Gait modelling for a context-aware user-adaptive robotic assistant platform

Abstract

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 development of an efficient leg tracker and the recognition of pathological gait are very important. In this work, we present the basic concept for the robot control 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 assessment. 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 commonly 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. I.