• Journal of Information Technology Services
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• v.12 no.4
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• pp.435-447
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• 2013

If we use a smartphone to analyze and detect falling, it is a huge advantage that the person with a sensor attached to one's body is free from awareness of difference and limitation of space, unlike attaching sensors on certain fixed areas. In this paper, we suggested effective posture analysis of smartphone users, and fall detecting system. Suggested algorithm enables to detect falling accurately by using the fact that instantaneous change of acceleration sensor is different according to user's posture. Since mobile applications working on smart phones are low in compatibility according to mobile platforms, it is a constraint that new development is needed which is suitable for sensor equipment's characteristics. In this paper, we suggested posture analysis algorithm using smartphones to solve the problems related to user's inconvenience and limitation of development according to sensor equipment's characteristics. Also, we developed fall detection system with the suggested algorithm, using hybrid mobile application which is not limited to platform.

• Journal of Sensor Science and Technology
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• v.23 no.4
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• pp.251-258
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• 2014

While the feasibility of vertical velocity as a threshold parameter for pre-impact fall detection has been verified, effects of sensor attachment locations and methods calculating vertical acceleration and velocity on the detection performance have not been studied yet. Regarding the vertical velocity-based pre-impact fall detection, this paper investigates detection accuracies of eight different cases depending on sensor locations (waist vs. sternum), vertical accelerations (accurate acceleration based on both accelerometer and gyroscope vs. approximated acceleration based on only accelerometer), and vertical velocities (velocity with attenuation vs. velocity difference). Test results show that the selection of waist-attached sensor, accurate acceleration, and velocity with attenuation based on accelerometer and gyroscope signals is the best in overall in terms of sensitivity and specificity of the detection as well as lead time.

• Journal of Korea Game Society
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• v.10 no.6
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• pp.47-55
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• 2010

In this research, a chair type game controller has been developed. We implement pressure sensors on a chair so that user's whole body movements can be signalized. The signals linked to a game system. This process occurs a meaningful exercise effect. Because users can play exergame setting on the chair type game controller, it is reduces risk of physical injury such as falling down and the Joint pressure compared existing exergame controllers. Based on the advantages of seating, elderly and obese can be provided appropriate exergame.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.173-181
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• 2016

Several falling-sensing terminals have been developed which are able to detect the fall/upset accident of the elderly and transmit the collapsing signals to the administrative server via RF communication using 400MHz bandwidth. Supposing there might be no communication problem within a sanatorium, just single terminal without relay function had been made as a prototype in the first stage of development. However some communication problems have discovered at the coner of the floor apart from the other floor diagonally in the same building. Same kind of problems have been happened in the area behind a steel fence or a car. To solve these kinds of communication problems, we have developed several terminals and added up relay function to them. In this paper we represented that the success ratio of the communication has been improved by using relay function among several terminals.

• Journal of Information Technology Services
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• v.11 no.sup
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.998-999
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• 2023

본 논문에서는 스마트 홈 기반 노약자 응급상황 탐지 및 대응 시스템을 설계하였다. 2 개의 레이다 센서를 활용하여 센서 데이터를 분석하고 분류하며, 사용자의 상태를 취침, 외출, 응급상황 총 3 가지 경우로 식별한다. AWS 서버의 데이터베이스를 통해 응급상황 및 낙상 감지 이력을 축적하여 맞춤형 서비스를 제공한다. 어플리케이션을 통해 응급상황 자동 신고 접수와 센서 오작동시 자동 신고 접수 수동 취소 기능을 제공하는 응급상황 탐지 및 대응 시스템을 소개한다.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.11
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.91-98
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• 2019

Accidents of worker that occur in isolated places are difficult to rescue, unlike general construction accidents. There is a problem of communication limitation when an accident occurs in an isolated place. Also, it is difficult to search the accident place due to the absence of CCTV. In order to solve these problems, this paper proposes a device that combines IoT technology with a safety helmet, which must be worn in the workplace. The proposed device additionally designs and implements a real-time PPG(Photoplethysmography) sensor, body temperature sensor, accelerometer sensor and a camera sensor on the helmet. The proposed helmet system allows the user and the control center to monitor the state of the worker. In addition, when an abnormal biological signal or fall occurs to the worker, the image is transmitted to the control center. By using the proposed system, it is possible to check the status of the worker in real time, so that it has an advantage that it can cope with the accident quickly.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.59-64
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• 2008

D. W. KANG, J. S. CHOI, and G. R. TACK, A Study on Real-Time Sports Activity Classification & Monitoring Using a Tri-axial Accelerometer. Korean Jouranl of Sport Biomechanics, Vol. 18, No. 2, pp. 59-64, 2008. This study was conducted to study the real-time sports activity classification and monitoring using single waist mounted tri-axial accelerometer. This monitoring system detects events of sports activities such as walking, running, cycling, transitions between movements, resting and emergency event of falls. Accelerometer module was developed small and easily attachable on waist using wireless communication system which does not constrain sports activities. The sensor signal was transferred to PC and each movement pattern was classified using the developed algorithm in real-time environment. To evaluate proposed algorithm, experiment was performed with several sports activities such as walking, running, cycling movement for 100sec each and falls, transition movements(sit to stand, lie to stand, stand to sit, lie to sit, stand to lie and sit to lie) for 20 times each with 5 healthy subjects. The results showed that successful detection rate of the system for all activities was 95.4%. In this study, through sports activity monitoring. it was possible to classify accurate sports activities and to notify emergency event such as falls. For further study, the accurate energy consumption algorithm for each sports activity is under development.