Treatment propriety determination system, treatment propriety determination device, treatment propriety determination method, and treatment propriety determination program

Abstract

To efficiently determine the propriety of treatment for a customer performed by a practitioner.SOLUTION: A practitioner wears a portable terminal 10 on the body and performs predetermined treatment, and the portable terminal 10 acquires acceleration and angular velocity data while the practitioner performs the predetermined treatment. The portable terminal 10 transmits the acquired acceleration and angular velocity data to a treatment state determination learned model 14b, and determines whether the treatment has been performed on the basis of output from the treatment state determination learned model 14b. Subsequently, the portable terminal 10 calculates treatment execution time during which the practitioner performed the treatment on the basis of the determination as to whether the treatment has been performed. The portable terminal 10 compares the treatment execution time with scheduled treatment time in scheduled treatment data, and determines treatment propriety as to whether the treatment has been appropriately performed. Subsequently, the portable terminal 10 displays a result of the determination of treatment propriety on an operation display unit 11.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a treatment suitability determination system, a treatment suitability determination device, a treatment suitability determination method, and a treatment suitability determination program that can efficiently determine the suitability of a treatment performed by a therapist on a customer. [Background technology]

[0002] Traditionally, in establishments that provide treatment, such as massage parlors, salons, beauty salons, chiropractic clinics, bone setting clinics, and hair and beauty salons, employees (practitioners) provide treatment to customers, and the establishments need to manage whether or not employees are providing appropriate treatment to customers.

[0003] Here, a conventional technology is known in which an image of a person is input into a trained model that has undergone deep learning, and abnormal behavior is judged based on the results output from the trained model (see, for example, Patent Document 1). For this reason, a method of understanding the treatment status of an employee using such a conventional technology is conceivable, but taking pictures during treatment is undesirable in terms of protecting the privacy of customers.

[0004] For this reason, there is known a conventional technique for estimating the working condition of a worker based on information acquired by a wearable device, without taking an image (see, for example, Patent Document 2). By using such a conventional technique, the treatment condition of an employee can be determined using a wearable device. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 7108395 [Patent Document 2] JP 2019-197565 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, the above-mentioned Patent Document 2 cannot determine whether the employee performed the treatment commensurate with the price paid by the customer. For example, if a customer selects a 90-minute full-body massage course and pays the price, but the employee's treatment time is less than 60 minutes, the store will not provide an appropriate service.

[0007] The present invention has been made to solve the problems (issues) of the conventional technology described above, and aims to provide a treatment suitability determination system, a treatment suitability determination device, a treatment suitability determination method, and a treatment suitability determination program that can efficiently determine the suitability of a treatment performed by a therapist on a customer. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems and achieve the object, the present invention provides a treatment suitability determination system for determining the suitability of treatment by an employee of a store who provides a specified treatment to a customer, and includes a movement information acquisition unit that acquires movement information of the employee during the treatment, a treatment state determination unit that determines whether the employee is in a treatment state based on the movement information acquired by the movement information acquisition unit, a treatment execution time calculation unit that calculates the treatment time to be performed by the employee based on the determination result by the treatment state determination unit, and a treatment suitability determination unit that determines the suitability of the treatment by the employee based on the treatment execution time calculated by the treatment execution time calculation unit and the planned treatment time to be performed by the employee.

[0009] In addition, in the above invention, the treatment state determination unit determines whether or not the treatment state is being performed by an employee based on a learned model machine-learned based on teacher data including teacher action information and correct answer data.

[0010] The present invention also provides a treatment suitability determination system for determining whether or not a treatment is performed by an employee of a store who provides a specified treatment to a customer, and includes a movement information acquisition unit that acquires movement information of the employee during the treatment, a treatment state determination unit that determines whether or not the employee is performing the treatment based on the movement information acquired by the movement information acquisition unit, a treatment execution time calculation unit that calculates the treatment time performed by the employee based on the determination result by the treatment state determination unit, and a treatment suitability determination unit that determines the suitability of the treatment performed by the employee based on the treatment execution time calculated by the treatment execution time calculation unit and sales information corresponding to the treatment execution time.

[0011] In addition, the present invention in the above-mentioned invention further comprises a notification unit that notifies a predetermined administrator terminal of a result of the determination made by the determination unit.

[0012] In addition, in the above invention, the present invention provides that the motion information acquisition unit acquires the motion information including an output value of an acceleration sensor that detects acceleration associated with the employee's treatment motion and an output value of an angular velocity sensor that detects angular velocity associated with the employee's treatment motion.

[0013] In the present invention, in the above-mentioned invention, the device further comprises a display control unit that controls the display of a result of the determination by the determination unit on a predetermined display unit.

[0014] In the present invention, in the above-mentioned invention, the predetermined treatment is a private medical treatment or a non-medical treatment provided to the customer.

[0015] The present invention also provides a treatment suitability determination device for determining the suitability of treatment by a store employee who provides a specified treatment to a customer, and includes a motion information acquisition unit that acquires motion information of the employee during treatment, a treatment state determination unit that determines whether or not the employee is in a treatment state based on the motion information acquired by the motion information acquisition unit, a treatment execution time calculation unit that calculates the treatment time to be performed by the employee based on the determination result by the treatment state determination unit, and a treatment suitability determination unit that determines the suitability of the treatment by the employee based on the treatment execution time calculated by the treatment execution time calculation unit and the planned treatment time to be performed by the employee.

[0016] The present invention also provides a treatment suitability determination method in a treatment suitability determination system that determines the suitability of treatment by an employee of a store who performs a specified treatment on a customer, the method comprising: a motion information acquisition process for acquiring motion information of the employee during treatment; a treatment state determination process for determining whether or not the employee is in a treatment state based on the motion information acquired by the motion information acquisition process; a treatment implementation time calculation process for calculating the treatment implementation time by the employee based on the determination result by the treatment state determination process; and a treatment suitability determination process for determining the suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation process and the planned treatment time that the employee should perform.

