• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.199-206
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• 2013

An emergency monitoring system for the elderly, which uses acceleration data measured with an accelerometer, angular velocity data measured with a gyroscope, and heart rate measured with an electrocardiogram, is proposed. The proposed fall detection algorithm uses multiple parameter combinations in which all parameters, calculated using tri-axial accelerations and bi-axial angular velocities, are above a certain threshold within a time period. Further, we propose an emergency detection algorithm that monitors the movements of the fallen elderly person, after a fall is detected. The results show that the proposed algorithms can distinguish various types of falls from activities of daily living with 100% sensitivity and 98.75% specificity. In addition, when falls are detected, the emergency detection rate is 100%. This suggests that the presented fall and emergency detection method provides an effective automatic fall detection and emergency alarm system. The proposed algorithms are simple enough to be implemented into an embedded system such as 8051-based microcontroller with 128 kbyte ROM.

• 한국정밀공학회지
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• 제28권11호
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• pp.1330-1338
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• 2011

Social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities increase, so does the occurrence of falls that could lead to fractures. Falls are serious health hazards to the elderly. Therefore, development of a device that can detect fall accidents and prevent fracture is essential. In this study, we developed a portable fall detection system for the fracture prevention system of the elderly. The device is intended to detect a fall and activate a second device such as an air bag deployment system that can prevent fracture. The fall detection device contains a 3-axis acceleration sensor and two 2-axis tilt sensors. We measured acceleration and tilt angle of body during fall and activities of daily(ADL) living using the fall detection device that is attached on the subjects'. Moving mattress which is actuated by a pneumatic system was used in fall experiments and it could provide forced falls. Sensor data during fall and ADL were sent to computer and filtered with low-pass filter. The developed fall detection device was successful in detecting a fall about 0.1 second before a severe impact to occur and detecting the direction of the fall to provide enough time and information for the fracture preventive device to be activated. The fall detection device was also able to differentiate fall from ADL such as walking, sitting down, standing up, lying down, and running.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.393-395
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• 2013

현대 사회에는 의학기술의 발전과 생활수준 향상 등으로 고령자들이 증가하고 있다. 고령자들의 낙상은 심한 경우 사망에 까지 이를 수 있는 상당히 큰 위협이 된다. 이러한 문제점을 해결하기 위해 낙상을 감지하는 여러 가지 알고리즘과 하드웨어 시스템의 필요성이 증가 하고 있으며 국내외에서 낙상 감지 시스템의 연구 결과가 발표 되고 있다. 본 논문에서는 3축 가속도 센서를 이용한 낙상 감지 시스템을 소개한다. 낙상 감지 시스템은 3축 가속도 센서 데이터로부터 몇 가지의 파라미터를 계산하여 낙상을 판별한다. 제안된 시스템을 이용하여 최대 98.3%의 sensitivity와 94.7%의 specificity 결과 값을 얻었다.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.370-375
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• 2023

As we enter a super-aged society, studies are being conducted to reduce complications and deaths caused by falls in elderly adults. Research is being conducted on interventions for preventing falls in the elderly, wearable devices for detecting falls, and methods for improving the performance of fall detection algorithms. Wearable devices for detecting falls of the elderly generally use gyro sensors. In addition, to improve the performance of the fall detection algorithm, an artificial intelligence algorithm is applied to the x, y, z coordinate data collected from the gyro sensor. In this paper, we develop a wearable device that uses a gyro sensor, body temperature, and heart rate sensor for health management as well as fall detection for the elderly. In addition, we develop a fall detection and health management system that works with wearable devices and a guardian's mobile app to improve the performance of the fall detection algorithm and provide health information to guardians.

• 한국정보통신학회논문지
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• 제19권11호
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• pp.2546-2553
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• 2015

낙상은 시간과 장소에 상관없이 언제든지 발생할 수 있으며 특히 65세 이상 고령자의 경우 사망에 까지 이를 수 있는 위험요소 중 하나이다. 최근 사물인터넷을 기반으로 하는 스마트 헬스케어 서비스로서 낙상 감지 기술에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 스마트 센서로 동작하는 아두이노와 스마트 디바이스를 연동하여 낙상을 감지하기 위한 시스템을 제안한다. 스마트 센서의 가속도 센서 정보를 블루투스 저전력 기술을 이용하여 전송하면 스마트 디바이스가 이 정보를 가공 및 분석하여 낙상 상황을 판단한다. 스마트 센서와 스마트 디바이스를 이용한 사물인터넷 기반 낙상 감지 시스템은 활동성과 휴대성의 제약을 극복할 수 있다는 장점이 있다.

