WO2021040126A1 - Intelligent wearable dangerous state determination device using complex environment measurement, and method therefor - Google Patents

Intelligent wearable dangerous state determination device using complex environment measurement, and method therefor Download PDF

Info

Publication number
WO2021040126A1
WO2021040126A1 PCT/KR2019/013719 KR2019013719W WO2021040126A1 WO 2021040126 A1 WO2021040126 A1 WO 2021040126A1 KR 2019013719 W KR2019013719 W KR 2019013719W WO 2021040126 A1 WO2021040126 A1 WO 2021040126A1
Authority
WO
WIPO (PCT)
Prior art keywords
worker
state
sensor
intelligent wearable
hazardous
Prior art date
Application number
PCT/KR2019/013719
Other languages
French (fr)
Korean (ko)
Inventor
전찬희
Original Assignee
전찬희
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 전찬희 filed Critical 전찬희
Publication of WO2021040126A1 publication Critical patent/WO2021040126A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0446—Sensor means for detecting worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C19/00—Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/003—Kinematic accelerometers, i.e. measuring acceleration in relation to an external reference frame, e.g. Ferratis accelerometers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0492—Sensor dual technology, i.e. two or more technologies collaborate to extract unsafe condition, e.g. video tracking and RFID tracking
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/14—Central alarm receiver or annunciator arrangements

Definitions

  • the present invention relates to an intelligent wearable risk state determination device and method using a composite environment measurement, and an intelligent wearable risk state using a composite environment measurement to measure the user's external environment and check the user's state based on the measured data. It relates to a judgment device and a method thereof.
  • a portable gas meter uses a battery to supply power, so it is inconvenient to carry it at all times due to its heavy weight and size. It is a method driven by a single sensor capable of detecting one gas on one sensor chip.
  • mobile terminals refer to portable electronic devices such as cell phones, portable computers, palm PCs, PDAs, electronic notebooks, digital cameras, camcorders, MP3 players, and PMPs.
  • portable electronic devices such as cell phones, portable computers, palm PCs, PDAs, electronic notebooks, digital cameras, camcorders, MP3 players, and PMPs.
  • the trend is becoming smaller.
  • Such a mobile terminal configures various internal circuits according to a maker and a model of the mobile terminal, and is provided to enable interfacing by installing a plurality of connectors on one side of the mobile terminal housing as an interface for communicating with the outside.
  • a portable gas meter has a battery, and the required power is supplied through the battery. In the case of being supplied, there is a problem in that the portable gas measuring device is inconvenient to carry due to the heavy weight and large size of the portable gas meter due to the characteristic of the structure supplying power from the battery.
  • An object of the present invention is to provide an intelligent wearable danger state determination device and method using complex environment measurement to measure the user's external environment and check the user's state based on the measured data.
  • an intelligent wearable danger state determination device using a complex environment measurement
  • a gas sensor that is detachable and capable of measuring the concentration of gas around the work environment, and the operator's
  • a sensor unit including a gyro sensor and an acceleration sensor capable of measuring the balance state and motion, and a camera for photographing the surrounding environment of the worker, and a hazardous state determination to determine whether or not there is a harmful state around the worker from the measured concentration of the gas Part
  • a learning unit that generates a learning model by applying the worker's fall according to the number of floors on which the worker is located, the measured value of the gyro sensor, the acceleration sensor and the camera's photographed image attached to the worker, to a machine learning technique.
  • a fall determination unit that determines whether the worker falls or not by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the photographed image, and the number of floors of the worker's current location to the learning model, Includes an output unit for outputting information on a hazardous state and information on whether the worker is falling through a display, and a control unit to transmit information on a harmful state of the work environment and information on whether the worker is falling through a network to another worker or a control center. do.
  • the sensor unit includes a read out integrated circuit (ROIC), and may measure at least one of oxygen, combustible gas, hydrogen sulfide, temperature, humidity, and carbon monoxide.
  • ROIC read out integrated circuit
  • the fall determination unit if the value measured from the gyro sensor is greater than the reference value, the value measured from the acceleration sensor is greater than the reference value, or the amount of change in the pixel value of the image captured by the camera is greater than the reference value. It can be determined that the worker simply fell.
  • the fall determination unit determines that the surrounding environment before the fall of the felled worker is in a hazardous state, it may be determined that the fall is caused by the fall of the worker.
  • the control unit When it is determined that the surrounding of the worker is in a hazardous state, the control unit transmits a danger alarm signal to another worker at a certain distance from the worker, and when it is determined that the worker has fallen, an alarm is generated and the other worker wears it.
  • An intelligent wearable complex environment measuring device can communicate the fall of the worker.
  • the control unit may distinguish whether the worker has fallen due to a simple fall or an unhealthy condition, and transmit it to the control center.
  • the learning unit may learn by increasing the weight of the gyro sensor and the acceleration sensor as the number of floors of the work environment increases, and decrease the weight of the gyro sensor and the acceleration sensor as the number of floors of the work environment decreases.
  • the control unit may estimate the current number of floors of the worker through a beacon signal attached to the work environment.
  • a method for determining a dangerous state using an intelligent wearable dangerous state determining device it is possible to attach and detach, measure the concentration of gas around the work environment, measure the balance state and motion of the worker, Photographing the surrounding environment of the worker, determining whether there is a harmful state around the worker from the measured concentration of the gas, measured values of the gyro sensor, acceleration sensor and camera attached to the worker, and the number of floors on which the worker is located.
  • Generating a learning model by applying whether the worker falls or not to a machine learning technique, the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the photographed image, and the number of floors in the current location of the worker.
  • the present invention it is possible to detect whether there is harmfulness by using harmful information obtained in real time, and when a harmful state occurs, it is possible to notify a worker in real time so that they can leave a corresponding area.
  • the senor module can be replaced and used, the efficiency and safety of work can be improved.
  • FIG. 1 is a diagram showing the configuration of an intelligent wearable danger state determination apparatus according to an embodiment of the present invention.
  • FIG. 2 is a flowchart illustrating a method of determining a dangerous state using an intelligent wearable dangerous state determining device according to an embodiment of the present invention.
  • step S210 of FIG. 2 is a diagram for explaining step S210 of FIG. 2.
  • FIG. 4 is a diagram illustrating an apparatus for determining an intelligent wearable danger state according to an embodiment of the present invention.
  • FIG. 1 is a diagram showing the configuration of an intelligent wearable danger state determination apparatus according to an embodiment of the present invention.
  • the intelligent wearable danger state determination device 100 includes a sensor unit 110, a hazardous state determination unit 120, a learning unit 130, a fall determination unit 140, an output unit 150, and a control unit ( 160).
  • the sensor unit 110 is detachable, a gas sensor capable of measuring the concentration of gas around the work environment, a gyro sensor and an acceleration sensor capable of measuring the balance and motion of the worker, and the surrounding environment of the worker. It includes a camera for shooting.
  • the gas sensor includes a read out integrated circuit (ROIC) and may measure at least one of oxygen, combustible gas, hydrogen sulfide, temperature/humidity, and carbon monoxide.
  • ROIC read out integrated circuit
  • the sensor unit 110 may change the type of gas that can be measured by changing the type of the gas sensor.
  • the harmful state determination unit 120 determines whether there is a harmful state around the worker from the concentration of the gas measured by the gas sensor.
