Infection risk estimation device, method, and program

Abstract

To enable highly accurate estimation of infection risk by taking into account the user's constitution and characteristics, and to enable highly accurate estimation of infection risk with minimal objective information.SOLUTION: An infection risk estimation system includes: a hand roughness index acquisition unit 211 that acquires a hand roughness index of a user; a user subjective information acquisition unit 212 that acquires user subjective information; a user objective information acquisition unit 213 that acquires user objective information; an antimicrobial capability index calculation unit 221 that calculates an index of the antimicrobial capability from the hand roughness index; an adhesion / invasion risk estimation unit 222 that estimates the risk of microbial adhesion and / or the risk of microbial invasion into the body from the user subjective information and the user objective information; and an infection risk calculation unit 223 that estimates the user's infection risk of the infectious disease from the antimicrobial capability index and the adhesion risk and / or invasion risk.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a technique for estimating the risk of contracting an infectious disease. [Background technology]

[0002] In recent years, infectious diseases such as COVID-19 have become more prevalent around the world, and preventive measures against infectious diseases have become an important social issue. One preventive measure against infectious diseases is to understand the degree of risk of infection of an infectious disease. Therefore, systems that estimate the risk of infection have been developed (see Patent Documents 1 and 2).

[0003] In the system described in Patent Document 1, a server collects location information and health information from a user's terminal at any time, calculates weights based on the location information and health information, determines the infection risk based on the weight of the infection risk of infectious diseases accumulated along the movement route, and notifies the user of the infection risk. In the system described in Patent Document 2, the infection risk is calculated based on the user's lifestyle information, health information, vaccination information, test result information, vital information, health observation information, and contact history information. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2015-161987 A [Patent Document 2] Patent No. 7188712 [Patent Document 3] JP 2021-159046 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, although the technique described in Patent Document 1 takes into account not only location information but also health information of the user when determining the risk of infection, this health information is intended to be the symptoms caused by the infectious disease that is the subject of the analysis. In other words, the technique described in Patent Document 1 performs an analysis from the perspective of how susceptible a user who is not infected with an infectious disease is to contracting the infectious disease through their movement.

[0006] Incidentally, the risk of infection is thought to be influenced not only by the place and route of travel, but also by various factors such as the person's own constitution and characteristics. For example, one of the important routes of infection for infectious diseases is contact infection, more specifically, infection via human fingers, and in the case of infection via fingers, the risk of infection is thought to be influenced by the characteristics of the skin on the fingers. However, the method described in Patent Document 1 does not perform an analysis from this perspective, so it cannot be said that the accuracy of estimating the infection risk is sufficient.

[0007] On the other hand, the infection risk estimation process in the cited document 2 takes into account vital information (body temperature, blood pressure, etc.), but does not take into account the user's constitution or characteristics. In particular, there is no mention of vital information related to fingers. Therefore, the estimation accuracy of the infection risk described in the cited document 2 cannot be said to be sufficient. Furthermore, the cited document 2 requires a lot of objective information to calculate the infection risk, and measurements using dedicated measuring equipment are also required to obtain this information. For this reason, the cited document 2 lacks convenience.

[0008] In addition, the risk of infection is thought to be influenced not only by the place and route of travel, but also by the behavioral tendencies of the person. For example, in the case of infectious diseases transmitted by contact, people who have the habit of unconsciously touching their faces with their hands are thought to have a higher risk of infection than those who do not have such a habit. Also, for example, people who frequently clean places where people come into contact with each other, such as walls and doorknobs at home, are thought to have a lower risk of infection than those who clean less frequently. These behavioral tendencies can be understood by measuring objective values ​​such as "the number of times you touch your face with your hands" and "the amount of microbial adhesion on the surface of walls and doorknobs." However, measuring and obtaining these objective values ​​is extremely cumbersome and requires dedicated measuring equipment, so it cannot be done casually.

