Design and Control of a Robotic Exoskeleton Glove Using a Neural Network Based Controller for Grasping Objects

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.