Control methods, programs, and load analysis systems

Abstract

This invention provides a control method that can send messages to individuals who are determined to be under high load based on activity level data. [Solution] A control method for a load analysis system that outputs a message based on the load of a subject, using an information processing device, a subject user device used by a subject, and a device attached to the subject, the control method comprising: acquiring activity data of the subject measured by the device; determining whether the load of the subject satisfies predetermined conditions based on the activity data; and outputting a message for accepting comments when it is determined that the load of the subject satisfies the predetermined conditions.

Description

Technical Field

[0001] This disclosure relates to a control method, a program, and a load analysis system.

Background Art

[0002] In the information processing method and the like described in Patent Document 1, a stress threshold value of the worker is set in advance using the biological information of the worker and the response information to the work, and it is determined whether the stress is determined according to whether the newly acquired biological information of the worker exceeds the threshold value (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the conventional technology, since response information is acquired from all workers, for example, it is necessary to cause even a worker who does not feel stress that may have a low load to input response information.

Means for Solving the Problems

[0005] In order to solve the above problems, one aspect is a control method of a load analysis system that outputs a message based on the load of a target person using an information processing device, a target person use device used by the target person, and a device attached to the target person, the method including: acquiring activity amount data of the target person measured by the device; determining whether the load of the target person satisfies a predetermined condition based on the activity amount data; and outputting the message for receiving a comment when it is determined that the load of the target person satisfies the predetermined condition.

[0006] To solve the above problems, one embodiment is a control method for a load analysis system comprising an information processing device, a user-accessible device used by a target person, and a device attached to the target person, the control method comprising: causing the device to measure the activity level data of the target person; causing the information processing device to acquire the activity level data; causing the information processing device to determine whether the load of the target person satisfies predetermined conditions based on the acquired activity level data; causing the information processing device to generate a message for receiving comments from the target person when it has determined that the load of the target person satisfies the predetermined conditions; causing the information processing device to transmit the message to the user-accessible device; and causing the user-accessible device to output the message.

[0007] To solve the above problem, one embodiment is a control method for a device attached to a target person, an information processing device that transmits a message, and a target person user device that communicates, wherein the control method acquires activity data of the target person from the device, stores the acquired activity data, determines whether at least one of the amount of data of the stored activity data and a predetermined first timing for communicating with the information processing device satisfies predetermined transmission conditions, transmits the stored activity data to the information processing device if it is determined that the transmission conditions are met, and after the information processing device determines that the load of the target person satisfies predetermined conditions based on the received activity data, receives the message for accepting comments from the target person.

[0008] To solve the above problem, one embodiment of the control method comprises the steps of: causing an information processing device to acquire activity data of a subject measured by a device attached to the subject; determining whether the subject's load meets predetermined conditions based on the activity data; and, if it is determined that the subject's load meets the predetermined conditions, outputting a message to a subject-user device used by the subject for receiving comments from the subject.

[0009] To solve the above problem, one embodiment is a program for which a computer is made to perform the following steps: to cause the computer to acquire activity data of a subject measured by a device attached to the subject; to determine whether the subject's load meets predetermined conditions based on the activity data; and, if it is determined that the subject's load meets the predetermined conditions, to output a message to a subject-user device used by the subject for receiving comments from the subject.

[0010] To solve the above problem, one embodiment of the control method comprises the steps of: causing a user-accessible device used by a target person to acquire activity data of the target person measured by a device attached to the target person; determining whether the target person's load meets predetermined conditions based on the activity data; and, if it is determined that the target person's load meets the predetermined conditions, outputting a message to receive comments from the target person.

[0011] To solve the above problem, one embodiment is a program for which a computer is made to perform the following steps: to make the computer acquire activity data of a subject measured by a device attached to the subject; to make the computer determine whether the subject's load meets predetermined conditions based on the activity data; and to output a message to receive comments from the subject if it is determined that the subject's load meets the predetermined conditions.

[0012] To solve the above problem, one embodiment of the control method comprises the steps of: causing a device attached to a subject to acquire activity data of the subject; determining whether the subject's load meets predetermined conditions based on the activity data; and, if it is determined that the subject's load meets the predetermined conditions, sending a message to a subject-user device used by the subject to receive comments from the subject.

[0013] To solve the above problem, one embodiment is a program that enables a computer attached to a subject to acquire activity data of the subject; to determine whether the subject's workload meets predetermined conditions based on the activity data; and, if it is determined that the subject's workload meets the predetermined conditions, to send a message to a subject-user device used by the subject to receive comments from the subject.

[0014] To solve the above problems, one embodiment is a load analysis system that uses an information processing device, a user device for a subject, and a device attached to the subject to output a message based on the subject's load, the system acquires activity data of the subject measured by the device, determines whether the subject's load satisfies predetermined conditions based on the activity data, and outputs a message to accept comments if it is determined that the subject's load satisfies the predetermined conditions. [Brief explanation of the drawing]

[0015] [Figure 1] This figure shows an example configuration of the first load analysis system according to the first embodiment. [Figure 2] This is a diagram showing an example configuration of the first user-accessible device according to the first embodiment. [Figure 3] This is a diagram showing an example configuration of the first server device according to the first embodiment. [Figure 4] This is a diagram showing an example configuration of the first administrator terminal device according to the first embodiment. [Figure 5A] This figure shows an example of a Table 1A representing information about input measurement data according to the first embodiment. [Figure 5B] This figure shows an example of Table 1B, which represents information regarding input attribute information and hearing data according to the first embodiment. [Figure 5C] This figure shows an example of Table 1C, which represents information regarding analytical materials, individual analysis, and organizational analysis according to the first embodiment. [Figure 6] It is a diagram showing an example of a second table related to personal information according to the first embodiment. [Figure 7A] It is a diagram showing an example of physical high-load time and mental high-load time according to the first embodiment. [Figure 7B] It is a diagram showing an example of a third table related to personal physical high-load time and mental high-load time according to the first embodiment. [Figure 7C] It is a diagram showing an example of a fourth table related to the ranking of physical high-load time and mental high-load time of an organizational unit according to the first embodiment. [Figure 8] It is a diagram showing an example of a first screen related to chat according to the first embodiment. [Figure 9] It is a diagram showing an example of chat summary data according to the first embodiment. [Figure 10] It is a diagram showing an example of a second screen related to a report according to the first embodiment. [Figure 11] It is a diagram showing an example of the procedure of processing performed in the first load analysis system according to the first embodiment. [Figure 12] It is a diagram showing an example of the procedure of processing in detail regarding the upload conditions by the first subject use device according to the first embodiment. [Figure 13] It is a diagram showing an example of the procedure of processing performed in the first load analysis system according to a modification example of the first embodiment. [Figure 14A] It is a diagram showing an example of a schematic functional block of the first load analysis system according to a specific example of the first embodiment. [Figure 14B] It is a diagram showing an example of the relationship of processing related to employee load analysis according to a specific example of the first embodiment. [Figure 15] It is a diagram showing a configuration example of the second load analysis system according to the second embodiment. [Figure 16] It is a diagram showing a configuration example of the second subject use device according to the second embodiment. [Figure 17] It is a diagram showing an example of the procedure of processing performed in the second load analysis system according to the second embodiment. [Figure 18] This figure shows an example configuration of the third load analysis system according to the third embodiment. [Figure 19] This figure shows an example configuration of the third device according to the third embodiment. [Figure 20] This figure shows an example of the processing procedure performed in the third load analysis system according to the third embodiment. [Modes for carrying out the invention]

[0016] The embodiments will be described below with reference to the drawings.

[0017] The first embodiment will be described.

[0018] Figure 1 shows an example of the configuration of the first load analysis system 1 according to the first embodiment. The first load analysis system 1 comprises a first device 11, a first user access device 12, a first server device 13, a first administrator terminal device 14, a first database 15, and a first network 21. Figure 1 also shows the first subject 31 and the first administrator 41. The first subject 31 operates the first subject user device 12. In this embodiment, the first subject 31 owns the first subject user device 12. The first administrator 41 operates the first administrator terminal device 14.

[0019] In this embodiment, the first device 11 and the first user device 12 communicate wirelessly. This communication may be, for example, Bluetooth® Low Energy. In this embodiment, the first user device 12, the first server device 13, the first administrator terminal device 14, and the first database 15 can communicate with each other via the first network 21.

[0020] In this embodiment, the first user access device 12 establishes a communication connection with the first network 21 wirelessly. The first user access device 12 may also establish a communication connection with the first network 21 via, for example, a base station device (not shown). In this embodiment, the first server device 13, the first administrator terminal device 14, and the first database 15 each communicate with the first network 21 via wired connections.

[0021] The network configuration in this embodiment is merely an example, and two or more devices may communicate using any communication method, either wired, wireless, or both. The network may utilize, for example, one or more of the following: LTE (Long Term Evolution), Wi-Fi, Bluetooth® Low Energy, etc. Furthermore, for example, the first device 11 may also communicate with the first network 21 wirelessly, and may communicate with the first user device 12, the first server device 13, the first administrator terminal device 14, and the first database 15 via the first network 21.

[0022] The first device 11 is a wearable device and is attached to the body of the first subject 31. In this embodiment, the first device 11 is directly attached to the skin of one arm of the first subject 31. In addition, any other location on the body may be used as the location on which the first device 11 is attached.

[0023] The first device 11 acquires activity level data from the first subject 31. In this embodiment, the activity data includes heartbeat data and body movement data, but may also include other data. For example, activity data may also include at least one of the following: data on the number of steps taken and data on sleep. In this embodiment, pulsation and pulse refer to essentially the same thing.

[0024] The first device 11 comprises a first pulse measurement unit Q1 and a first body movement detection unit Q2. The first pulse measurement unit Q1 measures the pulse rate of the first subject 31 and acquires pulse data, which is the data of the measurement result. For example, a pulse sensor may be used as the first pulse measurement unit Q1. The pulse sensor may also be called, for example, a pulse meter.

[0025] The first body motion detection unit Q2 detects the body movements of the first subject 31 and acquires body motion data, which is the data of the detection result. For example, an acceleration sensor that detects acceleration may be used as the first motion detection unit Q2. As another example, the first motion detection unit Q2 may be an angular velocity sensor that detects angular velocity. Alternatively, the first motion detection unit Q2 may be two or more sensors that detect different physical quantities, for example.

[0026] In this embodiment, the first device 11 is equipped with a pulse sensor and an acceleration sensor to measure activity data, including heartbeat data. The first device 11 has functions for performing the operations described in this embodiment, and for example, it has a function for communicating wirelessly with the outside. Such a communication function may be provided in, for example, the first pulse measurement unit Q1 and the first body movement detection unit Q2, or it may be provided as a separate functional unit from the first pulse measurement unit Q1 and the first body movement detection unit Q2.

[0027] In this embodiment, the first device 11 for measuring activity data is shown as a single device; however, in other examples, the first device 11 may be composed of multiple devices. As a specific example, if the first device 11 is equipped with multiple sensors, these multiple sensors may be distributed and provided across two or more devices, either in the same number or different numbers. Furthermore, if the first device 11 consists of multiple devices, for example, each device may be attached to a different part of the body of the first subject 31.

[0028] Figure 2 shows an example of the configuration of the first user-accessible device 12 according to the first embodiment. The first user device 12 comprises a first input unit 111, a first output unit 112, a first communication unit 113, a first storage unit 114, and a first control unit 115. The first input unit 111 includes a first operation unit 131. The first output unit 112 includes a first display unit 132.

[0029] The first user device 12 is configured using a computer. The first user device 12 may be, for example, a smartphone, a tablet device, or a personal computer.

[0030] The first input unit 111 has the function of inputting information. For example, the first operation unit 131 receives and inputs the details of the operation performed by the first target person 31. Furthermore, for example, the first input unit 111 may receive information from an external device. The first output unit 112 has the function of outputting information. For example, the first display unit 132 outputs the information to be displayed to the screen. Furthermore, for example, the first output unit 112 may output information to an external device. Here, the first operation unit 131 and the first display unit 132 may be shared, for example, by using a touch panel.

[0031] The first communication unit 113 has the function of performing communications. In this embodiment, the first communication unit 113 is shown separately from the first input unit 111 and the first output unit 112. However, for example, the receiving function of the first communication unit 113 may be included in the functions of the first input unit 111, and the transmitting function of the first communication unit 113 may be included in the functions of the first output unit 112.

[0032] The first memory unit 114 stores information. The first control unit 115 performs various processes or controls on the first user device 12. In this embodiment, the first control unit 115 is equipped with a predetermined processor, such as a CPU (Central Processing Unit), and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the first storage unit 114.

[0033] Figure 3 shows an example of the configuration of the first server device 13 according to the first embodiment. The first server device 13 includes a server input unit 211, a server output unit 212, a server communication unit 213, a server storage unit 214, and a server control unit 215. The names of the parts provided in the first server device 13 are for descriptive purposes only and may be referred to by any other name. The first server device 13 is configured using a computer.

