Emergency Monitoring System Based on a Newly-Developed Fall Detection Algorithm

Yi, Yun Jae,Yu, Yun Seop The Korea Institute of Information and Commucation 2013 Journal of information and communication convergen Vol.11 No.3

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.

Emergency Exit Signs Detecting Smart Glasses Based on Deep Learning for the Visually Impaired

Ranjai Baidya,Heon Jeong 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10

Emergency exit signs are crucial during misfortunate events such as fire, earthquakes or even human caused events such as robbery and, bombing. However, these signs are of no use to the visually impaired people. During emergency scenarios, the blind people need to rely on other individuals and sometimes they may even be left helpless. This raises a need for some assistive device that could benefit the visually impaired people during the time of emergency. In this paper, we propose a concept of smart glasses that could be tremendously beneficial to the blind people. These glasses will have camera and headphone speakers embedded to them. The device will be capable of detecting emergency signs using modern deep learning techniques during the times of need and could notify the user regarding the direction where the exit is.

계량정보분석 방법을 활용한 클라우드 컴퓨팅 분야의 미래기술 탐색

최병관(Byung Kwan Choi),김병근(Byung Keun Kim) 한국인터넷전자상거래학회 2013 인터넷전자상거래연구 Vol.13 No.1

Scientific progress in technology oriented research fields is made by incremental or fundamental inventions concerning natural science effects, materials, methods, tools and applications. Therefore our approach focuses on research activities of such technological elements on the basis of IPC in published patents. In this paper we show how emerging topics in the field of cloud computing based on IPC data from the USPTO-database can be identified. This methodology allows us to answer the following questions: Which technological aspects within our considered field can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?

An Emerging Technology Trend Identifier Based on the Citation and the Change of Academic and Industrial Popularity

김선호,이준규,Waqas Rasheed,여운동 한국기술혁신학회 2011 기술혁신학회지 Vol.14 No.S

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends. Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

First Isolates of OXA-48-Like Carbapenemase-Producing Enterobacteriaceae in A Specialized Cancer Center

Villanueva-Cotrina Freddy,Condori Dick Mamani,Gomez Tamin Ortiz,Yactayo Katia Mallma,Barron-Pastor Heli 대한감염학회 2022 Infection and Chemotherapy Vol.54 No.4