[0009] The present invention has been made in consideration of the above circumstances, and its first object is to provide an apparatus, method, and program capable of estimating the risk of infection with high accuracy taking into account the constitution and characteristics of the user. Also, its second object is to provide an apparatus, method, and program capable of estimating the risk of infection with high accuracy using a small amount of objective information. [Means for solving the problem]

[0010] In order to achieve the above-mentioned object, the infection risk estimation device of the present invention is characterized by comprising a hand roughness index acquisition means for acquiring a hand roughness index indicating the degree of roughness of a user's hands, an antimicrobial ability index calculation means for calculating an index of antimicrobial ability from the hand roughness index, and an infection risk estimation means for estimating the user's risk of infection with an infectious disease from the index of antimicrobial ability.

[0011] In addition, the present invention provides an infection risk estimation device as described above, which comprises a user information acquisition means for acquiring at least user subjective information, which is user subjective information regarding the user's behavior, and user objective information, which is objective information regarding the user's behavior, and an adhesion / intrusion risk estimation means for estimating at least one of the risk of microbial adhesion to the skin of the fingers and the risk of microorganisms present on the skin of the fingers invading the body based on the information acquired by the user information acquisition means, and the infection risk estimation means is characterized in that it estimates the user's risk of infection with an infectious disease from the risk estimated by the adhesion / intrusion risk estimation means and an indicator of the antimicrobial ability. Effect of the Invention

[0012] According to the present invention, an index of antimicrobial ability, which is the ability of the skin itself to fight against microorganisms such as viruses and bacteria, is calculated from a hand roughness index indicating the degree of roughness of the user's hands, and the user's risk of infection with an infectious disease is estimated at least from this index of antimicrobial ability. As mentioned above, the user's hands and fingers are very important in the route of infection of infectious diseases, and therefore the characteristics of the hands and fingers are thought to have a large impact on the risk of infection. Therefore, the present invention makes it possible to estimate the risk of infection with high accuracy without obtaining a large amount of other objective information.

[0013] Furthermore, according to the present invention, user subjective information, which is at least subjective information of the user regarding the user's behavior, is acquired, and the risk of microorganisms adhering to the skin of the hand and / or the risk of microorganisms present on the skin invading the body is estimated based on this acquired information, and the risk of infection of the user with an infectious disease is estimated from this risk of adhesion and / or invading and the index of the antimicrobial ability. Here, the user subjective information can include objective information that can be objectively acquired but is difficult to acquire. And since this user subjective information is based on the subjectivity of the user, it is extremely easy to acquire. Therefore, the present invention makes it possible to estimate the risk of infection with high accuracy using a small amount of objective information. [Brief description of the drawings]

[0014] [Figure 1]Overall configuration diagram of an infection risk estimation system according to a first embodiment [Diagram 2] Functional block diagram of an infection risk estimation system according to a first embodiment [Diagram 3] An example of the input screen for rough hands index [Figure 4] An example of a screen for inputting user subjective information [Diagram 5] An example of a screen for inputting user objective information [Figure 6] Diagram explaining the results of partial correlation analysis of each index [Figure 7] A flowchart explaining the operation of the infection risk estimation system according to the first embodiment. [Figure 8] Functional block diagram of a user terminal in an infection risk estimation system according to a second embodiment [Figure 9] A functional block diagram of an infection risk estimation server in an infection risk estimation system according to a third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] [Definition] In the present invention, "microorganisms" refer to pathogens such as bacteria and viruses that cause infectious diseases. In addition, in the present invention, "infection" refers to the growth or proliferation of microorganisms after their invasion into the body. It should be noted that the attachment of microorganisms to the surface of the skin or body hair does not mean "infection."

[0016] In the present invention, "hand" means one or more of the fingers, palm (also referred to as palmar region), back of the hand (also referred to as dorsal region), and wrist (also referred to as carpal region). In the present invention, "chapped hands" means rough skin caused by irritation of the hands due to dryness in winter, water work such as cooking and laundry, and handling of chemicals such as shampoo and hair dye, and refers to a state in which any of the following symptoms is observed: erythema, papules, vesicles, scales, crusts, lichenification, hangnails, chapped skin, redness, redness, dryness, gloss, roughness, disappearance of fingerprints, hardening, and cracks. The chapped hands to be evaluated in the present invention are preferably erythema, redness, papules, vesicles, inflammation, redness, chapped skin, scales, and hangnails.

