WO2017109910A1

WO2017109910A1 - Electronic device, determination method, and determination program

Info

Publication number: WO2017109910A1
Application number: PCT/JP2015/086106
Authority: WO
Inventors: 笠間　晃一朗
Original assignee: 富士通株式会社
Priority date: 2015-12-24
Filing date: 2015-12-24
Publication date: 2017-06-29

Abstract

An electronic device that obtains exercise information relating to exercise by a user or the environment in which the user exercises. The electronic device also obtains vital information such as the number of times the user coughs. The electronic device then uses the exercise information and identifies the regularity of the user exercise or environment. The electronic device uses the vital information and identifies chronological change in said vital information. Then the electronic device determines the health condition of the user, on the basis of the regularity and the chronological change in vital information.

Description

Electronic device, determination method, and determination program

The present invention relates to an electronic device, a determination method, and a determination program.

In recent years, electronic devices that can be worn on the body, such as wearable terminals, have become widespread, and health management has been carried out on their own. For example, the number of steps per day is measured with a wearable terminal, and the amount of exercise per day is determined by comparison with the standard number of steps. In addition, it is performed to measure a daily pulse rate with a wearable terminal, and to detect a change in the pulse rate to determine a health condition.

JP 2013-025380 A

However, in the above technology, since the health condition and exercise amount are determined by comparison with general standard values, it may be affected by the characteristics of the individual being monitored, environmental information such as temperature and humidity, and accurate health. It is difficult to manage.

An object of one aspect is to provide an electronic device, a determination method, and a determination program that can improve the accuracy of health management.

In the first proposal, the electronic device acquires exercise information related to the user's exercise or the environment in which the user exercises, and a resting pulse rate that is a pulse rate calculated when the user's exercise amount is a predetermined value or less. Part. The electronic device includes a first specifying unit that specifies the movement of the user or the regularity of the environment using the movement information. The electronic device includes a second specifying unit that specifies a time-series change of the resting pulse rate using the resting pulse rate. The electronic device includes a determination unit that determines a health state of the user based on the regularity and a time-series change in the resting pulse rate.

According to one embodiment, the accuracy of health management can be improved.

FIG. 1 is a diagram illustrating an example of the overall configuration of a system according to an embodiment. FIG. 2 is a diagram illustrating a hardware configuration example of the sensor terminal according to the embodiment. FIG. 3 is a functional block diagram of a functional configuration example of the system according to the embodiment. FIG. 4 is a flowchart illustrating a flow of processing according to the embodiment. FIG. 5 is a flowchart showing the flow of basic information calculation processing (center value, cumulative value). FIG. 6 is a flowchart showing the flow of the basic information calculation process (amplitude value). FIG. 7 is a flowchart showing the flow of analysis data calculation processing. FIG. 8 is a flowchart showing the flow of regularity analysis processing. FIG. 9 is a flowchart showing the flow of the date interval check process. FIG. 10 is a flowchart showing the flow of the reference value analysis process.

Hereinafter, embodiments of an electronic apparatus, a determination method, and a determination program according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

[overall structure]
FIG. 1 is a diagram illustrating an example of the overall configuration of a system according to an embodiment. As shown in FIG. 1, in this system, a sensor terminal 10, a communication terminal 20, a user terminal 30, a platform 40, and a management server 50 are connected to be communicable with each other via a communication network such as the Internet. That is, this system is a cloud system in which the platform 40 and the management server 50 are installed on the cloud, and is a system that determines the health state of the user who uses the sensor terminal 10.

The sensor terminal 10 is a wearable terminal worn on a user's arm or the like, and includes various sensors such as an acceleration sensor, a temperature / humidity sensor, and an optical sensor. The sensor terminal 10 transmits sensor values measured by various sensors to the communication terminal 20 using near field communication such as Bluetooth (registered trademark) or NFC (Near Field Radio Communication).

The communication terminal 20 is a gateway for communication between the sensor terminal 10 and each server on the cloud, and is a mobile terminal such as a smartphone or a mobile phone. The communication terminal 20 receives the sensor value from the sensor terminal 10 using short-range wireless communication and stores it in the platform 40. Moreover, the communication terminal 20 has a cough sensor, detects the cough of the user holding the communication terminal 20, and stores the detected date and time and the number of detections in the platform 40 in association with each other.

The user terminal 30 is an electronic device such as a smartphone, a mobile phone, a personal computer, or a server. The user terminal 30 accesses the platform 40 using a Web browser or the like, and browses a processing result by the management server 50 described later.

The platform 40 is a database server that is connected to various devices via a network and stores various data. For example, the platform 40 stores the sensor value transmitted from the communication terminal 20 in the DB, and stores the processing result by the management server 50 in the DB.

The management server 50 acquires various sensor values from the platform 40, and performs accurate health management in consideration of the characteristics of the individual to be monitored (user) and the influence of environmental information such as temperature and humidity.

In such a state, the management server 50 acquires exercise information regarding the user's exercise or the environment in which the user exercises, and user vital information. The management server 50 identifies regularity of the user's exercise or environment using the exercise information. And the management server 50 specifies the time-sequential change of the said vital information using vital information. Thereafter, the management server 50 determines the health status of the user based on regularity and time-series changes in vital information.

For example, is the management server 50 performing a healthy life according to a comparison result between a combination of vital history information such as a pulse rate and the number of coughs and environmental information corresponding to the history information and a predetermined condition? Is output and a warning is output.

[Hardware configuration]
Next, the hardware configuration of each device in the system will be described. Since each device is common in that it has a processor and a memory, here, the sensor terminal 10 will be described as an example. The communication terminal 20 has a cough sensor for detecting cough, but it may be a general cough sensor, and executes an algorithm for detecting cough using acceleration or the like. Also good.

FIG. 2 is a diagram illustrating a hardware configuration example of the sensor terminal 10 according to the embodiment. As shown in FIG. 2, the sensor terminal 10 includes an optical sensor 10a, a temperature / humidity sensor 10b, an acceleration sensor 10c, a short-range communication unit 10d, a memory 10e, and a processor 10f.

