This thesis describes the system design of EMG Site, a MATLAB-based application for collection and visualization of surface electromyograms (EMGs) and the real-time control of an upper limb prosthesis, including details pertaining to the design of the software and the graphical user interface (GUI). The application consists of features that aid in the visualization of the collected EMG data and the control of a prosthesis. Visualization of the collected EMG data is handled in one of two ways: an oscilloscope-like view showing the raw EMG data collected with respect to time, or a radial plot showing the processed EMG data collected with respect to the site of EMG data collection on the arm. The control of a hand-wrist prosthesis is primarily regulated through the use of signal processing designed to relate EMG to torque and is visualized in the tracking window - a plotting window showing both a user-control cursor and an either static (or dynamic) computer-controlled target. This thesis concludes with a description of the real-time capabilities of the application regarding both the visualization of the collected EMG data as well as the control of a prosthesis.

This thesis work provides a new way to detect and track the displacement of flexor tendons within the human arm, using a non-invasive, ultrasound-based, speckle tracking technique. By tracking the tendons in the arm, it provides a way to monitor a person’s intention to move their hands and fingers. This has application to hand prosthetic control, as well as tendon injury assessment, which has significant contributions to the medical and rehabilitation community. The system works by capturing and processing a sequence of B-scan ultrasound images, to detect and track the flexor tendon motion (excursion) in the wrist, as the user flexes their muscles. Given the biomechanics of the hand, tendon displacement is correlated to the user’s intention to move their finger. Several speckle tracking techniques using B-scan ultrasound image sequences are developed in this work, including: auto-location of the tendon, a stationary ROI (region of interest), and novel use of similarity measures such as FT (Fisher Tippett), and hybrid methods. As well, work is done to investigate various speckle tracking parameters, and their effects on tracking accuracy. The different speckle tracking techniques are developed using data obtained from cadaver hands, and human volunteers undergoing regular surgery. The tracking techniques are compared in terms of successfully detecting the tendon, accurately tracking tendon displacement, successfully tracking multiple tendons, successfully detecting and tracking the onset of low tendon displacement, and computational efficiency of the algorithms. Another major aspect of this work is the design of a novel quad-array transducer that can collect image sequences from up to four tendons simultaneously. This transducer is instrumental to the motivation for controlling an advanced prosthesis. As well, specialized hardware is designed for the cadaver-based studies. Overall, this thesis successfully demonstrated the proposed tracking algorithms and newly designed hardware, for tracking the displacement of single and multiple flexor tendons. It has provided several important contributions to the field. / Graduate / 0548 / 0986 / 0760

Beginning with the introduction of electrically powered prostheses more than 65 years ago surface electromyographic (sEMG) signals recorded from residual muscles in amputated limbs have served as the primary source of upper-limb myoelectric prosthetic control. The majority of these devices use one or more neural interfaces to translate the sEMG signal amplitude into voltage control signals that drive the mechanical components of a prosthesis. In so doing, users are able to directly control the speed and direction of prosthetic actuation by varying the level of muscle activation and the associated sEMG signal amplitude. Consequently, in spite of decades of development, myoelectric prostheses are prone to highly variable functional control, leading to a relatively high-incidence of prosthetic abandonment among 23-35% of upper-limb amputees. Efforts to improve prosthetic control in recent years have led to the development and commercialization of neural interfaces that employ pattern recognition of sEMG signals recorded from multiple locations on a residual limb to map different intended movements. But while these advanced algorithms have made strident gains, there still exists substantial need for further improvement to increase the reliability of pattern recognition control solutions amongst the variability of muscle co-activation intensities. In efforts to enrich the control signals that form the basis for myoelectric control, I have been developing advanced algorithms as part of a next generation neural interface research and development, referred to as Motor Unit Drive (MU Drive), that is able to non-invasively extract the firings of individual motor units (MUs) from sEMG signals in real-time and translate the firings into smooth biomechanically informed control signals. These measurements of motor unit firing rates and recruitment naturally provide high-levels of motor control information from the peripheral nervous system for intact limbs and therefore hold the greater promise for restoring function for amputees. The goal for my doctoral work was to develop advanced algorithms for the MU Drive neural interface system, that leverage MU features to provide intuitive control of multiple degrees-of-freedom. To achieve this goal, I targeted 3 research aims: 1) Derive real-time MU-based control signals from motor unit firings, 2) Evaluate feasibility of motor unit action potential (MUAP) based discrimination of muscle intent 3) Design and evaluate MUAP-based motion Classification of motions of the arm and hand.

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