  • the learning unit 130 generates a learning model by applying the measurement values of a gyro sensor, an acceleration sensor, and a camera attached to the worker, and whether the worker falls according to the number of floors on which the worker is located, to a machine learning technique.
  • the machine learning technique is an artificial intelligence learning method, and can generate optimal data by analyzing big data.
  • the learning unit 130 learns by increasing the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment increases, and decreases the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment decreases.
  • the fall determination unit 140 determines whether the worker falls by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the camera, and the number of floors of the worker's current location to the learning model.
  • the fall determination unit 140 may have a value measured from a gyro sensor greater than a reference value, a value measured from an acceleration sensor greater than a reference value, or the amount of change in the pixel value of the image captured by the camera is greater than the reference value. If it is large, it is determined that the worker has simply fallen.
  • a simple fall means a fall caused by a worker's failure.
  • the fall determination unit 140 determines that the surrounding environment before the fall of the fallen worker is in a hazardous state, it is determined that the worker has fallen due to the hazardous state.
  • the output unit 150 outputs information on a hazardous state of the work environment and information on whether a worker falls or not through the display.
  • the output unit 150 outputs information on the current harmful state around the worker through the display.
  • control unit 160 transmits information on the hazardous state of the work environment and information on whether the worker has fallen or not to another worker or a control center through a network.
  • the control unit 160 transmits a danger alarm signal to another worker at a certain distance from the worker, and when it is determined that the worker has fallen, an alarm is generated and the other worker wears it.
  • An intelligent wearable complex environment measuring device communicates the fall of the worker.
  • control unit 160 distinguishes whether the worker has simply fallen or has fallen due to a hazardous condition, and transmits it to the control center.
  • control unit 160 estimates the current number of floors of the worker through a beacon signal attached to the work environment.
  • FIG. 2 is a flowchart illustrating a method of determining a dangerous state using an intelligent wearable dangerous state determining device according to an embodiment of the present invention.
  • the sensor unit 110 measures the concentration of gas around the work environment using a detachable sensor module, measures the balance and motion of the worker using a gyro sensor and an acceleration sensor, and measures the operator's balance and motion using a camera. Take a picture of the surrounding environment (S210).
  • step S210 of FIG. 2 is a diagram for explaining step S210 of FIG. 2.
  • the gas sensor included in the sensor unit 110 may be implemented in the form of a plurality of sensor modules according to the type of gas to be measured.
  • the gas sensor may include any one of a temperature/humidity sensor module 111a, a CO 2 sensor module 111b, or a CO sensor module 111e. At least one of gas, hydrogen sulfide, temperature/humidity, and carbon monoxide can be measured.
  • a gyro sensor and an acceleration sensor included in the sensor unit 110 are used to detect a worker's balance state and motion, and a camera is used to photograph the worker's surrounding environment.
  • the hazardous state determination unit 120 determines whether the work environment around the worker is in a hazardous state from the measured concentration of the gas (S220).
  • the hazardous state determination unit 120 indicates that there is a hazardous state when the oxygen concentration is less than 18% or 23.5% or more, or the concentration of carbon dioxide is 1.5% or more, or the concentration of hydrogen sulfide is 10 ppm or more and the carbon monoxide concentration is 30 ppm. Judge.
  • the learning unit 130 generates a learning model by applying the measurement values of the gyro sensor, acceleration sensor and camera attached to the worker, and whether the worker falls according to the number of floors on which the worker is located, to a machine learning technique. (S230).
  • the learning unit 130 learns by increasing the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment increases, and decreases the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment decreases.
  • the learning unit 130 sets the weight for the floor on which the worker is located to a value greater than 1 to learn.
  • the learning unit 130 learns by increasing the weight differentially according to the final number of floors of the building.
  • the fall determination unit 140 determines whether the worker falls by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the amount of pixel change of the camera, and the number of floors of the worker's current location to the learning model (S240).
  • the fall determination unit 140 determines whether the value measured from the gyro sensor is greater than the reference value, the value measured from the acceleration sensor is greater than the reference value, or the amount of change in the pixel value of the image captured by the camera is the reference value. If it is greater than, it is judged that the worker has simply fallen.
  • the fall or not determining unit 140 determines that the surrounding environment before the fall of the fallen worker is in a hazardous state, it is determined that the worker has fallen due to the hazardous state.
  • the reference value is a work environment, a value that changes when the worker moves or works, and may be changed according to the worker or the attached position.
  • the output unit 150 outputs information on a hazardous state of the work environment and information on whether a worker falls or not through the display (S250).
  • the output unit 150 outputs information on the hazardous state of the work environment through the display so that the worker or other worker can check it.
  • control unit 160 transmits information on the hazardous state of the work environment and information on whether the worker has fallen to another worker or the control center through the network (S260).
  • control unit 160 transmits a danger alarm signal to another worker at a certain distance from the worker, and when it is determined that the worker has fallen, the control unit 160 causes an alarm to occur. Communicate the worker's status to other workers.
  • control unit 160 determines whether a fall due to a loss or a fall due to a harmful condition is determined, and transmits it to another worker or a control center.
  • the control center provides the rescuer with a list of necessary tools according to the hazardous condition.
  • the control center For example, if it is determined that the person has fallen due to carbon monoxide poisoning, the control center provides tools such as an oxygen tank to the rescuer, and if it is judged that the person has fallen due to poisonous gas poisoning, the control center provides a rescuer with a tool that can remove the toxic gas. To provide.
  • control unit 160 estimates the current number of floors of the worker using a beacon signal and a network signal attached to the work environment.
  • control unit 160 provides the operator's condition and harmful condition to another operator using a wired or wireless network, and transmits the operator's condition and harmful condition to another operator or the control center in real time to the control center.
  • the beacon is a short-range mobile communication device for forming IoT, and the controller 160 can know the exact location of the worker and the number of floors in the building using the beacon.
  • FIG. 4 is a diagram illustrating an apparatus for determining an intelligent wearable danger state according to an embodiment of the present invention.
  • the operator can wear the manufactured intelligent wearable danger state determination device 100 on a part of the worker's body, and the attachment position is the form of the user and the intelligent wearable danger state determination device 100 It can be changed according to.
  • an air inlet portion for inhaling ambient air and an air outlet portion for discharging the inhaled air are included in order to measure harmful gas, and each position may be changed.
  • each button generated on the left and right of the intelligent wearable complex environment measuring apparatus 100 may be any one of power, normal clock mode, communication mode, and gas sensor operation. Each location can be changed according to the user.
  • the senor module can be replaced and used, the efficiency and safety of work can be improved.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Chemical & Material Sciences (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Gerontology & Geriatric Medicine (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Toxicology (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Artificial Intelligence (AREA)
  • Multimedia (AREA)
  • Medical Informatics (AREA)
  • Combustion & Propulsion (AREA)
  • Environmental & Geological Engineering (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Emergency Alarm Devices (AREA)
  • Alarm Systems (AREA)

