• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.564-566
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• 2015

신호등은 적색, 녹색일 때 다른 의미를 나타낸다. 자동차 운전자와 횡단보도의 보행자는 신호등의 신호에 따라 움직이거나 멈춰야한다. 그러나 이러한 신호를 무시하거나 보지 않을 경우 사고가 발생할 확률이 높다. 또한 곡선형 횡단보도에서는 적외선 센서를 이용한 안내 방송 시스템을 설치하기가 어려운 실정이다. 본 논문에서는 카메라를 이용하여 보행자를 검지하는 방법을 설계 및 구현한다. 보행철주에 설치된 카메라가 보행자를 촬영하고, 촬영된 이미지를 통해 보행자 검지구간을 설정한다. 제안하는 시스템을 사용하면 곡선형 횡단보도에서 보행자 검지를 하는데 효율적이다.

• 한국멀티미디어학회논문지
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• 제17권4호
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• pp.506-513
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• 2014

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

• 한국정밀공학회지
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• 제25권12호
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• pp.65-74
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• 2008

This paper deals with the path creation for stable action of a robot and transformation by using the fuzzy algorithm. Also, the obstacle detection and environmental analysis are performed by a stereo vision device. The robot decides the range and the height using the fuzzy algorithm. Therefore the robot can be adapted in topography through a transformation by itself. In this paper, the robot is designed to have two advantages. One is the fast movability in flat topography with the use of wheels. The other is the moving capability in uneven ground by walking. It has six leg forms for a stable walk. The wheels are fixed on the legs of the robot, so that various driving is possible. The height and the width of robot can be changed variously using four joints of each leg. The wheeled joint has extra DOF for a rotation of vertical axis. So the robot is able to rotate through 360 degrees. The robot has various sensors for checking the own state. The stable action of a robot is achieved by using sensors. We verified the result of research through an experiment.

• The Journal of Korean Physical Therapy
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• 제22권2호
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• pp.31-38
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• 2010

Purpose: We investigated the relationship of depression and the ability to engage in activities of daily living in the elderly. Methods: Subjects (n = 182) were 60 years or older and who attended the Senior College of Gimhae Senior Welfare Center. We collected data via a questionnaire, through a Self- recording method and through individual interviews. We collected data on personal and general characteristics, level of depression, and activities of daily living. Results: Among all subjects, 51.1% reported having depressionMild depression was reported by 29.7% (54 subjects), moderate depression by 13.2% (24 subjects), and severe depression by 8.2% (15 subjects). Activities of daily living, including walking, climbing stairs, standing from a chair and sitting on and using toilets, using a telephone, bathing, shopping, cleaning house, and managing money were significantly lower in elderly subjects who were depressed (p<0.05). The greater the level of depression, the less able they were to engage in activities of daily living. Conclusion: These findings may help us achieve early detection of depression in the elderly and provide mediated arbitration so that they can have better health condition and greater ability to engage in activities of daily living.

• 한국멀티미디어학회논문지
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• 제25권3호
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• pp.510-518
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• 2022

This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.

• 동서간호학연구지
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• 제28권2호
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• pp.122-131
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• 2022

Purpose: This study aimed to identify the factors related to fear of falling (FOF) in different age groups from community-dwelling mid to late-adults. Methods: To identify the factors related to FOF, data of 162,684 adults over 45 years of age from 2019 Community Health Survey was analyzed using logistic regression with complex samples. Results: Factors related to FOF found in all age groups were sex, previous experience of falls, physical activity levels over moderate intensity, subjective health status, number of chronic diseases, stress, depression, and cognitive decline. In the 45-64 age group, the FOF was significantly higher in the groups of low education level and low monthly household income. In the 65-74 and over 75 age groups, the FOF was significantly higher in the groups of not living with spouse and walking not practiced. Conclusion: We suggests that understanding of risk factors and early detection of fall risk patients in each age group are necessary to establish and apply tailored fall prevention programs for prevention and management of the FOF in community-dwelling mid to late-adults.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.48-54
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• 2023

Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 하계학술대회
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• pp.1249-1252
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• 2022

In this paper, we propose a conceptual framework that identifies video frames in motion containing the movement of people and vehicles in traffic videos. The automatic selection of video frames in motion is an important topic in security and surveillance video because the number of videos to be monitored simultaneously is simply too large due to limited human resources. The conventional method to identify the areas in motion is to compute the differences over consecutive video frames, which has been costly because of its high computational complexity. In this paper, we reduced the overall complexity by examining only the keyframes (or I-frames). The basic assumption is that the time period between I-frames is rather shorter (e.g., 1/10 ~ 3 secs) than the usual length of objects in motion in video (i.e., pedestrian walking, automobile passing, etc.). The proposed method estimates the possibility of videos containing motion between I-frames by evaluating the difference of consecutive I-frames with the long-time statistics of the previously decoded I-frames of the same video. The experimental results showed that the proposed method showed more than 80% accuracy in short surveillance videos obtained from different locations while keeping the computational complexity as low as 20 % compared to the HM decoder.

