Jun-Yeup Kim

Real-Time Heart Rate Monitoring System based on Ring-Type Pulse Oximeter Sensor

With the continuous aging of the populations in developed countries, the medical requirements of the aged are expected to increase. In this paper, a ring-type pulse oximeter finger sensor and a 24-hour ambulatory heart rate monitoring system for the aged are presented. We also demonstrate the feasibility of extracting accurate heart rate variability measurements from photoelectric plethysmography signals gathered using a ring-type pulse oximeter sensor attached to the finger. We designed the heart rate sensor using a CPU with built-in ZigBee stack for simplicity and low power consumption. We also analyzed the various distorted signals caused by motion artifacts using a FFT, and designed an algorithm using a least squares estimator to calibrate the signals for better accuracy.

Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction

This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.

HMM-based Intent Recognition System using 3D Image Reconstruction Data

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer`s range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

A Binary PSO-Based Optimal EEG Channel Selection Method for a Motor Imagery Based BCI System

Brain-computer interface based on motor imagery is a system that transforms a subject’s intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject’s limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. Using many channels cause other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfitting problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a particle swarm optimization applied to CSP. This paper examines selecting optimal channels among all channels, and comparing the classification accuracy between CSP and CSP with PSO by linear discriminant analysis.

Optimal EEG Channel Selection for Motor Imagery BCI System Using BPSO and GA

A motor imagery brain-computer interface system is used to translate a subject’s intention into a control command of machine, such as electrical wheelchair, robot manipulator, and so on. The overall process of classification of the motor imagery EEG signals is based on the acquisition of raw data from multiple channel of scalp when the subject tries to imagine the movement of limbs. So far, we have been concentrated which channel are activated by the imagination of the movement of limbs. Therefore, we have expected that the more channels are selected, the better results can be acquired. However, the problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is a spatial feature extraction, many channels cause an overfitting problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization (BPSO) as an optimal channel selection method. This paper examines selecting optimal channels and their combination, and comparing accuracy and the number of selected channels obtained from BPSO and simple genetic algorithm.

Development of Intelligent Green Fountain Culture System for Healthy Emotional Self-Concept

In the growing standard of people`s lives, we want desire to create eco-friendly water space what is called the Green Technology that is in the limelight. These green space is introduced the cultural contents and we use the water, music, and nature as tool of emotional verbalism. Presently, when we want to make scenario, water landscape scenario is made by director. but these systems have some disadvantages as the cost and limitation of direction. There is a growing interest in the integrated control system based on PC and Internet. In this paper, it is about fountain control system. Previous research area was only one using programmable logic controller or industrial PC. we proposed the development of intelligent green fountain culture system for healthy emotional self-concept. And we made automatic weather sensing system that is designed by the intelligent green fountain culture system to estimate the time-variant system.

Optimal EEG Channel Selection using BPSO with Channel Impact Factor

Brain-computer interface based on motor imagery is a system that transforms a subject`s intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject`s limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).

Design of Communication System for Intelligent Multi Agent Robot System

In the ad-hoc wireless network environment, that the fixed sensor nodes and the sensor nodes to move are mixed, as the number of the sensor nodes with mobility are getting more, the costs to recover and maintain the whole network will increase more and more. This paper proposed the CDSR (Cost based Dynamic Source Routing) algorithm being motivated from the typical DSR algorithm, that is one of the reactive routing protocol. The cost function is defined through measuring the cost which any sensor node pays to participate in the whole network for communication. It is also showed in this paper that the proposed routing algorithm will increase the efficiency and life of whole sensor network through a series of experiments.

ERS Feature Extraction using STFT and PSO for Customized BCI System

This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.

Development of Mirror Neuron System-based BCI System using Steady-State Visually Evoked Potentials

Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user`s inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.