JP7307432B1 - Information processing device, excretion prediction method and program - Google Patents

Abstract

Kind Code: A1 A technology is provided that can reduce the burden on a person who supports a subject's daily life. An information processing device executes a first acquisition process, a registration process, and a prediction process. The first acquisition process repeatedly acquires the biometric information of the target person who is a prediction target of the desire to excrete. Urge craving is the feeling of wanting to excrete excretions, including one or both of urine and stool. In the registration process, when biometric information is acquired in the first acquisition process, a value corresponding to the biometric information is registered as a management value. The prediction process predicts the subject's excretion desire. The registration process accumulates the value corresponding to the biometric information to the registered management value. In the prediction process, when the management value exceeds a predetermined level standard value, the subject's excretion desire is predicted to be in the first desire state, and when the management value does not exceed the level reference value, the subject's excretion desire is predicted to be in the second desire state, which is weaker than the first desire state. [Selection drawing] Fig. 5

Description

The present invention relates to prediction of a subject's excretion desire.

Patent Literature 1 discloses an ecosystem. The ecosystem detects and predicts urination. The ecosystem includes wearable devices, urine detection sensors, hubs, network controllable devices, computing devices and application servers. In the ecosystem, such devices are directly connected or connected to a network. Wearable devices collect biometric data from users. Biometric data includes data relating to individuals and data relating to the user's environment. Examples of biometric data are heart rate, exercise, ambient temperature, body temperature, blood oxygenation, skin conductivity, blood pressure, electrocardiogram data, electroencephalogram data, electromyography data, respiration rate, sleep quality, sleep depth , resting or heart rate variability data. Biometric data can include user input regarding: The aforementioned points indicate micturition events, urge to urinate, absence of micturition events or lack of urge to urinate. The user presses a button on the wearable device when inputting. The application server performs processing for predicting expected urination time based on the user model. The data, expected voiding time and user model are associated with a user identifier that indicates the next individual. Biometric data is collected from this individual and an expected voiding time is applied to this individual. Biometric data is collected by the wearable device and uploaded to the application server continuously or periodically. The application server can determine when a voiding event is expected to occur based on the user model. Once the application server determines the expected voiding time, it can transmit the expected voiding time to the wearable device and/or the caregiver's computing device. The update or alert awakens the stimulation of the wearable device. Stimulation can alert the user of an impending voiding event. An update or alert can cause the network controllable device to perform the following functions. The aforementioned functions activate and/or change a network-controllable light bulb to a desired color or intensity, or provide an auditory stimulus to a network-controllable sound source. An update or alarm may cause the computing device to arouse stimulation and alert the caregiver or user of an impending voiding event. The wearable device can continuously monitor current biometric data and dynamically update the expected voiding time according to the current biometric data.

Japanese Patent Application Publication No. 2019-522500

As the population ages, the number of people who need human assistance in daily life is increasing. The inventors are aware that the treatment of the subject's excretion is a burden on the person who assists the subject in daily life. For example, the inventors know that treatment of the excrement of a person requiring care or support is a burden on the caregiver at a nursing care site. Therefore, the inventor studied a technique for predicting a subject's desire to excrete in order to reduce the burden on the person who supports the subject's daily life. Desire to excrete means the feeling of wanting to excrete excrement.

An object of the present invention is to provide a technology that can reduce the burden on a person who assists a subject in daily life.

One aspect of the present invention is a first acquisition process of repeatedly acquiring the heart rate of a subject who is a prediction target of excretion desire, which is a feeling of wanting to excrete excrement including one or both of urination and stool; A second acquisition process of acquiring a change amount between a first heart rate as the heart rate acquired at the timing and a second heart rate as the heart rate acquired at a second timing different from the first timing. a registration process of registering the amount of change as a management value when the heart rate is acquired in the first acquisition process and the amount of change is acquired in the second acquisition process; and a prediction process for predicting, wherein the registration process accumulates the amount of change in the registered control value, and the prediction process is performed when the control value exceeds a predetermined level reference value , predicting that the subject's excretion desire is in the first desire state, and if the management value does not exceed the level reference value, the subject's excretion desire is weaker than the first desire state; An information processing device that predicts a desire state.

According to this information processing device, the first state or the second state is determined by the management value obtained by accumulating the amount of change in the heart rate as a value corresponding to the repeatedly obtained heart rate of the subject's desire to excrete. can be predicted. A person's excretion desire gradually becomes stronger with the passage of time. By accumulating the amount of change as the control value, an increase in the control value can correspond to an increase in the desire to excrete over time. The amount of change in the heart rate over time between the first timing and the second timing can correspond to the change in desire to excrete.

When the amount of change is acquired in the second acquisition process, the information processing device executes a comparison process of comparing the amount of change with a predetermined cumulative reference value, and the registration process includes the amount of change. exceeds the cumulative reference value, accumulate the amount of change in the registered control value, and if the amount of change does not exceed the cumulative reference value, add the amount of change to the registered control value It may be non-cumulative.

According to this configuration, the amount of change can be accumulated or not accumulated in the management value using the cumulative reference value as the threshold.

The prediction processing comprises a first prediction processing for predicting the excretion desire of the subject according to a first level reference value as the level reference value, and a second prediction processing for predicting the excretion desire of the subject according to the level reference value. a second prediction process of predicting according to a reference value; and a selection process of selecting which of the first prediction process and the second prediction process to predict the excretion desire of the subject, wherein the first prediction The processing is executed when the first prediction processing is selected in the selection processing, and when the management value exceeds the first level reference value, the excretion desire of the subject is in the first desire state. and if the management value does not exceed the first-level reference value, it is predicted that the excretion desire of the subject person is in the second desire state, and the second prediction process includes: It is executed when the second prediction process is selected in the process, and when the management value exceeds the second level reference value, the excretion desire of the subject is predicted to be the first desire state. and, if the management value does not exceed the second level reference value, the excretion desire of the subject may be predicted to be in the second desire state.

According to this configuration, it is possible to predict the excretion desire suitable for the subject. The inventor knows that the desire to excrete differs from subject to subject. Assuming two subjects, the first prediction process can be selected for the first subject and the second prediction process can be selected for the second subject. It becomes possible to predict the excretion desire of the first subject by the first prediction processing, and it is possible to predict the excretion desire of the second subject by the second prediction processing.

The information processing device includes: a third acquisition process for acquiring excretion information indicating that the subject has excreted the excrement; to a predetermined initial value, and an initialization process may be executed.

According to this configuration, the management value can be initialized to the initial value as the subject's excrement is excreted. Excretion of excrement eliminates or weakens the subject's desire to excrete. By using the management value as the initial value, the management value can be matched with the subject's excretion desire.

Another aspect of the present invention is an excretion prediction method as an information processing method executed by software control, which is a prediction target of excretion desire, which is a feeling of wanting to excrete excrement including one or both of urination and stool. A first acquisition step of repeatedly acquiring the heart rate of the subject, the first heart rate as the heart rate acquired at the first timing, and the heart rate acquired at a second timing different from the first timing A second acquisition step of acquiring a second heart rate as a heart rate, and when the heart rate is acquired in the first acquisition step and the variation is acquired in the second acquisition step , a registration step of registering the amount of change as a control value; and a prediction step of predicting the excretion desire of the subject. The prediction step predicts that the subject's excretion desire is in a first desire state when the management value exceeds a predetermined level reference value, and the management value does not exceed the level reference value. In this case, the excretion prediction method predicts that the subject's desire to excrete is in a second desire state weaker than the first desire state.

According to this excretion prediction method, the first state or the second state is determined by a management value obtained by accumulating changes in the heart rate as a value corresponding to the heart rate obtained repeatedly by the subject's desire to excrete. can be predicted. A person's excretion desire gradually becomes stronger with the passage of time. By accumulating the amount of change as the control value, an increase in the control value can correspond to an increase in the desire to excrete over time. The amount of change in the heart rate over time between the first timing and the second timing can correspond to the change in desire to excrete.

The excretion prediction method includes, when the amount of change is acquired in the second acquisition step, a comparison step of comparing the amount of change with a predetermined cumulative reference value, and the registration step includes the step of comparing the amount of change with When the accumulated reference value is exceeded, the amount of change is accumulated in the registered control value, and when the amount of change does not exceed the accumulated reference value, the amount of change is accumulated in the registered control value. No, you can.

According to this configuration, the amount of change can be accumulated or not accumulated in the management value using the cumulative reference value as the threshold.

The prediction step includes a first prediction step of predicting the excretion desire of the subject according to a first level reference value as the level reference value, and a second level prediction step of predicting the excretion desire of the subject according to the level reference value. a second prediction step of predicting according to a reference value; and a selection step of selecting which of the first prediction step and the second prediction step to predict the excretion desire of the subject, wherein the first prediction The step is performed when the first prediction step is selected in the selection step, and when the management value exceeds the first level reference value, the excretion desire of the subject is in the first desire state. and predicting that the excretion desire of the subject is in the second desire state when the management value does not exceed the first level reference value, and the second prediction step includes: It is performed when the second prediction step is selected in the step, and when the management value exceeds the second level reference value, the excretion desire of the subject is predicted to be the first desire state. and, if the management value does not exceed the second level reference value, the excretion desire of the subject may be predicted to be in the second desire state.

