Won-chul Bang

Magic wand: a hand-drawn gesture input device in 3-D space with inertial sensors

This paper presents a gesture input device, magic wand, with which a user can input gestures in 3-D space, inertial sensors embedded in it generate acceleration and angular velocity signals according to a users hand movement. A trajectory estimation algorithm is employed to convert them into a trajectory on 2-D plane. The recognition algorithm based on Bayesian networks finds the gesture model with the maximum likelihood from it. The recognition performance of the proposed system is quite promising; the writer-independent recognition rate was 99.2% on average for the database of 15 writers and 13 gesture classes.

Self-contained spatial input device for wearable computers

In this paper, we present a pen-shaped input device forwearable computers which reproduce and recognizethree-dimensional (3D) hand motions with no externalreferences. The input device is equipped with inertialsensors. The inertial sensors measure accelerations andangular velocities produced by a users handwritingmotion in 2D/3D spaces. The measurements fromgyroscopes are integrated once to produce the attitude ofthe system and are consequently used to remove theeffects of the gravity and the inclination of the system.The compensated acceleration measurements are doublyintegrated to yield the position of the system. Due to theintegration processes involved in reproducing thehandwriting trajectory, the accuracy of the positionmeasurement significantly deteriorates along with time.To reproduce handwriting trajectory of a stroke on asurface and even in the air, we propose algorithms tosegment a stroke, to compensate the integration errors,and to reconstruct 2D trajectory on the x-y plane from 3Dtrajectory in the air. Real experimental results areincluded to show the effectiveness and feasibility of theproposed algorithms. The proposed methods provide anew approach for implementing a small, user-friendlykeyboard alternative input device for wearable computers.

Inertial sensor based recognition of 3-D character gestures with an ensemble classifiers

We present a 3-D input medium based on inertial sensors for on-line character recognition and an ensemble classification scheme for the recognition task. The system allows user to write a character in the air as a gesture, with a sensor-embedded device held in hand. The kinds of sensors used are 3-axis accelerometer and 3-axis gyroscope generating acceleration and angular velocity signals respectively. For character recognition, we used the technique of FDA (Fisher discriminant analysis). We tried different combinations of sensor signals to test the recognition performance. It is also possible to estimate a 2-D handwriting trajectory from the sensor signals. The best recognition rate of 93.23%, in case we use only raw sensor signals, was attained when all 6 sensor signals were combined. The recognition rate of 92.22% was reached if the estimated trajectory was used as input. Finally we tested the ensemble method and the generalization rate of 95.04% was attained on the ensemble recognizer consisting of 3 FDA recognizers based on acceleration-only, angular-velocity-only and handwriting trajectory respectively.

A novel hand gesture input device based on inertial sensing technique

In this paper, we present a novel gesture-based input device by using inertial sensing technique. The trajectories of users hand-drawn gestures in 3D space are captured and recognized by this device to fulfill user interaction task. The proposed device employs gyro-free inertial measurement unit (IMU) to track hand motions without requiring any external reference sensors or signals. Since the unbounded growing error of trajectory estimation, as a major drawback of IMU-based motion tracking technology, is carefully solved by using zero velocity compensation. Here, a deliberately-designed motion detection scheme is proposed to capture accurate hand motion period. Finally, the recognition algorithm based on Bayesian networks takes estimated trajectories and finds the corresponding gesture model with the maximum probability. Because the IMU provides outstanding capability of self-contained positioning, the proposed device is extraordinary simple and effective, comparing with the devices using other tracking technologies such as vision-based system. Experimental results also show its effectiveness and feasibility. Currently, after employing the trajectory estimation method provided in this paper, the recognition rate of 95.51% for 14 gestures has been achieved when this device is implemented as a TV remote controller. It can be used as a powerful, flexible interface for ubiquitous computing device.

