Analysis system, analysis method, and computer program

Abstract

An analysis system (11) is provided with: a work data server (14) that acquires work data, the work data including drive records of one or more machines (20); a health data server (13) that acquires health data, the health data including data relating to one or more health factors for each of a plurality of workers; and an analysis server (12). The analysis server (12) executes: generation of analysis data using the work data and the health data, the analysis data including data indicating workloads of work using the one or more machines (20) and data indicating health states when the work was performed; calculation to obtain correlations between the workload and the health factors using the analysis data; and outputting of results of the calculation.

Description

Analysis system, analysis method, and computer program

[0001] The present disclosure relates to an analysis system, an analysis method, and a computer program.

[0002] Labor productivity can fluctuate depending on the health condition of workers. Therefore, attempts have been made to use data related to the health condition of workers to evaluate labor productivity. For example, Patent Document 1 discloses a method for comparing data indicating a worker's past health condition with newly acquired data indicating the worker's health condition, and extracting, from among changing parameters, parameters that have a large impact on the amount of loss caused by a decrease in labor productivity as factors for improving the health condition.

[0003] Japanese Patent Application Laid-Open No. 2022-174010

[0004] Data on workers' health can be obtained, for example, from the results of health checkups or inferred from work environment factors such as the length of overtime hours. However, because there are a variety of factors that can affect people's health and the factors that affect each person vary, it is not easy to clarify the relationship between labor productivity and health.

[0005] An object of the present disclosure is to provide an analysis system, an analysis method, and a computer program for analyzing health factors that affect work productivity.

[0006] An analysis system according to one aspect of the present disclosure includes: a work data server configured to acquire work data, the work data including records acquired from one or more machines used by each of a plurality of workers for work; a health data server configured to acquire health data, the health data including data related to one or more health factors for each of the plurality of workers; and an analysis server, wherein the analysis server is configured to: generate analysis data using the work data and the health data, the analysis data including, for each of the plurality of workers, data indicating the amount of work performed using the one or more machines and data indicating the health state of the worker at the time of performing the work; use the analysis data to perform a calculation to obtain a correlation between the amount of work and at least one of the one or more health factors; and output the results of the calculation.

[0007] An analysis method according to one aspect of the present disclosure includes: acquiring work data, the work data including records acquired from one or more machines used by each of a plurality of workers in their work; acquiring health data, the health data including data results related to one or more health factors for each of the plurality of workers; generating analysis data using the work data and the health data, the analysis data including, for each of the plurality of workers, data indicating the amount of work performed using the one or more machines and data indicating the health state of the worker at the time of performing the work; using the analysis data to perform a calculation to obtain a correlation between the amount of work and at least one of the one or more health factors; and outputting the results of the calculation.

[0008] A computer program according to one aspect of the present disclosure is a computer program for causing a user terminal to receive analysis result data sent from a server, the result data including the results of an analysis of the correlation between workload and at least one of one or more health factors using work data and health data, the work data including records obtained from one or more machines used by each of a plurality of workers in their work, and the health data including data related to the one or more health factors for each of the plurality of workers; and displaying a result display screen showing the result data, the result display screen including analysis results for one or more health factors that are correlated with the workload, the analysis results including at least two of a chart, a numerical value, a graph, or text.

[0009] FIG. 1 is a schematic diagram of an analysis system according to an embodiment. FIG. 2 is a block diagram illustrating the electrical configuration of devices constituting the analysis system of FIG. 1. FIG. 3 is a table showing examples of morning questions. FIG. 4 is a table showing examples of questions related to menopause. FIG. 5 is a table showing examples of questions related to menstruation. FIG. 6 is a table showing examples of questions related to pain. FIG. 7 is a flowchart showing an analysis method according to an embodiment. FIG. 8 is an example of a result display screen showing analysis result data.

[0010] Examples of an analysis system 11 for analyzing health factors that affect work productivity, an analysis method, and a computer program (application) for displaying the analysis results will be described below with reference to Figures 1 to 7. The present invention is not limited to these examples, and is intended to include all modifications within the meaning and scope of the claims, in addition to the content set forth in the claims.

[0011] 1, the analysis system 11 includes one or more analysis servers 12. The one or more analysis servers 12 are configured to collect various types of data, analyze the data, and transmit the analysis results. The various functions of the analysis system 11, such as collecting data, analyzing the data, and transmitting the analysis results, may be realized by one analysis server 12 or by multiple analysis servers 12.

[0012] Data collected by analysis system 11 includes health data related to the health of workers and work data related to the amount of work done by the workers. Analysis system 11 may include a health data server 13 configured to acquire the health data and a work data server 14 configured to acquire the work data. The work data may include driving records of one or more machines 20 used by each of the multiple workers in their work. The health data may include data related to one or more health factors for each of the multiple workers.

[0013] At least one of the one or more analysis servers 12 may be configured to acquire worker data related to a plurality of workers. The worker data may include identification information for identifying each of the plurality of workers and basic data for each worker. The basic data may include data related to at least one of age, gender, marital status, number of children, and industry of employment. The basic data may include data related to health status, such as data related to at least one of bedtime, sleep duration, wake-up time, presence or absence of insomnia, presence or absence of pain, mental health, premenstrual or menstrual symptoms, and menopausal symptoms.

[0014] The analysis system 11 may include one or more databases 15. The one or more databases 15 store data acquired by the analysis system 11, such as worker data, work data, and other work data. The analysis server 12 may include part or all of the databases 15, or may be able to access the databases 15 via a network 17.

[0015] The network 17 includes, for example, the Internet, a wide area network (WAN), a local area network (LAN), a provider terminal, a wireless communication network, a wireless base station, a dedicated line, etc. It is not necessary for all combinations of devices shown in Fig. 1 to be able to communicate with each other, and the network 17 may include a local network in part.

