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

Bilal, Sara Mohammed Osman Saleh; Akmeliawati, Rini; Shafie, Amir Akramin; Salami, Momoh Jimoh Eyiomika

doi:10.1007/s10462-011-9292-0

Cited by 37 publications

(16 citation statements)

References 48 publications

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“…In [28], forward algorithm of HMM uses test frame T and standard frame S. It compares the two frames to find the probability of the given sequence. It compares State transition matrix with confusion matrix using forward algorithm [29] [30]. If the result of comparison satisfies the given requirement, then it is successful gesture recognition otherwise non gesture phase.…”

Section: B Hmmmentioning

confidence: 99%

Research trends in Hand Gesture Recognition techniques

Srividya,

2020

IJRTE

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Over years' key board and mouse are among the most effective input devices to the computers. Not much improvement has happened for other modes of inputs like voice and gestures. These kinds of inputs explore the limitations caused by the key board and mouse, which needs actual physical contact. With gestures and voice the physical proximity can be little relaxed and can be used by specially-abled people. Exploring effective Human Computer Interactions (HCI) has always been of great interest for development of new techniques and methods. Gestures are considered as more easy, natural, and intuitive method for communicating with systems. Hand recognition and analysis forms the core of these systems. This paper provides comparative study on state of art technologies that work in this area. The different representation, detection and recognition techniques are discussed. The strength and challenges of different techniques are highlighted. The main aim of this research is to summarize the research progress done till date with different technologies for gesture recognition algorithms, identify areas which need further research.

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Section: B Hmmmentioning

confidence: 99%

Research trends in Hand Gesture Recognition techniques

Srividya,

2020

IJRTE

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“…The cardinality of a dictionary for video gesture recognition systems is often less than 100 [24], [25].…”

Section: A Gesture Databasementioning

confidence: 99%

Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition

et al. 2016

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The most popular systems for automatic sign language recognition are based on vision. They are user-friendly, but very sensitive to changes in regards to recording conditions. This article presents a description of the construction of a more robust system -an accelerometer glove -as well as its application in the recognition of sign language gestures. The basic data regarding inertial motion sensors and the design of the gesture acquisition system as well as project proposals are presented. The evaluation of the solution presents the results of the gesture recognition attempt by using a selected set of sign language gestures with a described method based on HMM and Parallel HMM approaches. The proposed usage of Parallel HMM for sensor-fusion modeling reduced the equal error rate by more than 60%, while preserving 99.75% recognition accuracy.

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“…For example, Peng and Qian [19] proposed a framework for online gesture spotting from visual hull data using HMM. In addition, many efforts have also been made to address more complicated recognition problems by extending the conventional HMM approaches to more advanced ones, such as continuous HMM, discrete HMM, partial HMM, coupled HMM, parallel HMM, and parametric HMM [20]. In spite of their success in gesture recognition, HMM-based methods suffer from a problem called key gesture recognition [17], which refers to the discrimination of a meaningful gesture from non-gestures or garbage gestures.…”

Section: Probability Statistic-based Approachesmentioning

confidence: 99%

Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

Wu¹,

Wang

Zhang

2015

Vis Comput

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In the design of gesture-based user interfaces, continuously recognizing complex dynamic gestures is a challenging task, because of the high-dimensional information of gestures, ambiguous semantic meanings of gestures, and the presence of unpredictable non-gesture body motions. In this paper, we propose a hybrid model that can leverage the time-series modeling ability of hidden Markov model and the fuzzy inference ability of fuzzy neural network. First, a complex dynamic gesture is decomposed and fed into the hybrid model. The likelihood probability of an observation sequence estimated by the hidden Markov model is used as fuzzy membership degree of the corresponding fuzzy class variable in fuzzy neural network. Next, fuzzy rule modeling and fuzzy inference are performed by fuzzy neural network for gesture classification. To spot key gestures accurately, a threshold model is introduced to calculate the likelihood threshold of an input pattern and provide a reliability measure of whether to accept the pattern as a gesture. Finally, the proposed method is applied to recognize ten user-defined dynamic gestures for controlling interactive digital television in a smart room. Results of our experiment show that B Huiyue Wu the proposed method performed better in terms of spotting reliability and recognition accuracy than conventional gesture recognition methods.

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

Cited by 37 publications

References 48 publications

Research trends in Hand Gesture Recognition techniques

Research trends in Hand Gesture Recognition techniques

Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition

Combining hidden Markov model and fuzzy neural network for continuous recognition of complex dynamic gestures

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