JP2022052910A - Physical condition evaluation system, server, program, and physical condition evaluation service provision method - Google Patents

Abstract

To provide a physical condition evaluation system for evaluating a person's physical condition.SOLUTION: According to an embodiment, the physical condition evaluation system comprises: a human presence sensor for detecting a person; a server capable of communicating with the human presence sensor; and a database accessible from the server. The database includes a disease history and motion database that stores health history information for each person. The human presence sensor is provided with an analysis unit for analyzing image data obtained by imaging a prescribed area and generating sensor data that includes the motion information of persons. The server is provided with a reception unit, an acquisition unit and a calculation unit. The reception unit receives sensor data from the human presence sensor. The acquisition unit accesses the disease history and motion database and acquires the health history information. The calculation unit calculates a score in real time that indicates a trend of physical condition of the person on the basis of the motion information of the person included in the received sensor data and the health history information.SELECTED DRAWING: Figure 2

Description

An embodiment of the present invention relates to a physical condition evaluation system, a server, a program, and a method for providing a physical condition evaluation service.

In recent years, the employment system has been reviewed and while companies can work longer, the aging of employees in companies is advancing, and companies need to manage this limited human resources more appropriately. There is. One of the methods is to manage the physical condition of employees, and it is required to evaluate the physical condition objectively and appropriately, and then promptly take appropriate measures as necessary.

Conventionally, a technique for monitoring the physical and mental health of employees working in an office using an image sensor has been known. In the existing technology, the position and body movement of a person are monitored by an image sensor, and a state where there is no conversation and a long sitting state is warned to an administrator or the like as a sign that the mental state deteriorates.

Japanese Unexamined Patent Publication No. 2019-101952

However, even if a person working in the office happens to have no conversation and is seated for a long time, it may be that he / she was able to concentrate on his / her work and did a good job, so he / she can accurately judge his / her physical condition only from the movement of the person. As a rule of thumb, it is difficult to try, and there was a need for something that could better evaluate the physical condition of those people.

Therefore, an object of the present invention is to provide a physical condition evaluation system, a server, a program, and a physical condition evaluation service providing method for evaluating a person's physical condition.

According to the embodiment, the physical condition evaluation system includes a motion sensor for detecting a person, a server capable of communicating with the motion sensor, and a database accessible from the server. The database includes a medical history / behavior database that stores health history information for each person. The motion sensor includes an analysis unit that analyzes image data obtained by imaging a predetermined area and generates sensor data related to human motion information. The server includes a receiving unit, an acquisition unit, and a calculation unit. The receiving unit receives the sensor data from the motion sensor. The acquisition unit acquires health history information from the medical history / behavior database. The calculation unit calculates a score indicating a tendency of a person's physical condition based on the movement information of the person related to the received sensor data and the health history information.

FIG. 1 is a diagram showing an example in the floor of a building. FIG. 2 is a diagram showing an example of a physical condition evaluation system according to an embodiment. FIG. 3 is a block diagram showing an example of the image sensor 3. FIG. 4 is a diagram showing an example of image data obtained by the image sensor 3. FIG. 5 is a diagram showing an example of sensing items of the image sensor 3. FIG. 6 is a schematic diagram for explaining human motion information. FIG. 7 is a diagram showing an example of operation information. FIG. 8 is a diagram showing an example of information stored in the medical history / operation database 42. FIG. 9 is a diagram showing an example of information stored in the personal information database 43. FIG. 10 is a diagram showing an example of information stored in the office database 44. FIG. 11 is a block diagram showing an example of the cloud server 200. FIG. 12 is a diagram for explaining a score showing a tendency of a person's physical condition. FIG. 13 is a sequence diagram showing an example of a processing procedure in the physical condition evaluation system according to the embodiment. FIG. 14 is a diagram showing an example of message data for managing human health.

An image sensor, which is a motion sensor, can acquire more diverse information than a physical sensor or an infrared sensor. Some have a communication function and have a high affinity with IoT (Internet of Things) technology. Furthermore, it is expected as an edge device for big data analysis.