According to this configuration, it is possible to predict the excretion desire suitable for the subject. The inventor knows that the desire to excrete differs from subject to subject. Given two subjects, the first prediction step can be selected for the first subject and the second prediction step can be selected for the second subject. It becomes possible to predict the excretion desire of the first subject by the first prediction step, and it is possible to predict the excretion desire of the second subject by the second prediction step.

The excretion prediction method includes: a third acquisition step of acquiring excretion information indicating that the subject has excreted the excrement; to a predetermined initial value.

According to this configuration, the management value can be initialized to the initial value as the subject's excrement is excreted. Excretion of excrement eliminates or weakens the subject's desire to excrete. By using the management value as the initial value, the management value can be matched with the subject's excretion desire.

In still another aspect of the present invention, the processor repeatedly acquires the heart rate of the subject, who is the target of predicting excretion desire, which is the feeling of wanting to excrete excrement including one or both of urination and stool. Acquisition of a change amount between a first heart rate as the heart rate acquired at the first timing and a second heart rate as the heart rate acquired at a second timing different from the first timing. a registration process for registering the amount of change as a management value when the heart rate is obtained in the first obtaining process and the amount of change is obtained in the second obtaining process; a prediction process of predicting the excretion desire of a person, the registration process accumulating the amount of change in the registered control value, and the prediction process comprising a level reference value predetermined by the control value. is exceeded, the excretion desire of the subject is predicted to be in the first desire state, and when the management value does not exceed the level reference value, the excretion desire of the subject is the first desire It is a program that predicts that the second desire state is weaker than the state.

According to this program, either the first state or the second state is predicted based on the control value obtained by accumulating the amount of change in the heart rate as a value corresponding to the heart rate that the subject's excretion desire is repeatedly obtained. can do. A person's excretion desire gradually becomes stronger with the passage of time. By accumulating the amount of change as the control value, an increase in the control value can correspond to an increase in the desire to excrete over time. The amount of change in the heart rate over time between the first timing and the second timing can correspond to the change in desire to excrete.

The program causes the processor to execute a comparison process for comparing the amount of change with a predetermined cumulative reference value when the amount of change is acquired in the second acquisition process, and the registration process includes: When the amount of change exceeds the cumulative reference value, the amount of change is accumulated in the registered control value, and when the amount of change does not exceed the cumulative reference value, the change is added to the registered control value. Amounts may be non-cumulative.

According to this configuration, the amount of change can be accumulated or not accumulated in the management value using the cumulative reference value as the threshold.

The prediction processing comprises a first prediction processing for predicting the excretion desire of the subject according to a first level reference value as the level reference value, and a second prediction processing for predicting the excretion desire of the subject according to the level reference value. a second prediction process of predicting according to a reference value; and a selection process of selecting which of the first prediction process and the second prediction process to predict the excretion desire of the subject, wherein the first prediction The processing is executed when the first prediction processing is selected in the selection processing, and when the management value exceeds the first level reference value, the excretion desire of the subject is in the first desire state. and if the management value does not exceed the first-level reference value, it is predicted that the excretion desire of the subject person is in the second desire state, and the second prediction process includes: It is executed when the second prediction process is selected in the process, and when the management value exceeds the second level reference value, the excretion desire of the subject is predicted to be the first desire state. and, if the management value does not exceed the second level reference value, the excretion desire of the subject may be predicted to be in the second desire state.

According to this configuration, it is possible to predict the excretion desire suitable for the subject. The inventor knows that the desire to excrete differs from subject to subject. Assuming two subjects, the first prediction process can be selected for the first subject and the second prediction process can be selected for the second subject. It becomes possible to predict the excretion desire of the first subject by the first prediction processing, and it is possible to predict the excretion desire of the second subject by the second prediction processing.

a third acquisition process for acquiring excretion information indicating that the subject has excreted the excrement; and an initialization process for initializing to a predetermined initial value.

According to this configuration, the management value can be initialized to the initial value as the subject's excrement is excreted. Excretion of excrement eliminates or weakens the subject's desire to excrete. By using the management value as the initial value, the management value can be matched with the subject's excretion desire.

ADVANTAGE OF THE INVENTION According to this invention, the burden of a person who supports a subject's daily life can be reduced.

It is a block diagram which shows an example of schematic structure of an excretion prediction system. It is an image figure which shows an example of schematic structure of a notification screen. The upper part shows the initial state of the notification screen. The lower part shows the predicted state of the notification screen. The upper and lower notification screens include three target person areas for target persons "AAA", "BBB" and "CCC". It is explanatory drawing explaining an example of the outline of the setting method of a prediction model. 4 is a flowchart of measurement processing; It is a flow chart of the first part of excretion prediction processing. It is a flow chart of the second part of excretion prediction processing. FIG. 2 is a diagram showing an example of a schematic configuration of a heart rate database; FIG. A heart rate database for three users whose user IDs are "AAA", "BBB" and "CCC" is shown. It is a flow chart of information processing.

An embodiment for carrying out the present invention will be described with reference to the drawings. The present invention is not limited to the configurations described below, and various configurations can be adopted based on the same technical idea. For example, some of the configurations shown below may be omitted or replaced with other configurations. The invention may include other configurations. The drawings schematically show certain configurations.

<Excretion prediction system 10>
A schematic configuration of the excretion prediction system 10 will be described with reference to FIGS. The excretion prediction system 10 includes a measuring device 20, an information processing device 30, and a notification device 40 (see FIG. 1). In the excretion prediction system 10, the information processing device 30 performs data communication with the measurement device 20 and the notification device 40, respectively. For example, the measuring device 20 and the information processing device 30 are connected to each other via the communication network 60 , and the information processing device 30 and the notification device 40 are connected to each other via the communication network 60 . The communication network 60 is not limited to a communication network of a specific communication system, and includes various known communication networks. Accordingly, data communication over communication network 60 may be either wired communication, wireless communication, or a combination thereof. In addition, the communication network 60 may be the Internet, a LAN (Local Area Network), a communication system that locally connects two devices, or two of these. It may be a combination of species or more. Examples of communication schemes for locally connecting two devices include USB (Universal Serial Bus), Bluetooth (registered trademark), and Wi-Fi Direct.

The measurement device 20 executes measurement processing (see FIG. 4, which will be described later). The measuring device 20 implements the measuring method along with the execution of the measuring process, and measures the biological information of the subject. The measurement processing and measurement method will be described later. Examples of subjects include those who require human assistance in carrying out their daily activities. "Assistance" means helping others with their daily lives. For example, "support" includes care and assistance in care settings, includes nursing in medical settings, and care of infants and young children in home and educational settings. In the embodiment, a person who supports a subject is called a "supporter". Examples of biological information include heart rate, pulse rate, body temperature, blood pressure, blood oxygen concentration, and electrocardiogram waveform. The measurement device 20 repeats measurement of biological information at predetermined regular intervals.

When measuring the biological information, the measuring device 20 transmits the biological information to the information processing device 30 . It is assumed that the excretion prediction system 10 targets a plurality of subjects for prediction. In this case, the excretion prediction system 10 employs the user ID. A user ID is an identifier unique to a subject and identifies the subject. It is assumed that the excretion prediction system 10 is used at a predetermined facility and the location of the target person is specified within the facility. In this case, the user ID may be a location-specific identifier of the subject. Examples of predetermined facilities include nursing homes and hospitals. In embodiments, the excretion prediction system 10 employs a user ID. Along with this, the excretion prediction system 10 can target a plurality of subjects for prediction.

The measuring device 20 transmits the user ID to the information processing device 30 together with the biological information to be transmitted. That is, the biological information is transmitted from the measuring device 20 to the information processing device 30 while being associated with the user ID. The measuring device 20 may measure the biometric information repeatedly at intervals of one second, and may transmit the biometric information and the user ID at intervals of one second. The measurement interval and the transmission interval are appropriately determined in consideration of various conditions.

The information processing device 30 executes excretion prediction processing (see FIG. 5 described later). The information processing device 30 implements the excretion prediction method along with the execution of the excretion prediction processing, and predicts the subject's desire to excrete. Urgency refers to the feeling of wanting to excrete excrement, including one or both of urine and stool. The feeling of wanting to excrete excrement means "urine urge" when excretion is urine, and means "feces urge" when excretion is stool. The information processing device 30 uses the biological information from the measuring device 20 for the excretion prediction process and the excretion prediction method. Therefore, the excretion desire predicted by the excretion prediction process and the excretion prediction method targets the subject identified by the user ID associated with the biometric information.

The information processing device 30 transmits the desire state corresponding to the predicted excretion desire to the notification device 40 . The desire state indicates the degree of excretion desire. At that time, the information processing device 30 transmits the next user ID to the notification device 40 together with the desire state of the transmission target. This user ID is associated with biometric information used when predicting the desire state of the transmission target to excrete in the excretion prediction process and the excretion prediction method. That is, the desire state is transmitted from the information processing device 30 to the notification device 40 in a state associated with the user ID.

