Publication: Stochastic kinematic modeling and feature extraction for gait analysis
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KU Authors
Co-Authors
Dockstader, Shiloh L.
Berg, Michel J.
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Embargo Status
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Abstract
This research presents a new model-based approach toward the three-dimensional (3-D) tracking and extraction of gait and human motion. We suggest the use of a hierarchical, structural model of the human body that introduces the concept of soft kinematic constraints. These constraints take the form of a priori, stochastic distributions learned from previous configurations of the body exhibited during specific activities; they are used to supplement an existing motion model limited by hard kinematic constraints. We use time-varying parameters of the structural model to measure gait velocity, stance width, stride length, stance times, and other gait variables with multiple degrees of accuracy and robustness. To characterize tracking performance, we also introduce a novel geometric model of expected tracking failures. We demonstrate and quantify the performance of the suggested models using multi-view, video sequences of human movement captured in a complex home environment.
Source
Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Subject
Computer Science, Artificial intelligence, Electrical electronics engineering
Citation
Has Part
Source
IEEE Transactions on Image Processing
Book Series Title
Edition
DOI
10.1109/TIP.2003.815259