Information processing systems, information processing methods, and programs

Abstract

The present invention makes it possible to estimate a body temperature in order to predict an effect to a body caused by a change in balance between heat production and heat dissipation.　The present invention relates to an information processing system. The information system is characterized by comprising: a model storage unit which stores a prediction model that has been created by analyzing a relationship between past weather data and a past heat radiation amount measured by a sensor, which measures the past heat radiation amount from the body surface, and predicts the heat radiation amount from the weather data; a weather data acquisition unit which acquires a prediction value of the weather data; a heat radiation amount prediction unit which inputs the prediction value to the prediction model and predicts the heat radiation amount; and a core body temperature estimation unit which estimates a core body temperature of a user on the basis of the heat radiation amount.

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method, and a program.

Background Art

[0002] Patent Document 1 notifies an alert of a risk caused by an influence on the body due to a change in the balance between heat generation and heat dissipation.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <​​​​​​​​​​​​​​​

[0007] Further issues and solutions disclosed in this application will be made clear in the section on embodiments of the invention and in the drawings. [Effects of the Invention]

[0008] According to the present invention, body temperature can be estimated. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows an example of the overall configuration of the information processing system according to this embodiment. [Figure 2] This figure shows an example of the hardware configuration of management server 2. [Figure 3] This figure shows an example of the software configuration for management server 2. [Figure 4] This diagram illustrates the operation of management server 2. [Modes for carrying out the invention]

[0010] <Summary of the Invention> The embodiments of the present invention will be described by listing them. The present invention has, for example, the following configuration. [Item 1] A model storage unit stores a predictive model that predicts the amount of heat dissipation from weather data, which is created by analyzing the relationship between the amount of heat dissipation measured by a sensor that measures the amount of heat dissipation from the body surface in the past and past weather data including at least the temperature, A weather data acquisition unit that acquires predicted values ​​of the aforementioned weather data, A heat dissipation amount prediction unit that provides the predicted value to the prediction model to predict the heat dissipation amount, A body temperature estimation unit that estimates information regarding the user's body temperature based on the amount of heat dissipated, An information processing system characterized by comprising the following features. [Item 2] The information processing system described in item 1, The aforementioned prediction model is created by analyzing the relationship between the amount of heat dissipation and the meteorological data and site attributes. It includes a site attribute acquisition unit that acquires attributes related to the site, The heat dissipation amount prediction unit gives the meteorological data and the attributes to the prediction model to predict the heat dissipation amount, An information processing system characterized by the above. [Item 3] The information processing system according to Item 1, It includes a metabolic rate estimation unit that estimates the basal metabolic rate based on the attributes of the user, An activity amount acquisition unit that acquires the activity amount measured by the activity amount sensor carried by the user, A heat production amount estimation unit that estimates the heat production amount by the user based on the basal metabolic rate and the activity amount, and is equipped with, The body temperature estimation unit estimates the body temperature based on the heat production amount and the heat dissipation amount, An information processing system characterized by the above. [Item 4] The information processing system according to Item 1, It includes a risk estimation unit that estimates the health risk including heat stroke of the user, The risk estimation unit, estimates the first risk of health risk including heat stroke based on the first body temperature estimated by the body temperature estimation unit, acquires the second body temperature measured by the thermometer worn by the user, and estimates the second risk of health risk including heat stroke based on the acquired second body temperature, An information processing system characterized by the above. [Item 5] The information processing system according to Item 1, It includes a risk estimation unit that estimates the health risk including hypothermia of the user, The risk estimation unit, estimates the first risk of health risk including hypothermia based on the first body temperature estimated by the body temperature estimation unit, acquires the second body temperature measured by the thermometer worn by the user, and estimates the second risk of health risk including hypothermia based on the acquired second body temperature, An information processing system characterized by [Item 6] The information processing system according to Item 4 or Item 5, comprising a notification unit that notifies the health risk, The notification unit notifies at the timing when the first risk level exceeds a predetermined value, and notifies at the timing when the second risk level exceeds a predetermined value, An information processing system characterized by [Item 7] The information processing system according to Item 4 or Item 5, where the risk level estimation unit estimates the first risk level for a future time point and estimates the second risk level for the current time point, An information processing system characterized by [Item 8] A computer that stores in a storage unit a prediction model for predicting the heat dissipation amount from past meteorological data including at least the air temperature and the heat dissipation amount measured by a sensor that measures the heat dissipation amount from the body surface in the past, the step of obtaining a predicted value of the meteorological data, the step of giving the predicted value to the prediction model to predict the heat dissipation amount, the step of estimating the body temperature of the user based on the heat dissipation amount, An information processing method characterized by executing [Item 9] A computer that stores in a storage unit a prediction model for predicting the heat dissipation amount from past meteorological data including at least the air temperature and the heat dissipation amount measured by a sensor that measures the heat dissipation amount from the body surface in the past, the step of obtaining a predicted value of the meteorological data, the step of giving the predicted value to the prediction model to predict the heat dissipation amount, the step of estimating the body temperature of the user based on the heat dissipation amount, A program for causing the above to be executed.