[0017] In the present invention, "characteristics of rough hands" means any of the above-mentioned symptoms observed in rough hands, but preferably means one or more selected from red spots resulting from erythema, redness, papules, or small blisters, red areas resulting from inflammation, redness, or chapped skin, and white areas resulting from scales or hangnails.

[0018] [overview] The skin surface of the hand is the part of the human body that comes into contact with the outside world the most, and microorganisms such as bacteria and viruses often infect the human body via the skin of the hand. Therefore, the inventors of the present invention thought that recognizing the state of microorganisms attached to the skin of a subject would be useful for establishing hygiene habits and preventing diseases for the subject himself. From this perspective, the inventors of the present invention found that the ability to inactivate microorganisms (antimicrobial ability) is contributed to by a comprehensive analysis of human skin components and properties, and that skin with a particularly high amount of lactic acid has a high ability to reduce microorganisms such as bacteria and viruses, and is effective in preventing contact infection through the skin. In this case, inactivation means that microorganisms are killed or denatured to a state where they have lost their infectivity. Based on this knowledge, the inventors of the present invention conducted research and developed a technology for calculating an index of antimicrobial ability from indexes that can be relatively easily and quickly tested from a subject, such as a lactate amount index group, a hydrogen ion index group, a temperature index group, and a drying speed index group (see Patent Document 3).

[0019] However, although the above-mentioned technology can be used relatively easily and in a short time, it still requires a considerable amount of time and effort to test each indicator. The present inventors have found that the degree of "rough hands" is correlated with the amount of lactic acid, which is considered to be one of the important factors of the above-mentioned antimicrobial ability. Based on this finding, the inventors have developed a technology to calculate an index of antimicrobial ability from a rough hand index that indicates the degree of rough hands.

[0020] In addition, although the above-mentioned technology can calculate an index of antimicrobial ability, it does not calculate the risk of infection with bacteria or viruses. It is believed that the risk of infection is affected not only by a person's antimicrobial ability but also by many factors such as the person's behavior and environment. Although it is possible to observe and grasp each factor as an objective phenomenon, observing and grasping a large number of factors is not only a great effort but also requires dedicated measuring equipment, etc., so it cannot be done casually. On the other hand, it is believed that some of the factors that appear as objective phenomena are expressed as the result of actions based on subjectivity, such as people's habits and consciousness. In addition, some of the factors that appear as objective phenomena can be estimated by subjective and ambiguous information even if they are not objective and accurate numerical values, or the strictness of numerical values ​​may not be very important in calculating the risk of infection in the first place. Therefore, the present inventors have developed a technology to estimate the risk of infection using subjective information based on the user's subjectivity.

[0021] (First embodiment) An infection risk estimation system according to a first embodiment of the present invention will be described below with reference to the drawings. Fig. 1 is an overall configuration diagram of the infection risk estimation system, and Fig. 2 is a functional block diagram of the infection risk estimation system.

[0022] The infection risk estimation system includes one or more user terminals 100 carried by a user, and an infection risk estimation server 200 that provides an infection risk estimation service to the user, as shown in Fig. 1. The user terminal 100 and the infection risk estimation server 200 are connected to each other via a network 300 such as the Internet so as to be able to communicate data with each other.

[0023] The user terminal 100 can be configured by a conventionally known computer equipped with a main processing unit, a main memory unit, an auxiliary memory unit, an input device, an output device, an imaging device, a communication device, etc. The user terminal 100 can be implemented by installing a program on the computer. The user terminal 100 can be implemented as dedicated hardware. In this embodiment, the user terminal 100 is implemented by installing a program on a highly functional portable communication terminal known as a "smartphone."

[0024] The user terminal 100 functions as a user interface for the infection risk estimation service provided by the infection risk estimation server 200. As shown in Fig. 2, the user terminal 100 includes an input processing unit 110 that accepts various information input from a user using an input device (not shown) and transmits the information to the infection risk estimation server 200, and an infection risk output unit 120 that receives the infection risk of the user from the infection risk estimation server 200 and outputs the information to an output device (not shown).

