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Title: Bayesian nonparametric methods for dynamics identification and segmentation for powered prosthesis control
Author: Dhir, Neil
ISNI:       0000 0004 7652 4046
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2017
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Robots need to be able to adapt to their surroundings. Robots whose core function relates to the rehabilitation and assistance of humans, need to be able to adapt to humans. But not all humans, one human in particular: their user. This is an adaptive control problem which speaks of the need for powered prostheses to have anthropomorphic adaptive capabilities. However, it is inconceivable that all possible movements, dynamics and tasks can be preprogrammed into such a system. Prostheses, like robots, need to be able to learn and improve, either by themselves unsupervised, or with the help of human supervision. The United States alone is home to more than two million amputees, where below-knee amputations is the most common form. The same country is also home to 4.7 million stroke survivors, many of whom could make use of rehabilitation robots. Vascular diseases, such as diabetes, cause 54% of all amputations and the number of diabetes and pre-diabetes cases in the United States currently exceeds 100 million and growing. Whilst this thesis does not attack the root causes, it does present a three pronged approach for framing the adaptive control problem required for effective rehabilitation. We develop methodology that structures the problem into one of learning a control framework for lower extremity active prosthetics. First, we consider incidence detection as a key element of any control strategy. We study the dynamics of falling under the aegis of classification and state-space modelling. We use the standard Kalman smoother to inpaint missing observations. Then dimensionally reduce the feature space to demonstrate increased classification accuracy, compared with our reference study. Secondly, we move to the temporal segmentation of similar time-series. This is done using the Bayesian nonparametric paradigm within the framework of state-space modelling and probabilistic programming. Models are rarely correct for real world data. Rather than comparing models that vary in complexity, this approach fits a single model that can adapt its complexity to the observations. This unbounded analysis of the state-space is required given the need for the proposed control framework to grow with new observations. Finally, we investigate multiple incidents of walking velocity, and propose a learned control strategy able to smoothly transition between the incidents. We combine Gaussian process regression with impedance control, and gait-cycle regression to form a locomotion envelope. The learned control capabilities allow the wearer to smoothly transition between self-selected velocities.
Supervisor: Wood, Frank ; Posner, Ingmar ; Osborne, Michael Sponsor: CDT in Healthcare Innovation
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Robotics ; Bayesian nonparametrics ; Machine learning