JP7577503B2 - Information processing device, estimation method, program, storage medium, and health management support system. - Google Patents

Description

The present invention relates to an information processing device, an estimation method, a program, a storage medium, and a health management support system for estimating persons at risk of infection.

In facilities where people live together, there are quite a few outbreaks of infectious gastroenteritis and influenza every year. General infections often occur when three elements are present: the source of infection (infected people or animals, and their excrement and environment), the route of infection (pathway), and susceptibility to infection (especially people with weak resistance, the elderly, infants, etc.). Therefore, measures such as removing the source of infection, blocking the route of infection, and improving people's resistance (nutrition, rest, vaccinations, etc.) are effective. Specific examples include hand washing and disinfection, wearing masks and aprons, and regular sterilization of toys. However, it is difficult to completely prevent infection with the above measures. In such cases, it is necessary to take measures such as removing the source of infection as soon as possible and blocking the route of infection. As an effective technology for this, a technology has been disclosed that uses vital sensors and the like to accurately monitor a large number of people at once and determine their health condition (see Patent Document 1).

JP 2016-103178 A

However, for people who are infected but have not yet developed symptoms (potential carriers), they and their families are unaware that they are infected, and are therefore unable to make adequate preparations in the event that symptoms appear. This makes it difficult to take measures such as blocking the route of infection from potential carriers or procuring cold medicine in advance.

The purpose of the present invention is to understand the risk of infection with infectious diseases and reduce the spread of infection.

In order to solve the above problem, the present invention includes an acquisition means for acquiring an image captured by an imaging device that captures a predetermined area, an extraction means for extracting image data of an image including a contact who has come into contact with an infected person from image data of the image captured by the acquisition means, and an identification means for identifying a person at risk of infection with an infectious disease based on the image data of the image output from the extraction means, wherein the extraction means extracts image data in which the infected person is judged to be shouting from the image data of the image captured including the contact who has come into contact with the infected person, based on information about the shape of the infected person's mouth, and further extracts image data of an image in which the distance between the mouths of the infected person and the contact who are face-to-face is within a specified range, except when the contact is wearing a mask, from the image data in which it is judged that the infected person is shouting , except when the contact is wearing a mask, and outputs the image data of the image in which the distance between the mouths of the infected person and the contact who are face-to-face is within a specified range to the identification means, and the identification means identifies the contact included in the image data of the image in which the distance between the mouths of the infected person and the contact who are face-to-face is within a specified range as a person at risk of infection with an infectious disease .

The present invention makes it possible to grasp the risk of infection and reduce the spread of infection.

1 is a block diagram showing an overall configuration of a physical condition management support system according to a first embodiment. FIG. 2 is a block diagram showing a configuration of an estimation unit according to the first embodiment. FIG. 13 is a diagram illustrating an example of table data indicating indexes used for selecting an estimation process. FIG. 2 is a hardware configuration diagram of an estimation unit. 4 is a flowchart showing the flow of operations of the physical condition management support system. FIG. 2 is a block diagram showing an estimation processing unit and its peripheral configuration according to the first embodiment. FIG. 2 is a diagram illustrating a configuration of a storage unit according to the first embodiment. FIG. 4 is a block diagram showing a configuration of a second extracted data storage unit according to the first embodiment. 10 is a flowchart showing an example of the flow of an estimation process according to the first embodiment. 10 is a flowchart showing an example of the flow of an estimation process according to the first embodiment. FIG. 11 is a block diagram showing an estimation processing unit and its peripheral configuration according to a second embodiment. 13 is a flowchart showing an example of the flow of an estimation process according to the second embodiment. FIG. 11 is a block diagram showing an estimation processing unit and its peripheral configuration according to a third embodiment. 13 is a flowchart showing an example of the flow of an estimation process according to a third embodiment. FIG. 13 is a block diagram showing an estimation processing unit and its peripheral configuration according to a fourth embodiment. FIG. 13 is a block diagram showing an estimation processing unit and its peripheral configuration according to a fifth embodiment. FIG. 13 is a diagram illustrating an example of a score table stored in a judgment index storage unit.

The following describes an embodiment of the present invention with reference to the drawings. In the drawings, parts having the same functions are given the same numbers, and repeated explanations are omitted. Note that the configurations shown in the following embodiments are merely examples, and the present invention is not limited to the configurations shown in the drawings.

