• Journal of the Korean Society of Safety
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• v.35 no.5
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• pp.74-85
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• 2020

Smart phone zombie or Smombie means pedestrians who walk without attention to their surroundings because they are focused upon their smart phone. Because the traffic accidents and injuries caused by Smombie have been increased rapidly in recent years, the social attention and policies are needed to prevent it. This study was conducted to analyze Smombie's current status and some solutions used before and to propose new improved method through the latest ICT trend. In this study, we did the field survey to check Smombies at several places in Seoul through people counting, and found that a lot of pedestrians still use the smart phone while walking. And we analyzed many case studies about some solutions to prevent Smombies previously. The case studies include legal regulations, government policies, smart phone app services and facilities that are used before. We studied them through internet searches and reference studies and we also checked the current operating situation as visiting several places that the solutions actually has been operated. Therefore, we found there are some limitations in previous solutions in terms of effectiveness and management. To consider new solution that can be expected to overcome the limitations, we analyzed the latest ICT trends focused on features to utilize the Smombie prevention, especially video recognition and digital signage. In these days, video recognition has been developed rapidly with assistance of AI technology and it can recognize the specific pedestrian's characteristics such as holding smart phone as well as hair style, clothes, backpack and etc. On the other hands, the digital signage is the convergence device that includes big display, network connection and various IoT sensors. It can be used as public media in many places for public services as well as advertising. Through these analysis results, we show the requirements and the user scenario for the improved method to prevent Smombie. Finally, we propose to develop R&D technology to recognize Smombie exactly as pedestrian attributes and to spread creative contents to increase pedestrian's interest and engagement for Smombie prevention through digital signage.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.200-208
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• 2022

In this study, an automatic information collection system and geographic information construction algorithm for the transportation disadvantaged using electric mobility aids are implemented using an object recognition model. Recognizes objects that the disabled person encounters while moving, and acquires coordinate information. It provides an improved route selection map compared to the existing geographic information for the disabled. Data collection consists of a total of four layers including the HW layer. It collects image information and location information, transmits them to the server, recognizes, and extracts data necessary for geographic information generation through the process of classification. A driving experiment is conducted in an actual barrier-free zone, and during this process, it is confirmed how efficiently the algorithm for collecting actual data and generating geographic information is generated.The geographic information processing performance was confirmed to be 70.92 EA/s in the first round, 70.69 EA/s in the second round, and 70.98 EA/s in the third round, with an average of 70.86 EA/s in three experiments, and it took about 4 seconds to be reflected in the actual geographic information. From the experimental results, it was confirmed that the walking weak using electric mobility aids can drive safely using new geographic information provided faster than now.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.107-114
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• 2012

We have developed a textile capacitive pressure sensor for smart gait analysis. The proposed system can convert sensor signal into step counts and pressure levels by different posture. To evaluate the performance of insole type textile capacitive sensor, we measured capacitance change by increment of weights from 10 kg to 100 kg with 10 kg increment using M1 class rectangular weights (four 20 kg weights and two 10 kg weights) which have ${\pm}10%$ tolerance. The result showed non-linearity characteristic of a general capacitive pressure sensor. The test was performed according to a test protocol for four different postures (sitting, standing, standing on a left leg and standing on a right leg) and different walking speeds (1 km/h and 4 km/h). Five healthy male subjects were participated in each test. As we expected, the pressure level was changed by pressure distribution according to posture. Also, developed textile pressure sensor showed higher recognition rate (average 98.06 %) than commercial pedometer at all walking speed. Therefore, the proposed step counts and posture monitoring system using conductive textile capacitive pressure sensor proved to be a reliable and useful tool for monitoring gait parameters.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.325-331
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• 2016

In driving at evening or night, we are not able to recognize the speed bump and so stop suddenly. It could result in accidents. And also, we have a restriction of street light installation in farm road because it could be harmful to the crops and driver could not recognize the walking people. It needs to develop the speed bump with light and streetlight to be non harmful to the crops. So, we develop both the speed bump and streetlight with LED which could be non harmful to the crops and be increased recognition of walking people in farm road. For LED lighting power, we use the solar cells, and piezoelectric elements. It has automatic on/off according to power saving rates without illumination sensor. Minimization of circuit elements and design of minimum resisters and low power LED was used for power saving in assuring 3-days.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• The Journal of Pediatrics of Korean Medicine
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• v.24 no.2
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• pp.1-12
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• 2010

Objectives Taking detailed patient history helps earlier diagnosis and treatment of developmental disability. In this study we analyzed the clinical questionnaire to find out the clinical characteristics of those with five-retardation, five-limpness, or five-stiffness. Methods The data was collected from 484 children under the age of six who have visited H oriental medicine clinic for developmental delay. The clinical questionnaire was filled out by their parents and the data was analyzed statistically. Results 436 children showed symptoms of five-retardation, 90 children suffered from five-stiffness, 54 children showed five-limpness and 7 children suffered from five-stiffness and five-limpness complex. Generally, boys had higher chance to show disease symptoms than the girls (2.32:1) and 40 children (8.26%) reported family history of developmental disability. Cerebral palsy ranks the most common familial disease, followed by developmental delay, mental retardation, autistic disorder and language disorder. Among the children we have studied, 285 children (63.19%) showed delayed unassisted walk while 192 children (42.57%) had language disorder. Also, 138 children (28.51%) had both walk and language disorders. The children in this study also showed delayed toilet training and half of them had little stranger anxiety when they were infants. It was also found that 120 children (24.79%) experienced epilepsy. This study reaffirmed that low birth weight, premature birth, and suffocation are major risks causing neurological damage. Conclusions They had history which including family history, problems at birth, epilepsy, face recognition, muscle tone disorder, delayed walking without assistance, language ability, and toilet training.