If a fisheye lens, a wide-angle camera, or the like is used, the area that can be photographed by one image sensor can be enlarged, and the distortion of the image can be corrected by calculation. An image sensor having a function of setting a mask for an area in the field of view that is not desired to be sensed and a learning function is also known. The image sensor according to the embodiment can obtain, for example, human information or environmental information by taking an image captured in the field of view and processing the acquired image data.

Human information is information about humans existing in the target space, for example, the number of people in the area, the behavior of the person, the amount of activity of the person, the presence / absence indicating the existence or absence of the person, or the person walking or walking. Examples include walking / staying indicating whether or not the person stays in one place. An object (article) attached to a person, that is, the presence or absence of luggage is also an example of human information. The environmental information is information about the environment of the space to be controlled (target space), and as an example thereof, the illuminance distribution of the store, the temperature distribution, and the like can be mentioned.

Human information and environmental information can be calculated for each target space. Alternatively, human information and environmental information can be calculated for each small area (area) in which the target space is divided into a plurality of areas. In this embodiment, we pay particular attention to motion information that indicates human motion in an office building.

<Structure>
FIG. 1 is a diagram showing an example in the floor of a building. As shown in FIG. 1, the lighting device 1, the air outlet of the air conditioning device 2, and the image sensor 3 are arranged on each floor, for example, the ceiling. The image sensor 3 captures an image captured in the field of view and acquires image data. This image data is processed by the image sensor 3 to generate operation information.

<System>
FIG. 2 is a diagram showing an example of a physical condition evaluation system according to an embodiment. In FIG. 2, a plurality of image sensors 3 (3-1 to 3-n) are arranged on each floor of the office building 100, for example, on the ceiling so as to look down on the office area. The image sensor 3 captures the work area in the field of view, captures what is captured in the field of view, and generates image data. The image data includes, for example, office employees (people), desks and seats (chairs), furniture, and the like. The image sensor 3 analyzes the image data and generates sensor data including human motion information.

The surveillance camera 3-0 is installed near the entrance of the office building 100 or the entrance gate, and images the visitors from the front direction to acquire facial image data or whole body image data of employees and visitors.

Each image sensor 3 is connected to, for example, a gateway (GW) 7 arranged in a control room via a signal line 4. EtherCAT (registered trademark), BACnet (registered trademark), TCP / IP (Transmission Control Protocol / Internet Protocol), UDP / IP, and the like can be applied to the protocol of the signal line 4.

The gateway 7 is connected to the in-building network 5. Building Automation and Control Networking protocol (BACnet (registered trademark)), DALI (registered trademark), ZigBee (registered trademark), ECHONET Lite (registered trademark), etc. can be applied to the communication protocol of the network 5 in the building. be.

For example, the firewall 6 and the management server 8 are connected to the in-building network 5. The management server 8 manages the lighting, air conditioning, safety, etc. of the office building 100 based on various information acquired from the image sensor 3 and the like. The storage connected to the network 5 in the building stores the sensor database 9a. The sensor database 9a stores the sensor data generated by the image sensor 3. The firewall 6 is connected to the cloud 400 via, for example, a dedicated line network 300 formed on the Internet.

The cloud (cloud computing system) 400 has a cloud server 200 as a core, and includes a surveillance camera 41, a mail server 500, and the like. In addition, the cloud 400 includes a medical history / operation database 42, a personal information database 43, and an office database 44, all of which can be accessed from the cloud server 200. Further, the smartphone or tablet 20 owned by the user can access the cloud 400 via wireless communication.

<Image sensor>
FIG. 3 is a block diagram showing an example of the image sensor 3. The image sensor 3 includes a camera unit 31, a storage unit 32, a processor 33, and a communication unit 34. These are connected to each other via an internal bus 35.

The camera unit 31 includes a fisheye lens 31a, an aperture mechanism 31b, an image sensor (image sensor) 31c, and a register 30. The fisheye lens 31a forms an image in the visual field on the image pickup device 31c. In the embodiment, the fisheye lens 31a captures the work area in the field of view, and the image of the work area is imaged on the image sensor 31c. The work area may be divided into a plurality of areas, and each area is imaged by one image sensor, and the image data obtained by the multiple image sensors are integrated to cover the entire area of the work area. Is also good.

