Byoung-Ju Yun

Accelerometer Signal Processing for User Activity Detection

Estimation of human motion states is important enabling technologies for realizing a pervasive computing environment. In this paper, an improved method for estimating human states from accelerometer data is introduced. Our method for estimating human motion state utilizes various statistics of accelerometer data, such as mean, standard variation, skewness, kurtosis, eccentricity, as features for classification, and is expected to be more robust than other existing methods that rely on only a few simple statistics. A series of experiments for testing the effectiveness of the proposed method has been performed, and its result is presented.

Posture Monitoring System for Context Awareness in Mobile Computing

The posture of a user is one of the contextual information that can be used for mobile applications and the treatment of idiopathic scoliosis. This paper describes a method for monitoring the posture of a user during operation of a mobile device in three activities such as sitting, standing, and walking. The user posture monitoring system (UPMS) proposed in this paper is based on two major technologies. The first involves a tilt-angle measurement algorithm (TAMA) using an accelerometer. Unlike most previous studies, it is based on a relative computation using the dot product from the time-series acceleration data. Because TAMA does not require a physical calibration by a user, it is more robust and accurate compared to other methods that rely on absolute computations. The second technology is an effective signal-processing method that eliminates the motion acceleration component of the accelerometer signal using a second-order Butterworth low-pass filter (SLPF). Because the posture of a user is only related to the gravity acceleration component, the motion acceleration components should be removed. The TAMA and UPMS are implemented on a personal digital assistant (PDA). They are evaluated to verify the possibility of application to a mobile device. Additionally, a posture-based intelligent control interface in context-aware computing that reacts to the posture of a PDA user is implemented on the PDA to complement the poor user interface (UI) of the mobile device, and its results are presented.

A Sequence-Action Recognition Applying State Machine for User Interface

The user interface in accelerometer-enable mobile devices requires more advanced action recognition technology than that of the traditional input method due to its awkward handling (e.g., limited computational capability, miniaturized input/output controls, and so on). Many types of input technology and interaction styles are being tested for gesture recognition. We introduce a method based on state machines that constructs high-level interaction events from lower-level ones. This is a novel user interface that recognizes a users sequence-action by using the two-axis accelerometer in order to supplement the lacking user interface of mobile devices. To recognize a users actions (users gesture and posture) in the order of precedence, we used a state machine algorithm. We designed mobile games such as fishing and bowling to be played on mobile phones by using this algorithm. We will prove that this state machine is appropriate for sequence-action recognition.

Curvature based ECG signal compression for effective communication on WPAN

As electrocardiogram (ECG) signals are generally sampled with a frequency of over 200 Hz, a method to compress diagnostic information without losing data is required to store and transmit them efficiently on a wireless personal area network (WPAN). In this paper, an ECG signal compression method for communications on WPAN, which uses feature points based on curvature, is proposed. The feature points of P, Q, R, S, and T waves, which are critical components of the ECG signal, have large curvature values compared to other vertexes. Thus, these vertexes were extracted with the proposed method, which uses local extrema of curvatures. Furthermore, in order to minimize reconstruction errors of the ECG signal, extra vertexes were added according to the iterative vertex selection method. Through the experimental results on the ECG signals from Massachusetts Institute of Technology-Beth Israel hospital arrhythmia database, it was concluded that the vertexes selected by the proposed method preserved all feature points of the ECG signals. In addition, it was more efficient than the amplitude zone time epoch coding method.

A Development of the TFT-LCD Image Defect Inspection Method Based on Human Visual System

The TFT-LCD image has non-uniform brightness that is the major difficulty of finding the visible defect called Mura in the field. To facilitate Mura detection, background signal shading should level off and Mura signal must be amplified. In this paper, Mura signal amplification and background signal flattening method is proposed based on human visual system (HVS). The proposed DC normalized contrast sensitivity function (CSF) is used for the Mura signal amplification and polynomial regression (PR) is used to level off the background signal. In the enhanced image, tri-modal thresholding segmentation technique is used for finding Dark and White Mura at the same time. To select reliable defect, falsely detected invisible region is eliminated based on Webers Law. By the experimental results of artificially generated 1-d signal and TFT-LCD image, proposed algorithm has novel enhancement results and can be applied to real automated inspection system.