[0017] The present invention also relates to a treatment suitability judgment program executed by a treatment suitability judgment device that judges the suitability of treatment by an employee of a store who performs a specified treatment on a customer, and causes a computer to execute an action information acquisition procedure for acquiring action information of the employee during treatment, a treatment state judgment procedure for judging whether or not the employee is in a treatment state based on the action information acquired by the action information acquisition procedure, a treatment implementation time calculation procedure for calculating the treatment implementation time by the employee based on the judgment result by the treatment state judgment procedure, and a treatment suitability judgment procedure for judging the suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation procedure and the planned treatment time that the employee should perform. Effect of the Invention

[0018] According to the present invention, the suitability of a treatment performed by a therapist on a customer can be efficiently determined. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a diagram showing an overview of a treatment suitability determination device according to the first embodiment. [Diagram 2] FIG. 2 is a functional block diagram showing a configuration of the mobile terminal shown in FIG. [Diagram 3] FIG. 3 is an explanatory diagram for explaining generation of a trained model. [Figure 4] FIG. 4 is a diagram showing an example of the service implementation data and the service schedule data shown in FIG. [Diagram 5] FIG. 5 is a flowchart showing a processing procedure of the mobile terminal shown in FIG. [Figure 6] FIG. 6 is a flowchart illustrating the procedure of the data acquisition process shown in FIG. [Figure 7] FIG. 7 is an explanatory diagram for explaining an overview of the first modification. [Figure 8] FIG. 8 is a diagram showing an overview of a treatment suitability determination system according to the second embodiment. [Figure 9] FIG. 9 is a diagram showing a system configuration of a treatment suitability determination system according to the second embodiment. [Figure 10] FIG. 10 is a functional block diagram showing the configuration of the mobile terminal shown in FIG. [Figure 11] FIG. 11 is a functional block diagram showing the configuration of the treatment suitability determination server shown in FIG. [Figure 12] FIG. 12 is a functional block diagram showing the configuration of the administrator terminal shown in FIG. [Figure 13] FIG. 13 is a sequence diagram (part 1) showing the processing procedure of the treatment suitability determination system shown in FIG. [Figure 14] FIG. 14 is a sequence diagram (part 2) illustrating the processing procedure of the treatment suitability determination system illustrated in FIG. [Figure 15]FIG. 15 is a diagram showing a system configuration of a treatment suitability determination system according to the third embodiment. As shown in FIG. [Figure 16] FIG. 16 is a functional block diagram showing the configuration of the treatment suitability determination server shown in FIG. [Figure 17] FIG. 17 is a sequence diagram (part 1) showing the processing procedure of the treatment suitability determination system shown in FIG. [Figure 18] FIG. 18 is a sequence diagram (part 2) illustrating the processing procedure of the treatment suitability determination system illustrated in FIG. [Figure 19] FIG. 19 is a functional block diagram showing the configuration of a treatment suitability determination server of the treatment suitability determination system according to the fourth embodiment. As shown in FIG. [Figure 20] FIG. 20 is an explanatory diagram for explaining generation of a treatment judgment model using machine learning in the treatment suitability judgment system according to the second modification. [Figure 21] FIG. 21 is a diagram showing an overview of a treatment suitability determination system according to the third modification. [Figure 22] FIG. 22 is a diagram illustrating an example of the configuration. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of a treatment suitability determination system, a treatment suitability determination device, a treatment suitability determination method, and a treatment suitability determination program according to the present invention will be described in detail with reference to the drawings.

[0021] [Embodiment 1] <Outline of treatment suitability assessment device> An overview of the treatment suitability determination device according to the present embodiment 1 will be described. FIG. 1 is a diagram showing an overview of the treatment suitability determination device according to the embodiment 1. Here, a case will be described in which the treatment suitability determination device is a smartphone (mobile terminal) 10. The mobile terminal 10 has an acceleration sensor and an angular velocity sensor. It is assumed that a treatment condition determination learned model is downloaded and installed on the mobile terminal 10 from a specified server device or the like in order to determine whether or not treatment has been performed. It is also assumed that treatment schedule data is downloaded and stored on the mobile terminal 10 from a specified server device or the like in order to determine whether or not treatment is performed.

[0022] The mobile terminal 10 judges whether or not a treatment has been performed based on the acceleration data and the angular velocity data (hereinafter referred to as "acceleration-angular velocity data") using a treatment condition judgment trained model generated by supervised learning of deep learning. Here, a convolutional neural network (CNN) is used as the deep learning. The generation of a trained model using supervised learning of deep learning will be described later.

[0023] As shown in Fig. 1, a practitioner wears a mobile terminal 10 on his / her body and performs a treatment. The mobile terminal 10 acquires acceleration and angular velocity data while the practitioner is performing a predetermined treatment. The mobile terminal 10 inputs the acceleration and angular velocity data to a treatment condition determination trained model. The mobile terminal 10 then judges whether or not a treatment has been performed based on an output from the treatment determination trained model.

[0024] The mobile terminal 10 then compares the treatment schedule data with the treatment implementation data. The mobile terminal 10 then compares the planned treatment time included in the treatment schedule data with the treatment implementation time in the treatment implementation data, and if the difference between the planned treatment time and the treatment implementation time is within a predetermined threshold, it determines that the treatment was performed appropriately, and if the difference between the planned treatment time and the treatment implementation time is greater than or less than the predetermined threshold, it determines that the treatment was not performed appropriately. The mobile terminal 10 then displays the determination result data. After the treatment is completed, the administrator of the practitioner can check whether the practitioner performed the treatment based on the treatment schedule data.

[0025] <Configuration of mobile terminal 10> Next, the configuration of the mobile terminal 10 will be described. Fig. 2 is a functional block diagram showing the configuration of the mobile terminal 10 shown in Fig. 1. As shown in Fig. 2, the mobile terminal 10 has an operation display unit 11, an acceleration sensor 12, an angular velocity sensor 13, a storage unit 14, and a control unit 15. The operation display unit 11 is an input and display device such as a touch panel type liquid crystal display.

[0026] The acceleration sensor 12 detects the change in position when acceleration is applied to an integrated spring and weight, and measures the change in position when the attached weight moves due to an external force. The displacement is measured using changes in frequency, capacitance, and electrical resistance due to the piezoelectric effect. The angular velocity sensor 13 is a sensor that detects the change in rotation or direction of an object as angular velocity using the Coriolis force, and outputs an electrical signal. Angular velocity sensors come in mechanical, optical, and vibration types.