The optical sensor 10a is a sensor that measures a user's pulse rate. For example, the optical sensor 10a emits light, periodically measures the pulse rate using the reflected wave, and outputs the pulse rate to the processor 10f. Here, the optical sensor 10a is exemplified as the sensor for measuring the pulse rate, but the present invention is not limited to this, and other sensors such as a pulse sensor capable of measuring the pulse can also be employed.

The temperature / humidity sensor 10b periodically measures indoor (outdoor) or outdoor temperature (air temperature) and humidity in which the user is present, and outputs the result to the processor 10f. The acceleration sensor 10c is a sensor that detects an acceleration value (m / s ² ), for example, a three-axis sensor. For example, the acceleration sensor 10c measures acceleration values (acceleration vectors) for the x-axis, y-axis, and z-axis, and outputs the measurement results to the processor 10f.

The near field communication unit 10d is a communication interface that performs near field communication such as Bluetooth (registered trademark) or NFC. The memory 10e is a storage device that stores programs and data. Examples of the memory 10e include RAM (Random Access Memory) such as SDRAM (Synchronous Dynamic Random Access Memory), ROM (Read Only Memory), flash memory, and the like.

The processor 10f reads out and executes a program for executing processing to be described later from the memory 10e and starts various processes. Examples of the processor 10f include a CPU (Central Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), a PLD (Programmable Logic Device), and the like.

[Function configuration]
Next, the functional configuration of each device in the system will be described. The communication terminal 20 has the same functional configuration as that of a general smartphone, and the user terminal 30 and the platform 40 have the same functional configuration as that of a general server device. Here, the sensor terminal 10 and the management server 50 will be described.

(Functional configuration of sensor terminal 10)
FIG. 3 is a functional block diagram of a functional configuration example of the system according to the embodiment. As illustrated in FIG. 3, the sensor terminal 10 includes a sensor communication unit 11, a communication unit 12, a storage unit 13, and a control unit 14. For the sake of explanation, FIG. 3 shows the system configuration of FIG. 1 in a simplified manner.

The sensor communication unit 11 is a processing unit that controls communication with the optical sensor 10a, the temperature / humidity sensor 10b, and the acceleration sensor 10c of the sensor terminal 10, and is, for example, a sensor driver. The sensor communication unit 11 receives sensor values from each sensor and outputs them to the control unit 14.

The communication unit 12 is a processing unit that controls communication with the communication terminal 20, and is, for example, a communication interface. The communication unit 12 transmits various values generated by the control unit 14 to the platform 40. The transmission timing can be periodically executed, can be executed by a user operation, and can be arbitrarily changed.

The storage unit 13 is a storage device that stores various types of information, and corresponds to the memory 10e in FIG. For example, the storage unit 13 stores various sensor values received by the sensor communication unit 11 and various values generated by the control unit 14.

The control unit 14 is a processing unit that controls the entire sensor terminal 10, and is, for example, a processor. The control unit 14 includes a pulse calculation unit 15, a motion processing unit 16, and a temperature / humidity processing unit 17. For example, the pulse calculation unit 15, the motion processing unit 16, and the temperature / humidity processing unit 17 are an example of an electronic circuit such as a processor or an example of a process executed by the processor.

The pulse calculation unit 15 is a processing unit that calculates a resting pulse rate that is a pulse rate calculated when the amount of exercise is equal to or less than a predetermined value. Specifically, the pulse calculation unit 15 acquires the pulse rate measured by the optical sensor 10 a via the sensor communication unit 11. The pulse calculation unit 15 stores it in the storage unit 13 in association with the measured date and time.

Further, the pulse calculation unit 15 receives information in which the date and time and the amount of exercise of the user are associated from the exercise processing unit 16. Then, for each date and time when the pulse rate is measured, the pulse calculation unit 15 extracts the pulse rate calculated at a timing when the amount of exercise is equal to or less than a predetermined value as a resting pulse rate. Thereafter, the pulse calculation unit 15 stores the date and time and the resting pulse rate in association with each other in a predetermined storage unit of the platform 40. The pulse calculation unit 15 can also store the measurement date and time of the pulse rate in association with the pulse rate in a predetermined storage unit of the platform 40.

The exercise processing unit 16 is a processing unit that identifies the user's exercise status. Specifically, the motion processing unit 16 acquires the acceleration value measured by the acceleration sensor 10 c via the sensor communication unit 11. Then, the motion processing unit 16 calculates an exercise intensity that is captured as the magnitude of the motion that occurred during the exercise from the acquired acceleration value. Thereafter, the exercise processing unit 16 stores the measurement date and time and the exercise intensity in the storage unit 13 in association with each other. In addition, the exercise processing unit 16 stores the measurement date and time and the exercise intensity in association with each other in a predetermined storage unit of the platform 40.

For example, the motion processing unit 16 can use acceleration as the exercise intensity. The exercise processing unit 16 can also use METs (Metabolic equivalents) indicating how many times the metabolism (calorie consumption) of the resting state is performed when performing an activity or exercise. For example, the motion processing unit 16 can calculate METs by multiplying the acceleration by a predetermined coefficient. The exercise processing unit 16 can use an activity amount as the exercise intensity. For example, the exercise processing unit 16 calculates the amount of activity (METs · time) as exercise intensity (METs) × time.

Also, the exercise processing unit 16 can measure the number of steps using an acceleration or the like as an example of the amount of activity of the user, and can store the measurement date / time and the measured number of steps in the platform 40 in association with each other. For the number of steps, a step number sensor, a pedometer or the like can be adopted.

The temperature / humidity processing unit 17 is a processing unit that identifies temperature and humidity as user environment information. Specifically, the temperature / humidity processing unit 17 acquires the temperature and humidity measured by the temperature / humidity sensor 10 b via the sensor communication unit 11. Then, the temperature / humidity processing unit 17 corrects the acquired temperature and humidity by a general known method. Thereafter, the temperature / humidity processing unit 17 stores the measurement date and time and the measurement value (temperature or humidity) in the storage unit 13 in association with each other. The temperature / humidity processing unit 17 stores the measurement date and time and the measurement value (temperature or humidity) in a predetermined storage unit of the platform 40 in association with each other.

