• Smart Media Journal
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• v.10 no.3
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• pp.31-38
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• 2021

It is hard to predict when and where a fall accident will happen. Also, if rapid follow-up measures on it are not performed, a fall accident leads to a threat of life, so studies that can automatically detect a fall accident have become necessary. Among automatic fall-accident detection techniques, a fall detection scheme using an IMU (inertial measurement unit) sensor attached to a wrist is difficult to detect a fall accident due to its movement, but it is recognized as a technique that is easy to wear and has excellent accessibility. To overcome the difficulty in obtaining fall data, this study proposes an algorithm that efficiently learns less data through machine learning such as KNN (k-nearest neighbors) and SVM (support vector machine). In addition, to improve the performance of these mathematical classifiers, this study utilized feature data aquired in the frequency space. The proposed algorithm analyzed the effect by diversifying the parameters of the model and the parameters of the frequency feature extractor through experiments using standard datasets. The proposed algorithm could adequately cope with a realistic problem that fall data are difficult to obtain. Because it is lighter than other classifiers, this algorithm was also easy to implement in small embedded systems where SIMD (single instruction multiple data) processing devices were difficult to mount.

• ETRI Journal
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• v.44 no.4
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.11-17
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• 2017

To the elderly, sudden falls are one of the most frightening accidents. If an accident occurs, a prompt action has to be taken to deal with the situation. Recently, there have been a number of attempts to detect sudden falls using acceleration sensors embedded in the mobile devices, such as smart phones and wrist-bands. However, using the sensor readings only, the detection rate of the falls is around 65%. Ordinary daily activities such as running or jumping could not be well distinguished from the falls. In this paper, we describe our attempts on improving the fall-detection rate. We implemented a wrist-band fall detection module, using a three-axis acceleration sensor. With the pattern matching on the fall signal-strength frequency spectrum, in addition to the conventional signal strength measurement, we could improve the detection rate by 9% point. Furthermore, by applying two wrist-bands in the experiment, we could further improve the detection rate to 82%.

• Journal of IKEEE
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• v.18 no.1
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• pp.134-139
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• 2014

According to survey, more than 57.2% of the fall which is the most frequent safety accident of the elders takes place at home. This research aims to verify the fall by measuring and analyzing the floor vibration. And the vibration sensor module was designed with piezo film sensor and operation amplifier. The vibration signals are converted to digital signals through the data acquisition device and vibration sensor module. And then modified the signals into frequency domain to obtain characteristic vibration data. The characteristic signals are verified by K-Nearest Neighbor verification, and experimental results shows the recognition rate 93.6%. Also the fall detection sensor module is useful for extract the meaningful data for fall detection. 10 persons are participated for this experiment.

• Journal of IKEEE
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• v.17 no.1
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• pp.22-28
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• 2013

According to a survey, more than 50% of the elders fall which is the most frequent daily safety accident of the elders takes place at home. Furthermore, the elders fall is anticipated to increase as more elderly people are expected to live alone since, 67.1% of the elders of 65 or more do not hope to live with their children. This research aims to verify the fall by measuring and analyzing the floor vibration, and the hardware system was also designed was Piezo Film Sensor, Op-Amp, and DAQ. The system is consists of signal processing part for measuring floor vibration and alarm part for identifying the consciousness of the user when the fall occurs. The fall detection by vibration signals verified by k-Nearest Neighbor verification, and the results showed the error rate of 3.8%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.461-462
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• 2022

최근 지속적인 출산율의 감소와 평균수명의 증가로 인하여, 대한민국의 초고령 사회는 예상보다 훨씬 빠르게 증가하고 있다. 핵가족 형태가 보편화되며 1인 가구도 함께 늘고 있어서 홀로 사는 노인의 수 역시 증가하는 추세이다. 주거 공간에서 낙상사고와 같은 고령화 안전사고가 많이 발생하고 있다. 혼자 사는 독거노인들의 경우 사고 발생 즉시 대처가 가능한 보호자가 없다는 문제점이 있다. 본 논문에서는 MediaPipe를 이용한 낙상사고 감지 시스템을 개발한다. 먼저, 이 시스템은 MediaPipe를 이용해서 카메라를 통해 실시간으로 수신된 영상에서 사람을 인식하고, 자세 유형 분석을 통해 낙상사고 발생 여부를 판별하여 애플리케이션을 통해 보호자에게 현장 상황을 알려주는 시스템이다. 낙상사고가 발생했다면 보호자용 애플리케이션을 통해 사고 발생 알림 및 현장 사진을 보여준다. 이와 같은 기술을 활용하여 응급상황에 처한 노인을 빠르게 구조하며 독거노인의 생활안전사고 문제를 해결하는 데에 기여하고자 한다.

• 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.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.282-292
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• 2020

In order to minimize casualties from marine vessel accidents that occur frequently at home and abroad, it is important to ensure the safety of the passengers aboard the vessel in the event of an accident. There is an EPIRB system as a system for disaster preparedness in the marine situation currently on the market, but there is a problem that the price is very expensive. In order to overcome the cost problem, which is a disadvantage of previous system, LoRaWAN-based communication is used. LoRaWAN communication-based vessel positioning and risk detection system based on LoRaWAN communication transmits measurement data of each module using two Beacon and GPS modules to stably perform position measurement for both indoor and outdoor situations. The rider danger situation detection system can detect the safety status of the rider using the 3-axis acceleration sensor, collect data from the rider positioning system and the rider safety status detection system, and send to server using LoRa communication. When conducting communication experiments in the long-distance maritime situation and actual communication experiments using the implemented system, it was found that the two experiments showed over 90% communication success rate on average.

• 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.