Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction

Abstract This paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process.