Motion primitives for designing flexible gesture set in Human-Robot Interface
This paper proposes motion primitives for designing a gesture set in a gesture recognition system as Human-Robot Interface (HRI). Based on statistical analyses of angular tendency of hand movements in sign languages and hand motions in practical gestures, we construct four motion primitives as building blocks for basic hand motions. By combining these motion primitives, we design a discernable `fundamental hand motion set' toward improving machine based hand signal recognition. Novelty of combining the proposed motion primitives is demonstrated by a `fundamental hand motion set' recognizer based on Hidden Markov Model (HMM). The recognition system shows 99.40% recognition rate on the proposed language set. For connected recognition of the `fundamental motion set', the recognition system shows 97.95% recognition rate. The results validate that using the proposed motion primitives ensures flexibility and discernability of a gesture set. It is thus promising candidate for standardization when designing gesture sets for human-robot interface.