• 센서학회지
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• 제20권6호
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• pp.420-427
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• 2011

The population of elderly people increases rapidly as our society moves towards the aged one. Healthcare for the elderly becomes an important issue and falling down is one of the critical problems although not well recognized. In this study, a fall detection system was developed using a 3-axis accelerometer. Analyzing fall patterns, we took into account the degree of impact, posture angle, the repetitions of similar movements and the activities after a potential fall and proposed an algorithm of fall detection. Information of the fall sensor was sent to a remote healthcare server through the wireless networks of Zigbee and WLAN. Our system was designed to monitor multiples users. 12 persons participated in experiment and each one performed 24 different movements. Our proposed algorithm was compared with other reported ones. Our method produced the excellent results having a sensitivity of 96.4 % and a specificity of 100 % whereas other methods had a sensitivity range between 87.5 % and 94.8 % and a specificity range between 63.5 % and 83.3 %.

• 로봇학회논문지
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• 제13권1호
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• pp.45-54
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• 2018

This paper presents a vision-based fall detection system to automatically monitor and detect people's fall accidents, particularly those of elderly people or patients. For video analysis, the system should be able to extract both spatial and temporal features so that the model captures appearance and motion information simultaneously. Our approach is based on 3-dimensional convolutional neural networks, which can learn spatiotemporal features. In addition, we adopts a thermal camera in order to handle several issues regarding usability, day and night surveillance and privacy concerns. We design a pan-tilt camera with two actuators to extend the range of view. Performance is evaluated on our thermal dataset: TCL Fall Detection Dataset. The proposed model achieves 90.2% average clip accuracy which is better than other approaches.

• 터널과지하공간
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• 제20권3호
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• pp.207-216
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• 2010

국내에서 현재까지 개발되어 적용된 낙석감지 시스템은 주로 낙석방지망에 와이어나 광섬유, 또는 경사계를 설치하여 와이어의 장력을 측정하거나 낙석 발생에 의해 와이어 단락이나 낙석방지망의 변형 또는 기울어지는 것을 감지하여 낙석을 확인하는 시스템들이다. 이러한 방법들은 낙석방지망이 먼저 설치되어 있어야 하며 낙석이 발생하면 일반적으로 관련 센서와 와이어 등에 대하여 재정비를 해야 다시 원래의 기능을 수행할 수 있게 된다. 이러한 단점을 해결하기 위하여 본 연구에서는 장력이나 변위 측정방식이 아니라 광센서와 카메라가 설치된 구역에서 낙석 통과 여부를 비접촉 방식으로 실시간 감지가 가능하도록 하는 방법을 제시하고 이를 구현하였다. 개발된 시스템을 실내실험 및 현장 적용을 한 결과 낙석 관련 정보를 효과적으로 수집하고 분석할 수 있었다. 또한 낙석 감지 전후 획득한 2장의 사진에 대하여 lighten 또는 difference 연산을 적용할 경우 낙석의 크기와 이동 방향에 대한 개략적 평가가 가능하였다.

• 한국IT서비스학회지
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• 제11권sup호
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• pp.113-121
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• 2012

In this study, a real-time fall detection system based on a smartphone equipped with three-axis accelerometer and magnetometer was proposed and evaluated. The proposed system provides a service that detects falls in real time, triggers alarm sound, and sends emergency SMS(Short Message Service) if the alarm is not deactivated within a predefined time. When both of the acceleration magnitude and angle displacement of the smartphone attached to waist belt are greater than predefined thresholds, it is detected as a fall. The proposed system was evaluated against activities of daily living(walking, jogging, sitting down, standing up, ascending stairs, and descending stairs) and unintended falls induced by a proprietary pneumatic-powered mattress. With the thresholds of acceleration magnitude 1.7g and angle displacement $80^{\circ}$, it showed 96.5% accuracy to detect the falls while all the activities of daily living were not detected as fall.

• 한국정보전자통신기술학회논문지
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• 제9권1호
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• pp.38-44
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• 2016

본 논문에서는 움직임 벡터를 이용한 낙상 감지 시스템에 관해 기술한다. 두드러진 움직임을 위한 가중치 차영상 기법, 움직임 벡터를 이용하여 인간이라고 판단되는 블랍을 검출하고, 추출된 움직임 벡터를 이용하여 낙상 여부를 판단한다. 기존의 영상 기반 낙상 감지 시스템의 경우 특정 방향으로 낙상이 발생하는 경우에만 낙상 감지에 성공하였지만 제안 시스템의 경우 다양한 각도에서 낙상이 발생하여도 상황 판단이 가능하다는 장점이 있다. 실험을 위해서 150개의 상황을 연출하였으며, 약 85% ~ 97.1% 낙상 상황 판단 성공률을 보였다.