| Summary: | 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.
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