[0016] Worker data, health data, and work data are acquired for each of multiple workers. To perform the analysis appropriately, it is preferable to analyze multiple workers who are in charge of the same work. The analysis system 11 is configured to analyze the correlation between the workload of work using one or more machines 20 and health factors for multiple workers who are engaged in work that involves operating one or more machines 20. The one or more machines 20 may be, for example, manufacturing machines that produce products or processing machines that process materials. The analysis system 11 in this example analyzes multiple workers who are engaged in sewing work using sewing machines, which are an example of machines 20, at a single workplace.

[0017] The health data may include the results of a questionnaire regarding one or more health factors for each of the multiple workers. The results of the questionnaire may be collected via multiple user terminals 16 used by each of the multiple workers. For example, at least one of the one or more health data servers 13 may be configured to send a questionnaire to the multiple user terminals 16 used by the multiple workers, respectively, and then receive responses to the questionnaire sent by the multiple user terminals 16. The questionnaire may be administered in the language used by the worker responding.

[0018] A survey application for collecting health data may be installed on the user terminal 16. The survey application may be configured to, for example, display one or more questionnaire screens for a health questionnaire for each of a plurality of workers on the user terminal 16. The one or more questionnaire screens include a plurality of health-related questions and fields for answering the questions.

[0019] The survey application may be configured to transmit the input data as survey results from user terminal 16 to health data server 13 when the worker enters answers in the response fields. Alternatively, the survey for collecting health data may be conducted via a website, by email, or using a chatbot. After receiving the input data, health data server 13 may be configured to automatically tally the survey results. The basic data included in the worker data may be collected via user terminal 16, like the health data, or may be collected by a separate survey.

[0020] The health data may include vital data acquired from multiple wearable devices 18 worn by multiple workers. The vital data may include, but is not limited to, at least one of body temperature, heart rate, heart rate variability (HRV), blood oxygen level (transcutaneous arterial oxygen saturation, SpO2), and body movement. The wearable device 18 is not limited to a smartwatch type, and any device capable of acquiring vital data may be used. Furthermore, multiple wearable devices 18 may each acquire different vital data.

[0021] A management application for managing vital data acquired from wearable device 18 may be installed on user device 16. In this case, the management application may be configured to synchronize wearable device 18 with user device 16, or to transmit vital data acquired from wearable device 18 to health data server 13 via user device 16. Alternatively, wearable device 18 may be configured to transmit the acquired vital data directly to health data server 13.

[0022] The machine 20 used by the worker is equipped with a recording device 21 that records the operating status of the machine 20. Multiple workers may use a designated machine 20, or may use different machines 20 depending on the work content or date and time, but the work data is linked to the worker who performed the work. For example, an identification code such as a barcode or QC code (registered trademark) may be issued to each worker, and the work data may be linked to the worker by reading the identification code with a reading device linked to the machine 20.

[0023] For example, each recording device 21 may start recording work data when the machine 20 is powered on, and then stop recording when the machine 20 is powered off. Each of the multiple recording devices 21 may transmit the recorded work data to the work data server 14 when recording ends, or at any timing. The work data server 14 is configured to collect work data of the multiple machines 20 from the multiple recording devices 21, and store the work data as work data of each of the multiple workers.

[0024] The work data may include, for example, data related to at least one of the number of needles, thread cutting, power on / off, and raising / lowering of the needle presser. The work data may also include data related to work results, such as the number of work errors or the number of completed products, input by the worker. The machine (e.g., a computer) into which the worker inputs the work results may also be included in the machine 20 used by the worker.

[0025] Analysis server 12 is capable of communicating with health data server 13 and work data server 14 via network 17. Health data server 13 and work data server 14 may be configured to transmit collected health data and work data to analysis server 12, respectively, on a regular or irregular basis. Upon receiving the data, analysis server 12 may be configured to store the health data and work data in database 15 as data of the corresponding worker.

[0026] [Configuration of Server and User Terminal] Figure 2 shows the basic configuration of a computer, which is an example of the servers 12-14 and the terminals 16, 18. The machine 20 may also be equipped with a similar computer. A mobile terminal such as a smartphone, tablet, or smartwatch is also an example of a computer. The computer includes, for example, one or more processors 31, one or more memories 32, and a communication IF (interface) 33. The computer may further include an input device 34 and a display 35. The one or more processors 31, the one or more memories 32, the communication IF 33, the input device 34, and the display 35 are connected to each other by a communication bus 36. The configurations of the servers 12-14 and the user terminal 16 may differ from each other.

[0027] The one or more processors 31 are, for example, processing circuits configured to execute various software processes. The processing circuit may include a dedicated hardware circuit (e.g., an ASIC) that processes at least a part of the software processes. In other words, the software processes may be executed by processing circuitry that includes at least one of one or more software processing circuits and one or more dedicated hardware circuits.

[0028] The one or more processors 31 may be, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor unit (MPU), a field-programmable gate array (FPGA), or other computing device. The processor 31 executes a series of instructions included in a computer program stored in the memory 32 in response to a signal provided thereto or in response to the establishment of a predetermined condition.

[0029] The one or more memories 32 may include, for example, random access memory (RAM) or other volatile memory. The memory 32 may be configured to temporarily store programs and data. The one or more memories 32 may include storage for permanently storing data including programs. The programs include applications and an operating system. The storage may be, for example, read-only memory (ROM), a hard disk drive, flash memory, or other non-volatile storage device. The storage may be a removable storage device such as a memory card. At least a portion of the data, including applications used by the servers 12-14 and the user terminal 16, may be stored in a cloud server that can communicate via the network 17.