[0027] The storage unit 14 is a storage device such as a hard disk drive or a non-volatile memory, and stores acquired data 14a, a treatment state determination learned model 14b, treatment implementation data 14c, and treatment schedule data 14d. The acquired data 14a is acceleration and angular velocity data acquired when a therapist performs a predetermined treatment.

[0028] The treatment condition determination trained model 14b is a trained model downloaded from a server device (not shown) or the like. Specifically, as shown in Fig. 3, teacher data A consisting of a large amount of data is input to CNNB, and, for example, backpropagation is performed based on correct answer data R, and supervised learning for determining the weight of each path is repeated to generate a trained model M. Note that the correct answer data R, i.e., the probability that it is a known phenomenon, can be assigned by the person in charge of supervised learning.

[0029] The treatment implementation data 14c is data such as a treatment implementation ID, a time when the treatment started, and a treatment implementation duration. For example, as shown in FIG. 4(a), the data is data such as a treatment implementation ID, a treatment start time, and a treatment implementation duration. Here, the treatment implementation ID "0724001" is associated with a start time "10:00" and a treatment implementation duration "60". Also, the treatment implementation ID "0724002" is associated with a start time "11:15" and a treatment implementation duration "45".

[0030] The treatment schedule data 14d is data on treatment schedules downloaded from a server device (not shown) or the like. The treatment schedule data 14d is data on treatment schedule ID, scheduled time, scheduled treatment duration, fee, etc. For example, as shown in FIG. 4(b), the treatment schedule ID "0724001" is associated with the scheduled time "10:00", the scheduled treatment duration "60", and the fee "6000". In addition, the treatment schedule ID "0724002" is associated with the scheduled time "11:15", the scheduled treatment duration "45", and the fee "4500".

[0031] The control unit 15 is a control unit that performs overall control of the mobile terminal 10, and has an operation information acquisition unit 15a, a treatment state determination unit 15b, a treatment implementation time calculation unit 15c, a treatment suitability determination unit 15d, and a display control unit 15e. In practice, by loading these programs into a CPU and executing them, the operation information acquisition unit 15a, the treatment state determination unit 15b, the treatment implementation time calculation unit 15c, the treatment suitability determination unit 15d, and the display control unit 15e are caused to execute processes corresponding to each of them.

[0032] The motion information acquisition unit 15a is a processing unit that acquires acceleration data of output values ​​output from the acceleration sensor 12 connected to the control unit 15 and angular velocity data of output values ​​output from the angular velocity sensor 13 at predetermined time intervals (e.g., every minute) and stores the data in the memory unit 14 as acquired data 14a.

[0033] The treatment state determination unit 15b reads out the acquired data 14a from the storage unit 14 and inputs it to the treatment state determination trained model 14b. The treatment state determination unit 15b is a processing unit that determines whether or not a treatment has been performed based on the output of the treatment state determination trained model 14b. The treatment implementation time calculation unit 15c is a processing unit that, if the determination result of the treatment state determination unit 15b is "treatment is being performed", stores the time as the treatment start time, and, if the determination result of the treatment state determination unit 15b is subsequently determined to be "treatment is not being performed", stores the time as the treatment end time, and calculates the treatment implementation time based on the treatment start time and the treatment end time.

[0034] The treatment suitability determination unit 15d is a processing unit that reads out the treatment implementation data 14c and the treatment schedule data 14d and determines whether the treatment was performed as scheduled based on the treatment implementation data 14c and the treatment schedule data 14d. Specifically, the treatment suitability determination unit 15d selects schedule data whose scheduled time is within a predetermined threshold from the treatment start time of the treatment implementation data 14c based on the scheduled time included in the treatment schedule data 14d.

[0035] Then, the treatment suitability determination unit 15d compares the planned treatment time in the treatment schedule data 14d with the treatment implementation time in the treatment implementation data 14c. If the difference between the planned treatment time in the treatment schedule data 14d and the treatment implementation time in the treatment implementation data 14c is within a predetermined threshold, the treatment suitability is determined to be "suitable." If the difference between the planned treatment time in the treatment schedule data 14d and the treatment implementation time in the treatment implementation data 14c is larger or smaller than the predetermined threshold, the treatment suitability is determined to be "inappropriate." The display control unit 15e is a processing unit that controls the display of the determination result of the treatment suitability determination unit 15d on a predetermined operation display unit 11.

[0036] <Processing Procedure of the Mobile Terminal 10> Next, the processing procedure of the mobile terminal 10 will be described. Fig. 5 is a flowchart showing the processing procedure of the mobile terminal 10 shown in Fig. 2. As shown in Fig. 5, the mobile terminal 10 performs a data acquisition process to acquire acceleration and angular velocity data (step S101). Then, the mobile terminal 10 inputs the acceleration and angular velocity data to the treatment condition determination trained model 14b (step S103). After that, the mobile terminal 10 receives output data from the treatment condition determination trained model 14b (step S104).

[0037] Then, the mobile terminal 10 judges whether or not the treatment has started (step S104). If the output data is "treatment has started" (step S104: Yes), the mobile terminal 10 stores the treatment start time (step S105). On the other hand, if the output data is "treatment has not started", the mobile terminal 10 waits until the output data becomes "treatment has started" (step S105: No).

[0038] Thereafter, the mobile terminal 10 judges whether the treatment has ended (step S106). The mobile terminal 10 judges that the treatment has ended when the output data of the treatment status judgment trained model 14b changes from "treatment started" to "treatment not being performed". When the mobile terminal 10 judges that the treatment has ended (step S106: Yes), it calculates the difference between the treatment end time and the treatment start time as the treatment implementation time, and stores the treatment implementation time (step S107). When the treatment has not ended, the mobile terminal 10 waits until the treatment ends (step S106: No).

[0039] Then, the mobile terminal 10 compares the treatment implementation data 14c with the treatment schedule data 14d (step S108). After that, the mobile terminal 10 judges whether the treatment was performed appropriately (step S109). If the treatment was performed appropriately (step S109: Yes), the mobile terminal 10 controls the predetermined operation display unit 11 to display "Treatment OK" (step S110).

