• Anxiety and mood
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• v.6 no.1
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• pp.45-54
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• 2010

Objective : This study aimed to investigate and compare the prevalence of depression and suicidality among the elderly in a rural city according to their residence type. Methods : Participants were 311 elderly people (109 male and 202 female) in Icheon City, whom trained researchers interviewed and examined Geriatric Depression Scale, Social Support Scale, Instrumental Activities of Daily Living, Activities of Daily Living, Quality of Life Scale, and Mini International Neuropsychiatric Interview (MINI), module C. Using these data, we analyzed participants' risk factors associated with depression and suicidality. Results : The prevalence of depression was 28.0%. The prevalence in the elderly living alone and those admitted to residential facilities was significantly higher than that among the elderly living with family. Suicidality frequency was 19.6%. The suicidality frequency among the elderly living alone and those admitted to facilities was significantly higher than that among the elderly living with family. The risk factors for depression were age, admission to a facility, and low economic status. The suicidality risk factors were living alone, admission to a facility, poor social support, and a history of headache. Conclusion : These results showed the prevalence of depression and frequency of suicidality among the elderly in such a city was quite high. The results of this study remind psychiatrists of the importance of early detection and therapeutic intervention for the elderly with a high risk of depression and suicidality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2105-2110
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• 2008

Embedding the compact wearable units to monitor the health status of a person has been analysed as a convenient solution for the home health care. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring of the elderly and people with limited mobility can not only provide their general health status but also alarms whenever an emergency such as fall or gait has been occurred and a help is needed. A timely assistance in such a situation can reduce the loss of life. This work shows a detailed analysis of the data received from a chest worn sensor unit embedding a 3-axis accelerometer and depicts which features are important for the classification of human activities. How to arrange and reduce the features to a new feature set so that it can be classified using a simple classifier and also improving the classification resolution. Principal component analysis (PCA) has been used for modifying the feature set and afterwards for reducing the size of the same. Finally a Neural network classifier has been used to analyse the classification accuracies. The accuracy for detection of fall events was found to be 86%. The overall accuracy for the classification of Activities or daily living (ADL) and fall was around 94%.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.41-47
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• 2019

In this study, a smart assistive device is designed to recognize pedestrian signal and to provide audio instructions for visually impaired people in crossing streets safely. Walking alone is one of the biggest challenges to the visually impaired and it deteriorates their life quality. The proposed device has a camera attached on a pair of glasses which can detect traffic lights, recognize pedestrian signals in real-time using a machine learning algorithm on GPU board and provide audio instructions to the user. For the portability, the dimension of the device is designed to be compact and light but with sufficient battery life. The embedded processor of device is wired to the small camera which is attached on a pair of glasses. Also, on inner part of the leg of the glasses, a bone-conduction speaker is installed which can give audio instructions without blocking external sounds for safety reason. The performance of the proposed device was validated with experiments and it showed 87.0% recall and 100% precision for detecting pedestrian green light, and 94.4% recall and 97.1% precision for detecting pedestrian red light.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.175-182
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• 2020

The non-contact eye tracking is a nonintrusive human-computer interface providing hands-free communications for people with severe disabilities. Recently. it is expected to do an important role in non-contact systems due to the recent coronavirus COVID-19, etc. This paper proposes a novel approach for an eye mouse using an eye tracking method based on a context-aware based AdaBoost multi-region classifier and ASSL algorithm. The conventional AdaBoost algorithm, however, cannot provide sufficiently reliable performance in face tracking for eye cursor pointing estimation, because it cannot take advantage of the spatial context relations among facial features. Therefore, we propose the eye-region context based AdaBoost multiple classifier for the efficient non-contact gaze tracking and mouse implementation. The proposed method detects, tracks, and aggregates various eye features to evaluate the gaze and adjusts active and semi-supervised learning based on the on-screen cursor. The proposed system has been successfully employed in eye location, and it can also be used to detect and track eye features. This system controls the computer cursor along the user's gaze and it was postprocessing by applying Gaussian modeling to prevent shaking during the real-time tracking using Kalman filter. In this system, target objects were randomly generated and the eye tracking performance was analyzed according to the Fits law in real time. It is expected that the utilization of non-contact interfaces.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.10
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• pp.409-418
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• 2022

In this paper, we propose SAAnnot-C3Pap, a semi-automatic annotation method for obtaining ground truth of a player's posture. In order to obtain ground truth about the two-dimensional joint position in the existing music domain, openpose, a two-dimensional posture estimation method, was used or manually labeled. However, automatic annotation methods such as the existing openpose have the disadvantages of showing inaccurate results even though they are fast. Therefore, this paper proposes SAAnnot-C3Pap, a semi-automated annotation method that is a compromise between the two. The proposed approach consists of three main steps: extracting postures using openpose, correcting the parts with errors among the extracted parts using supervisely, and then analyzing the results of openpose and supervisely. Perform the synchronization process. Through the proposed method, it was possible to correct the incorrect 2D joint position detection result that occurred in the openpose, solve the problem of detecting two or more people, and obtain the ground truth in the playing posture. In the experiment, we compare and analyze the results of the semi-automated annotation method openpose and the SAAnnot-C3Pap proposed in this paper. As a result of comparison, the proposed method showed improvement of posture information incorrectly collected through openpose.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.1
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• pp.1-8
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• 2009

