WO2022118397A1 - Dispositif, système et procédé de calcul de valeur de risque et programme d'enregistrement de support lisible par ordinateur non transitoire - Google Patents

Dispositif, système et procédé de calcul de valeur de risque et programme d'enregistrement de support lisible par ordinateur non transitoire Download PDF

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Publication number
WO2022118397A1
WO2022118397A1 PCT/JP2020/044863 JP2020044863W WO2022118397A1 WO 2022118397 A1 WO2022118397 A1 WO 2022118397A1 JP 2020044863 W JP2020044863 W JP 2020044863W WO 2022118397 A1 WO2022118397 A1 WO 2022118397A1
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Prior art keywords
risk value
person
image data
symptom
visible light
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PCT/JP2020/044863
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English (en)
Japanese (ja)
Inventor
俊明 田中
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日本電気株式会社
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Priority to JP2022566547A priority Critical patent/JPWO2022118397A5/ja
Priority to PCT/JP2020/044863 priority patent/WO2022118397A1/fr
Publication of WO2022118397A1 publication Critical patent/WO2022118397A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu

Definitions

  • the present invention relates to a risk value calculation device, a system, a method and a program.
  • the technique of detecting or measuring the risk of infection for an infectious disease of a person existing in a predetermined space is known.
  • the infection risk identification system described in Patent Document 1 identifies the infection level of a person staying in a space, detects the position of a person in the space, and positions the person in the space based on the infection level and the position of the person. Generates information indicating the risk of infection with infectious substances for each.
  • Patent Document 2 a person is identified from an image taken by a video camera at a point where a lot of people come and go, the body temperature of the person is measured by a thermal image camera, and a person having a body temperature exceeding a predetermined threshold is selected. The technique of presuming a fever is described.
  • the present disclosure has been made in view of such problems, and an object of the present disclosure is to provide a risk value calculation device or the like that can suitably determine an infection risk.
  • the risk value calculation device includes an image data acquisition means, a temperature measurement means, a symptom detection means, a risk value calculation means, and an output means.
  • the image data acquisition means acquires the visible light image data generated by the visible light camera and the thermal image data generated by the infrared camera having a shooting range corresponding to at least a part of the visible light image data.
  • the temperature measuring means identifies a person included in the visible light image data and the thermal image data, and measures the body surface temperature of the specified person.
  • the symptom detecting means detects the symptom of a person's infectious disease based on the visible light image data.
  • the risk value calculation means calculates the infection risk value of a person based on the body surface temperature and the symptom.
  • the output means outputs the infection risk value.
  • the computer executes the following method.
  • the computer acquires the visible light image data generated by the visible light camera and the thermal image data generated by the infrared camera having a shooting range corresponding to at least a part of the visible light image data.
  • the computer identifies the person contained in the visible light image data and the thermal image data, and measures the body surface temperature of the specified person.
  • the computer detects the symptoms of an infectious disease in a person based on visible light image data.
  • the computer calculates the infection risk value of a person based on the body surface temperature and the symptom.
  • the computer outputs the infection risk value.
  • the program causes a computer to perform the following steps.
  • the computer acquires the visible light image data generated by the visible light camera and the thermal image data generated by the infrared camera having a shooting range corresponding to at least a part of the visible light image data.
  • the computer identifies the person contained in the visible light image data and the thermal image data, and measures the body surface temperature of the specified person.
  • the computer detects the symptoms of an infectious disease in a person based on visible light image data.
  • the computer calculates the infection risk value of a person based on the body surface temperature and the symptom.
  • the computer outputs the infection risk value.
  • FIG. It is a block diagram which shows the structure of the risk calculation apparatus which concerns on Embodiment 1.
  • FIG. It is a flowchart which shows the risk calculation method concerning Embodiment 1.
  • FIG. It is a flowchart which shows the risk calculation method concerning Embodiment 2.
  • FIG. It is a flowchart which shows the risk calculation method concerning Embodiment 3.
  • FIG. 1 is a block diagram showing a configuration of a risk value calculation device 11 according to the first embodiment.
  • the risk value calculation device 11 is used, for example, to grasp the possibility that a person existing in a predetermined facility is infected with a predetermined infection or the possibility of being infected.
  • the risk value calculation device 11 mainly includes an image data acquisition unit 110, a temperature measurement unit 111, a symptom detection unit 112, a risk value calculation unit 113, and an output unit 114. It should be noted that these configurations of the risk value calculation device 11 are connected so as to be communicable as appropriate.
  • the image data acquisition unit 110 acquires the visible light image data generated by the visible light camera and the thermal image data generated by the infrared camera having a shooting range corresponding to at least a part of the visible light image data.