[0025] The input processing unit 110 includes a roughness index input unit 111 for inputting a roughness index indicating the degree of roughness of the hands, a subjective information input unit 112 for inputting the subjective information of the user, and an objective information input unit 113 for inputting the objective information of the user.

[0026] Here, the roughness index is an index showing the goodness of the condition of the stratum corneum, which is the outermost layer of the skin of the hand. The roughness index can be calculated by evaluating roughness by various methods. For example, visual scoring evaluation, judgment by photography, transepidermal water loss using a skin property measuring device, measurement of stratum corneum moisture content, measurement of the amount of stratum corneum components such as intercellular lipids and natural moisturizing factors, etc. The roughness index can be calculated by any one or a combination of these multiple evaluation methods. The roughness index may be calculated as a continuous value or a discrete value.

[0027] In this embodiment, the rough hand index uses a rough hand score of multiple stages (for example, 5 stages) derived by visual scoring evaluation. The rough hand score is defined such that the higher the value, the less rough the hand, and the lower the value, the more rough the hand. In other words, the rough hand score is a score indicating the goodness of the condition of the skin of the hand. The rough hand score is defined by the characteristics of the rough hand appearance of the skin of the fingers. More specifically, the more scales there are on the skin surface as the characteristics of the appearance, the greater the degree of rough hand, i.e., the smaller the rough hand score, and the fewer scales there are, the less rough the hand, i.e., the larger the rough hand score. As shown in FIG. 3, the rough hand index input unit 111 displays sample images corresponding to each rough hand score on the user terminal 100 for the user, and acquires the rough hand index by having the user select one of the sample images.

[0028] The subjective information input unit 112 stores in advance in a predetermined storage unit, as subjective question answer information, a set of questions for a user about user information related to the user's habits, consciousness, behavior, etc., and a plurality of optional answers to the questions, in which the answers are determined based on the user's subjectivity. An example of the subjective question answer information is shown in Table 1.

[0029] [Table 1]

[0030] The types of questions and answers can be broadly classified into those in which the answer is related to the person's consciousness and those in which the question is related to the person's consciousness. An example of the former is the question "I touch my face unconsciously with my hands" and the answer "Often / Somewhat often / Neither / Rarely / Never." An example of the latter is the question "Please tell us when you are concerned about germs or viruses on your hands" and the answer "Before eating / Before getting on a train or bus / After touching money / When returning home." There may be multiple subjective question answer information as shown in Table 1. It is preferable that the subjective question answer information includes at least one type each of information related to infection consciousness and daily behavior consciousness.

[0031] Please note that subjective answer information can be set as subjective even if it is an objectively observable or comprehensible phenomenon. For example, "how many times a day you wash your hands" is objective information, but the question "how many times a day?" and the answer "a lot / a little / never" can be set as subjective answer information.

[0032] As shown in FIG. 4, the subjective information input unit 112 presents a question related to the subjective question answer information to the user and a plurality of answers to the question as options, and allows the user to select an answer, thereby acquiring user subjective information consisting of a pair of a question and an answer.

[0033] The objective information input unit 113 prestores in a predetermined storage unit, as objective question information, questions about user information related to the user's attributes, behavior history, etc., whose answers can be observed and understood as objective phenomena. Note that questions related to objective question information are about the user's attributes and behavior history, and therefore the answers are uniquely determined. Questions related to objective question information can include answer conditions such as answer options for the question and numerical ranges acceptable for answers as additional information. An example of objective question information is shown in Table 2.

[0034] [Table 2]

[0035] 5, the objective information input unit 112 presents a question related to the objective question information to the user and has the user input an answer, thereby acquiring the objective information of the user. Alternatively, the objective information input unit 112 presents a question related to the objective question information and answer options to the user and has the user select an answer, thereby acquiring the user's objective information consisting of a pair of a question and an answer.

[0036] It is not necessary for the user to recognize whether the question and its answer are subjective or objective. Therefore, as shown in Figures 4 and 5, the presentation of a question and the input of an answer by subjective information input unit 112 (questions 1 and 2 in Figure 4) and the presentation of a question and the input of an answer by objective information input unit 113 (questions 3 and 4 in Figure 5) can be treated as a series of processes, and the order can be changed as appropriate.