The objective of this study is to design a system that automatically monitors the state of interior spaces like offices where lots of people are coming and going through image data and environment data, which includes temperature, humidity, and other conditions, and implement and test related application programs. In practice, there are lots of image data automatically obtained by unmanned equipments, such as certain types of CCTVs, for monitoring situation in usual interior spaces. This image data can be used as a more effective manner by establishing a system that recognizes situation in specific interior spaces based on the relationship between image and environment data. For instance, it is possible to perform unmanned on/off controls for various electronic equipments, such as air conditioners, lights, and other devices, through analyzing the data acquisited from environment sensors (temperature, humidity, and illumination) as dynamic states are not maintained for a specified period of time. For implementing these controls, this study analyzes environment data acquisited from temperature and humidity sensors and image data input from wireless cameras to recognize situation and that can be used to automatically control environment variables configured by users. Experiments were applied in a laboratory where unmanned controls were effectively performed as automatic on/off controls for the air conditioner and lights installed in the laboratory as certain motions were detected or undetected for a specified period of time.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.103-110
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• 2023

With the spread of the Internet, anonymous communities emerged along with the activation of communities for communication between people, and many users are doing harm to others, such as posting aggressive posts and leaving comments using anonymity. In the past, administrators directly checked posts and comments, then deleted and blocked them, but as the number of community users increased, they reached a level that managers could not continue to monitor. Initially, word filtering techniques were used to prevent malicious writing from being posted in a form that could not post or comment if a specific word was included, but they avoided filtering in a bypassed form, such as using similar words. As a way to solve this problem, deep learning was used to monitor posts posted by users in real-time, but recently, the community uses words that can only be understood by the community or from a human perspective, not from a general Korean word. There are various types and forms of characters, making it difficult to learn everything in the artificial intelligence model. Therefore, in this paper, we proposes a preprocessing technique in which each character of a sentence is imaged using a CNN model that learns the consonants, vowel and spacing images of Korean word and converts characters that can only be understood from a human perspective into characters predicted by the CNN model. As a result of the experiment, it was confirmed that the performance of the LSTM, BiLSTM and CNN-BiLSTM models increased by 3.2%, 3.3%, and 4.88%, respectively, through the proposed preprocessing technique.

• Journal of Digestive Cancer Research
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• v.6 no.2
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• pp.69-72
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• 2018

Mucinous adenocarcinoma occurs in 1.6-25.4% of patients with colorectal cancer. We report a case of a 27-year-old man with negative findings on initial colonoscopic biopsy, but finally diagnosed with mucinous adenocarcinoma of the colon. After undergoing an abdominal CT due to persistent abdominal pain, he was transferred to our hospital. The abdominal CT showed a diffuse and irregular wall thickening in the distal transverse colon. Due to the edema and stenosis of colonic wall, it was difficult to insert the colonoscope into the proximal region; a biopsy revealed chronic colitis with lymphofollicular hyperplasia. Transverse colectomy and lymph node dissection were performed. The diagnosis was mucinous adenocarcinoma of approximately 20×4.5 cm. Compared to adenocarcinoma, mucinous adenocarcinoma is found in a younger population with an advanced stage and is less responsive to palliative chemotherapy. Therefore, recalcitrant abdominal pain even in young people warrants early detection through appropriate examinations such as abdominal CT and colonoscopy.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.40-55
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• 2023

Arbor Day is a day that encourages people to plant trees and symbolizes the timing of planting. Arbor Day has been honored on April 5th in Korea, but it often does not agree to actual planting time due to global warming. This study confirmed the discrepancy between Arbor Day and regional soil-thawing times and reviewed alternative dates for tree planting using satellite-based soil-thaw data (FT-ESDR) from 1991 to 2020. Study results showed that the start time of planting on the Korean Peninsula, which was indicated by soil-thaw dates, was March 24 during 1991-2000, and it progressively changed to March 17 during 2011-2020. Should Arbor Day be changed based on soil-thaw periods, mid-March would be the most comprehensive, suitable alternative period considering the number of governmental administration units (cities and counties) and the land area of soil-thaw. Tree-Planting Day (March 14) and International Day of Forests (March 21) were found suitable for alternative dates to Arbor Day because they were close to the average soil-thaw time of Korean Peninsula (March 19) and land area whose soil-thaw time was within 10 days from those two dates ranged from 52.5% to 58.8% centered geographically on the mid-section of the peninsula. Since the periods of soil-thaw will continue to change due to climate change, it is necessary to reflect the trend of advancing planting periods in the future if Arbor Day is changed to an earlier date.