[0030] The communication IF 33 is configured to connect to the network 17. The communication IF 33 is configured to communicate with other devices connected to the network 17. The communication IF 33 is realized, for example, as a LAN or other wired communication IF. The communication IF 33 can also be realized, for example, as Wi-Fi (registered trademark), Bluetooth (registered trademark), or other wireless communication IF, but is not limited to these.

[0031] The input device 34 is, for example, a keyboard and a mouse, and may also include buttons, keys, switches, a touchpad, or a microphone. The display 35 may be, for example, a liquid crystal monitor or an organic EL (Electro Luminescence) monitor, or a touch screen having a touch panel that also serves as the input device 34.

[0032] [Survey on Health Factors] Questions for the health factor questionnaire may be set based on worker data. For example, different questions may be set based on data such as the worker's age, gender, or whether or not they have children. For example, questions about menstruation or pregnancy may be set for female workers, and questions about menopause may be set for workers in their 40s to 60s. In addition, questions about each symptom may be set for workers who have specific symptoms such as menstruation, pregnancy, menopause, pain, or insomnia in the preliminary questionnaire.

[0033] Health data may be acquired at any time interval, for example, at a predetermined time or time period. For example, the questionnaire may be conducted every morning, or every morning and evening. In addition to questions that are asked periodically, the questionnaire may include specific questions aimed at workers who are menstruating, pregnant, postpartum, or menopausal. For example, questions about menstruating subjects may include the start and end dates of their most recent period. Alternatively, questions about menopausal subjects may include whether they are menstruating (whether they have reached menopause), the severity of menstrual symptoms, whether they have headaches, and whether they take painkillers.

[0034] Furthermore, for various symptoms, questions regarding newly started behaviors, increased behaviors, or newly started habits as behavioral changes to improve the symptoms may be set in an open-ended or multiple-choice format. Examples of behaviors include, but are not limited to, eating a balanced diet, eating three meals a day, consuming or not consuming specific foods or drinks, going to bed early, exercising, taking supplements or medicines, staying hydrated, stretching, soaking in a hot bath, and warming the body. The period for collecting health data can be set arbitrarily, but it is preferable to collect data for a period of one month or more so that the data can be compiled on a monthly basis.

[0035] Figure 3 shows example questions for morning use (before work), and Figures 4 to 6 show example questions for evening or night use (after work). Figure 4 shows example questions specifically related to menopause. For questions asking about the presence or absence of symptoms, such as those in Figure 4, a frequency score may be obtained for each symptom, such as "none = 0," "some symptoms, but not severe = 3," "every week = 6," or "occurs almost daily = 10." Figure 5 shows example questions related to menstruation. Figure 6 shows example questions related to pain. Additionally, as shown in Figure 6, questions may be asked about behavioral changes for various symptoms, such as what or what habits have been initiated to improve symptoms, for example, once a week. While Figure 6 allows for free description of behavioral changes, multiple options (examples of behavior) such as those described above may also be presented for selection. These questions may branch to the next question depending on the answer, as indicated by the "Next QID" in Figures 4 to 6. When "Next QID" reaches "END," the question is the last and the survey ends.

[0036] Examples of morning questions include sleep quality (e.g., a 5-point scale), sleep duration, whether or not breakfast was eaten, what was eaten for breakfast, and the reason for not eating breakfast. An example of a question regarding menopause is subjective symptoms (multiple choice). Examples of questions regarding menstruation (period) include whether or not the patient is on their period, the intensity of menstrual pain (e.g., a 6-point scale), and the intensity of menstrual symptoms. Examples of questions regarding pain include whether or not the patient has a headache, the intensity of the headache, whether or not the patient has taken painkillers, the amount of painkillers taken, and the reason for taking painkillers. Other questions regarding mental health may also be set.

[0037] [Generation of analytical data and data analysis] The analytical server 12 is configured to generate analytical data using the work data and health data prior to analysis. The analytical data is generated by linking the worker data, health data, and work data for each of the multiple workers. The analytical data includes, for each of the multiple workers, data indicating the amount of work performed using one or more machines 20 and data indicating the health status of each worker when performing the work.

[0038] That is, the analytical data includes data identifying each worker and comparing the worker's workload with the worker's health status at the time of performing that work. However, the data for each worker need only identify the worker using identification information, and need not include personal information such as the worker's name or affiliation. For example, the analysis server 12 may issue an identification code indicating each worker's identification information, and the worker may use that identification code to register in a survey application or have the identification code read by a reader linked to the machine 20. In this way, generating and analyzing analytical data anonymously can prevent workers from being treated unfavorably due to their health status.

[0039] The analysis server 12 may be configured to select data to be used for analysis based on basic data. Examples of basic data for data selection include age, gender, marital status, whether or not the person has children, the number of children, or the industry of the person's workplace. For example, the analysis server 12 may generate analysis data by age group based on age, by gender based on gender, or by industry.

[0040] The analysis server 12 may be configured to select data to be used for analysis based on at least one of one or more health factors. The one or more health factors may include at least one of various symptoms indicative of lifestyle habits or health conditions. Examples of lifestyle habits include, but are not limited to, sleep quality, sleep duration, whether or not breakfast was eaten, what was eaten for breakfast, and reasons for skipping breakfast. Examples of health factors include, but are not limited to, sleep quality, sleep duration, body temperature, menopausal symptoms, menstrual symptoms, and pain.

[0041] Examples of symptoms indicating health status include, but are not limited to, whether or not a person has menopausal symptoms, whether or not they are menstruating, the intensity of menstrual pain, and the presence and intensity of headaches. Menstrual pain and headaches are examples of pain, but pain can be directed to any part of the body, such as the lower back, shoulders, joints, muscles, stomach, intestines, chest, whole body, limbs, or eyes. In particular, if a person tends to experience pain in a particular part of the body due to work, it is a good idea to target that part.