First Embodiment
1 is a block diagram showing the overall configuration of a health management support system 1000 according to a first embodiment. Each block will be briefly described below. Here, as an example, the health management support system 1000 will be described as being used in a childcare facility.

The health management support system 1000 includes an estimation system 101, a management terminal 106, and a user terminal 107. The estimation system 101 and the management terminal 106 are connected, for example, via a network. The management terminal 106 and the user terminal 107 are also connected, for example, via a network. The network may use wireless communication such as WiFi, or may be a network form using a wired LAN. The user terminal 107 may be connected to the estimation system 101, and the estimation system 101, the management terminal 106, and the user terminal 107 may each be capable of communicating with each other.

The management terminal 106 is a terminal owned by an administrator who operates the estimation system 101 and operated by the administrator. The administrator may be, for example, a staff member of the facility. The user terminal 107 is a terminal owned by a user, such as a child attending a childcare facility or their family, and operated by the user. In this specification, the term "terminal" includes, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, etc.

The management terminal 106 receives information about the infected person from the user terminal 107, and transmits (outputs) the information about the infected person to the estimation system 101. The information about the infected person includes, for example, the name of the infected person, symptoms, and onset date. At this time, the administrator may receive a call from the user and input the information about the infected person into the management terminal 106, thereby transmitting the information about the infected person to the estimation system 101. The management terminal 106 then notifies the user terminal 107 of the result of the estimation process (processing result) by the estimation system 101. The management terminal 106 may notify all user terminals 107 of the processing result, or may notify only some users, such as users who are estimated to be at risk of infection, of the processing result.

The estimation system 101 includes a data control unit 102, an estimation unit 103, a monitoring unit 104, and an imaging data storage unit 105.

The monitoring unit 104 includes one or more imaging devices. The imaging devices, for example, capture images of a specific area (target area) within the childcare facility and capture images of people and objects located in the target area.

The data control unit 102 uploads or transmits data (captured images) captured by the monitoring unit 104 to the imaging data storage unit 105. Alternatively, based on a request from the estimation unit 103, the data control unit 102 downloads or receives specified imaging data from the imaging data storage unit 105 and transfers it to the estimation unit 103.

The imaging data storage unit 105 stores (memorizes) the data captured by the monitoring unit 104. The imaging data storage unit 105 can be realized, for example, by a HDD (Hard Disk Drive) or a NAS (Network Attached Storage).

The estimation unit 103 starts the estimation process when triggered by an instruction (input) from the management terminal 106, and notifies (outputs) the management terminal 106 of the processing result. Note that in this embodiment, the processing result is set to be notified to the management terminal 106, but the system configuration may be such that the processing result is notified to a device other than the management terminal 106, such as the user terminal 107, for example.

The estimation unit 103 of this embodiment will be described in detail below. FIG. 2 is a block diagram showing the configuration of the estimation unit 103 according to the first embodiment. The estimation unit 103 is, for example, an information processing device such as a smartphone, a tablet terminal, a notebook PC, a desktop PC, a server device, or cloud computing. The estimation unit 103 includes a notification unit 201, a selection unit 202, a selection index storage unit 203, a transmission/reception unit 204, an estimation processing unit 205, and a memory unit 206.

The notification unit 201 receives instructions and information about infected individuals from the management terminal 106 described above, or notifies the management terminal 106 of the processing results.

The selection unit 202 checks the input information from the notification unit 201, i.e., information about the infected person, and selects an appropriate estimation method in the estimation processing unit 205. The selection unit 202 also selects the period of imaging data to be downloaded from the imaging data storage unit 105 based on the information about the infected person.

The selection index storage unit 203 stores table data that the selection unit 202 refers to as an index used to select an inference process. FIG. 3 is a diagram showing an example of table data indicating the index used to select an inference process. The table data includes information on at least one infectious disease. The table data includes the name of an infectious disease, and information such as the infection route, the incubation period, and the characteristics of symptoms (e.g., coughing, infection through the hands) is linked to the infectious disease name. The selection unit 202 selects the inference process to be performed and the period of imaging data to be downloaded from the imaging data storage unit 105 based on the information on the infected person and the index used to select an inference process.