[0034] The server input unit 211 has the function of inputting information. For example, the server input unit 211 receives information from an external device. The server output unit 212 has the function of outputting information. For example, the server output unit 212 outputs information to an external device.

[0035] The server communication unit 213 has the function of performing communication. In this embodiment, the server communication unit 213 is shown separately from the server input unit 211 and the server output unit 212. However, for example, the receiving function of the server communication unit 213 may be included in the functions of the server input unit 211, and the transmitting function of the server communication unit 213 may be included in the functions of the server output unit 212.

[0036] The server memory unit 214 stores information. The server control unit 215 performs various processes or controls in the first server device 13. In this embodiment, the server control unit 215 is equipped with a predetermined processor, such as a CPU, and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the server storage unit 214.

[0037] Figure 4 shows an example of the configuration of the first administrator terminal device 14 according to the first embodiment. The first administrator terminal device 14 includes a management input unit 311, a management output unit 312, a management communication unit 313, a management storage unit 314, and a management control unit 315. The management input unit 311 includes a management operation unit 331. The management output unit 312 includes a management display unit 332. The names of the parts provided in the first administrator terminal device 14 are for descriptive purposes only and may be referred to by any other name.

[0038] The first administrator terminal device 14 is configured using a computer. The first administrator terminal device 14 may be, for example, a smartphone, a tablet device, or a personal computer.

[0039] The management input unit 311 has the function of inputting information. For example, the management operation unit 331 receives and inputs the details of the operation performed by the first administrator 41. Furthermore, for example, the management input unit 311 may receive information from an external device. The management output unit 312 has the function of outputting information. For example, the management display unit 332 outputs the information to be displayed on the screen. Furthermore, for example, the management output unit 312 may output information to an external device. Here, the management operation unit 331 and the management display unit 332 may be unified using, for example, a touch panel.

[0040] The management and communication unit 313 has the function of performing communications. In this embodiment, the management communication unit 313 is shown separately from the management input unit 311 and the management output unit 312. However, for example, the receiving function of the management communication unit 313 may be included in the functions of the management input unit 311, and the transmitting function of the management communication unit 313 may be included in the functions of the management output unit 312.

[0041] The management memory unit 314 stores information. The management control unit 315 performs various processes or controls on the first administrator terminal device 14. In this embodiment, the management control unit 315 is equipped with a predetermined processor, such as a CPU, and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the management memory unit 314.

[0042] The first database 15 stores various types of information. The first database 15 may store, for example, various types of information handled in the first load analysis system 1. The first database 15 is not necessarily required; for example, the necessary information may be stored in each of the first device 11, the first user device 12, the first server device 13, and the first administrator terminal device 14.

[0043] The processing performed in the first load analysis system 1 according to this embodiment will be described below. In this embodiment, we will explain the case where the first load analysis system 1 is applied to a nurse working in a hospital. In this embodiment, the first subject 31 is a nurse, and the first manager 41 is a supervisor or other person who manages the first subject 31.

[0044] In the first load analysis system 1, the pulse information acquired as pulse data is normalized and treated as load information. This pulse information may be, for example, heart rate information, heart rate variability information, or heart rate and heart rate variability information.

[0045] There are no particular limitations on the method used to quantify the load, and various methods may be employed. In this embodiment, as a method for quantifying the load, for example, a method using maximum heart rate (HRmax) as a reference, a method using resting heart rate (HRrest) as a reference, or a method using both of these as a reference may be used. As an example, heart rate activity level may be used. Heart rate activity level is expressed as (heart rate / HRrest). Another example that may be used is the percentage of maximum heart rate (%HRmax). This percentage is expressed as (heart rate / HRmax). Another example that may be used is heart rate reserve (%HR Reserve). Heart rate reserve is expressed as {(heart rate - HRrest) / (HRmax - HRrest)}.

[0046] In the first load analysis system 1, for the first subject 31, if the load data, which is load information based on heart rate, exceeds a threshold, it is determined that the load is high. This threshold may be called, for example, a load reference value. The first load analysis system 1 aggregates the length of time during which the load is determined to be high within a predetermined period. This predetermined period is not particularly limited and may be, for example, one day, the duration of one work period, or one week.

[0047] The first load analysis system 1 calibrates the load reference value. The first load analysis system 1 acquires the resting heart rate or resting RMSSD (Root Mean Square of Successive Differences), which is the basis for calculating the load standard value, for each individual. During this process, the necessary information is measured, and calculations are performed using the necessary formulas. Here, resting heart rate or resting RMSSD may be determined by referring to health checkup data such as blood pressure measurements and electrocardiograms, for example. Furthermore, resting heart rate may be determined, for example, based on a table that correlates age with resting heart rate. Furthermore, resting heart rate or resting RMSSD may be determined, for example, based on the heart rate during periods of rest and stillness. In this embodiment, the load threshold value may be adjusted based on the employee's subjective opinion.

[0048] The first load analysis system 1 distinguishes whether the load is caused by physical or psychological factors based on signals from sensors used to detect body movement data. These sensors are, for example, acceleration sensors. In the first load analysis system 1, the aggregation is separated into physical factors and psychological factors. In the first load analysis system 1, if the load data exceeds a threshold when there is body movement, it is determined that there is a high physical load. On the other hand, in the first load analysis system 1, if the load data exceeds a threshold when there is no physical movement, it is determined that there is a high mental load. Furthermore, the presence or absence of body movement may be determined, for example, based on whether or not there is body movement that meets a predetermined standard.

[0049] In the first load analysis system 1, attribute data representing attributes is attached to the load data. These attributes may include the personal attributes of the employee. This attribute may include the date and time. This attribute may include the work content. Personal attributes include, for example, the organization to which they belong, their job responsibilities, their work style, age, and gender. The work content will allow us to identify what the workload data corresponds to when an employee was performing their duties at each time period.

[0050] The first load analysis system 1 aggregates and visualizes the load status based on attribute data assigned to the load data. The load status may be compiled, for example, by organization or by team. The workload status can be compiled, for example, by assigned task or by role. The workload status may be compiled, for example, by work schedule. Examples of work schedules include day shifts, night shifts, and irregular shifts. The load status may be aggregated, for example, by generation. These generations could include, for instance, younger generations, mid-career generations, or veteran generations.

[0051] The first load analysis system 1 selects interviewees based on load data. In this embodiment, the interview is conducted using a chat robot. For the sake of explanation, in this embodiment, the robot will be referred to as the interview robot, but it may be called by any other name. In this embodiment, the hearing robot may, for example, perform the chat autonomously without using external functions, or it may perform the chat using external functions. Here, the external function may be, for example, an AI such as Artificial Intelligence. Furthermore, a robot is not necessarily required to be used as the function for conducting interviews. Furthermore, while the chat is conducted using text information in this embodiment, it may also be conducted using voice in other examples.

[0052] For example, those ranked highly in terms of workload could be selected as the people to be interviewed. Those selected for interviews may, for example, be chosen based on the time-series changes in their workload. Specifically, individuals whose workload has increased compared to the previous week may be selected as interviewees. Those selected for interviews may be individuals experiencing real-time changes in their system load. Examples of such real-time changes in load include sudden spikes in load.

[0053] The first load analysis system 1 generates personnel allocation support data. This personnel allocation support data includes, for example, advice such as recommending personnel transfers or increases in staff to equalize the workload of individuals or teams. Furthermore, the personnel allocation support data may also be called, for example, personnel allocation advice data.

[0054] Various pieces of information will be explained with reference to Figures 5A, 5B, and 5C. The information shown in Figures 5A, 5B, and 5C is illustrative, and not all of it is necessarily required to be used. Furthermore, information not shown may also be used.

[0055] Figure 5A shows an example of Table 1A Ta1A, which represents information about input measurement data according to the first embodiment. The input measurement data includes information such as pulse-derived load data, stress score, calories burned, steps taken, time spent moving, and time spent not moving.

[0056] Pulse-derived load data is acquired as real-time information, cumulative information, or both. Pulse-derived load data is, for example, a load value normalized by pulse zones and resting pulse rate. Here, pulse zones represent, for example, exercise intensity.

[0057] Stress scores are obtained as real-time information. A stress score is a value that quantifies factors such as pulse rate or sympathetic nervous system activity based on heart rate variability (HRV). Furthermore, accumulating the time spent with a high pulse rate results in the "excitement time" of the heart's balance.

[0058] Calorie expenditure is obtained as cumulative value information. Calorie expenditure, in a broad sense, is the same as load data derived from pulse rate, but it is data that is influenced by factors such as weight, gender, and physical fitness. Physical fitness, in this context, is, for example, muscle mass. The number of steps is obtained as cumulative information.

[0059] The amount of time spent moving your body is recorded as cumulative data. When considering pulse rate, the time spent moving the body is either the time spent in each pulse zone or the exercise time.

[0060] Time spent without physical activity is recorded as cumulative data. When you're not physically active, and you take your pulse rate into account, it represents either a time of relaxation or excitement in terms of mental balance.

[0061] Optional information for the input measurement data includes fatigue level, recovery level, sleep, and sleep score.

[0062] Fatigue levels are obtained as cumulative value information. Fatigue level is a score that represents the cumulative load based on pulse rate or HRV. This cumulative load is, for example, an integral value. Furthermore, fatigue levels decrease after a period of rest or sleep.

[0063] The recovery level is obtained as cumulative value information. The recovery level is a score that represents fatigue recovery based on pulse rate or HRV. The degree of recovery represents, for example, the degree of recovery achieved through rest, relaxation, and especially sleep, and is a value that quantifies the recovery effect.

[0064] Sleep information is obtained as cumulative data. Sleep information includes, for example, total sleep time and the cumulative time spent in light or deep sleep.

[0065] The sleep score is obtained as cumulative value information. The sleep score is a numerical value that quantifies the quality of sleep. A sleep score is a numerical value that quantifies factors such as sleep duration, sleep depth, and the occurrence of REM and WAKE sleep.

[0066] Figure 5B shows an example of Table 1B Ta1B, which represents information about input attribute information and hearing data according to the first embodiment. Input attribute information includes age, gender, height and weight, exercise habits, employment type, affiliated organization, job responsibilities, skill level, position and role, and work history. Exercise habits, for example, represent the level of physical fitness.

[0067] Work arrangements include, for example, day shifts, night shifts, and irregular days off. Organizational affiliations include, for example, departments, sections, and teams. Information about assigned tasks includes, for example, the name of the task or the process involved. Information regarding skill level includes, for example, years of experience. Examples of positions and roles include member, leader, and management.

[0068] Information about work history includes, for example, schedule history information showing what tasks were performed at each time of day. The operational history information may also include details of any trouble events and their timestamps.

[0069] For example, summary information may be used as interview data. The summary may include information such as recent load conditions, circumstances under which high load occurs, causes of high load, requests for load reduction, and suggestions for administrators. Furthermore, as a suggestion to administrators, information such as comments generated by an AI, including a generative AI, regarding how best to handle the situation may be used.

[0070] Figure 5C shows an example of Table 1C Ta1C, which represents information regarding the analytical material, individual analysis, and organizational analysis according to the first embodiment. The data used for analysis includes information such as the duration of high physical exertion, the duration of high mental exertion, the maximum physical exertion, and the maximum mental exertion.

[0071] High-intensity physical activity can be measured by, for example, the total time spent during physical activity when the heart rate exceeds (resting heart rate × 1.5) over a predetermined period. The specified period may be, for example, every hour or every day.

[0072] Examples of periods of high mental stress include time spent without physical activity, meeting the following conditions, and totaling these periods over a predetermined timeframe. These predetermined periods may be, for example, every hour or every day. These conditions include, for example, a pulse rate exceeding (resting pulse rate × 1.2), a sympathetic nervous system index value being above a predetermined threshold, and a parasympathetic nervous system index value being below a predetermined threshold.

[0073] The maximum physical load can be measured, for example, by the load index at the highest load on a given day. This load index corresponds, for example, to the maximum heart rate during work on a given day.

[0074] The maximum mental load could be measured by, for example, the load index at the highest point of the day in question. This load index corresponds, for example, to the maximum heart rate during a day of inactivity.

[0075] Information related to individual analysis includes daily workload trends and increases / decreases, workload levels for each type of work, the effectiveness of breaks, and the accumulation of chronic workload.

[0076] Regarding daily load trends and fluctuations, it is possible to identify, for example, the trends of periods with high load. These periods may include, for example, months or days of the week. Regarding daily workload trends and fluctuations, it is possible to analyze, for example, whether fatigue increases more easily when working consecutive days. It is possible to analyze daily load trends and fluctuations, such as whether the load increases from Monday to Friday.

[0077] Regarding the workload for each type of work, it is possible to identify, for example, which tasks or operations are particularly burdensome for a given employee. Regarding the workload for each type of work task, it is possible to analyze, for example, which processes an employee is good at and which they struggle with.