[0042] For example, the analysis server 12 may generate analysis data for workers who have menopausal symptoms, or for workers who are menstruating. Alternatively, the analysis server 12 may generate analysis data for workers who have headaches on a specified number of days or more within a specified period (e.g., one month). Alternatively, the analysis server 12 may generate analysis data for workers who have poor sleep quality and headaches, or may generate analysis data by industry.

[0043] The analysis server 12 is configured to use the analysis data to perform calculations to obtain a correlation between workload and at least one of one or more health factors and output the results of the calculations. For example, the analysis server 12 may be configured to perform correlation analysis to estimate health factors correlated with workload. Data indicating workload may be, for example, the number of stitches per day or the number of completed products per day. For example, if a negative correlation is confirmed, such as the stronger the menstrual pain or headache, the less workload there is, it can be said that health factors such as menstrual pain or headaches are affecting the workload.

[0044] The analysis server 12 may be configured to perform calculations to obtain correlations between lifestyle habits and each health symptom and output the results of the calculations. For example, the analysis server 12 may analyze whether the presence or absence of a particular lifestyle habit (e.g., eating breakfast) or the quality of a lifestyle habit (e.g., good or bad sleep quality) is correlated with the intensity of a symptom. This makes it possible to analyze, for example, which lifestyle habits affect symptoms (e.g., menstrual symptoms, menopausal symptoms, or headaches).

[0045] In correlation analysis, the analysis server 12 may convert the data used in the analysis into numerical values ​​(scores) before performing calculations. For example, the analysis server 12 converts data such as the presence or absence of specific lifestyle habits, the quality of those lifestyle habits, and the severity of symptoms into scores. The analysis server 12 then calculates a correlation coefficient between each score and the amount of work. The correlation coefficient can be calculated using any known calculation method.

[0046] To determine the strength of correlation using the correlation coefficient, standard values ​​such as 0.0 or more and less than 0.2 indicates almost no correlation, 0.2 or more and less than 0.4 indicates a slight correlation, 0.4 or more and less than 0.7 indicates a considerable correlation, and 0.7 or more and less than 1.0 indicates a strong correlation can be used.

[0047] The analysis server 12 may be configured to use the vital data to perform calculations to obtain correlations between lifestyle habits and various health symptoms and output the results. For example, the analysis server 12 can obtain accurate sleep duration data by acquiring sleep onset and wake-up times from body movement data. The analysis server 12 can also estimate sleep quality, such as light sleep, deep sleep, and REM sleep, from patterns of heart rate and heart rate variability. Furthermore, the analysis server 12 can measure whether a person is breathing with sufficient oxygen during sleep from blood oxygen levels.

[0048] These sleep conditions (sleep duration and sleep quality) can be scored from these vital data in addition to or instead of the questionnaire results. Lack of sleep itself can directly contribute to poor physical condition or reduced work efficiency, and can also indirectly contribute to reduced work efficiency by adversely affecting other health factors such as menstrual symptoms. Therefore, the analysis server 12 may calculate the correlation between the sleep-related vital data (or score) and health factors (e.g., sleep quality, menstrual symptoms, menopausal symptoms, headaches), and may further calculate the correlation between the sleep-related vital data (or score) and workload. When using vital data to analyze workload or questionnaire results, daily average values ​​or values ​​measured at a specified time or time period may be used.

[0049] Body temperature, which is one of the vital signs, can reveal symptoms of fever and hypothermia. Fever and hypothermia are symptoms of various diseases such as infection, heatstroke, and abnormal immune function. Therefore, body temperature may be used in analysis as one of the health factors. In this case, the body temperature data may include not only the average value for the day, but also the maximum or minimum value.

[0050] For example, for female workers, the physical condition is likely to change depending on the menstrual cycle. Therefore, the analysis server 12 may generate analysis data for female workers based on their menstrual cycles, using menstruation as a health factor. More specifically, the analysis server 12 may calculate a correlation coefficient for a target group including multiple female workers to obtain a correlation between the number of stitches per day and the intensity of menstrual pain in a menstrual cycle beginning on the first day of menstruation. This correlation coefficient may be calculated using the average value of multiple menstrual cycles for each worker, or may be calculated using the average value of multiple workers obtained at the same time. If a correlation coefficient equal to or greater than a specified value is calculated, the health factor is estimated to be correlated with productivity.

[0051] The analysis server 12 may calculate the menstrual cycle based on vital data. For example, the analysis server 12 may predict the menstrual cycle based on fluctuations in body temperature, and may further link this to questionnaire results for use in various analyses. In this case, body temperature data obtained early in the morning or before and after awakening may be used to determine basal body temperature.

[0052] Additionally, the analysis server 12 may score a stress level based on vital data such as heart rate, heart rate variability, and blood oxygen level. Stress itself can directly contribute to poor physical condition or reduced work efficiency, and can also indirectly contribute to reduced work efficiency by adversely affecting other health factors, such as menstrual symptoms. Therefore, the analysis server 12 may calculate a correlation between vital data (or a score) related to stress and health factors (e.g., sleep quality, menstrual symptoms, menopausal symptoms, pain), and may further calculate a correlation between vital data (or a score) related to stress and workload.

[0053] The analysis server 12 may be configured to output the calculation results in a graph. For example, even if a correlation coefficient is not calculated, if a graph shows that the amount of work is reduced due to poor physical condition such as pain, it suggests that the poor physical condition is leading to a decrease in productivity.

