Modeling and identification of emotional aspects of locomotion |
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| Institution: | 1. Department of Neurology, School of Medicine, University of Wisconsin, Madison, WI, USA;2. Department of Anesthesiology, School of Medicine, University of Wisconsin, Madison, WI, USA;3. Department of Psychiatry, School of Medicine, Washington University, St Louis, WA, USA;4. Wisconsin National Primate Research Center, Madison, WI, USA;5. Department of Anesthesiology, Columbia University, New York Presbyterian Hospital, Irving Medical Center, New York, NY, USA;1. Institute for Supply Chain Management, EBS University, Burgstrasse 5, Oestrich-Winkel, 65375 Germany;2. Poole College of Management, NC State University, Raleigh, NC 27695-7229, USA;1. Département de génie informatique et génie logiciel École Polytechnique Montréal, Montréal Québec H3T 1J4, Canada;2. Département d’informatique et de recherche opérationnelle Université de Montréal, Montréal, Québec H3C 3J7, Canada |
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| Abstract: | The studies of emotional facial expressions and emotional body language are currently receiving a lot of attention in the cognitive sciences. In this project, we study implicit bodily expression of emotions during standard motions, such as walking forwards.An underlying assumption of our work is that all human motion is optimal in some sense and that different emotions induce different objective functions, which result in different deformations of normal motion.We created a 3D rigid-body model of a human of which we use the forward dynamics simulation in an optimal control context. We performed two kinds of optimizations: (i) reconstruction of dynamic quantities, such as joint torques, of pre-recorded data of emotional walking motions and (ii) forward optimization that generates neutral and varied walking motions using different objective functions. Optimizations are performed with the software package MUSCOD-II, which uses a direct multiple-shooting discretization scheme. The results of this work form the foundation for further analysis of emotional motions using inverse optimal control methods. |
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