[0078] Regarding the effects of rest, for example, it is possible to analyze whether the workload is temporarily reduced by taking a break.

[0079] Regarding the accumulation of chronic workload, it is possible to analyze it based on factors such as whether the workload remains high or whether the workload is already high at the start of the work.

[0080] Information for organizational analysis includes comparisons of workload across different departments, across different age groups, across different time periods, across different work styles, and verification of the effectiveness of improvement measures.

[0081] Regarding the comparison of workloads across organizations, it is possible to compare the magnitude of the workload at various levels, such as between workplaces, between processes, or between teams. Regarding comparisons across age groups, for example, it is possible to analyze whether the workload is higher for older age groups. Regarding comparisons between different work patterns, it is possible to compare, for example, day shifts with night shifts, or regular work schedules with irregular work schedules. Regarding the evaluation of the effectiveness of improvement measures, it is possible to analyze improvements in work procedures, resource and workload distribution, and help-related matters.

[0082] Figure 6 shows an example of a second table Ta2 relating to personal information according to the first embodiment. Table 2, Ta2, stores user ID (which identifies the employee), name, age, affiliated organization, job responsibilities, work style, resting pulse rate, resting SDNN (Standard Deviation of Normal and Normal Interval), and chat summary data (a summary of the chat content) in an associated manner. Note that in the example in Figure 6, the description of specific examples for chat summary data has been omitted.

[0083] Here, the information in the second table Ta2 may be stored, for example, in a database that manages employee data, or in another storage device. Furthermore, this database stores, for example, employee attendance data. For example, the first database 15 may be used as the database in question.

[0084] In the example in Figure 6, the information in the second table Ta2 contains attribute information for each employee, such as their affiliated organization and personal age. In the example in Figure 6, chat summary data summarizing the key points of the interview regarding workload status is used, but data containing the results of the interview regarding workload status may be used together with or instead of this data. Note that the information in the second table, Ta2, is illustrative, and it is not necessary to use all of the information, or other information may be used instead.

[0085] Let me explain the load analysis cloud. In this embodiment, the load analysis cloud is a functional unit that performs load analysis in the cloud. In this embodiment, the load analysis cloud is configured, for example, by a first server device 13. As another example, the functional components of the load analysis cloud may be distributed and provided on the first server device 13 and the first administrator terminal device 14. As yet another example, the functional unit of the load analysis cloud may be comprised of a first administrator terminal device 14. In this case, for example, a first server device 13 may not be provided.

[0086] The load analysis cloud receives activity data measured by the first device 11 worn by the first subject 31, who are employees. In this embodiment, the activity data is relayed by a first user device 12, such as a smartphone provided to the employee, and acquired by a load analysis cloud. The load analysis cloud, for example, calculates load threshold values ​​by referencing activity data and employee data.

[0087] Here, we will illustrate three methods for calculating load threshold values: the first load threshold calculation method, the second load threshold calculation method, and the third load threshold calculation method.

[0088] In the first load baseline calculation method, the resting heart rate is calculated based on the heart rate before and after waking up. The resting heart rate is used as the average value over a predetermined period, such as 10 minutes. For example, the following formula may be used: {(Individual load threshold heart rate) = (Resting heart rate) × 1.5}. Specifically, using this formula, if the resting heart rate is 60, the individual load threshold heart rate would be 90. Note that the coefficient 1.5 in the calculation formula is just one example, and other values ​​may be used.

[0089] The second load threshold calculation method uses maximum heart rate. This method corresponds to %HRmax. This method calculates an approximate maximum heart rate based on age. For example, the formula {HRmax=220-(age)} may be used. Alternatively, the functions of the first device 11 may be used to calculate an estimated value of HRmax or to obtain an actual measured value of HRmax. For example, the following formula may be used: [(Individual load threshold heart rate) = {220 - (Age)} × 0.5]. Specifically, using this formula, if the age is 40, the individual load threshold heart rate would be 90. Note that the coefficients 220 and 0.5 in the calculation formula are merely examples, and other values ​​may be used.

[0090] The third load reference value calculation method uses resting heart rate and maximum heart rate. This method is equivalent to the %HRR method, heart rate reserve method, and Karvonen method. As an example, the formula [{(heart rate)-HRrest} / (HRmax-HRrest)>0.4] can be rearranged to {(individual load threshold heart rate)>(HRmax-HRrest)×0.3+HRrest}. Specifically, using this formula, if HRmax is 180 and HRrest is 60, the individual load threshold heart rate will be greater than 96. Note that the coefficient 0.3 in the calculation formula is just one example, and other values ​​may be used.

[0091] This section explains load analysis and result display. In this embodiment, the load analysis cloud considers a heart rate exceeding an individual's load threshold to be high. The load analysis cloud determines the presence or absence of body movement based on signals from acceleration sensors. Furthermore, the load analysis cloud considers high load as physical high load when there is physical movement. On the other hand, when there is no physical movement, the load analysis cloud considers high load as mental high load. Furthermore, as a method for determining whether or not there is body movement, for example, a method may be used in which body movement is determined to be present if it meets a predetermined standard, while otherwise it is determined that there is no body movement.

[0092] Here, as a condition for determining that the physical load is high, at least one of the following may be used: for example, the movement of the first subject 31 based on body movement data is at or above a predetermined first intensity, and the value based on the first subject 31's pulse data is at or above a predetermined first value; or, in the immediately preceding period, the first cumulative time during which the first subject 31's movement is at or above the said first intensity is at or above a predetermined first hour, and the second cumulative time during which the value based on the first subject 31's pulse data is at or above the said first value is at or above a predetermined second hour. Furthermore, as a condition for determining that there is a high mental load, at least one of the following may be used: for example, the movement of the first subject 31 based on body movement data is less than the first intensity, and the value based on the first subject 31's pulse data is greater than or equal to a predetermined second value; or, in the immediately preceding period, the first cumulative time during which the first subject 31's movement is greater than or equal to the first intensity is shorter than the first hour, and the third cumulative time during which the value based on the first subject 31's pulse data is greater than or equal to the second value is greater than or equal to a predetermined third hour.

[0093] In this example, the conditions for determining that there is physical movement and physical load on the first subject 31 are that the movement of the first subject 31, based on the body movement data, is at or above a predetermined intensity, or that the cumulative time during which the movement of the first subject 31, based on the body movement data, is at or above a predetermined intensity is at or above a predetermined time. Similarly, in this example, the conditions for determining that there is no physical movement by the first subject 31 and that the stress is mental are if the movement of the first subject 31 based on the body movement data is less than a predetermined intensity, or if the cumulative time during which the movement of the first subject 31 based on the body movement data is at or above a predetermined intensity is shorter than a predetermined time. In this example, the conditions for determining high load are that the value based on the pulse data of the first subject 31 is above a predetermined value, or that the cumulative time during which the value based on the pulse data of the first subject 31 is above a predetermined value is above a predetermined time. Similarly, in this example, the conditions for determining that the load is not high are that the value based on the pulse data of the first subject 31 is smaller than a predetermined value, or that the cumulative time during which the value based on the pulse data of the first subject 31 is equal to or greater than a predetermined value is shorter than a predetermined time. In this example, an index is used in which a higher value based on the pulse data of the first subject 31 indicates a higher load.

[0094] Here, various methods may be used to determine whether the high load is a physical high load or a mental high load. For example, a method for determining whether someone is experiencing high physical exertion could be one that uses the number of steps taken. In other words, the more steps taken, the higher the physical exertion might be considered to be. For example, as a method for determining whether a person is under high mental stress, a method based on the magnitude of variability in heart rate intervals, such as SDNN or RMSSD, may be used. In other words, if the variability in heart rate intervals is small, it may be considered that the person is under high mental stress because the sympathetic nervous system is overactive. Furthermore, mental burden may also be called, for example, psychological stress. Data relating to physical stress may be called physical stress data, and data relating to mental stress may be called mental stress data.

[0095] The load analysis cloud aggregates the time periods during which each individual experienced high load, in predetermined time units. For example, the period in question may be the period for each working day, in which case a high-load time for each working day is required. As another example, the period in question may be a period of time every hour, in which case the high-load time for each hour is required.

[0096] Figure 7A shows an example of the physical high-load time and mental high-load time according to the first embodiment. In the graph shown in Figure 7A, the horizontal axis represents time, and the vertical axis represents the high-load duration [minutes]. The graph shows the physical high-load time D1 and the mental high-load time D2 for each discrete time point. In the example in Figure 7A, the illustration is simplified, and only one physical high-load time D1 and one mental high-load time D2 corresponding to each time point are labeled; however, other time points are also indicated by the same bar-shaped mark.

[0097] Figure 7B shows an example of a third table Ta3 relating to an individual's physical high-load time and mental high-load time according to the first embodiment. Table 3, Ta3, contains information ranking the high-load time for all employees. Table 3, Ta3, stores the following information in association with rank, name, age, affiliated organization, job responsibilities, work style, and high-load time [minutes / day]. High-load time [minutes / day] includes physical high-load time, mental high-stress time, and the sum of these.

[0098] Here, the information in the third table Ta3 may be stored, for example, in a database that manages employee data, or in another storage device. For example, the first database 15 may be used as the database in question.

[0099] Figure 7C shows an example of the fourth table Ta4 relating to the ranking of physical high-load time and mental high-load time for organizational units according to the first embodiment. Table 4, Ta4, contains information on the results of aggregating and ranking high-load time for each organizational unit. In this example, the information used for high-load time is the average high-load time, which is the result of averaging the high-load time of employees belonging to each organization. Table 4, Ta4, stores the ranking, affiliated organization, and average high-load time [minutes / day] in a corresponding format. Average high-load time [minutes / day] includes average physical high-load time, average mental high-stress time, and the sum of these.

[0100] Here, the information in the fourth table Ta4 may be stored, for example, in a database that manages employee data, or in another storage device. For example, the first database 15 may be used as the database in question.

[0101] In the load analysis cloud, for example, data from the individual who reported high workload during that time period, along with data from surrounding employees, can be compiled and analyzed. The relevant individuals and their workload data can then be obtained. Here, the surrounding employees may be employees belonging to the same organization as the individual, and the relevant individuals may be some or all of the surrounding employees. This makes it possible to distinguish, for example, whether the workload is high for an individual or for the organization. In this case, the level of workload may be relative between the individual and the organization.

[0102] With a load analysis cloud, for example, you can understand load data from the perspective of when and what kind of tasks were being performed during periods of high load. This allows, for example, assigning task details as attribute data to hourly load data, enabling analysis and identification of which tasks are causing high workloads. This narrows the scope of load reduction measures that can be considered and makes it easier to implement countermeasures.

[0103] I will now explain how to gather information about the load situation. The load analysis cloud selects interviewees—those who will be interviewed about the load situation—based on the load data. For the interview, one or more individuals may be selected from, for example, those who rank highly in high-load time over a certain period, such as weekly; those whose load threshold has increased sharply compared to the average load time of the previous week; or those who have been detected to have an extremely high load.

[0104] The load analysis cloud service requests information about the load situation. In this example, we will explain the case where the first subject 31 is the person being interviewed. The first subject's device 12, such as a smartphone, used by the first subject 31, stores an application for conducting interviews in its first storage unit 114. This application may, for example, be pre-installed on the first subject's device 12.

[0105] The load analysis cloud sends a signal to the application on the first user device 12 to request a hearing regarding the load status. In response, the first user device 12 uses the application to perform a process of hearing about the load status from the first user 31.

[0106] In this embodiment, the interview is conducted using a chat function provided by a hearing robot. Another example would be when employees voluntarily report their workload status via chat and seek advice. In this embodiment, the function for conducting interviews is shown to be provided in the first user device 12, but as an alternative example, the function for conducting interviews may be provided in other devices.

[0107] The hearing robot in this embodiment will now be described. The interviewing robot processes inquiries from employees regarding their workload. The interviewing robot can handle interviews with employees about their workload, as well as assist employees in seeking advice about their workload.

[0108] The interview robot might ask the interviewee, for example, whether they feel overwhelmed. The interviewing robot may also ask questions such as, "In what ways is your workload high?" and "What would you like to do, or would you like others to do, to reduce your workload?" The interviewing robot may also convey to the person being interviewed that "we are aware of your high workload and you can consult with us at any time."

[0109] The interview robot may also ask the interviewee whether it is okay to share the interview results with their supervisor or the human resources department. If the interviewee agrees to share the interview results with their supervisor or the human resources department, the interview robot will, for example, notify them of the interview results, including the interviewee's name. On the other hand, if the interviewee responds that they will not share the interview results with their supervisor or the human resources department, the interview robot will, for example, anonymously notify them of the interview results.