Abstract

The present invention relates to an intelligent wearable complex environment measuring device, and a method therefor. An intelligent wearable dangerous state determination device using complex environment measurement, according to the present invention, comprises: a sensor unit comprising a detachable gas sensor capable of measuring the concentration of gas around a work environment, a gyro sensor and an acceleration sensor capable of measuring a balance state and motion of a worker, and a camera for photographing the surrounding environment of the worker; a hazardous state determination unit for determining, from the measured concentration of the gas, whether there is a hazardous state around the worker; a learning unit for generating a learning model by applying, to a machine learning technique, measurement values of the gyro sensor and the acceleration sensor attached to the worker, a photographed image of the camera, and whether the worker falls according to a floor on which the worker is located; a fall determination unit for determining whether the worker falls, by applying, to the learning model, the measurement value of the gyro sensor, the measurement value of the acceleration sensor, a pixel change value of the photographed image, and a floor on which the worker is currently located; an output unit for outputting, through a display, hazardous state information of the work environment and information on whether the worker falls; and a control unit for transmitting, to other workers or a control center through a network, the hazardous state information of the work environment and the information on whether the worker falls.

Description

복합환경 측정을 이용한 지능형 웨어러블 위험 상태 판단 장치 및 그 방법Intelligent wearable danger state determination device and method using complex environment measurement
본 발명은 복합환경 측정을 이용한 지능형 웨어러블 위험 상태 판단 장치 및 그 방법에 관한 것으로, 사용자의 외부환경을 측정하고 측정된 데이터를 바탕으로 사용자의 상태를 확인하기 위한 복합환경 측정을 이용한 지능형 웨어러블 위험 상태 판단 장치 및 그 방법에 관한 것이다.The present invention relates to an intelligent wearable risk state determination device and method using a composite environment measurement, and an intelligent wearable risk state using a composite environment measurement to measure the user's external environment and check the user's state based on the measured data. It relates to a judgment device and a method thereof.
일반적으로 휴대용 가스 측정기는 배터리를 사용하여 전원을 공급하므로 무게가 무겁고 크기가 커서 상시 휴대하는데 불편하며, 측정기에 실장되는 가스센서의 경우 종래 기술의 제품은 소비전력이 150㎽정도로 소비전력이 매우 크며 한 개의 센서칩에 한 개의 가스감지를 할 수 있는 단일센서로 구동되는 방식이다.In general, a portable gas meter uses a battery to supply power, so it is inconvenient to carry it at all times due to its heavy weight and size. It is a method driven by a single sensor capable of detecting one gas on one sensor chip.
이와 같은 문제를 해결하기 위하여 필요시 이동 단말기에 장착하여 가스를 측정하는 방법들이 시도되고 있다. In order to solve such a problem, methods of measuring gas by attaching to a mobile terminal when necessary have been attempted.
일반적으로 이동 단말기는 휴대폰, 휴대용 컴퓨터, 팜PC, PDA, 전자수첩, 디지털 카메라, 캠코더, MP3 플레이어, PMP 등의 휴대용 전자장치들을 지칭하는 것으로 이러한 이동 단말기의 사용은 점차 증가되고 있으며, 크기도 더욱 소형화되고 있는 추세이다. In general, mobile terminals refer to portable electronic devices such as cell phones, portable computers, palm PCs, PDAs, electronic notebooks, digital cameras, camcorders, MP3 players, and PMPs. The trend is becoming smaller.
이러한 이동 단말기는 제조사(Maker)와 이동 단말기의 기종에 따라서 다양한 내부회로를 구성하고, 외부와 통신 하기 위한 인터페이스로서 다수의 커넥터를 이동 단말기 하우징의 일측면에 설치하여 인터페이싱이 가능하도록 구비되어 있다.Such a mobile terminal configures various internal circuits according to a maker and a model of the mobile terminal, and is provided to enable interfacing by installing a plurality of connectors on one side of the mobile terminal housing as an interface for communicating with the outside.
그러나 최근에 들어 이동 단말기 특히 휴대폰이 박형화 및 경량화됨에 따라서 복수개의 커넥터를 휴대폰에 구비하는 것은 설계 및 적용상 바람직하지 않게 되었다. However, in recent years, as mobile terminals, especially mobile phones, have become thinner and lighter, having a plurality of connectors in a mobile phone is not desirable for design and application.
또한, 스마트폰의 경우 5핀의 커넥터로 통일되고 있는 추세이며 휴대용 가스 측정기를 스마트 폰의 5핀의 커넥터와 연결하여 이용하고 있는데, 휴대용 가스 측정기가 배터리를 구비하고, 해당 배터리를 통해 필요한 전원을 공급받도록 한 경우, 배터리에서 전원을 공급하는 구조의 특성상 휴대용 가스 측정기의 무게가 무겁고 크기가 커져서 휴대하는데 불편함이 있다는 문제가 있었다.In addition, in the case of smartphones, the trend is being unified with a 5-pin connector, and a portable gas meter is used by connecting a 5-pin connector of a smartphone. A portable gas meter has a battery, and the required power is supplied through the battery. In the case of being supplied, there is a problem in that the portable gas measuring device is inconvenient to carry due to the heavy weight and large size of the portable gas meter due to the characteristic of the structure supplying power from the battery.
따라서, 사용자에게 부착될 수 있는 웨어러블 형태의 가스측정기의 필요성이 높아지고 있으며, 가스센서를 탈부착할 수 있는 복합환경 측정 장치가 필요하게 되었다.Accordingly, the need for a wearable type gas measuring device that can be attached to a user is increasing, and a complex environment measuring device capable of attaching and attaching a gas sensor is required.
본 발명의 배경이 되는 기술은 대한민국 공개특허 제10-1362430호(2014.02.21)에 개시되어 있다.The technology behind the present invention is disclosed in Korean Patent Application Publication No. 10-1362430 (2014.02.21).
본 발명이 이루고자 하는 기술적 과제는 사용자의 외부환경을 측정하고 측정된 데이터를 바탕으로 사용자의 상태를 확인하기 위한 복합환경 측정을 이용한 지능형 웨어러블 위험 상태 판단 장치 및 그 방법을 제공하기 위한 것이다.An object of the present invention is to provide an intelligent wearable danger state determination device and method using complex environment measurement to measure the user's external environment and check the user's state based on the measured data.
이러한 기술적 과제를 이루기 위한 본 발명의 실시예에 따르면, 복합환경 측정을 이용한 지능형 웨어러블 위험 상태 판단 장치에 있어서, 탈부착이 가능하며, 작업 환경 주변의 가스의 농도를 측정할 수 있는 가스 센서, 작업자의 균형 상태 및 동작을 측정할 수 있는 자이로 센서 및 가속도 센서 및 상기 작업자의 주변 환경을 촬영하기 위한 카메라를 포함하는 센서부, 상기 측정된 가스의 농도로부터 작업자 주변의 유해 상태 여부를 판단하는 유해 상태 판단부, 상기 작업자에게 부착된 자이로 센서, 가속도 센서의 측정 값 및 카메라의 촬영 영상, 작업자가 위치한 층수에 따른 상기 작업자의 낙상 여부를 머신러닝(Machine Learning)기법에 적용하여 학습 모델을 생성하는 학습부, 상기 자이로 센서의 측정 값, 가속도 센서의 측정 값, 촬영 영상의 픽셀 변화값 및 상기 작업자의 현재 위치 층수를 상기 학습 모델에 적용하여 상기 작업자의 낙상 여부를 판단하는 낙상 판단부, 상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 디스플레이를 통하여 출력하는 출력부, 그리고 상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 네트워크를 통하여 다른 작업자 또는 관제센터에 전달하도록 하는 제어부를 포함한다.According to an embodiment of the present invention for achieving such a technical problem, in an intelligent wearable danger state determination device using a complex environment measurement, a gas sensor that is detachable and capable of measuring the concentration of gas around the work environment, and the operator's A sensor unit including a gyro sensor and an acceleration sensor capable of measuring the balance state and motion, and a camera for photographing the surrounding environment of the worker, and a hazardous state determination to determine whether or not there is a harmful state around the worker from the measured concentration of the gas Part, a learning unit that generates a learning model by applying the worker's fall according to the number of floors on which the worker is located, the measured value of the gyro sensor, the acceleration sensor and the camera's photographed image attached to the worker, to a machine learning technique. , A fall determination unit that determines whether the worker falls or not by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the photographed image, and the number of floors of the worker's current location to the learning model, Includes an output unit for outputting information on a hazardous state and information on whether the worker is falling through a display, and a control unit to transmit information on a harmful state of the work environment and information on whether the worker is falling through a network to another worker or a control center. do.