Background OXA-48-like carbapenemases have been found in a growing and varied number of carbapenemase-producing Enterobacteriaceae (CPE) isolates, and they are spreading to several countries. Although this oxacillinase leads to weak resistance to carbapenems without affecting broad-spectrum cephalosporin activity, when they are associated with other resistance mechanisms, the level of resistance to these antibiotics may be significantly higher. This weak resistance against carbapenems and cephalosporins, along with the absence of other resistance mechanisms, could render OXA-48-like harboring isolates undetected in the laboratory routine. In addition, the lack of a specific screening test for this enzyme complicates the detection of these isolates. This report characterizes the first isolates of OXA-48-like CPE detected in our laboratory. Materials and Methods The study was carried out at the Instituto Nacional de Enfermedades Neoplasicas, Lima - Peru, between March and December 2021. OXA-48-like CPE isolates were detected as part of the routine microbiological study, and clinical data were obtained by reviewing medical records. The automated microbiological system provides the bacterial identification and antimicrobial susceptibility profile by the dilution method. Additionally, the column chromatography test is used to detect carbapenemase enzymes, including OXA-48-like. Finally, the molecular identification of the OXA-48-like enzyme was carried out by Polymerase Chain Reaction PCR amplification for the blaOXA-48-like. Results Seven OXA-48-like CPE strains were isolated. Notably, in all cases, the automated system issued a minimum inhibitory concentration (MIC) of ≥1 ug/mL for ertapenem and a MIC of >64/4 ug/mL for piperacillin/tazobactam. In addition, resistance category to imipenem and meropenem was found (2/7), at least one indeterminate category for any of these carbapenems (5/7), and other serine β-lactamases such as Extended-spectrum beta-lactamases (3/7) and AmpC (3/7). The immunochromatographic study confirmed the presence of the OXA-48-like enzyme in all isolates, while class A and class B were ruled out for them. Finally, the multiplex PCR, for the five isolates that could be recovered, showed amplification for carbapenemase OXA-48-like, while none of the other carpabemases was amplified for class A or class B carbapenemase genes. Conclusion We confirm the emergence of OXA-48-like CPE isolates in our cancer center and highlight the need to implement surveillance and detection measures of these strains, for controlling their dissemination. We found practical and inexpensive methodologies for the detection of OXA-48-like CPE: (1) the finding of resistance to ertapenem and piperacillin/tazobactam in the antibiogram in the absence of class A and B carbapenemases, for screening and (2) immunochromatographic study, for confirmation. Background OXA-48-like carbapenemases have been found in a growing and varied number of carbapenemase-producing Enterobacteriaceae (CPE) isolates, and they are spreading to several countries. Although this oxacillinase leads to weak resistance to carbapenems without affecting broad-spectrum cephalosporin activity, when they are associated with other resistance mechanisms, the level of resistance to these antibiotics may be significantly higher. This weak resistance against carbapenems and cephalosporins, along with the absence of other resistance mechanisms, could render OXA-48-like harboring isolates undetected in the laboratory routine. In addition, the lack of a specific screening test for this enzyme complicates the detection of these isolates. This report characterizes the first isolates of OXA-48-like CPE detected in our laboratory. Materials and Methods The study was carried out at the Instituto Nacional de Enfermedades Neoplasicas, Lima - Peru, between March and December 2021. OXA-48-like CPE isolates were detected as part of the routine microbiological study, and clinical data were obtained by reviewing medical records. The automated microbiological system provides the bacterial identification and antimicrobial susceptibility profile by the dilution method. Additionally, the column chromatography test is used to detect carbapenemase enzymes, including OXA-48-like. Finally, the molecular identification of the OXA-48-like enzyme was carried out by Polymerase Chain Reaction PCR amplification for the blaOXA-48-like. Results Seven OXA-48-like CPE strains were isolated. Notably, in all cases, the automated system issued a minimum inhibitory concentration (MIC) of ≥1 ug/mL for ertapenem and a MIC of >64/4 ug/mL for piperacillin/tazobactam. In addition, resistance category to imipenem and meropenem was found (2/7), at least one indeterminate category for any of these carbapenems (5/7), and other serine β-lactamases such as Extended-spectrum beta-lactamases (3/7) and AmpC (3/7). The immunochromatographic study confirmed the presence of the OXA-48-like enzyme in all isolates, while class A and class B were ruled out for them. Finally, the multiplex PCR, for the five isolates that could be recovered, showed amplification for carbapenemase OXA-48-like, while none of the other carpabemases was amplified for class A or class B carbapenemase genes. Conclusion We confirm the emergence of OXA-48-like CPE isolates in our cancer center and highlight the need to implement surveillance and detection measures of these strains, for controlling their dissemination. We found practical and inexpensive methodologies for the detection of OXA-48-like CPE: (1) the finding of resistance to ertapenem and piperacillin/tazobactam in the antibiogram in the absence of class A and B carbapenemases, for screening and (2) immunochromatographic study, for confirmation.

Emergency Signal Detection based on Arm Gesture by Motion Vector Tracking in Face Area

Rabia Fayyaz,박대준,이은주 한국정보전자통신기술학회 2019 한국정보전자통신기술학회논문지 Vol.12 No.1

This paper presents a method for detection of an emergency signal expressed by arm gestures based on motion segmentation and face area detection in the surveillance system. The important indicators of emergency can be arm gestures and voice. We define an emergency signal as the ‘Help Me’ arm gestures in a rectangle around the face. The ‘Help Me’ arm gestures are detected by tracking changes in the direction of the horizontal motion vectors of left and right arms. The experimental results show that the proposed method successfully detects ‘Help Me’ emergency signal for a single person and distinguishes it from other similar arm gestures such as hand waving for ‘Bye’ and stretching. The proposed method can be used effectively in situations where people can’t speak, and there is a language or voice disability.