[0110] Here, the data from the interviews may be stored, for example, in a database that manages employee data, or in another storage device. For example, the first database 15 may be used as the database in question. Furthermore, if the supervisor or HR representative of the interviewee has been granted access rights, the data from the interview results can be accessed by that supervisor or HR representative. Furthermore, the interviewing robot can, for example, refer to the data from the previous interview to start the next interview from where it left off.

[0111] The hearing robot may offer advice to reduce the workload. The hearing robot may seek confirmation of the system's willingness to share the situation with the supervisor. The interviewing robot may refer to personnel information and ask people in the same workplace as the person being interviewed about their workload.

[0112] Figure 8 shows an example of the first screen G1 related to the chat according to the first embodiment. Screen G1 is an example of an interface screen for the hearing robot, which is part of the first subject user device 12, to chat with the first subject 31, who is the subject of the hearing. In the example shown in Figure 8, the first screen G1 includes the first frame W1, the second frame W2, the third frame W3, and the fourth frame W4.

[0113] The text in the first frame W1 and the question in the third frame W3 are the content of a conversation conducted by a listening robot. The text in the second box, W2, is the response written by the first subject 31 to the question in the first box, W1. Slot 4, W4, is a space for the first subject 31 to write their answer to the question in slot 3, W3.

[0114] Here, we will explain the differences in the content of the chat used for the interview, specifically regarding cases where the interviewee experiences a high physical burden and cases where the interviewee experiences a high mental burden. If the physical load is high, for example, a message containing advice that can be given during work may be displayed. On the other hand, if the mental burden is high, for example, a message may be output that does not include advice that can be given during work hours, but includes advice that can be given outside of work hours.

[0115] First, here is an example of how to structure a message in a chat. In this example, for the sake of explanation, we will show a case where the message consists of eight components, such as the first component M1 to the eighth component M8. Note that a message does not necessarily have to include all of its components, and may include other components as well.

[0116] The first component, M1, is a comment that conveys the results of the analysis of the activity level data of the interviewees. The second component, M2, consists of comments that express appreciation for the interviewee and inquire about their well-being. The third component, M3, consists of comments expressing concern for the person being interviewed. The fourth component, M4, consists of comments regarding advice for the interviewees. The fifth component, M5, consists of comments regarding what we want to hear from the interviewees. The sixth component M6, the seventh component M7, and the eighth component M8 are comments that encourage the interviewees to seek advice. These comments may also include, for example, the current load status, the circumstances under which the load becomes high, the causes of high load, whether or not the interviewees have made requests for load reduction and the content of those requests, and suggestions for administrators regarding load reduction.

[0117] Next, I will explain the difference between messages related to physical stress and messages related to mental stress. In the first component M1, comments indicating a high physical load are provided for the physical load. In the first component M1, the comment regarding mental load will indicate that the mental load is high.

[0118] In the second component M2, regarding physical load, since it can be inferred that the employee is performing a large volume of work due to the high physical load during work, comments should be made acknowledging their hard work. In the second component M2, regarding mental stress, since mental stress during work may be related to workplace relationships or unexpected troubles, comments should be made asking whether any troubles occurred.

[0119] In the third component, M3, regarding physical load, comments express concern about the feeling of physical fatigue associated with physical load. In the third component, M3, regarding mental load, comments expressing concern about the mental stress associated with mental load should be included.

[0120] In the fourth component, M4, comments regarding physical stress should include advice on measures that can be taken during work. In the fourth component, M4, comments regarding mental stress should include advice on how to address it outside of work. In this context, mental stress often stems from interpersonal relationships at work or unexpected work-related problems. Even if advice is given to the interviewee regarding actions they can take during work hours, it is often difficult for them to resolve the root cause of their mental stress on their own. Therefore, to provide advice that can be taken on by the interviewee outside of work hours, the system outputs advice that can alleviate their mental stress. "Outside of work hours" refers to, for example, their personal life.

[0121] In the fifth component, M5, for physical load, comments are made to inquire about the nature or volume of work that contributes to the physical load. In the fifth component, M5, regarding mental stress, comments will be made to ask about workplace interpersonal relationships or unexpected troubles that contribute to mental stress.

[0122] Next, I will show some specific examples of messages. Here are some specific examples of messages to send when someone is under a high level of physical strain. An example of a message for the first component M1 in the case of high physical load is the message, "We have found that your work has put a greater physical strain on you than before over the past week." An example of a message for the second component M2 in a situation of high physical load is the message, "You're working hard. How are you doing?" An example of a message for the third component M3 in cases of high physical stress is the message, "I'm worried if you're feeling tired or if you can't shake off your fatigue."

[0123] An example of a message for the fourth component M4 in cases of high physical load is: "If you are feeling tired, it may be helpful to change your work methods, reduce the amount of work, or take breaks between tasks." An example of a message for the fifth component, M5, in cases of high physical load is the message, "If you have any problems with the work content or workload, or if there is anything you would like to talk about," An example of a message for the sixth component, M6, in cases of high physical load is the message, "Please feel free to consult with this hearing robot or those around you." In this example, the fifth component M5 and the sixth component M6 constitute a single message.

[0124] An example of a message for the seventh component, M7, in cases of high physical stress is: "Let's think together about what we can improve. Your health and well-being are our top priority." An example of a message for the eighth component M8 in cases of high physical strain is the message, "Please don't overexert yourself, and ask for help when needed."

[0125] Here are some specific examples of messages to send when under high mental stress. An example of a message for the first component M1 in cases of high mental stress is the message, "We have observed that your work has placed a greater mental burden on you than usual over the past week." An example of a message in the second component M2 when the mental load is high is the message, "Is there some kind of trouble? How are you feeling?" An example of a message from the third component M3 in cases of high mental stress is the message, "I am concerned if you continue to experience stress or are unable to relieve it."

[0126] An example of a message for the fourth component M4 in cases of high mental stress is: "If you feel stressed, it is effective to take rest in your personal life, such as getting more sleep." An example of a message for the fifth component, M5, in cases of high mental stress is the message, "If you are having trouble with relationships or unexpected problems, or if there is anything you would like to talk about." An example of a message for the sixth component, M6, in cases of high mental load is the message, "Please feel free to consult with this listening robot or those around you." In this example, the fifth component M5 and the sixth component M6 constitute a single message.

[0127] An example of a message from the seventh component, M7, in cases of high mental stress is: "Let's think together about what we can improve. Your health and happiness are our top priority." An example of a message for the eighth component, M8, in cases of high mental stress is the message, "Please don't push yourself too hard, and ask for help when you need it."

[0128] Figure 9 shows an example of chat summary data H1 according to the first embodiment. Chat summary data H1 contains a text that summarizes the content of the chat between the interviewing robot and the first subject 31, who is the interviewee.

[0129] I will now explain the personnel management support data. The load analysis cloud generates HR allocation support data. In doing so, the load analysis cloud may refer to one or more of the following, for example, the results of load analysis, employee attribute data, attendance data, and the results of chats for interviews.

[0130] Here, the personnel allocation support data may include, for example, proposals for transferring or sending personnel to positions with lower workloads. The load analysis cloud could, for example, record a list of tasks or work areas that employees can handle as part of their attribute information, calculate their ability to handle tasks and suitability, and generate a list of personnel to be transferred or assigned to support roles. Furthermore, the transfer or support may be, for example, a permanent transfer or support, or it may be support only during times when the workload is high.

[0131] Figure 10 shows an example of the second screen G2 related to the report according to the first embodiment. Screen 2, G2, contains the contents of the report regarding personnel allocation. The information on the second screen G2 may be displayed by a predetermined device. In this embodiment, the predetermined device may be the first administrator terminal device 14. In this case, the first server device 13 transmits the information on the second screen G2 to the first administrator terminal device 14, and the first administrator terminal device 14 displays the received information to the first administrator 41 using the management display unit 332.

[0132] Referring to Figures 11, 12, and 13, an example of the processing procedure performed in the first load analysis system 1 according to the first embodiment is shown. Figure 11 shows an example of the processing procedure performed in the first load analysis system 1 according to the first embodiment.

[0133] Figure 11 shows the first user device 12, the first device 11, the first server device 13, and the first administrator terminal device 14. In this example, the load analysis cloud functionality is provided on the first server device 13. In this example, the first subject, 31, is the person being interviewed. In this example, the first manager 41 is the person who manages the first subject 31, and is, for example, the first subject 31's superior or a human resources person. In this example, the first device 11 is attached to the body of the first subject 31.

[0134] In process T1, the first server device 13 manages employee data. In this example, the employee data may be stored in the first database 15. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0135] During processing T2, the first device 11 acquires activity data of the first subject 31. This activity data includes pulsation data and body movement data. In processing T3, the first device 11 transmits the acquired activity data to the first user device 12. In process T4, the first user device 12 transmits the activity data received from the first device 11 to the first server device 13 at a predetermined timing. This transmission may be called, for example, uploading.

[0136] During processing T5, the first server device 13 analyzes the activity level data received from the first user device 12. In process T6, the first server device 13 stores the analysis results linked to employee data. In this example, the first database 15 may also store the analysis results linked to employee data. This linkage connects each employee with the analysis results of the workload associated with that employee. This linking process may also be called, for example, correspondence.

[0137] In processing T7, the first server device 13 identifies high-load users according to the analysis results. In processing T8, the first server device 13 selects individuals to be interviewed from among the identified high-load users. In process T9, the first server device 13 transmits predetermined message information to the selected interviewee. In this example, the first server device 13 transmits the message information to the first interviewee's user device 12 of the selected interviewee.

[0138] In process T10, the first user device 12 initiates a chat for interviewing purposes using the functions of the interviewing robot, in response to message information from the first server device 13. In process T11, the first user device 12 sends the chat content to the first server device 13.

[0139] In process T12, the first server device 13 analyzes the chat content received from the first user device 12 and generates a summary of the chat content.

[0140] In process T13, the first server device 13 generates a report from the analysis results and the chat summary. Here, the analysis results may include, for example, the results of the analysis of activity level data and the results of the analysis of chat content.

[0141] In process T14, the first server device 13 sends the generated report to the first administrator terminal device 14.

[0142] In process T15, the first administrator terminal device 14 displays the report received from the first server device 13.

[0143] In process T21, the first administrator terminal device 14 can access and display the analysis results linked to employee data. Furthermore, process T21 can be executed at any time after process T6, for example, after process T15.

[0144] Process T31 is a variation of this flow and may not be performed if the above processes are already performed. As a variation of this flow, process T31 may be executed instead of processes T3 and T4. In other words, in process T31, the first device 11 directly transmits the acquired activity data to the first server device 13. In other words, in this flow, activity data is transmitted from the first device 11 to the first server device 13 via the first user device 12 during processes T3 and T4. However, in a modified version of this flow, the activity data is transmitted directly from the first device 11 to the first server device 13.

[0145] In the example shown in Figure 11, the processing sections T5 to T9 are shown as the first processing section P1. In the example shown in Figure 11, the first processing unit P1 is performed by the first server device 13.

[0146] Figure 12 is a diagram showing an example of a detailed processing procedure regarding the conditions for uploading by the first user device 12 according to the first embodiment. In the flowchart shown in Figure 12, processes T51 to T54 are shown as more detailed examples of process T4 shown in Figure 11. Furthermore, in the flowchart shown in Figure 12, other processes are the same as in the example in Figure 11 and are denoted by the same reference numerals. This section explains processes T51 to T54, and omits explanations for processes similar to the example in Figure 11.

[0147] In process T51, the first user device 12 stores the activity level data received from the first device 11 in the first storage unit 114.

[0148] In process T52, the first user device 12 determines whether or not it has stored a certain amount of activity data. Here, the threshold for this certain amount of data may be, for example, a predetermined number of data points, or a predetermined amount of data. Based on this determination, if the first user device 12 determines that it has stored a certain amount of activity data, it proceeds to process T53. On the other hand, if, as a result of this determination, the first user device 12 determines that it does not have certain data stored regarding activity level data, it continues to perform process T51. In this example, the first user device 12 is configured not to upload activity data if sufficient data has not been stored.

[0149] In process T53, the first user device 12 determines whether the predetermined timing for uploading has arrived. As a result of this determination, if the first user device 12 determines that it is time to perform the upload, it proceeds to process T54. On the other hand, if the first user device 12 determines as a result of this determination that it is not yet the predetermined time for uploading, it will wait until that predetermined time arrives.

[0150] Here, the predetermined timing may, for example, be set in advance. For example, the predetermined timing may be the timing when a predetermined application is opened by or automatically by the first subject 31. The predetermined application may be, for example, an application that uses the activity data of the first subject 31. As another example, the predetermined timing may be a regular timing. A regular timing could be, for example, every hour or every day.

[0151] During processing T54, the first user device 12 uploads the stored activity data to the first server device 13 at a predetermined timing.

[0152] Note that processes T51 to T54 are just examples for illustrative purposes, and for example, in the first user device 12, the process of waiting for the upload of already stored activity data until a predetermined time and the process of storing newly received activity data to be uploaded next time during that waiting period may overlap within the same time period.

