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S.: Attractor-Shape for Dynamical Analysis of Human Movement

By Vinay Venkataraman, Pavan Turaga, Nicole Lehrer, Michael Baran, Thanassis Rikakis and Steven L. Wolf


In this paper, we propose a novel shape-theoretic frame-work for dynamical analysis of human movement from 3D data. The key idea we propose is the use of global descrip-tors of the shape of the dynamical attractor as a feature for modeling actions. We apply this approach to the novel ap-plication scenario of estimation of movement quality from a single-marker for future usage in home-based stroke re-habilitation. Using a dataset collected from 15 stroke sur-vivors performing repetitive task therapy, we demonstrate that the proposed method outperforms traditional methods, such as kinematic analysis and use of chaotic invariants, in estimation of movement quality. In addition, we demon-strate that the proposed framework is sufficiently general for the application of action and gesture recognition as well. Our experimental results reflect improved action recogni-tion results on two publicly available 3D human activity databases. 1

Year: 2013
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