The image sensor 31c is an image sensor typified by CMOS (complementary metal oxide semiconductor), and generates a video signal having a frame rate of, for example, 30 frames per second. This video signal is digitally coded to generate image data. The image data may include human face image data. The amount of light incident on the image sensor 31c is adjusted by the aperture mechanism 31b.

The register 30 stores the camera information 30a. The camera information 30a is information about the camera unit 31, such as the state of the auto gain control function, the gain value, and the exposure time, or information about the image sensor 3 itself.

The storage unit 32 is a semiconductor memory such as an SDRAM (Synchronous Dynamic RAM) or an EPROM (Erasable Programmable ROM). The storage unit 32 uniquely distinguishes the image data 32a including the face image data acquired by the camera unit 31, the program 32b for causing the processor 33 to execute various functions related to the embodiment, and the image sensor. The sensor ID (IDentification) 32c and the dictionary data 32d are stored.

The dictionary data 32d is data similar to template data prepared according to detection items (people, objects, men, women, etc.) in the image sensor 3. For example, the chair is detected from the image data by performing the template matching process using the dictionary data 32d prepared for the detection of the "chair". Various dictionary data 32d are prepared in advance in order to make it possible to distinguish the accuracy, the type of chair (office chair, sofa, sitting chair, etc.) and the color. In addition, it is possible to create dictionary data for detecting wheelchairs, strollers, children, carts, white cane users, wandering elderly people, suspicious persons, lost children, persons who have been late to escape, and the like. As an example of the creation method, a machine learning framework such as a support vector machine or a Boltzmann machine can be used.

The processor 33 realizes various functions described in the embodiment by loading and executing the program stored in the storage unit 32. The processor 33 is, for example, an LSI (Large Scale Integration) equipped with a multi-core CPU (Central Processing Unit) and tuned for executing image processing at high speed. The processor 15 can also be configured by FPGA (Field Programmable Gate Array) or the like. MPU (Micro Processing Unit) is also one of the processors.
The communication unit 34 can be connected to the signal line 4 and mediates the exchange of data with the gateway 7, the management server 8, the cloud 400, and the cloud server 200.

By the way, the processor 33 includes a detection unit 33a, a feature amount calculation unit 33b, an analysis unit 33c, and a learning unit 33d as processing functions according to the embodiment. The detection unit 33a, the feature amount calculation unit 33b, the analysis unit 33c, and the learning unit 33d are, for example, in a process generated in the process of executing arithmetic processing by the processor 33 according to the program 32b loaded in the register of the processor 33. be.

The detection unit 33a detects an object in the visual field using the dictionary data 32d. In the embodiment, a person is assumed as a detection target. That is, the detection unit 33a detects a person in the work area from the image data 32a by using the dictionary data set prepared for each detection item.

The feature amount calculation unit 33b processes the image data 32a stored in the storage unit 32 by a predetermined algorithm, and extracts the feature amount related to a person or an object in the visual field. For example, it is possible to calculate the motion feature amount by tracing the change in the brightness of each frame of the image data for each pixel and analyzing the time series. Histograms of oriented gradients (HOG) Features such as features, contrast, resolution, S / N ratio, and color tone are known. Further, a co-occurrence histogram (Co-occurrence HOG: Co-HOG) feature quantity, a Haar-Like feature quantity, and the like are also known as feature quantities.

The analysis unit 33c analyzes the image data 32a and generates sensor data including the position of a person, attribute information, staying time, and the like. That is, the analysis unit 33c is, for example, based on the feature amount obtained by the feature amount calculation unit 33b, human information (position of person, presence / absence of person, number of people, behavior of person, amount of activity of person, presence / absence of luggage, etc.). And environmental information (illuminance distribution, temperature distribution, etc.) are calculated. There are also sensing items such as human cell position, presence / absence, staying time, walking / staying. The analysis unit 33c can further sense more detailed attribute information such as male, female, young, middle-aged, and elderly.