[0011] <System Overview> The following describes an information processing system according to one embodiment of the present invention. Figure 1 is a diagram showing an example of the overall configuration of the information processing system according to this embodiment.

[0012] As an example, this information processing system aims to predict and notify workers (users) of heatstroke occurring at a construction site.

[0013] The information processing system of this embodiment includes a management server 2. The management server 2 is connected to the user terminal 1 via a communication network 3. The communication network 3 is, for example, the internet and is constructed using public telephone networks, mobile phone networks, wireless communication channels, Ethernet (registered trademark), etc.

[0014] User terminal 1 is a computer operated by the user. User terminal 1 can be, for example, a smartphone, a tablet computer, or a personal computer.

[0015] The management server 2 may be a general-purpose computer such as a workstation or personal computer, or it may be logically implemented through cloud computing.

[0016] <Management Server> Figure 2 shows an example of the hardware configuration of the management server 2. Note that the illustrated configuration is just one example, and other configurations are also possible. The management server 2 includes a CPU 201, memory 202, storage device 203, communication interface 204, input device 205, and output device 206. The storage device 203 stores various data and programs, such as a hard disk drive, solid-state drive, or flash memory. The communication interface 204 is an interface for connecting to the communication network 3, such as an adapter for connecting to Ethernet®, a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or RS232C connector for serial communication. The input device 205 is for inputting data, such as a keyboard, mouse, touch panel, button, or microphone. The output device 206 is for outputting data, such as a display, printer, or speaker. Furthermore, each functional unit of the management server device 2, described later, is realized by the CPU 201 reading a program stored in the storage device 203 into memory 202 and executing it, and each storage unit of the management server 2 is realized as part of the storage area provided by memory 202 and storage device 203.

[0017] Figure 3 shows an example of the software configuration of the management server 2. The management server 2 includes a model storage unit 231, a user information storage unit 232, a field information storage unit 233, a weather data acquisition unit 211, a heat dissipation prediction unit 212, a body temperature estimation unit 213, a field attribute acquisition unit 214, a metabolic rate estimation unit 215, an activity level acquisition unit 216, a heat production estimation unit 217, a risk level estimation unit 218, and a notification unit 219. Note that "field" in this embodiment is a concept that includes not only a "construction site" but also the entire space in which the user to be predicted is located, whether it is their home, a facility, or indoors or outdoors.

[0018] <Storage section> The model memory unit 231 stores a predictive model that predicts heat dissipation from meteorological data, which is created by analyzing the relationship between the amount of heat dissipation measured by a sensor that measures the amount of heat dissipation from the body surface in the past and past meteorological data, including at least the temperature. The predictive model is created by analyzing the relationship between the amount of heat dissipation and the attributes of meteorological data and information about the environment around the user (examples include, but are not limited to, working environment, work environment, standby environment, etc.). The "meteorological data" includes temperature, wind speed, wind direction, precipitation, snowfall, humidity (absolute humidity and relative humidity), weather, solar radiation / radiant heat, and atmospheric pressure, either individually or in combination, as well as qualitative and quantitative information (which may include indices, scores, and other forms of expression) obtained by processing this information.

