• Therapeutic Science for Rehabilitation
• /
• v.6 no.1
• /
• pp.55-69
• /
• 2017

Objective : The purpose of this study was to investigate the reliability and concurrent validity of the computerized cognitive function test system (called CNFT) for evaluating the cognitive function and to provide its normative data. Methods : For this purpose, 140 normal adults participated in a investigation to provide the normative data of CNFT. 40 normal adults participated in an evaluating experiment to verify the reliability and validity. CNFT consists of attention, memory, sensori-motor coordination, and frontal lobe & higher cognitive function domains. Because CNFT is a computerized evaluation tool, all results and operations are processed consistently and automatically. Results : In the results, as the age of subjects increased, the average accuracy decreased and response time increased. Additionally, memory and frontal lobe & higher cognitive function was lower than other domains. Test-retest reliability of 2 weeks interval was highly correlated (r=.48~.85) and there is no significant difference between test and retest scores. CNFT was highly correlated with computerized neurocognitive function test (r=.67~.79; p<.05). Conclusion : Normative data of CNFT were obtained, and the guidelines for the interpretation were provided. A reliable and valid clinically applicable computerized cognitive function test was developed.

• Science of Emotion and Sensibility
• /
• v.25 no.2
• /
• pp.79-86
• /
• 2022

With external air pollution forcing many people indoors, new methods of facilitating healthier indoor life are necessary. This study, therefore, investigates the effects of indoor oxygen concentration and respiration methods on biosignals and cognitive ability. The study included twenty healthy subjects who inhaled air through a mask from a gas delivery system. All subjects were asked to perform three types of breathing (nasal, oral, and oral breathing with high oxygenation) and respond to cognitive stimuli (rest close eye, rest open eye, 1-back and 2-back working memory tasks). The changes in cognitive load according to respiration were analyzed by measuring response time, accuracy, and biosignals to stimuli. The result showed that, in all three respirations, heart rate significantly increased with the increase in cognitive load. Also, in oral respiration, the airway respiration rate significantly increased according to the increase in cognitive load. The change appeared to compensate for insufficient oxygen supply in oral respiration during cognitive activity. Conversely, there was no significant change in airway respiration rate during oral respiration with a high concentration oxygen supply as in nasal respiration. This result suggests that a high concentration oxygen supply might play a role in compensating for insufficient oxygen concentration or inefficient oxygen inhalation, such as oral respiration. Based on the results of this study, a follow-up study is necessary to determine the impact of changes in the autonomic nervous system, such as stress and emotions, to find out more precise and comprehensive effects of oxygen concentration and breathing type.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.05a
• /
• pp.300-300
• /
• 2004

본 연구에서는 언어과제 수행 시 일반 공기 중의 산소 농도 (21%) 환경에 비해 외부에서 고 농도 (30%)의 산소 공급이 혈중 산소 포화도(SPO$_2$), 심박동율(Heart Rate), 정답률(Accuracy), 반응속도(Reaction Time)에 어떠한 영향을 미치는지를 검증하고자 한다. 30%와 21%의 산소를 8L/min의 양으로 일정하게 공급할 수 있는 산소 공급 장치를 이용하였고, 10명의 대학생(오른손잡이, 평균나이 23.4세)을 대상으로 실험을 수행하였다. 난이도가 비슷한 두 가지 언어과제를 28문제씩 피험자에게 풀게하여 정답률과 반응속도를 계산하였다.(중략)

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.216-226
• /
• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

• Journal of Information Technology Applications and Management
• /
• v.14 no.4
• /
• pp.75-96
• /
• 2007

The effects of information quality and the importance of information have been reported in the Information Systems (IS) literature. However, little has been learned about the impact of information visualization types and contextual information on decision quality. Therefore, this study investigated the interaction effects of these variables on decision quality by conducting a laboratory experiment. Based on two types of information visualization and the availableness of contextual information, this study had a $2{\times}2$ factorial design. The dependent variables used to measure the outcomes of decision quality were decision accuracy and time. The results demonstrated that the effects of contextual information on decision quality were significant. In addition, there was a significant main effect of information visualization on decision accuracy. The findings suggest that decision makers can expect to improve their decision quality by enhancing information visualization types and contextual information. This research may extend a body of research examining the effects of factors that can be tied to human decision-making performance.

• Journal of information and communication convergence engineering
• /
• v.21 no.4
• /
• pp.281-286
• /
• 2023

Diagnosing reading disorders involves complex procedures to evaluate complex cognitive processes. For an accurate diagnosis, a series of tests and evaluations by human experts are required. In this study, we propose a quantitative tool to diagnose reading disorders based on natural reading behaviors using minimal human input. The eye movements of the third- and fourth-grade students were recorded while they read a text at their own pace. Seven machine learning models were used to evaluate the gaze patterns of the words in the presented text and classify the students as normal or having a reading disorder. The accuracy of the machine learning-based diagnosis was measured using the diagnosis by human experts as the ground truth. The highest accuracy of 0.8 was achieved by the support vector machine and random forest classifiers. This result demonstrated that machine learning-based automated diagnosis could substitute for the traditional diagnosis of reading disorders and enable large-scale screening for students at an early age.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3583-3597
• /
• 2020

In this paper, a parallel deep learning model using a convolutional neural network and a dilated convolutional neural network is proposed to classify Alzheimer's disease with high accuracy in PET/CT images. The developed model consists of two pipelines, a conventional CNN pipeline, and a dilated convolution pipeline. An input image is sent through both pipelines, and at the end of both pipelines, extracted features are concatenated and used for classifying Alzheimer's disease. Complimentary abilities of both networks provide better overall accuracy than single conventional CNNs in the dataset. Moreover, instead of performing binary classification, the proposed model performs three-class classification being Alzheimer's disease, mild cognitive impairment, and normal control. Using the data received from Dong-a University, the model performs classification detecting Alzheimer's disease with an accuracy of up to 95.51%.

• The Journal of Korea Robotics Society
• /
• v.13 no.1
• /
• pp.38-44
• /
• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.233-242
• /
• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Korean Journal of Cognitive Science
• /
• v.19 no.1
• /
• pp.89-105
• /
• 2008

In this paper, we suggest a rule-based system for the prediction of natural prosodic phrase breaks from Korean texts. For the implementation of the rule-based system, (1) sentence constituents are sub-categorized according to their syntactic functions, (2) syntactic phrases are recognized using the dependency relations among sub-categorized constituents, (3) rules for predicting prosodic phrase breaks are created. In addition, (4) the length of syntactic phrases and sentences, the position of syntactic phrases in a sentence, sense information of contextual words have been considered as to determine the variable prosodic phrase breaks. Based on these rules and features, we obtained the accuracy over 90% in predicting the position of major break and no break which have high correlation with the syntactic structure of the sentence. As for the overall accuracy in predicting the whole prosodic phrase breaks, the suggested system shows Break_Correct of 87.18% and Juncture Correct of 89.27% which is higher than that of other models.