[0040] On the other hand, if the treatment was not performed appropriately (step S109: No), the mobile terminal 10 controls the predetermined operation display unit 11 to display "Treatment NG" (step S111). Then, the mobile terminal 10 determines whether or not to end the process (step S112). If the process has ended (step S112: Yes), the mobile terminal 10 ends all the processes. On the other hand, if the process has not ended, the mobile terminal 10 proceeds to step S101.

[0041] <Data acquisition process procedure> Next, the processing procedure of the motion information acquisition unit will be described. Fig. 6 is a flowchart showing the processing procedure of the data acquisition process shown in Fig. 5. As shown in Fig. 6, the mobile terminal 10 removes gravity and other noises (step S201). Then, the mobile terminal 10 determines whether the strength of the acceleration is greater than a threshold (step S202). If the strength of the acceleration is not greater than the threshold (step S202: No), the mobile terminal 10 waits until the strength of the acceleration becomes greater than the threshold.

[0042] On the other hand, if the acceleration strength is greater than the threshold (step S202: Yes), the mobile terminal 10 acquires acceleration data (step S203). After that, it acquires angular velocity data (step S204). Then, the mobile terminal 10 determines whether the acceleration strength is less than the threshold (step S205). If the acceleration strength is not less than the threshold (step S205: No), the mobile terminal 10 proceeds to step S203.

[0043] On the other hand, if the magnitude of the acceleration is smaller than the threshold (step S205: Yes), the mobile terminal 10 determines whether the magnitude of the angular velocity is smaller than the threshold (step S206). If the angular velocity is not smaller than the threshold (step S206: No), the mobile terminal 10 proceeds to step S203. On the other hand, if the magnitude of the angular velocity is smaller than the threshold (step S206: Yes), the mobile terminal 10 proceeds to step S102 in FIG. 5.

[0044] As described above, in the present embodiment 1, the practitioner wears the mobile terminal 10 and performs a predetermined treatment, and the mobile terminal 10 acquires acceleration and angular velocity data while the practitioner is performing the predetermined treatment. Then, the mobile terminal 10 transmits the acquired acceleration and angular velocity data to the treatment state determination learned model 14b, and judges whether or not the treatment has been performed based on the output of the treatment state determination learned model 14b. Then, the mobile terminal 10 calculates the treatment implementation time during which the practitioner performed the treatment based on the judgment of whether or not the treatment has been performed. Then, the mobile terminal 10 compares the treatment implementation time with the planned treatment time of the planned treatment, and judges whether or not the treatment has been performed appropriately. Then, the mobile terminal 10 displays the judgment result of whether or not the treatment is appropriate on the operation display unit 11, so that the administrator of the practitioner can check whether or not the practitioner performed the treatment based on the treatment plan data after the treatment is completed.

[0045] <Variation 1> In the above embodiment 1, we have described a case in which the appropriateness of treatment is determined by comparing the actual treatment time with the planned treatment time.However, in variant example 1, we will describe a case in which the determination is made based on the type of treatment, the treatment time for each treatment, and the order of the treatments.

[0046] In the mobile terminal of variant example 1, acceleration and angular velocity data based on a plurality of types of treatment and a treatment condition determination trained model that has been subjected to supervised learning based on ground-truth data are downloaded from a server device (not shown) and installed.

[0047] Fig. 7 is an explanatory diagram for explaining an overview of the modified example 1. As shown in Fig. 7(a), the treatment schedule data stores the type of treatment and the scheduled treatment time for each treatment. Then, when a therapist performs a treatment, the mobile terminal inputs the acquired acceleration and angular velocity data to the treatment state determination learned model, and stores the type of treatment and its treatment implementation time as treatment implementation data based on the output of the treatment state determination learned model.

[0048] In this example, the planned treatment data is 20 minutes of leg massage, 10 minutes of shoulder massage, and 30 minutes of lower back massage, while the treatment execution data is 10 minutes of leg massage, 30 minutes of shoulder massage, and 20 minutes of lower back massage. Since there is a discrepancy between the planned treatment time and the actual treatment time for each treatment, it may be determined that the treatment was not performed appropriately.

[0049] Also, as shown in FIG. 7(b), the treatment plan data is 20 minutes of leg massage, 10 minutes of shoulder massage, and 30 minutes of lower back massage, while the treatment implementation data is 30 minutes of lower back massage, 20 minutes of leg massage, and 10 minutes of shoulder massage. There is no discrepancy between the planned treatment time and the treatment implementation time for each treatment in the treatment plan data and the treatment implementation data, but if there is a discrepancy in the order of the treatments, it may be determined that the treatment was not performed appropriately.

[0050] [Embodiment 2] In the above-mentioned embodiment 1, a case has been described in which the judgment of treatment and judgment of treatment suitability are performed by the mobile terminal 10, but in the embodiment 2, a case will be described in which acceleration and angular velocity data is transmitted from the mobile terminal 20 to the treatment suitability judgment server 30, the treatment suitability judgment server 30 judges the treatment and the suitability of the treatment, transmits the judgment result to the manager terminal 40, and displays the judgment result on the manager terminal 40. Note that the same reference numerals are used for the same parts as in the embodiment 1, and detailed explanations thereof will be omitted.

[0051] <Overview of treatment suitability assessment system> Fig. 8 is a diagram showing an overview of a treatment suitability determination system according to embodiment 2. As shown in Fig. 8, the treatment suitability determination system has a mobile terminal 20, a treatment suitability determination server 30, and an administrator terminal 40. The mobile terminal 20 has an acceleration sensor 12 and an angular velocity sensor 13.

[0052] The mobile terminal 20 acquires acceleration and angular velocity data when the practitioner performs treatment, and transmits the acceleration and angular velocity data to the treatment suitability determination server 30, which will be described later, to determine whether or not treatment has been performed.

[0053] The treatment suitability determination server 30 transmits the acceleration and angular velocity data received from the mobile terminal 20 to the treatment condition determination trained model 14b, and determines whether or not the treatment has been performed based on the output data of the treatment condition determination trained model 14b. The treatment suitability determination server 30 then compares the treatment schedule data 14d and the treatment implementation data 14c stored in advance to determine whether or not the treatment has been performed appropriately, and transmits the determination result data to the administrator terminal 40.

