• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.79-85
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• 2016

This paper proposes a threshold-based fall recognition algorithm to discriminate between falls and activities of daily living (ADL) using a tri-axial accelerometer and a bi-axial gyroscope sensor mounted on the upper sternum. The experiment was executed ten times according to the proposed experimental protocol. The output signals of the tri-axial accelerometer and the bi-axial gyroscope were measured during eight falls and eleven ADL action sequences. The threshold values of the signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) parameter were calculated using MATLAB. From the preliminary study, three thresholds (TH1, TH2, and TH3) were set so that the falls could be distinguished from ADL. When the parameter SVM_Acc is greater than 2.5 g (TH1), ${\omega}_{res}$ is greater than 1.75 rad/s (TH2), and ${\theta}_{res}$ is greater than 0.385 rad (TH3), these action sequences are recognized as falls. If at least one or more of these conditions is not satisfied, the sequence is classified as ADL.

• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.93-104
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• 2017

The objective of this study was two fold: firstly, we analyzed how the Geoparks have been run since the first one had been designated on December 31th, 2015. We then investigated how visitors' geographical and geological recognitions on the parks have changes. We visited geosites and investigated how well these sites accorded with the conditions for running Geoparks. In addition, scenery pictures and hashtags uploaded in Instagram between 2015 and 2016 were collected in order to analyze visitors preferences on the geosites along the, Hantan Imjingang River Geopark. Results showed that the hotspots were Bidulginang Waterall, Art Valley, and Jaein Waterfall. Compared to the ratio of geographical and geological references in 2015, the hashtags in all of these three geosites increased. The increases were as much as 3% in Bidulginang Falls, 0.6% in Art Valley, and 5% in Jaein Falls. In labelling the geographical and geological terms in Bidulginang Falls and Jaein Falls, the most frequently mentioned hashtags was "columnar joint", followed by "natural monument", "Geopark", and "basalt canyon". This study includes the study of visitors recognition which is one of the most important, but somehow neglected factor for the geopark's management.

• The Korean Journal of Emergency Medical Services
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• v.20 no.3
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• pp.69-83
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• 2016

Purpose: The purpose of this study was to explore the factors influencing evidence-based fall prevention nursing performance of hospital nurses. Methods: A self-reported questionnaire was completed by 344 nurses from three general hospitals from January 20 to March 10, 2013. The study instruments included general characteristics of the subjects, and awareness and performance of fall prevention. Data were analyzed by t test, ANOVA, Pearson's correlation, and multiple regression using SPSS v. 20.0. Results: There were statistically significant differences in awareness and performance according to age, marital status, clinical experiences, workplace, experience of fall prevention education, knowledge of fall prevention, compliance with fall prevention, attention level toward prevention, recognition level of potential falls, nurse responsibility for falls, importance of fall prevention, efforts level for fall prevention, and awareness score of falls prevention. There was a positive correlation among awareness and performance of fall prevention. Based on the multiple regression analysis, compliance with fall prevention, efforts level for fall prevention, and awareness score of falls prevention were significant predictors for performance of fall prevention. The explanation power of the model was 64.1%. Conclusion: The findings revealed the need to develop an effective nursing intervention to improve hospital nurses' performance for fall prevention.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.254-259
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• 2013

A newly developed fall recognition algorithm using gravity weighted 3-axis accelerometer data as the input of HMM (Hidden Markov Model) is introduced. Five types of fall feature parameters including the sum vector magnitude(SVM) and a newly-defined gravity-weighted sum vector magnitude(GSVM) are applied to a HMM to evaluate the accuracy of fall recognition. A GSVM parameter shows the best accuracy of falls which is 100% of sensitivity and 97.96% of specificity, and comparing with SVM, the results archive more improved recognition rate, 5.2% of sensitivity and 4.5% of specificity. GSVM shows higher recognition rate than SVM due to expressing falls characteristics well, whereas SVM expresses the only momentum.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.7-14
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• 2017

A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) were calculated using MATLAB. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ${\omega}_{res}$ was larger than 1.75 rad/s (TH2), and ${\theta}_{res}$ was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.395-399
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• 2006

A method to recognize unpaved road region using a 3D depth measurement system is proposed for mobile robots. For autonomous maneuvering of mobile robots, recognition of obstacles or recognition of road region is the essential task. In this paper, the 3D depth measurement system which is composed of a rotating mirror, a line laser and mono-camera is employed to detect depth, where the laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The obtained depth information is converted into an image. Such depth images of the road region represent even and plane while that of off-road region is irregular or textured. Therefore, the problem falls into a texture identification problem. Road region is detected employing a simple spatial differentiation technique to detect the plain textured area. Identification results of the diverse situation of unpaved trail are included in this paper.

• Speech Sciences
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• v.10 no.1
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• pp.27-39
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• 2003

This study attempts to verify the reliability of automatically generated segment labels as compared to those obtained by conventional labelling by hand. First of all, an autolabeller is constructed using the standard HMM speech recognition technique. For evaluation, we compare the automatically generated labels with manually annotated labels for the same speech data. The comparison is performed by calculating the temporal difference between an autolabel boundary and its corresponding hand label boundary. When the mismatched duration between two labels falls within 10 msec, we consider the autolabel as correct. The results suggest that overall 78% of autolabels are correctly obtained. It is found that the boundary of obstruents is better aligned than that of sonorants and vowels. In case of stop sound classes, strong stops in manner-of-articulation wise and velar stops in place-of-articulation wise show better performance in boundary alignment. The result suggests that more phone-specific consideration is necessary to improve autosegmentation performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.169-173
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• 2021

Currently, safety accidents in hospitals are steadily occurring. In particular, safety accidents of elderly patients with weak immunity, such as nursing hospitals, continue to occur, and countermeasures are needed. Most accidents are caused by patient movement. As a method of reducing safety accidents by analyzing and recognizing the sound of the inpatient room according to the movement of the patient, this paper classifies the sound pattern for sound recognition in the hospital inpatient room using DTW (Dynamic Time Warping), an algorithm applicable to time-series pattern recognition. It was analyzed by applying it to the inpatient room environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.129-137
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• 1995

Recently the recognition of Logistics becomes important to enterprises as a means for improving their competition, but the Korea enterpries falls for far behind in management techniques for analyzing realities and problems in the logistic compare to the advanced countries. In describing a distribution network, we have stated that it is basically a system or a set of locations that ship, receive or store material plus the routes that connect these locations. Using a Dynamic Programming on logistics, we can decrease the inventory level and increase the service level on logistics and the management performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.552-554
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• 2021

We propose a fall recognition system using a deep learning algorithm using motion data acquired by a Doppler radar sensor. Among the deep learning algorithms, an RNN that has an advantage in time series data is used to recognize falls. The fall data of the Doppler radar sensor has a temporal characteristic as time series data, and the structure of the RNN is sequenced because the result only determines whether a fall or not It is designed in a structure that outputs a fixed size to the input.