The estimation processing unit 205 proceeds with the estimation process according to the estimation method instructed by the selection unit 202, in other words, the estimation process selected by the selection unit 202. Details of the estimation process will be described later. The estimation processing unit 205 also transfers the period of the imaging data selected by the selection unit 202 to the transmission/reception unit 204.

The transmission/reception unit 204 requests the imaging data for the date and time specified by the estimation processing unit 205 from the data control unit 102. The data transferred from the data control unit 102 is then stored in the storage unit 206.

In addition to storing the transferred data from the data control unit 102, the memory unit 206 also functions as a working memory used by the estimation processing unit 205.

Figure 4 is a hardware configuration diagram of the estimation unit 103. The estimation unit 103 may include a memory unit 206, a RAM (Random Access Memory) 21, a ROM (Read Only Memory) 22, a CPU (Central Processing Unit) 23, and a communication unit 24.

The CPU 23 operates based on a program stored in the ROM 22 or the memory unit 206, and controls each unit of the estimation processing unit 205. The ROM 22 stores a boot program executed by the CPU 23 when the estimation processing unit 205 is started, and programs that depend on the hardware of the estimation processing unit 205. The CPU 23 realizes a flow of the estimation process, for example, by executing a program loaded on the RAM 21. The CPU 23 may obtain and execute these programs from another device, for example, via a network. The communication unit 24 is used, for example, for communication with an external device via a network.

The estimation unit 103 may also include an input unit 25 such as a keyboard or a mouse, and a display unit 26 such as a display.

Figure 5 is a flowchart showing the flow of operations of the health management support system 1000. S601 to S602 describe the operations of the peripheral blocks before the estimation unit 103 starts operating. First, in S601, the user terminal 107 sends a message to the management terminal 106 about Child A's absence from school and his/her condition. Next, in S602, the management terminal 106, which has received the message, sends information about the infected person, including Child A's name, condition, onset date, etc., to the estimation system 101.

The specific operation of the estimation unit 103 is described below in S603 to S608. In S603, the notification unit 201 receives the transmitted data in S602. If the notification unit 201 has successfully received the data (Yes), it transfers the received data, i.e., information on the infected person, to the selection unit 202. If the data has not been successfully received in S603 (No), it notifies the management terminal 106 of the resend request flag and starts over from S602.

In S604, the selection unit 202 checks the received data, selects (decides) the shooting date/time of the imaging data to be downloaded from the imaging data storage unit 105 and the estimation process to be executed, and inputs them to the estimation processing unit 205. When selecting the shooting date/time of the imaging data to be downloaded and the estimation process to be executed, the selection unit 202 refers to the table data stored in the selection index storage unit 203. As described above, the table data includes information such as that shown in FIG. 3, and information such as contact infection, droplet infection, or airborne infection, the number of days of the incubation period, and characteristics of the symptoms are linked to the symptoms. The selection unit 202 selects the estimation process to be executed based on at least one of the infection route and the characteristics of the symptoms included in the table data, for example. The selection unit 202 also selects the period of the imaging data to be downloaded from the imaging data storage unit 105 based on the incubation period included in the table data.

In S605, the estimation processing unit 205 instructs the transmission/reception unit 204 to download the imaging data for the specified date and time from the imaging data storage unit 105. In response to the instruction from the estimation processing unit 205, the transmission/reception unit 204 downloads the imaging data via the data control unit 102. In S606, the transmission/reception unit 204 stores the imaging data transferred from the data control unit 102 in the memory unit 206. In S607, the estimation processing unit 205 performs estimation processing in accordance with the estimation processing method instructed by the selection unit 202. The detailed operation of the estimation processing will be described later.

S608 to S609 describe the operation of the peripheral blocks following the operation of the estimation unit 103. In S608, the result of the estimation process by the estimation processing unit 205 is sent to the management terminal. Specifically, the estimation unit 103 notifies the management terminal 106 of the name of the child who is estimated to be at risk of infection and the level of risk via the notification unit 201. In S609, the management terminal 106, which has received the notification, notifies the user terminal 107 of the family of the child at risk of infection of that fact. This is the flow of the operation of the estimation unit 103.