Further, the temperature / humidity processing unit 17 calculates the WBGT of the measurement date / time using the measured temperature and humidity, stores the measurement date / time and the WBGT in association with each other in the storage unit 13, and stores the predetermined storage in the platform 40. Store in the department.

For example, the temperature / humidity processing unit 17 can calculate the WBGT using a calculation formula designated in advance. For example, the temperature / humidity processing unit 17 can calculate “WBGT = α × temperature + β × humidity (α and β are predetermined counts)”.

The temperature / humidity processing unit 17 can also use a table that uniquely identifies the WBGT from the temperature and humidity. For example, the temperature / humidity processing unit 17 can store the association in which the humidity and the temperature are associated with each other in the storage unit 13 or the like, and can uniquely specify the WBGT from the measured humidity and temperature. For example, when the humidity is 70% and the temperature is 20 ° C., the temperature / humidity processing unit 17 determines WBGT as X2. Note that numerical examples of WBGT are 23 and 25.

(Functional configuration of the management server 50)
As illustrated in FIG. 3, the management server 50 includes a communication unit 51, a storage unit 52, and a control unit 53. The communication unit 51 is a processing unit that controls communication with other devices such as the platform 40, and is, for example, a communication interface. The communication unit 51 receives various types of information from the platform 40 and transmits various types of information generated by the control unit 14 to the platform 40.

The storage unit 52 is a storage device that stores various types of information, such as a memory or a hard disk. The storage unit 52 stores an analysis data DB 52a and an analysis result DB 52b.

The analysis data DB 52a is a database for storing analysis data used for user health management. Specifically, the analysis data DB 52a stores data acquired from the platform 40, data generated by the control unit 53 described later, intermediate data, and the like.

The analysis result DB 52b is a database that stores the estimation result of the user's health condition. Specifically, various analysis results generated by the control unit 53 to be described later, estimation results indicating the health state of each user, and the like are stored.

The control unit 53 is a processing unit that controls the entire management server 50, and is, for example, a processor. The control unit 53 includes an acquisition unit 54, a data extraction unit 55, a regularity analysis unit 56, a reference value analysis unit 57, a rhythm analysis unit 58, and a warning unit 59. For example, the acquisition unit 54, the data extraction unit 55, the regularity analysis unit 56, the reference value analysis unit 57, the rhythm analysis unit 58, and the warning unit 59 are an example of an electronic circuit such as a processor or an example of a process executed by the processor. is there.

The acquisition unit 54 is a processing unit that acquires various data from the platform 40, outputs the data to each processing unit of the control unit 53, and stores the data in the storage unit 52. For example, when the acquisition unit 54 receives an instruction to start the estimation process of the user's health status from an administrator or the like, the acquisition unit 54 obtains information such as the pulse rate, resting pulse rate, exercise intensity, WBGT, and cough count generated by the sensor terminal 10. , Acquired from the platform 40 and stored in the analysis data DB 52a.

The data extraction unit 55 is a processing unit that extracts data used for regularity analysis, reference value analysis, and rhythm analysis from various sensor values acquired by the sensor terminal 10. Specifically, when an instruction to start processing is given, the data extraction unit 55 reads various data from the analysis data DB 52a. Then, the data extraction unit 55 extracts various data to be used for analysis in accordance with conditions specified in advance by an administrator or the like, and stores the extraction results in the analysis data DB 52a.

Here, as an extraction example of each information extracted by the data extraction unit 55, basic information and analysis data will be described with specific examples.

(Example of basic information (center value) extraction)
The data extraction unit 55 calculates the central value of the time series change of the resting pulse rate every 24 hours. For example, the data extraction unit 55 acquires the resting pulse rate for the past 90 days from the current time from the analysis data DB 52a. Subsequently, the data extraction unit 55 classifies the acquired resting pulse rate by dividing it every 24 hours from the current time. And the data extraction part 55 calculates the average of the pulse rate at rest for every 24 hours, and makes it the center value for every 24 hours. Thereafter, the data extraction unit 55 stores the calculated center value of the resting pulse rate every 24 hours in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the central value of the resting pulse rate on August 1 and the central value of the resting pulse rate at each time from 0:00 to 24:00 on August 1. The data extraction unit 55 calculates these data for 90 days worth of data. If the resting pulse rate is 0 in 24 hours, the center value is 0.

(Example of basic information (amplitude value) extraction)
The data extraction unit 55 calculates the amplitude value of the time series change of the resting pulse rate every 24 hours. For example, the data extraction unit 55 acquires the resting pulse rate for the past 90 days from the current time from the analysis data DB 52a. Subsequently, the data extraction unit 55 classifies the acquired resting pulse rate by dividing it every 24 hours from the current time. And the data extraction part 55 calculates | requires the maximum value and minimum value of the time-sequential change of the pulse rate at rest for every 24 hours, and calculates the difference value of the adjacent minimum value and maximum value. Here, when there are a plurality of difference values, the data extraction unit 55 calculates the average value and stores it as an amplitude value in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the amplitude value of the resting pulse rate on August 1 and the amplitude value of the resting pulse rate at each time from 0:00 to 24:00 on August 1. The data extraction unit 55 calculates these data for 90 days worth of data. If there is no pulse rate at rest in 24 hours, or if there is no amplitude and a maximum or minimum value cannot be obtained, the amplitude value is set to zero.

(Extraction example of basic information (cumulative cough))
The data extraction unit 55 calculates the cumulative value of the number of coughs every 24 hours. For example, the data extraction unit 55 acquires cough detection data for the past 90 days from the current time from the analysis data DB 52a. At this time, the data extraction unit 55 counts the number of coughs on each day for 90 days, and also acquires the number of coughs per hour for each of 90 days. Subsequently, the data extraction unit 55 classifies the number of coughs by dividing it every 24 hours from the current time. Thereafter, the data extraction unit 55 calculates a cumulative value of the calculated number of coughs every 24 hours and stores it in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the cumulative value of the number of coughs on August 1 and the cumulative value of the number of coughs at each time from 0:00 to 24:00 on August 1. The data extraction unit 55 calculates these data for 90 days worth of data.