The notification device 40 notifies the desire state of the subject's excretion desire (see the lower part of FIG. 2). When the excretion prediction system 10 targets a plurality of subjects for prediction, the notification device 40 may notify the subject information along with the desire state. The target person information corresponds to the user ID associated with the desire state. The notification device 40 performs notification processing (see FIG. 8 described later) and receives excretion information. Excretion information indicates that the subject excreted excrement. Excretion information is associated with a subject's user ID. The notification device 40 transmits the excretion information and the user ID to the information processing device 30 .

In the embodiment, the biological information is the heart rate, the excretion desire to be predicted is the desire to urinate, and the excretion is urine. In this case, the excretion prediction process and the excretion prediction method predict the subject's urge to urinate. When a certain amount or more of urine is stored in the bladder, the pressure inside the bladder increases, and this stimulus is transmitted to the parasympathetic nerves, causing the urge to urinate. A person's heart rate changes according to the desire to urinate. The amount of urine stored in the bladder increases over time. Along with this, the urge to urinate also becomes stronger over time. Examples of craving states include "strong urge to urinate", "weak urge to urinate" and "no urge to urinate". However, in the embodiment, there are two desire states, "with urge to urinate" and "without urge to urinate". The mode to be set as the desire state is appropriately determined in consideration of various conditions. Excretion information indicates that the subject has urinated. It is assumed that the notification of the desire state by the notification device 40 is performed by "display". Three persons "AAA", "BBB" and "CCC" are exemplified as subjects (see FIG. 2). Assume that the user ID of the target person "AAA" is "AAA", the user ID of the target person "BBB" is "BBB", and the user ID of the target person "CCC" is "CCC" (see FIG. 7 described later).

<Measuring device 20>
The measuring device 20 includes a processor 21, a storage 22, a memory 23, a measuring device 24, an operation device 25, and a communication device 26 (see FIG. 1). The processor 21 executes arithmetic processing. The storage 22 is a storage medium readable by the processor 21 . Examples of storage 22 include flash memory and ROM. The storage 22 stores programs and data. An example of the program is a program for measurement processing (see FIG. 4). An example of data is a user ID. The user ID is registered in the program for measurement processing. The storage 22 may be externally connected to the main body (control device) of the measuring device 20 . In this case, the measuring device 20 has a connection interface. The connection interface connects the storage 22 to the main body of the measuring device 20 . Processor 21 accesses storage 22 via a connection interface.

The memory 23 serves as a storage area used when the processor 21 executes programs stored in the storage 22 . The memory 23 stores predetermined data used in processing in a predetermined storage area during execution of the processing. For example, the processor 21 uses the memory 23 to execute a measurement processing program stored in the storage 22 . Along with this, the measuring device 20 implements the measuring method.

The measuring instrument 24 measures biological information. The processor 21 acquires biological information from the measuring instrument 24 . In the embodiment, the measuring device 24 is a heart rate monitor and measures heart rate as biological information. The communication device 26 connects the measurement device 20 to the communication network 60 and communicates with the information processing device 30 via the communication network 60 .

The operation device 25 receives input of various instructions to the measuring device 20 . For example, the operation device 25 receives an instruction to start measurement processing. Examples of the operating device 25 include hardware keys and a touch panel. Examples of hardware keys include operation buttons. In the embodiment, description and illustration of a display device that serves as a touch panel together with the operation device 25 in the measuring device 20 are omitted. The type of the operation device 25 is appropriately determined in consideration of various conditions.

The communication device 26 transmits the heart rate measured by the measuring device 24 to the information processing device 30 . In the embodiment, the communication device 26 supports a wireless communication method and connects the measuring device 20 to the communication network 60 via wireless communication. However, the communication method of the communication device 26 may be wired communication. The communication method of the communication device 26 is appropriately determined in consideration of various conditions.

The measuring device 20 measures the heart rate with the measuring device 24 and transmits the heart rate from the communication device 26 to the information processing device 30 via the communication network 60 when performing the measurement method associated with the execution of the measurement processing. An example of the measuring device 20 is a wearable device. A smart watch is an example of a wearable device as the measuring device 20 .

The measuring device 20 may include the control device and the measuring device 24 separately, and the control device and the measuring device 24 may be connected so as to be capable of data communication. The control device includes a processor 21, a storage 22, a memory 23, a communication device 26, and a connection interface. A connection interface connects the meter 24 to the controller. The processor 21 acquires biometric information through the connection interface. Examples of controllers include computers. A small computer called Raspberry Pi (registered trademark) may be employed as the control device. An example of the measuring device 24 that can be employed in the measuring device 20 including the control device is a mat-type sensor. A mat-type sensor as the measuring device 24 is installed under a futon or mattress and measures the heart rate of the subject lying on it.

A measuring device having both functions of heart rate measurement and communication has already been put into practical use and is publicly known. The measuring device 20 may include, in addition to the above-described elements, elements included in known measuring devices. However, the measurement device 20 differs from known measurement devices in that a program for measurement processing is stored in the storage 22 .

<Information processing device 30>
The information processing device 30 includes a processor 31, a storage 32, a memory 33, a communication device 34, and a timer 35 (see FIG. 1). The processor 31 executes arithmetic processing. The storage 32 is a storage medium readable by the processor 31 . Examples of the storage 32 include flash memory and HDD (Hard Disk Drive). When a storage medium using flash memory is employed as the storage 32, an example of such a storage medium is an SSD (Solid State Drive). Alternatively, storage 32 may include ROM. The storage 32 may be provided inside the main body of the information processing device 30 together with the processor 31 and the memory 33 and may be externally connected to the main body of the information processing device 30 . In this case, the information processing device 30 has a connection interface. The connection interface connects the storage 32 to the main body of the information processing device 30 . The processor 31 accesses the storage 32 through the connection interface.

Storage 32 stores programs and data. An example of the program is a program for excretion prediction processing (see FIGS. 5 and 6). The program for excretion prediction processing includes a program for selection processing (see S202 in FIG. 5). Examples of data include multiple prediction models and multiple heart rate databases. In the excretion prediction process and the excretion prediction method, the desire to excrete is predicted according to level reference values respectively set for a plurality of prediction models. The level reference value is appropriately determined according to the interval between two consecutive excretion times. In the embodiment, excretion is treated as urine, so the interval between two consecutive excretion times is the interval between two consecutive excretion times of urine (urination). A plurality of prediction models are registered in a program for excretion prediction processing. The programmatic process for excretion prediction processing accesses multiple prediction models. The heart rate database registers the time and heart rate in association with the user ID (see FIG. 7).

A program for excretion prediction processing and a plurality of prediction models are installed in the storage 32 in advance. A heart rate database is created when excretion prediction processing is executed.

In the embodiment, two prediction models are exemplified as the plurality of prediction models. When distinguishing between the two prediction models, they are referred to as "first prediction model" and "second prediction model". When the first prediction model and the second prediction model are not distinguished, or when they are collectively referred to, they are simply referred to as "prediction model". The difference between the first prediction model and the second prediction model is the difference in level reference value. The number of prediction models may be three or more. The number of prediction models is appropriately determined in consideration of various conditions. For example, the inventors consider an excretion prediction system with five prediction models. A method for setting the prediction model will be described later.

In the embodiment, a "first level reference value" is defined as the level reference value for the first prediction model, and a "second level reference value" is defined as the level reference value for the second prediction model. The first level reference value and the second level reference value are to be compared with the control value. Management values will be described later. In the prediction of excretion desire using the first prediction model, when the management value exceeds the first level reference value (see S214: Yes in FIG. 5), the desire state is predicted to be "feeling to urinate". If the control value does not exceed the first level reference value (see S214: No in FIG. 5), the desire state is predicted to be "no urge to urinate". In the prediction of excretion desire using the second prediction model, when the control value exceeds the second-level reference value, the desire state is predicted to be "feeling to urinate". If the control value does not exceed the second level reference value, the craving state is predicted to be "no desire to urinate."

However, in the prediction of excretion desire using the first prediction model, when the management value is the first-level reference value, the desire state may be predicted as "having the urge to urinate." In the prediction of excretion desire using the second prediction model, when the control value is the second level reference value, the desire state may be predicted as "having urge to urinate".

"Exceeds" and "does not exceed" in the excretion prediction process and the excretion prediction method will be described (see S210 and S214 in FIG. 5). In this description, "comparison value" and "reference value" are used as examples. The comparison value corresponds to the change amount and the reference value corresponds to the cumulative reference value (see S208 to S212 in FIG. 5), or the comparison value corresponds to the control value and the reference value corresponds to the level reference value (see FIG. 5 (see S212 and S214 of ). The amount of change and the cumulative reference value will be described later. Assume that the amount of change when the urge to urinate increases is set to "minus" and the reference value is set to "minus value". In this case, "the comparative value exceeds the reference value" includes "the comparative value is less than the reference value", and "the comparative value does not exceed the reference value" includes "the comparative value is greater than the reference value" and Contains "comparison value equals reference value". Assume that the amount of change when the urge to urinate increases is set to "plus" and the reference value is set to "plus value". In this case, "the comparative value exceeds the reference value" includes "the comparative value is greater than the reference value", and "the comparative value does not exceed the reference value" includes "the comparative value is less than the reference value" and Contains "comparison value equals reference value". In the embodiment, the excretion prediction process and the excretion prediction method set the amount of change to "minus" when the urge to urinate increases. However, in the excretion prediction process and the excretion prediction method, the amount of change when the urge to urinate increases may be set to "plus".