Next, the specific configuration and detailed operation of the estimation processing unit 205 of the estimation unit 103 will be described. FIG. 6 is a block diagram showing the estimation processing unit 205 and its peripheral configuration according to the first embodiment. The estimation processing unit 205 includes a first extraction unit 301, a second extraction unit 302, a third extraction unit 303, a risk level determination unit 304, and a determination index storage unit 305. FIG. 7 is a diagram showing the configuration of the storage unit 206 according to the first embodiment. The storage unit 206 includes an imaging data storage unit 401, a first extracted data storage unit 402, a second extracted data storage unit 403, and a third extracted data storage unit 404. FIG. 8 is a block diagram showing the configuration of the second extracted data storage unit 403 according to the first embodiment. The second extracted data storage unit 403 includes a person data storage unit 501 and an object data storage unit 502.

The first extraction unit 301 retrieves the imaging data from the imaging data storage unit 401 and extracts image data showing Child A, who is infected. The first extraction unit 301 then writes the extracted image data into the first extracted data storage unit 402 and stores it there.

The second extraction unit 302 uses the extraction result of the first extraction unit 301 to extract image data of a first contact, which is a person who has come into contact with an infected person, or a contact object, which is an object that has come into contact with an infected person. Specifically, the second extraction unit 302 retrieves image data including Child A from the first extracted data storage unit 402. Then, it extracts image data showing a child (first contact) and an object (contact object) who has come into contact with Child A, and writes and stores the image data in the second extracted data storage unit 403. Note that, as an example, the second extracted data storage unit 403 in this embodiment is configured to separate a person data storage unit 501 that stores image data including a contact person, and an object data storage unit 502 that stores image data including a contact object.

The third extraction unit 303 uses the extraction result of the second extraction unit 302 to extract image data of the second contactor who is the person who came into contact with the contact object. Specifically, the third extraction unit 303 retrieves image data from the object data storage unit 502, extracts image data of the child (second contactor) who touched the contact object, and writes and stores the image data in the third extracted data storage unit 404.

The risk level determination unit 304 retrieves image data from the person data storage unit 501 and the third extracted data storage unit 404. Then, it checks the name of the child who has been in contact with child A, i.e., the first contact, and counts the number of times that child has been in contact with child A. It also checks the name of the child who has been in contact with the contact object, i.e., the second contact, and counts the number of times that child has been in contact with the contact object. The risk level determination unit 304 then determines whether or not there is an infection risk for the child who has been confirmed to have been in contact, according to the risk determination index stored in the determination index storage unit 305. For example, in a case where the number of contacts in the determination index is set to 5 times, it is assumed that child B has been in contact with child A 10 times, and child C has been in contact with child A 3 times. In this case, the risk level determination unit 304 determines that child B, who has been in contact more than 5 times, is at risk of infection, and child C, who has been in contact less than 5 times, is not at risk of infection. In other words, it presumes that child B is an infection risk carrier.

In addition, the risk level determination unit 304 may determine the degree of risk of infection (infection risk) of being infected with an infectious disease based on, for example, the number of contacts, instead of the presence or absence of an infection risk. Specifically, in a case where the number of contacts as a determination index is set to 5 times, if the number of contacts exceeds 5 times, it is determined that there is a "high risk of infection." On the other hand, if the number of contacts is 5 times or less, it is determined that there is a "low risk of infection." By setting multiple thresholds as a determination index, it is also possible to determine the degree of infection risk in more detail, for example, as "high," "medium," or "low."

Figure 9 is a flowchart showing an example of the flow of the estimation process according to the first embodiment. Each operation (step) shown in this flowchart can be executed by the control of each unit by the CPU 23. First, in S704, the first extraction unit 301 retrieves imaging data from the imaging data storage unit 401 and extracts image data showing an infected person. Then, the first extraction unit 301 writes the extracted image data into the first extracted data storage unit 402 for storage.

In S705, the second extraction unit 302 retrieves image data including the infected person from the first extracted data storage unit 402, and extracts image data showing a person (first contact person) and object (contact object) who has come into contact with the infected person. The second extraction unit 302 then writes the extracted image data into the second extracted data storage unit 403 for storage.

In S706, the third extraction unit 303 retrieves image data including the contacted object from the object data storage unit 502, and extracts image data of the person (second contactor) who touched the contacted object. The third extraction unit 303 then writes the extracted image data into the third extracted data storage unit 404 and stores it.

