• Korean Journal of Applied Biomechanics
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• v.25 no.4
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• pp.383-391
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• 2015

Objective : The purpose of this study was to investigate the effects of dynamic squats with slope-whole body vibration (WBV) on the trunk and lower limb in muscle activities. Method : 9 healthy women (age: $21.1{\pm}0.6years$, height: $160.5{\pm}1.4cm$, body weight: $50.5{\pm}2.4kg$) were recruited for this study. Muscle activities in the trunk and lower limb muscles, including biceps femoris (BF), rectus femoris (RF), rectus abdominum (RA), gastrocnemius (GCM), iliocostalis lumborum (IL) and tibialis anterior (TA), were recorded using an EMG measurement system. The test was performed by conducting dynamic squats with slope-WBV using frequency (10Hz, 50Hz), amplitude (0.5mm), and degree ($0^{\circ}$, $5^{\circ}$). Experimental method consisted of 2-pre-sessions and 1-test-session for 20 seconds. Results : The results showed that the muscle activities of the trunk and low limb muscles increased significantly with the $5^{\circ}$ slope and lower frequency (10Hz) except for in the TA. From this result, we confirmed that the slope and WBV could efficiently affect stimulation, enhancing muscle activities by facilitating neural control trail and muscle chain tightness. Conclusion : Utilizing the slope-WBV device while squatting could give positive effects on muscle activation in the trunk and lower limb muscles and provide neural stimulation, enhancing muscle chain of control subsystem through TVR (tonic vibration reflex).

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.238-244
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• 2019

In this study, we compared and analyzed the performance of each Convolution Neural Network (CNN) by implementing the CNN that reflected the characteristics of the cerebral structure, in order to analyze the CNN that was used for the prediction of cybersickness, and provided the performance varying depending on characteristics of the brain. Dizziness has many causes, but the most severe symptoms are considered attributable to vestibular dysfunction associated with the brain. Brain waves serve as indicators showing the state of brain activities, and tend to exhibit differences depending on external stimulation and cerebral activities. Changes in brain waves being caused by external stimuli and cerebral activities have been proved by many studies and experiments, including the thesis of Martijn E. Wokke, Tony Ro, published in 2019. Based on such correlation, we analyzed brain wave data collected from dizziness-inducing environments and implemented the dizziness predictive artificial neural network reflecting characteristics of the cerebral structure. The results of this study are expected to provide a basis for achieving optimal performance of the CNN used in the prediction of dizziness, and for predicting and preventing the occurrence of dizziness under various virtual reality (VR) environments.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of the Ergonomics Society of Korea
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• v.19 no.2
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• Korean Journal of Biological Psychiatry
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• v.6 no.1
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• pp.3-11
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• 1999

At the beginning, researches on the biology of depression or affective illness have focused mainly on the receptor functions and neuroendocrine activities. And the studies of the past years did not break new theoretical background, but the recent advances in the research on the molecular mechanisms underlying neural communication and signal transduction do add some insights to many established ideas. This article will overview some of the more recent advances in the clinical researches of depression. Our major concerns to be presented here include the followings : (1) alterations in the post-synaptic neural transduction ; (2) changes in the neurons of hypothalamic neuropeptides ; (3) decreased peptidase enzyme activities ; (4) associations of hypothalamic-pituitary-adrenal axis abnormalities with serotonin neurotransmission ; (5) role of serotonin transporter ; (6) changes in the responsiveness of intracellular calcium ion levels ; (7) the inositol deficiency theory of lithium and depression ; (8) the transcription factors including immediate early genes ; (9) recent genetic studies in some families. This brief overview will suggest that changes in DNA occur during antidepressant therapy. These changes at the DNA level initiating a cascade of events underlying antidepressant modality will give us the insights on the molecular biological basis of the pathogenesis of depression and cues for a new class of antidepressants.

• The Journal of Korean Physical Therapy
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• v.17 no.1
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• pp.108-122
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• 2005

The gait training strategy in very important things for central nervous system(CNS) injury patients. There are many method and strategy for regaining of the gait who had CNS injury. A human being has central pattern generator(CPG) is spinal CPG for locomotion. It is a neural network which make the cyclical patterns and rhythmical activities for walking. Sensory input from loading and hip position is essential for CPG stimulation that makes the central neural rhythm and pattern generating structure. From sensory input, the proprioceptive information facilitate proximal muscles that controlled in voluntarily from cortical level and visual and / or acoustical information facilitate distal muscles that controlled voluntarily from subcortical level. Gait training method can classify that is functional level and structural level. Functional level includ level surface gait, going up and down the stair. It is important to facilitate a guide tempo in order to activate the central pattern generators. During the functional test or functional activities, can point out the poor period in gait that have to be facilitate in structural level. There are many access methods with patient position and potentiality. The methods are using of rhythmic initiation, replication and combination of isotonic with standing position. Clinically using it on weight transfer onto the stance leg, loading response, loading response and pre-swing, terminal stance, up and downwards stairs.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• Journal of Biomedical Engineering Research
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• v.32 no.1
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• pp.55-60
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• 2011

Although sound intensity is considered as one of important factors in auditory processing, its neural mechanism in auditory neurons with limited dynamic range of firing rates is still unclear. In this study, we examined the effect of sound intensity adaptation on the change of glucose metabolism in a rat brain using [F-18] micro positron emission tomography (PET) neuroimaging technique. In the experiment, broadband white noise sound was given for 30 minutes after the [F-18]FDG injection in order to explore the functional adaptation of rat brain into the sound intensity levels. Nine rats were scanned with four different sound intensity levels: 40 dB, 60 dB, 80 dB, 100 dB sound pressure level (SPL) for four weeks. When glucose uptake during the adaptation of a high intensity sound level (100 dB SPL) was compared with that during adaptation to a low intensity level (40 dB SPL) in the experiment, the former induced a greater uptake at bilateral cochlear nucleus, superior olivary complexes and inferior colliculi in the auditory pathway. Expectedly, the metabolic activity in those areas linearly increased as the sound intensity level increased. In contrast, significant decrease interestingly occurred in the bilateral auditory cortices: The activities of auditory cortex proportionally decreased with higher sound intensities. It may reflect that the auditory cortex actively down-regulates neural activities when the sound gets louder.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.155-160
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• 2017

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.27 no.3
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• pp.196-206
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• 2016

Objectives: Individuals with autism spectrum disorder (ASD) are considered to have problems with empathy. It has recently been suggested that there are two systems for empathy; cognitive and emotional. We aimed to investigate the neural response to cognitive and emotional empathy and elucidate the neurobiological aspects of empathy in patients with ASD. Methods: We recruited patients with ASD (N=17, ASD group) and healthy controls (HC) (N=22, HC group) for an functional magnetic resonance imaging study. All of the subjects were scanned while performing cognitive and emotional empathy tasks. The differences in brain activation between the groups were assessed by contrasting their neural activity during the tasks. Results: During both tasks, the ASD group showed greater neural activities in the bilateral occipital area compared to the HC group. The ASD group showed more activation in the bilateral precunei only during the emotional empathy task. No brain regions were more activated in the HC group than in the ASD group during the cognitive empathy task. While performing the emotional empathy task, the HC group exhibited greater neural activities in the left middle frontal gyrus and right anterior cingulate gyrus than the ASD group. Conclusion: This study showed that the brain regions associated with cognitive and emotional empathy in ASD patients differed from those in healthy individuals. The results of this study suggest that individuals with ASD might have defects both in cognitive empathy and in emotional empathy.