Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
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Date
2013-12-01
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Netherlands
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 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.
Description
Keywords
HCI applications, HMM, Artificial intelligence, Hand posture recognition, Hand gesture recognition
Citation
Bilal, 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.