In S707, the risk level determination unit 304 retrieves image data from the person data storage unit 501, and counts the names of people who have come into contact with the infected person, i.e., the first contacts, and the number of times each first contact has come into contact with the infected person. Then, in S708, the risk level determination unit 304 reads out the risk determination index stored in the determination index storage unit 305. Then, it compares the number of contacts of the risk determination index with the count value of each first contact, and determines whether or not there is an infection risk for each first contact.

In S709, the risk level determination unit 304 transmits the processing results for each first contact to the notification unit 201. At this time, it may be possible to transmit only the risk level of the first contact who is determined to be at risk of infection.

In this flow, the flow for determining whether or not there is an infection risk for the first contact has been described, but a similar flow can be used for the second contact. When determining whether or not there is an infection risk for the second contact, in S707, the risk level determination unit 304 retrieves image data from the third extracted data storage unit 404. Then, it counts the names of the people who have come into contact with the contact object, i.e., the second contacts, and the number of times each second contact has come into contact with the contact object. Next, in S708, the risk level determination unit 304 reads out the risk determination index stored in the determination index storage unit 305. Then, it compares the number of contacts of the risk determination index with the count value of each second contact, and determines the risk level of each second contact.

In addition, if the first contact and the second contact overlap, the total number of times they have come into contact with the infected person and the number of times they have come into contact with the infected object is compared with the number of contacts in the risk assessment index to determine the level of risk.

The weighting of the count values for contact with an infected person and contact with a contact object may also be changed. Specifically, for example, in a case where the number of contacts in the judgment index is set to 5 times, the weighting of contact with an infected person is set to 1, and the weighting of contact with a contact object is set to 0.5. Suppose that Child D has come into contact with Child A, who is infected, 3 times, and Child D has come into contact with a contact object 3 times. In this case, the count value of the number of contacts is 4.5 times. Therefore, the risk level judgment unit 304 judges Child D to have no risk of infection. The weighting may also be changed depending on the contact time.

Furthermore, the risk assessment index may be dynamically changed to reflect past performance, thereby making it possible to improve the accuracy of the estimation. In addition, the risk assessment index may be different for the number of times of contact between the first contact and the second contact.

Furthermore, the risk assessment index does not necessarily have to be the number of contacts. For example, it may be the time of contact. Figure 10 is a flowchart showing an example of the flow of the estimation process according to the first embodiment. As an example, Figure 10 shows an operation flow when the time of contact is set as the risk assessment index. Each operation (step) shown in this flowchart can be executed by the control of each part by the CPU 23. Note that steps S704 to S706 in Figure 10 are the same as those in Figure 9, so a description thereof will be omitted.

In S801, the risk level determination unit 304 acquires the time that the first contact was in contact with the infected person. The time of contact is acquired, for example, by converting it from the number of frames of the captured image data. For example, if there are 20 consecutive images of contact in an image captured at 5 frames/second, the contact time is 4 seconds. Note that the time may be calculated by another method. In S802, the risk level determination unit 304 reads out the risk determination index stored in the determination index storage unit 305. Then, the risk level determination unit 304 compares the contact time of the risk determination index with the acquired contact time between each first contact and the infected person, and determines whether or not there is an infection risk for each first contact. If the contact time of the determination index stored in the determination index storage unit 305 exceeds the contact time, it is determined that there is an infection risk, and if it is less than the contact time, it is determined that there is no infection risk.

For example, in the case where the contact time of the judgment index is 60 seconds, if Child E was in contact with Child A for 10 minutes and Child F was in contact with Child A for 30 seconds, the risk level judgment unit 304 will judge Child E to be at risk of infection and Child F to be at no risk of infection. In other words, Child E is presumed to be at risk of infection. Note that, as with the number of contacts, the risk judgment index may be dynamically changed to reflect past performance.

The above is an explanation of the configuration and operation of the first embodiment. This embodiment makes it possible to grasp the risk of infection in advance, and to take measures to prepare for onset of symptoms before symptoms appear. As a result, the spread of infectious diseases can be reduced.

Second Embodiment
When the infection route of the infected person is contact infection or droplet infection, the key point is what gesture, posture, or state the infected person made contact with another person. In the second embodiment, contact is determined only when the infected person makes contact with another person with a gesture, posture, or state that may cause infection. By adopting this configuration, it is possible to further reduce the estimation processing load with the same estimation accuracy as the first embodiment. In addition, the capacity of the memory unit 206 used in the estimation process can be reduced.

