• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1406-1409
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• 2010

본 연구에서는 호출기를 착용한 환자가 낙상/전도 사고를 당했을 때, 자동으로 구조요청 하는 시스템을 개발하고자 한다. 자동 호출기에는 사고의 감지, 사고발생 위치의 추적, 관제센터로의 통신 등의 기능이 필요하다. 이를 위해 3축 가속도센서를 탑재하고, 낙상판단 알고리즘을 구현하여, MCU에 포팅하고, RF 송수신기와 알람을 집적하였다. 자동 호출기와 관제소와의 통신방법으로는 400MHz 대역의 RF 송수신기를 채택하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1226-1274
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• 2022

In this paper, we dealt with a safety helmet for detecting dangerous situations that focuses on falling accidents and gas leaks, which are the main causes of industrial accidents. the fall situation range was set through gravity acceleration measurement using an acceleration sensor, and as a result, a fall detection rate of 80% could be confirmed. .In addition, the dangerous gas concentration was measured through a gas sensor, and when a digital value of 188 or more was output through a serial monitor, it was determined as a gas dangerous situation, and a fall warning message and a gas warning message could be checked through a smart-phone application produced based on the app inventor program.

• 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 the Korea Convergence Society
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• v.8 no.12
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• pp.63-69
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• 2017

This study is about the development of a smart bed control system to be able to detect the human position and body signal on bed. The main control board in the bed control system consists of the human sensing part, motor driving part and MCU. Here, to increase the credibility to check the human presence on bed, the human sensing part is combined with the human position part by membrane sensor and the body-signal detecting part of EMFI sensor. Also, remotely connecting the two detected signal to the application program of the app mode makes it possible to monitor human information on bed. In this paper, the remote function monitoring of the on-bed human information by bluetooth communication will be abe to make it applicable to the technical prevention method of the bed fall and absence accident in hospital and care facilities.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.458-463
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• 2015

This paper suggests detection system for the movement of patient on bed based on IEEE802.15.4 by using pressure pad and guard sensor. The system is designed to detect ordinary activities of patients on bed as well as patients' falling from the bed while sleeping at night. The node that is installed at bed sends data to gather when the pressure pad and sensor of guard detect patients' activities and falling. These data sent to gather are transmitted to monitor at help desk by TCP/IP communication. To remove unnecessary data that occurred due to switch chattering during tossing and turning, timer of MCU is used. Also, Communication module can change transmission power to apply this system to various environments of hospital room. Therefore, the nurse can take care of patients on bed in real time with data about patients' conditions.

• Journal of IKEEE
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• v.24 no.4
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• pp.1180-1183
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• 2020

Recently, the daily life safety detection functionalities such as fall accident detection and burn danger detection are widely disseminated along with the development of IoT and smart home. These safety detection functionalities are mostly performed by artificial intelligence. However, simple accuracy measurement of the safety detection in laboratory environment is often far from practical performance in daily life environment. To mitigate this problem, this paper introduces two techniques, i.e. living lab and confusion matrix. Living lab is more than simple simulation of daily life environment, and it enables users to directly participate technology development and product design. Various performance measures induced from confusion matrix significantly help to evaluate the performance of artificial intelligence system for proper application purposes.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.51-52
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• 2024

본 논문에서는 고령 휠체어 사용자를 대상으로 능동적인 이동을 위한 보도 탐지와 낙상 사고 감지의 기능을 담은 어플리케이션을 제안하고자 한다. 보도 파손 데이터를 담은 데이터베이스 지도를 형성함으로써, 다른 사용자의 2차 사고 방지도 예방할 수 있을 것이다.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.211-218
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• 2021

A recent spread of personal mobility devices such as electric kickboards has brought about a rapid increase in accident cases. Such vehicles are susceptible to falling accidents due to their low dynamic stability and lack of outer protection chassis. This paper presents the development of an automatic emergency braking system and a safe starting system as driving assistance devices for electric kickboards. The braking system employed artificial intelligence to detect nearby threaening objects. The starting system was developed to disable powder to the motor until when the driver's boarding is confirmed. This study is meaningful in that it proposes the convergence technology of advanced driver assistance systems specialized for personal mobility devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.402-409
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• 2019

Patients with dementia or elderly patients who can not move at all by themselves are at a high risk of falls and bedsore due to lack of caregivers. In this paper, to solve this problem, we propose an algorithm to determine the patient's lying postures by discriminating the main body parts such as head, shoulders, and hips based on the pressure intensity sensed at regular intervals. A smart mat with a lattice structure in which a pressure sensor is arranged so that the body part can be discriminated irrespective of the physical characteristics has been implemented. It consists of two modules of $7{\times}7$ array size. Each module consists of 49 FSR-406 sensors and independently senses pressure. For each module, the body part corresponding to the upper body or the lower body is sequentially discriminated by using a pressure distribution such as a cumulative pressure sum using a filter. The proposed algorithm can identify five lying positions by examining the inclusion relationship between body parts belonging to layer-1 such as head, shoulder, and hip area.