[0153] In the example shown in Figure 12, the transmission conditions used to determine whether or not the first user device 12 uploads activity data include both the amount of stored activity data and a predetermined timing. However, in other examples, transmission conditions relating to either one of these may be used. Note that the specified timing is an example of the first timing.

[0154] Figure 13 shows an example of the processing procedure performed in the first load analysis system 1 according to a modified example of the first embodiment. Figure 13 shows the first user device 12, the first device 11, and the first administrator terminal device 14. In this example, the load analysis cloud functionality is provided on the first administrator terminal device 14.

[0155] In general terms, this flow differs from the flow shown in Figure 11 in that the processing performed by the first server device 13 in the example in Figure 11 is performed by the first administrator terminal device 14. When this flow is processed, for example, the first server device 13 does not need to be provided.

[0156] In process T101, the first administrator terminal device 14 manages employee data. In this example, employee data may be stored in the first database 15. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0157] Processes T102 and T103 are the same as processes T2 and T3 performed by the first device 11 in the example in Figure 11. In process T104, the first user device 12 transmits the activity data received from the first device 11 to the first administrator terminal device 14 at a predetermined timing. This transmission may be called, for example, an upload.

[0158] In process T105, the first administrator terminal device 14 analyzes the activity level data received from the first user device 12. Processes T106, T107, and T108 are the same as processes T6, T7, and T8 performed by the first server device 13 in the example of Figure 11, except that they are performed by the first administrator terminal device 14, respectively. In process T109, the first administrator terminal device 14 transmits predetermined message information to the selected interviewee. In this example, the first administrator terminal device 14 transmits the message information to the first interviewee's user device 12 of the selected interviewee.

[0159] In process T110, the first user device 12 initiates a chat for interviewing purposes using the functions of the interviewing robot, in response to message information from the first administrator terminal device 14. In process T111, the first user device 12 sends the chat content to the first administrator terminal device 14.

[0160] In process T112, the first administrator terminal device 14 analyzes the chat content received from the first target user device 12 and generates a summary of the chat content. Process T113 is the same as process T13 performed by the first server device 13 in the example of Figure 11, except that it is performed by the first administrator terminal device 14. In process T114, the first administrator terminal device 14 displays the generated report.

[0161] Process T131 is a variation of this flow and may not be performed if the above processes are already performed. As a variation of this flow, process T131 may be executed instead of processes T103 and T104. In other words, in process T131, the first device 11 directly transmits the acquired activity data to the first administrator terminal device 14. In other words, in this flow, activity data is transmitted from the first device 11 to the first administrator terminal device 14 via the first user device 12 during processes T103 and T104. However, in a modified version of this flow, activity data is transmitted directly from the first device 11 to the first administrator terminal device 14.

[0162] In the example shown in Figure 13, the processing portion from T105 to T109 is shown as the second processing portion P2. In the example shown in Figure 13, the second processing part P2 is performed by the first administrator terminal device 14. The second processing section P2 is a part where similar processing is performed, although the main entity is different, compared to the first processing section P1 in the example in Figure 11.

[0163] In this flow, the same processing as processes T51 to T54 shown in Figure 12 may be applied to process T104.

[0164] Figure 14A is a diagram showing an example of a schematic functional block of the first load analysis system 1 according to a specific example of the first embodiment. The first load analysis system 1 includes a wearable device B1, a listening robot B2, a load analysis cloud B3, a user interface B4 for administrators, and an employee data management database B5.

[0165] In this embodiment, the wearable device B1 is composed of the first device 11. In this embodiment, the hearing robot B2 is configured with the functions of the application provided by the first user device 12. In this embodiment, the load analysis cloud B3 is composed of a first server device 13, or a first administrator terminal device 14, or both of these devices. In this embodiment, the administrator user interface B4 is configured by the first administrator terminal device 14. In this embodiment, the employee data management database B5 is composed of the first database 15.

[0166] The wearable device B1 comprises a pulse rate measurement unit C1 and a body movement detection unit C2. The pulse measurement unit C1 measures the pulse rate of the first subject 31 and acquires the pulse data resulting from that measurement. The motion detection unit C2 detects the motion of the first subject 31 and acquires motion data as a result of that detection.

[0167] The hearing robot B2 includes a chat interface C11 and a chat data output unit C12. The chat interface C11 initiates a chat with the first target person 31 for the purpose of conducting an interview. The chat data output unit C12 outputs chat data, which is data such as the content of the chat. In the example in Figure 14A, the chat data output unit C12 outputs the chat data to the employee data management database B5 and stores it.

[0168] The employee data management database B5 stores employee attribute data C41, attendance data C42, and chat data C43. Here, employee attribute data C41 may, for example, be stored in advance and updated as needed. Furthermore, attendance data C42 may be updated as needed, for example. Furthermore, chat data C43 may be updated as needed.

[0169] The load analysis cloud B3 comprises a load standard value setting unit C21, a load calculation unit C22, a load analysis unit C23, a hearing target selection instruction unit C24, and a personnel allocation support data generation unit C25. The load reference value setting unit C21 sets reference values, such as thresholds, used in load-related judgments. This setting may be done in advance or as needed. The load calculation unit C22 calculates load-related values ​​based on the pulsation data and body movement data acquired by the wearable device B1. In this case, the load calculation unit C22 may refer to the reference value set by the load reference value setting unit C21.

[0170] The load analysis unit C23 performs load analysis based on the calculation results from the load calculation unit C22. In this process, the load analysis unit C23 may refer to data stored in the employee data management database B5. This data may be, for example, either or both of the employee attribute data C41 and attendance data C42. Alternatively, the term "analysis" may be used instead of "analysis."

[0171] The interview participant selection instruction unit C24 selects interview participants based on the results of the analysis performed by the load analysis unit C23. Furthermore, the interviewee selection instruction unit C24 instructs the interview robot B2 of the selected interviewee, the first interviewee 31, to conduct a chat to inquire about the load status.

[0172] The personnel allocation support data generation unit C25 generates data to support personnel allocation based on chat data C43, which reflects the results of a chat for gathering information on workload status. In this process, the personnel allocation support data generation unit C25 may refer to one or more of the following: employee attribute data C41, attendance data C42, and the results of the analysis by the workload analysis unit C23.

[0173] The administrator user interface B4 includes a load status display unit C31 and a personnel allocation support data display unit C32. The load status display unit C31 displays information regarding the load status based on the results of the analysis performed by the load analysis unit C23. The personnel allocation support data display unit C32 displays the contents of the personnel allocation support data generated by the personnel allocation support data generation unit C25.

[0174] FIG. 14B is a diagram showing an example of the relationship of processes related to employee load analysis according to a specific example of the first embodiment. Note that the relationship of the plurality of processes shown in FIG. 14 is an example and is not limited to this example.

[0175] Process E1 is a process for employee load analysis. Process E2 is a process for adding attribute information to employee data. Process E3 is a process for analyzing the load based on the attribute information. Process E4 is a process for generating personnel allocation support data. Process E5 is a process for selecting hearing subjects. Process E6 is a process for selecting hearing subjects based on the time-series change of the load. Process E7 is a process for selecting hearing subjects based on the real-time change of the load. Process E8 is a process for generating report data of hearing results.

[0176] Process E11 is a process for setting a threshold for determining a high-load state. Process E12 is a subjective calibration of the load reference value. Process E13 is an objective calibration of the load reference value. Process E14 is a process for measuring the resting heart rate. Process E15 is a process for performing objective calibration in cooperation with medical examination data.

[0177] In process E1, process E2 may be performed, and further, process E3 may be performed. In process E1, process E5 may be performed, and further, process E8 may be performed. Process E8 may be performed based on the result of process E5 and process E1. In process E5, process E6 or process E7 may be performed. Process E4 may be performed based on the result of process E1 and the result of process E8. In process E1, process E11 may be performed, and furthermore, either or both of processes E12 and E13 may be performed. In process E13, at least one of process E14 and process T15 may be performed.

[0178] In addition, in process E14, the resting heart rate is measured and the load reference value is objectively calibrated, without being linked to the health check data. In process E15, the resting heart rate is determined in conjunction with health checkup data, and objective calibration of the load reference value is performed.

[0179] As described above, the first load analysis system 1 and control method according to this embodiment can send a message to a subject who is determined to have a high load based on activity data. For example, if you send messages only to users who are determined to have a high load, you can reduce the amount of processing required to send the messages. In the first load analysis system 1 and control method according to this embodiment, the load of the first subject 31 is determined using activity data detected by the first device 11 attached to the first subject 31, and if it is determined that the load is high, a message is sent to the first subject 31 for interviewing. Thus, the first load analysis system 1 and control method according to this embodiment can send a message to the first subject 31 who has been determined to have a high load based on the activity data.

[0180] In the first load analysis system 1 and control method according to this embodiment, for example, by analyzing the workload, the workload related to employees' work can be quantified based on pulsation data, and the workload status at the individual or organizational level can be aggregated, summarized, and trend-analyzed. In the first load analysis system 1 and control method according to this embodiment, for example, the perceived burden on individual employees in their work can be quantified as data, attribute information can be added to the quantified burden data to visualize or analyze the workload situation at the organizational level, and based on this workload situation, actions that are effective for improving work can be proposed. Furthermore, in the first load analysis system 1 and control method according to this embodiment, when an employee is under a high load, the system can directly ask the employee about the cause and collect the data, thereby supporting the implementation of improvement measures that focus on more realistic work-related problems appropriate to the situation.

[0181] In the first load analysis system 1 and control method according to this embodiment, when quantifying the burden on individual employees in their work as data, the data is normalized by taking into account the individual's physical characteristics. This makes it possible to compare the degree of burden by absorbing differences in how individuals' heart rates rise or differences in their physical strength. Examples of individual physical characteristics include resting heart rate. In the first load analysis system 1 and control method according to this embodiment, the magnitude of the load can be compared and ranked under normalized conditions, making it easy to detect high-load employees. This makes it easier to narrow down who to contact among a large number of employees to check on their situation.

[0182] For example, in the background technology, simply attempting to smooth out workload based on differences in work duration or the number of action items sometimes failed to take into account the high level of workload and burden associated with those individual tasks. In contrast, the first load analysis system 1 and control method according to this embodiment make it possible to compare the workload and perceived burden for each individual or organization using the same criteria, thereby facilitating the optimization of work allocation and employee assignments.

[0183] In the first load analysis system 1 and control method according to this embodiment, for example, physical load and mental load can be distinguished by determining the presence or absence of body movement based on signals from an acceleration sensor attached to the first subject 31. Furthermore, it is also possible to configure the system to calculate load data based on different measurement data, such as using heart rate to quantify physical load and heart rate variability to quantify mental load. This makes it possible to obtain load data that is appropriate for both physical and mental load.

[0184] In the first load analysis system 1 and control method according to this embodiment, for example, attribute information related to the circumstances under which the load occurred can be added to the load data, making it easier to analyze what factors caused the load to increase. In other words, if such attribute information is not available, only values ​​indicating the amount of load, such as the high load time, can be obtained, so while it may be possible to compare the magnitude of the load, it may be difficult to pinpoint the cause of the load. However, in this embodiment, attribute information does not necessarily have to be used.

[0185] In the first load analysis system 1 and control method according to this embodiment, an interview regarding the workload status is conducted with the first subject 31 based on the load data of the first subject 31. As a result, the first subject 31, who is the employee being interviewed, can explain the work situation while taking into account the circumstances in which they are located. In the first load analysis system 1 and control method according to this embodiment, attribute information can be added to the quantified burden data to visualize or analyze the workload situation at the organizational level, and based on this workload situation, it becomes possible to propose actions that are effective for improving operations.

[0186] In this embodiment, we have shown a case where the workload situation within a single hospital is digitized and personnel allocation is proposed. However, as another example, the workload situation can be compared across multiple hospitals. This allows for the optimization of employee allocation among multiple hospitals within the same group. Another example is the ability to check the workload levels among multiple hospitals located in the same area. This allows for a more objective examination of which departments or wards within each hospital are experiencing high workloads. Furthermore, there are no particular limitations on the length of time for analyzing workload conditions; it is possible to compare and verify workload conditions over long periods. For example, based on the results of the workload analysis, it becomes possible to understand the seasonality of peak seasons.

[0187] In this embodiment, the first load analysis system 1 and control method were shown as being applied to a system for analyzing the workload of hospital employees. However, the first load analysis system 1 and control method are not limited to this and may be applied to various other systems, such as a system for analyzing the workload of employees of a company. Furthermore, the load is not limited to, for example, the workload of work in a hospital or company, but may be applied to various other loads such as the workload of studying or the workload of work at home. Furthermore, various methods may be used for analyzing the load. Furthermore, a variety of topics may be used in the chat for gathering information regarding the load status. Furthermore, the content of reports regarding workload status is not necessarily limited to information supporting personnel allocation; a variety of content may be used.