[0054] The treatment suitability determination server 30 uses the treatment condition determination trained model 14b generated by deep learning as in the first embodiment, and employs an algorithm using supervised learning and CNNB as deep learning. The administrator terminal 40 receives the determination result from the treatment suitability determination server 30 and controls the display on a predetermined operation display unit.

[0055] The practitioner wears the mobile terminal 20 on his / her body and performs a prescribed treatment. The mobile terminal 20 acquires acceleration and angular velocity data while the practitioner is performing the prescribed treatment. The mobile terminal 20 then transmits the acceleration and angular velocity data to the treatment suitability determination server 30.

[0056] The treatment suitability determination server 30 receives the acceleration and angular velocity data transmitted from the mobile terminal 20 and transmits it to the treatment condition determination learned model 14b. Then, the treatment suitability determination server 30 stores the treatment implementation time of the practitioner as treatment implementation data 14c based on the output of the treatment condition determination learned model 14b. Thereafter, the treatment suitability determination server 30 compares the treatment implementation data 14c with the treatment schedule data 14d, determines the suitability of the treatment based on whether the treatment was performed appropriately, and transmits the treatment suitability determination result data to the administrator terminal 40.

[0057] The administrator terminal 40 receives the judgment result data transmitted from the treatment suitability judgment server 30, and controls the display of the judgment result data. After the treatment is completed, the administrator of the practitioner can confirm whether the practitioner performed the treatment based on the treatment schedule data.

[0058] <Configuration of treatment suitability judgment system> Next, the system configuration of the treatment suitability determination system will be described. Fig. 9 is a diagram showing the system configuration of the treatment suitability determination system according to embodiment 2. As shown in Fig. 9, the treatment suitability determination system includes a plurality of mobile terminals 20a, 20b, 20c (hereinafter, sometimes collectively referred to as "mobile terminals 20"), a treatment suitability determination server 30, and an administrator terminal 40, which are connected to a network N.

[0059] The mobile terminal 20 is a device that acquires acceleration and angular velocity data while the practitioner is performing a specified treatment, and transmits the data to the treatment suitability determination server 30. The treatment suitability determination server 30 receives the acceleration and angular velocity data from the mobile terminal 20, transmits the acceleration and angular velocity data to the treatment status determination learned model 14b, determines whether or not the treatment has been performed based on the output of the treatment status determination learned model 14b, calculates the treatment implementation time based on the treatment start time and treatment end time, and stores the calculated time as treatment implementation data 14c.

[0060] Thereafter, the treatment suitability determination server 30 is a device that determines whether the treatment has been performed appropriately based on the treatment implementation data 14c and the treatment schedule data 14d, and transmits the treatment suitability determination result data to the administrator terminal 40. The administrator terminal 40 is a device that receives and displays the treatment suitability determination result data from the treatment suitability determination server 30.

[0061] <Configuration of the mobile terminal 20a> Next, the configuration of the mobile terminal 20a shown in Fig. 9 will be described. Fig. 10 is a functional block diagram showing the configuration of the mobile terminal 20a shown in Fig. 9. The mobile terminals 20b and 20c also have the same configuration. As shown in Fig. 10, the mobile terminal 20a has an operation display unit 11, an acceleration sensor 12, an angular velocity sensor 13, a communication I / F unit 23, a storage unit 24, and a control unit 25.

[0062] The communication I / F unit 23 is an interface unit for communicating with the treatment suitability determination server 30, and performs wireless communication using a mobile communication standard such as a third generation mobile communication system, a fourth generation mobile communication system, or a fifth generation mobile communication system, or short-distance wireless communication using a wireless LAN (Local Area Network), etc. The storage unit 24 is a storage device such as a hard disk drive or a nonvolatile memory, and stores the acquired data 14a.

[0063] The control unit 25 is a control unit that performs overall control of the mobile terminal 20a, and includes an operation information acquisition unit 15a and a data transmission processing unit 25a. In practice, these programs are loaded into the CPU and executed, causing the operation information acquisition unit 15a and the data transmission processing unit 25a to execute processes corresponding thereto.

[0064] The data transmission processing unit 25a is a processing unit that transmits the acquired data 14a acquired from the acceleration sensor 12 and the angular velocity sensor 13 to the treatment suitability determination server 30. Specifically, the data transmission processing unit 25a reads out the acquired data 14a from the storage unit 24, and transmits the acquired data 14a to the treatment suitability determination server 30 via the communication I / F unit 23.

[0065] <Configuration of treatment suitability determination server 30> Next, the configuration of the treatment suitability determination server 30 will be described. Fig. 11 is a functional block diagram showing the configuration of the treatment suitability determination server 30 shown in Fig. 9. As shown in Fig. 11, the treatment suitability determination server 30 has a display unit 31, an input unit 32, a communication I / F unit 33, a storage unit 34, and a control unit 35. The display unit 31 is a display device such as a liquid crystal display that displays various information. The input unit 32 is an input device such as a mouse or a keyboard.

[0066] The communication I / F unit 33 is an interface unit for communicating with the mobile terminal 20, and performs wireless communication by a mobile communication standard such as a third generation mobile communication system, a fourth generation mobile communication system, or a fifth generation mobile communication system, or short-distance wireless communication by a wireless LAN (Local Area Network) or the like. The storage unit 34 is a storage device such as a hard disk drive or a non-volatile memory, and stores the treatment condition determination learned model 14b, treatment implementation data 14c, treatment schedule data 14d, and determination result data 34a. The determination result data 34a is data of the treatment appropriateness determination result as to whether or not the treatment was performed appropriately by comparing the treatment implementation data 14c and the treatment schedule data 14d.

[0067] The control unit 35 is a control unit that performs overall control of the treatment suitability determination server 30, and has a data reception processing unit 35a, a treatment state determination unit 15b, a treatment implementation time calculation unit 15c, a treatment suitability determination unit 15d, and a determination result data transmission processing unit 35b. In practice, by loading these programs into a CPU and executing them, the data reception processing unit 35a, the treatment state determination unit 15b, the treatment implementation time calculation unit 15c, the treatment suitability determination unit 15d, and the determination result data transmission processing unit 35b are caused to execute processes corresponding to each of them.

