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Proceedings ArticleDOI

Low-Cost Gesture Detector Using Neural Networks

04 Apr 2014-pp 41-45

TL;DR: This project proposes a novel low cost approach to gesture recognition using neural networks for end user applications control in contrary to more expensive devices prevalent in the market.
Abstract: This project proposes a novel low cost approach to gesture recognition using neural networks for end user applications control in contrary to more expensive devices prevalent in the market. The final outcome is proposed to be a versatile product with variety of applications which can cater to many user needs. In fact, a person with minimal programming knowledge can design an application with the proposed and prototyped device. The use of neural networks has made this possible, as the pattern recognition of neural networks is highly accurate than conventional programming.
References
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Journal ArticleDOI
Abstract: Clumsy intermediary devices constrain our interaction with computers and their applications. Glove-based input devices let us apply our manual dexterity to the task. We provide a basis for understanding the field by describing key hand-tracking technologies and applications using glove-based input. The bulk of development in glove-based input has taken place very recently, and not all of it is easily accessible in the literature. We present a cross-section of the field to date. Hand-tracking devices may use the following technologies: position tracking, optical tracking, marker systems, silhouette analysis, magnetic tracking or acoustic tracking. Actual glove technologies on the market include: Sayre glove, MIT LED glove, Digital Data Entry Glove, DataGlove, Dexterous HandMaster, Power Glove, CyberGlove and Space Glove. Various applications of glove technologies include projects into the pursuit of natural interfaces, systems for understanding signed languages, teleoperation and robotic control, computer-based puppetry, and musical performance. >

728 citations


Journal ArticleDOI
TL;DR: A hand gesture recognition system to recognize continuous gesture before stationary background consisting of a real time hand tracking and extraction, feature extraction, hidden Markov model (HMM) training, and gesture recognition.
Abstract: In this paper, we introduce a hand gesture recognition system to recognize continuous gesture before stationary background. The system consists of four modules: a real time hand tracking and extraction, feature extraction, hidden Markov model (HMM) training, and gesture recognition. First, we apply a real-time hand tracking and extraction algorithm to trace the moving hand and extract the hand region, then we use the Fourier descriptor (FD) to characterize spatial features and the motion analysis to characterize the temporal features. We combine the spatial and temporal features of the input image sequence as our feature vector. After having extracted the feature vectors, we apply HMMs to recognize the input gesture. The gesture to be recognized is separately scored against different HMMs. The model with the highest score indicates the corresponding gesture. In the experiments, we have tested our system to recognize 20 different gestures, and the recognizing rate is above 90%.

487 citations


Proceedings ArticleDOI
Rung-Huei Liang, Ming Ouhyoung1Institutions (1)
14 Apr 1998
TL;DR: A large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a data glove using hidden Markov models for 51 fundamental postures, 6 orientations, and 8 motion primitives.
Abstract: A large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a data glove. Sign language, which is usually known as a set of natural language with formal semantic definitions and syntactic rules, is a large set of hand gestures that are daily used to communicate with the hearing impaired. The most critical problem, end-point detection in a stream of gesture input is first solved and then statistical analysis is done according to four parameters in a gesture: posture, position, orientation, and motion. The authors have implemented a prototype system with a lexicon of 250 vocabularies and collected 196 training sentences in Taiwanese Sign Language (TWL). This system uses hidden Markov models (HMMs) for 51 fundamental postures, 6 orientations, and 8 motion primitives. In a signer-dependent way, a sentence of gestures based on these vocabularies can be continuously recognized in real-time and the average recognition rate is 80.4%,.

433 citations


Journal ArticleDOI
Ruize Xu1, Shengli Zhou2, Wen J. Li2Institutions (2)
TL;DR: A recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.
Abstract: This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle, and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally, the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.

212 citations


"Low-Cost Gesture Detector Using Neu..." refers methods in this paper

  • ...In [1], three MEMS (Micro-Electro Mechanical System) based accelerometers have been used to detect non-user specific hand gestures and an algorithm developed to classify the gestures in a personal computer....

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Journal ArticleDOI
Chan Wah Ng1, Surendra Ranganath1Institutions (1)
TL;DR: A vision-based system that can interpret a user's gestures in real time to manipulate windows and objects within a graphical user interface and users who tested it found the gestures intuitive and the application easy to use.
Abstract: In this paper, we consider a vision-based system that can interpret a user's gestures in real time to manipulate windows and objects within a graphical user interface. A hand segmentation procedure first extracts binary hand blob(s) from each frame of the acquired image sequence. Fourier descriptors are used to represent the shape of the hand blobs, and are input to radial-basis function (RBF) network(s) for pose classification. The pose likelihood vector from the RBF network output is used as input to the gesture recognizer, along with motion information. Gesture recognition performances using hidden Markov models (HMM) and recurrent neural networks (RNN) were investigated. Test results showed that the continuous HMM yielded the best performance with gesture recognition rates of 90.2%. Experiments with combining the continuous HMMs and RNNs revealed that a linear combination of the two classifiers improved the classification results to 91.9%. The gesture recognition system was deployed in a prototype user interface application, and users who tested it found the gestures intuitive and the application easy to use. Real time processing rates of up to 22 frames per second were obtained.

153 citations