  • the image data acquisition unit 110 may supply the acquired visible light image data and thermal image data to the temperature measurement unit 111 and the symptom detection unit 112, respectively.
  • Visible light cameras and infrared cameras are installed in a predetermined facility, in the vicinity of the facility, or in a predetermined place outdoors.
  • the visible light camera captures a landscape including a person, generates visible light image data related to the captured landscape image, and supplies the generated visible light image data to the risk value calculation device 11.
  • the infrared camera captures a landscape including a person, generates thermal image data related to the captured landscape image, and supplies the generated thermal image to the risk value calculation device 11.
  • the visible light camera and the infrared camera overlap at least a part of the shooting range.
  • the infrared camera has a shooting range corresponding to at least a part of the visible light image taken by the visible light camera.
  • the visible light camera and the infrared camera are fixed so that their positional relationship does not change.
  • the photographing apparatus can associate an object included in the visible light image data generated by the visible light camera with an object included in the thermal image data generated by the infrared camera.
  • the visible light camera and the infrared camera may be one or more.
  • the visible light camera or the infrared camera may be movable so that the positional relationship with each other changes. In this case, the visible light camera or the infrared camera may temporarily pan, tilt, or zoom by the user's operation, for example, and even if the angle of view is changed, it may automatically return to the predetermined position. preferable.
  • the temperature measuring unit 111 identifies a person included in the visible light image data and the thermal image data, and measures the body surface temperature of the specified person. When the temperature measuring unit 111 measures the body surface temperature of a person, the temperature measuring unit 111 supplies the measured data on the body surface temperature to the risk value calculating unit 113.
  • the temperature measuring unit 111 first extracts an image of a person from visible light image data. At this time, the temperature measuring unit 111 may extract only a specific part such as a face image in the body of the person. When the visible light camera captures a plurality of persons at the same time, the temperature measuring unit 111 extracts and identifies each of the plurality of persons from the visible light image data.
  • the temperature measuring unit 111 extracts the thermal image data corresponding to the image of the specified person. Further, the temperature measuring unit 111 measures the body surface temperature of the person specified from the extracted thermal image data. The temperature measuring unit 111 may measure the body surface temperature from the portion showing the highest temperature in the extracted thermal image data. Further, the temperature measuring unit 111 may calculate a statistical value of the temperature of the extracted thermal image data, and the calculated statistical value may be used as the body surface temperature of the person.
  • the symptom detection unit 112 When the symptom detection unit 112 receives the visible light image data, the symptom detection unit 112 detects the symptom of the infectious disease of the person from the image data of the person included in the received visible light image data. When the symptom detection unit 112 detects a symptom from the image data of a person, the symptom detection unit 112 supplies data indicating that the symptom has been detected to the risk value calculation unit 113.
  • the symptom detection unit 112 identifies the image data of a person included in the visible light image data. Next, the symptom detection unit 112 analyzes the image data of the specified person and estimates the posture of this person. Further, the symptom detection unit 112 determines whether or not the estimated posture matches the symptom shown when suffering from a preset disease. Then, when the symptom detection unit 112 determines that the posture of the person matches the symptom, the symptom detection unit 112 detects the symptom from the specified person.
  • the preset disease is, for example, pneumonia, and more specifically, infectious pneumonia.
  • Infectious pneumonia includes viral pneumonia, bacterial pneumonia and mycoplasma pneumonia.
  • the preset disease may be another disease in which a peculiar symptom appears.
  • the risk value calculation unit 113 calculates the infection risk value of the specified person from the body surface temperature and the symptom of the person. When the risk value calculation unit 113 calculates the infection risk value of a person, the calculated infection risk value is supplied to the output unit 114.
  • the symptom detection unit 112 may detect the symptom by using the thermal image acquired from the thermal camera 92 in addition to the visible light image data acquired from the visible light camera.
  • the risk value calculation unit 113 receives data on the body surface temperature of a person from the temperature measurement unit 111. Further, the risk value calculation unit 113 receives data on a person's symptom from the symptom detection unit 112. Then, the risk value calculation unit 113 calculates the infection risk value of the person using these received data.
  • the infection risk value is an index for estimating that the patient is infected with a preset disease, and is indicated by a preset numerical value.
  • the infection risk value is defined by a numerical value from 0 to 1. In this case, for example, 0 means the lowest risk of infection and 1 means the highest risk of infection. The higher the "infection risk", the higher the possibility of infection.
  • the body surface temperature of a person is classified into a plurality of levels, and each classification is set to a preset value in the range of 0 to 1.
  • the symptom of the person is set to a preset value in the range of 0 to 1, for example, for each corresponding symptom.
