• Clinical and Experimental Pediatrics
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• v.60 no.10
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• pp.312-319
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• 2017

Purpose: To evaluate the usefulness of the Korean Developmental Screening Test (K-DST) for infants and children for developmental delay assessment. Methods: This study was based on retrospective studies of the results of the K-DST, Preschool Receptive-Expressive Language Scale (PRES), Sequenced Language Scale for Infants (SELSI), Childhood Autism Rating Scale (CARS), Modified Checklist for Autism in Toddlers (M-CHAT), electroencephalography, magnetic resonance imaging, and extensive tests conducted in 209 of 1,403 patients, of whom 758 underwent the K-DST at the Korea University Guro Hospital between January 2015 and December 2016 and 645 were referred from local clinics between January 2015 and June 2016. Results: Based on the K-DST results, the male children significantly more frequently required further or follow-up examination than the female children in most test sections, except for gross motor. The male children had notably lower mean scores than the female children. The PRES/SELSI results showed that when more further or follow-up evaluations were required in the K-DST communication section, significantly more problems in language delay or disorder emerged. When further or follow-up evaluation was required in the cognitive section in the CARS/M-CHAT, the possibility of autism increased significantly. A child tended to score low in the CARS test and show autism when further or follow-up evaluation was recommended in the K-DST. Conclusion: This study demonstrated the usefulness of the K-DST as a screening test early in the development of infants and children in Korea. Data of normal control groups should be examined to determine the accuracy of this investigation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.91-99
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• 2010

The purpose of this study is to design and develop an e-mentoring system for full inclusion. Through the literature review of related studies, the e-mentoring performance model was drawn. Based on this model, an e-mentoring system was designed and developed. Especially the horizontal mentoring was mainly considered during the procedure. To evaluate the usability of the system, 2 system managers, 5 mentors, and 5 mentees were chosen and they were interviewed by researchers. Researchers used an interview instrument(10 items) consisting of mentoring support(cognitive support, affective support, adaptability) and technical support(convenience, accuracy, look & feel) categories. On the whole affirmative responses were given in both mentoring support and technical support areas. It can be concluded that the e-mentoring system is very effective in supporting full inclusion. In future it is recommended that several empirical studies of utilizing the e-mentoring should be performed.

• PNF and Movement
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• v.7 no.4
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• pp.31-36
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• 2009

Many daily activities require people to complete a motor task while walking. Substantial gait decrements during simultaneous attention to a variety of cognitive tasks have been shown by a group of severely injured neurological patients of mixed etiology. And previous studies have shown that the attentional load of a walking-associated task increased with its level of difficulty. The purpose of this study was to analyze subjects' gait changes are affected by the effects of arithmetic task difficulty and performance level. Participants performed a walking task alone, three different Arithmetic tasks while seated, and among them, two kinds of the simillar Arithmetic tasks in combination with walking. Reaction time and accuracy were recorded for two of the Arithmetic tasks. The mean values of the gait were measured using a Timed Up and Go test among 11 with post-stroke patients while walking with and without forward counting (WFC) and backward counting(WBC).There was significant Arithmetic Task Difficulty level between the 10-forward counting task condition(FC) and the 10-backward counting task condition(BC)(p=0.008). The mean values of T.U.G time were significantly higher under backward counting dual-task condition than during a simple walking task(p=0.009) and WFC(p=0.009). The change in T.U.G time during WFC was higher when compared with the change during a simple walking, but there was no significant difference (p=0.246). This study suggesting that a high interference could be linked with a high level of difficulty, whereas adaptive task enabled participants to perfectly share their attention between two concurrent tasks. Future research should determine whether dual task training can reduce gait decrements in dual task situations in people after stroke. And the dual-task-based exercise program is feasible and beneficial for improving walking ability in subjects with stroke.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.295-301
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• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

• Clinical Psychopharmacology and Neuroscience
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• v.16 no.4
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• pp.449-460
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• 2018

