Pattern Recognition Based Myoelectric Control for Emerging Human-Machine Interfaces

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Rapid changes in the availability and utility of technology have left society in a state of constant adaptation. Now realizing the dangers of sedentary lifestyles and pervasive computer and mobile device use, many are scrambling to find alternatives. With the advent of emerging heads-up virtual and augmented reality devices, there is growing demand for hands-free interaction approaches that could enable interaction on the go. For decades, myoelectric control, the use of electrical signals generated during muscle contractions to decode user intent, has been used to control prostheses for amputees. Recently, advances in technology, machine learning, and user training have revolutionized the way myoelectric control is achieved, enabling more intuitive control of multiple degrees of freedom. In comparison, however, myoelectric control has been relatively unexplored as a potential human-machine interface for use with other consumer products. Those who have pursued this direction have mostly directly applied the knowledge from the body of prosthetics research, limiting its effectiveness. Consequently, there are many outstanding questions about the potential, robustness and intuitiveness of myoelectric control for consumer use. In this work, we will develop new human-machine interaction models and devices specifically designed for use with emerging consumer hands-free technologies. As the Innovation Research Chair at the Institute of Biomedical Engineering, a world-renowned leader and influencer in the field of myoelectric control, we will leverage lessons learned in developing the state-of-the-art in myoelectric control for prosthetics, while designing for this very different use-case. We will determine appropriate gesture sets that balance intuitiveness for the application with the information content and robustness of the control scheme and improve the training and learning process by considering the user as part of the control system. Finally, we will explore alternative sensors and the role of sensor fusion in developing versatile and robust hands-free human-machine interfaces. This work will present engaging multidisciplinary training opportunities for students and develop highly sought-after skillsets in problem solving, critical thinking, signal processing, machine learning, and human machine interaction. The results of this program will produce foundational research that will broaden the applications for myoelectric control and inform the design of novel technologies. The development of intuitive, hands-free human machine interfaces will reduce the restrictions and risks associated with current work environments.