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Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.
Dong-Woo Koh; Jin-Kook Kwon; Sang-Goog Lee. Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals. Sensors 2021, 21, 4607 .
AMA StyleDong-Woo Koh, Jin-Kook Kwon, Sang-Goog Lee. Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals. Sensors. 2021; 21 (13):4607.
Chicago/Turabian StyleDong-Woo Koh; Jin-Kook Kwon; Sang-Goog Lee. 2021. "Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals." Sensors 21, no. 13: 4607.
The elderly are more susceptible to stress than younger people. In particular, heart palpitations are one of the causes of heart failure, which can lead to serious accidents. To prevent heart palpitations, we have devised the Safe Driving Intensity (SDI) and Cardiac Reaction Time (CRT) as new methods of estimating the correlations between effects on the driver’s heart and the movement of a vehicle. In SDI measurement, recommended acceleration value of vehicle for safe driving is inferred from the suggested correlation algorithm using machine learning. A higher SDI value than other people means less pressure on the heart. CRT is an estimated value of the occurring time of heart palpitations caused by stressful driving. In particular, it is proved by SDI that elderly subjects tend to overestimate their driving abilities in personal assessment questionnaires. Furthermore, we validated our SDI using other general statistical methods. When comparing the results using a t-test, we obtained reliable results for the equivalent variance. Our results can be used as a basis for evaluating elderly people’s driving ability, as well as allowing for the implementation of a personalized safe driving system for the elderly.
Dong-Woo Koh; Sang-Goog Lee. An Evaluation Method of Safe Driving for Senior Adults Using ECG Signals. Sensors 2019, 19, 2828 .
AMA StyleDong-Woo Koh, Sang-Goog Lee. An Evaluation Method of Safe Driving for Senior Adults Using ECG Signals. Sensors. 2019; 19 (12):2828.
Chicago/Turabian StyleDong-Woo Koh; Sang-Goog Lee. 2019. "An Evaluation Method of Safe Driving for Senior Adults Using ECG Signals." Sensors 19, no. 12: 2828.
This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized.
Woo-Hyuk Jung; Sang-Goog Lee. ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method. Applied Sciences 2017, 7, 1205 .
AMA StyleWoo-Hyuk Jung, Sang-Goog Lee. ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method. Applied Sciences. 2017; 7 (11):1205.
Chicago/Turabian StyleWoo-Hyuk Jung; Sang-Goog Lee. 2017. "ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method." Applied Sciences 7, no. 11: 1205.
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain–computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation–maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.
David Lee; Sang-Hoon Park; Sang-Goog Lee. Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors. Sensors 2017, 17, 2282 .
AMA StyleDavid Lee, Sang-Hoon Park, Sang-Goog Lee. Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors. Sensors. 2017; 17 (10):2282.
Chicago/Turabian StyleDavid Lee; Sang-Hoon Park; Sang-Goog Lee. 2017. "Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors." Sensors 17, no. 10: 2282.
David Lee; Sang-Hoon Park; Hee-Jae Lee; Sang-Goog Lee. Improving Classification Accuracy of Motion Imaginary Electroencephalogram Using Mutual Information and Rare Learning. The Journal of Korean Institute of Information Technology 2017, 15, 67 -74.
AMA StyleDavid Lee, Sang-Hoon Park, Hee-Jae Lee, Sang-Goog Lee. Improving Classification Accuracy of Motion Imaginary Electroencephalogram Using Mutual Information and Rare Learning. The Journal of Korean Institute of Information Technology. 2017; 15 (8):67-74.
Chicago/Turabian StyleDavid Lee; Sang-Hoon Park; Hee-Jae Lee; Sang-Goog Lee. 2017. "Improving Classification Accuracy of Motion Imaginary Electroencephalogram Using Mutual Information and Rare Learning." The Journal of Korean Institute of Information Technology 15, no. 8: 67-74.
Sang-Hoon Park; Ha-Young Kim; David Lee; Sang-Goog Lee. Filter-Bank Based Regularized Common Spatial Pattern for Classification of Motor Imagery EEG. Journal of KIISE 2017, 44, 587 -594.
