• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1353-1362
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• 2023

Various methods are being attempted to resolve the inconvenience of blood glucose meters used to check blood sugar levels. In this paper, we attempted to estimate blood sugar levels non-invasively using machine learning technology from spectral data acquired using a near-infrared sensor. The non-invasive blood glucose meter used in the study has a total of six near-infrared ray emitters, including visible rays, and a light receiver that receives them. It is a device created to collect spectral data on specific parts of the human body, such as the fingers. To verify whether there was a significant difference depending on blood sugar level, we attempted to estimate blood sugar level through machine learning algorithms. As a result of applying five machine learning algorithm techniques to the collected data and adjusting various hyper parameters, it was confirmed that the support vector regression algorithm showed the best performance.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.56-64
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• 2024

This study aims to compare and evaluate the usability of domestic and overseas XR devices. With the recent release of 'Apple Vision Pro', interest in the XR field is increasing rapidly. XR devices are being used in various fields such as defense, medical care, education, and entertainment, but the evaluation system for evaluating usability is still insufficient. Therefore, this study aims to derive improvements in domestic equipment through comparative evaluation of usability for two HMD-type devices and one glasses-type device that are released. In order to conduct the study, 20 participants in their 20s to 30s who were interested in XR devices and had no visual sensory organ-related disabilities were evaluated by wearing VR equipment. As a quantitative evaluation, electromyography through an EMG sensor and the device and body temperature of the device through a thermal imaging camera were measured. As a qualitative evaluation, the safety of wearing, ease of wearing, comfort of wearing, and satisfaction of wearing were evaluated. As a result of comparing the usability of the devices based on the results, it was confirmed that domestic HMD-type device needs improvement in the strap part.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.39-46
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• 2024

In this paper, we propose a low-cost wearable device that can monitor the health status of the elderly living alone in real-time. As aging is accelerating, the elderly population is rapidly increasing, and the social isolation of the elderly living alone is causing physical and mental difficulties and the number of elderly people dying alone is increasing, becoming a social problem. In this study, we propose a belly band-type wearable device that can monitor the biometric information of elderly living alone. The proposed device transmits electromyogram, electrocardiogram, and body temperature information to a remote server through an Arduino-based sensor built into the abdominal band. Transmitted information can be monitored in a web environment in real-time, and it has the feature of enabling remote monitoring of a large number of subjects with a small amount of management manpower. The research results will contribute to improving the safety and welfare of seniors living alone by not only detecting lonely deaths in advance but also responding immediately to dangerous situations that may occur in daily life.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Journal of Preventive Medicine and Public Health
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• v.25 no.2 s.38
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• pp.141-147
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• 1992

This study was carried out not only to determine blood alcohol levels by time but also to examine the changes of working ability and reaction speed after ingestion of alcohol. Fifteen healthy students aged from 21 to 27 volunteered as subjects for this study, Liguor (Sojoo) in concentration of 25% ethyl alcohol was administrated with the amount of 1ml of ethyl alcohol per kg of body weight to the subjects. The concentration of alcohol in the blood were determined by the 'Alcohol Sensor 100' at 5, 30, 60 and 90 minutes after the administration of alcohol. Also, the choice reactiontest, the eye-hand coordination test and kraepelin test were examined at the same time after checking of alcohol concentration in the blood. The results of this study can be summarized as follows. 1. Mean blood alcohol level changes resulting from administration of 1ml of ethyl alcohol per kg of body weight were $0.16%(160{\pm}57mg/100ml,\;0.10%(100{\pm}42mg/100ml),\;0.08%(80{\pm}36mg/100ml)\;and\;0.03%(30{\pm}24mg/100ml)$ at the 3, 30, 60 and 90 minutes after the administration respectively The peak in the concentration of blood alcohol was 5 miniutes after the ingestion according to alcohol examination by the respiration. 2. As for choice reaction test, reaction times became prolonged as blood alcohol levels increased. The reaction time showed a significant changes when the blood alcohol concentration reached 0.08% or more after alcohol ingestion. 3. In eye and hand coordination test, the accuracy of the performance became decreased as blood alcohol levels increased. The difference of accuracy of the test was significantly shown when alcohol levels in the blood reached 0.08% or more after alcohol intake. 4. As for kraepelin test, the abilities of calculation also became lowered as blood alcohol levels increased. The abilities of calculation differed signigicantly from control group when alchool levels of 0.08% and more.