Further, the analysis unit 33c refers to human information, environmental information, feature quantities, and the like, and acquires human motion information in the work area by, for example, rule-based recognition or machine learning recognition. For example, pattern recognition technology using motion features can detect information indicating the tendency of human movement (movement path information, motion features, individual motion characteristics of a group of people (cluster), etc.). ..

The learning unit 33d performs learning in order to improve the correct answer rate of the analysis result by the analysis unit 33c based on the output of the image data 32a and the feature amount calculation unit 33b.

FIG. 4 is a diagram showing an example of image data obtained by an image sensor 3 arranged on each floor, for example, on the ceiling so as to look down on the office area. The frame of this image data is divided into 7 cells in the (x) axis direction and the (y) axis direction, respectively, and is divided into 7 × 7 = 49 cells. In the field of view of the image sensor 3 in the center, four tables and four seats are captured for each table. The image sensor 3 detects a person in the cells (x, y) = (2,2), (2,6), (6,6), (1,4), and is shown in FIG. 4 as the person cell position. Get the sensor data.

As shown in FIG. 5, the presence flag 1 is set in the cells (2, 2), (2, 6), and (6, 6) corresponding to the environment of FIG. For cells (1 and 4), the presence flag is 0, indicating that (moving) has been detected. Collectively, sensor data of [absence] = (1,1,1,0) can be obtained. For each cell in which a person is detected, for example, (30, 30, 40, 00) is detected as a [stay (attendance) time] item. The unit is (minutes). (0,0,0,1) is detected in each cell as [walking retention].

Further, the attributes of the person at each cell position (x, y) = (2,2), (2,6), (6,6), (1,4) are specified as, for example, male, female, female, and male. Then, identification information (person ID) for distinguishing each is given as, for example, 001,002,003,004.

Examples of sensor data calculated by the analysis unit 33c are (1) presence / absence of people, (2) number of people, (3) activity amount of people, (4) illuminance, and (5) pedestrian retention status (walking retention). , Will be described in more detail.

(1) Detection of presence / absence of a person (presence / absence of a person) For example, the analysis unit 33c may detect the presence / absence of a person by identifying a person area in the image based on the difference between the images. Alternatively, the presence or absence of a person can be detected by applying the person recognition technology. As the difference between the images, a difference from the background image, a difference between temporally continuous images (difference between frames), and the like may be used.

(2) Estimating the number of people For example, the analysis unit 33c identifies a person area in the image by the same method as in (1), processes the image data of the identified person area into an image, and exists in the target space. You may estimate the number of people.

(3) Estimating the amount of human activity For example, the analysis unit 33c may extract a movement region using an inter-frame difference or the like, and estimate the amount of activity based on the size, shape, or the like of the extracted movement region. .. For example, the amount of activity may be estimated at stages such as “none”, “small”, “medium”, and “large”, or may be estimated as an index value such as METs (Metabolic Equivalents). The analysis unit 33c may extract a motion region for each subspace and estimate the amount of activity for each subspace.

(4) Illuminance estimation For example, the analysis unit 33c may estimate the illuminance of the target space (or each subspace) based on the image and the image pickup set value (for example, gain, exposure time, etc.) of the camera unit 31. good.

(5) Detection of gait retention For example, the analysis unit 33c may extract a movement region using a difference between frames or the like, and detect gait retention based on the size, shape, or the like of the extracted motion region.

The image sensor 3 repeatedly generates sensor data as shown in FIG. The sensor data is transmitted from the image sensor 3 to the cloud server 200 via the gateway 7.

Further, in the embodiment, the analysis unit 33c of the image sensor 3 calculates human motion information.
FIG. 6 is a schematic diagram for explaining human motion information. The motion information is information expressing, for example, a person's movement, its tendency, a movement trajectory, or a behavior, and is acquired in real time by each image sensor 3. As shown in FIG. 6, if a person's flow line follows an irregular trajectory beyond the limit, the person may be dizzy, intoxicated, or out of balance. In any case, I can see that I am not feeling well. The tendency of such movements can be quantified, and can be classified into two types, healthy / unhealthy, by, for example, a machine learning method.