상기 센서부는, 판독회로(ROIC, Read Out Integrated Circuit)를 포함하고, 산소, 가연성 가스, 황하수소, 온도, 습도 및 일산화 탄소 중에서 적어도 하나를 측정할 수 있다.The sensor unit includes a read out integrated circuit (ROIC), and may measure at least one of oxygen, combustible gas, hydrogen sulfide, temperature, humidity, and carbon monoxide.
상기 낙상 판단부는, 상기 자이로 센서로부터 측정되는 값이 기준 값 보다 크거나, 가속도 센서로부터 측정되는 값이 기준 값보다 크거나, 상기 카메라에 의해 촬영된 영상의 픽셀 값의 변화량이 기준 값보다 크면 상기 작업자가 단순 낙상한 것으로 판단할 수 있다.The fall determination unit, if the value measured from the gyro sensor is greater than the reference value, the value measured from the acceleration sensor is greater than the reference value, or the amount of change in the pixel value of the image captured by the camera is greater than the reference value. It can be determined that the worker simply fell.
상기 낙상 판단부는, 상기 낙상한 작업자가 낙상하기 전의 주변 환경이 유해 상태로 판단되면, 상기 작업자는 유해 상태로 인해 낙상한 것으로 판단할 수 있다.When the fall determination unit determines that the surrounding environment before the fall of the felled worker is in a hazardous state, it may be determined that the fall is caused by the fall of the worker.
상기 제어부는, 상기 작업자 주변이 유해 상태인 것으로 판단되면, 상기 작업자로부터 일정 거리에 있는 다른 작업자에게 위험 알람 신호를 전달하고, 상기 작업자가 낙상한 것으로 판단하면, 알람이 발생시키고 다른 작업자가 착용한 지능형 웨어러블 복합환경 측정 장치로 상기 작업자의 낙상 사실을 전달할 수 있다.When it is determined that the surrounding of the worker is in a hazardous state, the control unit transmits a danger alarm signal to another worker at a certain distance from the worker, and when it is determined that the worker has fallen, an alarm is generated and the other worker wears it. An intelligent wearable complex environment measuring device can communicate the fall of the worker.
상기 제어부는, 상기 작업자가 단순 낙상한 것인지, 유해 상태에 의해 낙상한 것인지를 구분하여 상기 관제센터에 전달할 수 있다.The control unit may distinguish whether the worker has fallen due to a simple fall or an unhealthy condition, and transmit it to the control center.
상기 학습부는, 작업 환경의 층수가 높을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 증가시켜 학습시키고, 작업 환경의 층수가 낮을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 감소시켜 학습시킬 수 있다.The learning unit may learn by increasing the weight of the gyro sensor and the acceleration sensor as the number of floors of the work environment increases, and decrease the weight of the gyro sensor and the acceleration sensor as the number of floors of the work environment decreases.
상기 제어부는, 상기 작업환경에 부착된 비콘 신호를 통하여 상기 작업자의 현재 층수를 추정할 수 있다.The control unit may estimate the current number of floors of the worker through a beacon signal attached to the work environment.
본 발명의 다른 실시예에 따르면, 지능형 웨어러블 위험 상태 판단 장치를 이용한 위험상태 판단 방법에 있어서, 탈부착이 가능하며, 작업 환경 주변의 가스의 농도를 측정하고, 작업자의 균형 상태 및 동작을 측정하고, 상기 작업자의 주변 환경을 촬영하는 단계, 상기 측정된 가스의 농도로부터 작업자 주변의 유해 상태 여부를 판단하는 단계, 상기 작업자에게 부착된 자이로 센서, 가속도 센서 및 카메라의 측정값, 작업자가 위치한 층수에 따른 상기 작업자의 낙상 여부를 머신러닝(Machine Learning)기법에 적용하여 학습 모델을 생성하는 단계, 상기 자이로 센서의 측정 값, 가속도 센서의 측정 값, 촬영 영상의 픽셀 변화값 및 상기 작업자의 현재 위치 층수를 상기 학습 모델에 적용하여 상기 작업자의 낙상 여부를 판단하는 단계, 상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 디스플레이를 통하여 출력하는 단계, 그리고 상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 네트워크를 통하여 다른 작업자 또는 관제센터에 전달하도록 하는 단계를 포함한다.According to another embodiment of the present invention, in a method for determining a dangerous state using an intelligent wearable dangerous state determining device, it is possible to attach and detach, measure the concentration of gas around the work environment, measure the balance state and motion of the worker, Photographing the surrounding environment of the worker, determining whether there is a harmful state around the worker from the measured concentration of the gas, measured values of the gyro sensor, acceleration sensor and camera attached to the worker, and the number of floors on which the worker is located. Generating a learning model by applying whether the worker falls or not to a machine learning technique, the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the photographed image, and the number of floors in the current location of the worker. Applying to the learning model to determine whether the worker falls, outputting information on the harmful state of the work environment and information on whether the worker falls through a display, and information on the harmful state of the work environment and the worker's It includes the step of transmitting information on whether a fall has occurred to another worker or a control center through a network.
이와 같이 본 발명에 따르면, 실시간으로 획득되는 유해정보를 활용하여 유해성 여부를 검지할 수 있으며, 유해상태 발생시 실시간으로 작업자에게 알려주어 해당지역에서 이탈할 수 있게 알려줄 수 있다.As described above, according to the present invention, it is possible to detect whether there is harmfulness by using harmful information obtained in real time, and when a harmful state occurs, it is possible to notify a worker in real time so that they can leave a corresponding area.
또한, 센서모듈을 교체하여 사용이 가능하기 때문에 작업의 효율성 및 안전성을 높일 수 있다.In addition, since the sensor module can be replaced and used, the efficiency and safety of work can be improved.
도 1은 본 발명의 실시예에 따른 지능형 웨어러블 위험 상태 판단 장치의 구성을 나타낸 도면이다.1 is a diagram showing the configuration of an intelligent wearable danger state determination apparatus according to an embodiment of the present invention.
도 2는 본 발명의 실시예에 따른 지능형 웨어러블 위험 상태 판단 장치를 이용한 위험 상태 판단 방법을 설명하기 위한순서도이다.2 is a flowchart illustrating a method of determining a dangerous state using an intelligent wearable dangerous state determining device according to an embodiment of the present invention.
도 3은 도 2의 S210 단계를 설명하기 위한 도면이다.3 is a diagram for explaining step S210 of FIG. 2.
도 4는 본 발명의 실시예에 따른 지능형 웨어러블 위험 상태 판단 장치를 나타낸 도면이다.4 is a diagram illustrating an apparatus for determining an intelligent wearable danger state according to an embodiment of the present invention.
아래에서는 첨부한 도면을 참조하여 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 본 발명의 실시 예를 상세히 설명한다. 그러나 본 발명은 여러 가지 상이한 형태로 구현될 수 있으며 여기에서 설명하는 실시예에 한정되지 않는다. 그리고 도면에서 본 발명을 명확하게 설명하기 위해서 설명과 관계없는 부분은 생략하였으며 명세서 전체를 통하여 유사한 부분에 대해서는 유사한 도면 부호를 붙였다.Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are attached to similar parts throughout the specification.
명세서 전체에서, 어떤 부분이 어떤 구성요소를 "포함"한다고 할 때, 이는 특별히 반대되는 기재가 없는 한 다른 구성요소를 제외하는 것이 아니라 다른 구성요소를 더 포함할 수 있는 것을 의미한다.Throughout the specification, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary.
그러면 첨부한 도면을 참고로 하여 본 발명의 실시 예에 대하여 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자가 용이하게 실시할 수 있도록 상세히 설명한다.Then, with reference to the accompanying drawings, embodiments of the present invention will be described in detail so that those of ordinary skill in the art can easily implement the present invention.
도 1은 본 발명의 실시예에 따른 지능형 웨어러블 위험 상태 판단 장치의 구성을 나타낸 도면이다.1 is a diagram showing the configuration of an intelligent wearable danger state determination apparatus according to an embodiment of the present invention.
도 1에서 나타낸 것처럼 지능형 웨어러블 위험 상태 판단 장치(100)는 센서부(110), 유해상태 판단부(120), 학습부(130), 낙상 판단부(140), 출력부(150) 및 제어부(160)를 포함한다.As shown in FIG. 1, the intelligent wearable danger state determination device 100 includes a sensor unit 110, a hazardous state determination unit 120, a learning unit 130, a fall determination unit 140, an output unit 150, and a control unit ( 160).
먼저, 센서부(110)는 탈부착이 가능하며, 작업 환경 주변의 가스의 농도를 측정할 수 있는 가스 센서, 작업자의 균형을 및 동작을 측정할 수 있는 자이로 센서 및 가속도 센서 및 작업자의 주변 환경을 촬영하기 위한 카메라를 포함한다.First, the sensor unit 110 is detachable, a gas sensor capable of measuring the concentration of gas around the work environment, a gyro sensor and an acceleration sensor capable of measuring the balance and motion of the worker, and the surrounding environment of the worker. It includes a camera for shooting.
이때, 가스 센서는 판독회로(ROIC, Read Out Integrated Circuit)를 포함하고, 산소, 가연성 가스, 황하수소, 온/습도 및 일산화 탄소 중에서 적어도 하나를 측정할 수 있다.At this time, the gas sensor includes a read out integrated circuit (ROIC) and may measure at least one of oxygen, combustible gas, hydrogen sulfide, temperature/humidity, and carbon monoxide.