The Defect Detection Using Human Visual System and Wavelet Transform in TFT-LCD Image

The thin film transistor liquid crystal display (TFT- LCD) image has non-uniform brightness, which is the major difficulty in finding the defect region called Mura. To facilitate Mura segmentation, globally widely varying background signal has to be flattened and Mura signal must be enlarged. In this paper, Mura signal magnification and background signal flattening method is proposed using wavelet coefficients processing and the properties of human visual system (HVS). The wavelet approximation coefficients are used for background signal flattening while wavelet detail coefficients are employed to magnify Mura signal based on adapted contrast sensitivity function (ACSF). For the enhanced image, tri-modal thresholding segmentation technique is used for finding dark and white Mura at the same time. For final reliable defect confirmation, false region elimination algorithms based on Webers law are also proposed. By the experimental results of TFT-LCD image, the proposed algorithms can have promising results and can be applied to the real automated TFT-LCD inspection system.

Intelligent video tracking based on fuzzy-reasoning segmentation

In our previous work [J. Cho, D. Kim, D. Park, Robust centroid target tracker based on new distance features in cluttered image sequences. IEICE Transactions on Information and Systems, Vol. E83-D, No. 12, December, 2000.], we presented a novel centroid target tracker based on new distance features in cluttered image sequences. A real-time adaptive segmentation method based on new distance features was proposed for the binary centroid tracker. The target classifier by the Bayes decision rule for minimizing the probability of error should properly estimate the state-conditional densities. In this correspondence, the proposed target classifier adopts the fuzzy-reasoning segmentation instead of the estimation of the state-conditional probability densities. Comparative experiments show that the performance of the proposed fuzzy-reasoning segmentation is superior to that of the conventional thresholding methods. The usefulness of the fuzzy-reasoning segmentation for practical applications is demonstrated by considering two sequences of real target images. The tracking results are good and stable without difficulty of the probability densities estimation.

Video rate control using an adaptive quantization based on a combined activity measure

A new rate control algorithm for videos is presented. The method comes from the MPEG-2 Test Model 5(TM5) rate control, while a buffer constraint and a new measure for the macroblock (MB) activity based on spatio-temporal sensitivity are introduced. Experimental results show that the proposed method outperforms the TM5 rate control in picture quality.

ECG Signal Compression using Feature Points based on Curvature

As electrocardiogram(ECG) signals are generally sampled with a frequency of over 200Hz, a method to compress diagnostic information without losing data is required to store and transmit them efficiently. In this paper, an ECG signal compression method, which uses feature points based on curvature, is proposed. The feature points of P, Q, R, S, T waves, which are critical components of the ECG signal, have large curvature values compared to other vertexes. Thus, these vertexes are extracted with the proposed method, which uses local extremum of curvatures. Furthermore, in order to minimize reconstruction errors of the ECG signal, extra vertexes are added according to the iterative vertex selection method. Through the experimental results on the ECG signals from MIT-BIH Arrhythmia database, it is concluded that the vertexes selected by the proposed method preserve all feature points of the ECG signals. In addition, they are more efficient than the AZTEC(Amplitude Zone Time Epoch Coding) method.

Human computer interaction for the accelerometer-based mobile game

As a result of growth of sensor-enabled mobile devices such as PDA, cellular phone and other computing devices, in recent years, users can utilize the diverse digital contents everywhere and anytime. However, the interfaces of mobile applications are often unnatural due to limited resources and miniaturized input/output. Especially, users may feel this problem in some applications such as the mobile game. Therefore, novel interaction forms have been developed in order to complement the poor user interface of the mobile device and to increase the interest for the mobile game. In this paper, we describe the demonstration of the gesture and posture input supported by an accelerometer. The application example we created are AM-Fishing game on the mobile device that employs the accelerometer as the main interaction modality. The demos show the usability for the gesture and posture interaction