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Journal ArticleDOI

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

Sara Mohammed Osman Saleh Bilal, +3 more
- 01 Dec 2013 - 
- Vol. 40, Iss: 4, pp 495-516
TLDR
A survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications is provided.
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.

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Citations
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Journal ArticleDOI

Gesture recognition for human-robot collaboration: A review

Hongyi Liu, +1 more
- 06 Mar 2017 - 
TL;DR: In this article, an overall model of gesture recognition for human-robot collaboration is also proposed, including sensor technologies, gesture identification, gesture tracking, gesture classification, and gesture classification.
Journal ArticleDOI

Lane changing intention recognition based on speech recognition models

Keqiang Li, +3 more
- 01 Aug 2016 - 
TL;DR: A novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering techniques to recognize a driver’s lane changing intention and can achieve a recognition accuracy of 93.5% and 90.3% which is a significant improvement compared with the HMM-only algorithm.
Journal ArticleDOI

Human motion prediction for human-robot collaboration

Hongyi Liu, +1 more
- 01 Jul 2017 - 
TL;DR: In this article, a human-robot collaborative assembly system is proposed to predict human workers' intention and assist human during assembly operations, where a hidden Markov model is used to generate a motion transition probability matrix.
Journal ArticleDOI

Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition

Jakub Galka, +3 more
- 22 Jun 2016 - 
TL;DR: The construction of a more robust system-an accelerometer glove-as well as its application in the recognition of sign language gestures with a described method based on Hidden Markov Model (HMM) and parallel HMM approaches are presented.
Journal ArticleDOI

Dynamic Gesture Recognition in the Internet of Things

Gongfa Li, +4 more
- 01 Jan 2019 - 
TL;DR: The Kinect-based gesture recognition is analyzed in detail, and a dynamic gesture recognition method based on HMM and D-S evidence theory is proposed, which lays a good foundation for human–computer interaction under the IoTs technology.
References
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Journal ArticleDOI

A tutorial on hidden Markov models and selected applications in speech recognition

Lawrence R. Rabiner
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI

C ONDENSATION —Conditional Density Propagation forVisual Tracking

Michael Isard, +1 more
- 01 Aug 1998 - 
TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.
Journal ArticleDOI

Gesture Recognition: A Survey

Sushmita Mitra, +1 more
TL;DR: A survey on gesture recognition with particular emphasis on hand gestures and facial expressions is provided, and applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail.
Proceedings ArticleDOI

Recognizing human action in time-sequential images using hidden Markov model

Junji Yamato, +2 more
TL;DR: The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer.
Proceedings ArticleDOI

Real-time American Sign Language recognition from video using hidden Markov models

Thad Starner, +1 more
TL;DR: A real-time HMM-based system for recognizing sentence level American Sign Language (ASL) which attains a word accuracy of 99.2% without explicitly modeling the fingers.