[0054] [Analysis Method] The analysis method of the present disclosure will be described with reference to Figure 7. First, in step S11, the work data server 14 acquires the operation records of one or more machines 20 as work data. Furthermore, the analysis server 12 acquires the work data from the work data server 14 at a predetermined timing. In this way, the analysis server 12 obtains records of one or more machines 20 used by each of multiple workers in their work. If the analysis system 11 does not include a work data server 14, the analysis server 12 may acquire work data directly from one or more machines 20.

[0055] In step S12, health data server 13 transmits questionnaire data to multiple user terminals 16 at a predetermined timing, and acquires questionnaire responses from user terminals 16 as health data. Health data server 13 also receives vital data transmitted by wearable device 18 or user terminal 16. Analysis server 12 then acquires the health data from health data server 13 at a predetermined timing. As a result, analysis server 12 obtains data related to one or more health factors for each of multiple workers. If analysis system 11 does not include health data server 13, analysis server 12 may acquire the health data directly. Note that steps S11 and S12 may be performed in reverse order, or simultaneously.

[0056] Then, in step S13, the analysis server 12 generates analysis data using the work data, health data, and worker data. The analysis data includes data indicating the amount of work performed by each of a plurality of workers obtained from one or more machines 20, and data indicating the health status of each worker when performing that work. A portion of the work data and health data to be analyzed may be selected based on the worker data.

[0057] Next, in step S14, the analysis server 12 performs calculations to obtain a correlation between the workload and at least one of the one or more health factors. Furthermore, in step S15, the analysis server 12 outputs the calculation results of step S14 as analysis results. The analysis results may include a correlation coefficient or a graph. In step S16, the analysis server 12 may send the analysis results to a destination (e.g., another server or computer) specified by the person requesting the analysis. Alternatively, in step S16, the analysis server 12 may send analysis result data to multiple user terminals 16. The analysis result data may include feedback as described below. The feedback may be intended to notify the user of the analysis results and propose solutions to health problems.

[0058] [Feedback of Analysis Results] The analysis server 12 may transmit analysis result data as feedback to multiple user terminals 16. The result data includes the results of an analysis of the correlation between the workload and at least one of one or more health factors using the work data and health data.

[0059] The display of the analysis results on each user terminal 16 may be performed by an application for displaying the analysis results (also referred to as a feedback application) installed on the user terminal 16. The feedback application is configured to, upon receiving the analysis result data transmitted from the analysis server 12, cause a result display screen 40 (see FIG. 8 ) showing the result data to be displayed on the user terminal 16. The result data may include the analysis results of all health factors, or may include only the analysis results of one or more health factors that have a particularly strong correlation with workload.

[0060] The feedback application includes a computer program for causing the user terminal 16 to perform various operations. The feedback application may also serve as a survey application, or may be another application for displaying results. The analysis server 12 and the feedback application are configured to work together to display the analysis results on the user terminal 16.

[0061] The frequency of feedback can be set for each health factor, or can be set to a specified cycle, such as once a week or once a month. The timing of feedback can be set according to the menstrual cycle, for example, if there is a strong correlation between menstrual symptoms and workload. For example, in response to premenstrual syndrome (PMS), the analysis server 12 may transmit the analysis results to the target worker a specified number of days after the end of menstruation or a specified number of days before the next expected menstrual date. In this case, the analysis results may be aggregated based on the menstrual cycle.

[0062] Using vital signs data, it is possible to predict the start date of the next period from changes in body temperature. This allows workers to take measures such as adjusting their work or managing their health based on feedback before menstrual symptoms actually appear. The start date of the next period can also be predicted from past survey results.

[0063] The analysis result data may include data on the relationship between health symptoms and behavioral changes, in addition to the correlation between workload and health data. Whether each behavioral change contributes to symptom improvement may be determined, for example, by the analysis server 12 converting the intensity of symptoms into a numerical value (score). For example, if the score before and after a certain behavioral change decreases or increases by a specified value or more, the analysis server 12 may output an analysis result indicating that the behavior improved the symptoms. In this case, the analysis result data may include, in addition to the scores before and after the behavior, a comment (text) such as, "My symptoms have improved compared to last month, and my workload has also improved. This is probably due in part to the elimination of sleep deprivation."

[0064] 8 shows a result display screen 40 showing result data that the feedback application causes the user terminal 16 to display. The result display screen 40 includes the questionnaire results (severity of menstrual symptoms) and analysis results for one or more health factors (PMS) correlated with workload. The result display screen 40 may also include analysis results of data acquired from the wearable terminal 18. The analysis results may include at least one or two of a chart, numerical values, a graph, or text. The feedback application may include a component, module, or library for displaying a chart or graph in addition to numerical values ​​(scores) and text. In this case, the feedback application can reflect the result data in a chart or graph and display it on the user terminal 16.

[0065] The result display screen 40 may include, for example, as the analysis results related to menstrual symptoms, at least one of a ledger chart 41 showing the severity of menstrual symptoms and a line graph 42 showing the daily workload for one menstrual cycle. The ledger chart 41 and the graph 42 may be accompanied by text 43 and 44 explaining the data analysis results, respectively. This allows each worker to confirm whether their own menstrual symptoms are affecting their workload. The ledger chart 41 may show average menstrual symptoms as a comparison target for individual menstrual symptoms. The line graph 42 may show the difference between each worker's workload and a workload reference value (number of stitches required per day, work quota) as an impact on performance (workload). The workload may be compared with each worker's past data (e.g., data from the previous month) or an average value including each worker or other workers.