The memory 33 serves as a storage area used when the processor 31 executes programs stored in the storage 32 . The memory 33 stores predetermined data used in processing in a predetermined storage area during execution of the processing. For example, the processor 31 uses the memory 33 to execute a program for excretion prediction processing stored in the storage 32 . Along with this, the information processing device 30 implements the excretion prediction method.

The communication device 34 connects the information processing device 30 to the communication network 60 and communicates with the measurement device 20 and the notification device 40 via the communication network 60 . The communication device 34 receives the heart rate and user ID transmitted from the measuring device 20 . The communication device 34 receives the excretion information and user ID transmitted from the notification device 40 . The processor 31 acquires the heart rate and user ID, excretion information and user ID via the communication device 34 . The communication device 34 transmits the desire state and the user ID to the notification device 40 . In the embodiment, the communication device 34 is compatible with a wired communication method, and connects the information processing device 30 to the communication network 60 via wired communication. However, the communication method of the communication device 34 may be wireless communication. The communication method of the communication device 34 is appropriately determined in consideration of various conditions.

A timer 35 measures the passage of time. Alternatively, the timer 35 may measure the current time. The timer 35 may be configured by hardware or may be configured by software.

An example of the information processing device 30 is a server device. The information processing device 30 may include, in addition to the above-described elements, elements of known server devices. However, the information processing device 30 differs from a known server device in that a program for excretion prediction processing and a plurality of prediction models are stored in the storage 32 .

<Notification device 40>
The notification device 40 includes a processor 41, a storage 42, a memory 43, a notification device 44, an operation device 45, and a communication device 46 (see FIG. 1). The processor 41 executes arithmetic processing. The storage 42 is a storage medium readable by the processor 41 . Examples of the storage 42 include flash memory and HDD (Hard Disk Drive). When a storage medium using flash memory is employed as the storage 42, an example of such a storage medium is an SSD (Solid State Drive). Alternatively, storage 42 may include ROM. The storage 42 may be provided together with the processor 41 and the memory 43 within the main body (control device) of the notification device 40 and may be externally connected to the main body of the notification device 40 .

Storage 42 stores programs and data. An example of the program is a program for notification processing (see FIG. 8). Examples of data include notification screen data and a user ID. The notification screen data represents the notification screen 50 (see FIG. 2). The notification screen data includes layout data. The layout data defines the layout of the notification screen 50. FIG. The notification screen data and the user ID are registered in the notification processing program.

The storage 42 may be externally connected to the body of the notification device 40 . In this case, the notification device 40 has a connection interface. The connection interface connects the storage 42 to the body of the notification device 40 . The processor 41 accesses the storage 42 via the connection interface.

The memory 43 serves as a storage area used when the processor 41 executes programs stored in the storage 42 . The memory 43 stores predetermined data used in processing in a predetermined storage area during execution of the processing. For example, the processor 41 uses the memory 43 to execute a notification processing program stored in the storage 42 . Along with this, the notification device 40 implements the notification method.

The notification device 44 notifies the desire state of the subject's excretion desire. In the embodiment, the notification device 44 is a display, and displays a notification screen 50 to notify the desire state of excretion desire (see FIG. 2). The notification screen 50 includes target person areas 51 divided for each target person. The notification screen 50 includes the same number of target person areas 51 as the user IDs registered in the program for notification processing. The subject area 51 includes a first area 52 , a second area 53 and an excretion button 54 . Such a layout of the notification screen 50 is defined by layout data as described above.

The first area 52 displays subject information. The target person information corresponds to the user ID. A second area 53 displays the desire state. In the target person area 51, the target person information and the desire state are displayed in association with each other. That is, the subject area 51 indicates the subject's excretion desire. The supporter can confirm the target person information and desire state displayed in the target person area 51 . The supporter understands the target person's desire state indicated by the target person information, and determines whether support for this target person is necessary or not. Excretion information and the next user ID are set in the excretion button 54 . This user ID corresponds to the target person information displayed in the first area 52 of the target person area 51 provided with the excretion button 54 . Excretion information and user ID are associated.

The operation device 45 receives input of various instructions to the notification device 40 . For example, the operation device 45 receives pressing of the excretion button 54 on the notification screen 50 . The operation device 45 may include hardware such as a mouse and keyboard, or may be integrated with the notification device 44 to form a touch panel. The operation device 45 includes hardware such as a mouse and keyboard, and may form a touch panel together with the notification device 44 . The type of the operation device 45 is appropriately determined in consideration of various conditions. When the operator 45 accepts an operation on the excretion button 54, the processor 41 acquires the excretion information and the next user ID. This user ID corresponds to the subject information displayed together with the excretion button 54 whose depression has been accepted.

The communication device 46 connects the notification device 40 to the communication network 60 and communicates with the information processing device 30 via the communication network 60 . The communication device 46 receives the desire state and user ID transmitted from the information processing device 30 . The processor 41 acquires the desire state and user ID via the communication device 46 . The communication device 46 transmits the excretion information and the user ID to the information processing device 30 in response to the acquisition of the excretion information and the user ID by the processor 41 . In the embodiment, the communicator 46 supports a wireless communication method and connects the notification device 40 to the communication network 60 via wireless communication. However, the communication method of the communication device 46 may be wired communication. The communication method of the communication device 46 is appropriately determined in consideration of various conditions.

The notification device 40 displays the notification screen 50 on the notification device 44 and transmits the excretion information from the communication device 46 to the information processing device 30 via the communication network 60 when the notification process is executed (when the notification method is performed). An example of the notification device 40 is a combination of a control device and a display as the notification device 44 . In the notification device 40, the controller and display may be separate or integrated. An example of a separate controller and display is a display-separate computer (desktop computer). Examples of integrated controllers and displays include display-integrated computers (laptop computers), tablet terminals, and smart phones. The control device includes a processor 41, a storage 42, a memory 43, a communication device 46, and a connection interface. The operation device 45 may be provided in the control device or may be provided in the display. A connection interface connects the display to the controller. Processor 41 outputs a display command to the display via the connection interface.

A notification device having both functions of notifying information and receiving instructions has already been put into practical use and is publicly known. The notification device 40 may include elements of known notification devices in addition to the elements described above. However, the notification device 40 differs from known notification devices in that a program for notification processing is stored in the storage 42 .

<How to set the prediction model>
An overview of the method of setting the prediction model implemented by the inventor will be described with reference to FIG. In setting up a plurality of prediction models, the inventor conducted an experiment with a plurality of subjects, and set up a plurality of prediction models using the results of this experiment. All of the multiple subjects were elderly people with no underlying disease. In the experiment, the subject's heartbeat data was collected continuously for a predetermined period, and changes in the subject's desire to excrete (desire state) were electronically recorded. The recording of the desire to excrete was performed at the same timing as the collection of heartbeat data, and the desire to excretion was recorded during the collection period of the heartbeat data.

The method of recording the desire to excrete was generally as follows. In this method, a recording device was used. The subject enters the first command into the recording device when he or she feels the urge to urinate. The first indication is associated with "there is an urge to urinate". The recording device records the first time stamp when receiving the input of the first instruction. The first timestamp indicates the input time of the first instruction. The subject enters a second instruction when going to the bathroom or urinating. The second instruction is associated with "excretion (urination)". The recording device records the second time stamp when receiving the input of the second instruction. The second timestamp indicates the input time of the second instruction. The subject inputs the third instruction when he no longer feels the urge to urinate. The third indication is associated with "no urge to urinate". The recording device records the third time stamp when receiving the input of the third instruction. The third timestamp indicates the input time of the third instruction.

The first time stamp, the second time stamp and the third time stamp identify the subject's desire to void. For example, the subject does not feel the urge to urinate during the following first period and second period. The first period is from the start of recording to the first time stamp. The second period is from the third timestamp to the first timestamp. The subject feels the urge to urinate during the following third and fourth periods. A third time period is from the first timestamp to the second timestamp. A fourth time period is from the first timestamp to the third timestamp.

Through the experiment, several hundred patterns of heartbeat data are collected in association with the excretion desire of the subject from whom the heartbeat data is collected. Additionally, the heart rate data can be associated with a first, second, third, or fourth time period identified as described above from the first time stamp, the second time stamp, and the third time stamp. For example, in this experiment, the heart rate of the subject in the first state, the heart rate of the subject in the second state, and the change in the heart rate of the subject in the case of changing from the first state to the second state collect. In the first state, the subject does not feel the urge to urinate. In the second state, the subject feels an urge to urinate.