Further, even a person who does not move from one place may exhibit symptoms such as sneezing and coughing, and these characteristic movements are analyzed by the image sensor 3 and classified into behavioral information. Furthermore, suppose that the movement of another person moving away from the person in the center of the group in a radial pattern is detected. In such cases, the person in the center is likely to have coughed or sneezeed without a mask. In view of the social situation caused by COVID-19 in recent years, it is considered that such characteristic movements are often observed, but these can also be quantified and used as operation information.

FIG. 7 is a diagram showing an example of operation information. According to the classification, it is possible to classify a person's movement into three types, for example, sneezing, light-headedness, and coughing, and score the certainty of each movement. The score is, for example, 100 points, and the larger the value, the higher the possibility. According to FIG. 7, for example, it can be seen that a person with ID = 001 is likely to sneeze and a person with ID = 002 is likely to cough at a certain point in time. The operation information is calculated in real time, and the score changes one after another.

FIG. 8 is a diagram showing an example of information stored in the medical history / motion database 42 of FIG. The medical history / behavior database 42 is a database in which history information (health history information) related to health such as medical history, constitution, or smoking history is associated and registered for each ID (IDentification) associated with an individual. .. This type of database collects necessary information from the database on the medical history of patients in related hospitals with permission as a company, and in the form of interviews with industrial physicians and questionnaires to employees in corporate health examinations. It is constructed by collecting from each employee by having them answer the questions. Further, the personal ID can be uniquely specified by, for example, an IC card.

FIG. 9 is a diagram showing an example of information stored in the personal information database 43 of FIG. The personal information database 43 is a database in which personal information such as age, address, or travel history is associated with an individual's ID and registered. This type of database can be constructed, for example, by collecting necessary information with permission from the database of the local government, collecting information in the form of a questionnaire, or inquiring to the database of the place of employment.

FIG. 10 is a diagram showing an example of information stored in the office database 44 of FIG. The office database 44 is a database in which schedules and actual employment information are registered for each employee, such as daily actual entry / exit times, daily work schedules, business trip destinations, etc., in association with individual IDs. be. This type of database can be constructed by collecting individual schedules and attendance information from their respective workplaces via the intranet.

<Cloud server>
FIG. 11 is a block diagram showing an example of the cloud server 200. The cloud server 200 is a computer including a processor 210 such as a CPU and an MPU, a ROM (Read Only Memory) 220, and a RAM (Random Access Memory) 230. The cloud server 200 further includes a storage unit 240 such as a hard disk drive (HDD), an optical media drive 260, and a communication unit 270.

The ROM 220 stores basic programs such as BIOS (Basic Input Output System) and UEFI (Unified Extensible Firmware Interface), and various setting data. The RAM 230 temporarily stores programs and data loaded from the storage unit 240.

The optical media drive 260 reads digital data recorded on a recording medium such as a CD-ROM 280. Various programs executed by the cloud server 200 are recorded and distributed on, for example, a CD-ROM 280. The program stored in the CD-ROM 280 is read by the optical media drive 260 and installed in the storage unit 240.

The communication unit 270 controls communication with the image sensor system via the leased line network 300 and the firewall 6.

In addition to the program 240a executed by the processor 210, the storage unit 240 stores a medical history / operation database 42, a personal information database 43, and an office database 44 equivalent to each database of the cloud 400. The program 240a includes an instruction for making the cloud server 200 function as a receiving unit 200a, an instruction for making it function as an acquisition unit 200b, and an instruction for making it function as a calculation unit 200c.

By the way, in FIG. 11, the processor 210 includes a receiving unit 200a, an acquisition unit 200b, and a calculation unit 200c as its functions.

The receiving unit 200a receives the sensor data generated by the image sensors 3 (FIGS. 2 and 3) directly from the respective image sensors 3 or via the sensor database 9a. For example, the cloud server 200 transmits a sensor data acquisition request to the gateway 7 at a constant polling cycle, for example. The gateway 7 that has received this request transmits the sensor data to the cloud server 200. The receiving unit 200a receives the transmitted sensor data.