  • the infection risk value is calculated by taking the average value of each value set in this way.
  • the infection risk value is calculated higher when the body surface temperature of a person exceeds a predetermined threshold value than when the threshold value is not exceeded.
  • the infection risk value is calculated to be higher when the person shows symptoms of an infectious disease than when the person does not show the symptoms.
  • the above-mentioned method for calculating the infection risk value is an example, and the method for calculating the infection risk value is not limited to the above-mentioned method.
  • the output unit 114 When the output unit 114 receives the infection risk value calculated by the risk value calculation unit 113, the output unit 114 outputs the received infection risk value to a predetermined display device.
  • the predetermined display device may be included in the risk value calculation device 11.
  • the predetermined display device may be included in a predetermined terminal device that is communicably connected to the risk value calculation device 11.
  • FIG. 2 is a flowchart showing a risk calculation method according to the first embodiment.
  • the flowchart shown in FIG. 2 is started by, for example, activating the risk value calculation device 11.
  • the image data acquisition unit 110 acquires the visible light image data from the visible light camera and the thermal image data from the infrared camera (step S11).
  • the image data acquisition unit 110 may acquire the visible light image data and the thermal image data in parallel, or may sequentially acquire the visible light image data and the thermal image data according to a preset protocol.
  • the temperature measuring unit 111 identifies a person included in the visible light image data and the thermal image data (step S12), and measures the body surface temperature of the specified person (step S13).
  • the temperature measuring unit 111 generates the body surface temperature data in such a manner that the risk value calculation unit 113 can identify each identified person.
  • the symptom detection unit 112 detects the symptom of a person's infectious disease based on the visible light image data (step S14).
  • the symptom detection unit 112 detects the symptom of a plurality of persons
  • the symptom detection unit 112 generates data related to the symptom in such a manner that the risk value calculation unit 113 can identify each identified person.
  • the risk value calculation unit 113 calculates the infection risk value of the specified person from the data on the body surface temperature received from the temperature measurement unit 111 and the data on the symptom received from the symptom detection unit 112 (step S15). ).
  • the output unit 114 receives the infection risk value calculated by the risk value calculation unit 113, and outputs the received infection risk value (step S16).
  • step S13 and step S14 may be executed in parallel, or step S12 may be executed after step S13.
  • the risk value calculation device 11 uses the visible light image data acquired from the visible light camera and the thermal image data acquired from the thermal camera.
  • the risk value calculation device 11 calculates the infection risk value by combining the body surface temperature of the person measured from the thermal image data and the symptom of the person detected from the visible light image data. Therefore, according to the present embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program capable of suitably determining an infection risk.
  • the risk value calculation device 11 has a processor and a storage device as a configuration (not shown).
  • the storage device included in the risk value calculation device 11 includes a storage device including a flash memory and a non-volatile memory such as an SSD (Solid State Drive).
  • the storage device of the risk value calculation device 11 stores a computer program (hereinafter, also simply referred to as a program) for executing the risk value calculation method according to the present embodiment.
  • the processor also reads a computer program from the storage device into the memory and executes the program.
  • Each configuration of the risk value calculation device 11 may be realized by dedicated hardware. Further, a part or all of each component may be realized by a general-purpose or dedicated circuitry, a processor, or a combination thereof. These may be composed of a single chip or may be composed of a plurality of chips connected via a bus. A part or all of each component of each device may be realized by the combination of the circuit or the like and the program described above. Further, as a processor, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an FPGA (field-programmable gate array), or the like can be used.
  • a CPU Central Processing Unit
  • GPU Graphics Processing Unit
  • FPGA field-programmable gate array
  • each component of the risk value calculation device 11 when a part or all of each component of the risk value calculation device 11 is realized by a plurality of arithmetic units, circuits, etc., the plurality of arithmetic units, circuits, etc. may be centrally arranged or distributed. It may be arranged.
  • the arithmetic unit, the circuit, and the like may be realized as a form in which each is connected via a communication network, such as a client-server system and a cloud computing system.
  • the function of the risk value calculation device 11 may be provided in the SAAS (Software as a Service) format.
  • SAAS Software as a Service
  • FIG. 3 is a block diagram showing a configuration of the risk value calculation device according to the second embodiment.
  • the risk value calculation device 12 shown in FIG. 3 is communicably connected to the user terminal 21, the visible light camera 91, and the thermal camera 92 via the network 500.
  • the network 500 is a communication network such as a wide area network such as the Internet or a local area network such as a line in a predetermined facility.
  • the visible light camera 91 and the thermal camera 92 are installed in a predetermined facility 90 and are communicably connected to the risk value calculation device 12 via the network 500. There is a person in the facility 90.