Multiple combinations of heartbeat data and voiding desire recordings collected by the experiment are processed by a computer to generate a normal training data set and a pre-voiding (pre-urination) training data set. An example of a computer is the information processing device 30 . However, the computer may be different from the information processing device 30 in terms of hardware. Examples of processing include data cleansing. Through this processing, the heartbeat waveform indicated by the heartbeat data is edited into a normal heartbeat waveform and a heartbeat waveform before excretion. The normal state corresponds to a state in which the subject does not feel the urge to urinate as a desire to excrete. Before excretion corresponds to a state in which the subject feels an urge to urinate as a desire to excrete.

The computer repeats machine learning using the normal teacher data set and the pre-excretion teacher data set to provisionally construct a prediction model. Further, the computer verifies the accuracy of the temporarily constructed prediction model using test data corresponding to the prediction model, and tunes the prediction model according to the verification results. A computer constructs a plurality of prediction models in such a manner. Different level reference values are set for the plurality of prediction models.

It is assumed that the prediction model constructed by the computer is the first prediction model, and the first prediction model is set for a subject whose interval between two consecutive excretion times is less than arbitrary N hours. In this case, the computer may extract a combination of the heartbeat data and the voiding desire record of the subject whose interval between two consecutive voidings is less than N hours. Subsequently, the computer processes the combination of the extracted heart rate data and voiding desire recordings to generate a normal training data set and a pre-voiding (pre-urination) training data set. Next, the computer repeats machine learning using the generated normal training data set and the pre-excretion training data set to provisionally construct a first prediction model. Further, the computer verifies the accuracy of the temporarily constructed first prediction model using test data corresponding to the first prediction model, and tunes the first prediction model according to the verification result. The computer constructs the first prediction model in such a manner. A first level reference value is set for the first prediction model.

It is assumed that the prediction model constructed by the computer is the second prediction model, and the second prediction model is set for a subject whose interval between two consecutive excretions is arbitrary N hours or more. In this case, the computer may extract a combination of the heartbeat data and the excretion desire record of the subject whose interval between two consecutive excretion is N hours or more. Subsequently, the computer processes the combination of the extracted heart rate data and voiding desire recordings to generate a normal training data set and a pre-voiding (pre-urination) training data set. Next, the computer repeats machine learning using the generated normal training data set and the pre-excretion training data set to provisionally construct a second prediction model. Furthermore, the computer verifies the accuracy of this second prediction model using test data corresponding to the temporarily constructed second prediction model, and tunes this second prediction model according to the verification result. The computer constructs the second prediction model in such a manner. A second level reference value is set for the second prediction model.

<Measurement processing>
The measurement process will be described with reference to FIG. A measurement process is executed by the measurement device 20 . In the measurement device 20 , the operation device 25 receives an instruction to start measurement processing. The processor 21 acquires the measurement process start instruction when the operation device 25 receives the measurement process start instruction. Subsequently, the processor 21 activates the measurement processing program stored in the storage 22 in response to acquisition of the measurement processing start instruction, and starts the measurement processing. Along with this, the measuring device 20 starts the measuring method. Processor 21 outputs a start command to measuring instrument 24 . The measuring device 24 starts measuring the heart rate as biological information of the subject. The measuring device 24 continues to measure the subject's heart rate and repeatedly outputs the measured heart rate.

The processor 21 acquires the heart rate output from the measuring device 24 (S100). Subsequently, the processor 21 causes the heart rate and user ID to be transmitted (S102). The processor 21 outputs a heart rate and user ID transmission command to the communication device 26 . The heart rate and the user ID are transmitted to the information processing device 30 . The communication device 26 transmits the heart rate and the user ID to the information processing device 30 according to the transmission command.

After that, the processor 21 determines whether an instruction to end the measurement process has been acquired (S104). In the measurement device 20 , an instruction to end the measurement process is received by the operation device 25 . The processor 21 acquires the measurement process end instruction when the operation device 25 receives the measurement process end instruction. If the processor 21 has not acquired an end instruction (S104: No), the process returns to S100. After that, the processor 21 repeatedly executes the processes after S100. If the processor 21 acquires the end instruction (S104: Yes), it ends the measurement process. Along with this, the measuring device 24 finishes measuring the heart rate.

<Excretion prediction processing>
Excretion prediction processing will be described with reference to FIGS. The excretion prediction process is executed by the information processing device 30 . In the information processing device 30, the excretion prediction process may be started as follows. That is, the processor 31 automatically activates the excretion prediction processing program stored in the storage 32 after the information processing device 30 is activated, and starts the excretion prediction processing. Along with this, the information processing device 30 starts the excretion prediction method. However, the procedure for starting such excretion prediction processing is an example. The procedure for starting excretion prediction processing is appropriately determined in consideration of various conditions. The processor 31 executes processes by the program for excretion prediction processing as many times as there are subjects to be predicted. A programmatic process for excretion prediction processing is simply referred to as a "process." One process corresponds to one subject. Multiple processes are identified by user IDs.

The processor 31 starts acquiring heart rate and user ID (S200). The heart rate and user ID are repeatedly transmitted from the measuring device 20 in S102 of FIG. 4 and received by the communication device 34 each time. Assume that the excretion prediction system 10 includes a plurality of measuring devices 20 . In this case, the heart rate and the user ID are transmitted from each measuring device 20 in S102 of FIG. The processor 31 obtains from the communicator 34 the heart rate received by the communicator 34 and the user ID.

The processor 31 associates and registers the heart rate acquired in S200 and the user ID in the heart rate database, and also registers the time (see FIG. 7). In other words, processor 31 registers the heart rate and time in the heart rate database associated with the user ID. "Time" indicates the time when the heart rate and user ID were acquired. When a plurality of target persons are targeted for prediction, the heart rate database may be managed for each user ID (see FIG. 7). Assume that a heart rate database associated with the user ID obtained in S200 along with the heart rate has not been created. In this case, processor 31 sets this user ID in a new heart rate database and also registers the time and heart rate. The heart rate database may be in a format divided for each user ID (see FIG. 7), or may be in a format (not shown) in which a plurality of user IDs are registered in one heart rate database. . The format of the heart rate database is appropriately determined in consideration of various conditions.

Next, the processor 31 executes selection processing (S202). In the selection process, a prediction model that matches the target audience of the process being executed is selected from a plurality of prediction models. In an embodiment, the user ID targeted by the running process corresponds to the user ID of S200 in that process. That is, the processor 31 selects the first prediction model or the second prediction model through selection processing. In the selection process, statistical analysis is performed using a cox proportional hazards model using heart rate above a certain number and age of the subject as data. In the selection process in S202, the heart rate acquired in the past 20 minutes from the start of this is used as data. It is assumed that the transmission of the heart rate and the user ID (see S102 in FIG. 4) by the measuring device 20 is performed at intervals of one second. In this case, 1200 heart rates are acquired in 20 minutes. However, the number of heartbeats used for selecting the prediction model is appropriately determined in consideration of various conditions. In S202, the processor 31 selects a prediction model that matches the subject's excretion interval estimated from the analysis result of the cox proportional hazard model. Processor 31 stores the selected prediction model in memory 43 . The predictive model is associated with the user identity for which the running process is intended. Processor 31 terminates the selection process without selecting a prediction model when a heart rate equal to or greater than a certain number is not registered in the heart rate database.

Subsequently, processor 31 determines whether a prediction model has been selected (S204). When the prediction model is not selected (S204: No), the processor 31 returns the process to S202 and executes the selection process again. If the processor 31 ends the selection process because the heart rate for 20 minutes is not registered in the heart rate database, it denies the determination in S204.

When the prediction model is selected (S204: Yes), the processor 31 determines whether there is an unacquired amount of change (S206). The amount of change is the difference between two heart rates registered in the heart rate database. The target heart rate database is associated with the target user ID of the running process. The amount of change is calculated from two heart rates obtained at different times for a certain period of time. In this case, the heart rate that is acquired later is acquired after a certain period of time has elapsed from the time of the heart rate that was acquired earlier. Regarding calculation of the heart rate, the heart rate database associated with "user ID: AAA" in FIG. 7 is taken as an example. In the embodiment, for convenience of explanation, the amount of change is calculated from two heart rates continuously acquired at intervals of 1 second. In this case, the acquired variation amounts are "80 (No. 1) - 79 (No. 2) = 1", "79 (No. 2) - 80 (No. 3) = -1", "80 (No. .3) -81 (No. 4) = -1", . . . "85 (No. X - 1) - 86 (No. X) = -1". "1 second" as the time interval between two heart rate acquisitions for calculating the amount of change is an example. This interval may be a time different from 1 second. For example, this interval may be longer than 1 second. This interval is appropriately determined in consideration of various conditions.

If there is no change amount that has not yet been acquired (S206: No), the processor 31 proceeds to S214. If there is an unacquired amount of change (S206: Yes), the processor 31 acquires the amount of change (S208). Take the heart rate database associated with “user ID: AAA” in FIG. 7 as an example. Assume that the amount of change for the two records "No. X, X-1" has not yet been obtained. In this case, the processor 31 acquires "85 (No. X-1)-86 (No. X)=-1" as the amount of change.

