My research is regarding the construction of high-function, low cost prosthetic fingers. Current solutions for prosthetic hands can cost upwards of $20,000 to $100,000, due to the intensive costs of manufacturing, labor, and materials. This actually leads to a decreased usage by patients because of the fear of damaging their expensive prosthetic. Using 3D printing technologies and unconventional filaments, I am trying to create fingers with advanced capabilities in a single, simple process. This method could allow prosthetic users to 3D print their own replaceable fingers at home for less than a few dollars per finger. 

Modern hand prosthetics are generally lacking in the area of intuitive, reliable, and versatile user control. One method of control that is promising is force myography. This method uses force or pressure sensors in an arrangement on the user's forearm. Residual movement within the forearm creates an activation pattern that can be used to actuate the user's prosthetic. 

There are some drawbacks to force myographic methods that have been studied and published in literature. This project seeks to address some of the major drawbacks, including inaccuracy from shifting of sensors on the arm, changes in activation patterns with short-term and long-term use, and the loss of useable data due to sensor drift. 

This project will study the use of different types of custom, low cost to manufacture, sensors, and custom wrist band designs to see how they compare to conventional sensors and wrist band designs for force myography-based control. Additionally, software-based methods will be implemented in an effort to reduce the effect of shifting sensors, continuously changing user input, etc.

My research focuses on developing and optimizing a lightweight delta manipulator capable of manipulating various power tools for use in infrastructure repair. There are over 3800 wind turbines damaged by lightning yearly, and current repair procedures require workers to don uncomfortable safety equipment, using unwieldy tools, in a risky environment to repair the damage. A drone mounted with a robot arm and a power tool, capable of landing on a damaged wind turbine blade to remove damage composite material greatly simplifies the repair process. After creating the kinematic models that describe the behaviour of the arm, an algorithm is constructed that implies what physical characteristics of the arm are ideal to perform the given task. This is done by using a gradient descent algorithm to find the combination of physical robot dimensions that produce the most amount of output force for the least amount of input. After completing these optimization calculations, the final device build, informed by kinematic understanding, will perform with great efficiency.

 

 

 Modern advanced robotic hand prostheses face many challenges with respect to user control of the devices, which can often result in their rejection by users. A vision-based control system is proposed, which could bypass many of these issues by having the computational component of the system control the grasping of the hand based on the spacial relation of the prosthesis and the grasp object as provided from object tracking. This eliminates complicated control and the need for training for use of the prosthesis. In this thesis a control system that consists of a camera, image processing software, and other software to handle robot control is explored. The particular version realized for the study uses a webcam paired with Vuforia software in the Unity environment to track the prosthesis and the grasp object, while the SynGrasp toolbox for Matlab is used to control a virtual hand. Successful use of the proposed technology would require: 1) accurate object recognition, 2) determining what object a user is reaching to, 3) predicting user’s arm motion for the near future, 4) measuring the kinematics of the arm, hand, and object relative to each other, and 5) controlling the hand to close around the target object such that an adequate grasp is achieved. This thesis focuses on Points 4 and 5, though the other points are addressed in sum- mary form. Therefore, in this context, feasibility entails accurate, camera-based kinematics measurement and appropriate grip section for a set of target objects. Kinematic errors were determined under realistic static and dynamics conditions. It was determined that translation error typically had a standard deviation of about half a millimeter when stationary and nine millimeters when in realistic worst case motion. Rotational error was found to have a standard deviation of about half a degree. These error estimates were then propagated through grasp selection algorithms via the SynGrasp toolbox for Matlab. It was determined that the stationary error had little impact on the grasp with 73 to 99 percent of simulations maintaining 90 percent or better relative performance quality compared to its control case. The motion error resulted in significant differences in grasp compared to the control case with 10 to 30 percent of simulations maintaining 90 percent or better relative performance quality compared to its control case. The particular embodiment of the concept was ultimately ruled to be feasible since

most grasp situations a prosthesis would expect to encounter would better relate to a stationary environment than one in motion.