(Example of extracting basic information (cumulative number of steps))
The data extraction unit 55 calculates the cumulative value of the number of steps for every 24 hours. For example, the data extraction unit 55 acquires detection data of the number of steps for the past 90 days from the current time from the analysis data DB 52a. At this time, the data extraction unit 55 counts the number of steps for each day for 90 days, and also acquires the number of steps for each hour for 90 days. Subsequently, the data extraction unit 55 classifies the number of steps by dividing the number of steps every 24 hours from the current time. Thereafter, the data extraction unit 55 calculates the cumulative value of the calculated number of steps every 24 hours and stores it in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the cumulative value of the number of steps on August 1 and the cumulative value of the number of steps on each time from 0:00 to 24:00 on August 1. The data extraction unit 55 calculates these data for 90 days worth of data.

(Example of extracting data for analysis)
Next, an example of calculating an average value of each data used for analysis using the basic information and various data stored in the analysis data 52a will be described. In addition, the numerical example illustrated here is an illustration to the last, and can change a setting arbitrarily.

First, the data extraction unit 55 uses the data calculated from the basic information (center value) to calculate the average center value of the past 90 days and the average center of the past three days regarding the time series change of the resting pulse rate every 24 hours. Value and the average center value of the past one day are calculated and stored in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the average value for the past 90 days from the current time and sets it as the “average center value for the past 90 days”. Further, the data extraction unit 55 calculates an average value for the past three days from the current time and sets it as “average center value for the past three days”. Further, the data extraction unit 55 calculates an average value for the past one day from the current time, and sets it as “average center value for the past one day”. If the data calculated with the basic information (center value) includes an invalid value of 0, the average is calculated with the number of remaining cases excluding the corresponding data.

Subsequently, the data extraction unit 55 uses the data calculated with the basic information (amplitude value), the average amplitude value for the past 90 days and the average for the past three days for the time series change of the resting pulse rate every 24 hours. The amplitude value and the average amplitude value for the past one day are calculated and stored in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the amplitude value for the past 90 days from the current time and sets it as the “average amplitude value for the past 90 days”. In addition, the data extraction unit 55 calculates an average value for the past three days from the current time, and sets it as “average amplitude value for the past three days”. Further, the data extraction unit 55 calculates an average value for the past one day from the current time and sets it as “average amplitude value for the past one day”. If the data calculated with the basic information (amplitude value) contains an invalid value of 0, the average is calculated with the number of remaining cases excluding the corresponding data.

Subsequently, the data extraction unit 55 uses the data calculated from the basic information (cumulative cough), the average value of the past 90 days, the average value of the past three days, about the cumulative value of the number of coughs every 24 hours, The average value for the past one day is calculated and stored in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the average value of cumulative values of cough for the past 90 days from the current time. In addition, the data extraction unit 55 calculates an average value of cumulative values of cough for the past three days from the current time. In addition, the data extraction unit 55 calculates an average value of cumulative values of cough for the past one day from the current time.

Subsequently, the data extraction unit 55 uses the data calculated with the basic information (accumulation of the number of steps), the average value of the past 90 days, the average value of the past three days, The daily average value is calculated and stored in the analysis data DB 52a.

For example, the data extraction unit 55 calculates the average number of steps for the past 90 days from the current time. Further, the data extraction unit 55 calculates an average value of the number of steps in the past three days from the current time. Further, the data extraction unit 55 calculates the average value of the number of steps in the past one day from the current time.

3, the regularity analysis unit 56 is a processing unit that identifies regularity of the user's exercise or environment. Specifically, the regularity analysis unit 56 specifies the regularity of each user regarding the number of steps, the amount of activity, the WBGT, and the like using the basic information, analysis data, and the like calculated by the data extraction unit 55. Here, the regularity analysis of the number of steps will be described as an example.

For example, the regularity analysis unit 56 extracts all data in the 7 am range from the data for the past 30 days calculated by the basic information (cumulative number of steps), and determines a determination threshold (for example, 20 steps) from each value. ) Is subtracted. Subsequently, the regularity analysis unit 56 extracts all dates having “− (minus)” in the calculated value, and determines “regular” when the date intervals are substantially the same (for example, within ± 2 days). For example, if the date interval varies within ± 3 days or less, for example, it is judged as “short-term (within 5 days) irregular”, and if it is over 6 days, it is determined as “long-term (6 days or more). It is determined as “irregular”.

Note that the regularity analysis unit 56 determines a long-term irregularity if there is at least six days or more, and a short-term irregularity if there is no more than six days and there is at least three days. The regularity analysis unit 56 stores the regularity of the regularity analysis data of the number of steps (7 am) in the analysis result DB 52b.

Further, the regularity analysis unit 56 extracts all data in the 3 pm range from the data for the past 30 days calculated by the basic information (cumulative number of steps), and determines a determination threshold (for example, 20 steps) from each value. ) Is subtracted. Subsequently, the regularity analysis unit 56 extracts all dates having “− (minus)” in the value calculated here, and determines that the date interval is almost the same (for example, within ± 2 days) as “regular”. However, if the date interval varies within ± 3 days or less, it is judged as “short-term (less than 5 days) irregular”, and if it is more than 6 days, “long-term (more than 6 days) irregularity” "Rule". Then, the regularity analysis unit 56 stores the regularity of the step count regularity analysis data (3 pm) in the analysis result DB 52b.

In addition, the regularity analysis unit 56 extracts all data in the midnight range from the data for the past 30 days calculated by the basic information (cumulative number of steps), and determines a determination threshold value (for example, 20 steps) from each value. ) Is subtracted. Subsequently, the regularity analysis unit 56 extracts all dates having “− (minus)” in the value calculated here, and determines that the date interval is almost the same (within ± 2 days) as “regular”. If the date interval varies within ± 3 days or less, it will be judged as “short-term (within 5 days) irregular”, and if it is over 6 days, “long-term (within 6 days) irregular” Is determined. The regularity analysis unit 56 stores the regularity of the step count regularity analysis data (0:00 am) in the analysis result DB 52b.