In the embodiment, as described above, the amount of change is set to "minus" when the urge to urinate increases. Therefore, in S208, the processor 31 subtracts the later acquired heart rate from the earlier acquired heart rate to calculate the amount of change. Suppose that the amount of change is set to "plus" when the urge to urinate increases, unlike in the embodiment. In this case, in S208, the processor 31 subtracts the previously acquired heart rate from the later acquired heart rate to calculate the amount of change.

Subsequently, processor 31 compares the amount of change obtained in S208 with the cumulative reference value, and determines whether the amount of change exceeds the cumulative reference value (S210). The cumulative reference value is set in advance and registered in a program for excretion prediction processing. When the amount of change when the urge to urinate increases is set to "minus" as in the embodiment, the cumulative reference value is set to a negative value. Unlike the embodiment, when the amount of change when the urge to urinate increases is set to "plus", the cumulative reference value is set to a plus value. Heart rate varies due to various events. Based on the results obtained through various experiments, the inventor considered the amount of change in heart rate in the process in which the desire to excrete became stronger over time, and set a cumulative reference value. The experiment described above is an example of this investigation. The cumulative reference value shall be common to multiple prediction models. However, the cumulative reference value may be set to a different value for some or all of the multiple prediction models. Thereby, the cumulative reference value can be appropriately optimized for each prediction model. Processor 31 determines S210 according to the cumulative reference value set for the prediction model selected in the selection process of S202.

If the amount of change does not exceed the cumulative reference value (S210: No), processor 31 returns the process to S206. After that, the processor 31 repeatedly executes the processes after S206. If the amount of change exceeds the cumulative reference value (S210: Yes), the processor 31 accumulates the amount of change acquired in S208 to the management value (S212). The control value is the cumulative amount of change. In the excretion prediction processing and the excretion prediction method, the cumulative amount of change is managed as a control value. The processor 31 manages the management value for each process when executing the excretion prediction process. The initial value of the management value is set to a predetermined value. An example of the initial value of the management value is "0". After executing S212, the processor 31 returns the process to S206.

In S214, the processor 31 compares the management value and the level reference value to determine whether the management value exceeds the level reference value. Assume that the first prediction model is selected in the selection process of S202. In this case, the processor 31 determines whether the management value exceeds the first level reference value. Assume that the second prediction model is selected in the selection process of S202. In this case, the processor 31 determines whether the management value exceeds the second level reference value. In an embodiment, "the control value exceeds the level reference value" means "the control value exceeds the first level reference value" or "the control value exceeds the second level reference value" . "The control value does not exceed the level reference value" means "the control value does not exceed the first level reference value" or "the control value does not exceed the second level reference value". If the management value exceeds the level reference value (S214: Yes), the processor 31 shifts the process to S216 of FIG. If the management value does not exceed the level reference value (S214: No), the processor 31 shifts the process to S218 of FIG.

In S216, the processor 31 causes the desire state "with urge to urinate" and the user ID to be transmitted. A user ID is intended for a running process. The processor 31 outputs to the communicator 34 a command to transmit the desire state “with urge to urinate” and the user ID. The notification device 40 is set as the transmission destination of the desire state “with urge to urinate” and the user ID. The communication device 34 transmits the desire state “with urge to urinate” and the user ID to the notification device 40 in accordance with the transmission command. After that, the processor 31 shifts the process to S220.

In S218, the processor 31 causes the desire state "no desire to urinate" and the user ID to be transmitted. A user ID is intended for a running process. The processor 31 outputs to the communication device 34 a command to transmit the desire state “no desire to urinate” and the user ID. The destination of the desire state “no desire to urinate” and the user ID is the notification device 40 . The communication device 34 transmits the desire state “no urge to urinate” and the user ID to the notification device 40 in accordance with the transmission command. After that, the processor 31 shifts the process to S220.

At S220, the processor 31 determines whether the excretion information and the user ID have been acquired. The excretion information and the user ID are transmitted from the notification device 40 in S308 of FIG. 8, which will be described later. The information processing device 30 receives the excretion information and the user ID through the communication device 34 . The processor 31 acquires the excretion information and the user ID received by the communication device 34 from the communication device 34 . Processor 31 stores the excretion information and the user ID in memory 33 . Processor 31 returns processing to S206 of Drawing 5, when excretion information and user ID are not acquired (S220:No). After that, the processor 31 repeatedly executes the processes after S206.

When the excretion information and the user ID are acquired (S220: Yes), the processor 31 initializes the management value (S222). The managed value to be initialized is managed in association with the user ID targeted by the running process. After executing S222, the processor 31 returns the process to S202 of FIG. After that, the processor 31 repeatedly executes the processes after S202. However, the processor 31 acquires the excretion information (see S220: Yes in FIG. 6) and the heart rate acquired together with the user ID from the communication device 34 after the processing after S202 that is executed again, and the change acquired from this heart rate Run with quantity.

<Notification processing>
The notification processing will be described with reference to FIG. The notification process is executed by the notification device 40 . When the operation device 45 accepts the instruction to start the notification process, the processor 41 acquires the instruction to start the notification process. The processor 41 activates the notification processing program stored in the storage 42 and starts the notification processing. Along with this, the notification device 40 starts the notification method.

After starting the notification process, the processor 41 causes the notification screen 50 to be displayed on the notification device 44 (S300). The processor 41 generates a notification screen 50 from the notification screen data stored in the storage 42 and outputs a command to display the notification screen 50 to the notification device 44 . The notification device 44 displays a notification screen 50 (see the upper part of FIG. 2).

Subsequently, the processor 41 determines whether the desire state and the user ID have been acquired (S302). The desire state and the user ID are transmitted from the information processing device 30 in S216 or S218 of FIG. The notification device 40 receives the desire state and the user ID through the communication device 46 . The processor 41 obtains from the communicator 46 the desire state and the user ID received by the communicator 46 . Processor 41 stores the desire state and user ID in memory 43 . If the processor 41 has not acquired the desire state and the user ID (S302: No), the process proceeds to S306.

When the desire state and the user ID are acquired (S302: Yes), the processor 41 updates the notification screen 50 being displayed (see S304). The updated notification screen 50 includes the newly acquired desire state in the second area 53 of the next target person area 51 . This target person area 51 includes, in the first area 52, the target person information corresponding to the same user ID as the user ID acquired this time. That is, the updated notification screen 50 displays the desire state acquired this time and the target person information corresponding to the user ID acquired this time in association with each other. Processor 41 outputs an update command to annunciator 44 . The update command updates the notification screen 50 being displayed to the updated notification screen 50 described above. The notification device 44 displays the updated notification screen 50 (see the lower part of FIG. 2). After executing S304, the processor 41 shifts the process to S306.

Assume that the desire state acquired this time is the same as the desire state being displayed in the second area 53 in association with the subject information corresponding to the user ID acquired this time. In this case, the processor 41 may omit S304. That is, in the case described above, the processor 41 may shift the process to S306 without executing S304.

In S306, the processor 41 determines whether the excretion information and the user ID have been acquired. When an operation on the excretion button 54 is accepted by the operation device 45 , the processor 41 acquires the excretion information and the user ID set on the operated excretion button 54 . If the processor 41 has not acquired the excretion information and the user ID (S306: No), the process proceeds to S310.

When the excretion information and the user ID are acquired (S306: Yes), the processor 41 transmits the excretion information and the user ID (S308). The processor 41 outputs a command to transmit the excretion information and the user ID to the communication device 46 . The destination of the excretion information and the user ID is the information processing device 30 . The communication device 46 transmits the excretion information and the user ID to the information processing device 30 according to the transmission command.

After that, the processor 41 determines whether an instruction to end the notification process has been acquired (S310). In the notification device 40 , an instruction to end the notification process is received by the operation device 45 . When the operation device 45 receives an instruction to end the notification process, the processor 41 acquires the instruction to end the notification process. If the processor 41 has not acquired an end instruction (S310: No), the process returns to S302. After that, the processor 41 repeatedly executes the processes after S302. If the processor 41 acquires the end instruction (S310: Yes), it ends the notification process. Along with this, the notification device 40 ends the notification of the desire state of the subject's excretion desire. That is, the notification device 40 ends the display of the notification screen 50 .

<Effects of Embodiment>
According to the embodiment, the following effects can be obtained.

(1) In the information processing device 30, the processor 31 executes excretion prediction processing (see FIGS. 5 and 6). Along with this, the information processing device 30 implements the excretion prediction method. According to the information processing device 30 and the excretion prediction method, it is possible to predict whether the subject's desire to excrete is in a desire state of "with urge to urinate" or "without urge to urinate" based on the management value. A person's excretion desire gradually becomes stronger with the passage of time. By setting the management value as an accumulation of changes in heart rate obtained repeatedly, an increase in the management value can be made to correspond to an increase in the desire to excrete over time. It is possible to reduce the burden on a person (supporter) who supports the subject's daily life.