Objective: Prior functional magnetic resonance imaging (fMRI) work has revealed that children/adolescents with disruptive behavior disorders (DBDs) show dysfunctional reward/non-reward processing of non-social reinforcements in the context of instrumental learning tasks. Neural responsiveness to social reinforcements during instrumental learning, despite the importance of this for socialization, has not yet been previously investigated. Methods: Twenty-nine healthy children/adolescents and 19 children/adolescents with DBDs performed the fMRI social/non-social reinforcement learning task. Participants responded to random fractal image stimuli and received social and non-social rewards/non-rewards according to their accuracy. Results: Children/adolescents with DBDs showed significantly reduced responses within the caudate and posterior cingulate cortex (PCC) to non-social (financial) rewards and social non-rewards (the distress of others). Connectivity analyses revealed that children/adolescents with DBDs have decreased positive functional connectivity between the ventral striatum (VST) and the ventromedial prefrontal cortex (vmPFC) seeds and the lateral frontal cortex in response to reward relative to non-reward, irrespective of its sociality. In addition, they showed decreased positive connectivity between the vmPFC seed and the amygdala in response to non-reward relative to reward. Conclusion: These data indicate compromised reinforcement processing of both non-social rewards and social non-rewards in children/adolescents with DBDs within core regions for instrumental learning and reinforcement-based decision-making (caudate and PCC). In addition, children/adolescents with DBDs show dysfunctional interactions between the VST, vmPFC, and lateral frontal cortex in response to rewarded instrumental actions potentially reflecting disruptions in attention to rewarded stimuli.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.657-665
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• 2021

Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.83-92
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• 2021

Recently many organizations have adopted 'untact' work due to social distancing caused by Coronavirus-19. To clarify some controversy about the effectiveness from 'untact' work, it is necessary to examine the cognition of organizational members. This study identified issues in 'untact' work from the literature review, analyzed the content of in-depth interviews with workers and managers experiencing 'untact' work, and compared both groups' cognition by applying the co-orientation model. Both groups pointed out the communication difficulty as the top disadvantage and showed no significant differences in job satisfaction, organizational commitment, and work-life balance. However, the two groups showed significant differences in their cognition about performance evaluation (agreement and workers' congruence) and productivity enhancement (workers' accuracy). This paper has an academic contribution in that it has focused on cognitive gaps between workers and managers, urges organizations to devise ways to reduce the gaps, and suggests future studies with quantitative approaches.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.189-196
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• 2020

The study seeks to conduct an oral stereognosis assessment of the elderly, identify characteristics and confirm the association with cognition, Instrumental Activities of Daily Living(IADL), Quality of Life(QOL). Oral stereognosis(OS) was evaluated on 20 senior citizens aged 75 or older living in Gyeonggi-do. Cognition was used as MoCA-K(Montreal Cognitive Assessment-Korean), IADL as K-IADL(Korean Instrumental Activities of Daily Living), and QOL as GQOL(Geriatric Quality of Life scale). OS decreases accuracy with age, unaffected by the level of education. Shapes with clear edges and broad sides were found to be easy to recognize. OS is related to cognition, IADL and QOL. Through this study, the OS of the elderly could predict the functional level and QOL, including cognition. Therefore, it can be used as a basic research for the physical and mental health management of the elderly through oral lectures, and the development of oral stereognosis tools for the elderly through large scale subjects should be made.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Journal of Korean geriatric psychiatry
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• v.16 no.1
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• pp.17-23
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• 2012

The diagnosis of Alzheimer's disease (AD) is still obscure even to specialists. To improve the diagnostic accuracy, to find at-risk people as early as possible, to predict the efficacy or adverse reactions of pharmacotherapy on an individual basis, to attain more reliable results of clinical trials by recruiting better defined participants, to prove the disease-modifying ability of new candidate drugs, to establish prognosis-based therapeutic plans, and to do more, is now increasing the need for biomarkers for AD. Among AD-related biochemical markers, cerebrospinal beta-amyloid and tau have been paid the most attention since they are materials directly interfacing the brain interstitium and can be obtained through the lumbar puncture. Level of beta-amyloid is reduced whereas tau is increased in cerebrospinal fluid of AD patients relative to cognitively normal elderly people. Remarkably, such information has been found to help predict AD conversion of mild cognitive impairment. Despite inconsistent findings from previous studies, plasma beta-amyloid is thought to be increased before the disease onset, but show decreasing change as the disease progress. Regarding other peripheral biochemical markers, omics tools are being widely used not only to find useful biomarkers but also to generate novel hypotheses for AD pathogenesis and to lead new personalized future medicine.