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.

Authors

• Salvatore Iengo
• Silvia Rossi 0002
• Mariacarla Staffa
• Alberto Finzi

Keywords

• Hidden Markov models
• Robots
• Gesture recognition
• Training
• Human-robot interaction
• Data models
• Vectors
• Flexible Interaction
• Continuous Recognition
• Continuous Gesture
• Continuous Gesture Recognition
• Training Set
• Recognizable
• Continuous Process
• Hidden Markov Model
• Recognition Process
• Interactive
• Usability
• True Positive
• Vector Control
• Transition Probabilities
• Simple Task
• Search String
• State Machine
• Posterior Density
• Gesture Classification
• Recognition Phase
• Robotic Arm
• Small Training Set
• Levenshtein Distance
• Time Stamp
• Qualitative Performance
• Position Estimation
• Probability Matrix