AMA StyleSang-Hoon Park, Ha-Young Kim, David Lee, Sang-Goog Lee. Filter-Bank Based Regularized Common Spatial Pattern for Classification of Motor Imagery EEG. Journal of KIISE. 2017; 44 (6):587-594.
Chicago/Turabian StyleSang-Hoon Park; Ha-Young Kim; David Lee; Sang-Goog Lee. 2017. "Filter-Bank Based Regularized Common Spatial Pattern for Classification of Motor Imagery EEG." Journal of KIISE 44, no. 6: 587-594.
Seung-Ju Lim; Sung-Dae Park; Hyung-Gi Byun; Sang-Goog Lee; Ji-Hoon Choi; Chung-Hyun Ahn; Jeong-Do Kim. Olfactory Data Structure for Converged Multimedia Services. Sensor Letters 2014, 12, 1160 -1164.
AMA StyleSeung-Ju Lim, Sung-Dae Park, Hyung-Gi Byun, Sang-Goog Lee, Ji-Hoon Choi, Chung-Hyun Ahn, Jeong-Do Kim. Olfactory Data Structure for Converged Multimedia Services. Sensor Letters. 2014; 12 (6):1160-1164.
Chicago/Turabian StyleSeung-Ju Lim; Sung-Dae Park; Hyung-Gi Byun; Sang-Goog Lee; Ji-Hoon Choi; Chung-Hyun Ahn; Jeong-Do Kim. 2014. "Olfactory Data Structure for Converged Multimedia Services." Sensor Letters 12, no. 6: 1160-1164.
Sang-Goog Lee; Jeong-Hwan Lee; Jung-Ju Kim; Jin-Ho Ahn; Hyung-Gi Byun; Jeong-Do Kim. IEEE 1451.2 Standard Applicable to Array Based Chemical Sensing System. Sensor Letters 2011, 9, 444 -449.
AMA StyleSang-Goog Lee, Jeong-Hwan Lee, Jung-Ju Kim, Jin-Ho Ahn, Hyung-Gi Byun, Jeong-Do Kim. IEEE 1451.2 Standard Applicable to Array Based Chemical Sensing System. Sensor Letters. 2011; 9 (1):444-449.
Chicago/Turabian StyleSang-Goog Lee; Jeong-Hwan Lee; Jung-Ju Kim; Jin-Ho Ahn; Hyung-Gi Byun; Jeong-Do Kim. 2011. "IEEE 1451.2 Standard Applicable to Array Based Chemical Sensing System." Sensor Letters 9, no. 1: 444-449.
The purpose of a web-sensor is to transmit recorded data and related information to a remote user. Since the user is at a remote location, the sensor information must be reliable and secure, and the diagnosis for sensors should be easy to handle. To ensure these outcomes, the IEEE 1451 has been used in the past for smart sensor. This paper proposes a new smart web sensor model. Since the proposed smart web sensor is based on IEEE 1451.0, most of the existing sensor interfaces may be used, and the smart web sensor can be achieved using TEDS information. In addition, as XML is used, the web service is user friendly and a remote user can easily handle all kinds of information related to the sensor. This research presents a reference model for a smart web sensor and, to prove how valuable it is, a web-service using a gas sensor is utilized.
Jeong-Do Kim; Jung-Ju Kim; Sung-Dae Park; Chul-Ho Hong; Hyung-Gi Byun; Sang-Goog Lee. A Smart Web-Sensor Based on IEEE 1451 and Web-Service Using a Gas Sensor. Econometrics for Financial Applications 2011, 365, 219 -235.
AMA StyleJeong-Do Kim, Jung-Ju Kim, Sung-Dae Park, Chul-Ho Hong, Hyung-Gi Byun, Sang-Goog Lee. A Smart Web-Sensor Based on IEEE 1451 and Web-Service Using a Gas Sensor. Econometrics for Financial Applications. 2011; 365 ():219-235.