여기서, 센서부(110)는 가스 센서의 종류를 변경함으로써 측정할 수 있는 가스의 종류를 변경시킬 수 있다.Here, the sensor unit 110 may change the type of gas that can be measured by changing the type of the gas sensor.
다음으로, 유해상태 판단부(120)는 가스 센서로부터 측정된 가스의 농도로부터 작업자 주변의 유해 상태 여부를 판단한다.Next, the harmful state determination unit 120 determines whether there is a harmful state around the worker from the concentration of the gas measured by the gas sensor.
그리고, 학습부(130)는 작업자에게 부착된 자이로 센서, 가속도 센서 및 카메라의 측정값, 작업자가 위치한 층수에 따른 작업자의 낙상 여부를 머신러닝(Machine Learning)기법에 적용하여 학습 모델을 생성한다.In addition, the learning unit 130 generates a learning model by applying the measurement values of a gyro sensor, an acceleration sensor, and a camera attached to the worker, and whether the worker falls according to the number of floors on which the worker is located, to a machine learning technique.
여기서, 머신러닝(Machine Learning)기법은 인공지능의 학습방법으로, 빅 데이터를 분석하여 최적의 데이터를 생성할 수 있다.Here, the machine learning technique is an artificial intelligence learning method, and can generate optimal data by analyzing big data.
또한, 학습부(130)는 작업 환경의 층수가 높을수록 자이로 센서 및 가속도 센서에 대한 가중치를 증가시켜 학습시키고, 작업 환경의 층수가 낮을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 감소시켜 학습시킨다.In addition, the learning unit 130 learns by increasing the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment increases, and decreases the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment decreases. .
다음으로, 낙상 판단부(140)는 자이로 센서의 측정 값, 가속도 센서의 측정 값, 카메라의 픽셀 변화값 및 작업자의 현재 위치 층수를 학습 모델에 적용하여 작업자의 낙상 여부를 판단한다.Next, the fall determination unit 140 determines whether the worker falls by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the camera, and the number of floors of the worker's current location to the learning model.
이때, 낙상 판단부(140)는 자이로 센서로부터 측정되는 값이 기준 값 보다 크거나, 가속도 센서로부터 측정되는 값이 기준 값보다 크거나, 카메라에 의해 촬영된 영상의 픽셀 값의 변화량이 기준 값보다 크면 상기 작업자가 단순 낙상한 것으로 판단한다.In this case, the fall determination unit 140 may have a value measured from a gyro sensor greater than a reference value, a value measured from an acceleration sensor greater than a reference value, or the amount of change in the pixel value of the image captured by the camera is greater than the reference value. If it is large, it is determined that the worker has simply fallen.
여기서, 단순 낙상은 작업자의 실족에 의한 낙상을 의미한다.Here, a simple fall means a fall caused by a worker's failure.
또한, 낙상 판단부(140)는 낙상한 작업자가 낙상하기 전의 주변 환경이 유해 상태로 판단되면, 상기 작업자는 유해 상태로 인해 낙상한 것으로 판단한다.In addition, if the fall determination unit 140 determines that the surrounding environment before the fall of the fallen worker is in a hazardous state, it is determined that the worker has fallen due to the hazardous state.
다음으로, 출력부(150)는 작업환경의 유해 상태 정보와 작업자의 낙상 여부 정보를 디스플레이를 통하여 출력한다.Next, the output unit 150 outputs information on a hazardous state of the work environment and information on whether a worker falls or not through the display.
즉, 출력부(150)는 작업자 주위의 현재 유해상태 정보를 디스플레이를 통하여 출력한다.That is, the output unit 150 outputs information on the current harmful state around the worker through the display.
다음으로, 제어부(160)는 작업환경의 유해 상태 정보와 작업자의 낙상 여부 정보를 네트워크를 통하여 다른 작업자 또는 관제센터에 전달한다.Next, the control unit 160 transmits information on the hazardous state of the work environment and information on whether the worker has fallen or not to another worker or a control center through a network.
이때, 제어부(160)는 작업자 주변이 유해 상태인 것으로 판단되면, 작업자로부터 일정 거리에 있는 다른 작업자에게 위험 알람 신호를 전달하고, 작업자가 낙상한 것으로 판단하면, 알람이 발생시키고 다른 작업자가 착용한 지능형 웨어러블 복합환경 측정 장치로 상기 작업자의 낙상 사실을 전달한다.At this time, when it is determined that the surroundings of the worker are in a hazardous state, the control unit 160 transmits a danger alarm signal to another worker at a certain distance from the worker, and when it is determined that the worker has fallen, an alarm is generated and the other worker wears it. An intelligent wearable complex environment measuring device communicates the fall of the worker.
또한, 제어부(160)는 작업자가 단순 낙상한 것인지, 유해 상태에 의해 낙상한 것인지를 구분하여 상기 관제센터에 전달한다.In addition, the control unit 160 distinguishes whether the worker has simply fallen or has fallen due to a hazardous condition, and transmits it to the control center.
그리고, 제어부(160)는 작업환경에 부착된 비콘 신호를 통하여 작업자의 현재 층수를 추정한다.Then, the control unit 160 estimates the current number of floors of the worker through a beacon signal attached to the work environment.
이하에서는 도 2 내지 도 4를 이용하여 지능형 웨어러블 복합환경 측정 장치(100)를 이용한 복합환경 측정 방법을 설명한다.Hereinafter, a method of measuring a complex environment using the intelligent wearable complex environment measuring apparatus 100 will be described with reference to FIGS. 2 to 4.
도 2는 본 발명의 실시예에 따른 지능형 웨어러블 위험 상태 판단 장치를 이용한 위험 상태 판단 방법을 설명하기 위한 순서도이다.2 is a flowchart illustrating a method of determining a dangerous state using an intelligent wearable dangerous state determining device according to an embodiment of the present invention.
먼저, 센서부(110)는 탈부착이 가능한 센서모듈을 이용하여 작업 환경 주변의 가스의 농도를 측정하고, 자이로 센서 및 가속도 센서를 이용하여 작업자의 균형을 및 동작을 측정하고, 카메라를 이용하여 작업자의 주변 환경을 촬영한다(S210).First, the sensor unit 110 measures the concentration of gas around the work environment using a detachable sensor module, measures the balance and motion of the worker using a gyro sensor and an acceleration sensor, and measures the operator's balance and motion using a camera. Take a picture of the surrounding environment (S210).
도 3은 도 2의 S210 단계를 설명하기 위한 도면이다.3 is a diagram for explaining step S210 of FIG. 2.
도 3에서 나타낸 것처럼, 센서부(110)에 포함된 가스 센서는 측정하고자 하는 가스의 종류에 따라 복수의 센서 모듈 형태로 구현이 될 수 있다. As shown in FIG. 3, the gas sensor included in the sensor unit 110 may be implemented in the form of a plurality of sensor modules according to the type of gas to be measured.
즉, 도 3과 같이 가스 센서는 온습도 센서모듈(111a), CO2 센서모듈(111b) 쪋 CO 센서모듈(111e) 중에서 어느 하나를 포함할 수 있으며, 장착된 센서 모듈의 종류에 따라 산소, 가연성 가스, 황하수소, 온/습도 및 일산화 탄소 중에서 적어도 하나를 측정할 수 있다.That is, as shown in FIG. 3, the gas sensor may include any one of a temperature/humidity sensor module 111a, a CO 2 sensor module 111b, or a CO sensor module 111e. At least one of gas, hydrogen sulfide, temperature/humidity, and carbon monoxide can be measured.
그리고, 센서부(110)에 포함된 자이로 센서 및 가속도 센서를 이용하여 작업자의 균형 상태 및 동작을 감지하고, 카메라를 이용하여 작업자의 주변 환경을 촬영한다.In addition, a gyro sensor and an acceleration sensor included in the sensor unit 110 are used to detect a worker's balance state and motion, and a camera is used to photograph the worker's surrounding environment.
다음으로, 유해상태 판단부(120)는 측정된 가스의 농도로부터 작업자 주변 작업환경의 유해 상태 여부를 판단한다(S220).Next, the hazardous state determination unit 120 determines whether the work environment around the worker is in a hazardous state from the measured concentration of the gas (S220).
즉, 유해상태 판단부(120)는 산소농도가 18% 미만이거나 23.5% 이상일 경우 또는 탄산가스의 농도가 1.5% 이상일 경우 또는 황화수소의 농도 10ppm 이상일 경우 및 일산화탄소 농도 30ppm일 경우에 유해상태가 있는 것으로 판단한다.In other words, the hazardous state determination unit 120 indicates that there is a hazardous state when the oxygen concentration is less than 18% or 23.5% or more, or the concentration of carbon dioxide is 1.5% or more, or the concentration of hydrogen sulfide is 10 ppm or more and the carbon monoxide concentration is 30 ppm. Judge.
다음으로, 학습부(130)는 작업자에게 부착된 자이로 센서, 가속도 센서 및 카메라의 측정값, 작업자가 위치한 층수에 따른 작업자의 낙상 여부를 머신러닝(Machine Learning)기법에 적용하여 학습 모델을 생성한다(S230).Next, the learning unit 130 generates a learning model by applying the measurement values of the gyro sensor, acceleration sensor and camera attached to the worker, and whether the worker falls according to the number of floors on which the worker is located, to a machine learning technique. (S230).