The acquisition unit 200b accesses the medical history / motion database 42 to acquire health history information. Further, the acquisition unit 200b accesses the personal information database 43 and acquires the personal information of each individual. Further, the acquisition unit 200b accesses the office database 44 and acquires employment information of each individual.

The calculation unit 200c obtains a score indicating a tendency of a person's physical condition in real time based on the person's movement information included in the sensor data received from the image sensor and the health history information acquired from the medical history / movement database 42. calculate. This score is calculated in real time for each person, for example, each time sensor data is acquired. If a person whose score deviates from a predetermined range appears, the mail server 500 sends an e-mail indicating that the person is concerned about his / her physical condition to at least one of the person himself / herself or a person related to the person (such as a boss). And warn you.

FIG. 12 is a diagram for explaining a score showing a tendency of a person's physical condition. For example, it is possible to calculate a numerical score (probability) for each person for three types of physical conditions: health, rhinitis (without fever), and cold (with fever). The score is 100 points, and the larger the number, the higher the possibility.

According to the motion information of FIG. 7, the motion score of the person with ID = 001 is sneezing = 80, wobbling = 5, and cough = 3. According to the medical history / behavior database of FIG. 8, since this person has a constitution of pollen allergy, it is comprehensively determined that this person is highly likely to have rhinitis. Therefore, the score for a person with ID = 001 is calculated as, for example, a set (vector) of numerical values (health, rhinitis, cold) = (30, 80, 5) (FIG. 12). This kind of score calculation is feasible using multivariate analysis and machine learning frameworks. That is, if real-time operation information and each information of the pre-registered medical history / operation database 42, personal information database 43, and office database 44 are input to the input layer of the pre-created trained model, the intermediate layer can be used. A score can be obtained from the output layer via a weighting operation.

According to FIG. 12, a person with ID = 002 has a cold (with fever) score of 90 points, which is extremely high. It is possible that he has COVID-19 because he is suspected of having a fever and has traveled abroad (AAA country) according to the personal information database (Fig. 9). If the medical history / behavior database does not have a history of COVID-19, the possibility is likely to increase. In such a case, it is preferable to notify the warning message not only to the person himself / herself but also to his / her boss and further to the close contact person with ID = 002 found by the image data analysis. Image data analysis may be used to obtain knowledge about mask wearers / non-wearers and warn only non-wearers. Next, the operation in the above configuration will be described.

<Action>
FIG. 13 is a sequence diagram showing an example of a processing procedure in the physical condition evaluation system according to the embodiment. In FIG. 13, each image sensor 3 captures its own assigned area (step S1), analyzes the image data (step S2), and generates sensor data including human motion information. The obtained operation information is transmitted to the sensor database 9a and the cloud server 200 (step S3). The procedures of steps S1 to S3 are repeatedly executed, operation information is generated in real time, and is transmitted to the cloud server 200.

When the cloud server 200 receives the operation information (FIG. 7) (step S4), the cloud server 200 identifies an individual related to the operation information (step S5) and acquires an ID. Next, the cloud server 200 acquires health history information from the medical history / operation database 42 (FIG. 8) (step S6) and personal information from the personal information database 43 (FIG. 9) for each ID of the specified person. (Step S7), employment information is acquired from the office database 44 (FIG. 10) (step S8). Then, based on this information, the cloud server 200 calculates the physical condition score (step S9).

Next, the cloud 40 compares a predetermined threshold value with the physical condition score for each person's ID, and requests the mail server 500 to notify the warning to the person whose score exceeds the threshold value (in step S10). Yes).

Upon receiving this request, the mail server 500 sends a message such as "It looks like you are starting to catch a cold. Let's take a rest without overdoing it" to the employee himself (step S11), for example, as shown in FIG. ), A message is sent to notify the administrator about the employee and the content of the message, or this is displayed on the screen of the administrator's management PC (not shown). By reading the message displayed on the screen of the smartphone or tablet 20 possessed by the person who receives this e-mail, he / she will be able to quickly take measures such as returning home early.

