• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.186-192
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• 2021

In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1337-1346
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• 2005

In this paper, we describe an algorithm which can automatically recognize human gesture for Human-Robot interaction. In early works, many systems for recognizing human gestures work under many restricted conditions. To eliminate these restrictions, we have proposed the method that can represent 3D and 2D gesture information simultaneously, APM. This method is less sensitive to noise or appearance characteristic. First, the feature vectors are extracted using APM. The next step is constructing a gesture space by analyzing the statistical information of training images with PCA. And then, input images are compared to the model and individually symbolized to one portion of the model space. In the last step, the symbolized images are recognized with HMM as one of model gestures. The experimental results indicate that the proposed algorithm is efficient on gesture recognition, and it is very convenient to apply to humanoid robot or intelligent interface systems.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.51-56
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• 2023

Hand gesture recognition is an essential technology for the people who have difficulties using spoken language to communicate. Electromyogram (EMG), which is often utilized for hand gesture recognition, is expected to have difficulties in hand gesture recognition because its people's movements varies depending on prior postures, but the study on this subject is rare. In this study, we conducted tests to confirm if the prior postures affect on the accuracy of gesture recognition. Data were recorded from 20 subjects with different prior postures. We achieved average accuracies of 89.6% and 52.65% when the prior states between the training and test data were unique and different, respectively. The accuracy was increased when both prior states were considered, which confirmed the need to consider a variety of prior states in hand gesture recognition with EMG.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.3
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• pp.143-150
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• 2015

As smart devices get popular, research on gesture recognition using their embedded-accelerometer draw attention. As Dynamic Time Warping(DTW), recently, has been used to perform gesture recognition on data sequence from accelerometer, in this paper we propose Feature-Strengthened Gesture Recognition(FsGr) Model which can improve the recognition success rate when DTW is used. FsGr model defines feature-strengthened parts of data sequences to similar gestures which might produce unsuccessful recognition, and performs additional DTW on them to improve the recognition rate. In training phase, FsGr model identifies sets of similar gestures, and analyze features of gestures per each set. During recognition phase, it makes additional recognition attempt based on the result of feature analysis to improve the recognition success rate, when the result of first recognition attempt belongs to a set of similar gestures. We present the performance result of FsGr model, by experimenting the recognition of lower case alphabets.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.503-510
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• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.518-520
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• 2021

In this paper, we propose a hand gesture recognition method by modifying the textNAS used for text classification so that it can be applied to multivariate time series data. It can be applied to various fields such as behavior recognition, emotion recognition, and hand gesture recognition through multivariate time series data classification. In addition, it automatically finds a deep learning model suitable for classification through training, thereby reducing the burden on users and obtaining high-performance class classification accuracy. By applying the proposed method to the DHG-14/28 and Shrec'17 datasets, which are hand gesture recognition datasets, it was possible to obtain higher class classification accuracy than the existing models. The classification accuracy was 98.72% and 98.16% for DHG-14/28, and 97.82% and 98.39% for Shrec'17 14 class/28 class.

• Smart Media Journal
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• v.5 no.3
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• pp.35-41
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• 2016

In this paper, we propose hand-gesture signal recognition based on EPS(Electronic Potential Sensor) using Deep learning model. Extracted signals which from Electronic field based sensor, EPS have much of the noise, so it must remove in pre-processing. After the noise are removed with filter using frequency feature, the signals are reconstructed with dimensional transformation to overcome limit which have just one-dimension feature with voltage value for using convolution operation. Then, the reconstructed signal data is finally classified and recognized using multiple learning layers model based on deep learning. Since the statistical model based on probability is sensitive to initial parameters, the result can change after training in modeling phase. Deep learning model can overcome this problem because of several layers in training phase. In experiment, we used two different deep learning structures, Convolutional neural networks and Recurrent Neural Network and compared with statistical model algorithm with four kinds of gestures. The recognition result of method using convolutional neural network is better than other algorithms in EPS gesture signal recognition.

• Smart Media Journal
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• v.5 no.2
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• pp.15-23
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• 2016

In this paper we propose the depth-poselets using body-part-poses and also propose the method to recognize the gesture. Since the gestures are composed of sequential poses, in order to recognize a gesture, it should emphasize to obtain the time series pose. Because of distortion and high degree of freedom, it is difficult to recognize pose correctly. So, in this paper we used partial pose for obtaining a feature of the pose correctly without full-body-pose. In this paper, we define the 16 gestures, a depth image using a learning image was generated based on the defined gestures. The depth poselets that were proposed in this paper consists of principal three-dimensional coordinates of the depth image and its depth image of the body part. In the training process after receiving the input defined gesture by using a depth camera in order to train the gesture, the depth poselets were generated by obtaining 3D joint coordinates. And part-gesture HMM were constructed using the depth poselets. In the testing process after receiving the input test image by using a depth camera in order to test, it extracts foreground and extracts the body part of the input image by comparing depth poselets. And we check part gestures for recognizing gesture by using result of applying HMM. We can recognize the gestures efficiently by using HMM, and the recognition rates could be confirmed about 89%.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.3
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• pp.92-97
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• 2011

In this paper, a controller recognizing human gestures using EMG signal is shown. The tiny and band-type controller is developed for image training to excercise the specific area in the body, and uses a dry-type silver fiber electrode easy to be attached or detached itself to a skin. The captured EMG signals are converted to 10-bit digital values via amplification and frequency filtering processes within the controller, and are transmitted to the server by wireless. As the gesture recognition ratio using the proposed controller on biceps is up to 80%, we expect the practical potential of the controller is very promising.