  • the visible light camera 91 and the thermal camera 92 each photograph an internal landscape including a person in the facility 90, and transmit the image data (visible light image data and thermal image data) generated by the images to the risk value calculation device 12, respectively. do.
  • the user terminal 21 is an information processing terminal such as a personal computer, a smartphone or a tablet terminal.
  • the user terminal 21 is communicably connected to the risk value calculation device 12 via the network 500, and receives the infection risk value from the risk value calculation device 12.
  • the user terminal 21 displays the infection risk value received from the risk value calculation device 12, and notifies the user who operates the user terminal 21 of the infection risk value.
  • the user can grasp the infection risk value of the person existing in the facility 90 via the user terminal 21. As a result, for example, the user can perform an activity to reduce the risk of infection to a person existing in the facility 90.
  • the risk value calculation device 12 according to the present embodiment is different from the risk value calculation device 11 according to the first embodiment in that the storage unit 120 is included.
  • the storage unit 120 is a storage device including a non-volatile memory such as an EPROM (ErasableProgrammableReadOnlyMemory) or a flash memory.
  • the storage unit 120 stores the symptom DB 121.
  • the symptom DB 121 contains information about a preset symptom of the disease. More specifically, the symptom DB 121 includes preset symptoms of the disease that are related to the posture of the affected person. Further, the symptom DB 121 includes information on a mode that can be collated with the posture of the person extracted by the symptom detection unit 112. The mode that can be collated with the extracted posture of the person is, for example, image data showing the posture pattern of the person or feature amount data obtained by extracting the feature amount of the posture pattern of the person.
  • the symptom DB 121 may include symptom attribute information associated with the memorized posture pattern. The symptom attribute information is information indicating the possibility of infection and the severity of the symptom corresponding to each symptom posture pattern.
  • the storage unit 120 supplies the symptom DB 121 to the symptom detection unit 112.
  • the symptom detection unit 112 reads the symptom DB 121 from the storage unit 120, and collates the read symptom DB 121 with the extracted posture of the person. As a result of the collation, the symptom detection unit 112 determines whether or not the posture of the extracted person matches the posture pattern indicating the symptom included in the symptom DB 121. When the symptom detection unit 112 determines that the extracted posture of the person is included in the symptom DB 121, the symptom detection unit 112 detects the symptom from the posture of this person.
  • the symptom detection unit 112 detects a symptom, for example, when the posture of the person and the posture pattern included in the symptom DB 121 match at a predetermined ratio or more within a predetermined period. Specifically, for example, the symptom detection unit 112 detects a symptom when it matches a posture pattern showing the symptom in 10 seconds out of 60 seconds.
  • the symptom detection unit 112 may supply the symptom attribute information to the risk value calculation unit 113 together with the detected symptom.
  • the risk value calculation device 12 can improve the accuracy of the calculated infection risk value.
  • FIG. 4 is a flowchart of the risk calculation method according to the second embodiment.
  • the flowchart shown in FIG. 4 is different from the flowchart according to the first embodiment shown in FIG. 2 in that the processing between steps S13 and S15 is step S21 and step S22 instead of step S14.
  • Steps S11 to S13 are the same as the flowchart shown in FIG.
  • the symptom detection unit 112 extracts the posture of the specified person from the visible light image data received from the image data acquisition unit 110 (step S21).
  • the symptom detection unit 112 reads the symptom DB 121 from the storage unit 120, collates the extracted posture of the person with the symptom DB 121, and detects the symptom (step S22). As a result of collation between the posture of the extracted person and the symptom DB 121, if it is determined that they match, the symptom detection unit 112 detects the symptom from the posture of the extracted person. As a result of collation between the posture of the extracted person and the symptom DB 121, if it is not determined that they match, the symptom detection unit 112 does not detect the symptom from the posture of the extracted person.
  • the symptom detection unit 112 supplies data related to the detected symptom to the risk value calculation unit 113.
  • the risk value calculation device 12 proceeds to step S15.
  • the processing after step S15 is the same as the flowchart shown in FIG.
  • the risk value calculation device 12 is not limited to the above configuration.
  • a visible light camera 91 and a thermal camera 92 are shown one by one.
  • the risk value calculation device 12 may acquire image data from a plurality of visible light cameras 91 and a thermal camera 92, respectively.
  • the visible light camera 91 and the thermal camera 92 may be configured in different numbers.
  • the risk value calculation device 12 collates the extracted posture of the person with the information of the symptom DB 121. As a result, the risk value calculation device 12 can accurately calculate the infection risk value.