Emergency Signal Detection based on Arm Gesture by Motion Vector Tracking in Face Area

Fayyaz, Rabia,Park, Dae Jun,Rhee, Eun Joo Korea Information Electronic Communication Technol 2019 한국정보전자통신기술학회논문지 Vol.12 No.1

This paper presents a method for detection of an emergency signal expressed by arm gestures based on motion segmentation and face area detection in the surveillance system. The important indicators of emergency can be arm gestures and voice. We define an emergency signal as the 'Help Me' arm gestures in a rectangle around the face. The 'Help Me' arm gestures are detected by tracking changes in the direction of the horizontal motion vectors of left and right arms. The experimental results show that the proposed method successfully detects 'Help Me' emergency signal for a single person and distinguishes it from other similar arm gestures such as hand waving for 'Bye' and stretching. The proposed method can be used effectively in situations where people can't speak, and there is a language or voice disability.

Advances in gamma radiation detection systems for emergency radiation monitoring

K.A. Pradeep Kumar,G.A. Shanmugha Sundaram,B.K. Sharma,S. Venkatesh,R. Thiruvengadathan 한국원자력학회 2020 Nuclear Engineering and Technology Vol.52 No.10

The study presents a review of research advancements in the field of gamma radiation detection systems for emergency radiation monitoring, particularly two major sub-systems namely (i) the radiation detector and (ii) the detection platform – air-borne and ground-based. The dynamics and functional characteristics of modern radiation detector active materials are summarized and discussed. The capabilities of both ground-based and aerial vehicle platforms employed in gamma radiation monitoring are deliberated in depth.

24/7 Elderly Guard Robot: Emergency Detecting, Reacting, and Reporting

Deok-won Lee,Ahmed Elsharkawy,Kooksung Jun,Yun-dong Lee,SeungJun Kim,Mun Sang Kim 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10

As the number of elderly persons increases, greater attention must be given to how they or their caregivers deal with emergency situations. This paper describes an automated tracking, fall detection, and emergency recovery system for elderly persons, and shows that efficient a Socially Assistive Robot (SAR) can resolve emergency situations and abnormal behaviors for at-risk populations. Our assistant robot uses position data provided by Ultra-WideBand (UWB) wireless network and motion sensor information to detect potentially dangerous situations for elderly persons. In this context, a deep neural network–based double-check method has been developed to detect and confirm fall situation with high accuracy using in-house developed sensory hardware. We then simulated four typical emergency scenarios using SILBOT-3 robot. Interaction scenarios were demonstrated to 28 caregivers, who were then invited to complete a short questionnaire regarding benefits and improvements for our system. Caregivers responded positively to our system’s performance and stated that they would accept an assistant robot that could notify them quickly about a dangerous situation or possibly resolve the situation autonomously.

Detection of Emergency Disaster using Human Action Recognition based on LSTM Model

Yull Kyu Han,Young Bok Choi 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.3

We propose a deep learning model for human action recognition in order to identify quickly the location and occurrence of disasters such as fire and terrorism. Using the acceleration and gyroscope sensors built in the smartphone, we obtained data on human behavior and we classified human behavior through the LSTM deep learning model. There are four categories of human behavior, usually stopping, walking and running, and running helter-skelter in the event of a disaster. We compared analysis of four types of human behavior data such as stop, walking, running, and running helter-skelter, with the existing NN model and the LSTM model proposed in this paper. As a result, we confirmed that the LSTM model can be classified more accurately than the NN model with 81.50% for the conventional NN model and 95.32% for the LSTM model. We expect that the proposed model can be used to detect disaster occurrences quickly.