2015
IMAACA 2015 Proceeding
Gait modelling for a context-aware user-adaptive robotic assistant platform
Authors: Georgia G. Chalvatzaki, Xanthi S. Papageorgiou, Costas S. Tzafestas
Abstract
For a context-aware robotic assistant platform that follows patients with moderate mobility impairment and adapts its motion to the patient?s needs, the de- velopment of an efficient leg tracker and the recogni- tion of pathological gait are very important. In this work, we present the basic concept for the robot con- trol architecture and analyse three essential parts of the Adaptive Context-Aware Robot Control scheme; the detection and tracking of the subject?s legs, the gait modelling and classification and the computation of gait parameters for the impairment level assess- ment. We initially process raw laser data and estimate the legs? position and velocity with a Kalman Filter and then use this information as input for a Hidden Markov Model-based framework that detects specific gait patterns and classifies human gait into normal or pathological. We then compute gait parameters com- monly used for medical diagnosis. The recognised gait patterns along with the gait parameters will be used for the impairment level assessment, which will activate certain control assistive actions regarding the pathological state of the patient.
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