[0188] Now, let me explain Patent Document 1. In Patent Document 1, in order to appropriately investigate the magnitude of stress received by the subject, mental stress is quantified based on the heart rate and the like, and a questionnaire is taken regarding the sense of burden for each business implemented for the subject. Based on the questionnaire results, a weighted heart rate stress value for each business is calculated. Further, in Patent Document 1, the information processing apparatus conducts a measure questionnaire for the subject regarding the businesses up to a predetermined rank from the ones with larger weighted heart rate stress values among a plurality of businesses. With such a configuration, in Patent Document 1, a mathematical formula that can convert the heart rate stress value into a subjective stress value and a threshold value for the subjective stress value can be generated without depending on the operation input of the administrator. Thereby, the intensity of the subjective load received by an individual according to the type of business can be evaluated. However, in Patent Document 1, only the load is quantified for routine and easily definable businesses. For example, even for the same business due to a large workload in the business situation at that time, the sense of burden received by the employee was not taken into account. Further, for example, in view of the fact that different stresses may be received depending on the human relationship between employees and customer service that depends on the humanity of customers, in the technology described in Patent Document 1, the handling of load data may not be sufficient. In contrast, in the present embodiment, for example, it is possible to analyze the physical load and mental load for each individual of each subject. Further, in the present embodiment, for example, it is also possible to analyze the physical load and mental load for each predetermined organization such as for each department.

[0189] The second embodiment will be described. In the present embodiment, mainly, the parts different from the first embodiment will be described in detail.

[0190] FIG. 15 is a diagram showing a configuration example of a second load analysis system 1a according to the second embodiment. The second load analysis system 1a comprises a second device 11a, a second user access device 12a, a second administrator terminal device 14a, a second database 15a, and a second network 21a. Figure 15 also shows the second subject 31a and the second administrator 41a. The second device 11a comprises a second pulse measurement unit Q1a and a second body movement detection unit Q2a.

[0191] Here, the configuration and operation of the second load analysis system 1a differ in general terms from the configuration and operation of the first load analysis system 1 shown in Figure 1 of the first embodiment in that the load analysis cloud functions in the first embodiment are provided in the second user device 12a. In this embodiment, a server device corresponding to the first server device 13 in the first embodiment is not provided.

[0192] In this embodiment, the second device 11a having a second pulse measurement unit Q1a and a second body movement detection unit Q2a, the second user device 12a, the second administrator terminal device 14a, the second database 15a, and the second network 21a each have functions similar to, for example, the first device 11 having a first pulse measurement unit Q1 and a first body movement detection unit Q2, the first user device 12, the first administrator terminal device 14, the first database 15, and the first network 21 in the first embodiment, except for the differences described in this embodiment. In this embodiment, the second subject 31a and the second administrator 41a are the same as the first subject 31 and the first administrator 41 in the first embodiment, respectively.

[0193] Figure 16 shows an example of the configuration of the second user-accessible device 12a according to the second embodiment. The second user device 12a comprises a second input unit 111a, a second output unit 112a, a second communication unit 113a, a second storage unit 114a, and a second control unit 115a. The second input unit 111a includes a second operation unit 131a. The second output unit 112a includes a second display unit 132a.

[0194] In this embodiment, the second input unit 111a, the second output unit 112a, the second communication unit 113a, the second storage unit 114a, the second control unit 115a, the second operation unit 131a, and the second display unit 132a each have functions similar to, for example, the first input unit 111, the first output unit 112, the first communication unit 113, the first storage unit 114, the first control unit 115, the first operation unit 131, and the first display unit 132 in the first embodiment, except for the differences described in this embodiment.

[0195] Figure 17 shows an example of the processing procedure performed in the second load analysis system 1a according to the second embodiment.

[0196] Figure 17 shows the second user device 12a, the second device 11a, and the second administrator terminal device 14a. In this example, the second user device 12a is equipped with a function to perform load analysis. In this example, the second subject, 31a, is the person being interviewed. In this example, the second manager 41a is the person who manages the second subject 31a, and is, for example, the second subject 31a's superior or a human resources person. In this example, the second device 11a is attached to the body of the second subject 31a.

[0197] In this embodiment, the second target user device 12a manages data relating to at least the second target user 31a from the employee data. In this example, for instance, employee data may be stored in the second database 15a. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0198] In process T301, the second device 11a acquires activity data of the second subject 31a. This activity data includes pulsation data and body movement data. In process T302, the second device 11a transmits the acquired activity data to the second user device 12a. In process T303, the second user device 12a analyzes the activity level data received from the second device 11a. In process T304, the second subject user device 12a stores the analysis results. In this example, the analysis results are those relating to the second subject 31a.

[0199] In process T305, the second user device 12a determines whether the user is a high-load user based on the analysis results. In this example, the second target user device 12a indicates the case where the second target user 31a is determined to be a high-load user and a target for interview. Furthermore, the second user device 12a may terminate the processing of this flow if it determines that the second user 31a is not a high-load user. In this case, for example, the processing of this flow may be terminated after processing T306 has been performed.

[0200] In process T306, the second user device 12a transmits the analysis results to the second administrator terminal device 14a. For example, the second database 15a may store the analysis results linked to employee data. In this linking, each employee is associated with the analysis results of the workload related to that employee.

[0201] In process T307, the second subject user device 12a displays a predetermined message to the second subject 31a, who is the subject of the hearing. This message may, for example, be displayed in the chat during process T308.

[0202] In process T308, the second user device 12a initiates a chat for the purpose of conducting an interview using the functions of the interview robot. In process T309, the second user device 12a sends the chat content to the second administrator terminal device 14a.

[0203] In process T310, the second administrator terminal device 14a analyzes the chat content received from the first target user device 12 and generates a summary of the chat content.

[0204] In process T311, the second administrator terminal device 14a generates a report from the analysis results and the chat summary. Here, the analysis results may include, for example, the results of the analysis of activity level data and the results of the analysis of chat content.

[0205] In process T312, the second administrator terminal device 14a displays the generated report.

[0206] In the example shown in Figure 17, the processing section from T303 to T307 is shown as the third processing section P3. In the example shown in Figure 17, the third processing section P3 is performed by the second user-accessible device 12a.

[0207] As described above, the second load analysis system 1a and control method according to this embodiment can send a message to a subject who is determined to have a high load based on activity data. In this embodiment, except for the difference that load analysis is performed in the second user device 12a, it is possible to obtain the same effects as in the first embodiment, for example.

[0208] A third embodiment will be described. In this embodiment, we will primarily describe in detail the parts that differ from the first embodiment.

[0209] Figure 18 shows an example of the configuration of the third load analysis system 1b according to the third embodiment. The third load analysis system 1b comprises a third device 11b, a third user access device 12b, a third administrator terminal device 14b, a third database 15b, and a third network 21b. Figure 18 also shows the third target person 31b and the third administrator 41b. The third device 11b comprises a third pulse measurement unit Q1b and a third body motion detection unit Q2b.

[0210] Here, the configuration and operation of the third load analysis system 1b differ in general terms from the configuration and operation of the first load analysis system 1 shown in Figure 1 of the first embodiment in that the load analysis cloud functionality in the first embodiment is provided on the third device 11b. In this embodiment, a server device corresponding to the first server device 13 in the first embodiment is not provided.

[0211] In this embodiment, the third device 11b having a third pulse measurement unit Q1b and a third body movement detection unit Q2b, the third user device 12b, the third administrator terminal device 14b, the third database 15b, and the third network 21b each have functions similar to, for example, the first device 11 having a first pulse measurement unit Q1 and a first body movement detection unit Q2, the first user device 12, the first administrator terminal device 14, the first database 15, and the first network 21 in the first embodiment, except for the differences described in this embodiment. In this embodiment, the third subject 31b and the third administrator 41b are the same as the first subject 31 and the first administrator 41 in the first embodiment, respectively.

[0212] Figure 19 shows an example configuration of the third device 11b according to the third embodiment. The third device 11b includes a device input unit 411, a device output unit 412, a device communication unit 413, a device storage unit 414, a device control unit 415, and a device sensor unit 416. The device input unit 411 includes a device operation unit 431. The device output unit 412 includes a device display unit 432. The device sensor unit 416 includes a third pulse measurement unit Q1b and a third body motion detection unit Q2b. The names of the parts provided in the third device 11b are for descriptive purposes only and may be referred to by any other name.

[0213] Here, the example configuration of the third device 11b shown in Figure 19 is just one example, and other configurations may be used. For example, if operation and display are not required for the third device 11b, the device operation unit 431 and the device display unit 432 do not need to be provided. Furthermore, for example, if the third device 11b does not require information input or output other than communication, the device input unit 411 and the device output unit 412 do not need to be provided.

[0214] The third device 11b is configured using a computer. The device input unit 411 has the function of inputting information. For example, the device operation unit 431 receives and inputs the details of the operation performed by the third subject 31b. Furthermore, for example, the device input unit 411 may receive information from an external device. The device output unit 412 has the function of outputting information. For example, the device display unit 432 outputs the information to be displayed to the screen. Furthermore, for example, the device output unit 412 may output information to an external device. Here, the device operation unit 431 and the device display unit 432 may be shared, for example, by using a touch panel.

[0215] The device communication unit 413 has the function of performing communication. In this embodiment, the device communication unit 413 is shown separately from the device input unit 411 and the device output unit 412. However, for example, the receiving function of the device communication unit 413 may be included in the functions of the device input unit 411, and the transmitting function of the device communication unit 413 may be included in the functions of the device output unit 412.

[0216] The device storage unit 414 stores information. The device control unit 415 performs various processes or controls on the third device 11b. In this embodiment, the device control unit 415 includes a predetermined processor, such as a CPU, and performs various processes or controls by executing a predetermined program using this processor. The program may be stored, for example, in the device storage unit 414.

[0217] Figure 20 shows an example of the processing procedure performed in the third load analysis system 1b according to the third embodiment. Figure 20 shows the third user device 12b, the third device 11b, and the third administrator terminal device 14b. In this example, the third device 11b is equipped with a function for performing load analysis. In this example, the third subject, 31b, is the person being interviewed. In this example, the third manager 41b is the person who manages the third subject 31b, and is, for example, the superior or HR manager of the third subject 31b. In this example, the third device 11b is attached to the body of the third subject 31b.

[0218] In this embodiment, the third device 11b manages employee data, specifically data relating to at least the third subject 31b. In this example, for instance, employee data may be stored in the third database 15b. Furthermore, employee data management may, for example, be performed on an ongoing basis.

[0219] In process T401, the third device 11b acquires activity data of the third subject 31b. This activity data includes pulsation data and body movement data. In process T402, the third device 11b analyzes the acquired activity data. In process T403, the third device 11b stores the analysis results. In this example, the analysis results pertain to the third subject 31b.

[0220] In process T404, the third device 11b determines whether the user is a high-load user based on the analysis results. In this example, the third device 11b indicates the case where the third subject 31b is determined to be a high-load individual and therefore a subject for interview. Furthermore, the third device 11b may terminate the processing of this flow if it determines that the third target person 31b is not a high-load user. In this case, for example, the processing of this flow may be terminated after process T405 has been performed.

[0221] In process T405, the third device 11b transmits the analysis results to the third user device 12b. The third user device 12b then transmits the analysis results received from the third device 11b to the third administrator terminal device 14b. For example, the third database 15b may store the analysis results linked to employee data. In this linking, each employee is associated with the analysis results of the workload related to that employee.

[0222] In process T406, the third device 11b transmits a predetermined message to the third subject user device 12b of the third subject 31b, who is the subject of the hearing.

[0223] In process T407, the third user device 12b initiates a chat for the purpose of conducting an interview, using the functions of the interview robot. In process T408, the third user device 12b sends the chat content to the third administrator terminal device 14b.

[0224] In process T409, the third administrator terminal device 14b analyzes the chat content received from the third target user device 12b and generates a summary of the chat content.

[0225] In process T410, the third administrator terminal device 14b generates a report from the analysis results and chat summary. Here, the analysis results may include, for example, the results of the analysis of activity level data and the results of the analysis of chat content.

[0226] In process T411, the third administrator terminal device 14b displays the generated report.

[0227] Process T431 is a variation of this flow and may not be performed if the above processes are already performed. As a variation of this flow, process T431 may be executed instead of process T405. In other words, in process T431, the third device 11b directly transmits the analysis results to the third administrator terminal device 14b. In other words, in this flow, during process T405, the analysis results are transmitted from the third device 11b to the third administrator terminal device 14b via the third user device 12b. However, in a modified version of this flow, the analysis results are transmitted directly from the third device 11b to the third administrator terminal device 14b.

[0228] In the example shown in Figure 20, the processing section from T402 to T406 is shown as the fourth processing section P4. In the example shown in Figure 20, the fourth processing section P4 is performed by the third device 11b.

