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Metaxas, Kapadia and Li awarded NSF CHS grant

Wednesday, April 19, 2017

Congratulations to Dimitri Metaxas, Mubbasir Kapadia, and their collaborator Kang Li (Industrial and Systems Engineering) on their new NSF grant:

"CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain,”

Size: $1,183,465 for 3 years.

Project Overview:

Modeling human gait parametrically, efficiently and accurately is an open and challenging problem with many applications such as ergonomics, animation, biomechanics, rehabilitation, physical therapy, virtual reality and entertainment. Gait is a very complex process because a large number of joint degrees of freedom have to be simultaneously coordinated and adapted to varying terrain, types of gait and related kinematics and dynamics. To date, however, no efficient, accurate, general-purpose models of gait kinematics on unconstrained complex terrains exist. In addition, even basic kinematic gait databases and related ground forces that can form the basis for developing such models do not provide accurate information. Most gait data are collected by the commonly used surface marker systems which suffer from soft tissue artifacts and are not sufficient for the full understanding of gait kinematics. The primary goal of this proposal is to develop an efficient, accurate, and general purpose parameterized model of human gait kinematics that can be used in the above applications. Our approach will use novel gait data and ground forces on unconstrained terrains, procedural gait modeling techniques, biomechanical constraints, and efficient optimization methods. Procedural methods will be used to generalize the gait data for new terrains while biomechanical constraints and gait manifolds will be used to reduce the search space of possible joint configurations. Gait models will cover a velocity space from very slow shuffling to normal walking on complex terrain. A secondary goal is to develop reliable methods to track accurately human gait motions. Our motion gait data will be collected using an innovative method, which can measure the gross whole body movement and the in vivo bone movement of body joints of interest with high accuracy. These data and associated ground forces will then be integrated to derive detailed gait kinematics and associated manifolds.