[0037] The input processing unit 110 transmits the hand roughness index acquired by the hand roughness index input unit 111, the user subjective information acquired by the subjective information input unit 112, and the user objective information acquired by the objective information input unit 113 to the infection risk estimation server 200 via the network 300.

[0038] The infection risk output unit 120 receives the infection risk of the user estimated by the infection risk estimating server 200 from the infection risk estimating server 200 as described below, and displays / outputs it on the display device of the user terminal 100.

[0039] The infection risk estimation server 200 can be configured by a conventionally known computer equipped with a main processing unit, a main memory unit, an auxiliary memory unit, an input device, an output device, a communication device, etc. The infection risk estimation server 200 can be implemented by installing a program on a computer. The infection risk estimation server 200 can be implemented as dedicated hardware. The infection risk estimation server 200 can be implemented in a distributed manner on multiple devices. The location where the infection risk estimation server 200 is installed and the communication path with the user terminal 100 are not important. In this embodiment, the infection risk estimation server 200 is implemented as a cloud server on the Internet.

[0040] As shown in Figure 2, the infection risk estimation server 200 includes an information collection unit 210 that collects various information used in calculating the infection risk, an infection risk estimation unit 220 that estimates the user's infection risk based on the collected information, and an infection risk transmission unit 230 that transmits the estimated infection risk to the user's user terminal 100.

[0041] The information collecting section 210 includes a rough hand index acquiring section 211, a user subjective information acquiring section 212, and a user objective information acquiring section 213. The rough hand index acquiring section 211 acquires a rough hand index inputted from the user terminal 100 via a rough hand index input section 111. The user subjective information acquiring section 212 acquires user subjective information inputted from the user terminal 100 via a subjective information input section 112. The user objective information acquiring section 213 acquires user objective information inputted from the user terminal 100 via an objective information input section 113.

[0042] The infection risk estimation unit 220 includes an antimicrobial capability index calculation unit 221 , an adhesion / invasion risk estimation unit 222 , and an infection risk calculation unit 223 .

[0043] The antimicrobial ability index calculation unit 221 calculates an index of antimicrobial ability from the roughness index. Here, the antimicrobial ability is the ability to reduce the number of bacteria on the skin, and the higher the value, the higher the possibility of reducing the number of microorganisms attached to the hands. As described above, four factors, namely, the amount of lactic acid, the hydrogen ion exponent, the temperature, and the drying speed, contribute to this antimicrobial ability, and the inventors have found that the degree of a person's "rough hands" is correlated with the amount of lactic acid, which is considered to be the main factor of the antimicrobial ability. In this embodiment, a rough hands score defined by the amount of scales is used as the rough hands index, and the higher the rough hands score, the better the condition of the skin on the hands. Therefore, the antimicrobial ability index calculation unit 221 calculates the index of antimicrobial ability from the rough hands score using a function in which the rough hands score and the index of antimicrobial ability have a positive correlation.

[0044] The adhesion / invasion risk estimation unit 222 estimates the risk of microbial adhesion to the skin of the fingers (hereinafter referred to as the "adhesion risk") and / or the risk of microorganisms present on the skin of the fingers invading the body (hereinafter referred to as the "invasion risk") based on the user's subjective information and user objective information.

[0045] Specifically, in order to calculate each risk from the user subjective information, the adhesion / intrusion risk estimation unit 222 pre-stores questions related to the above-mentioned subjective question answer information, each answer to the question, and conversion information for converting each answer into an adhesion risk and / or an intrusion risk. For example, the risk values ​​of the adhesion risk and / or intrusion risk corresponding to each answer are pre-stored as conversion information. The adhesion / intrusion risk estimation unit 222 can calculate each risk from the user subjective information by referring to this conversion information.

