• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.1
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• pp.38-44
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• 2016

In this paper, Author of this article presents a system to ensure the safety of residents in case the residents occurs an fall situation. Author of this article use weighted difference image and motion vector. Proposed system suggested the fall detection algorithm using weighted difference image and motion vector. Fall detection algorithm showed a success rate of 85% ~ 97.1% through 150 experiments. Proposed algorithm showed a litter higher or similar success rate than the existing camera based system.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.19-24
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• 2013

The problem of elderly people with weak physical health has become a very important issue in the aging society. Elderly people with very low judgment and decision-making skills often falls because of the degradation of the strength and balance. Due to the fall triggered off fractures, parenchyma damage, and casualties, generally fast emergency treatment is needed. In this paper, an automatic fall detection system consisting of a triaxial accelerometer and tilt sensor. Using the fall system, the performance of the system was analyzed in many situations. The experimental results showed more than 92% analytical skills.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.547-550
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• 2020

노인인구의 급증에 따라 노인 건강에 대한 관심이 증가하였고 노인 낙상을 발견하는 방법에 대한 관심도 함께 대두되기 시작하였다. 낙상 사고의 경우 낙상을 일으킨 원인보다 낙상이 제때 감지되지 않아 발생하는 이후의 상황이 더욱 심각한 결과를 초래한다. 따라서 낙상이 발생했을 때, 바로 낙상을 감지할 수 있는 시스템 구축이 필요하다. 다양한 낙상 검출을 위한 방법이 존재하지만 그 중 착용이 쉽고 원격지에서 관찰 및 관리가 가능한 웨어러블(Wearable) 기기의 센서 데이터를 사용한 낙상 검출을 진행하였다. 본 논문에서는 머신 러닝 모델들을 사용해서 낙상 검출 성능 비교 및 적절한 모델을 제안한다. 기계 학습 기반의 모델인 결정 트리(Decision Tree), 랜덤 포래스트(Random Forest), SVM(Support Vector Machine)을 사용하여 실제 측정된 데이터에 낙상 검출 학습 능력을 정량화하였다. 또한, 모델의 입력 값에 적용한 데이터 분할, 전처리 및 특징 추출 방법을 통해서 효율적인 낙상 검출을 위한 기계학습 관점에서의 타당성을 판단하고자 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.781-788
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• 2013

In this paper, we introduce a quadruped robot-based alarm system for monitoring the emergency situation due to falling in the elderly. Quadruped robot includes the FPGA Board(Field Programmable Gate Array) applying a red-color tracking algorithm. To detect a falling of the elderly, a sensor node is worn on chest and accelerations and angular velocities measured by the sensor node are transferred to quadruped robot, and then the emergency signal is transmitted to manager if a fall is detected. Manager controls the robot and then he judges the situation by monitoring the real-time images transmitted from the robot. If emergency situation is decided by the manager, he calls 119. When the fall detection system using only sensor nodes is used, sensitivity of 100% and specificity of 98.98% were measured. Using the combination of the fall detection system and portable camera (robot), the emergency situation was detected to 100 %.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.441-442
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• 2022

현재 고령화 시대에 접어들며 대한민국 고령화 노인 비율이 2022년 기준 17%에 도달했다. 그만큼 대한민국의 고령화 시대가 급격하게 진행되고 있다. 노약자 수가 증가함에 따라 안전사고 발생 빈도가 증가했다. 본 논문은 노약자들이 심박, 체온 센서의 측정값을 그래프로 표현한 후 검사결과를 토대로 의사와 원격진료가 가능하여 병원을 방문하지 않고 의사와 상담 및 진료가 가능하다. 또한, 낙상 상황 발생 시에는 낙상 상황 발생 후 일정 시간 동안 움직임이 감지되지 않으면 보호자에게 위치 및 안전문자가 전송되어 안전사고 문제를 예방할 수 있다.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.2
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• pp.31-37
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• 2016

Falling situations are extremely critical events for the elderly person who requires timely and adequate emergency service. For the case of emergency, the information of falling and its direction can be used as an important information for the first aid treatment of the injured person. In this paper, a falling detection system which can pinpoint the falling event with the falling direction is implemented. In order to detect the fall situation, a single gyroscope (MPU-6050) is used in the developed system. The fall detection algorithm that can classify 8 different fall directions such as front, back, left, right and in between falls is proposed. The direction of the fall is decided by examining the acceleration values of X and Y directions of the sensor. It is shown that the proposed algorithm successfully detects the falling event and the falling direction with probability of 97% for a selected value of acceleration threshold.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2546-2553
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• 2015

Falling can happen to anyone, anywhere at anytime and especially it is one of the risk factor that can lead causes of death of persons aged 65 and over. Recently, the study of fall detection mechanisms as a smart healthcare service based on the IoT(Internet of Things) are being actively investigated. In this paper, we implement a fall detection system using arduino as a smart sensor communicates with a smart device. When transmitting the information of the acceleration on a sensor smart sensor with a BLE(Bluetooth Low Energy), the smart device processing and analyzing this information. and determines a fall situation. A fall detection system based on the Internet of Things which using smart sensor and smart device, has the advantage of being able to overcome the mobility and portability constraints.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1226-1274
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• 2022

In this paper, we dealt with a safety helmet for detecting dangerous situations that focuses on falling accidents and gas leaks, which are the main causes of industrial accidents. the fall situation range was set through gravity acceleration measurement using an acceleration sensor, and as a result, a fall detection rate of 80% could be confirmed. .In addition, the dangerous gas concentration was measured through a gas sensor, and when a digital value of 188 or more was output through a serial monitor, it was determined as a gas dangerous situation, and a fall warning message and a gas warning message could be checked through a smart-phone application produced based on the app inventor program.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.25-27
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• 2021

With the recent development of deep learning technology, computer vision-based AI technologies have been studied to analyze the abnormal behavior of objects in image information acquired through CCTV cameras. There are many cases where surveillance cameras are installed in dangerous areas or security areas for crime prevention and surveillance. For this reason, companies are conducting studies to determine major situations such as intrusion, roaming, falls, and assault in the surveillance camera environment. In this paper, we propose a real-time abnormal behavior analysis algorithm using object detection and tracking method.