[0229] As described above, the third load analysis system 1b and control method according to this embodiment can send a message to a subject who is determined to have a high load based on activity data. In this embodiment, except for the difference that load analysis is performed in the third device 11b, it is possible to obtain the same effects as in the first embodiment, for example.

[0230] As another possible configuration, the third device 11b may be equipped with a function for conducting a chat for the purpose of hearing. In this case, in the flow shown in Figure 20, process T431 is used instead of process T405, a predetermined message is displayed to the third target person 31b instead of process T406, and processes T407 and T408 are performed by the third device 11b. Furthermore, in this case, the third-party user device 12b does not necessarily have to be provided.

[0231] An example configuration according to the above embodiment is shown. As an example configuration, the control method for the load analysis system uses an information processing device, a user-accessible device used by the target person, and a device attached to the target person to output messages based on the target person's load. This control method acquires activity data of the subject measured by the device. In this control method, based on the activity data, it is determined whether the workload of the subject meets predetermined conditions. In this control method, when it is determined that the load on the target person meets the predetermined conditions, a message is output to accept comments.

[0232] Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Here, in the example shown in Figure 1 according to the first embodiment, the first load analysis system 1, the first target person 31, the first target person user device 12, and the first device 11 are examples of a load analysis system, an example of a target person, an example of a user device, and an example of a device, respectively. Also, in the example shown in Figure 1 according to the first embodiment, the first server device 13, or the first administrator terminal device 14, or both, are examples of information processing devices. Furthermore, in the example shown in Figure 13 according to the second embodiment, the second load analysis system 1a, the second subject 31a, the second subject user device 12a, and the second device 11a are examples of a load analysis system, an example of a subject, an example of a subject user device, and an example of a device, respectively. Also, in the example shown in Figure 13 according to the second embodiment, the second administrator terminal device 14a is an example of an information processing device. Furthermore, in the example shown in Figure 18 according to the third embodiment, the third load analysis system 1b, the third target person 31b, the third target person user device 12b, and the third device 11b are examples of a load analysis system, an example of a target person, an example of a user device, and an example of a device, respectively. Also, in the example shown in Figure 18 according to the third embodiment, the third administrator terminal device 14b is an example of an information processing device.

[0233] As an example configuration, in the control method of a load analysis system, the activity level data includes pulse data related to the subject's heartbeat. Therefore, the load can be determined based on the pulse data. In this way, a determination can be made according to the subject's pulse rate.

[0234] As an example configuration, in the control method of a load analysis system, the activity level data includes body movement data related to the subject's body movements. Therefore, based on body movement data, it is possible to determine whether or not the subject is moving. In this way, it is possible to make judgments in accordance with the subject's pulse rate and body movement.

[0235] As an example configuration, the control method of the load analysis system calculates the subject's first type of load based on pulse data and body movement data, and determines whether the subject's load meets predetermined conditions based on the calculated first type of load. Therefore, physical load can be determined based on pulse data and body movement data. Here, in the first to third embodiments, the first type of load is a physical load.

[0236] As an example configuration, in the control method of the load analysis system, the predetermined conditions are that the subject's movement based on body movement data is at or above a predetermined first intensity, and the value based on the subject's pulse data is at or above a predetermined first value. As another example of a configuration, in the control method of a load analysis system, the predetermined conditions are that, in the immediately preceding period, the first cumulative time during which the subject's movement is at or above the first intensity is a predetermined first hour or longer, and the second cumulative time during which the value based on the subject's pulse data is at or above the first value is a predetermined second hour or longer. The specified conditions include at least one of these conditions.

[0237] Therefore, conditions can be used to determine if something is a physical burden. For example, the conditions for determining physical stress may be set separately from the conditions for determining mental stress. Here, any value may be used as the first intensity of the subject's movement based on the body movement data. The subject's movement may be defined, for example, by acceleration, velocity, or energy. Any value may be used as the first value based on the subject's heartbeat data. Various indicator values ​​may be used as the values ​​based on the subject's heartbeat data. Any period may be used as the immediately preceding period. For example, the cumulative time in the immediately preceding period may be used as the first cumulative time. Any time may be used as the first period. For the second cumulative time, for example, the cumulative time during the immediately preceding period may be used. Any time may be used as the second period.

[0238] As an example configuration, in the control method of a load analysis system, the messages include messages regarding the physical stress on the subject. Therefore, for example, the content of the message sent when the physical load meets the conditions can be made appropriate. This content may include, for example, information that can be gathered about the subject's physical condition.

[0239] As an example configuration, the control method of the load analysis system calculates the subject's second type load based on pulse data and body movement data, and determines whether the subject's load meets predetermined conditions based on the calculated second type load. Therefore, mental load can be determined based on pulse data and body movement data. Here, in the first to third embodiments, the second type of load is a mental load.

[0240] As an example configuration, in the control method of the load analysis system, the predetermined conditions are that the subject's movement based on body movement data is less than the first intensity, and the value based on the subject's pulsation data is greater than or equal to a predetermined second value. Another example of a configuration is a predetermined condition in which, in the immediately preceding period, the first cumulative time during which the subject's activity is at or above the first intensity is shorter than the first hour, and the third cumulative time during which the value based on the subject's pulse data is at or above the second value is a predetermined third hour or longer. The specified conditions include at least one of these conditions.

[0241] Therefore, conditions can be used to determine if something constitutes a mental burden. For example, the conditions for determining mental stress may be set separately from the conditions for determining physical stress. Any value may be used as the second value based on the subject's pulse data. Furthermore, the first and second values ​​based on the subject's pulse data may be the same, or they may be different. Furthermore, the second and third periods of time may, for example, be of the same length, or they may be of different lengths.

[0242] As one example of a configuration, in the control method of a load analysis system, the message includes a message about the mental stress of the subject. Therefore, for example, the content of the message sent when the mental burden meets the conditions can be made appropriate. Such content may include, for example, information that allows for an interview about the subject's state of mind.

[0243] As an example configuration, in the control method of a load analysis system, the target is the person performing the work. The control method of the load analysis system outputs a message to receive comments regarding the task when it is determined that the workload of the person in question meets predetermined conditions. Therefore, depending on the circumstances of the work performed by the person concerned, we may accept interviews regarding that work. In this context, the control method for the load analysis system involves, for example, receiving comments about the subject's work during or after their work.

[0244] As an example configuration, the control method for a load analysis system comprises an information processing device, a user-accessible device used by the target person, and a device attached to the target person. The control method includes the steps of: causing the device to measure the activity level data of the subject; causing the information processing device to acquire the activity level data; causing the information processing device to determine whether the subject's load meets predetermined conditions based on the acquired activity level data; causing the information processing device to generate a message for receiving comments from the subject if it determines that the subject's load meets the predetermined conditions; causing the information processing device to send the message to the subject's user device; and causing the subject's user device to output the message. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data.

[0245] As an example configuration, the control method for a device used by a target person includes a device attached to the target person, an information processing device that transmits messages, and a control method for the device used by the target person that communicates with the target person. In this control method, activity level data of the subject is acquired from the device. This control method stores the acquired activity level data. This control method determines whether at least one of the amount of stored activity data and a predetermined first timing for communication with the information processing device satisfies predetermined transmission conditions. In this control method, when it is determined that the transmission conditions are met, the stored activity data is transmitted to the information processing device, and after the information processing device determines that the subject's workload meets predetermined conditions based on the received activity data, it receives a message from the subject to accept comments.

[0246] In other words, in this control method, when it is determined that the transmission conditions are met, the stored activity data is transmitted to the information processing device and a message is received from the subject to receive comments. At this time, the information processing device, based on the received activity data, determines that the subject's workload meets predetermined conditions and then sends a message to receive comments from the subject.

[0247] Therefore, a message can be sent to individuals who are determined to have a high workload based on activity data. In this case, activity data can be transmitted from the user's device to the information processing device at an appropriate time when predetermined transmission conditions are met. Here, the predetermined transmission conditions may be, for example, transmission conditions relating to either the amount of activity data or the first timing, or transmission conditions relating to both. Furthermore, various conditions may be used as transmission conditions.

[0248] As an example configuration, the control method includes the steps of: causing the information processing device to acquire activity data of the subject measured by a device attached to the subject; determining whether the subject's load meets predetermined conditions based on the activity data; and, if it is determined that the subject's load meets the predetermined conditions, outputting a message to the subject's user device used by the subject for receiving comments from the subject. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Here, this configuration example is a configuration example according to the first embodiment.

[0249] As an example configuration, the program is a program that enables the following steps: causing a computer to acquire activity data of a subject measured by a device attached to the subject; determining whether the subject's workload meets predetermined conditions based on the activity data; and, if it is determined that the subject's workload meets the predetermined conditions, outputting a message to the subject's user device used by the subject to receive comments from the subject. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Herein, this configuration example is applicable to a program for controlling a computer that constitutes the first server device 13 or the first administrator terminal device 14 according to the first embodiment.

[0250] As an example configuration, the control method includes the steps of: causing a user-accessible device used by a user to acquire activity data of the user measured by a device attached to the user; determining whether the user's load meets predetermined conditions based on the activity data; and, if it is determined that the user's load meets the predetermined conditions, outputting a message to receive comments from the user. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Here, this configuration example is a configuration example according to the second embodiment.

[0251] As an example configuration, the program is designed to perform the following steps: cause a computer to acquire activity data of a subject measured by a device attached to the subject; determine whether the subject's workload meets predetermined conditions based on the activity data; and, if it is determined that the subject's workload meets the predetermined conditions, output a message to receive comments from the subject. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Herein, this configuration example is applicable to a program for controlling the computer that constitutes the second user-accessible device 12a according to the second embodiment.

[0252] As an example configuration, the control method includes the steps of: having a device attached to a subject acquire activity data of the subject; having the device determine, based on the activity data, whether the subject's load meets predetermined conditions; and, if it is determined that the subject's load meets the predetermined conditions, having the device send a message to the subject's user device used by the subject to receive comments from the subject. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Here, this configuration example is a configuration example according to the third embodiment.

[0253] As an example configuration, the program is designed to perform the following steps: cause a computer attached to the subject to acquire activity data of the subject; determine whether the subject's workload meets predetermined conditions based on the activity data; and, if it is determined that the subject's workload meets the predetermined conditions, send a message to the subject's user device used by the subject to receive comments from the subject. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Here, this configuration example is applicable to a program for controlling the computer that constitutes the third device 11b according to the third embodiment.

[0254] As an example configuration, a load analysis system uses an information processing device, a device used by the subject, and a device attached to the subject to output messages based on the subject's load. The load analysis system acquires activity data of the subject measured by the device. The load analysis system determines, based on the activity data, whether the load on the subject meets predetermined conditions. The load analysis system outputs a message to accept comments when it determines that the load of the subject meets the predetermined conditions. Therefore, it is possible to send messages to individuals who are determined to be under high load based on their activity data. Here, this example configuration is an example configuration according to the first to third embodiments.

[0255] A program for realizing the function of any component in any of the devices described above may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed. Here, "computer system" includes the operating system and hardware such as peripheral devices. "Computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROM (Read Only Memory), CD (Compact Disc)-ROMs, and storage devices such as hard disks built into the computer system. "Computer-readable recording medium" also includes volatile memory within a computer system that acts as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line, which retains the program for a certain period of time. Such volatile memory may be RAM. The recording medium may also be a non-temporary recording medium.

[0256] The above program may be transmitted from a computer system that stores this program in a memory device or the like to another computer system via a transmission medium, or by transmission waves within the transmission medium. The "transmission medium" used to transmit the program refers to a medium that has the function of transmitting information, such as a network like the Internet or a communication line like a telephone line. The above program may be intended to implement some of the functions described above. The above program may also be a so-called differential file, capable of implementing the aforementioned functions in combination with programs already recorded in the computer system. A differential file may also be called a differential program.

[0257] The functions of any component in any device described above may be implemented by a processor. Each process in the embodiment may be implemented by a processor that operates based on information such as a program, and a computer-readable recording medium that stores information such as a program. The functions of each part of the processor may be implemented by separate hardware, or the functions of each part may be implemented by integrated hardware. The processor includes hardware, and the hardware may include at least one of a circuit that processes digital signals and a circuit that processes analog signals. The processor may be configured using one or more circuit devices or one or both of one or more circuit elements mounted on a circuit board. ICs (Integrated Circuits) may be used as circuit devices, and resistors or capacitors may be used as circuit elements.

[0258] The processor may be a CPU. However, the processor is not limited to a CPU; various types of processors such as a GPU (Graphics Processing Unit) or a DSP (Digital Signal Processor) may be used. The processor may be a hardware circuit using an ASIC (Application Specific Integrated Circuit). The processor may consist of multiple CPUs, or it may consist of hardware circuits using multiple ASICs. The processor may consist of a combination of multiple CPUs and hardware circuits using multiple ASICs. The processor may include one or more amplifier circuits or filter circuits that process analog signals.