[0019] The user information storage unit 232 stores information about the user (hereinafter referred to as "user information"). User information includes information that identifies the user (user ID), the user's normal body temperature (normal core body temperature), which may include surface body temperature, core body temperature, or both, but will be referred to as "core body temperature" in the following explanation), and various attributes of the user. Normal core body temperature may be, for example, the average value of core body temperature measured over a predetermined period. Normal core body temperature may also be set based on information entered by the user. User attributes may include, for example, age, gender, place of residence, weight, exercise habits (intensity and frequency of exercise, etc.), lifestyle habits (amount and frequency of alcohol consumption, amount and frequency of smoking, sleep duration, bedtime, etc.), work content, occupation, presence and degree of physical effects due to past changes in the balance between heat production and heat dissipation (for example, information on whether or not heatstroke or hypothermia has occurred), clothing, medical history, medication history, chronic illnesses, allergies, etc.

[0020] The site information storage unit 233 stores information about the site (hereinafter referred to as "site information"). Site information may include the location of the site, working hours, whether it is indoors or outdoors, and the amount of clothing worn, etc., associated with information that identifies the site (site ID). The location may be the position of the site on a map, for example, the latitude and longitude of a representative point of the site (or a geofence surrounding the site). The working hours are the time period during which work is performed, for example, it may be distinguished as daytime or nighttime, or it may be specified by the start time and end time. Indoors or outdoors may be a flag value indicating whether the work is indoors or outdoors. The amount of clothing worn is the amount of clothing worn when working at the site.

[0021] The setting information storage unit 234 stores setting information. The setting information may include unit body temperature increase and unit body temperature decrease, associated with the user's attributes. The user's attributes may be the values ​​or ranges of attributes included in the user information. Unit body temperature increase can be the body temperature that decreases when the amount of heat produced by the human body is 0 and the body releases 1 unit (e.g., joule) of heat. Unit body temperature decrease can be the body temperature that rises when the amount of heat released from the human body is 0 and the body produces 1 unit of heat.

[0022] <Functional Section> The weather data acquisition unit 211 acquires predicted values ​​of weather data. The weather data acquisition unit 211 can acquire weather data from, for example, the servers of the Japan Meteorological Agency or weather companies. The weather data acquisition unit 211 may also acquire current weather data from, for example, environmental sensors, and predict weather data based on the acquired weather data. The weather data acquisition unit 211 can also, for example, record the history of acquired current weather data in a storage unit and predict weather data through time series analysis.

[0023] The heat dissipation prediction unit 212 predicts the amount of heat dissipation by providing predicted values ​​to a prediction model. The heat dissipation prediction unit 212 can predict the amount of heat dissipation by providing meteorological data and site attributes to a prediction model.

[0024] The site attribute acquisition unit 214 acquires attributes related to the site. Site attributes can be, for example, items included in the site information. The site attribute acquisition unit 214 can register the site information with the acquired attributes set in the site information storage unit 233.

[0025] The metabolic rate estimation unit 215 estimates the basal metabolic rate based on the user's attributes. The metabolic rate estimation unit 215 can estimate the basal metabolic rate based on age, gender, etc., set in the user information, for example, using a known method. Alternatively, the basal metabolic rate may be set in the user information so that the metabolic rate estimation unit 215 can obtain the basal metabolic rate from the user information. The metabolic rate estimation unit 215 may further correct the basal metabolic rate according to attributes such as lifestyle, exercise habits, and weight.

[0026] The activity level acquisition unit 216 acquires the activity level measured by an activity level sensor carried by the user. The activity level acquisition unit 216 may, for example, acquire the IMU measurement value of a smartphone carried by the user. Alternatively, the activity level acquisition unit 216 may, for example, acquire the activity level from an activity level sensor worn by the user.

[0027] The heat production estimation unit 217 estimates the amount of heat produced by the user based on the basal metabolic rate and activity level. The heat production estimation unit 217 obtains the basal metabolic rate and activity level as values ​​expressed in terms of heat, and can sum these heat values.