Here, the regularity analysis unit 56 determines that there is an irregularity when there is a “long-term (6 days or more) irregularity” in the above-described determination of the regularity of the number of steps, and the regularity analysis data of the number of steps Are stored in the analysis result DB 52b.

Referring back to FIG. 3, the reference value analysis unit 57 is a processing unit that analyzes a health abnormality by specifying a time-series change of the vital information using the vital information and comparing it with the reference value. Specifically, the reference value analysis unit 57 performs reference value analysis on cough, the number of steps, the amount of activity, and the like using the basic information and analysis data calculated by the data extraction unit 55. Here, as an example, reference value analysis of cough and number of steps will be described.

(Reference value analysis of cough)
For example, the reference value analysis unit 57 subtracts “the average value of the cumulative value of the cough frequency every 24 hours for the past 90 days” from the “average value of the cumulative value of the cough frequency every 24 hours” for the past 90 days. Then, it is stored in the analysis result DB 52b as “difference between the current average value and the long-term average value” of the reference value analysis data of the number of coughs.

Further, the reference value analysis unit 57 subtracts “the average value of the cumulative value of the cough frequency every 24 hours for the past one day” from the “average value of the cumulative value of the cough frequency for every 24 hours” for the past three days. Then, it is stored in the analysis result DB 52b as “difference between the current average value and the short-term average value” of the reference value analysis data of the number of coughs.

Further, the reference value analysis unit 57 calculates a difference between the “difference between the current average value and the long-term average value” and the “difference between the current average value and the short-term average value”, and a difference equal to or greater than a threshold (for example, +5) is calculated. It is determined whether or not there is. Here, when there is a difference greater than or equal to the threshold, the reference value analysis unit 57 stores the difference in the cough regularity analysis data in the analysis result DB 52b.

In addition, the reference value analysis unit 57 includes “an average value for the past 90 days of the cumulative value of the number of coughs every 24 hours”, “an average value of the cumulative value of the number of coughs every 24 hours for the past three days”, “ The cough count threshold value (for example, 10 times) is subtracted from each of the “average value of the past 1 day of the cumulative value of cough counts every 24 hours”. Then, the reference value analysis unit 57 “difference between long-term average value and threshold value”, “difference between short-term average value and threshold value”, “difference between current average value and threshold value” in the reference value analysis data 1 of the number of coughs Is stored in the analysis result DB 52b.

Then, the reference value analysis unit 57 indicates that each of “difference between long-term average value and threshold value”, “difference between short-term average value and threshold value”, and “difference between current average value and threshold value” is “+ (plus)”. It is determined whether or not. When there are two or more “+ (plus)” values, the reference value analysis unit 57 stores the difference in the cough regularity analysis data in the analysis result DB 52b.

(Regularity analysis of the standard value of the number of steps)
For example, the reference value analysis unit 57 subtracts “the average value of the accumulated value of the number of steps every 24 hours in the past 90 days” from “the average value of the accumulated value of the number of steps every 24 hours” to calculate the number of steps. It is stored in the analysis result DB 52b as “difference between current average value and long-term average value” of the reference value analysis data.

Further, the reference value analysis unit 57 subtracts the “average value of the cumulative value of the number of steps every 24 hours for the past three days” from the “average value of the cumulative value of the number of steps for every 24 hours” to calculate the number of steps. It is stored in the analysis result DB 52b as “difference between the current average value and the short-term average value” of the reference value analysis data.

Further, the reference value analysis unit 57 sets the difference between the current average value and the long-term average value and the difference between the current average value and the short-term average value as threshold values (for example, −6000). ) It is determined whether it is the following value. Here, when there is a difference equal to or smaller than the threshold value, the reference value analysis unit 57 stores the difference in the reference value analysis data of the number of steps in the analysis result DB 52b.

In addition, the reference value analysis unit 57 includes “an average value for the past 90 days of the cumulative value of the number of steps every 24 hours”, “an average value of the accumulated value of the number of steps every 24 hours for the past three days”, “ A step count threshold value (for example, 8000 times) is subtracted from each of the “average value of the accumulated number of steps in the past one day”. Then, the reference value analysis unit 57 includes “difference between long-term average value and threshold”, “difference between short-term average value and threshold”, and “difference between current average value and threshold” of the reference value analysis data 1 of the number of steps. And stored in the analysis result DB 52b.

Then, the reference value analysis unit 57 determines that “difference between long-term average value and threshold value”, “difference between short-term average value and threshold value”, and “difference between current average value and threshold value” are “− (minus)”. It is determined whether or not. When there are two or more “− (minus)” values, the reference value analysis unit 57 stores the difference alarm of the reference value analysis data of the number of steps in the analysis result DB 52b.

The rhythm analysis unit 58 is a processing unit that analyzes circadian rhythms using time-series changes in the pulse rate at rest and analyzes health abnormalities. Specifically, the rhythm analysis unit 58 analyzes the circadian rhythm of the resting pulse rate using the basic information and analysis data calculated by the data extraction unit 55, and stores the analysis result in the analysis result DB 52b. .

For example, the rhythm analysis unit 58 determines that each of the above-described “average center value for the past 90 days”, “average center value for the past three days”, and “average center value for the past one day” is in a certain range (for example, 10 bpm). ). Then, the rhythm analysis unit 58 stores the determination result in the analysis result DB 52b. Further, when there is a value exceeding a certain range, the rhythm analysis unit 58 stores it in the analysis result DB 52b as “alarm” of the circadian rhythm analysis data of the central value.

Further, the rhythm analysis unit 58 has a predetermined range (for example, 10 bpm) of the above-mentioned “average amplitude value for the past 90 days”, “average amplitude value for the past 3 days”, and “average amplitude value for the past 1 day”. ). Then, the rhythm analysis unit 58 stores the determination result in the analysis result DB 52b. When there is a value exceeding a certain range, the rhythm analysis unit 58 stores the amplitude value in the analysis result DB 52b as an “alarm” of circadian rhythm analysis data.