The configuration and operation of the second embodiment will be described in detail below. Note that blocks having equivalent functions to those of the first embodiment are given the same reference numerals. FIG. 11 is a block diagram showing the estimation processing unit 205 and its peripheral configuration according to the second embodiment. In the second embodiment, the estimation processing unit 205 includes a first detection unit 901. And the storage unit 206 includes a first detection data storage unit 902.

The first detection unit 901 detects a specific movement of an infected person from the imaging data stored in the memory unit 206, and extracts the image data at that time. It then writes the image data to the first detection data memory unit 902. The first detection data memory unit 902 stores the image data extracted by the first detection unit 901.

FIG. 12 is a flowchart showing an example of the flow of the estimation process according to the second embodiment. In S1001, the first detection unit 901 extracts image data from the imaging data stored in the imaging data storage unit 401 when the sick child (infected person) performs a predetermined action, and writes the image data to the first detection data storage unit 902. The predetermined action of the infected person here refers to shouting at other children, putting something in the mouth, etc. The extraction of these predetermined actions is performed by analyzing the image data. For example, the first detection unit 901 judges whether or not there has been contact using information on the direction of the infected person's face or the shape of the infected person's mouth. Specifically, for example, the area of the open mouth of the infected person is calculated from the image data, and if the area is equal to or greater than a predetermined area, it is determined that the infected person is shouting. However, when an infected person is detected wearing a mask, the risk of droplet infection is eliminated, so image data is not extracted even in a scene where the infected person is shouting at other children.

Next, in S1002, the second extraction unit 302 reads image data including an infected person performing a predetermined action from the first detection data storage unit 902, and extracts from that image data image data showing a person (first infected person) who has been in contact with the infected person and an object (contact object). The subsequent operations, S706 to S709, are almost the same as those in FIG. 9 of the first embodiment, and therefore will not be described.

As described above, in the second embodiment, the efficiency of the estimation process can be further improved by extracting image data when an infected child comes into contact with another person using gestures, postures, or conditions that can cause infection.

Third Embodiment
The third embodiment is configured to estimate the gestures, posture, or state of a person who has come into contact with an infected person or a contact object when they came into contact with the infected person or the contact object. By adding this configuration to the configuration of the second embodiment, it becomes possible to perform a more efficient estimation process.

The configuration and operation of the third embodiment will be described below. Note that blocks having equivalent functions to those of the first and second embodiments are given the same reference numerals. FIG. 13 is a block diagram showing the estimation processing unit 205 and its peripheral configuration according to the third embodiment. Note that in this figure, some of the configuration within the memory unit 206 is omitted. In the third embodiment, the estimation processing unit 205 further includes a second detection unit 1101 and a third detection unit 1103. And the memory unit 206 further includes a second detection memory unit 1102 and a third detection memory unit 1104.

Figure 14 is a flowchart showing an example of the flow of the estimation process according to the third embodiment. S1201 is almost the same as S704 in Figure 9 of the first embodiment, so a description thereof will be omitted. S1202 is almost the same as S1001 in Figure 12 of the second embodiment, but in S1202, the first detection unit 901 retrieves image data including an infected person from the first extracted data storage unit 402. Then, image data when the infected person performs a predetermined movement is extracted and written to the first detection data storage unit 902. Note that S1201 may be omitted as in the second embodiment.

In S1203, the second extraction unit 302 retrieves image data from the first detection data storage unit 902 when the infected person performs a predetermined action, and extracts image data showing a person (first contact person) and an object (contact object) who has come into contact with the infected person. The second extraction unit 302 then writes the extracted image data into the second extracted data storage unit 403 for storage.

In S1204, the second detection unit 1101 detects a predetermined motion of the first contact from the image data of the first contact extracted in S1202, and extracts image data at that time. Then, writes it to the second detection storage unit 1102. The predetermined motion of the first contact here refers to the face facing the infected person, the infected person's hand touching the face of the first contact, holding hands with the infected person, etc. It should be noted that the determination of whether it is a predetermined motion is performed by analyzing the image data, as in the second embodiment. In other words, using information on the motion of the first contact toward the infected person, it is determined whether contact has occurred. Specifically, for example, the distance between the mouth of the infected person and the first contact who are facing each other is calculated by image analysis, and if the distance is within a specified range (for example, within 1 m), it is determined that the motion is a predetermined motion. However, when the first contact is wearing a mask (state), there is no risk of droplet infection, so image data is not extracted even in a scene where the face of the first contact is facing the infected person.