[0028] The body temperature estimation unit 213 estimates the user's core body temperature based on the amount of heat dissipated. For example, the body temperature estimation unit 213 can obtain user information corresponding to the user from the user information storage unit 232 and obtain setting information that matches the attributes included in the obtained user information from the setting information storage unit 234. The body temperature estimation unit 213 can calculate the increased body temperature, which is the value obtained by multiplying the basal metabolic rate estimated by the metabolic rate estimation unit 215 by the unit increased body temperature. The body temperature estimation unit 213 can calculate the decreased body temperature, which is the value obtained by multiplying the amount of heat dissipated predicted by the heat dissipation prediction unit 212 by the unit decreased body temperature. The body temperature estimation unit 213 can calculate an estimated value of core body temperature by adding the increased body temperature to the user's normal core body temperature and subtracting the decreased body temperature.

[0029] Furthermore, the body temperature estimation unit 213 can also take heat production into consideration. The body temperature estimation unit 213 calculates the increased body temperature by multiplying the amount of heat production estimated by the heat production estimation unit 217 by the unit increased body temperature, adds this increased body temperature to the normal core body temperature in the user information, and subtracts the decrease in body temperature corresponding to the amount of heat dissipation to calculate an estimated value of the core body temperature.

[0030] The risk estimation unit 218 estimates the user's risk of heatstroke. The risk estimation unit 218 can calculate the degree to which the core body temperature estimated by the body temperature estimation unit 213 exceeds a predetermined threshold as the risk level. The body temperature estimation unit 213 may estimate the change in core body temperature for multiple future points in time based on the cumulative value of heat dissipation and the cumulative value of heat production from the calculation start time (e.g., the start of work), for example, by subtracting the decrease in body temperature obtained by multiplying the cumulative value of heat dissipation by a unit decrease in body temperature from the normal core body temperature, and adding the increase in body temperature obtained by multiplying the cumulative value of heat production by a unit increase in body temperature. The risk estimation unit 218 may then calculate the risk level based on how short the time until the core body temperature estimated by the body temperature estimation unit 213 exceeds a predetermined threshold.

[0031] Furthermore, the risk estimation unit 218 can estimate a first risk level for a future point in time and a second risk level for the present time. The risk estimation unit 218 can estimate a first risk level for heatstroke based on the first core body temperature estimated by the body temperature estimation unit, obtain a second core body temperature measured by a core body thermometer worn by the user, and estimate a second risk level for heatstroke based on the obtained second core body temperature.

[0032] The notification unit 219 notifies the risk level of heatstroke. The notification unit 219 can notify when the first risk level exceeds a predetermined value, and can also notify when the second risk level exceeds a predetermined value.

[0033] <Operation> Figure 4 is a diagram illustrating the operation of the management server 2.

[0034] Management Server 2 acquires future weather data for the site (S301) and predicts the amount of heat dissipation based on the weather data (S302). Management Server 2 estimates the user's heat output based on user information (S303) and estimates the first core body temperature at a future point in time based on the heat balance of heat dissipation and heat output (S304). Management Server 2 can issue an alert according to the estimated core body temperature (S305).

[0035] The management server 2 can obtain a second core body temperature reading from the core body thermometer worn by the user (S306), and can also issue an alert according to the obtained second core body temperature reading (S307).

[0036] As described above, the information processing system of this embodiment can estimate core body temperature based on weather data, and can notify the risk of heatstroke based on this core body temperature.

[0037] Although these embodiments have been described above, they are intended to facilitate understanding of the present invention and are not intended to limit its interpretation. The present invention can be modified and improved without departing from its spirit, and equivalents thereof are also included.

[0038] For example, in this embodiment, an alert for heatstroke is issued based on core body temperature, but the alert may also be issued based on the amount of heat dissipation. For example, an alert may be issued when the amount of heat dissipation falls below a predetermined threshold. In this case, the alert may also be issued in response to the fact that the amount of heat dissipation falls below a predetermined value and that environmental values ​​such as temperature and humidity meet predetermined conditions.

[0039] Furthermore, in this embodiment, heat production is evaluated and the heat balance is calculated including basal metabolic rate, but basal metabolic rate may be omitted. For example, the amount of heat produced may include a correction amount for basal metabolic rate according to user attributes and the amount of activity.