(2) Excretion prediction processing includes S208 to S212 (see FIG. 5). According to this configuration, the amount of change in the biological information over time can be made to correspond to the change in desire to excrete. By using the cumulative reference value as a threshold, it is possible to accumulate or not accumulate the amount of change in the management value.

(3) Excretion prediction processing includes S202 and S214 (see FIG. 5). According to this configuration, it is possible to predict the excretion desire suitable for the subject. The inventor knows that the desire to excrete differs from subject to subject. Assume the subjects "AAA", "BBB" and "CCC" (see FIGS. 2 and 7). In this case, the first prediction model is selected for the first target, which is part of the three subjects, and the second prediction model, which is the other part of the three subjects, is selected A second prediction model can be selected for The excretion desire of the first subject can be predicted by S214 using the first prediction model, and the excretion desire of the second subject can be predicted by S214 using the second prediction model.

(4) Excretion prediction processing includes S220 and S222 (see FIG. 6). According to this configuration, the management value can be initialized to the initial value as the subject's excrement is excreted. Excretion of excrement eliminates or weakens the subject's desire to excrete. By using the management value as the initial value, the management value can be matched with the subject's excretion desire.

<Modification>
Embodiments can also be as follows. Some of the configurations of the modifications shown below can also be employed in combination as appropriate. In the following, points that are different from the above will be described, and descriptions of similar points will be omitted as appropriate.

(1) In the excretion prediction system 10, the measurement device 20 and the notification device 40 are separate devices (see FIG. 1). The measurement device 20 and notification device 40 may be devices or systems that include a processor, storage, memory, meter, notification device, manipulator, and communication device.

(2) The information processing device 30 sets, as prediction models, a first prediction model and a second prediction model according to the difference between two consecutive excretion intervals. The information processing device 30 may subdivide the prediction model according to the following information. Examples of this information include the subject's gender and subject's build. A subject's build includes one or both of the subject's height and weight. For example, the information processing device 30 sets a first prediction model and a second prediction model that match the difference between two consecutive excretion intervals for the age of 70s, the age of 80s, and the age of 90s or older, respectively. good too. In this case, the information processing device 30 stores information for segmentation in association with the user ID. For example, when subdividing the prediction model according to the age of the target person, the information processing device 30 stores the age or generation of the target person in association with the user ID. The selection process (see S202 in FIG. 5) selects a prediction model according to the interval between two consecutive excretion times and other information.

(3) In the excretion prediction process (see FIGS. 5 and 6), the processor 31 determines S210 in FIG. 5 depending on whether the amount of change exceeds the cumulative reference value. The determination condition of S210 in FIG. 5 may be that the amount of change exceeds the cumulative reference value or the amount of change is equal to the cumulative reference value. That is, when the change amount is equal to the cumulative reference value, the processor 31 affirms the determination in S210 of FIG. 5 (see S210 of FIG. 5: Yes). After that, the processor 31 executes S212 of FIG. 5 in the same manner as described above. On the other hand, if the amount of change does not exceed the cumulative reference value and the amount of change is not equal to the cumulative reference value, the processor 31 denies the determination of S210 in FIG. 5 (see S210: No in FIG. 5). . After that, the processor 31 returns the process to S206 of FIG. 5 as described above.

(4) In the excretion prediction process, the processor 31 accumulates the amount of change in the management value in S212 of FIG. The control value does not have to be the cumulative amount of change. The management value may be an accumulation of biometric information. For example, processor 31 may accumulate the next heart rate into the control value. This heart rate is one of the two heart rates for calculating the amount of change for which the determination in S210 of FIG. 5 is affirmative (see S210 of FIG. 5: Yes). For example, the processor 31 may accumulate a previously acquired heart rate to the control value, or may accumulate a later acquired heart rate to the control value. In the prediction model, a level reference value is appropriately set according to the management value.

(5) The desire state of excretion desire was defined as "with urge to urinate" and "without urge to urinate" (see lower part of Fig. 2). However, as described above, examples of the desire state of excretion desire include "strong urge to urinate", "weak urge to urinate" and "no urge to urinate". Therefore, a case where "strong urge to urinate", "weak urge to urinate" and "no urge to urinate" is adopted as the desire state of the desire to excrete will be described. The first prediction model defines a first level reference value and a third level reference value as level reference values. In the first prediction model, the first level reference value is a threshold for distinguishing between "strong urge to urinate" and "weak urge to urinate", and the third level reference value is a threshold for distinguishing between "weak urge to urinate" and "no urge to urinate". The second prediction model defines a second level reference value and a fourth level reference value as level reference values. In the second prediction model, the second level reference value is a threshold for distinguishing between "strong urge to urinate" and "weak urge to urinate", and the fourth level reference value is a threshold for distinguishing between "weak urge to urinate" and "no urge to urinate". The processor 31 executes the excretion prediction process from S214 in FIG. 5 to S218 in FIG. 6 as follows.

Assume that the first prediction model is selected in the selection process (see S202 in FIG. 5). If the determination in S206 of FIG. 5 is negative (see S206: No in FIG. 5), the processor 31 determines whether the management value exceeds the first level reference value. If the management value exceeds the first level reference value, the processor 31 causes the desire state "strong urge to urinate" and the user ID to be transmitted. If the controlled value does not exceed the first level reference value, the processor 31 determines whether the controlled value exceeds the third level reference value. If the control value exceeds the third level reference value, the processor 31 causes the desire state "weak urge to urinate" and the user ID to be transmitted. If the control value does not exceed the third level reference value, the processor 31 causes the desire state "no desire to urinate" and the user ID to be transmitted. If the control value is the first level reference value, the desire state may be predicted to be "strong urge to urinate". If the control value is the third level reference value, the desire state may be predicted to be "weak urge to urinate".

Assume that the second prediction model is selected in the selection process (see S202 in FIG. 5). If the determination in S206 of FIG. 5 is negative (see S206: No in FIG. 5), the processor 31 determines whether the management value exceeds the second level reference value. The processor 31 causes the desire state "strong urge to urinate" and the user ID to be transmitted when the management value exceeds the second level reference value. If the controlled value does not exceed the second level reference value, the processor 31 determines whether the controlled value exceeds the fourth level reference value. If the management value exceeds the fourth level reference value, the processor 31 causes the desire state "weak urge to urinate" and the user ID to be transmitted. If the management value does not exceed the fourth level reference value, the processor 31 causes the desire state "no desire to urinate" and the user ID to be transmitted. If the control value is the second level reference value, the desire state may be predicted to be "strong urge to urinate". If the control value is the fourth level reference value, the desire state may be predicted to be "weak urge to urinate".

The processor 31 transmits the desire state and the user ID in the same manner as S216 or S218 in FIG. After that, the processor 31 shifts the process to S220 in FIG. The desire state of excretion desire may be further subdivided. In the prediction model, the level reference value is appropriately set in correspondence with the subdivided desire state of the desire to excrete. The processor 31 compares the management value and the level reference value similar to those described above using the set level reference value, and determines whether the management value exceeds the level reference value. After that, the processor 31 causes the desire state and the user ID to be transmitted in the same manner as described above.

(6) In the excretion prediction process, the processor 31 returns the process to S206 in FIG. 5 after executing S222 in FIG. After executing S222 of FIG. 6, the processor 31 may return the process to S206 of FIG. In this case, the processor 31 may apply the prediction model selected in the already-executed selection process (see S202 and S204 in FIG. 5: Yes) in the subsequent processes after S206 in FIG. 5 .

(7) The excretion prediction system 10 receives excretion information with the notification device 40 . The excretion information may be received by the measuring device 20 . In this case, in the measuring device 20 , excretion information and the next user ID are set for a specific operation on the operation device 25 . This user ID indicates the subject who uses the measuring device 20 . The processor 21 in the measuring device 20 executes processing corresponding to S306 and S308 in FIG. In this case, the measurement process may include processes corresponding to S306 and S308 in FIG. 8 between S102 and S104 in FIG. The processor 21 denies the determination corresponding to S306 in FIG. 8 (see S306 in FIG. 8: No) and shifts the process to S104 in FIG. . On the other hand, the processor 21 affirms the determination corresponding to S306 of FIG. Corresponding processing is executed, and then the processing proceeds to S104 in FIG.

(8) The notification device 40 includes the notification device 44, and displays the notification screen 50 on the notification device 44 (see the lower part of FIG. 2). The notification screen 50 includes a target person area 51, and displays the target person information and the desire state in a first area 52 and a second area 53 in association with each other. The notification screen may have a different aspect from the notification screen 50 . The mode of the notification screen is appropriately determined in consideration of various conditions.

The desire state may be in a form that allows the supporter to visually recognize the subject's desire to excrete, unlike text information. Examples of visually recognizable formats include color-coded distinctions and graphical distinctions. Examples of graphics include graphs. Desire states may be a combination of textual information and visually perceptible forms.

In addition, the notification of the desire state by the notification device 40 may be performed by sound or by light. Furthermore, the notification of the desire state by the notification device 40 may be a combination of two or more of the notification screen, sound and light.