• 한국컴퓨터정보학회논문지
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• 제28권4호
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• pp.41-51
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• 2023

본 논문에서는 보행자의 걸음걸이로부터 분노 감정 검출을 위한 다중 시간 윈도 특징 추출 기술을 제안한다. 기존의 걸음걸이 기반 감정인식 기술에서는 보행자의 보폭, 한 보폭에 걸리는 시간, 보행 속력, 목과 흉부의 전방 기울기 각도(Forward Tilt Angle)를 계산하고, 전체 구간에 대해서 최솟값, 평균값, 최댓값을 계산해서 이를 특징으로 활용하였다. 하지만 이때 각 특징은 보행 전체 구간에 걸쳐 항상 균일하게 변화가 발생하는 것이 아니라, 때로는 지역적으로 변화가 발생한다. 이에 본 연구에서는 장기부터 중기 그리고 단기까지 즉, 전역적인 특징과 지역적인 특징을 모두 추출할 수 있는 다중 시간 윈도 특징 추출(Multi-Time Window Feature Extraction) 기술을 제안한다. 또한, 제안하는 특징 추출 기술을 통해 각 구간에서 추출된 특징들을 효과적으로 학습할 수 있는 앙상블 모델을 제안한다. 제안하는 앙상블 모델(Ensemble Model)은 복수의 분류기로 구성되며, 각 분류기는 서로 다른 다중 시간 윈도에서 추출된 특징으로 학습된다. 제안하는 특징 추출 기술과 앙상블 모델의 효과를 검증하기 위해 일반인에게 공개된 3차원 걸음걸이 데이터 세트를 사용하여 시험 평가를 수행했다. 그 결과, 4가지 성능 평가지표에 대해서 제안하는 앙상블 모델이 기존의 특징 추출 기술로 학습된 머신러닝(Machine Learning) 모델들과 비교하여 최고의 성능을 달성하는 것을 입증하였다.

• 한국운동역학회지
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• 제18권2호
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• pp.59-64
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• 2008

본 연구는 3축 가속도 센서를 허리에 부착하여 실시간으로 스포츠 동작분류를 할 수 있는 모니터 링에 관한 연구를 실시하였다. 이 모니터링 시스템은 스포츠 동작의 기본이라고 할 수 있는 걷기, 달리기, 자세변화 동지 정지상태의 동작들과 추가적으로 사이클링 동작을 분류할 수 있도록 하였다. 또한 운동 시에 발생할 수 있는 낙상을 감지하여 위급상황에 대한 정보도 나타나게 하였다. 가속도센서모듈은 인체에 부착된 형태로 스포츠 활동을 모니터링하기 위하여 소형으로 설계되었으며 활동에 방해가 되지 않게 허리에 부착되었다. 측정된 데이터는 RF통신을 통해 PC로 전송되며 알고리즘을 통해 실시간으로 동작분류를 시행하게 된다. 개발된 알고리즘을 검증하기 위한 실험으로 5명의 피험자를 대상으로 서로 다른 속도의 걷기, 달리기, 사이클링 동작을 각각 100초간 실시하였으며 낙상과 자세변화 동작(앉았다 일어서기, 누웠다 일어서기, 서있다 앉기, 누웠다 앉기, 서있다 눕기, 앉았다 눕기)은 각각 20회씩 실행하였다. 그 결과 동작분류 정확도는 95.4%를 나타내었다. 이번 연구에서 스포츠 모니터링을 통하여 정확한 자신의 운동 정보를 알려주고 운동 시에 발생하는 낙상에 대한 위급상황을 알려줌으로써 스포츠 활동에 도움을 주고자 하였으며, 추가적인 연구로 각각의 스포츠 활동에 대한 정확한 에너지 소비 추정 알고리즘을 개발 중에 있다.