[0068] The data receiving processing unit 35a is a processing unit that receives, via the communication I / F unit 33, the acceleration / angular velocity acquisition data 14a acquired when the practitioner performs a predetermined treatment, which is transmitted from the mobile terminal 20. The judgment result data transmission processing unit 35b is a processing unit that reads out the judgment result data 34a from the storage unit 34 and transmits it to the manager terminal 40 via the communication I / F unit 33.

[0069] <Configuration of administrator terminal 40> Next, the configuration of the administrator terminal 40 will be described. Fig. 12 is a functional block diagram showing the configuration of the administrator terminal 40 shown in Fig. 9. As shown in Fig. 12, the administrator terminal 40 has an operation display unit 11, a communication I / F unit 23, a storage unit 44, and a control unit 45. The storage unit 44 is a storage device such as a hard disk device or a non-volatile memory.

[0070] The control unit 45 is a control unit that performs overall control of the administrator terminal 40, and has a judgment result data reception processing unit 45a and a display control unit 45b. In practice, by loading these programs into the CPU and executing them, the judgment result data reception processing unit 45a and the display control unit 45b are made to execute processes corresponding to them, respectively.

[0071] The judgment result data receiving processing unit 45a is a processing unit that receives the judgment result data 34a transmitted from the treatment suitability judgment server 30 via the communication I / F unit 23. The display control unit 45b is a processing unit that controls the display of the judgment result data 34a received from the treatment suitability judgment server 30 on a predetermined operation display unit 11.

[0072] <Processing procedure for treatment suitability judgment system> Next, a process procedure of the treatment suitability determination system shown in Fig. 9 will be described. Fig. 13 and Fig. 14 are sequence diagrams showing the process procedure of the treatment suitability determination system shown in Fig. 9. As shown in Fig. 13, the mobile terminal 20a performs a data acquisition process (step S301). Note that the data acquisition process is similar to the process procedure of the data acquisition process shown in Fig. 6, and therefore a detailed description thereof will be omitted.

[0073] Then, the mobile terminal 20a transmits the acceleration and angular velocity data as acquired data to the treatment suitability determination server 30 (step S302). The treatment suitability determination server 30 determines whether or not it has received the acquired data transmitted from the mobile terminal 20a (step S303). If the treatment suitability determination server 30 has not received the acquired data from the mobile terminal 20a (step S303: No), it waits until it receives the acquired data.

[0074] On the other hand, if the treatment suitability determination server 30 receives the acquired data from the mobile terminal 20a (step S303: Yes), it inputs the acquired data to the treatment condition determination trained model 14b (step S304). After that, the treatment suitability determination server 30 receives output data from the treatment condition determination trained model 14b (step S305) and determines whether the treatment has started (step S306).

[0075] When the output of the treatment condition determination trained model 14b is "treatment has not started" (step S306: No), the treatment suitability determination server 30 waits until the output of the treatment condition determination trained model 14b is determined to be "treatment has started." On the other hand, when the output of the treatment condition determination trained model 14b is "treatment has started" (step S306: Yes), the treatment suitability determination server 30 stores the treatment start time (step S307).

[0076] Thereafter, the treatment suitability determination server 30 judges whether the treatment has been completed (step S308). The treatment suitability determination server 30 judges that the treatment has been completed when the output data of the treatment condition determination trained model 14b changes from "treatment started" to "treatment not being performed". When the treatment suitability determination server 30 judges that the treatment has been completed (step S308: Yes), it calculates the difference between the treatment end time and the treatment start time as the treatment implementation time, and stores the treatment implementation time (step S309). When the treatment has not been completed, the treatment suitability determination server 30 waits until the treatment is completed (step S308: No).

[0077] Then, the treatment suitability determination server 30 compares the treatment implementation data 14c with the treatment schedule data 14d (step S310). Thereafter, the treatment suitability determination server 30 determines whether the treatment was performed appropriately (step S311). If the treatment was performed appropriately (step S311: Yes), the treatment suitability determination server 30 determines the determination result as "Treatment OK" (step S312). On the other hand, if the treatment was not performed appropriately (step S311: No), the treatment suitability determination server 30 determines the determination result as "Treatment NG" (step S312).

[0078] Then, the treatment suitability determination server 30 transmits the determination result data to the manager terminal 40 (step S314). The manager terminal 40 determines whether or not the determination result data has been received (step S315). If the manager terminal 40 has not received the determination result data (step S315: No), the manager terminal 40 waits until the determination result data is received.

[0079] On the other hand, if the manager terminal 40 receives the determination result data (step S315: Yes), the manager terminal 40 controls the display of the received determination result data on a predetermined operation display unit (step S316).

[0080] As described above, in the second embodiment, the practitioner wears the mobile terminal 20 and performs a predetermined treatment, and the mobile terminal 20 acquires acceleration and angular velocity data while the practitioner is performing the predetermined treatment. The mobile terminal 20 then transmits the acquired acceleration and angular velocity data as acquired data to the treatment suitability determination server 30. The treatment suitability determination server 30 then inputs the received acquired data into the treatment state determination learned model 14b and judges whether the treatment has been performed based on the output of the treatment state determination learned model 14b. The treatment suitability determination server 30 then compares the treatment implementation time of the treatment implementation data 14c with the treatment planned time of the treatment planned data 14d to judge whether the treatment has been performed appropriately. The treatment suitability determination server 30 then transmits the judgment result data of the treatment suitability to the administrator terminal 40. The administrator terminal 40 receives the judgment result data transmitted from the treatment suitability determination server 30 and displays the judgment result data on the operation display unit 11, so that the administrator of the practitioner can check whether the practitioner has performed the treatment based on the treatment planned data after the treatment is completed.

[0081] [Embodiment 3] In the above-mentioned first and second embodiments, the treatment schedule data 14d is set in advance, but in the third embodiment, the treatment schedule data 14d is acquired from a POS (Point of Sales) terminal 60. The same reference numerals are used for the same parts as those in the first and second embodiments, and detailed description thereof will be omitted.

[0082] <Configuration of treatment suitability judgment system> Fig. 15 is a diagram showing the system configuration of a treatment suitability determination system according to embodiment 3. As shown in Fig. 15, in the treatment suitability determination system, a mobile terminal 20a and a POS terminal 60 in a store, an administrator terminal 40, and a treatment suitability determination server 50 are connected to a network N.

