To have a voice link with other diving partners or surface personnel, divers need to put on a communication mask. The second stage regulator or mouthpiece is equipped with a circuit to pick up the voice of the diver. Then the voice is frequency-modulates into ultrasonic signal to be transmitted into water. A receiver on the other side picks up the ultrasonic signal and demodulates it back to voice, and plays back in diver's earphone set. This technology is mature but not widely adopted for its price. Most divers still use their favorite way to communicate with each other, i.e. DSL (divers' sign language.) As more and more intelligent machines or robots are built to help divers for their underwater task, divers not only need to exchange messages with their human partners but also machines. However, it seems that there are not many input devices available other than push buttons or joysticks. We know that divers¡¦hands are always busy with holding tools or gauges. Additional input devices will further complicate their movement, also distract their attention for safety measures. With this consideration, this paper intends to develop an algorithm to read the DSL as input commands for computer-aided diving system. To simplify the image processing part of the problem, we attach an LED at the tip of each finger. The gesture or the hand sign is then captured by a CCD camera. After thresholding, there will only five or less than five bright spots left in the image. The remaining part of the task is to design a classifier that can identify if the unknown sign is one from the pool. Furthermore, a constraint imposed is that the algorithm should work without knowing all of the signs in advance. This is an analogy to that human can recognize a face is someone known seen before or a stranger. We modify the concept of eigenfaces developed by Turk and Pentland into eigenhands. The idea is to choose geometrical properties of the bright spots (finger tips), like distance from fingertips to the centroid or the total area of the polygon with fingertips as its vertices as the features of the corresponding hand sign. All these features are quantitative, so we can put several features together to construct a vector to represent a specific hand sign. These vectors are treated as the raw data of the hand signs, and an essential subset or subspace can be spanned by the eigen vectors of the first few large corresponding values. It is less than the total number of hand signed involved. The projection of the raw vector along these eigen vectors are called the principal components of the hand sign. Principal components are abstract but they can serve as keys to match the candidate from a larger pool. With these types of simple geometrical features, the success rate of cross identification among 30 different subjects' 16 gestures varies to 91.04% .

This thesis consists of the research on linkage-based fingertips for prosthetic hands. These fingertips consists of small polycentric mechanisms attached to what would be the pulp in normal anatomical fingers. These mechanisms allow the prosthetic hand to conform to the shape of objects during grasp. The goal of these prosthetic fingertips is to maximize the functionality of the hand while minimizing the number of inputs that the user has to control. The stability of the fingertip mechanisms is analyzed using the principle of virtual work. From this analysis we are able to show that the fingertip mechanism is stable for a large range of rotation of the link and for a large range of directions on which the force is applied, and that the mechanism is indifferent to the magnitude of the force applied to it (assuming that the force does not damage/deform the mechanism). To assess if the four-bar mechanisms (fingertips) improve the grasping capabilities in robotics and prosthetics, tests were performed on prosthetic hands and robot grippers with and without the fingertips. Comparisons were made using the Southampton Hand Assessment Procedure (SHAP) protocol, which tests the differences and measures the functionality of particular types of grasp, such as power, spherical, lateral, tripod, tip and extension. In the human testing, the overall Index of Functionality (IOF) of the Hosmer hook is 66.65 and 66.21 for the hook with the fingertips. The hook with the fingertips had a better IOF in the spherical and power prehensile pattern. When the IOF is calculated for the tasks that the fingertips were used, in 10 of 11 of the tasks, the IOF is higher than using the Hosmer hook. In the robotic gripper testing, the Index of Functionality was not be calculated because the time to perform the tasks depended more on the robotic control system than on the physical characteristics of the gripper.