특히, 학습부(130)는 작업 환경의 층수가 높을수록 자이로 센서 및 가속도 센서에 대한 가중치를 증가시켜 학습시키고, 작업 환경의 층수가 낮을수록 자이로 센서 및 가속도 센서에 대한 가중치를 감소시켜 학습시킨다.In particular, the learning unit 130 learns by increasing the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment increases, and decreases the weights for the gyro sensor and the acceleration sensor as the number of floors in the work environment decreases.
예를 들어, 14층에 해당되는 가중치가 1이라고 가정하면, 작업자가 현재 위치한 층이 28층일 경우, 학습부(130)는 작업자가 위치한 층에 대한 가중치를 1보다 큰 값으로 설정하여 학습시킨다.For example, assuming that the weight corresponding to the 14th floor is 1, when the floor where the worker is currently located is the 28th floor, the learning unit 130 sets the weight for the floor on which the worker is located to a value greater than 1 to learn.
또한, 학습부(130)는 건물의 최종 층수에 따라서 차등으로 가중치를 증가시켜 학습한다.In addition, the learning unit 130 learns by increasing the weight differentially according to the final number of floors of the building.
그러면, 낙상여부 판단부(140)는 자이로 센서의 측정 값, 가속도 센서의 측정 값, 카메라의 픽셀 변화량 및 작업자의 현재 위치 층수를 학습 모델에 적용하여 작업자의 낙상 여부를 판단한다(S240).Then, the fall determination unit 140 determines whether the worker falls by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the amount of pixel change of the camera, and the number of floors of the worker's current location to the learning model (S240).
즉, 낙상여부 판단부(140)는 자이로 센서로부터 측정되는 값이 기준 값 보다 크거나, 가속도 센서로부터 측정되는 값이 기준 값보다 크거나, 카메라에 의해 촬영된 영상의 픽셀 값의 변화량이 기준 값보다 크면 작업자가 단순 낙상한 것으로 판단한다.That is, the fall determination unit 140 determines whether the value measured from the gyro sensor is greater than the reference value, the value measured from the acceleration sensor is greater than the reference value, or the amount of change in the pixel value of the image captured by the camera is the reference value. If it is greater than, it is judged that the worker has simply fallen.
또한, 낙상여부 판단부(140)는 낙상한 작업자가 낙상하기 전의 주변 환경이 유해 상태로 판단되면, 작업자는 유해 상태로 인해 낙상한 것으로 판단한다.In addition, if the fall or not determining unit 140 determines that the surrounding environment before the fall of the fallen worker is in a hazardous state, it is determined that the worker has fallen due to the hazardous state.
여기서, 기준 값은 작업환경, 작업자가 이동 또는 작업시 변동되는 값이며, 작업자 또는 부착된 위치에 따라서 변경될 수 있다.Here, the reference value is a work environment, a value that changes when the worker moves or works, and may be changed according to the worker or the attached position.
다음으로, 출력부(150)는 작업환경의 유해상태 정보와 작업자의 낙상 여부 정보를 디스플레이를 통하여 출력한다(S250).Next, the output unit 150 outputs information on a hazardous state of the work environment and information on whether a worker falls or not through the display (S250).
즉, 도 3에서 나타낸 것처럼, 출력부(150)는 작업환경의 유해상태 정보를 작업자 또는 다른 작업자가 확인 할 수 있도록 디스플레이를 통하여 출력한다.That is, as shown in FIG. 3, the output unit 150 outputs information on the hazardous state of the work environment through the display so that the worker or other worker can check it.
그러면, 제어부(160)는 작업환경의 유해 상태 정보와 작업자의 낙상 여부 정보를 네트워크를 통하여 다른 작업자 또는 관제센터에 전달한다(S260).Then, the control unit 160 transmits information on the hazardous state of the work environment and information on whether the worker has fallen to another worker or the control center through the network (S260).
즉, 제어부(160)는 작업자가 유해 상태인 것으로 판단되면, 작업자로부터 일정 거리에 있는 다른 작업자에게 위험 알람 신호를 전달하고, 작업자가 낙상한 것으로 판단되면, 제어부(160)는 알람이 발생하도록 하여 다른 작업자에게 작업자의 상태를 전달한다.That is, when it is determined that the worker is in a hazardous state, the control unit 160 transmits a danger alarm signal to another worker at a certain distance from the worker, and when it is determined that the worker has fallen, the control unit 160 causes an alarm to occur. Communicate the worker's status to other workers.
또한, 제어부(160)는 실족으로 인한 낙상여부 또는 유해 상태로 인한 낙상 여부인지 판단하여 다른 작업자 또는 관제센터에 전달한다.In addition, the control unit 160 determines whether a fall due to a loss or a fall due to a harmful condition is determined, and transmits it to another worker or a control center.
이때, 유해 상태로 인한 낙상인 것으로 전달 받으면, 관제센터는 구조자에게 해당 유해상태에 따른 필요 도구목록을 제공한다.At this time, if it is reported that it is a fall due to a hazardous condition, the control center provides the rescuer with a list of necessary tools according to the hazardous condition.
예를 들어, 일산화탄소 중독으로 낙상한 것으로 판단되면, 관제센터는 산소통과 같은 도구를 구조자에게 제공하고, 유독가스 중독으로 낙상한 것으로 판단되면, 관제센터는 해당 유독가스를 제거할 수 있는 도구를 구조자에게 제공한다.For example, if it is determined that the person has fallen due to carbon monoxide poisoning, the control center provides tools such as an oxygen tank to the rescuer, and if it is judged that the person has fallen due to poisonous gas poisoning, the control center provides a rescuer with a tool that can remove the toxic gas. To provide.
또한, 제어부(160)는 작업환경에 부착된 비콘 신호 및 네트워크 신호를 이용하여 작업자의 현재 층수를 추정한다.In addition, the control unit 160 estimates the current number of floors of the worker using a beacon signal and a network signal attached to the work environment.
여기서, 제어부(160)는 유, 무선 네트워크를 이용하여 다른 작업자에게 작업자의 상태 및 유해상태를 제공하고, 관제센터에 실시간으로 작업자의 상태 및 유해상태를 다른 작업자 또는 관제센터에 전송한다.Here, the control unit 160 provides the operator's condition and harmful condition to another operator using a wired or wireless network, and transmits the operator's condition and harmful condition to another operator or the control center in real time to the control center.
여기서, 비콘은 IoT를 형성하기 위한 근거리 이동통신 기기로서, 제어부(160)는 비콘을 이용하여 작업자의 정확한 위치 및 건물의 층수를 알 수 있다. Here, the beacon is a short-range mobile communication device for forming IoT, and the controller 160 can know the exact location of the worker and the number of floors in the building using the beacon.
도 4는 본 발명의 실시예에 따른 지능형 웨어러블 위험 상태 판단 장치를 나타낸 도면이다.4 is a diagram illustrating an apparatus for determining an intelligent wearable danger state according to an embodiment of the present invention.
즉, 도 4에서 나타낸 것처럼, 작업자는 제작된 지능형 웨어러블 위험 상태 판단 장치(100)를 작업자의 신체 일부분에 착용할 수 있으며, 부착이 가능한 위치는 사용자 및 지능형 웨어러블 위험 상태 판단 장치(100)의 형태에 따라 변경이 가능하다.That is, as shown in FIG. 4, the operator can wear the manufactured intelligent wearable danger state determination device 100 on a part of the worker's body, and the attachment position is the form of the user and the intelligent wearable danger state determination device 100 It can be changed according to.
여기서, 유해 가스를 측정하기 위해 주변 공기를 흡입하기 위한 공기 유입부분과 흡입된 공기를 배출하기 위한 공기배출 부분을 포함하고 있으며, 각각의 위치는 변경될 수 있다.Here, an air inlet portion for inhaling ambient air and an air outlet portion for discharging the inhaled air are included in order to measure harmful gas, and each position may be changed.
또한, 도 4에서 나타낸 것처럼, 지능형 웨어러블 복합환경 측정장치(100)의 좌측 및 우측에 생성된 각각의 버튼은 전원, 일반시계모드, 통신모드 및 가스센서동작 여부 중에서 어느 하나가 될 수 있으며, 해당되는 각각의 위치는 사용자에 따라서 변경이 가능하다.In addition, as shown in FIG. 4, each button generated on the left and right of the intelligent wearable complex environment measuring apparatus 100 may be any one of power, normal clock mode, communication mode, and gas sensor operation. Each location can be changed according to the user.
이와 같이 본 발명의 실시예에 따르면, 실시간으로 획득되는 유해정보를 활용하여 유해성 여부를 검지할 수 있으며, 유해상태 발생시 실시간으로 작업자에게 알려주어 해당지역에서 이탈할 수 있게 알려줄 수 있다.As described above, according to an embodiment of the present invention, it is possible to detect whether there is harmfulness by using harmful information obtained in real time, and when a harmful state occurs, it is possible to notify an operator in real time so that they can leave a corresponding area.
또한, 센서모듈을 교체하여 사용이 가능하기 때문에 작업의 효율성 및 안전성을 높일 수 있다.In addition, since the sensor module can be replaced and used, the efficiency and safety of work can be improved.
본 발명은 도면에 도시된 실시 예를 참고로 설명 되었으나 이는 예시적인 것이 불과하며, 본 기술 분야의 통상의 지식을 가진 자라면 이로부터 다양한 변형 및 균등한 다른 실시 예가 가능하다는 점을 이해할 것이다. 따라서, 본 발명의 진정한 기술적 보호 범위는 첨부된 특허청구범위의 기술적 사상에 의하여 정해져야 할 것이다.Although the present invention has been described with reference to the embodiments shown in the drawings, this is only exemplary, and those of ordinary skill in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (16)