Chicago/Turabian StyleJeong-Do Kim; Jung-Ju Kim; Sung-Dae Park; Chul-Ho Hong; Hyung-Gi Byun; Sang-Goog Lee. 2011. "A Smart Web-Sensor Based on IEEE 1451 and Web-Service Using a Gas Sensor." Econometrics for Financial Applications 365, no. : 219-235.
Jeongmin Yu; Sang-Goog Lee; Moongu Jeon. Medical image segmentation by hybridizing ant colony optimization and fuzzy clustering algorithm. Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11 2011, 1 .
AMA StyleJeongmin Yu, Sang-Goog Lee, Moongu Jeon. Medical image segmentation by hybridizing ant colony optimization and fuzzy clustering algorithm. Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11. 2011; ():1.
Chicago/Turabian StyleJeongmin Yu; Sang-Goog Lee; Moongu Jeon. 2011. "Medical image segmentation by hybridizing ant colony optimization and fuzzy clustering algorithm." Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11 , no. : 1.
In this paper we discuss efficient methods of the state estimation which are robust against unknown outlier measurements. Unlike existing Kalman filters, we relax the Gaussian assumption of noises to allow sparse outliers. By doing so spikes in channels, sensor failures, or intentional jamming can be effectively avoided in practical applications. Two approaches are suggested: median absolute deviation (MAD) and L1-norm regularized least squares (L1-LS). Through a numerical example two methods are tested and compared.
Du Yong Kim; Sang-Goog Lee; Moongu Jeon. Outlier Rejection Methods for Robust Kalman Filtering. Communications in Computer and Information Science 2011, 316 -322.
AMA StyleDu Yong Kim, Sang-Goog Lee, Moongu Jeon. Outlier Rejection Methods for Robust Kalman Filtering. Communications in Computer and Information Science. 2011; ():316-322.
Chicago/Turabian StyleDu Yong Kim; Sang-Goog Lee; Moongu Jeon. 2011. "Outlier Rejection Methods for Robust Kalman Filtering." Communications in Computer and Information Science , no. : 316-322.
Jeong-Do Kim; Kap-Jin Kim; Gi-Su Chung; Jung-Hwan Lee; Jin-Ho Ahn; Sang-Goog Lee. The Mobile Health-Care Garment System for Measurement of Cardiorespiratory Signal. The KIPS Transactions:PartA 2010, 17A, 145 -152.
AMA StyleJeong-Do Kim, Kap-Jin Kim, Gi-Su Chung, Jung-Hwan Lee, Jin-Ho Ahn, Sang-Goog Lee. The Mobile Health-Care Garment System for Measurement of Cardiorespiratory Signal. The KIPS Transactions:PartA. 2010; 17A (3):145-152.
Chicago/Turabian StyleJeong-Do Kim; Kap-Jin Kim; Gi-Su Chung; Jung-Hwan Lee; Jin-Ho Ahn; Sang-Goog Lee. 2010. "The Mobile Health-Care Garment System for Measurement of Cardiorespiratory Signal." The KIPS Transactions:PartA 17A, no. 3: 145-152.
Jeong-Do Kim; Jung-Hwan Lee; Yu-Kyung Ham; Chul-Ho Hong; Byoung-Woon Min; Sang-Goog Lee. Sensor-Ball system based on IEEE 1451 for monitoring the condition of power transmission lines. Sensors and Actuators A: Physical 2009, 154, 157 -168.
AMA StyleJeong-Do Kim, Jung-Hwan Lee, Yu-Kyung Ham, Chul-Ho Hong, Byoung-Woon Min, Sang-Goog Lee. Sensor-Ball system based on IEEE 1451 for monitoring the condition of power transmission lines. Sensors and Actuators A: Physical. 2009; 154 (1):157-168.
Chicago/Turabian StyleJeong-Do Kim; Jung-Hwan Lee; Yu-Kyung Ham; Chul-Ho Hong; Byoung-Woon Min; Sang-Goog Lee. 2009. "Sensor-Ball system based on IEEE 1451 for monitoring the condition of power transmission lines." Sensors and Actuators A: Physical 154, no. 1: 157-168.