Motion intention recognition in robot assisted applications

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. The problems is challenging mainly because of the lack of a general mathematical model to describe human skills. One of the common approaches is to divide the task that the operator is executing into several subtasks or low-level subsystems in order to provide manageable modeling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gesteme classifier that classifies motions into basic actionprimitives, or gestemes. The gesteme classifiers are then used in a LHMM to model a teleoperated task. The proposed methodology uses three different HMM models at the gesteme level: one-dimensional HMM, multi-dimensional HMM and multi-dimensional HMM with Fourier transform. The online and off-line classification performance of these three models is evaluated with respect ...

• Type: Journal
• Year: 2008
• Where: RAS
• Authors: Daniel Aarno, Danica Kragic
• Keywords: Gesteme Classifiers, Hidden Markov Models, Human Skills, RAS 2008