In S1205, the third extraction unit 303 retrieves image data including the contacted object from the object data storage unit 502, and extracts image data of the person (second contactor) who touched the contacted object. The third extraction unit 303 then writes the extracted image data into the third extracted data storage unit 404 and stores it.

In S1206, the third detection unit 1103 reads the image data extracted in S1205, detects a specific action of the second contactor, and extracts the image data at that time. Then, it writes it to the third detection storage unit 1104. The specific action of the second contactor here refers to touching the contact object with a hand, or touching the contact object with a mouth or nose, etc.

After S1206 is completed, the process proceeds to S707 in FIG. 9 of the first embodiment. The subsequent operations S707 to S709 are almost the same as those in FIG. 9 of the first embodiment, so a description thereof will be omitted.

As described above, this embodiment can further improve the efficiency of the estimation process.

<Fourth embodiment>
In the above-mentioned embodiment, the configuration was such that infection from an infected person was estimated, but the fourth embodiment adopts a configuration capable of estimating whether or not there is a further secondary infection from a person who has been determined to be at risk of infection or to have a high degree of risk of infection in the above-mentioned embodiment. Fig. 15 is a block diagram showing an estimation processing unit 205 and its peripheral configuration according to the fourth embodiment.

In the fourth embodiment, the configuration of the first embodiment is configured by providing a feedback means 1301. In this embodiment, the first extraction unit 301 can send a command to the risk level determination unit 304 by the feedback means 1301.

First, the child who has developed symptoms is set as an infected person, and the estimation process described in the first embodiment is carried out. Next, the risk level determination unit 304 uses the feedback means 1301 to set the person who is determined to be at risk of infection to the first extraction unit 301 as an infected person. Thereafter, the estimation process described in the first embodiment is carried out. This makes it possible to estimate those who are at risk of secondary infection.

Furthermore, by repeating the same process thereafter, it is also possible to estimate tertiary and quaternary infection risk holders. The extent to which the estimation process is repeated is up to the user.

According to this embodiment, it is possible to estimate people who are at risk of secondary or tertiary infection.

Fifth embodiment
In the first to fourth embodiments, children who have had direct/indirect contact with an infected child are extracted, and the risk level of the infected child is estimated. In contrast, the fifth embodiment is configured to estimate the risk level of all children except for the infected child. This embodiment is particularly effective when a child has developed a disease that is airborne, rather than droplet infection or skin contact infection. The configuration and operation of the fifth embodiment will be described below. Note that blocks that perform the same processes as those in the first to fourth embodiments are given the same reference numerals.

Fig. 16 is a block diagram showing the estimation processing unit 205 and its peripheral configuration according to the fifth embodiment. The estimation processing unit 205 according to the fifth embodiment has a person extraction unit 1401. The person extraction unit 1401 extracts image data of a person from the imaging data downloaded from the imaging data storage unit 105. In other words, the person extraction unit 1401 extracts image data of a person who was in the target area captured by the imaging device within the period selected by the selection unit 202.

First, the selection unit 202 selects to perform estimation processing on all children except for the infected child A, that is, all people included in the captured image, based on the symptoms of the sick child (infected, referred to here as child A) obtained from the notification unit 201 (not shown). Specifically, when the symptoms are of an airborne type such as measles or chickenpox, the selection unit 202 decides to perform estimation processing on all children. In this embodiment, it is assumed that a child has measles.

After receiving instructions from the selection unit 202, the person extraction unit 1401 starts the estimation process for all children except for Child A who is infected. First, via the transmission/reception unit 204 (not shown), imaging data from two days before the onset date is downloaded and stored in the storage unit 206. In this embodiment, since the incubation period for measles is set to two days (see FIG. 3), imaging data from two days before the onset date is downloaded. The period of imaging data to be downloaded is preferably determined according to the incubation period. Information on the incubation period is stored in the selection index storage unit 203.

