• 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 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 %.

• 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.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.384-391
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• 2023

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1751-1768
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• 2011

Falls are one of the most concerned accidents for elderly people and often result in serious physical and psychological consequences. Many researchers have studied fall detection techniques in various domain, however none released to a commercial product satisfying user requirements. We present a systematic modeling and evaluating procedure for best classification performance and then do experiments for comparing the performance of six procedures to get a statistical classifier based wrist-type fall detector to prevent dangerous consequences from falls. Even though the wrist may be the most difficult measurement location on the body to discern a fall event, the proposed feature deduction process and fall classification procedures shows positive results by using data sets of fall and general activity as two classes.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.86-91
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• 2020

As the number of elderly people living alone increases, the frequency of fall accidents is also increasing. Falls are a threat to the health of older adults and can reduce their ability to remain independent. To solve this problem, we need real-time technology to recognize and respond to the critical condition of the elderly living alone. Therefore, this paper proposes a modified fall detection algorithm based on YOLO-KCF that can check one of the emergency situations in real time for the elderly living alone. YOLO can detect not only the detection of objects, but also the behavior of objects, namely stand and fall. Therefore, this paper can detect fall using the ratio of change of boundary box between stand and falling situation, and this algorithm can improve the shortcomings of KCF.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.401-406
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• 2008

Fall event detection is one of the most common problems for elderly people, especially those living alone because falls result in serious injuries such as joint dislocations, fractures, severe head injuries or even death. In order to prevent falls or fall-related injuries, several previous methods based on video sensor showed low fall detection rates in recent years. To improve this problem and outperform the system performance, this paper presented a novel approach for fall event detection in the elderly using a subtraction between successive difference images and temporal templates in real time environment. The proposed algorithm obtained the successful detection rate of 96.43% and the low false positive rate of 3.125% even though the low-quality video sequences are obtained by a USB PC camera sensor. The experimental results have shown very promising performance in terms of high detection rate and low false positive rate.

• 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 Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.249-251
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• 2021

In this paper, we introduce the deep-learning system using Tensorflow for recognizing situations that can occur fall situations when the elderly are moving or standing. Fall detection uses the LSTM (long short-term memory) learned using Tensorflow to determine whether it is a fall or not by data measured from wearable accelerator sensor. Learning is carried out for each of the 7 behavioral patterns consisting of 4 types of activity of daily living (ADL) and 3 types of fall. The learning was conducted using the 3-axis acceleration sensor data. As a result of the test, it was found to be compliant except for the GDSVM(Gravity Differential SVM), and it is expected that better results can be expected if the data is mixed and learned.