  • the symptom DB 121 may be configured to be updatable. As a result, the risk value calculation device 12 can collate the symptoms according to the type of the prevalent disease. Therefore, according to the present embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can appropriately and flexibly determine the infection risk.
  • FIG. 5 is a block diagram showing a configuration of the risk value calculation device according to the third embodiment.
  • the risk value calculation device 13 shown in FIG. 5 is different from the risk value calculation device 11 and the risk value calculation device 12 described above in that the congestion degree calculation unit 115 is included.
  • the image data acquisition unit 110 according to the present embodiment also supplies the acquired visible light image data to the congestion degree calculation unit 115.
  • the congestion degree calculation unit 115 receives visible light image data from the image data acquisition unit 110, and calculates the degree of congestion in the space from the received visible light image data.
  • the degree of congestion in a space is calculated by the number of people existing in a predetermined space. When the degree of congestion is high, the number of people present in the space is larger than when the degree of congestion is low. That is, the congestion degree calculation unit 115 can calculate the congestion degree by counting the number of people in the predetermined space. For example, the congestion degree calculation unit 115 may set the entire visible light image data as a predetermined space, or may set a part of the image included in the visible light image data as a predetermined space. Further, the congestion degree calculation unit 115 may divide the space included in the visible light image data into a plurality of spaces and calculate the congestion degree for each divided space. The congestion degree calculation unit 115 supplies the calculated congestion degree to the risk value calculation unit 113.
  • the congestion degree calculation unit 115 may treat a person included in a predetermined space as a crowd and calculate the congestion degree from the state of the crowd.
  • a crowd refers to a state in which a plurality of people appear to overlap in a predetermined space.
  • the congestion degree calculation unit 115 detects the crowd and identifies the crowded state of the detected crowd.
  • the congestion degree calculation unit 115 calculates the congestion degree. More specifically, for example, the congestion degree calculation unit 115 extracts a predetermined local image smaller than the image size of the visible light image data, and determines the congestion state of the crowd in the extracted local image. In determining the congestion state, the congestion degree calculation unit 115 may use, for example, a classifier generated by machine learning. By adopting such a means, the congestion degree calculation unit 115 can calculate the congestion degree without counting the exact number of people in the image.
  • the congestion degree calculation unit 115 used the visible light image data when calculating the congestion degree.
  • the data used in the calculation of the degree of congestion performed by the degree of congestion calculation unit 115 is not limited to the visible light image data. That is, the congestion degree calculation unit 115 may calculate the congestion degree by using the thermal image data.
  • the risk value calculation unit 113 calculates the infection risk value by further considering the degree of congestion in the predetermined space including the person in the visible light image data in addition to the body surface temperature and the symptom of the specified person. That is, the risk value calculation unit 113 calculates the infection risk value of a person existing in a space having a relatively high degree of congestion higher than the infection risk value of a person existing in a space having a relatively low degree of congestion. By calculating the infection risk value in consideration of the degree of congestion in this way, the risk value calculation device 13 can improve the accuracy of the infection risk value.
  • the risk value calculation unit 113 may calculate the infection risk value in consideration of the situation of surrounding persons as follows.
  • the risk value calculation unit 113 may calculate the infection risk value of a person by further considering the body surface temperature and the symptom of the surrounding person existing around the person. More specifically, for example, when there is a peripheral person in the vicinity of the person who calculates the infection risk value, the body surface temperature of the peripheral person exceeds normal temperature, or the posture of the peripheral person is a symptom. If it matches the posture pattern of, the risk value calculation unit 113 takes this into consideration and calculates a relatively high infection risk value.
  • the risk value calculation unit 113 calculates the infection risk value in consideration of the infection risk value of the peripheral person. For example, when the infection risk value of a peripheral person is the first risk value (for example, 0.7), the risk value calculation unit 113 is higher than the case where the infection risk value of the peripheral person is the second risk value (for example, 0.3). Calculate the infection risk value relatively high.
  • the risk value calculation unit 113 may calculate the infection risk value of the person by further considering the distance to the surrounding person existing in the vicinity of the person. More specifically, for example, when the distance to the surrounding person is the first distance (for example, 1 meter), the risk value calculation unit 113 has the second distance (for example, 3) in which the infection risk value of the peripheral person is farther than the first distance. Calculated relatively higher than the infection risk value calculated in the case of (meter). Based on the above contents, for example, the infection risk value can be calculated as follows. First, each element for calculating the infection risk value is set to a value between 0 and 1 as follows.
  • the body surface temperature of a person is classified into a plurality of levels, and each classification is set to a preset value in the range of 0 to 1.
  • the symptom of the person is set to a preset value in the range of 0 to 1, for example, for each corresponding symptom.