(9) In the embodiment, the heart rate is exemplified as biological information. As a result of the studies conducted so far, the inventor believes that it is possible to predict a subject's desire to excrete based on biological information other than the heart rate. That is, the inventors believe that it is possible to predict the excretion desire of a subject by any of the pulse rate, body temperature, blood pressure, blood oxygen concentration, and electrocardiogram waveform, which are exemplified as biological information other than the heart rate. think. Furthermore, the inventor believes that the above-mentioned biometric information can be used to predict a subject's defecation urge. That is, the inventors found that the management value obtained by accumulating the amount of change in information specified from any one of the heart rate, pulse rate, body temperature, blood pressure, blood oxygen concentration, and electrocardiogram waveform is the excretion such as the subject's urge to urinate and defecate. I think it has a relationship with the degree of desire. The degree of the subject's desire to excrete may be predicted from a combination of multiple types of biological information.

10 Excretion Prediction System 20 Measuring Device 21 Processor 22 Storage 23 Memory 24 Measuring Instrument 25 Operation Device 26 Communication Device 30 Information Processing Device 31 Processor 32 Storage 33 Memory 34 Communication Device 35 Timer 40 Notification Apparatus 41 Processor 42 Storage 43 Memory 44 Alarm 45 Manipulator 46 Communication Device 50 Notification Screen 51 Subject Area 52 First Area 53 Second Area 54 Excretion Button 60 Communication Network

Claims (15)

a first acquisition process of repeatedly acquiring the heart rate of the subject, who is the prediction target of excretion desire, which is the feeling of wanting to excrete excrement including one or both of urination and stool;
A second step of acquiring a change amount between a first heart rate as the heart rate acquired at the first timing and a second heart rate as the heart rate acquired at a second timing different from the first timing an acquisition process;
a registration process of registering the amount of change as a management value when the heart rate is acquired in the first acquisition process and the amount of change is acquired in the second acquisition process;
a prediction process for predicting the excretion desire of the subject; and
The registration process accumulates the amount of change in the registered management value,
The prediction process includes
when the management value exceeds a predetermined level reference value, predicting that the subject's excretion desire is in the first desire state;
The information processing device predicts that the subject's excretion desire is in a second desire state weaker than the first desire state when the management value does not exceed the level reference value. When the amount of change is acquired in the second acquisition process, executing a comparison process for comparing the amount of change with a predetermined cumulative reference value,
The registration process includes
if the amount of change exceeds the cumulative reference value, accumulating the amount of change in the registered control value;
2. The information processing apparatus according to claim 1 , wherein the amount of change is not accumulated in the registered management value when the amount of change does not exceed the cumulative reference value. The prediction process includes
a first prediction process of predicting the excretion desire of the subject according to a first level reference value as the level reference value;
a second prediction process of predicting the excretion desire of the subject according to a second level reference value as the level reference value;
a selection process for selecting which of the first prediction process and the second prediction process to predict the excretion desire of the subject,
The first prediction process includes
Executed when the first prediction process is selected in the selection process,
predicting that the excretion desire of the subject is the first desire state when the management value exceeds the first level reference value;
predicting that the excretion desire of the subject is in the second desire state when the management value does not exceed the first level reference value;
The second prediction process is
Executed when the second prediction process is selected in the selection process,
predicting that the excretion desire of the subject is in the first desire state when the management value exceeds the second level reference value;
The information processing apparatus according to claim 1 or 2 , wherein, when the management value does not exceed the second level reference value, the excretion desire of the subject is predicted to be the second desire state. a third acquisition process for acquiring excretion information indicating that the subject has excreted the excrement;
An initialization process for initializing the registered control value to a predetermined initial value when the excretion information is acquired in the third acquisition process, according to claim 1 or 2 . information processing equipment. a third acquisition process for acquiring excretion information indicating that the subject has excreted the excrement;
4. The information processing apparatus according to claim 3, further comprising an initialization process of initializing the registered management value to a predetermined initial value when the excretion information is acquired in the third acquisition process. . An excretion prediction method as an information processing method executed under software control,
a first acquiring step of repeatedly acquiring the heart rate of the subject, who is the prediction target of excretion desire, which is the feeling of wanting to excrete excrement including one or both of urination and stool;
A second step of acquiring a change amount between a first heart rate as the heart rate acquired at the first timing and a second heart rate as the heart rate acquired at a second timing different from the first timing an acquisition step;
a registration step of registering the amount of change as a control value when the heart rate is obtained in the first obtaining step and the amount of change is obtained in the second obtaining step;
a prediction step of predicting the excretion desire of the subject;
The registration step accumulates the amount of change in the registered control value,
The prediction step includes
when the management value exceeds a predetermined level reference value, predicting that the subject's excretion desire is in the first desire state;
An excretion prediction method, wherein when the management value does not exceed the level reference value, the excretion desire of the subject is predicted to be in a second desire state weaker than the first desire state. A comparison step of comparing the amount of change with a predetermined cumulative reference value when the amount of change is obtained in the second obtaining step,
The registration step includes
if the amount of change exceeds the cumulative reference value, accumulating the amount of change in the registered control value;
7. The excretion prediction method according to claim 6 , wherein when the amount of change does not exceed the cumulative reference value, the amount of change is not accumulated in the registered management value. The prediction step includes
a first prediction step of predicting the excretion desire of the subject according to a first level reference value as the level reference value;
a second prediction step of predicting the excretion desire of the subject according to a second level reference value as the level reference value;
a selection step of selecting which one of the first prediction step and the second prediction step to predict the excretion desire of the subject;
The first prediction step includes
Performed when the first prediction step is selected in the selection step,
predicting that the excretion desire of the subject is the first desire state when the management value exceeds the first level reference value;
predicting that the excretion desire of the subject is in the second desire state when the management value does not exceed the first level reference value;
The second prediction step includes
Performed when the second prediction step is selected in the selection step,
predicting that the excretion desire of the subject is in the first desire state when the management value exceeds the second level reference value;
8. The excretion prediction method according to claim 6 or 7 , wherein, when said management value does not exceed said second level reference value, said subject's desire to excrete is said to be in said second desire state. a third acquisition step of acquiring excretion information indicating that the subject has excreted the excrement;
8. The method according to claim 6 , comprising an initialization step of initializing the registered management value to a predetermined initial value when the excretion information is obtained in the third obtaining step. Excretion prediction method. a third acquisition step of acquiring excretion information indicating that the subject has excreted the excrement;
9. The excretion prediction method according to claim 8 , further comprising an initialization step of initializing the registered control value to a predetermined initial value when the excretion information is acquired in the third acquisition step. to the processor,
a first acquisition process of repeatedly acquiring the heart rate of the subject, who is the prediction target of excretion desire, which is the feeling of wanting to excrete excrement including one or both of urination and stool;
A second step of acquiring a change amount between a first heart rate as the heart rate acquired at the first timing and a second heart rate as the heart rate acquired at a second timing different from the first timing an acquisition process;
a registration process of registering the amount of change as a management value when the heart rate is acquired in the first acquisition process and the amount of change is acquired in the second acquisition process;
executing a prediction process for predicting the excretion desire of the subject;
The registration process accumulates the amount of change in the registered management value,
The prediction process includes
when the management value exceeds a predetermined level reference value, predicting that the subject's excretion desire is in the first desire state;
The program predicts that the excretion desire of the subject is in a second desire state weaker than the first desire state when the management value does not exceed the level reference value. causing the processor to perform a comparison process for comparing the amount of change with a predetermined cumulative reference value when the amount of change is obtained in the second obtaining process;
The registration process includes
if the amount of change exceeds the cumulative reference value, accumulating the amount of change in the registered control value;
12. The program according to claim 11 , wherein the amount of change is not accumulated in the registered management value if the amount of change does not exceed the cumulative reference value. The prediction process includes
a first prediction process of predicting the excretion desire of the subject according to a first level reference value as the level reference value;
a second prediction process of predicting the excretion desire of the subject according to a second level reference value as the level reference value;
a selection process for selecting which of the first prediction process and the second prediction process to predict the excretion desire of the subject,
The first prediction process includes
Executed when the first prediction process is selected in the selection process,
predicting that the excretion desire of the subject is the first desire state when the management value exceeds the first level reference value;
predicting that the excretion desire of the subject is in the second desire state when the management value does not exceed the first level reference value;
The second prediction process is
Executed when the second prediction process is selected in the selection process,
predicting that the excretion desire of the subject is in the first desire state when the management value exceeds the second level reference value;
13. The program according to claim 11 or 12 , which predicts that the excretion desire of the subject is the second desire state when the management value does not exceed the second level reference value. to the processor;
a third acquisition process for acquiring excretion information indicating that the subject has excreted the excrement;
13. The method according to claim 11 or 12 , wherein when the excretion information is acquired in the third acquisition process, an initialization process of initializing the registered management value to a predetermined initial value is executed. program. to the processor;
a third acquisition process for acquiring excretion information indicating that the subject has excreted the excrement;
14. The program according to claim 13 , which executes an initialization process of initializing the registered management value to a predetermined initial value when the excretion information is acquired in the third acquisition process.

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