  1. 복합환경 측정을 이용한 지능형 웨어러블 위험 상태 판단 장치에 있어서,In the intelligent wearable danger state determination device using complex environment measurement,
    탈부착이 가능하며, 작업 환경 주변의 가스의 농도를 측정할 수 있는 가스 센서, 작업자의 균형 상태 및 동작을 측정할 수 있는 자이로 센서 및 가속도 센서 및 상기 작업자의 주변 환경을 촬영하기 위한 카메라를 포함하는 센서부, Detachable, including a gas sensor that can measure the concentration of gas around the work environment, a gyro sensor and acceleration sensor that can measure the balance state and motion of the worker, and a camera for photographing the surrounding environment of the worker. Sensor,
    상기 측정된 가스의 농도로부터 작업자 주변의 유해 상태 여부를 판단하는 유해 상태 판단부,A hazardous state determination unit that determines whether or not there is a hazardous state around the worker from the measured concentration of the gas,
    상기 자이로 센서, 가속도 센서의 측정 값 및 카메라의 촬영 영상, 작업자가 위치한 층수에 따른 상기 작업자의 낙상 여부를 머신러닝(Machine Learning)기법에 적용하여 학습 모델을 생성하는 학습부,A learning unit that generates a learning model by applying a fall of the worker according to the number of floors on which the worker is located, the measured value of the gyro sensor and the acceleration sensor, and the image taken by the camera, to a machine learning technique,
    상기 자이로 센서의 측정 값, 가속도 센서의 측정 값, 촬영 영상의 픽셀 변화값 및 상기 작업자의 현재 위치 층수를 상기 학습 모델에 적용하여 상기 작업자의 낙상 여부를 판단하는 낙상 판단부,A fall determination unit that determines whether the worker falls or not by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the photographed image, and the number of floors of the worker's current location to the learning model,
    상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 디스플레이를 통하여 출력하는 출력부, 그리고 An output unit that outputs information on the hazardous state of the work environment and information on whether the worker falls through a display, and
    상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 네트워크를 통하여 다른 작업자 또는 관제센터에 전달하도록 하는 제어부를 포함하는 지능형 웨어러블 위험 상태 판단 장치.An intelligent wearable danger state determination device comprising a control unit configured to transmit information on a hazardous state of the work environment and information on whether the worker is falling or not to another worker or a control center through a network.
  2. 제1항에 있어서,The method of claim 1,
    상기 가스 센서는,The gas sensor,
    판독회로(ROIC, Read Out Integrated Circuit)를 포함하고, 산소, 가연성 가스, 황하수소, 온도, 습도 및 일산화 탄소 중에서 적어도 하나를 측정할 수 있는 지능형 웨어러블 위험 상태 판단 장치.An intelligent wearable hazard condition determination device that includes a read out integrated circuit (ROIC) and can measure at least one of oxygen, combustible gas, hydrogen sulfide, temperature, humidity, and carbon monoxide.
  3. 제1항에 있어서,The method of claim 1,
    상기 낙상 판단부는,The fall determination unit,
    상기 자이로 센서로부터 측정되는 값이 기준 값 보다 크거나, 가속도 센서로부터 측정되는 값이 기준 값보다 크거나, 상기 카메라에 의해 촬영된 영상의 픽셀 값의 변화량이 기준 값보다 크면 상기 작업자가 단순 낙상한 것으로 판단하는 지능형 웨어러블 위험 상태 판단 장치.If the value measured from the gyro sensor is greater than the reference value, the value measured from the acceleration sensor is greater than the reference value, or the amount of change in the pixel value of the image captured by the camera is greater than the reference value, the operator simply falls. An intelligent wearable dangerous state determination device that determines that it is.
  4. 제3항에 있어서,The method of claim 3,
    상기 낙상 판단부는,The fall determination unit,
    상기 낙상한 작업자가 낙상하기 전의 주변 환경이 유해 상태로 판단되면, 상기 작업자는 유해 상태로 인해 낙상한 것으로 판단하는 지능형 웨어러블 위험 상태 판단 장치.An intelligent wearable danger state determination device that determines that the worker who has fallen due to a fall due to the harmful state is determined to be in a hazardous state when the surrounding environment before the fall of the fallen worker is determined to be in a hazardous state.
  5. 제1항에 있어서,The method of claim 1,
    상기 제어부는,The control unit,
    상기 작업자 주변이 유해 상태인 것으로 판단되면, 상기 작업자로부터 일정 거리에 있는 다른 작업자에게 위험 알람 신호를 전달하고, If it is determined that the vicinity of the worker is in a hazardous state, a danger alarm signal is transmitted to another worker at a certain distance from the worker,
    상기 작업자가 낙상한 것으로 판단하면, 알람이 발생시키고 다른 작업자가 착용한 지능형 웨어러블 복합환경 측정 장치로 상기 작업자의 낙상 사실을 전달하는 지능형 웨어러블 위험 상태 판단 장치.When it is determined that the worker has fallen, an alarm is generated and an intelligent wearable danger state determination device that communicates the fall of the worker to an intelligent wearable complex environment measuring device worn by another worker.
  6. 제5항에 있어서,The method of claim 5,
    상기 제어부는, The control unit,
    상기 작업자가 단순 낙상한 것인지, 유해 상태에 의해 낙상한 것인지를 구분하여 상기 관제센터에 전달하는 지능형 웨어러블 위험 상태 판단 장치.An intelligent wearable danger state determination device that distinguishes whether the worker has simply fallen or has fallen due to an unhealthy condition, and transmits it to the control center.
  7. 제1항에 있어서,The method of claim 1,
    상기 학습부는, The learning unit,
    작업 환경의 층수가 높을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 증가시켜 학습시키고, As the number of floors in the work environment increases, the weight of the gyro sensor and the acceleration sensor is increased to learn,
    작업 환경의 층수가 낮을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 감소시켜 학습시키는 지능형 웨어러블 위험 상태 판단 장치.An intelligent wearable danger state determination device that learns by reducing weights for the gyro sensor and acceleration sensor as the number of floors in the work environment decreases.
  8. 제7항에 있어서,The method of claim 7,
    상기 제어부는, The control unit,
    상기 작업환경에 부착된 비콘 신호를 통하여 상기 작업자의 현재 층수를 추정하는 지능형 웨어러블 위험 상태 판단 장치.An intelligent wearable danger state determination device that estimates the number of floors of the worker through a beacon signal attached to the work environment.
  9. 지능형 웨어러블 위험 상태 판단 장치를 이용한 위험상태 판단 방법에 있어서,In the method for determining a dangerous state using an intelligent wearable dangerous state determining device,
    탈부착이 가능한 가스 센서를 이용하여 작업 환경 주변의 가스의 농도를 측정하는 단계, Measuring the concentration of gas around the working environment using a detachable gas sensor,
    상기 측정된 가스의 농도로부터 작업자 주변의 유해 상태 여부를 판단하는 단계,Determining whether there is a harmful state around the worker from the measured concentration of the gas,
    자이로 센서 및 가속도 센서를 이용하여 작업자의 균형 상태 및 동작을 측정하고, 카메라를 이용하여 상기 작업자의 주변 환경을 촬영하는 단계,Measuring the balance state and motion of the worker using a gyro sensor and an acceleration sensor, and photographing the surrounding environment of the worker using a camera,
    상기 자이로 센서, 가속도 센서 및 카메라의 측정값, 작업자가 위치한 층수에 따른 상기 작업자의 낙상 여부를 머신러닝(Machine Learning)기법에 적용하여 학습 모델을 생성하는 단계,Generating a learning model by applying a fall of the worker according to the measured values of the gyro sensor, acceleration sensor and camera, and the number of floors on which the worker is located to a machine learning technique,
    상기 자이로 센서의 측정 값, 가속도 센서의 측정 값, 촬영 영상의 픽셀 변화값 및 상기 작업자의 현재 위치 층수를 상기 학습 모델에 적용하여 상기 작업자의 낙상 여부를 판단하는 단계,Determining whether or not the worker falls by applying the measured value of the gyro sensor, the measured value of the acceleration sensor, the pixel change value of the photographed image, and the number of floors of the worker's current location to the learning model,
    상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 디스플레이를 통하여 출력하는 단계, 그리고 Outputting information on the hazardous state of the work environment and information on whether the worker falls through a display, and
    상기 작업환경의 유해 상태 정보와 상기 작업자의 낙상 여부 정보를 네트워크를 통하여 다른 작업자 또는 관제센터에 전달하도록 하는 단계를 포함하는 위험 상태 판단 방법.And transmitting information on the hazardous state of the work environment and information on whether the worker has fallen to another worker or a control center through a network.
  10. 제9항에 있어서,The method of claim 9,
    상기 가스 센서는,The gas sensor,
    판독회로(ROIC, Read Out Integrated Circuit)를 포함하고, 산소, 가연성 가스, 황하수소, 온도, 습도 및 일산화 탄소 중에서 적어도 하나를 측정할 수 있는 위험 상태 판단 방법.A method for determining a hazardous state that includes a read out integrated circuit (ROIC) and can measure at least one of oxygen, combustible gas, hydrogen sulfide, temperature, humidity, and carbon monoxide.
  11. 제9항에 있어서,The method of claim 9,
    상기 낙상 여부를 판단하는 단계는,The step of determining whether the fall or not,
    상기 자이로 센서로부터 측정되는 값이 기준 값 보다 크거나, 가속도 센서로부터 측정되는 값이 기준 값보다 크거나, 상기 카메라에 의해 촬영된 영상의 픽셀 값의 변화량이 기준 값보다 크면 상기 작업자가 단순 낙상한 것으로 판단하는 위험 상태 판단 방법.If the value measured from the gyro sensor is greater than the reference value, the value measured from the acceleration sensor is greater than the reference value, or the amount of change in the pixel value of the image captured by the camera is greater than the reference value, the operator simply falls. How to judge a dangerous state that is judged to be.
  12. 제11항에 있어서,The method of claim 11,
    상기 낙상 여부를 판단하는 단계는,The step of determining whether the fall or not,
    상기 낙상한 작업자가 낙상하기 전의 주변 환경이 유해 상태로 판단되면, 상기 작업자는 유해 상태로 인해 낙상한 것으로 판단하는 위험 상태 판단 방법.A method of determining a dangerous state of determining that the fallen worker has fallen due to the harmful state when the surrounding environment before the fall is determined to be in a hazardous state.
  13. 제9항에 있어서,The method of claim 9,
    상기 다른 작업자 또는 관제센터에 전달하도록 하는 단계는,The step of transmitting to the other operator or control center,
    상기 작업자 주변이 유해 상태인 것으로 판단되면, 상기 작업자로부터 일정 거리에 있는 다른 작업자에게 위험 알람 신호를 전달하고, If it is determined that the vicinity of the worker is in a hazardous state, a danger alarm signal is transmitted to another worker at a certain distance from the worker,
    상기 작업자가 낙상한 것으로 판단하면, 알람이 발생시키고 다른 작업자가 착용한 지능형 웨어러블 복합환경 측정 장치로 상기 작업자의 낙상 사실을 전달하는 위험 상태 판단 방법.When it is determined that the worker has fallen, an alarm is generated and a risk state determination method of transmitting the fact of the worker's fall to an intelligent wearable complex environment measuring device worn by another worker.
  14. 제13항에 있어서,The method of claim 13,
    상기 다른 작업자 또는 관제센터에 전달하도록 하는 단계는, The step of transmitting to the other operator or control center,
    상기 작업자가 단순 낙상한 것인지, 유해 상태에 의해 낙상한 것인지를 구분하여 상기 관제센터에 전달하는 위험 상태 판단 방법.A method for determining a dangerous state by distinguishing whether the worker has simply fallen or has fallen due to a hazardous condition, and delivers it to the control center.
  15. 제9항에 있어서,The method of claim 9,
    상기 학습 모델을 생성하는 단계는, Generating the learning model,
    작업 환경의 층수가 높을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 증가시켜 학습시키고, As the number of floors in the work environment increases, the weight of the gyro sensor and the acceleration sensor is increased to learn,
    작업 환경의 층수가 낮을수록 상기 자이로 센서 및 가속도 센서에 대한 가중치를 감소시켜 학습시키는 위험 상태 판단 방법.As the number of floors in the work environment decreases, weights for the gyro sensor and the acceleration sensor are reduced and learned.
  16. 제15항에 있어서,The method of claim 15,
    상기 다른 작업자 또는 관제센터에 전달하도록 하는 단계는,The step of transmitting to the other operator or control center,
    상기 작업환경에 부착된 비콘 신호를 통하여 상기 작업자의 현재 층수를 추정하는 위험 상태 판단 방법.A method of determining a danger state for estimating the current number of floors of the worker through a beacon signal attached to the work environment.
PCT/KR2019/013719 2019-08-26 2019-10-18 Intelligent wearable dangerous state determination device using complex environment measurement, and method therefor WO2021040126A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2019-0104518 2019-08-26
KR1020190104518A KR102123008B1 (en) 2019-08-26 2019-08-26 Apparatus for intelligent wearable hazard determination device using complex environment measurement and method thereof

