Persons with transfemoral (above knee) and transtibial (below knee) prostheses experience changes in the volume of their residual limb during the course of the day. These changes in volume unavoidably lead to changes in quality of fit of the prosthesis, skin irritations, and soft tissue injuries. The associated pain and discomfort can become debilitating by reducing one's ability to perform daily activities. While significant advancements have been made in prostheses, the undesirable pain and discomfort that occurs due to the volume change is still a major challenge that needs to be solved. The overall goal of this research is to develop smart prosthetic sockets that can accommodate for volume fluctuations in the residual limb. In this research, fluidic flexible matrix composite wafers (f2mc) are integrated into the prosthetic socket for volume regulation. The f2mc's are flexible tubular elements embedded in a flexible matrix. These tubular elements are connected to a reservoir, and contain an internal fluid such as air or water. Fluid flow between the tubes and reservoir is controlled by valves. A linear finite element model has been created to better understand output response and stiffness of the f2mc wafers for different design variables. Results demonstrate that wind angle, latex thickness, and material selection can be used to tailor the wafers for different applications. Through experiments, f2mc's have been shown to achieve nearly 100% strain through the thickness when pressurized to about 482.6 kPa (70 psi). The displacement results shown through these tests show great promise in applications of socket integration to compensate for volume change. / Master of Science

Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications. In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field. The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer. / Master of Science