Real-time Machine Intelligence for Assistive Technology

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During every-day life, humans leverage a detailed stream of sensation and actuation to interpret and manipulate the world around them. However, when a body part is lost or damaged—for example, through accident, illness, or birth complications—an individual can suffer a significant reduction in their ability to interact with the world and, as a consequence, reduced quality of life. New computing methods and assistive technologies are essential to augmenting and restoring an injured individual's sensory and motor abilities. My proposed research program focuses on a concrete example of this theme: using machine intelligence to create richer and more powerful interfaces to bridge the gap between a human and a robotic artificial limb that is affixed to their body. Designers of assistive robots of this kind face a number of challenges, perhaps the most significant of which is the development of natural, flexible control and communication methods that connect users to their assistive devices. It is apparent that traditional approaches to control and communication will become insufficient when faced with an increasing repertoire of diverse, data-dense sensor and actuation technologies. The literature also indicates that artificial limbs are in need of adaptive control systems—interfaces that adapt to both the unique factors in each individual and the day-to-day changes that occur in the user and their environment.