[0259] Although embodiments have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and includes designs and the like that do not depart from the gist of this disclosure.

[0260] [Note] The following are configuration examples 1 through 19. Furthermore, the lower-level configuration examples may or may not be applied to the higher-level configuration examples. Furthermore, a lower-level configuration example applicable to any of the two or more higher-level configuration examples may be applied to any of those two or more higher-level configuration examples. Moreover, if two or more application examples arise in this manner, a configuration example even lower than the lower-level example may be applied to any of those two or more application examples.

[0261] <Configuration Example 1> A control method for a load analysis system that outputs a message based on the load of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, The device acquires the activity level data of the subject measured by the device, Based on the activity data, it is determined whether the workload of the subject meets the predetermined conditions. When it is determined that the workload of the person concerned meets the predetermined conditions, the message for accepting comments is output. Control method.

[0262] <Configuration Example 2> The activity data includes pulse data relating to the subject's heartbeat. The control method described in <Configuration Example 1>.

[0263] <Configuration Example 3> The activity data includes body movement data relating to the subject's body movements. The control method described in <Configuration Example 2>. Note that <Configuration Example 3> can also be subordinate to <Configuration Example 1>.

[0264] <Configuration Example 4> Based on the pulsation data and the body movement data, the first type of load of the subject is calculated, and based on the calculated first type of load, it is determined whether the load of the subject satisfies the predetermined conditions. The control method described in <Configuration Example 3>.

[0265] <Configuration Example 5> The aforementioned predetermined conditions are: The conditions are that the subject's movement based on the aforementioned body movement data is at or above a predetermined first intensity, and the value based on the subject's pulse data is at or above a predetermined first value, or The conditions are met if, in the immediately preceding period, the first cumulative time during which the subject's movements are at or above the first intensity is a predetermined first hour or longer, and the second cumulative time during which the value based on the subject's pulse data is at or above the first value is a predetermined second hour or longer, including at least one of the following: The control method described in <Configuration Example 4>.

[0266] <Configuration Example 6> The aforementioned message includes a message regarding the physical stress of the subject, The control method described in <Configuration Example 5>.

[0267] <Configuration Example 7> Based on the pulsation data and the body movement data, the second type of load of the subject is calculated, and based on the calculated second type of load, it is determined whether the subject's load satisfies the predetermined conditions. The control method described in <Configuration Example 6>. Note that <Configuration Example 7> can also be subordinate to <Configuration Example 3>.

[0268] <Configuration Example 8> The aforementioned predetermined conditions are: The condition is that the subject's movement based on the aforementioned body movement data is less than the first intensity, and the value based on the subject's pulsation data is greater than or equal to a predetermined second value, or In the period immediately preceding the aforementioned period, the conditions are met if the first cumulative time during which the subject's movements are at or above the first intensity is shorter than the first hour, and the third cumulative time during which the value based on the subject's pulse data is at or above the second value is a predetermined third hour or longer, including at least one of the following: The control method described in <Configuration Example 7>.

[0269] <Configuration Example 9> The aforementioned message includes a message regarding the mental stress of the subject, The control method described in <Configuration Example 8>.

[0270] <Configuration Example 10> The aforementioned person is a person who performs the work, and when it is determined that the workload of the aforementioned person meets the predetermined conditions, the message for receiving the aforementioned comments regarding the work is output. The control method described in <Configuration Example 9>.

[0271] <Configuration Example 11> A control method for a load analysis system comprising an information processing device, a user-accessible device used by a target person, and a device attached to the target person, The process of having the device measure the activity level data of the subject, The process of causing the information processing device to acquire the activity data, The process of causing the information processing device to determine whether the workload of the subject meets predetermined conditions based on the acquired activity data, The information processing device includes a step of generating a message to receive comments from the subject when it is determined that the subject's workload meets the predetermined conditions, The process of causing the information processing device to transmit the message to the user-accessible device, The process of causing the user device to output the message, A control method including

[0272] <Configuration Example 12> A device attached to a person, an information processing device that transmits messages, and a control method for a person-use device that communicates, The device acquires activity data of the subject, The acquired activity data is stored, It is determined whether at least one of the amount of data of the stored activity data and a predetermined first timing for communicating with the information processing device satisfies predetermined transmission conditions. If it is determined that the transmission conditions are met, the stored activity data is transmitted to the information processing device, and the information processing device, based on the received activity data, determines that the subject's workload meets predetermined conditions, and then receives the message for receiving comments from the subject. Control method.

[0273] <Configuration Example 13> In an information processing device, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to the subject user device used by the subject for receiving comments from the subject. A control method comprising:

[0274] <Configuration Example 14> On the computer, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to the subject user device used by the subject for receiving comments from the subject. A program to achieve this.

[0275] <Configuration Example 15> On the device used by the person concerned, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to receive comments from the subject, A control method comprising:

[0276] <Configuration Example 16> On the computer, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to receive comments from the subject, A program to achieve this.

[0277] <Configuration Example 17> The device attached to the subject, A step of obtaining activity data of the aforementioned subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves sending a message to the subject's user device used by the subject to receive comments from the subject. A control method comprising:

[0278] <Configuration Example 18> On the computer attached to the subject, A step of obtaining activity data of the aforementioned subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves sending a message to the subject's user device used by the subject to receive comments from the subject. A program to achieve this.

[0279] <Configuration Example 19> A load analysis system that uses an information processing device, a user-accessible device for the subject, and a device attached to the subject to output a message based on the subject's load, The device acquires the activity level data of the subject measured by the device, Based on the activity data, it is determined whether the workload of the subject meets the predetermined conditions. When it is determined that the workload of the person concerned meets the predetermined conditions, the message for accepting comments is output. Load analysis system. [Explanation of Symbols]

[0280] 1...First load analysis system, 1a...Second load analysis system, 1b...Third load analysis system, 11...First device, 11a...Second device, 11b...Third device, 12...First user device, 12a...Second user device, 12b...Third user device, 13...First server device, 14...First administrator terminal device, 14a...Second administrator terminal device, 14b...Third administrator terminal device, 15...First database, 15a...Second database, 15b...Third database, 21...First network, 21a...Second network, 21b...Third network, 31...First pair Elephant, 31a...Second target, 31b...Third target, 41...First administrator, 41a...Second administrator, 41b...Third administrator, 111...First input unit, 111a...Second input unit, 112...First output unit, 112a...Second output unit, 113...First communication unit, 113a...Second communication unit, 114...First storage unit, 114a...Second storage unit, 115...First control unit, 115a...Second control unit, 131...First operation unit, 131a...Second operation unit, 132...First display unit, 132a...Second display unit, 211...Server input unit, 212...Server output unit, 213...Server communication unit, 214...Server storage unit, 215 ...Server control unit, 311...Management input unit, 312...Management output unit, 313...Management communication unit, 314...Management storage unit, 315...Management control unit, 331...Management operation unit, 332...Management display unit, 411...Device input unit, 412...Device output unit, 413...Device communication unit, 414...Device storage unit, 415...Device control unit, 431...Device operation unit, 432...Device display unit, 416...Device sensor unit, B1...Wearable device, B2...Hearing robot, B3...Load analysis cloud, B4...User interface for administrators, B5...Employee data management database -, C1...Pulse measurement unit, C2...Body movement detection unit, C11...Chat interface, C12...Chat data output unit, C21...Load reference value setting unit, C22...Load calculation unit, C23...Load analysis unit, C24...Hearing target selection instruction unit, C25...Personnel allocation support data generation unit, C31...Load status display unit, C32...Personnel allocation support data display unit, C41...Employee attribute data, C42...Attendance data, C43...Chat data, D1...Physical high load time, D2...Mental high load time, G1...First screen, G2...Second screen, H1...Chat summary data, Q1...First pulse measurement unit,Q1a...2nd pulse measurement unit, Q1b...3rd pulse measurement unit, Q2...1st body movement detection unit, Q2a...2nd body movement detection unit, Q2b...3rd body movement detection unit, Ta1A...1st A table, Ta1B...1st B table, Ta1C...1st C table, Ta2...2nd table, Ta3...3rd table, Ta4...4th table, W1...1st frame, W2...2nd frame, W3...3rd frame, W4...4th frame,

Claims

1. A control method for a load analysis system that outputs a message based on the load of a target person, using an information processing device, a target person user device used by the target person, and a device attached to the target person, The device acquires the activity level data of the subject measured by the device, Based on the activity data, it is determined whether the workload of the subject meets the predetermined conditions. When it is determined that the workload of the person concerned meets the predetermined conditions, the message for accepting comments is output. Control method.

2. The activity data includes pulse data relating to the subject's heartbeat. The control method according to claim 1.

3. The activity data includes body movement data relating to the subject's body movements. The control method according to claim 2.

4. Based on the pulsation data and the body movement data, the first type of load of the subject is calculated, and based on the calculated first type of load, it is determined whether the load of the subject satisfies the predetermined conditions. The control method according to claim 3.

5. The aforementioned predetermined conditions are: The conditions are that the subject's movement based on the aforementioned body movement data is at or above a predetermined first intensity, and the value based on the subject's pulse data is at or above a predetermined first value, or The conditions are met if, in the immediately preceding period, the first cumulative time during which the subject's movements are at or above the first intensity is a predetermined first hour or longer, and the second cumulative time during which the value based on the subject's pulse data is at or above the first value is a predetermined second hour or longer. Including at least one of the following: The control method according to claim 4.

6. The aforementioned message includes a message regarding the physical stress of the subject, The control method according to claim 5.

7. Based on the pulsation data and the body movement data, the second type of load of the subject is calculated, and based on the calculated second type of load, it is determined whether the subject's load satisfies the predetermined conditions. The control method according to claim 6.

8. The aforementioned predetermined conditions are: The condition is that the subject's movement based on the aforementioned body movement data is less than the first intensity, and the value based on the subject's pulsation data is greater than or equal to a predetermined second value, or In the period immediately preceding the aforementioned period, the conditions are met if the first cumulative time during which the subject's movement is at or above the first intensity is shorter than the first hour, and the third cumulative time during which the value based on the subject's pulse data is at or above the second value is a predetermined third hour or longer, Including at least one of the following: The control method according to claim 7.

9. The aforementioned message includes a message regarding the mental stress of the subject, The control method according to claim 8.

10. The aforementioned person is a person who performs the work, and when it is determined that the workload of the aforementioned person meets the predetermined conditions, the message for receiving the aforementioned comments regarding the work is output. The control method according to claim 9.

11. A control method for a load analysis system comprising an information processing device, a user-accessible device used by a target person, and a device attached to the target person, The process of having the device measure the activity level data of the subject, The process of causing the information processing device to acquire the activity data, The process of causing the information processing device to determine whether the workload of the subject meets predetermined conditions based on the acquired activity data, The information processing device includes a step of generating a message to receive comments from the subject when it is determined that the subject's workload meets the predetermined conditions, The process of causing the information processing device to transmit the message to the user-accessible device, The process of causing the user device to output the message, A control method including

12. A device attached to a person, an information processing device that transmits messages, and a control method for a person-use device that communicates, The device acquires activity data of the subject, The acquired activity data is stored, It is determined whether at least one of the amount of data of the stored activity data and a predetermined first timing for communicating with the information processing device satisfies predetermined transmission conditions. If it is determined that the transmission conditions are met, the stored activity data is transmitted to the information processing device, and the information processing device, based on the received activity data, determines that the subject's workload meets predetermined conditions, and then receives the message for receiving comments from the subject. Control method.

13. In an information processing device, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to the subject user device used by the subject for receiving comments from the subject. A control method comprising:

14. On the computer, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to the subject user device used by the subject for receiving comments from the subject. A program to achieve this.

15. On the device used by the person concerned, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to receive comments from the subject, A control method comprising:

16. On the computer, A step of acquiring activity data of the subject measured by a device attached to the subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves outputting a message to receive comments from the subject, A program to achieve this.

17. The device attached to the subject, A step of obtaining activity data of the aforementioned subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves sending a message to the subject's user device used by the subject to receive comments from the subject. A control method comprising:

18. On the computer attached to the subject, A step of obtaining activity data of the aforementioned subject, A step of determining whether the workload of the subject meets predetermined conditions based on the activity data, When it is determined that the workload of the subject meets the predetermined conditions, the process involves sending a message to the subject's user device used by the subject to receive comments from the subject. A program to achieve this.

19. A load analysis system that uses an information processing device, a user-accessible device for the subject, and a device attached to the subject to output a message based on the subject's load, The device acquires the activity level data of the subject measured by the device, Based on the activity data, it is determined whether the workload of the subject meets the predetermined conditions. When it is determined that the workload of the person concerned meets the predetermined conditions, the message for accepting comments is output. Load analysis system.

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