[0066] In the case of comparing the workload of each worker with a reference value, for example, if the workload exceeds the reference value when there are no menstrual symptoms and falls below the reference value when there are menstrual symptoms, it can be inferred that menstrual symptoms have a negative impact on the workload. Therefore, in this case, the feedback to the worker may include the analysis result that menstrual symptoms may be affecting the workload.

[0067] The analysis server 12 or the feedback application may store patterned feedback content in advance, such as when the workload or symptom score satisfies or does not satisfy a reference value. In this case, the analysis server 12 or the feedback application may be configured to transmit or display the corresponding pattern of feedback content to the user terminal 16 depending on the result.

[0068] The analysis server 12 is configured to acquire data related to the menstrual cycle, lifestyle habits, various health-related symptoms, workload, and behavioral changes, and to perform analysis and feedback. For example, the analysis server 12 generates analysis data based on the menstrual cycle, and calculates correlations between lifestyle habits or health symptoms and workload.

[0069] As a result, the analysis system 11 identifies one or more health factors that affect the amount of work, which indicates productivity. For example, if a negative correlation is found between severe menstrual pain and reduced productivity, or between poor sleep quality and reduced productivity, workers who have the health factors can be extracted as targets, and measures can be taken to improve their working environment or health condition. For example, even for workers who use machines 20 that do not have a recording device 21, measures can be taken for those workers if they have the target health factors (e.g., lack of sleep or menstrual pain).

[0070] Examples of measures to improve health include, but are not limited to, providing workers with information on health improvement methods, introducing applications to record menstrual cycles, introducing methods or products to alleviate symptoms, and encouraging them to undergo health checkups or visit a doctor. Examples of measures to improve the working environment include, but are not limited to, installing air conditioning, adjusting work shifts, adjusting workloads, or changing work content.

[0071] For example, if a worker is suffering from periodic illnesses such as menstrual pain, a decline in labor productivity can be prevented by systematically shortening working hours during periods of poor health. For workers, avoiding excessive work during periods of poor health can prevent their health from worsening. Furthermore, if a worker's work tends to cause pain in a specific part of the body, employers can proactively take measures to address this, which can not only improve labor productivity but also extend the healthy lifespan of workers. If such measures help workers maintain good health, it can be expected that employee retention rates will improve.

[0072] In particular, the analysis server 12 can provide both workers and employers with regular or continuous feedback based on the analysis results and behavioral changes, enabling more appropriate measures to be implemented. In this case, the history of improvement measures and their results is recorded as numerical data such as a score, which can be used to formulate further measures or for other subjects.

[0073] If the worker's personal information were linked to the analysis results, there is a risk that the worker may become suspicious that poor health will lead to an unfavorable evaluation. In this regard, if work data is automatically acquired from the machine 20 or the questionnaire is conducted anonymously, it is easier to gain understanding of data collection by not including personal information in the data for analysis. Furthermore, by automatically acquiring work data and health data by the servers 12-14, it is difficult for human intervention to be involved in data collection, making it easier to maintain anonymity.

[0074] Once health factors that affect productivity are identified, measures to improve health conditions can be taken based on the health data without collecting work data. In this case, health data can be exchanged directly between the analysis server 12 and the user terminal 16, and information on measures can be provided, without the employer having to provide work data. Therefore, workers can provide data and receive information without fear of their work performance being evaluated.

[0075] Health factors that affect productivity can be identified, for example, through a questionnaire that asks about "factors of poor health that lead to a decrease in one's productivity." However, judgments regarding a decrease in productivity vary from person to person, so this may lack objectivity. In this regard, by using machine 20 records as work data, more objective data can be obtained. Furthermore, by obtaining the work data separately from the health-related questionnaire, arbitrary survey results can be prevented.

[0076] The effort required for data collection can be reduced and data can be obtained in real time by automatically obtaining records of the operating status of the machine 20 as work data, obtaining health data through a questionnaire using a survey application, and obtaining vital data with the wearable terminal 18. In particular, when multiple work locations are in different countries, collecting data can be difficult or take a long time.

[0077] In this regard, collecting data via the network 17 solves issues such as geographical distance, time difference, and the need to secure personnel to collect data. Furthermore, the analysis server 12 automatically generates analytical data, automating the collection, generation, and analysis of data. This allows for real-time analysis of the actual physical discomfort felt by workers on a daily basis and the actual workload for that day. Therefore, for example, it is possible to analyze and implement measures for workers working in different countries or even for workers who have just been hired.

[0078] The survey application, management application, and feedback application are operated by programs included in each application. Therefore, by translating the survey questions and answer options into the language of the workers who will be answering, it can be easily applied to different countries. In particular, by making the survey answers multiple choice and converting the data used for analysis into numbers, it is possible to automate most of the analysis process even if the languages ​​used by the workers, the data analysts, and the employer (the person requesting the analysis) are different.

[0079] Furthermore, by using a feedback application, quantified data can be displayed in charts or graphs, eliminating the need for translation even when different languages ​​are used by workers, analysts, and employers.If different languages ​​are used by workers, analysts, and employers, the questionnaire and analysis can be more automated by translating related sentences between them using machine translation.

[0080] Effects of the Present Disclosure The analysis system 11 and analysis method of the present disclosure can achieve the following effects: (1) It is possible to obtain a correlation between labor productivity and health factors based on the actual operating status of the machine 20 and health data related to the worker himself / herself (for example, vital signs data or questionnaire results). This makes it possible to accurately analyze health factors that affect labor productivity.

[0081] (2) The number of stitches on a sewing machine reflects the amount of work done by a worker. Therefore, the number of stitches on a sewing machine can be used as an indicator of labor productivity. Furthermore, by adding data that reflects the work content, such as thread cutting or raising and lowering the needle presser, more accurate analysis becomes possible.