[0046] Similarly, in order to calculate each risk from the user objective information, the adhesion / intrusion risk estimation unit 222 pre-stores questions related to the above-mentioned objective question information and conversion information for converting the answers to the questions into adhesion risk and / or intrusion risk. If the answers to the questions are numerical, a function for converting the answer value into adhesion risk and / or intrusion risk is pre-stored as conversion information. If the answers to the questions are non-numerical and limited to predetermined options, the risk values ​​of adhesion risk and / or intrusion risk corresponding to each answer are pre-stored as conversion information. The adhesion / intrusion risk estimation unit 222 can calculate each risk from the user objective information by referring to this conversion information.

[0047] The adhesion / intrusion risk estimation unit 222 can refer to other information as necessary when calculating the adhesion risk and / or intrusion risk. Examples of this reference information include statistical information and geographical distribution information regarding the adhesion risk and / or intrusion risk. This reference information can be stored and managed in advance in the infection risk estimation server 200. Furthermore, the adhesion / intrusion risk estimation unit 222 may be configured to appropriately acquire the reference information from an external device that stores and manages the reference information.

[0048] The infection risk calculation unit 223 calculates the infection risk using a predetermined algorithm based on the antimicrobial ability index calculated by the antimicrobial ability index calculation unit 221 and the adhesion risk and / or intrusion risk calculated by the adhesion / intrusion risk estimation unit 222.

[0049] The present inventors have found that there is a correlation between the amount of lactic acid, which is considered to be an important factor of the antimicrobial ability index, and the infection risk. FIG. 6 shows a diagram of partial correlation analysis of the hand roughness score, the amount of lactic acid, the amount of sweating, the skin pH, and the infection risk of influenza virus. As shown in FIG. 6, the hand roughness score correlates with the antimicrobial ability index through the amount of lactic acid. It was also found that the infection risk correlates with the index of antimicrobial ability through the amount of lactic acid. Therefore, one example of processing by the infection risk calculation unit 223 is to calculate the infection risk by weighting and adding the antimicrobial ability index multiplied by the correlation coefficient, the adhesion risk, and the intrusion risk. The infection risk calculation unit 223 may divide the infection risk into a plurality of categories according to the magnitude of the value, and may obtain the category to which the calculated infection risk value belongs as the calculation result, or may obtain the calculation result by adding the category to the infection risk value.

[0050] The infection risk transmission unit 230 transmits the calculation result to the user terminal 100 via the network 300.

[0051] The operation of the infection risk estimation system according to the present embodiment will be described with reference to the flowchart of FIG. 7. First, the user inputs the hand roughness index, the user subjective information, and the user objective information in the user terminal 100 (step S1), and the infection risk estimation server 200 acquires each piece of information (step S2). The infection risk estimation server 200 calculates the index of antimicrobial ability from the acquired hand roughness index (step S3). Next, the infection risk estimation server 200 calculates the risk of adhesion and / or invasion from the acquired user subjective information and user objective information (step S4). Next, the infection risk estimation server 200 calculates the infection risk from the calculated index of antimicrobial ability and the risk of adhesion and / or invasion (step S5). Finally, the infection risk estimation server 200 transmits the calculated infection risk to the user terminal 100 (step S6). The user terminal 100 displays and outputs the received infection risk (step S7). By performing such operations, when a user inputs a hand roughness index using the user terminal 100 and answers the questions presented, the infection risk estimation server 200 calculates the infection risk based on the input information, and the infection risk is displayed on the user terminal 100.

[0052] As described above in detail, the infection risk estimation system according to the present embodiment calculates an index of antimicrobial ability, which is the ability of the skin itself to fight against microorganisms such as viruses and bacteria, from a hand roughness index that indicates the degree of roughness of the user's hands, and estimates the user's risk of infection from at least this index of antimicrobial ability. This makes it possible to estimate the infection risk with high accuracy without acquiring a large amount of other objective information.

[0053] Furthermore, according to the infection risk estimation system of this embodiment, user subjective information, which is at least subjective information of the user regarding the user's behavior, is acquired, the risk of adhesion and / or invasion is estimated based on this acquired information, and the risk of infection of the user with an infectious disease is estimated from this risk of adhesion and / or invasion and the index of the antimicrobial ability. Here, the user subjective information can include objective information that can be objectively acquired but is difficult to acquire. And since this user subjective information is based on the subjectivity of the user, it is extremely easy to acquire. This makes it possible to estimate the infection risk with high accuracy using little objective information.