The warning unit 59 is a processing unit that issues a warning to the user according to the analysis result by the control unit 53. Specifically, the warning unit 59 reads the analysis results by the regularity analysis unit 56, the reference value analysis unit 57, and the rhythm analysis unit 58 with reference to the analysis result DB 52b. Then, when an alarm is registered as an analysis result, the warning unit 59 transmits a warning message to a pre-designated mail address or transmits a warning message to a display or the like.

The warning unit 59 can also change the warning message depending on the type of alarm and the number of alarms. For example, when an alarm for circadian rhythm analysis data is registered, the warning unit 59 transmits a circadian rhythm warning message. Further, the warning unit 59 can classify warning levels into five levels according to the number of alarms, and can transmit a message corresponding to each warning level.

[Process flow]
Next, each process described above will be described. Here, the overall processing flow, extraction of basic information, calculation of analysis data, analysis of regularity, analysis of standardity, and analysis of circadian rhythm will be described.

(Overall processing)
FIG. 4 is a flowchart illustrating a flow of processing according to the embodiment. As shown in FIG. 4, the management server 50 sets a reference date and time (S101), and executes extraction of the corresponding data (S102). For example, the management server 50 uses the data sensed by the sensor terminal 10 as input data, and sets the date and time of the top data of the number of steps in the input data as the reference current date and time. Also, the management server 50 reads the data from the file, extracts the data for 89 days and 23 hours from the current date, adds the input data to the extracted data, overwrites the file with the added data, and outputs the extracted data To do.

Subsequently, when the input data is for a predetermined number of days (for example, 90 days) (S103: Yes), the management server 50 executes basic information (center value) calculation processing (S104), and the basic information (amplitude value). ) Is calculated (S105), and basic information (cumulative value) is calculated (S106). In addition, if demonstrated in the said example, in S103 to S105, a calculation process will be performed for the pulse rate at rest. In S106, calculation processing is executed for the number of coughs and the number of steps. If there is no input data for a predetermined number of days (for example, 90 days) (S103: No), the process ends.

Then, the management server 50 executes analysis data calculation processing (S107). In the above example, in S107, calculation processing is executed for the center value and amplitude value of the resting pulse rate, the cumulative number of coughs, and the cumulative number of steps.

Thereafter, the management server 50 executes regularity analysis processing (S108), reference value analysis processing (S109), and circadian rhythm analysis processing (S110).

(Basic information calculation processing: center value / cumulative value)
Next, the basic information calculation process for each of the center value and the accumulated value will be described. FIG. 5 is a flowchart showing the flow of basic information calculation processing (center value, cumulative value).

As shown in FIG. 5, the data extraction unit 55 generates a parent list and a child list (S201), and when there is unprocessed data (S202: Yes), selects the unprocessed data (S203). For example, when there is data every 24 hours, the data extraction unit 55 selects unprocessed data. Also, here, initial values are set in the parent list and the child list, and the reference date and time is initially set.

Subsequently, when the date (or date / time) of the selected data is different from the date of the child list (S204: Yes), the data extraction unit 55 adds the data of the child list to the parent list, and creates a new child list. The target data is added to the child list (S206).

Thereafter, the data extraction unit 55 returns to S202 and repeats the subsequent processing. In S204, if the date (or date) of the selected data is the same as the date of the child list (S204: No), the data extraction unit 55 executes S206 without executing S205. Note that the processing from S201 to S206 is referred to as 24-hour classification processing for classifying the sensing data to be analyzed.

When there is no unprocessed data in S202 (S202: No), the data extraction unit 55 determines whether there is data for 24 hours (S207). Here, if there is no data for 24 hours (S207: No), the process is terminated.

On the other hand, if there is data for 24 hours (S207: Yes), the data extraction unit 55 determines whether there is data that has not been processed by the basic information calculation process (S208).

Then, when there is unprocessed data for basic information calculation processing (S208: Yes), the data extraction unit 55 extracts unprocessed data (S209), and sets the value of the extracted unprocessed data as a total value. Add (S210). Thereafter, the data extraction unit 55 repeats S208 and subsequent steps.

On the other hand, when there is no unprocessed data for the basic information calculation process (S208: No), the data extraction unit 55 calculates the total value or calculates the average value as the center value (S211).

(Basic information calculation processing: amplitude value)
Next, basic information calculation processing will be described with respect to amplitude values. FIG. 6 is a flowchart showing the flow of the basic information calculation process (amplitude value).

As shown in FIG. 6, the data extraction unit 55 executes the 24-hour classification process (S301), and then sets a reference value (S302). For example, the data extraction unit 55 acquires the top data as the reference value.

Subsequently, when there is data for 24 hours (S302: Yes), the data extraction unit 55 executes S303 and subsequent steps, and ends the process when there is no data for 24 hours (S302: No).

Then, when there is unprocessed data (S304: Yes), the data extraction unit 55 determines whether or not the value of the data to be processed is larger than the reference value (S305).

Here, when the value of the data to be processed is larger than the reference value (S305: Yes), the data extraction unit 55 determines whether or not the minimum value is being calculated (S306). Then, when the local minimum value is being calculated (S306: Yes), the data extraction unit 55 sets the reference value to the local minimum value, and if there is a local maximum value, adds the difference to the list (S307).

Thereafter, the data extraction unit 55 updates the reference value with the value of the data to be processed (S308), sets the state to the maximum value calculation (S309), and then repeats S304 and subsequent steps. On the other hand, if it is determined in S306 that the minimum value is not being calculated (S306: No), the data extraction unit 55 executes S308 without executing S307.

In S305, when the value of the processing target data is equal to or less than the reference value (S305: No), the data extraction unit 55 determines whether the value of the processing target data is smaller than the reference value (S310).

Then, when the value of the data to be processed is smaller than the reference value (S310: Yes), the data extraction unit 55 determines whether the maximum value is being calculated (S311). If the local maximum is being calculated (S311: Yes), the data extraction unit 55 sets the reference value to the local maximum, and if there is a local minimum, adds the difference to the list (S312).

Thereafter, the data extraction unit 55 updates the reference value with the value of the data to be processed (S313), sets the state to the minimum value calculation (S314), and then repeats S304 and subsequent steps. In S310, when the value of the data to be processed is the same as the reference value (S310: No), S304 and subsequent steps are repeated. On the other hand, when it is determined in S310 that the maximum value is not being calculated (S311: No), the data extraction unit 55 executes S313 without executing S312.

