Aaron M. Bestick

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In a game-theoretic framework, given parametric agent utility functions, we solve the inverse problem of computing the feasible set of utility function parameters for each individual agent, given that they play a correlated equilibrium strategy. We model agents as utility maximizers, then cast the problem of computing the parameters of players' utility(More)
— We present a framework for parameter and state estimation of personalized human kinematic models from motion capture data. These models can be used to optimize a variety of human-robot collaboration scenarios for the comfort or ergonomics of an individual human collaborator. Our approach offers two main advantages over prior approaches from the literature(More)
Human behavioral interventions aimed at improving health can benefit from objective wearable sensor data and mathematical models. Smartphone-based sensing is particularly practical for monitoring behavioral patterns because smartphones are fairly common, are carried by individuals throughout their daily lives, offer a variety of sensing modalities, and can(More)
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