Here, the process of identifying an individual in step S5 is supplemented. In the explanation regarding the medical history / operation database 42 (FIG. 8), it was explained that the individual ID can be uniquely specified by, for example, an IC card. Then, for example, when passing through the entrance / exit gate to the office building 100, by holding the IC card over the reader, the person who has passed through the gate and the personal ID can be uniquely associated with each other. After that, by continuously tracking the people who have passed through the gate by the plurality of cameras 3 in the office building 100, it becomes possible to manage each person who entered the building in association with the personal ID. By managing a data table in which visitors and personal IDs are associated with each other, for example, in the management server 8 (FIG. 2) and transferring this data table to the cloud server 200, for example, the cloud server 200 manages the physical condition. It becomes possible to identify the individual to be targeted.

<Effect>
As described above, in the embodiment, the physical condition evaluation and the motion information are calculated in real time from the motion of the office employee sensed by the image sensor 3. The operation information is passed to the cloud server 200. The cloud is a behavioral relationship that is linked to the person's physical condition by comparing it with the normal operation information shown in the person's past medical history, personal information (age, etc.), and employment information (meeting participation information, office entry / exit information). Recognize any abnormalities. When an employee who behaves abnormally is found, tracking and monitoring by the surveillance camera 3-0 is started, and the person or an administrator such as a boss is warned. By doing so, it is possible to assist in returning the abnormal behavior related to the physical condition to the normal behavior.

With existing technology, from the perspective of office health, information related to human movements in the office (information with real-time characteristics) is combined with past medical history, health / personal information, and office information (information without real-time characteristics). Therefore, it was not possible to detect the movements associated with the physical condition of each individual. For example, by measuring body temperature and pulse during work (using a device that comes into contact with the body), it is possible to visualize physical condition and promote health, or to diagnose physical condition from past medical history and employment status without real-time characteristics. Only those that visualize physical condition and promote health were known.

On the other hand, according to the embodiment, it is possible to construct a system for everyone to be healthy by utilizing the operation information, health history information, personal information, and employment information of office employees. That is, the movement of an employee is observed in real time to evaluate the physical condition, and the normal movement information (movement certainty) based on the person's past medical history, personal information, and business information is used to determine the movement related to the person's physical condition. Can recognize abnormalities. In addition, it will be possible to identify employees who behave abnormally, selectively monitor them, and send warning messages.

Based on these facts, according to the embodiment, a physical condition evaluation system, a server, a program, and a physical condition evaluation service providing method for evaluating a person's physical condition by acquiring real-time information with an image sensor and using a database created in advance are provided. It will be possible to provide.

The present invention is not limited to the above embodiment. For example, in the embodiment, a mode in which a receiving unit 200a, an acquisition unit 200b, and a calculation unit 200c are mounted on the cloud server 200 has been described. Alternatively, it is of course possible to implement these objects in the cloud 400.

For example, in a service form known as SaaS (Software as a Service), a receiving unit 200a, an acquisition unit 200b, and a calculation unit 200c are implemented as an application (cloud application) implemented in a virtual server constructed in a cloud 400. Can be done. This kind of function can be realized by linking a server that has an entity as hardware and an application installed on the server. There may be one server or multiple servers.

In this way, some or all of the functions provided in the cloud server 200 can be implemented in the cloud 400. Further, the sensor database 9a may be provided in the cloud 400. Furthermore, various functions can be realized by linking with the gateway 7.

In addition, when calculating the physical condition score, not only the travel history overseas, but also where and with whom the meeting was held, the tendency of friendships judged by SNS, the place of residence of friends, etc. can be used as judgment materials. obtain. In the embodiment, such information can also be stored in a database and referred to. Furthermore, it is also possible to physically detect the body temperature of an individual with an infrared sensor or the like provided separately to assist in the judgment.

Further, in the embodiment, the e-mail as shown in FIG. 14 is used as a warning message sent to a person whose physical condition score deviates from the threshold value. Nor does it require a server 500. Furthermore, the mail sending function may be an option. In short, the purpose will be achieved if the physical condition of each individual can be grasped in real time.