The data extraction unit 55 ends the process when there is no unprocessed data in S304 (S304: No).

(Analysis data calculation process)
Next, analysis data calculation processing will be described. FIG. 7 is a flowchart showing the flow of analysis data calculation processing.

As illustrated in FIG. 7, when there is unprocessed data (S401: Yes), the data extraction unit 55 reads the data, and when the read data is not invalid data (S402: Yes), the value of the read data Is added to the total value (S403), and the number of data is counted up (S404). If the read data is invalid data (S402: No), the data extraction unit 55 executes S405 without executing S403 and S404.

Thereafter, when 1 is set as the number of loops (S405: Yes), the data extraction unit 55 calculates and holds an average value for the past one day (S406). On the other hand, after executing S406, or when 1 is not set for the number of loops (S405: No), the data extraction unit 55 determines whether 3 is set for the number of loops (S407).

Here, when 3 is set as the number of loops (S407: Yes), the data extraction unit 55 calculates and holds an average value for the past three days (S408). On the other hand, after executing S408, or when 3 is not set as the number of loops (S407: No), the data extraction unit 55 repeats S401 and subsequent steps.

Then, when there is no unprocessed data in S401 (S401: No), the data extraction unit 55 calculates and holds an average value for the past 90 days (S409).

(Regularity analysis processing)
Next, regularity analysis processing will be described. FIG. 8 is a flowchart showing the flow of regularity analysis processing. As shown in FIG. 8, the regularity analysis unit 56 extracts data for 30 days from data for 90 days (S501), and determines whether there is data to be processed (S502).

Then, when there is data (S502: Yes), the regularity analysis unit 56 determines whether the data is data at the time of AM7 and no determination result (S503). Here, the regularity analysis unit 56 performs the date interval check (S504) and repeats S502 and subsequent steps when the data is data at AM7 and no determination result (S503: Yes).

On the other hand, the regularity analysis unit 56 determines whether or not the data is data at PM3 and no determination result when the data is not data at AM7 or is not data without a determination result (S503: No). Is determined (S505).

Here, when the data is PM3 and there is no determination result (S505: Yes), the regularity analysis unit 56 performs the date interval check (S506), and then repeats S502 and subsequent steps.

On the other hand, the regularity analysis unit 56 determines whether or not the data is the data at the time of AM0 and without the determination result when the data is not the data at the time of PM3 or the data without the determination result (S505: No). Is determined (S507).

Here, when the data is AM0 and no determination result (S507: Yes), the regularity analysis unit 56 performs a date interval check (S508), and then repeats S502 and subsequent steps. If the data is not at AM0 or is not data without a determination result (S507: No), the regularity analysis unit 56 repeats S502 and subsequent steps without executing S508.

In S502, when there is no data to be processed (S502: No), the regularity analysis unit 56 indicates that the determination result is “No determination result in each time zone” or “No. “Short-circuit irregularity” is set, and “Regular” is set otherwise (S509).

(Date interval check process)
Next, the date interval check process executed in the regularity analysis process will be described. FIG. 9 is a flowchart showing the flow of the date interval check process.

As shown in FIG. 9, the regularity analysis unit 56 subtracts the determination result from the data to be processed (S601), and if the subtraction value is positive (S602: No), the process ends.

On the other hand, if the subtraction value is negative (S602: Yes), the regularity analysis unit 56 calculates a date difference if there is a previous negative date and time (S603). Subsequently, if the date difference is 6 days or more, the regularity analysis unit 56 sets “long-term irregular” as the determination result (S604). The regularity analysis unit 56 holds the subtraction value if it is larger than the difference between the previous dates (S605).

(Standard value analysis processing)
Next, the reference value analysis process will be described. FIG. 10 is a flowchart showing the flow of the reference value analysis process. As shown in FIG. 10, the reference value analysis unit 57 calculates and holds the difference (A) between the average value for one day and the average value for 90 days (S701). Subsequently, the reference value analysis unit 57 calculates and holds the difference (B) between the average value for one day and the average value for three days (S702).

Thereafter, the reference value analysis unit 57 performs determination based on the determination value for the difference (A) and the difference (B), and holds the determination result (S703). For example, the reference value analysis unit 57 determines whether there is a value greater than or equal to the determination value when the determination value is positive among the differences (A) or the difference (B), and determines when the determination value is negative. It is determined whether there is a value less than or equal to the value.

Subsequently, the reference value analysis unit 57 calculates and holds the difference between the average value for 90 days and the threshold (S704), and calculates and holds the difference between the average value for 3 days and the threshold (S705). ) Calculate and hold the difference between the average value for one day and the threshold (S706).

Thereafter, the reference value analysis unit 57 performs determination using the threshold value and each difference, and holds the determination result (S707). For example, the reference value analysis unit 57 determines whether or not there is a value greater than or equal to the determination value when the determination value is positive among the differences from the threshold, and a value equal to or less than the determination value when the determination value is negative. It is determined whether or not there is, and the determined number is held.

Then, the reference value analysis unit 57 executes alarm determination (S708). For example, the reference value analysis unit 57 sets an alarm based on the determination result of the difference from the average value for one day and the determination result of the difference from the threshold.

(Circadian rhythm analysis process)
Finally, the circadian rhythm analysis process will be described. The rhythm analysis unit 58 determines whether all of the average values for 90 days, 3 days, and 1 day are within the determination value. If there is an average value that is not within the determination value or an average value that is not within the determination value, Set alarms based on numbers, etc.

[effect]
As described above, the management server 50 estimates whether a healthy life is being performed according to a comparison result between a combination of vital history information and environmental information corresponding to the history information and a predetermined condition, and warns. Can be output. As a result, since the management server 50 can take into account the characteristics of the individual being monitored and the influence of environmental information such as temperature and humidity, it is possible to improve the accuracy of health management.