  • the degree of congestion is normalized so that the more crowded it is, the closer it is to 1, and the less crowded it is, the closer it is to 0.
  • the infection risk value is calculated by taking the average value of each value set in this way.
  • the body surface temperature of the surrounding person and the symptom of the surrounding person are set to a value between 0 and 1. Then, the risk value calculation unit 113 calculates the average value of these elements as the infection risk value.
  • the risk value calculation unit 113 may perform weighting so that the influence of a specific element is reflected relatively large when calculating the average value.
  • the above-mentioned infection risk value indicates the risk that the infection spreads to the surrounding persons existing around the predetermined person.
  • the risk value calculation unit 113 may calculate the risk of spreading the infection from a peripheral person to a predetermined person, or may calculate the risk of spreading the infection in a predetermined crowd.
  • the above-mentioned method for calculating the infection risk value is an example, and the method for calculating the infection risk value is not limited to the above-mentioned method.
  • FIG. 6 is a flowchart of the risk calculation method according to the third embodiment.
  • the flowchart shown in FIG. 6 is different from the flowchart according to the second embodiment shown in FIG. 4 in that it has steps S31 and S32 instead of step S15.
  • step S31 the congestion degree calculation unit 115 calculates the congestion degree from the visible light image data (step S31).
  • the risk value calculation unit 113 specifies from the data on the body surface temperature received from the temperature measurement unit 111, the data on the symptom received from the symptom detection unit 112, and the congestion degree received from the congestion degree calculation unit 115.
  • the infection risk value of the person who has been infected is calculated (step S32).
  • the output unit 114 outputs the infection risk value received from the risk value calculation unit 113 (step S16).
  • step S31 may be executed between steps S11 and S32 at a timing different from the order shown in FIG.
  • the order of the processes of measuring the body surface temperature (step S13), detecting the symptoms of a person (steps S21 and S22), and calculating the degree of congestion (step S31) does not matter. Further, these processes may be executed in parallel.
  • the embodiment 3 has been described above.
  • the risk value calculation device 13 according to the third embodiment calculates the degree of congestion, and calculates the risk value by adding the calculated degree of congestion.
  • the risk value calculation device 12 can accurately calculate the infection risk value.
  • the risk value calculation unit 113 can calculate the infection risk value more accurately by further considering the situation of the people around the person who calculates the infection risk value. Therefore, according to the present embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can determine the infection risk in a suitable and accurate manner.
  • the fourth embodiment is a system including at least a risk value calculation device and an authentication device.
  • FIG. 7 is a block diagram showing a configuration of the risk calculation system according to the fourth embodiment.
  • FIG. 7 shows the risk value calculation system 700.
  • the risk value calculation system 700 includes a risk value calculation device 14, an authentication device 200, a user terminal 21, a visible light camera 91, and a thermal camera 92.
  • the risk value calculation device 14 is communicably connected to the authentication device 200 via the network 500.
  • the risk value calculation device 14 also authenticates a person in cooperation with the authentication device 200 when identifying the person.
  • the risk value calculation device 11 can use the attribute information associated with the specified person.
  • the risk value calculation device 11 can store the history of the infection risk value in the specified person and directly notify the individual of the calculated infection risk value.
  • the storage unit 120 included in the risk value calculation device 14 stores the attribute information 122.
  • the attribute information includes the attribute information associated with the person involved in the authentication.
  • the attribute information 122 includes personal information such as a person's name and contact information, for example. Further, the attribute information 122 may include information on the medical history and chronic illness of the person involved in the authentication.
  • the risk value calculation device 14 may use the attribute information when calculating the infection risk value. For example, the risk value calculation unit 113 may calculate the infection risk value relatively high for a person having a chronic disease of the immune system.
  • the authentication device 200 is communicably connected to the risk value calculation device 14 via the network 500.
  • the authentication device 200 cooperates with the risk value calculation device 14 to authenticate a person from the face image data of the person. More specifically, the authentication device 200 receives the visible light image data from the risk value calculation device 14, and authenticates the face image included in the received visible light image data. Further, the authentication device 200 supplies the authentication result to the risk value calculation device 14.
  • FIG. 8 is a block diagram showing the configuration of the authentication device 200.
  • the authentication device 200 includes a face feature DB 210, a face detection unit 220, a feature point extraction unit 230, a registration unit 240, and an authentication unit 250.
  • the face feature DB 210 is a face feature database that stores a user ID of a person in association with the face feature information of the person.
  • the face detection unit 220 detects the face region included in the captured image and outputs it to the feature point extraction unit 230.
