• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.517-518
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• 2023

최근 뇌-컴퓨터 인터페이스 분야에서는 뇌파 신호를 이용한 운동 심상 분류 연구가 활발히 이루어지고 있다. 뇌파는 개인별 차이가 큰 생체 신호로, 사용자에 독립적인 경우 추론이 어려워지는 문제가 있어 운동 심상 분류에서는 주로 피험자 종속적인 연구가 행해져 왔다. 본 논문에서는 컨볼루션 신경망 기반의 뇌파 분류 모델인 EEGNet 에 새로운 방식으로 개선한 Squeeze-and-Excitation block 을 적용해 피험자에 대해 독립적인 운동 심상 분류 성능을 향상시키는 방법을 제안하며, 제안한 Squeeze-and-Excitation block 을 적용한 모델이 기존 모델보다 높은 분류 성능을 보여주는 것을 실험적으로 확인하였다.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.325-326
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• 2017

물은 색, 맛, 향이 없는 물질이지만 인간을 구성하는 주요 요소이며 모든 생명력의 근원이다. 물은 여러 성질을 지니고 있지만 그 중 물의 운동성에서 호기심이 자극된다. 또한 물은 형체가 없을 뿐만 아니라 스스로의 운동성이 없고 물과 에너지의 관계는 어떤 면에서 무용수와 안무가의 관계와 많은 유사성을 가지고 있다. 무용수는 안무가의 의도와 만나 움직임으로 표현하며 그 움직임들은 각 무용수 개성에 따라 또 다른 이미지를 발산하게 된다. 본 연구는 심상으로부터 인지되는 수류(Flow of Water)가 다양한 움직임의 이미지로 만들어지는 과정을 표현한다.

• Journal of the Korean Applied Science and Technology
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• v.35 no.3
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• pp.886-904
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• 2018

The 3D(dimension) vritual reality(VR) has already been used in various sports fields, especially in the training of elite athletes. It is mainly used to maximize the effectiveness of image training, and the use of VR-based image training has received special attention as evidence-based pratices for its feasibility, practicality, and appropriateness. However, in recent years, the use of VR is no longer used only for the training of elite athletes, but is widely used in social sports. This is because, the advantage of exercise in VR is that it is highly stable and has fewer restrictions from the external environment. Considering these advantages, it can be used for the elderly physical activity. This study identifies and reviews studies applying VR-based image training. Several recommendations for the future study on VR-based image training for the older such as interdisciplinary approach to VR-based image training, support needs regarding characteristics of the older, and generalization and maintenance of acquired technology were discussed.

• Journal of the Korean Applied Science and Technology
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• v.36 no.4
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• pp.1041-1049
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• 2019

This study gathered previous studies on the effects of image training in school physical education conducted in korea in order to investigate the average effect size as well as the factors that influence the effect sizes. This study connoted findings of individual studies related to image training in school physical education from 1995 to 2018. The results of this study were as follows: Firstly, the overall mean effect size of the image training in school physical education was large size(Cohen, 1988). Secondly, motor skills showed the large effect size than psychological variable. Thirdly, major factors that influence the effect of image training in school physical education appeared to be the type of motor learning, age, gender, training period, training frequency, training ime. Based on these findings, implications for future research and application of image training in school physical education were suggested.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.357-358
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• 2023

본 연구는 실제 행동과 운동 심상으로 팔과 다리 동작 인식을 위한 BCI 패러다임을 제안하고 유도성 분석을 한다. 이 페러다임은 각 팔과 양다리의 특정 움직임을 인식하기 위해 ERP를 기반 페러다임을 구성한다. BCI 페러다임은 왼팔, 오른팔, 양다리를 움직이는 영상 자극을 주며 이를 기반으로 왼팔, 오른팔, 양다리 움직임에 대한 인식을 한다. 거울뉴런은 실제 행동과 실제 행동을 보았을때와 운동심상을 통한 자극을 받았을 때 같은 뉴런이 활성화된다는 성질을 가지고 있다. 이러한 성질을 이용하여 운동심상만과 실제 행동을 동시에 학습할 경우를 유도성 분석을 진행한다. 또한 유도성 특징 분석을 통해 나타난 결과를 바탕으로 BCI 패러다임을 제안한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2837-2845
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• 2009

This study certified that the mentality training that utilized ICT learning has been working as an important base having much effect on learner's basic attitude on physical education class, improvement of bodily exercise function, and class satisfaction, and that the exercise ability was improved in the scope of speed, form(posture), accuracy(shooting success rate), and adaptability(performance ability). It means it is a much more step-forwarded educational method that the advantages of ICT learning and mentality training at the existing learning method were applied to the reality. Regarding the object of this study, it is a little bit unreasonable to generalize its study results in that it wasn't intended for national unit sampling. Therefore, in the future study, it is necessary to continue to advance the study that its representative-ness was supplemented through the balanced sampling between area and area, and between grade and grade.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• PNF and Movement
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• v.13 no.2
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• pp.55-72
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• 2015

Purpose: The purpose of this study was to propose a new therapy algorithm that combines motor imagery and physiotherapy as a physiotherapeutic clinical intervention technique that can stimulate the recovery of damaged physical function for patients with stroke. Methods: A variety of scientific research results related to motor imagery were reviewed and analyzed to investigate their applicability to physiotherapy in clinics. Results: As a new therapy algorithm for the therapeutic approach of motor imagery in stroke rehabilitation, a therapy algorithm that combines motor imagery with physiotherapy is proposed, which consists of three stages or steps: STEP 1 motor imagery familiarization, STEP 2 explicit learning stage, and STEP 3 implicit learning. Conclusion: The new therapy algorithm proposed in this study is expected to be a very useful clinical therapeutic approach for stimulating the recovery of damaged physical function in patients with stroke. It is believed that it will be necessary to confirm and standardize the effects of the therapeutic algorithm proposed in this study in the future by conducting diverse clinical studies.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.5
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• pp.440-448
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• 2015

Kinetic energy(KE) rod warhead system is a new interceptor which combines advantages of existing ones. This system is less dependant on a precision guidance than direct hit type warhead and gives high penetration rates than blast fragmentation type warhead. In this paper, isotropic KE rod warhead system is introduced with detonation/deployment model. A penetration effects of the deployed rods are calculated using TATE penetration equation. Also, an engagement performance analysis method is suggested. Finally, an optimal detonation time and engagement geometry is derived by Monte-Carlo simulation in various engagement situation using the performance analysis factor.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.747-752
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• 2011

HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.