Although embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. This novel embodiment can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. This embodiment and its modifications are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

1 ... lighting equipment, 2 ... air conditioning equipment, 3 ... image sensor, 3-0 ... surveillance camera, 4 ... signal line, 5 ... building network, 6 ... firewall, 7 ... gateway, 8 ... management server, 9a ... sensor Database, 15 ... Processor, 20 ... Tablet, 30 ... Register, 30a ... Camera information, 31 ... Camera unit, 31a ... Fish-eye lens, 31b ... Aperture mechanism, 31c ... Imaging element, 32 ... Storage unit, 32a ... Image data, 32b ... Program, 32d ... dictionary data, 33 ... processor, 33a ... detection unit, 33b ... feature amount calculation unit, 33c ... analysis unit, 33d ... learning unit, 34 ... communication unit, 35 ... internal bus, 40 ... cloud, 41 ... monitoring Camera, 42 ... medical history / operation database, 43 ... personal information database, 44 ... office database, 100 ... office building, 200 ... cloud server, 200a ... receiver, 200b ... acquisition unit, 200c ... calculation unit, 210 ... processor, 220 ... ROM, 230 ... RAM, 240 ... storage unit, 240a ... program, 260 ... optical media drive, 270 ... communication unit, 300 ... dedicated line network, 400 ... cloud, 500 ... mail server.

Claims (9)

A motion sensor that detects people and
A server that can communicate with the motion sensor,
It has a database that can be accessed from the server.
The database is
Includes a medical history / behavior database that stores health history information for each person
The motion sensor is
It is equipped with an analysis unit that analyzes the image data obtained by imaging a predetermined area and generates sensor data related to human motion information.
The server
A receiving unit that receives the sensor data from the motion sensor,
An acquisition unit that acquires the health history information from the medical history / operation database,
A physical condition evaluation system including a calculation unit that calculates a score indicating a tendency of the physical condition of the person based on the motion information of the person included in the received sensor data and the health history information. The physical condition evaluation system according to claim 1, further comprising a message server for notifying at least one of a person whose score deviates from a predetermined range or a person concerned with the warning message. It also has a personal information database that stores personal information for each person.
The physical condition evaluation system according to claim 1 or 2, wherein the calculation unit calculates the score based on the operation information, the health history information, and the personal information. It also has an office database that stores employment information for each person.
The physical condition evaluation system according to claim 1 or 2, wherein the calculation unit calculates the score based on the operation information, the health history information, and the employment information. Further equipped with a sensor database for storing the sensor data,
The physical condition evaluation system according to claim 1 or 2, wherein the calculation unit calculates the score based on the motion information of the person included in the sensor data of the sensor database and the health history information. A receiving unit that receives the sensor data from a motion sensor that generates sensor data related to human motion information from image data obtained by imaging a predetermined area.
The acquisition unit that acquires the health history information from the medical history / operation database that stores the health history information for each person, and
A server including a calculation unit that calculates a score indicating a tendency of the physical condition of the person based on the movement information of the person included in the received sensor data and the health history information. A command for causing the computer to function as a receiving unit for receiving the sensor data from a motion sensor that analyzes image data obtained by imaging a predetermined area and generates sensor data including human motion information.
Instructions for making the computer function as an acquisition unit that acquires the health history information from the medical history / operation database that stores the health history information for each person.
An instruction for causing the computer to function as a calculation unit for calculating a score indicating a tendency of the physical condition of the person based on the movement information of the person included in the received sensor data and the health history information. Including, program. A process in which a motion sensor that detects a person analyzes image data obtained by imaging a predetermined area and generates sensor data related to human motion information.
A process in which a server capable of communicating with the motion sensor receives the sensor data from the motion sensor, and
The process in which the server acquires the health history information from the medical history / operation database that stores the health history information for each person, and
A physical condition evaluation service comprising a process in which the server calculates a score indicating a tendency of the physical condition of the person based on the motion information of the person included in the received sensor data and the health history information. Providing method. The method for providing a physical condition evaluation service according to claim 8, wherein the server further comprises a process of notifying at least one of a person whose score deviates from a predetermined range or a person concerned with the person.

Images