Movement intention detection for intuitive and non-intrusive prosthetic arm control

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Prosthetic arms have the potential to improve the lives of individuals who have lost arms. Yet their functioning is limited as they are not connected to the human neural system. My goal is to design and improve the interface between a prosthetic arm and the user by detecting human movement intention. When performing a goal-directed movement, humans employ their eyes and other sensory information for guiding arm movement. Comprehensive signals from both eyes and arm muscles would help us to detect movement intention. For example, during a reach-and-grasp movement, we firstly use our eyes to scan the environment, identifying a target, extracting its location, size, shape and texture, and then reaching and grabbing. However, most previous algorithms for motion intention detection were based on muscle activities and brain signals. To the best of our knowledge, utilizing eye-metrics for improving prosthesis control has rarely been explored. This knowledge gap needs to be filled in terms of how eye-motions correlate to movement intention and how fusion of eye-metrics and arm muscles signals can be employed to improve prosthesis control. The long-term objective of my research program is to advance assistive devices including human prosthesis through the inclusion of non-invasive and affordable technologies, and by improving the detection of movement intention of the users for better control of the artificial body part. My short-term objectives (within the next 5 years) are to 1) develop a prosthetic arm sock with sensors to collect muscle activities data, 2) examine the relationship between muscle and eye signals to movement intention, 3) improve grasp detection using multiple module inputs including eye-tracking technology, 4) accurately estimate finger movement using machine learning from multiple channels of bio-signals and by integrating eye-tracking (pupil diameter), and 5) develop a machine learning model to accurately predict a series of movement intentions during a sequential goal-directed task. Sensor fusion technologies will also be implemented throughout my research to utilize the advantages of multiple modules input. This work will generate important tools to convert eye motion and muscle activity signals into movement intention, which is an innovative approach to movement intention detection for intuitive prosthesis control. Prosthetic devices have been greatly improved in recent years whereas man-machine interfaces for prosthesis control do not fulfill the device potential. Bridging this gap would greatly advance prosthesis systems and improve quality of life among users. My research will benefit both the large number of people who have lost limbs and the prosthesis industry (increase in jobs and research funding). I expect that the knowledge generated from this work will enable us to design a smart prosthetic arm and further improve our understanding of human-machine/robot interaction, remote manipulation and assistive device control.