• Economic and Environmental Geology
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• v.54 no.3
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• pp.399-408
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• 2021

Natural soil was artificially contaminated with heavy metals (Cd, Pb, and Zn), and the movement of earthworm was characterized in real time using the ViSSET system composed of vibration sensor and the other components. The manifestation mechanism of ecological toxicity of heavy metals was interpreted based on the accumulative frequency of earthworm movement obtained from the real-time monitoring as well as the conventional indices of earthworm behavior, such as the change in body weight before and after tests and biocumulative concentrations of each contaminant. The results showed the difference in the earthworm movement according to the species of heavy metal contaminants. In the case of Cd, the earthworm movement was decreased with increasing its concentration and then tended to be increased. The activity of earthworm was severely increased with increasing Pb concentration, but the movement of earthworm was gradually decreased with increasing Zn concentration. The body weight of earthworm was proved to be greatly decreased in the Zn-contaminated soil, but it was similarly decreased in Cd- and Pb-contaminated soils. The bioaccumulation factor (BAF) was higher in the sequence of Cd > Zn > Pb, and particularly the biocumulative concentration of Pb did not show a clear tendency according to the Pb concentrations in soil. It was speculated that Cd is accumulated as a metallothionein-bound form in the interior of earthworm for a long time. In particular, Cd has a bad influence on the earthworm through the critical effect at its higher concentrations. Pb was likely to reveal its ecotoxicity via skin irritation or injury of sensory organs rather than ingestion pathway. The ecotoxicity of Zn seemed to be manifested by damaging the cell membranes of digestive organs or inordinately activating metabolism. Based on the results of real-time monitoring of earthworm movement, the half maximal effective concentration (EC50) of Pb was estimated to be 751.2 mg/kg, and it was similar to previously-reported ones. The study confirmed that if the conventional indices of earthworm behavior are combined with the results of newly-proposed method, the mechanism of toxicity manifestation of heavy metal contaminants in soils is more clearly interpreted.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.4
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• pp.257-267
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• 2023

Purpose. The purpose of the study was to fabricate a prototype robotic simulator for dental education, to test whether it could simulate mandibular movements, and to assess the possibility of the stimulator responding to stimuli during dental practice. Materials and methods. A virtual simulator model was developed based on segmentation of the hard tissues using cone-beam computed tomography (CBCT) data. The simulator frame was 3D printed using polylactic acid (PLA) material, and dentiforms and silicone face skin were also inserted. Servo actuators were used to control the movements of the simulator, and the simulator's response to dental stimuli was created by pressure and water level sensors. A water level test was performed to determine the specific threshold of the water level sensor. The mandibular movements and mandibular range of motion of the simulator were tested through computer simulation and the actual model. Results. The prototype robotic simulator consisted of an operational unit, an upper body with an electric device, a head with a temporomandibular joint (TMJ) and dentiforms. The TMJ of the simulator was capable of driving two degrees of freedom, implementing rotational and translational movements. In the water level test, the specific threshold of the water level sensor was 10.35 ml. The mandibular range of motion of the simulator was 50 mm in both computer simulation and the actual model. Conclusion. Although further advancements are still required to improve its efficiency and stability, the upper-body prototype simulator has the potential to be useful in dental practice education.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.41-54
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• 2021

It is very essential to estimate the water body area using remote exploration for water resource management, analysis and prediction of water disaster damage. Hydrophysical detection using satellites has been mainly performed on large satellites equipped with optical and SAR sensors. However, due to the long repeat cycle, there is a limitation that timely utilization is impossible in the event of a disaster/disaster. With the recent active development of Micro satellites, it has served as an opportunity to overcome the limitations of time resolution centered on existing large satellites. The Micro satellites currently in active operation are ICEYE in Finland and Capella satellites in the United States, and are operated in the form of clusters for earth observation purposes. Due to clustering operation, it has a short revisit cycle and high resolution and has the advantage of being able to observe regardless of weather or day and night with the SAR sensor mounted. In this study, the operation status and characteristics of micro satellites were described, and the water area estimation technology optimized for micro SAR satellite images was applied to the Daecheong Dam basin on the Korean Peninsula. In addition, accuracy verification was performed based on the reference value of the water generated from the optical satellite Sentinel-2 satellite as a reference. In the case of the Capella satellite, the smallest difference in area was shown, and it was confirmed that all three images showed high correlation. Through the results of this study, it was confirmed that despite the low NESZ of Micro satellites, it is possible to estimate the water area, and it is believed that the limitations of water resource/water disaster monitoring using existing large SAR satellites can be overcome.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.