[0040] In the embodiments described above, "heatstroke" was used as an example to explain the effects on the body caused by a change in the balance between heat production and heat dissipation, but the method can also be applied to notifying "hypothermia" and other conditions. [Explanation of Symbols]

[0041] 1 User terminal 2 Management Server

Claims

1. A model storage unit that stores a predictive model for predicting heat dissipation from weather data, which was created by analyzing the relationship between the amount of heat dissipation measured by a sensor that measures the amount of heat dissipation from the body surface of past users and past weather data, A weather data acquisition unit that acquires predicted values ​​of weather data, A heat dissipation amount prediction unit that provides the predicted value to the prediction model to predict the amount of heat dissipation, A core body temperature estimation unit estimates the user's core body temperature based on the heat dissipation amount predicted by the heat dissipation amount prediction unit, An information processing system characterized by comprising the following features.

2. The information processing system according to claim 1, The aforementioned prediction model is created by analyzing the relationship between the amount of heat dissipation and the meteorological data and site attributes. It is equipped with a site attribute acquisition unit that acquires attributes related to the site, The heat dissipation prediction unit provides the weather data and attributes to the prediction model to predict the heat dissipation amount. An information processing system characterized by the following.

3. The information processing system according to claim 1, A metabolic rate estimation unit that estimates the basal metabolic rate based on the user's attributes, The activity level acquisition unit acquires the activity level measured by the activity level sensor carried by the user, A heat production estimation unit that estimates the amount of heat produced by the user based on the basal metabolic rate and the activity level, Equipped with, The core body temperature estimation unit estimates the core body temperature based on the heat production and heat dissipation. An information processing system characterized by the following.

4. The information processing system according to claim 1, The system includes a risk estimation unit that estimates the user's health risks, including heatstroke. The aforementioned risk estimation unit, Based on the first core body temperature estimated by the core body temperature estimation unit, the first degree of risk of health risks, including heatstroke, is estimated. The process involves obtaining a second body temperature measured by a thermometer worn by the user, and estimating a second risk level for health risks, including heatstroke, based on the obtained second body temperature. An information processing system characterized by the following.

5. The information processing system according to claim 1, The system includes a risk estimation unit that estimates the user's health risks, including hypothermia. The aforementioned risk estimation unit, Based on the first core body temperature estimated by the core body temperature estimation unit, the first degree of risk of health risks, including hypothermia, is estimated. The process involves obtaining a second body temperature measured by a thermometer worn by the user, and estimating a second degree of risk for health risks, including hypothermia, based on the obtained second body temperature. An information processing system characterized by the following.

6. An information processing system according to claim 4 or claim 5, It is equipped with a notification unit that notifies the aforementioned health risks, The aforementioned notification department, The system will notify when the first level of risk exceeds a predetermined value, The second risk level mentioned above is to be notified when it exceeds a predetermined value. An information processing system characterized by the following.

7. An information processing system according to claim 4 or claim 5, The risk estimation unit estimates the first risk level for a future point in time and estimates the second risk level for the present time. An information processing system characterized by the following.

8. A computer that stores a predictive model in its memory that predicts heat dissipation from weather data, created by analyzing the relationship between the amount of heat dissipation measured by a sensor that measures the amount of heat dissipation from the body surface of past users and past weather data, including at least the temperature, Steps to obtain predicted values ​​for weather data, The steps include: providing the predicted values ​​to the prediction model to predict the amount of heat dissipation; A step of estimating the user's core body temperature based on the predicted heat dissipation, An information processing method characterized by performing the following.

9. A computer that stores a predictive model in its memory unit, created by analyzing the relationship between the amount of heat dissipated from past users' body surfaces (measured by sensors) and past weather data, including at least the temperature, in order to predict the amount of heat dissipation from the weather data, Steps to obtain predicted values ​​for weather data, The steps include: providing the predicted values ​​to the prediction model to predict the amount of heat dissipation; A step of estimating the user's core body temperature based on the predicted heat dissipation, A program to execute.

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