[0054] (Second embodiment) An infection risk estimation system according to a second embodiment of the present invention will be described with reference to the drawings. Fig. 8 is a functional block diagram of a user terminal in the infection risk estimation system according to the second embodiment.

[0055] The difference between the present embodiment and the first embodiment is that some of the objective information of the user is acquired by means other than input from the user. The other configurations and operations are the same as those of the first embodiment, so only the differences will be explained here.

[0056] 8, objective information input unit 113 of user terminal 100 includes objective information collection unit 113a that collects objective information of the user by means other than input from the user. Objective information collection unit 113a acquires objective information from various sensors included in user terminal 100, various measuring devices connected to user terminal 100, and other devices that manage the objective information of the user.

[0057] In this embodiment, user terminal 100 includes health information management section 130, and objective information collection section 113a acquires objective information of the user via health information management section 190. Health information management section 130 stores and manages the user's body composition information and activity amount information on its own terminal. The information stored and managed by health information management section 130 is also stored and managed in health management server 400 deployed on the Internet, and information is synchronized between health information management section 130 and health management server 400. In this embodiment, health information management section 190 can acquire the number of steps measured using acceleration sensor 101 provided in its own terminal and movement information measured using position information acquisition section 102. In addition, health information management section 130 can acquire the activity amount information of the user from activity meter 191. Examples of the activity amount information include the number of steps, heart rate, skin temperature, and movement information. In addition, health information management section 130 can acquire the user's body composition information from body composition meter 192. The body composition information includes, for example, weight and body fat percentage.

[0058] Objective information input unit 113 transmits objective information acquired by objective information collection unit 113a, in addition to objective information acquired by input from the user, to infection risk estimating server 200. Adhesion / intrusion risk estimating unit 222 of infection risk estimating server 200 calculates the risk of adhesion and / or intrusion based on the user subjective information and user objective information input by the user at user terminal 100, as well as the user objective information collected at user terminal 100.

[0059] According to the infection risk estimation system of this embodiment, in addition to the user objective information input by the user, other objective information can be collected without the user having to perform any input work, making it possible to estimate the infection risk with greater accuracy.

[0060] (Third embodiment) An infection risk estimation system according to a third embodiment of the present invention will be described with reference to the drawings. Fig. 9 is a functional block diagram of an infection risk estimation server in the infection risk estimation system according to the third embodiment.

[0061] The difference between this embodiment and the first embodiment is that, like the second embodiment, some of the objective information of the user is acquired by means other than input from the user. Since the other configurations and operations are the same as those of the first embodiment, only the differences will be explained here.

[0062] In the second embodiment described above, the user terminal acquires objective information by means other than input from the user. Meanwhile, in this embodiment, acquisition of the user's objective information at user terminal 100 is similar to that of the first embodiment. And in this embodiment, infection risk estimation server 200 acquires, in addition to the objective information input by the user at user terminal 100, other objective information from devices other than the user terminal.

[0063] As shown in Figure 9, the user information acquisition unit 213 of the infection risk estimation server 200 is equipped with a collaboration processing unit 213a that collaborates with a health management server 400 deployed on the Internet to acquire objective information of the user from the health management server 400.

[0064] The health management server 400 manages various information such as activity amount information and body composition information in synchronization with a user's terminal having a health information management unit. Here, the user's terminal synchronized with the health management server 400 may be the user terminal 100 in the infection risk estimation system or another terminal.

[0065] The infection risk estimation server 200's adhesion / intrusion risk estimation unit 222 calculates the adhesion risk and / or intrusion risk based on the user subjective information and user objective information input by the user at the user terminal 100, as well as the user objective information collected from the health management server 400.

[0066] According to the infection risk estimation system of this embodiment, in addition to the user objective information input by the user, other objective information can be collected without the user having to perform any input work, making it possible to estimate the infection risk with greater accuracy.

