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

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dc.contributor.authorBilal, Sara
dc.contributor.authorAkmeliawati, Rini
dc.contributor.authorShafie, Amir A.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.date.accessioned2019-10-17T08:58:01Z
dc.date.available2019-10-17T08:58:01Z
dc.date.issued2013-12-01
dc.description.abstractHuman 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 anymovement 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.en_US
dc.identifier.citationBilal, S., Akmeliawati, R., Shafie, A. A., & Salami, M. J. E. (2013). Hidden Markov model for human to computer interaction: a study on human hand gesture recognition. Artificial Intelligence Review, 40(4), 495-516.en_US
dc.identifier.uri10.1007/s10462-011-9292-0
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/567
dc.language.isoenen_US
dc.publisherSpringer Netherlandsen_US
dc.subjectHCI applicationsen_US
dc.subjectHMMen_US
dc.subjectArtificial intelligenceen_US
dc.subjectHand posture recognitionen_US
dc.subjectHand gesture recognitionen_US
dc.titleHidden Markov model for human to computer interaction: a study on human hand gesture recognitionen_US
dc.typeArticleen_US
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