[0083] The treatment suitability determination server 50 receives and stores the treatment schedule data from the POS terminal 60. Then, the treatment suitability determination server 50 receives the acceleration and angular velocity data from the mobile terminal 20a, transmits the acceleration and angular velocity data to the treatment condition determination learned model 14b, determines whether or not the treatment has been performed based on the output of the treatment condition determination learned model 14b, and stores the result as treatment implementation data 14c.

[0084] Thereafter, the treatment suitability determination server 50 is a device that determines whether the treatment was performed appropriately based on the treatment implementation data 14c and the treatment schedule data, and transmits treatment suitability determination result data to the administrator terminal 40.

[0085] The POS terminal 60 is a terminal for inputting a treatment schedule ID, scheduled time, scheduled treatment duration, fee, etc. based on the implementation schedule. The POS terminal 60 is equipped with a communication I / F unit, and when the treatment schedule data is input, the POS terminal 60 transmits the treatment schedule data to the treatment suitability determination server 50 via the communication I / F unit.

[0086] <Configuration of treatment suitability determination server 50> Next, a description will be given of the configuration of the treatment suitability determination server 50 shown in Fig. 15. Fig. 16 is a functional block diagram showing the configuration of the treatment suitability determination server shown in Fig. 15. As shown in Fig. 15, the treatment suitability determination server 50 has a display unit 31, an input unit 32, a communication I / F unit 33, a storage unit 54, and a control unit 55.

[0087] The storage unit 54 is a storage device such as a hard disk drive or a non-volatile memory, and stores the treatment condition determination trained model 14b, the treatment implementation data 14c, the determination result data 34a, and the treatment schedule data 54a. The treatment schedule data 54a is the treatment schedule data received from the POS terminal 60.

[0088] The control unit 55 is a control unit that performs overall control of the treatment suitability determination server 50, and has a data reception processing unit 35a, a treatment state determination unit 15b, a treatment implementation time calculation unit 15c, a treatment suitability determination unit 15d, a determination result data transmission processing unit 35b, and a POS data reception processing unit 55a. In practice, by loading these programs into a CPU and executing them, the data reception processing unit 35a, the treatment state determination unit 15b, the treatment implementation time calculation unit 15c, the treatment suitability determination unit 15d, the determination result data transmission processing unit 35b, and the POS data reception processing unit 55a are caused to execute processes corresponding to each of them.

[0089] The POS data receiving processor 55a receives the treatment schedule data transmitted from the POS terminal 60 and stores it as treatment schedule data 54a. The treatment schedule data 54a includes information such as a treatment schedule ID, a scheduled time, a scheduled treatment duration, and a fee.

[0090] <Processing procedure for treatment suitability judgment system> Next, the processing procedure of the treatment suitability determination system shown in Fig. 15 will be described. Fig. 17 and Fig. 18 are sequence diagrams showing the processing procedure of the treatment suitability determination system shown in Fig. 15. The POS terminal 60 acquires treatment schedule data (step S401). After that, the POS terminal 60 transmits the treatment schedule data to the treatment suitability determination server 50 (step S401).

[0091] The treatment suitability determination server 50 determines whether or not the treatment schedule data has been received from the POS terminal 60 (step S403). If the treatment suitability determination server 50 has not received the treatment schedule data from the POS terminal 60 (step S403: No), the treatment suitability determination server 50 waits until the treatment schedule data is received.

[0092] On the other hand, if the treatment suitability determination server 50 receives the treatment schedule data from the POS terminal (step S403: Yes), the treatment schedule data is stored in the storage unit 44 as treatment schedule data 44a (step S404). The mobile terminal 20a performs a data acquisition process (step S405). Note that the data acquisition process is similar to the procedure of the data acquisition process shown in FIG. 6, and therefore a detailed description thereof will be omitted.

[0093] Then, the mobile terminal 20a transmits the acceleration and angular velocity data as acquired data to the treatment suitability determination server 50 (step S405). The treatment suitability determination server 50 determines whether or not it has received the acquired data transmitted from the mobile terminal 20a (step S407). If the treatment suitability determination server 50 has not received the acquired data from the mobile terminal 20a (step S407: No), it waits until it receives the acquired data.

[0094] On the other hand, if the treatment suitability determination server 50 receives the acquired data from the mobile terminal 20a (step S407: Yes), it inputs the acquired data to the treatment condition determination trained model 14b (step S408). After that, the treatment suitability determination server 50 receives output data from the treatment condition determination trained model 14b (step S409) and determines whether the treatment has started (step S410).

[0095] When the output of the treatment condition determination trained model 14b is "treatment has not started" (step S410: No), the treatment suitability determination server 50 waits until the output of the treatment condition determination trained model 14b is determined to be "treatment has started." On the other hand, when the output of the treatment condition determination trained model 14b is "treatment has started" (step S410: Yes), the treatment suitability determination server 50 stores the treatment start time (step S411).

[0096] Thereafter, the treatment suitability determination server 50 determines whether the treatment has been completed (step S412). The treatment suitability determination server 50 determines that the treatment has been completed when the output data of the treatment condition determination trained model 14b changes from "treatment started" to "treatment not being performed." When the treatment suitability determination server 50 determines that the treatment has been completed (step S412: Yes), it calculates the difference between the treatment end time and the treatment start time as the treatment implementation time, and stores the treatment implementation time (step S413). When the treatment has not been completed, the treatment suitability determination server 50 waits until the treatment is completed (step S412: No).

[0097] Then, the treatment suitability determination server 50 compares the treatment implementation data 14c with the treatment schedule data 54a (step S414). Thereafter, the treatment suitability determination server 50 determines whether the treatment was performed appropriately (step S415). If the treatment was performed appropriately (step S415: Yes), the treatment suitability determination server 50 determines the result as "Treatment OK" (step S416). On the other hand, if the treatment was not performed appropriately (step S415: No), the treatment suitability determination server 50 determines the result as "Treatment NG" (step S417).

[0098] Then, the treatment suitability determination server 50 transmits the determination result data to the manager terminal 40 (step S418). The manager terminal 40 determines whether or not the determination result data has been received (step S419). If the manager terminal 40 has not received the determination result data (step S419: No), the manager terminal 40 waits until the determination result data is received.