Publications (1)

Publication Number Publication Date
WO2021040126A1 true WO2021040126A1 (en) 2021-03-04

Family

ID=71081652

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2019/013719 WO2021040126A1 (en) 2019-08-26 2019-10-18 Intelligent wearable dangerous state determination device using complex environment measurement, and method therefor

Country Status (2)

Country Link
KR (1) KR102123008B1 (en)
WO (1) WO2021040126A1 (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112533166A (en) * 2020-11-27 2021-03-19 上海建工集团股份有限公司 Mobile information transmission instrument
KR102582490B1 (en) 2021-06-30 2023-09-22 한성대학교 산학협력단 Apparatus and method for safety management
CN117877198B (en) * 2024-03-12 2024-05-17 成都蜀诚通信技术有限公司 Safe operation and maintenance system applied to construction site

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140349707A1 (en) * 2012-02-01 2014-11-27 Young-ki Bang Gas detection system and method using smart phone
KR101663572B1 (en) * 2016-03-30 2016-10-07 (주)네트 Monitoring system of working environment for ensuring safety of workers
KR20180059356A (en) * 2016-11-25 2018-06-04 (재)한국재난안전기술원 Integrated control system for urgent response against disaster situations including chemical hazard, and disaster response method using thereof
KR20180097091A (en) * 2017-02-22 2018-08-30 광주과학기술원 Apparatus and method for estimating fall risk based on machine learning
KR20190009481A (en) * 2017-07-19 2019-01-29 주식회사 선경 이.엔.아이 Realtime Process Safety Management System based worker and facility information of position and status

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140349707A1 (en) * 2012-02-01 2014-11-27 Young-ki Bang Gas detection system and method using smart phone
KR101663572B1 (en) * 2016-03-30 2016-10-07 (주)네트 Monitoring system of working environment for ensuring safety of workers
KR20180059356A (en) * 2016-11-25 2018-06-04 (재)한국재난안전기술원 Integrated control system for urgent response against disaster situations including chemical hazard, and disaster response method using thereof
KR20180097091A (en) * 2017-02-22 2018-08-30 광주과학기술원 Apparatus and method for estimating fall risk based on machine learning
KR20190009481A (en) * 2017-07-19 2019-01-29 주식회사 선경 이.엔.아이 Realtime Process Safety Management System based worker and facility information of position and status

Also Published As

Publication number Publication date
KR102123008B1 (en) 2020-06-15

Similar Documents

Publication Publication Date Title
WO2021040126A1 (en) Intelligent wearable dangerous state determination device using complex environment measurement, and method therefor
WO2019132112A1 (en) System for monitoring abnormal states of facilities to be managed
WO2012005559A2 (en) Method and portable terminal for estimating step length of pedestrian
WO2019143185A1 (en) Electronic device and method of detecting status of battery thereof
WO2014069776A1 (en) Head mounted display and method of outputting audio signal using the same
WO2014007533A1 (en) Method and apparatus for controlling sleep mode in portable terminal
WO2013168920A1 (en) Waterproof inspection device for terminal
WO2017099479A1 (en) Internet-of-things system for recognizing user behavior and controlling things
EP3860446A1 (en) Electronic device for obtaining blood pressure value using pulse wave velocity algorithm and method for obtaining blood pressure value
WO2019124816A1 (en) On-site worker safety management system including enclosed space
WO2014046313A1 (en) Method for automatically recognizing and setting attachment and device therefor
KR20130021060A (en) Removable smart module for helmet
WO2023113058A1 (en) Drone control method for precise landing
WO2014073728A1 (en) Smart helmet having black box function and operation method therefor
WO2016140389A1 (en) Wearable device and system for user monitoring
WO2022098181A1 (en) System and method for battery diagnosis according to altitude using atmospheric pressure sensor
CN113280265A (en) Working condition identification method and device, computer equipment and storage medium
WO2020190087A1 (en) Gas detection apparatus, detection method, and operating method
WO2021162269A1 (en) Wireless charging method and electronic device supporting same
WO2021025492A1 (en) Headgear-type device for hazardous air quality warning and air quality improvement
WO2020145653A1 (en) Electronic device and method for recommending image capturing place
WO2017034254A1 (en) Fire extinguisher management apparatus and fire extinguisher
WO2020096311A1 (en) Electronic device and method for identifying occurrence of hypotension
WO2019139409A1 (en) Method and electronic device for correcting and generating data related to outside air on basis of movement
WO2021086005A1 (en) Electronic device, and method for providing evacuation information according to occurrence of disaster by using same

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19943441

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19943441

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 12/09/2022)

122 Ep: pct application non-entry in european phase

Ref document number: 19943441

Country of ref document: EP

Kind code of ref document: A1