• The Korean Journal of Food And Nutrition
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• v.30 no.1
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• pp.83-95
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• 2017

This study examined the socioeconomic factors that affect self-rated health (SRH) in healthy adults, and the relationship of SRH to health-related habits, serum biochemical indices, and nutritional intakes. Subjects consisted of 1,154 healthy adults without any known disease, aged 19 to 65 years (average age of 36.7), whose information was obtained from the 2013 Korean National Health and Nutritional Examination Survey data. Of these subjects, 73 rated themselves as 'very healthy,' 460 indicated that they were 'healthy,' 568 self-identified as 'ordinary', and 53 put themselves in the 'unhealthy' category. The proportion of subjects who chose 'unhealthy' was significantly increased with higher frequencies of disruptions in their daily lives (p<0.05), regret after drinking (p<0.05), smoking (p<0.001), and higher levels of stress (p<0.001). On the other hand, the proportion of subjects reported as 'very healthy' was significantly higher with regular intense (p<0.001) or moderate physical activities (p<0.05), regular walking (p<0.05), a perception of being 'normal' in their body image (p<0.01), a decrease of body weight more than 3 kg in the past year (p<0.05), and without risk factors for metabolic syndrome (p<0.05). Serum triglyceride level was lower, and 25-(OH) vitamin D content was significantly higher, in the 'very healthy' group as compared to the 'unhealthy' group (p<0.05). Dietary fiber and vitamin C intakes were significantly higher in the 'very healthy' group than 'unhealthy' group (p<0.05). The overall results suggest that a healthy lifestyle, including regular exercise, non-smoking, good stress management, and higher intakes of fiber and vitamin C, may be potential factors that affect one's positive perception of health.

• Journal of Biosystems Engineering
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• v.28 no.1
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• pp.65-76
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• 2003

The analysis of cow body parameters is important to provide some useful information fur cow management and cow evaluation. Present methods give many stresses to cows because they are invasive and constrain cow postures during measurement of body parameters. This study was conducted to develop the stereo vision system fur non-invasive analysis of cow body features. Body feature parameters of 16 heads at two farms(A, B) were measured using scales and nineteen stereo images of them with walking postures were captured under outdoor illumination. In this study, the camera calibration and inverse perspective transformation technique was established fer the stereo vision system. Two calibration results were presented for farm A and fm B, respectively because setup distances from camera to cow were 510 cm at farm A and 630cm at farm B. Calibration error values fer the stereo vision system were within 2 cm for farm A and less than 4.9 cm for farm B. Eleven feature points of cow body were extracted on stereo images interactively and five assistant points were determined by computer program. 3D world coordinates for these 15 points were calculated by computer program and also used for calculation of cow body parameters such as withers height. pelvic arch height. body length. slope body length. chest depth and chest width. Measured errors for body parameters were less than 10% for most cows. For a few cow. measured errors for slope body length and chest width were more than 10% due to searching errors fer their feature points at inside-body positions. Equation for chest girth estimated by chest depth and chest width was presented. Maximum of estimated error fur chest girth was within 10% of real values and mean value of estimated error was 8.2cm. The analysis of cow body parameters using stereo vision system were successful although body shape on the binocular stereo image was distorted due to cow movements.

• Journal of Digital Convergence
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• v.9 no.5
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• pp.55-63
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• 2011

The purpose of this study is analyzes the violate behavior the Parking Area for the handicapped and monitor the management problem, therefore presents the improvement alternatives of it. We use the field interview survey method by visiting the government office of cities of 16, commercial facility, housings, medical centers, cultural and sports facilities, express highway rest area. We visited the 50 places and interviewed the 227 violating persons. As a result, violate behavior is divided into the not attachment of sticker on parking possibility and not accompaniment with walking disabled person. We presented the improvement alternatives for the lawful usage of the parking area for the handicapped. First, We have to magnify a recognition and a advertisement about the parking area for the handicapped. Second, We have to improve a management of expense, manpower and civil complains, etc. We have to prepare the legal revision which strengthens the concrete punishment for a violate behavior improvement.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.6-11
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• 2011

In this paper, we propose a gait-based human identification system using eigenfeature regularization and extraction (ERE). First, a gait feature for human identification which is called gait energy image (GEI) is generated from walking sequences acquired from a camera sensor. In training phase, regularized transformation matrix is obtained by applying ERE to the gallery GEI dataset, and the gallery GEI dataset is projected onto the eigenspace to obtain galley features. In testing phase, the probe GEI dataset is projected onto the eigenspace created in training phase and determine the identity by using a nearest neighbor classifier. Experiments are carried out on the CASIA gait dataset A to evaluate the performance of the proposed system. Experimental results show that the proposed system is better than previous works in terms of correct classification rate.