  • the feature point extraction unit 230 extracts feature points from the face region detected by the face detection unit 220, and outputs face feature information to the registration unit 240. Face feature information is a set of extracted feature points.
  • the registration unit 240 newly issues a user ID when registering facial feature information.
  • the registration unit 240 registers the issued user ID and the face feature information extracted from the registered image in the face feature DB 210 in association with each other.
  • the authentication unit 250 collates the face feature information extracted from the face image with the face feature information in the face feature DB 210. If the face feature information matches, the authentication unit 250 determines that the face recognition was successful, and if the face feature information does not match, determines that the face recognition has failed.
  • the authentication unit 250 returns the success or failure of face authentication to the risk value calculation device 14.
  • the presence or absence of matching of facial feature information corresponds to the success or failure of authentication.
  • the authentication unit 250 identifies the user ID associated with the successful face feature information, and sets the authentication result including the specified user ID and the fact that the authentication is successful as a risk value. Reply to the calculation device 14.
  • the risk value calculation system 700 may be a combination of the risk value calculation device 14 and the authentication device 200.
  • the user terminal 21 may be a terminal of a person to be authenticated. In that case, the user terminal 21 is possessed by each of the plurality of persons involved in the authentication. Therefore, the risk value calculation system 700 may have a plurality of user terminals 21 corresponding to a plurality of persons involved in authentication.
  • the embodiment 4 has been described above.
  • the risk value calculation system 700 according to the fourth embodiment can authenticate a person and calculate an infection risk value for the person to be authenticated. Therefore, the risk value calculation system 700 can calculate the infection risk value in consideration of personal information. Alternatively, the risk value calculation system 700 can notify the individual of the calculated infection risk value. Therefore, according to the present embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can suitably determine an infection risk in consideration of personal information.
  • Non-temporary computer-readable media include various types of tangible recording media.
  • Examples of non-temporary computer-readable media include magnetic recording media (eg, flexible disks, magnetic tapes, hard disk drives), optomagnetic recording media (eg, optomagnetic disks), CD-ROM (Read Only Memory) CD-R, CDs. -R / W, including semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (RandomAccessMemory)).
  • the program may also be supplied to the computer by various types of temporary computer-readable media. Examples of temporary computer readable media include electrical, optical, and electromagnetic waves.
  • the temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.
  • An image data acquisition means for acquiring visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data.
  • a temperature measuring means for identifying a person included in the visible light image data and the thermal image data and measuring the body surface temperature of the specified person.
  • a symptom detecting means for detecting a symptom of an infectious disease of the person based on the visible light image data, and a symptom detecting means.
  • a risk value calculating means for calculating an infection risk value of the person based on the body surface temperature and the symptom, and a risk value calculating means.
  • a risk value calculation device including an output means for outputting the infection risk value.
  • the symptom detecting means detects the symptom from the posture of the person.
  • the risk value calculation device according to Appendix 1. (Appendix 3) Further equipped with a storage unit for storing postural patterns associated with the symptoms of the infectious disease.
  • the symptom detecting means detects the symptom by collating the posture of the person with the posture pattern.
  • the risk value calculation device described in Appendix 2. (Appendix 4)
  • the symptom detecting means detects the symptom when the posture of the person and the posture pattern match at a predetermined ratio or more within a predetermined period.
  • the risk value calculation device described in Appendix 3. (Appendix 5)
  • the storage unit stores the posture pattern associated with a predetermined pneumonia symptom.
  • the risk value calculation device according to Appendix 3 or 4.
  • Appendix 6 Further provided with a congestion degree calculation means for calculating the congestion degree of the space from the visible light image data is provided.
  • the risk value calculating means calculates the infection risk value by further adding the degree of congestion of the predetermined space including the person in the visible light image data.
  • the risk value calculation device according to any one of Supplementary note 1 to 5.
  • the congestion degree calculation means calculates the congestion degree based on the number of people in a predetermined space.
  • the risk value calculation device according to Appendix 6.
  • the risk value calculating means calculates the infection risk value of the person by further considering the body surface temperature of the surrounding person existing around the person and the symptom.
  • the risk value calculation device according to Appendix 6 or 7.
  • the risk value calculating means calculates when the infection risk value of the peripheral person is the first risk value, the infection risk value of the person is such that the infection risk value of the peripheral person is lower than the first risk value. 2 Calculated relatively higher than the infection risk value calculated in the case of risk value, The risk value calculation device according to Appendix 8. (Appendix 10) The risk value calculating means calculates the infection risk value of the person by further adding the distance to the surrounding person existing in the vicinity of the person. The risk value calculation device according to any one of Supplementary note 6 to 9. (Appendix 11) The risk value calculating means calculates the infection risk value of the person when the distance of the peripheral person is the first distance, when the infection risk value of the peripheral person is the second distance farther than the first distance.
