Seung-Min Park

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.

Specified Object Tracking Problem in an Environment of Multiple Moving Objects

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

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.

A Study on Swarm Robot-Based Invader-Enclosing Technique on Multiple Distributed Object Environments

Interest about social security has recently increased in favor of safety for infrastructure. In addition, advances in computer vision and pattern recognition research are leading to video-based surveillance systems with improved scene analysis capabilities. However, such video surveillance systems, which are controlled by human operators, cannot actively cope with dynamic and anomalous events, such as having an invader in the corporate, commercial, or public sectors. For this reason, intelligent surveillance systems are increasingly needed to provide active social security services. In this study, we propose a core technique for intelligent surveillance system that is based on swarm robot technology. We present techniques for invader enclosing using swarm robots based on multiple distributed object environment. The proposed methods are composed of three main stages: location estimation of the object, specified object tracking, and decision of the cooperative behavior of the swarm robots. By using particle filter, object tracking and location estimation procedures are performed and a specified enclosing point for the swarm robots is located on the interactive positions in their coordinate system. Furthermore, the cooperative behaviors of the swarm robots are determined via the result of path navigation based on the combination of potential field and wall-following methods. The results of each stage are combined into the swarm robot-based invader-enclosing technique on multiple distributed object environments. Finally, several simulation results are provided to further discuss and verify the accuracy and effectiveness of the proposed techniques.

Feature classification of EEG signal with binary heuristic optimization algorithms

In previous paper, we proposed the novel method of nonlinear unsupervised feature classification for EEG (Electroencephalography) signal based on HS (Harmony Search) algorithm. Using this method, we could convert classification problem into finding the smallest sum of Euclidean distances between vectors belonging to each class. Therefore the performance of proposed method was influenced by the performance of optimization algorithm. In this paper, to compare efficiency and performance of various heuristic algorithm for this method, we applied three different heuristic optimization algorithm, HS, PSO (Particle Swarm Optimization), and DS (Differential Search). For the simulation, we used EEG signal data from BCI Competition IV Dataset I. Two class data from two subject with 100 Hz sampling rate were used. For feature extraction, common spatial pattern algorithm was used. In conclusion, the fastest algorithm was HS algorithm with about 4.4 seconds of an average computational time, the algorithm with best classification rate was also HS algorithm and the average classification rates of subject `f and `g were 84.08 % and 81.95 %. The slowest heuristic algorithm was PSO algorithm with about 7.5 second in an average computational time, and the worst average classification rate was 57.27 % from subject `g with PSO algorithm. We could draw a conclusion that the best algorithm for proposed classification method was HS algorithm.

A study on hybrid model of HMMs and GMMs for mirror neuron system modeling using EEG signals

For our present life anytime, anywhere access to the network can communicate with the ubiquitous computing. it is essential to human life. We should be able to agree that communication will be enabled. For our present life, anytime, anywhere access to the network can communicate with the ubiquitous computing. Such as the ubiquitous era approached, interaction between the user and the computer has become an important issue. In this paper we use EEG signals to extract the users intention recognition data, which the Mirror Neuron System Based on HMMs and GMMs to model the convergence of the hybrid model is proposed. This is based on a kind of biological signals using EEG signals to the users intention recognition techniques have been studied. In addition, EEG signals is generated based on the model, using the user intention recognition method have been studied. The proposed model will be applied in the field of neuro robotics.

Optimal EEG feature selection by genetic algorithm for classification of imagination of hand movement

Brain computer interface (BCI) system allows users to direct interact with the surrounded environment just a blink of their thought. In doing this, the most relevant informative from the electroencelography (EEG) signals need to be extracted from the electrodes of the scalp. Neurophysiology studies have proved that the power density from corresponding electrodes and frequency could identify imagination of the left or right hand movement. They also proved that these feature vary strongly from one subject to another. These spatial-time-frequency components are the keys to unlock the optimal features from the large space of these power density features. In this paper, we proposed the optimal feature extracting method from the basic power density of EEG signal. At first, the all EEG signal from the electrodes were filter using both spatial and temporal filters to enhance the signal to noise ratio of EEG. Then, the time-frequency features were extracted using short-time Fourier transform (STFT) and average power in sub-window band. Genetic algorithm was applied to search for the optimal features. In our simulation, we used the dataset from BCI competition III, IV and the data experimented in our laboratory. To ensure the improvement of our proposed feature extraction method, we applied the extracted feature into the support vector machine.

A study on the analysis of auditory cortex active status by music genre: Drawing on EEG

Music is the pattern of sounds produced by people singing of playing instruments. Thus, the music is made by people. Music composer, will be planted in the human emotions, put the listener is able to feel a similar sentiment. Especially, interesting from our point of view is that music analysis, the emotions of the people can be analyzed. This paper focuses on the analysis of auditory cortex active status by music genre. In this paper, the musical stimuli in EEG signals by amplifying the corresponding reaction to the averaging method, ERP(Event-Related Potentials) experiments based on the process of extracting sound methods for removing noise from the ICA algorithm to extract the tone and noise removal according to the results are applied to analyze the characteristics of EEG. In addition, drawing on LORETA(Low Resolution Brain Electromagnetic Tomography)program have attempt to design location of electrode in brain. And finally, using EEGLAB, the relationship of music and the auditory cortex active status could be concluded.

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.