently. As an attempt to overcome these design challenges, the work of this thesis presents a mechanism design for a novel hand exoskeleton that can serve as a general purpose glove across several applications. The design of the mechanism is described in detail with preliminary analysis. In addition, this thesis presents the design and development of several prototypes, which were made by extending the mechanism into fully integrated systems. The experimental validations of these prototypes are presented as well as their application potentials. To conclude the thesis, a discussion of the on-going future work is given.

Patients suffering from brachial plexus injury or other spinal cord related injuries often lose their hand functionality. They need a device which can help them to perform day to day activities by restoring some form of functionality to their hands. A popular solution to this problem are robotic exoskeletons, mechanical devices that help in actuating the fingers of the patients, enabling them to grasp objects and perform other daily life activities. This thesis presents the design of a novel exoskeleton glove which is controlled by a neural network-based controller. The novel design of the glove consists of rigid double four-bar linkage mechanisms actuated through series elastic actuators (SEAs) by DC motors. It also contains a novel rotary series elastic actuator (RSEA) which uses a torsion spring to measure torque, passive abduction and adduction mechanisms, and an adjustable base. To make the exoskeleton glove grasp objects, it also needs to have a robust controller which can compute forces that needs to be applied through each finger to successfully grasp an object. The neural network is inspired from the way human hands can grasp a wide variety of objects with ease. Fingertip forces were recorded from a normal human grasping objects at different orientations. This data was used to train the neural network with a R2 value of 0.81. Once the grasp is initiated by the user, the neural network takes inputs like orientation, weight, and size of the object to estimate the force required in each of the five digits to grasp an object. These forces are then applied by the motors through the SEA and linkage mechanisms to successfully grasp an object autonomously. / Master of Science / Humans are one of the few species to have an opposable thumb which allows them to not only perform tasks which require power, but also tasks which require precision. However, unfortunately, thousands of people in the United States suffer from hand disabilities which hinder them in performing basic tasks. The RML glove v3 is a robotic exoskeleton glove which can help these patients in performing day to day activities like grasping semi-autonomously. The glove is lightweight and comfortable to use. The RML glove v3 uses a neural network based controller to predict the grasp force required to successfully grasp objects. After the user provides the required input, the glove estimates the object size and uses other inputs like object orientation and weight to estimate the grasp force in each finger linkage mechanism. The motors then drive the linkages till the required force is achieved on the fingertips and the grasp is completed.

Controlling an exoskeleton glove with a highly efficient human-machine interface (HMI), while accurately applying force to each joint remains a hot topic. This paper proposes a fast, secure, accurate, and portable solution to control an exoskeleton glove. This state of the art solution includes both hardware and software components. The exoskeleton glove uses a modified serial elastic actuator (SEA) to achieve accurate force sensing. A portable electronic system is designed based on the SEA to allow force measurement, force application, slip detection, cloud computing, and a power supply to provide over 2 hours of continuous usage. A voice-control-based HMI referred to as the integrated trigger-word configurable voice activation and speaker verification system (CVASV), is integrated into a robotic exoskeleton glove to perform high-level control. The CVASV HMI is designed for embedded systems with limited computing power to perform voice-activation and voice-verification simultaneously. The system uses MobileNet as the feature extractor to reduce computational cost. The HMI is tuned to allow better performance in grasping daily objects. This study focuses on applying the CVASV HMI to the exoskeleton glove to perform a stable grasp with force-control and slip-detection using SEA based exoskeleton glove. This research found that using MobileNet as the speaker verification neural network can increase the speed of processing while maintaining similar verification accuracy. / Master of Science / The robotic exoskeleton glove used in this research is designed to help patients with hand disabilities. This thesis proposes a voice-activated grasping system to control the exoskeleton glove. Here, the user can use a self-defined keyword to activate the exoskeleton and use voice to control the exoskeleton. The voice command system can distinguish between different users' voices, thereby improving the safety of the glove control. A smartphone is used to process the voice commands and send them to an onboard computer on the exoskeleton glove. The exoskeleton glove then accurately applies force to each fingertip using a force feedback actuator.This study focused on designing a state of the art human machine interface to control an exoskeleton glove and perform an accurate and stable grasp.