Hyoung-Ki Lee

Principles and clinical application of ultrasound elastography for diffuse liver disease

Accurate assessment of the degree of liver fibrosis is important for estimating prognosis and deciding on an appropriate course of treatment for cases of chronic liver disease (CLD) with various etiologies. Because of the inherent limitations of liver biopsy, there is a great need for non-invasive and reliable tests that accurately estimate the degree of liver fibrosis. Ultrasound (US) elastography is considered a non-invasive, convenient, and precise technique to grade the degree of liver fibrosis by measuring liver stiffness. There are several commercial types of US elastography currently in use, namely, transient elastography, acoustic radiation force impulse imaging, supersonic shear-wave imaging, and real-time tissue elastography. Although the low reproducibility of measurements derived from operator-dependent performance remains a significant limitation of US elastography, this technique is nevertheless useful for diagnosing hepatic fibrosis in patients with CLD. Likewise, US elastography may also be used as a convenient surveillance method that can be performed by physicians at the patients’ bedside to enable the estimation of the prognosis of patients with fatal complications related to CLD in a non-invasive manner.

Application of Electronic Compass for Mobile Robot in an Indoor Environment

This paper presents a novel approach to electronic compassing for robot in an indoor environment. Operation of compass (magnetometer) is assumed on a mobile robot that is capable to traverse a complete circular path. The electronic compass is used to estimate a robot absolute heading with respect to the magnetic North. This is only compass approach where an evaluation of quality of calibration and magnetic environment is important as much as calibration itself. In this method, compass is able to detect the external magnetic interference and estimate it numerically. The approach also relates to automatic calibration and requires one full 360-degree rotation and multiple points for further analysis. This enhanced calibration procedure is performed in the magnetic field domain and implemented using a non-iterative algebraic technique. The quality of calibration is a function of input data goodness and how successful is the fitting procedure. The magnetic environment evaluation is performed by the distortion factor that is calculated at the end of calibration and helps to estimate quantitatively local external magnetic distortions and their influence on a heading measurement. The validity of this approach has been verified experimentally by using robot with electronic compass.

Development stress monitoring system based on personal digital assistant (PDA)

We have developed nonintrusive type stress monitoring system based on the PDA (Personal Digital Assistance). This system separated sensing part of the physiological signal and estimating part of the stress states. First, sensing part consists of four electrodes such as one PPG electrode, two EDA electrodes and one SKT electrode. Sensing part was able to measuring heart rate, skin temperature variation, and electrodermal activity, all of which can be acquired without discomfort from finger. Second, estimating part was developed and verified for physiological signal database that was obtained from multiple subjects by presenting stress stimuli that were elaborated to effectively induce stress. This system is a useful measure of human stress in portabel device as PDA and smart phone.

Formal Verification of Robot Movements - a Case Study on Home Service Robot SHR100

Home service robots have received much attention from both academia and industry because home service robots have wide range of potential applications such as home security, cleaning, etc. The robots need to add or update services frequently according to the changing needs of human users. Furthermore, reactive nature of the robots add complexity to develop robot applications. These challenges raise safety issues seriously. Considering that safe operation of home service robots is crucial, current practice of validating robot applications is, however, not mature enough for wide deployment of home service robots. In this paper, we present our experience of developing and formally verifying discrete control software of Samsung Home Robot (SHR) using Esterel. We give a brief background on Esterel, then illuminate our result in formally verifying stopping behavior of SHR. Through the verification, we could detect and solve a feature interaction problem which caused the robot not to stop when a user commanded the robot to stop.

Improvement of dead reckoning accuracy of a mobile robot by slip detection and compensation using multiple model approach

Although dead reckoning based on odometry and inertial sensors is essential for a robotic localization system, none of previous works gives reliable and accurate position estimates on irregular terrain over long periods of time. Classical approaches use one estimator (such as a Kalman filter) with a single system model. However the single system model is not good to deal with both of slip and no-slip situations because of the dynamics changes. In this paper, a multiple model approach that uses two Kalman filters is presented: one Kalman filter accounting for no-slip condition and the other for slip condition. The Interacting Multiple Model (IMM) is adopted to switch two Kalman filters depending on whether slip occurs or not, and gives the weighted sum of two filter estimates. Experimental results are included to validate our approach.

Structured light 2D range finder for simultaneous localization and map-building (SLAM) in home environments

Map building and localization of mobile robots is one of the keys to implementing autonomous navigation systems. Owing to the many efforts in the past decade it has proved that map building and localization simultaneously (SLAM, simultaneous localization and map-building) is possible both theoretically and practically. Features or landmarks obtained from sensor measurements are registered and associated in SLAM if they are sustainable and correctly matched ones. To have such features it is essential to use a sensor system that provides precise range measurements. Typical sensors for SLAM are such as laser range finder (LIDAR) or ultrasonic sensors. LIDARs provide very accurate and long range measurements while measurements from ultrasonic sensors are shorter and coarse due to cone shaped beam patterns. In this paper we propose a SLAM system using a structured light sensor to SLAM in home environment. The accuracy is less than that of LIDAR (10 cm at 3 meters), but measurements are very dense laterally so that a small corners, often existing in home environments, can be determined as feature points in 3 meters. We introduce the implemented system for home use and prove its efficacy by showing the SLAM experimental results.

Constrained Kalman Filter for Mobile Robot Localization with Gyroscope

The odometry information used in localization can be quite erroneous when the robot follows the curved path or suffers from slippage. Thus the use of the low-cost gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering methods that fuse the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the nonapproximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time

Monocular SLAM with undelayed initialization for an indoor robot

This paper presents a new feature initialization method for monocular EKF SLAM (Extended Kalman Filter Simultaneous Localization and Mapping) which utilizes a 3D measurement model in the camera frame rather than 2D pixel coordinates in the image plane. The key idea is to regard a camera as a range and bearing sensor, of which the range information contains numerous uncertainties. 2D pixel coordinates of measurement are converted to 3D points in the camera frame with an assumed depth. The element of the measurement noise covariance corresponding to the depth of the feature is set to a very high value. And it is shown that the proposed measurement model has very little linearization error, which can be critical for the EKF performance. Furthermore, this paper proposes an EKF SLAM system that combines odometry, a low-cost gyro, and low frame rate (1-2 Hz) monocular vision. Low frame rate is crucial for reducing the price of the processor. This system combination is cost-effective enough to be commercialized for a real vacuum cleaning application. Simulations and experimental results show the efficacy of the proposed method with computational efficiency in indoor environments.

Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach.

Real-time audio-visual localization of user using microphone array and vision camera

In home environments, demands for a robot to serve a user are on the increase, such as cleaning rooms, bringing something to the user, and so on. To achieve these tasks, it is essential for developing a natural way of human-robot interaction (HRI). One of the most natural ways is that the robot approaches the user to do some tasks after recognizing the users call and localizing its position. In this case, user localization becomes a key technology. In this paper, we propose a novel audio visual user localization system. It consists of a microphone array with eight sensors and a video camera. Estimating calling direction is achieved by the spectral subtraction of the spatial spectra. In particular, a novel beam forming method is proposed to suppress the nonstationary audio noises where they always occur in a real world. Furthermore, a robust method for face detection is proposed to double check the user based on an Adaboost classifier. It is improved to reduce the false alarms remarkably through a new postprocessing on face candidates. Successful results in a real home environment show its efficacy and feasibility. The implementation issues, limitations, and their possible solutions are also discussed.