Then, the person extraction unit 1401 extracts image data from the imaging data stored in the memory unit when Child B, the person to be estimated, performs a specified action, and stores the image data in the memory unit 206. The specified action here refers to actions such as washing hands, touching the face with hands, wearing a mask, and being fussy. Note that, as in the second and third embodiments, the determination of whether or not an action is a specified action is made by analyzing the image data. Next, the risk level determination unit 304 reads out the image data extracted from the memory unit 206 when Child B performs a specified action, and calculates the risk level using the score table for each specified action stored in the determination index storage unit 305.

Figure 17 is a diagram showing an example of a score table stored in the judgment index storage unit 305. In the score table, a score value is set for each predetermined action. The risk level judgment unit 304 calculates the infection risk level by adding or subtracting the score value corresponding to each predetermined action. The risk level judgment unit 304 may transmit the calculated infection risk level to the outside as a processing result for child B, or may judge that child B is an infection risk carrier if the infection risk level exceeds a threshold value and transmit the judgment result to the outside as a processing result.

When risk estimation processing for Child B is completed, the person extraction unit 1401 is notified via the completion notification means 1403 that estimation processing for Child B is complete. Upon receiving the notification, the person extraction unit 1401 starts estimation processing for Child C, who is the next person to be estimated. Subsequent processing is the same as described above. By repeating the above, risk estimation processing is performed for all children. This concludes the explanation of the configuration and operation of the fifth embodiment.

According to this embodiment, it is possible to grasp the risk of infection in advance, even for infectious diseases that are transmitted through the air, and the spread of infection can be reduced.

<Other embodiments>
Although the embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications are possible within the scope of the gist of the present invention. For example, the estimation process of the first to fifth embodiments may be selected based on the infection route and symptoms of the developed infectious disease.

In the above embodiment, the health management support system 1000 is used in a childcare facility, but the use is not limited to this. It can also be used in welfare facilities, including elderly care facilities, schools, hospitals, etc.

The present invention can also be realized by a process in which a program that realizes one or more of the functions of the above-mentioned embodiments is supplied to a system or device via a network or storage medium, and one or more processors in the computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., an ASIC) that realizes one or more functions. Furthermore, the imaging device can execute the functions of the present invention by storing the processing of the present invention in the ROM of the imaging device included in the monitoring unit 104. In this case, the processing of the above-mentioned flowchart by the imaging device is realized by the CPU of the imaging device executing the processing based on the program stored in the ROM. Also, in this case, the imaging device outputs the processing results to an external device by a communication control unit or the like.

101 Estimation system 103 Estimation unit 106 Management terminal 107 User terminal 201 Notification unit 202 Selection unit 203 Selection index storage unit 205 Estimation processing unit 301 First extraction unit 302 Second extraction unit 303 Third extraction unit 304 Risk level determination unit

Claims (6)

An acquisition means for acquiring an image captured by an imaging device that captures an image of a predetermined area;
An extraction means for extracting image data of an image including a contact person who has come into contact with an infected person from image data of an image including an infected person among the captured images acquired by the acquisition means;
and a means for identifying a person at risk of infection with an infectious disease based on image data of the captured image output from the extraction means,
The extraction means includes :
Extracting image data in which it is determined that the infected person is shouting, based on information about the shape of the infected person's mouth , from image data of a captured image including a contact person who has come into contact with the infected person ;
From the image data in which it is determined that the infected person is shouting , image data of a captured image in which the distance between the mouths of the infected person and the contact person who are facing each other is within a specified range is extracted , except in cases where the contact person is wearing a mask;
outputting, to the identification means, image data of a captured image in which the distance between the mouths of the infected person and the contact person who are facing each other is within a specified range, except when the contact person is wearing a mask;
The identification means is
An information processing device characterized by identifying a contact person included in image data of an captured image in which the distance between the mouths of the infected person and the contact person who are face-to-face is within a specified range as a person at risk of infection with an infectious disease . The information processing device according to claim 1, further comprising an output means for outputting to the outside information on the person identified by the identification means who is at risk of infection with the infectious disease. The information processing device according to claim 1 or 2, characterized in that the information about the infected person is acquired from an externally connected device. The information processing device according to claim 1, further comprising an estimation means for estimating the infection risk or degree of a person at risk of infection with the infectious disease identified by the identification means. The information processing device according to claim 1, characterized in that the extraction means extracts image data of the captured image for a period based on the incubation period of the infectious disease that the infected person has developed. A program for causing a computer to function as the information processing device according to any one of claims 1 to 5.

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