[0067] Although the embodiment of the present invention has been described in detail above, the present invention is not limited to the above embodiment, and various improvements and modifications may be made without departing from the spirit and scope of the present invention.

[0068] For example, in each of the above embodiments, the infection risk estimation process is performed in the infection risk estimation server 200, and the user terminal 100 functions as an interface for inputting various information, but the infection risk estimation unit 220 may be implemented in the user terminal 100 to perform the infection risk estimation process. That is, although each of the above embodiments is a client-server type system, it may be implemented as a stand-alone type system.

[0069] In addition, in the above embodiment, both user subjective information and user objective information were obtained as user information, and this user information was used to calculate the risk of adhesion and / or the risk of intrusion, but it is also possible to use only the user subjective information.

[0070] In the above embodiment, only the roughness score derived by visual scoring evaluation is used as the roughness index, but the score determined by photographing, the transepidermal water loss using a skin property measuring device, or the stratum corneum moisture content may be used, or a combination of scores from these evaluation methods may be used. For example, when the score determined by photographing is used, an image of the user's hand is captured by the user terminal 100. Then, the hand roughness feature is extracted from the captured image, and the hand roughness score can be calculated based on the feature. The process of calculating the hand roughness score from the captured image may be performed by the user terminal 100 or the infection risk estimation server 200.

[0071] In the above embodiment, the adhesion / intrusion risk estimation unit 222 calculates the adhesion risk and / or intrusion risk with a predetermined algorithm using pre-stored conversion information based on the user subjective information and user objective information, but the estimation process may be performed using an estimation engine using machine learning technology, for example. In this case, the freedom of input questions and answers is improved, making it possible to build a more flexible system. [Explanation of symbols]

[0072] 100...User terminal 101...Accelerometer 102...Position information acquisition unit 110...input processing unit 111…Hand roughness index input section 112...Subjective information input unit 113…Objective information input section 113a…Objective Information Collection Department 120…Infection risk output unit 191…Activity meter 192…Body composition monitor 200…Infection risk estimation server 210…Information Gathering Department 211…Hand roughness index acquisition section 212...User subjective information acquisition unit 213…User Objective Information Acquisition Department 214... Collaboration processing unit 220…Infection Risk Estimation Division 221…Antimicrobial ability index calculation unit 222…Adhesion and Invasion Risk Estimation Section 223…Infection Risk Calculation Department 230…Infection Risk Transmission Section 300…Network 400: Health management server

Claims

1. A hand roughness index acquiring means for acquiring a hand roughness index indicating a degree of roughness of a user's hands; An antimicrobial ability index calculation means for calculating an index of antimicrobial ability from the hand roughness index; and an infection risk estimation means for estimating the infection risk of a user from at least the index of antimicrobial ability. An infection risk estimation device characterized by:

2. The hand roughness index includes a hand roughness appearance index defined by the characteristics of the appearance of the skin of the fingers.

2. The infection risk estimation device according to claim 1 .

3. The hand roughness appearance index is defined by the amount of scales occurring on the skin surface of the fingers.

3. The infection risk estimation device according to claim 2.

4. a user information acquiring means for acquiring at least user subjective information out of user subjective information which is subjective information of a user regarding a user's behavior and user objective information which is objective information regarding the user's behavior; and an adhesion / invasion risk estimation means for estimating at least one of a risk of microbial adhesion to the skin of the fingers and a risk of microbial invasion into the body of the body based on the information acquired by the user information acquisition means, The infection risk estimation means estimates the infection risk of the user from the risk estimated by the adhesion / invasion risk estimation means and the index of the antimicrobial ability.

4. An infection risk estimation device according to claim 1, wherein the infection risk estimation device is a device for estimating a risk of infection.

5. A method for estimating a user's risk of infection with an infectious disease using a computer, comprising: A step of acquiring a rough hand index indicating a degree of rough hands of a user; Calculating an index of antimicrobial ability from the hand roughness index; and estimating the risk of infection of the user from at least the index of antimicrobial ability. A method for estimating an infection risk.

6. A computer is caused to function as the infection risk estimation device according to claim 1. An infection risk estimation program characterized by:

Images