Motion Compensation for Ultrasound Thermal Imaging Using Motion-Mapped Reference Model: An in vivo Mouse Study

Ultrasound (US)-based thermal imaging is very sensitive to tissue motion, which is a major obstacle to apply US temperature monitoring to noninvasive thermal therapies of in vivo subjects. In this study, we aim to develop a motion compensation method for stable US thermal imaging in in vivo subjects. Based on the assumption that the major tissue motion is approximately periodic caused by respiration, we propose a motion compensation method for change in backscattered energy (CBE) with multiple reference frames. Among the reference frames, the most similar reference to the current frame is selected to subtract the respiratory-induced motions. Since exhaustive reference searching in all stored reference frames can impede real-time thermal imaging, we improve the reference searching by using a motion-mapped reference model. We tested our method in six tumor-bearing mice with high intensity focused ultrasound (HIFU) sonication in the tumor volume until the temperature had increased by 7°C. The proposed motion compensation was evaluated by root-meansquare-error (RMSE) analysis between the estimated temperature by CBE and the measured temperature by thermocouple. As a result, the mean±SD RMSE in the heating range was 1.1 ± 0.1°C with the proposed method, while the corresponding result without motion compensation was 4.3 ± 2.6°C. In addition, with the idea of motion-mapped reference frame, total processing time to produce a frame of thermal image was reduced in comparison with the exhaustive reference searching, which enabled the motioncompensated thermal imaging in 15 frames per second with 150 reference frames under 50% HIFU duty ratio.

A TV pointing device using LED directivity

A low-cost, absolute pointing device for the TV exploiting the directivity of LEDs is presented. The main technical issues to solve were 1) the irregularity of the directivity of LEDs, 2) susceptibility to external infrared noise, and 3) instability due to hand tremor. The efficiency of the prototype was evaluated in comparison with GyroPoint and Wii remote.

Orientation estimation in mobile virtual environments with inertial sensors

The purpose of this study is to contribute to orientation sensing technology in mobile virtual environments based on inertial sensors and to show the possibility of its application as a motion interface in mobile environments. The proposed orientation estimation algorithm is cost-effective utilizing only a tri-axis accelerometer and a dual-axis angular rate sensor. The data shows that the roll and pitch estimates are accurate to about five degrees under dynamic motion. As an application, it is shown that the algorithm can be used for intelligent motion-based navigation of large images spread out on a virtual space in front of the user through a mobile display.

IrCube tracker: an optical 6-DOF tracker based on LED directivity

Six-degrees-of-freedom (6-DOF) trackers, which were mainly for professional computer applications, are now in demand by everyday consumer applications. With the requirements of consumer electronics in mind, we designed an optical 6-DOF tracker where a few photo-sensors can track the position and orientation of an LED cluster. The operating principle of the tracker is basically source localization by solving an inverse problem. We implemented a prototype system for a TV viewing environment, verified the feasibility of the operating principle, and evaluated the basic performance of the prototype system in terms of accuracy and speed. We also examined its application possibility to different environments, such as a tabletop computer, a tablet computer, and a mobile spatial interaction environment.

Controlling virtual world by the real world devices with an MPEG-V framework

The recent online networked virtual worlds such as SecondLife, World of Warcraft and Lineage have been increasingly popular. A life-scale virtual world presentation and the intuitive interaction between the users and the virtual worlds would provide more natural and immersive experience for users. The emergence of novel interaction technologies such as sensing the facial expression and the motion of the users and the real world environments could be used to provide a strong connection between them. For the wide acceptance and use of the virtual world, a various type of novel interaction devices should have a unified interaction formats between the real world and the virtual world and interoperability among virtual worlds. Thus, MPEG-V Media Context and Control (ISO/IEC 23005) standardizes such connecting information. The paper provides an overview and its usage example of MPEG-V from the real world to the virtual world (R2V) on interfaces for controlling avatars and virtual objects in the virtual world by the real world devices. In particular, we investigate how the MPEG-V framework can be applied for the facial animation of an avatar in various types of virtual worlds.

Robust real-time respiratory motion tracking using ultrasound image sequences

Respiratory motion monitoring is important to deal with tumor tracking in radiotherapy and imaging. In this paper, we propose a system that measures directly the motion of organ itself only using ultrasound image. Our system locates robustly diaphragm even though diaphragm may be blurred in ultrasound images by a fitting technique. In addition, it can extract robustly respiratory signal by automatically selecting the best area to monitor respiratory area in all image sequences.