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Hidden Markov model for human to computer interaction: a study on human hand gesture recognition

Research paper by Sara Bilal, Rini Akmeliawati, Amir A. Shafie, Momoh Jimoh E. Salami

Indexed on: 28 Dec '11Published on: 28 Dec '11Published in: Artificial Intelligence Review



Abstract

Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.