[0082] (3) By conducting health surveys via the user terminals 16 used by workers, the cost and effort required to obtain survey results can be reduced. Therefore, more detailed health data can be obtained by conducting surveys more frequently, such as daily or twice a day. For example, detailed analysis can be performed to identify the days or days during a worker's menstrual period that have a particularly large impact on the amount of work.

[0083] (4) By acquiring vital data using the wearable device 18, actual measurement data related to the worker's health condition can be acquired in real time. In addition, the acquired vital data can be used to obtain information related to health factors such as sleep status, stress level, or menstrual cycle. By performing analysis and prediction using actual measurements such as vital data, more accurate and detailed results can be obtained.

[0084] (5) By selecting the data to be used for analysis based on attributes such as age, gender, marital status, number of children, and industry of employment, it becomes possible to analyze the relationship with health factors in more detail.

[0085] (6) By selecting the data to be used for analysis based on various health factors such as sleep quality, sleep duration, menopausal symptoms, menstrual symptoms, body temperature, and pain, it becomes possible to analyze in more detail the impact of each health factor on labor productivity.

[0086] (7) The analysis server 12 notifies the worker of the analysis results by transmitting analysis result data based on data related to, for example, menstrual cycle, lifestyle habits, various health-related symptoms, workload, and behavioral changes to multiple user terminals 16. This reduces the time and cost required for providing feedback to the worker, and enables efficient implementation of periodic or continuous measures.

[0087] (8) By using a feedback application, data can be visualized in an easy-to-understand manner using charts or graphs and displayed on the user terminal 16. [Modifications of the Present Disclosure] This embodiment can be modified and implemented as follows: This embodiment and the following modifications can be implemented in combination with each other to the extent that no technical contradiction occurs.

[0088] Machine 20 is not limited to a sewing machine, but may also be a device or terminal including a computer or controller, or a measuring machine capable of measuring the amount of work. For example, work data may be the amount of data entered by a worker, the amount of data processed, the number of measurements taken, the number of items transported, or the number of inspections performed. Additionally, work data may be the amount or number of materials consumed in the work, or the quantity or weight of processed or manufactured products. In other words, machine 20 is not limited to a machine that is directly operated by a worker, but may be any machine capable of obtaining a record of its work.

[0089] Analysis system 11 may include a transmitting device or server for transmitting analysis result data to multiple user terminals 16, separate from analysis server 12. Analysis system 11 may include a health data server 13 for receiving vital data acquired from wearable terminal 18, separate from health data server 13 for sending questionnaires and acquiring results.

[0090] The analysis system 11 may use only either the questionnaire results or the actual measurement data obtained from the wearable device as the health data. The following are examples of aspects that can be understood based on the above-described embodiment and modifications. [1] An analysis system comprising: a work data server configured to acquire work data, the work data including records acquired from one or more machines used by each of a plurality of workers for work; a health data server configured to acquire health data, the health data including data related to one or more health factors for each of the plurality of workers; and an analysis server, wherein the analysis server is configured to: generate analysis data using the work data and the health data, the analysis data including, for each of the plurality of workers, data indicating the amount of work performed using the one or more machines and data indicating the health state of the worker at the time of performing the work; use the analysis data to perform a calculation to obtain a correlation between the amount of work and at least one of the one or more health factors; and output the results of the calculation. [2] The analysis system described in [1] above, wherein the machine is a sewing machine, and the work data includes data related to at least one of the number of stitches, thread cutting, or raising and lowering of a needle presser foot. [3] The analysis system described in [1] or [2] above, wherein the health data includes results of a questionnaire regarding one or more health factors for each of the plurality of workers, and the health data server is configured to transmit the questionnaire to a plurality of user terminals used by the plurality of workers respectively, and then receive responses to the questionnaire transmitted by the plurality of user terminals. [4] The analysis system described in any of [1] to [3] above, wherein the health data includes vital data acquired from a plurality of wearable devices worn by each of the plurality of workers, and the vital data includes at least one of body temperature, heart rate, heart rate variability, blood oxygen level, and body movement, and the analysis server is configured to perform calculations to obtain a correlation between the vital data and the workload or at least one of the one or more health factors.[5] The analysis system according to any of [1] to [4] above, wherein the analysis server is configured to acquire worker data, the worker data including identification information for identifying each of the plurality of workers and basic data for each worker, the basic data including data related to at least one of age, gender, marital status, number of children, and industry of employment, and the analysis server is configured to select data to be used for the analysis data based on the basic data. [6] The analysis system according to any of [1] to [5] above, wherein the one or more health factors include at least one of sleep quality, sleep duration, body temperature, menopausal symptoms, menstrual symptoms, and pain, and the analysis server is configured to select data to be used for the analysis data based on at least one of the one or more health factors. [7] The analysis system according to any one of [1] to [6] above, wherein the analysis server is configured to transmit result data of analysis using the analysis data to a plurality of user terminals used by the plurality of workers, respectively, wherein the result data includes analysis results for one or more health factors correlated with the workload, and the analysis results include at least two of a chart, a numerical value, a graph, or text. [8] The analysis system according to any one of [1] to [7] above, wherein the analysis server is configured to transmit result data of analysis using the analysis data to a plurality of terminals used by the plurality of workers, respectively, wherein the result data includes a comparison between the data of the corresponding worker and a reference value, and the reference value of the workload is either an average value for that worker, an average value including other workers, or a work quota. [9] The analysis system according to any one of [1] to [8] above, wherein the health data includes data on menstrual symptoms, and the analysis server is configured to perform calculations to obtain a correlation between the workload and the menstrual symptoms, and the analysis results by the analysis server are compiled based on the menstrual cycle.