Also, it is possible to estimate whether a healthy life is being performed based on the output results of each of the processes executed in a plurality of spans such as the past 1 day, the past 3 days, and the past 90 days. Therefore, if it is determined only by a long span or one span, it is assumed that it cannot be accurately estimated due to a recent poor physical condition. However, by measuring in a plurality of spans, recent physical condition can be taken into consideration, and accuracy can be improved.

The embodiments of the present invention have been described so far, but the present invention may be implemented in various different forms other than the above-described embodiments.

[Numeric]
Each numerical value described in the above embodiment is an example, and can be arbitrarily changed. For example, the average value may be calculated in the past 30 days instead of the past 90 days. In the above embodiment, an example using the pulse rate and the number of coughs as an example of vital information has been described. However, the present invention is not limited to this example. For example, blood pressure, body temperature, sweating amount, etc. Can be processed.

[Processing order]
In addition, the order of the processes described in the above embodiments can be changed as appropriate within a consistent range. For example, the regularity analysis process, the reference value analysis process, and the circadian rhythm analysis process may be executed first, and are in no particular order.

[Process Item]
In the above-described embodiment, the target of regularity determination processing has been described by the number of steps. However, the amount of activity and WBGT can be similarly processed. In addition, although the reference value determination target has been described by taking cough and the number of steps as an example, the amount of activity can be similarly processed.

[period]
In addition, the period in the reference information extraction process described in the above embodiment can be arbitrarily changed. In addition, the reference information is extracted in the past 1 day, the past 3 days, and the past 90 days, and the alarm is determined. Similarly, the reference information is extracted in the past 1 day, the past 5 days, and the past 30 days, and the alarm is determined. It is also possible to output a warning when an alarm is detected in each. By measuring in a plurality of spans in this way, recent physical condition can be taken into consideration, and accuracy can be improved.

[warning]
In the above embodiment, an example is described in which an alarm is determined in each analysis process, and a warning is issued when an alarm is detected. However, the present invention is not limited to this. For example, a warning can be issued only when an alarm is detected in all analysis processes or when a specified number of alarms are detected, and the type of warning is changed depending on the number of alarms in each analysis process. You can also.

[system]
2 and FIG. 3 does not necessarily have to be physically configured as illustrated. That is, it can be configured to be distributed or integrated in arbitrary units. For example, the acquisition unit 54 and the data extraction unit 55 can be integrated. Moreover, the sensor terminal 10 may have each process part which the management server 50 has, and the communication terminal 20 may have each process part which the management server 50 has, and each process part which the sensor terminal 10 has. . Further, all or any part of each processing function performed in each device is realized by a CPU (Central Processing Unit) and a program analyzed and executed by the CPU, or hardware by wired logic. Can be realized as

Of all the processes described in the present embodiment, all or a part of the processes described as being automatically performed can be manually performed. Alternatively, all or part of the processing described as being performed manually can be automatically performed by a known method. In addition, the processing procedure, control procedure, specific name, and information including various data and parameters shown in the above-described document and drawings can be arbitrarily changed unless otherwise specified.

DESCRIPTION OF SYMBOLS 10 Sensor terminal 11 Sensor communication part 12 Communication part 13 Storage part 14 Control part 15 Pulse calculation part 16 Motion processing part 17 Temperature / humidity processing part 50 Management server 51 Communication part 52 Storage part 52a Data DB for analysis
52b Analysis result DB
53 Control Unit 54 Acquisition Unit 55 Data Extraction Unit 56 Regularity Analysis Unit 57 Reference Value Analysis Unit 58 Rhythm Analysis Unit 59 Warning Unit

Claims

An acquisition unit that acquires a user's exercise or exercise information related to an environment in which the user exercises, and a resting pulse rate that is a pulse rate calculated when the user's exercise amount is equal to or less than a predetermined value;
A first specifying unit that specifies the user's exercise or regularity of the environment using the exercise information;
A second specifying unit for specifying a time-series change of the resting pulse rate using the resting pulse rate;
An electronic apparatus comprising: a determination unit that determines the health state of the user based on the regularity and a time-series change of the resting pulse rate.
The acquisition unit acquires the exercise information and the resting pulse rate in a plurality of periods,
The first specifying unit specifies regularity of the exercise information in each of the plurality of periods,
The second specifying unit specifies a time-series change of the resting pulse rate in each of the plurality of periods,
The determination unit detects the rest specified in each of the plurality of periods when a regularity different from other regularity among the regularity of the exercise information specified in each of the plurality of periods is detected. 2. The electronic device according to claim 1, wherein a warning for the health condition of the user is output when a change different from other time series changes is detected among time series changes in time pulse rate. .
The acquisition unit further acquires vital information of the user,
The determination unit calculates a comparison result by comparing the vital information with a predetermined reference value,
The electronic device according to claim 1, wherein the health condition of the user is determined by using the regularity, a time-series change in the resting pulse rate, and the comparison result.
The acquisition unit acquires the number of steps of the user within a predetermined period as the exercise information, acquires the number of coughs of the user within the predetermined period as the vital information,
The first specifying unit specifies regularity of the number of steps in the predetermined period,
The determination unit calculates a comparison result comparing the number of coughs and a threshold, using the regularity of the number of steps, the comparison result of the number of coughs, and the time series change of the resting pulse rate, The electronic device according to claim 3, wherein a health state of the user is determined.
Electronics
Obtaining resting pulse rate which is a pulse rate calculated when the exercise information of the user or the environment in which the user exercises and the amount of exercise of the user is a predetermined value or less,
Using the exercise information to identify the user's exercise or the regularity of the environment,
Using the resting pulse rate to identify a time-series change of the resting pulse rate,
The determination method characterized by performing the process which determines the said user's health state based on the said regularity and the time-sequential change of the said pulse rate at rest.
Electronic equipment,
Obtaining resting pulse rate which is a pulse rate calculated when the exercise information of the user or the environment in which the user exercises and the amount of exercise of the user is a predetermined value or less,
Using the exercise information to identify the user's exercise or the regularity of the environment,
Using the resting pulse rate to identify a time-series change of the resting pulse rate,
A determination program for executing a process for determining a health state of the user based on the regularity and a time-series change of the resting pulse rate.