  • the risk value calculation device calculates relatively higher than the calculated infection risk value, The risk value calculation device according to Appendix 10.
  • Appendix 12 An authentication device that authenticates a person from the visible light image data, A risk value calculation system including the risk value calculation device according to any one of Supplementary note 1 to 11, which calculates and outputs an infection risk value of the person to be authenticated.
  • Appendix 13 The computer The visible light image data generated by the visible light camera and the thermal image data generated by the infrared camera having a shooting range corresponding to at least a part of the visible light image data are acquired, respectively. Identify the person included in the visible light image data and the thermal image data, The body surface temperature of the identified person was measured and Based on the visible light image data, the symptom of the infectious disease of the person is detected.
  • the infection risk value of the person is calculated.
  • Output the infection risk value, Risk value calculation method. (Appendix 14) The process of acquiring the visible light image data generated by the visible light camera and the thermal image data generated by the infrared camera having a shooting range corresponding to at least a part of the visible light image data, respectively.
  • a process for identifying a person included in the visible light image data and the thermal image data The process of measuring the body surface temperature of the identified person and The process of detecting the symptom of the infectious disease of the person based on the visible light image data, The process of calculating the infection risk value of the person based on the body surface temperature and the symptom, and The process of outputting the infection risk value and A non-temporary computer-readable medium containing a risk value calculation program that causes a computer to execute.
  • Risk value calculation device 11 Risk value calculation device 12 Risk value calculation device 13 Risk value calculation device 14 Risk value calculation device 21 User terminal 90 Facility 91 Visible light camera 92 Thermal camera 110 Image data acquisition unit 111 Temperature measurement unit 112 Symptom detection unit 113 Risk value calculation unit 114 Output unit 115 Congestion degree calculation unit 120 Storage unit 121 Symptom DB 122 Attribute information 200 Authentication device 210 Face feature DB 220 Face detection unit 230 Feature point extraction unit 240 Registration unit 250 Authentication unit 500 Network 700 Risk value calculation system

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Abstract

Dispositif de calcul de valeur de risque (11) comprenant : une unité d'acquisition de données d'image (110), une unité de mesure de température (111), une unité de détection de symptôme (112), une unité de calcul de valeur de risque (113) et une unité de sortie (114). L'unité d'acquisition de données d'image (110) acquiert des données d'image de lumière visible générées par une caméra de lumière visible, et des données d'image thermique générées par une caméra infrarouge ayant une plage de prise de vues correspondant à au moins une partie des données d'image de lumière visible. L'unité de mesure de température (111) spécifie une personne incluse dans les données d'image de lumière visible et les données d'image thermique, et mesure la température de surface corporelle de la personne spécifiée. L'unité de détection de symptôme (112) détecte le symptôme de la maladie infectieuse de la personne, sur la base des données d'image de lumière visible. L'unité de calcul de valeur de risque (113) calcule une valeur de risque d'infection de la personne, sur la base de la température de surface corporelle et du symptôme. L'unité de sortie (114) génère la valeur de risque d'infection.
PCT/JP2020/044863 2020-12-02 2020-12-02 Dispositif, système et procédé de calcul de valeur de risque et programme d'enregistrement de support lisible par ordinateur non transitoire WO2022118397A1 (fr)

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PCT/JP2020/044863 WO2022118397A1 (fr) 2020-12-02 2020-12-02 Dispositif, système et procédé de calcul de valeur de risque et programme d'enregistrement de support lisible par ordinateur non transitoire

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019083395A (ja) * 2017-10-30 2019-05-30 パナソニックＩｐマネジメント株式会社 感染性物質モニタリングシステム、及び、感染性物質モニタリング方法
JP2020027364A (ja) * 2018-08-09 2020-02-20 株式会社リクルート 医療機関受付システム、医療機関受付装置、およびプログラム
WO2020059442A1 (fr) * 2018-09-21 2020-03-26 パナソニックＩｐマネジメント株式会社 Système de nettoyage d'espace et procédé de nettoyage d'espace

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2019083395A (ja) * 2017-10-30 2019-05-30 パナソニックＩｐマネジメント株式会社 感染性物質モニタリングシステム、及び、感染性物質モニタリング方法
JP2020027364A (ja) * 2018-08-09 2020-02-20 株式会社リクルート 医療機関受付システム、医療機関受付装置、およびプログラム
WO2020059442A1 (fr) * 2018-09-21 2020-03-26 パナソニックＩｐマネジメント株式会社 Système de nettoyage d'espace et procédé de nettoyage d'espace

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