Continuous gesture recognition for flexible human-robot interaction

In this work, we present a reliable and continuous gesture recognition method that supports a natural and flexible interaction between the human and the robot. The aim is to provide a system that can be trained online with few samples and can cope with intra user variability during the gesture execution. The proposed approach relies on the generation of an ad-hoc Hidden Markov Model (HMM) for each gesture exploiting a direct estimation of the parameters. Each model represents the best prototype candidate from the associated gesture training set. The generated models are then employed within a continuous recognition process that provides the probability of each gesture at each step. The proposed method is evaluated in two case studies: a hand-performed letters recognizer and a natural gesture recognizer. Finally, we show the overall system at work in a simple human-robot interaction scenario.