• IEIE Transactions on Smart Processing and Computing
• /
• v.6 no.1
• /
• pp.21-26
• /
• 2017

In this paper, we suggest an automated sleep scoring method using machine learning algorithms on accelerometer data from a wristband device. For an experiment, 36 subjects slept for about eight hours while polysomnography (PSG) data and accelerometer data were simultaneously recorded. After the experiments, the recorded signals from the subjects were preprocessed, and significant features for sleep stages were extracted. The extracted features were classified into each sleep stage using five machine learning algorithms. For validation of our approach, the obtained results were compared with PSG scoring results evaluated by sleep clinicians. Both accuracy and specificity yielded over 90 percent, and sensitivity was between 50 and 80 percent. In order to investigate the relevance between features and PSG scoring results, information gains were calculated. As a result, the features that had the lowest and highest information gain were skewness and band energy, respectively. In conclusion, the sleep stages were classified using the top 10 significant features with high information gain.

• Journal of IKEEE
• /
• v.6 no.2 s.11
• /
• pp.169-177
• /
• 2002

In this paper, we introduce a new technology to extract the unique features from an iris image, which uses scale-space filtering. Resulting iris code can be used to develop a system for rapid and automatic identification of persons, with high reliability and confidence levels. First, an iris part is separated from the whole image. Then the radius and center of the iris are obtained. Once the regions that have a high possibility of being noise are discriminated, the features presented in the highly detailed pattern is then extracted from the iris image. Scale-space filtering technique is applied for feature extraction.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.1
• /
• pp.84-95
• /
• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Journal of the Ergonomics Society of Korea
• /
• v.34 no.5
• /
• pp.411-426
• /
• 2015

Objective: The goal of this thesis is to design the interaction structure and framework of system to recognize sign language. Background: The sign language of meaningful individual gestures is combined to construct a sentence, so it is difficult to interpret and recognize the meaning of hand gesture for system, because of the sequence of continuous gestures. This being so, in order to interpret the meaning of individual gesture correctly, the interaction structure and framework are needed so that they can segment the indication of individual gesture. Method: We analyze 700 sign language words to structuralize the sign language gesture interaction. First of all, we analyze the transformational patterns of the hand gesture. Second, we analyze the movement of the transformational patterns of the hand gesture. Third, we analyze the type of other gestures except hands. Based on this, we design a framework for sign language interaction. Results: We elicited 8 patterns of hand gesture on the basis of the fact on whether the gesture has a change from starting point to ending point. And then, we analyzed the hand movement based on 3 elements: patterns of movement, direction, and whether hand movement is repeating or not. Moreover, we defined 11 movements of other gestures except hands and classified 8 types of interaction. The framework for sign language interaction, which was designed based on this mentioned above, applies to more than 700 individual gestures of the sign language, and can be classified as an individual gesture in spite of situation which has continuous gestures. Conclusion: This study has structuralized in 3 aspects defined to analyze the transformational patterns of the starting point and the ending point of hand shape, hand movement, and other gestures except hands for sign language interaction. Based on this, we designed the framework that can recognize the individual gestures and interpret the meaning more accurately, when meaningful individual gesture is input sequence of continuous gestures. Application: When we develop the system of sign language recognition, we can apply interaction framework to it. Structuralized gesture can be used for using database of sign language, inventing an automatic recognition system, and studying on the action gestures in other areas.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.837-848
• /
• 2018

Monitoring technology of machining has a long history since unmanned machining was introduced. Despite the long history, many researchers have presented new approaches continuously in this area. Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sound is corrupted by the surrounding work environment. Therefore, the most important part of the diagnosis is to find hidden elements inside the data that can represent the error pattern. This paper presents a feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by tools. The magnitude spectrum of the sound is extracted using the Fourier analysis and the band-pass filter is applied to further characterize the data. Statistical functions are also used as input to the nonlinear classifier for the final response. The results prove that the proposed feature extraction method accurately captures the hidden patterns of the sound generated by the tool, unlike the conventional features. Therefore, it is shown that the proposed method can be applied to a sound based automatic diagnosis system.

• Journal of KIISE:Software and Applications
• /
• v.27 no.7
• /
• pp.712-722
• /
• 2000

In this paper, we suggest and test a classification technique of offence pattern from group formation to automatically index highlights of soccer games. A BP (Back-propagation) neural nets technique was applied to the information of the position of both the player and the ball on a ground, and the distance between the player and the ball to identify the group formation in space and time. The real soccer game scenes including '98 France World Cup were used to extract 297 video clips of various types of offence patterns; Left Running 60, Right Running 74, Center Running 72, Corner-kick 39 and Free-kick 52. The results are as follows: Left Running comes to 91.7%, Right Running 100%. Center Running 87.5%, Corner-kick 97.4% and Free-kick 75%, and these showed quite a satisfactory rate of recognition.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.1
• /
• pp.81-89
• /
• 2017

In this paper, we propose a gait pattern recognition method for intelligent prosthesis that enables walking in various environments of femoral amputees. The proposed gait mode changing method is a single sensor based algorithm which can discriminate gait surface and gait phase using only strain gauges sensor, and it is designed to simplify the algorithm based on multiple sensors of existing intelligent prosthesis and to reduce cost of prosthesis system. For the recognition algorithm, we analyzed characteristics of the ground reaction force generated during gait of normal person and defined gait step segmentation and gait detection condition, A gait analyzer was constructed for the gait experiment in the environment similar to the femoral amputee. The validity of the paper was verified through the defined detection conditions and fabricated instruments. The accuracy of the algorithm based on the single sensor was 95%. Based on the proposed single sensor-based algorithm, it is considered that the intelligent prosthesis system can be made inexpensive, and the user can directly grasp the state of the walking surface and shift the walking mode. It is confirmed that it is possible to change the automatic walking mode to switch the walking mode that is suitable for the walking mode.

• The KIPS Transactions:PartA
• /
• v.16A no.3
• /
• pp.209-216
• /
• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• The Journal of the Korea Contents Association
• /
• v.9 no.2
• /
• pp.432-438
• /
• 2009

In this paper, we evaluated the performance of the automatic classifier applied for the discrimination of acute alcoholic liver injury and chronic liver fibrosis. The classifier uses the discriminant peaks of the preprocessed Raman spectrum as a feature set. In preprocessing step, we subtract baseline and apply Savitzky-Golay smoothing filter which is known to be useful at preserving peaks. After identifying discriminant peaks from the spectra, we carried out the classification experiments using MAP and neural networks. According to the experimental results, the classifier shows the promising results to diagnosis alcoholic liver injury and chronic liver fibrosis. Classification results over 80% means that the peaks used as a feature set is useful for diagnosing liver disease.

• Journal of Korea Multimedia Society
• /
• v.17 no.3
• /
• pp.364-372
• /
• 2014

This research enables the user to have access to the desired service which is on the multi-platform display device by establishment customized Digital Signage System using the SaaS method. This system is significantly favorable due to the following points: the expandibility and portability is outstanding compared with the existing signage system, establishment expenses may be reduced because the platform can be established in various configurations independently, maintenance and management, and the strong point of the system is that costs can be reduced due to the fact that the electric power can be controlled according to environmental situations. Various researches should be conducted simultaneously such as researches on automatic pattern recognition technologies which recognizes the sex, age, location among other data of the user and various methods of image processing for the production of contents to elaborate lively contents to provide diverse experience and enjoyable configurations for the future generation.