• Journal of information and communication convergence engineering
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• v.11 no.3
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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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.393-395
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• 2013

In modern society, the elderly over 65 years old are increasing due to development of medical technology and improvement of their standard of living. Severe fall of the elderly can lead to death threats. To solve this problem, several algorithms and hardware systems for fall detection have been studied and developed. In this paper, a fall detection system using 3-axial acceleration sensor is presented. In the fall detection system, several types of fall-feature parameters are calculated and then the fall is determined by using them. Using this system, best sensitivity and specificity are 98.3% and 94.7%, respectively.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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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.

• 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.

• Journal of Sensor Science and Technology
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• v.20 no.6
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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 %.

• The Journal of Korea Robotics Society
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• v.13 no.1
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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.

• Tunnel and Underground Space
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• v.20 no.3
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• pp.207-216
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• 2010

Rockfall monitoring systems generally used in the country are mainly based on the detection of tension of protection wire or tilting of protection post due to rock fall. However, rock fall protection net must be installed prior to the monitoring system and continual maintenance work after each rock fall event is required for a normal operation of these detection systems. To solve these problems, we suggested and implemented a non-contact rock fall detection system using multiple photo sensors and additional camera. After a laboratory experiment and field application, we can conclude that this system is effective and reliable for detecting, collecting and analyzing the rock fall information. In addition, lighten and difference operations on two captured images were able to yield rough estimation of size and direction of rock fall.

• 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.

• 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.