[10] The analysis system according to any one of [1] to [9] above, wherein the health data includes the results of a questionnaire regarding menopausal symptoms, and the analysis server is configured to perform calculations to obtain a correlation between the workload and the menopausal symptoms.

[11] An analysis method comprising: acquiring work data, the work data including records acquired from one or more machines used by each of a plurality of workers for work; acquiring health data, the health data including data related to one or more health factors for each of the plurality of workers; generating analysis data using the work data and the health data, the analysis data including, for each of the plurality of workers, data indicating the amount of work performed using the one or more machines and data indicating the health state of the worker at the time of performing the work; using the analysis data to perform a calculation to obtain a correlation between the amount of work and at least one of the one or more health factors; and outputting the results of the calculation.

[12] A computer program for causing a user terminal to execute the following steps: receive analysis result data sent from a server, the result data including the result of analyzing the correlation between workload and at least one of one or more health factors using work data and health data, the work data including records obtained from one or more machines used by each of a plurality of workers in their work, and the health data including data related to the one or more health factors for each of the plurality of workers; and display a result display screen showing the result data, the result display screen including analysis results for one or more health factors correlated with the workload, the analysis results including at least two of charts, numbers, graphs, and text.

[13] An analysis system comprising: an acquisition unit configured to acquire work data and health data, wherein the work data includes data recording the operating status of one or more machines used by each of a plurality of workers in their work, and the health data includes data related to one or more health factors for each of the plurality of workers; a generation unit configured to generate analysis data from the work data and the health data; and a calculation unit configured to use the analysis data to perform calculations to obtain a correlation between labor productivity of work using the one or more machines and health factors.

Claims

1. An analysis system comprising: a work data server configured to acquire work data, the work data including records acquired from one or more machines used by each of a plurality of workers for work; a health data server configured to acquire health data, the health data including data related to one or more health factors for each of the plurality of workers; and an analysis server, wherein the analysis server is configured to: generate analysis data using the work data and the health data, the analysis data including, for each of the plurality of workers, data indicating the amount of work performed using the one or more machines and data indicating the health state of the worker at the time the work was performed; use the analysis data to perform a calculation to obtain a correlation between the amount of work and at least one of the one or more health factors; and output the result of the calculation.

2. The analysis system according to claim 1, wherein the machine is a sewing machine, and the operation data includes data relating to at least one of the number of stitches, thread cutting, or raising and lowering of a needle presser.

3. The analysis system of claim 1 or 2, wherein the health data includes the results of a questionnaire regarding one or more health factors for each of the plurality of workers, and the health data server is configured to transmit the questionnaire to a plurality of user terminals used respectively by the plurality of workers, and then receive responses to the questionnaire transmitted by the plurality of user terminals.

4. The analysis system of any one of claims 1 to 3, wherein the health data includes vital data obtained from a plurality of wearable devices worn by each of the plurality of workers, the vital data including at least one of body temperature, heart rate, heart rate variability, blood oxygen level, and body movement, and the analysis server is configured to perform calculations to obtain a correlation between the vital data and the workload or at least one of the one or more health factors.

5. The analysis system described in any one of claims 1 to 4, wherein the analysis server is configured to acquire worker data, the worker data including identification information for identifying each of the plurality of workers and basic data for each worker, the basic data including data relating to at least one of age, gender, marital status, number of children, and industry of workplace, and the analysis server is configured to select data to be used for the analysis data based on the basic data.

6. The analysis system of any one of claims 1 to 5, wherein the one or more health factors include at least one of sleep quality, sleep duration, body temperature, menopausal symptoms, menstrual symptoms, and pain, and the analysis server is configured to select data to be used for the analysis data based on at least one of the one or more health factors.

7. The analysis system of any one of claims 1 to 6, wherein the analysis server is configured to transmit analysis result data using the analysis data to a plurality of user terminals used by the plurality of workers respectively, and the result data includes analysis results for one or more health factors correlated with the workload, and the analysis results include at least two of charts, numbers, graphs, or text.

8. The analysis system of any one of claims 1 to 7, wherein the analysis server is configured to transmit analysis result data using the analysis data to a plurality of terminals used by the plurality of workers respectively, the result data including a comparison between the data of the corresponding worker and a standard value, and the standard value of the workload is either an average value for the worker, an average value including other workers, or a work quota.

9. The analysis system of any one of claims 1 to 8, wherein the health data includes data on menstrual symptoms, the analysis server is configured to perform calculations to obtain a correlation between the workload and the menstrual symptoms, and the analysis results by the analysis server are compiled based on the menstrual cycle.

10. An analysis system according to any one of claims 1 to 9, wherein the health data includes the results of a questionnaire regarding menopausal symptoms, and the analysis server is configured to perform calculations to obtain a correlation between the workload and the menopausal symptoms.

11. An analysis method comprising: acquiring work data, the work data including records obtained from one or more machines used by each of a plurality of workers in their work; acquiring health data, the health data including data related to one or more health factors for each of the plurality of workers; generating analysis data using the work data and the health data, the analysis data including, for each of the plurality of workers, data indicating the amount of work performed using the one or more machines and data indicating the health state at the time of performing the work; using the analysis data to perform a calculation to obtain a correlation between the amount of work and at least one of the one or more health factors; and outputting a result of the calculation.

12. A computer program for causing a user terminal to execute the following steps: receive analysis result data sent from a server, the result data including results of an analysis of the correlation between workload and at least one of one or more health factors using work data and health data, the work data including records obtained from one or more machines used by each of a plurality of workers in their work, and the health data including data related to the one or more health factors for each of the plurality of workers; and display a result display screen showing the result data, the result display screen including analysis results for one or more health factors correlated to the workload, the analysis results including at least two of charts, numbers, graphs, and text.

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