[0099] On the other hand, if the manager terminal 40 receives the determination result data (step S419: Yes), it controls the display of the received determination result data on a predetermined operation display unit (step S420).

[0100] As described above, in the third embodiment, the POS terminal 60 acquires treatment schedule data and transmits the acquired treatment schedule data to the treatment suitability determination server 50. When the treatment suitability determination server 50 receives the treatment schedule data from the POS terminal 60, it stores the data in the storage unit 54 as treatment schedule data 54a. Then, the therapist wears the mobile terminal 20a and performs a predetermined treatment, and the mobile terminal 20a acquires acceleration and angular velocity data while the therapist is performing the predetermined treatment. Then, the mobile terminal 20a transmits the acquired acceleration and angular velocity data as acquired data to the treatment suitability determination server 50. Then, the treatment suitability determination server 50 inputs the received acquired data to the treatment status determination learned model 14b and determines whether the treatment has been performed based on the output of the treatment status determination learned model 14b. Then, the treatment suitability determination server 50 compares the treatment implementation time with the treatment scheduled time and determines whether the treatment has been performed appropriately. Then, the treatment suitability determination server 50 transmits the treatment suitability determination result data to the administrator terminal 40. The manager terminal 40 receives the judgment result data transmitted from the treatment suitability judgment server 50 and displays the judgment result data on the operation display unit 11, so that the practitioner's manager can check, after the treatment is completed, whether the practitioner performed the treatment based on the treatment schedule data.

[0101] In the above third embodiment, the case where the treatment schedule is inputted through the POS terminal 60 has been described, however, the present invention is not limited to this, and a ticket vending machine or the like may be used to obtain the treatment schedule.

[0102] [Embodiment 4] In the above embodiments, the case where the appropriateness of treatment is judged based on the treatment implementation time and the scheduled treatment time is described, but in the fourth embodiment, the case where the appropriateness of treatment is judged based on the sales information as the payment for the treatment and the fee (sales) included in the treatment schedule data 14d is described. Note that the same reference numerals are used for the same parts as in the above embodiments, and detailed description thereof is omitted.

[0103] Fig. 19 is a functional block diagram showing the configuration of a treatment suitability determination server 90 of the treatment suitability determination system according to embodiment 4. As shown in Fig. 19, the treatment suitability determination server 90 has a display unit 31, an input unit 32, a communication I / F unit 33, a storage unit 94, and a control unit 95.

[0104] The storage unit 34 is a storage device such as a hard disk drive or a non-volatile memory, and stores the treatment condition judgment learned model 14b, the treatment implementation data 14c, the treatment schedule data 14d, the judgment result data 34a, and the treatment fee data 94a. The treatment fee data 94a is data on the unit price for the time of the treatment (for example, 1000 yen / 10 minutes). The treatment fee data may be, for example, a set fee of 5500 yen for a series of treatments such as 10 minutes of shoulder massage, 20 minutes of foot massage, and 30 minutes of lower back massage.

[0105] The control unit 95 is a control unit that performs overall control of the treatment suitability determination server 90, and has a data reception processing unit 35a, a treatment state determination unit 15b, a treatment implementation time calculation unit 15c, a determination result data transmission processing unit 35b, a treatment consideration calculation unit 95a, and a treatment suitability determination unit 95b. In practice, by loading these programs into a CPU and executing them, the data reception processing unit 35a, the treatment state determination unit 15b, the treatment implementation time calculation unit 15c, the determination result data transmission processing unit 35b, the treatment consideration calculation unit 95a, and the treatment suitability determination unit 95b are caused to execute processes corresponding to each of them.

[0106] The service fee calculation unit 95a is a processing unit that calculates sales as a fee corresponding to the service implementation time based on the service fee data 94a from the service implementation time calculated by the service implementation time calculation unit 15c. For example, if the service fee in the service fee data 94a is 1000 yen / 10 minutes and the service implementation time is 60 minutes, the sales of the service is calculated as 6000 yen.

[0107] The treatment appropriateness determination unit 95b is a processing unit that compares the sales data calculated in the treatment fee calculation unit 95a based on the treatment time with the fee (planned sales) included in the treatment schedule data 14d, and determines that the treatment was performed appropriately if the difference between the sales during the treatment time and the planned sales is within a predetermined threshold.

[0108] In addition, the treatment appropriateness determination unit 95b compares the sales data calculated in the treatment fee calculation unit 95a based on the treatment time with the fee (planned sales) data included in the treatment schedule data 14d, and if the difference between the sales during the treatment time and the planned sales is greater or smaller than a predetermined threshold, it determines that the treatment was not performed appropriately.

[0109] The treatment suitability determination server 90 transmits the determination result of the treatment suitability determination unit 95b to the manager terminal 40, and the manager terminal 40 displays the received determination result.

[0110] As described above, in this embodiment 4, the treatment suitability determination server 90 calculates sales as a compensation corresponding to the treatment implementation time based on the treatment fee data 94a from the treatment implementation time calculated by the treatment implementation time calculation unit 15c, and performs treatment suitability determination based on the sales data calculated based on the treatment implementation time and the fee (planned sales) data included in the treatment schedule data 14d. The treatment suitability determination server 90 then transmits the determination result to the manager terminal 40, and the manager terminal 40 displays the received determination result, so that the manager of the practitioner can check whether the practitioner performed the treatment based on the treatment schedule data after the treatment is completed.

[0111] <Variation 2> In the above embodiments, a case where a trained model using deep learning is used for treatment determination is described. In the second variant example, a case where a trained model using machine learning is used is described.

[0112] FIG. 20 is an explanatory diagram for explaining generation of a treatment judgment model using machine learning in the treatment suitability judgment system according to the modified example 2. Here, LightGBM (registered trademark) is used as the machine learning. LightGBM (registered trademark) is a machine learning framework of gradient boosting based on a decision tree algorithm. Note that a nearest neighbor method, a support vector machine, a random forest, XGBoost, deep learning, etc. can also be used.

[0113] As shown in Fig. 20, a server device (not shown) or the like performs machine learning to determine whether or not a treatment has been performed based on acceleration and angular velocity data when a practitioner performs a treatment and a treatment judgment criterion (S1). By performing training using this machine learning, a treatment condition judgment trained model for judging whether or not a treatment has been performed is generated (S2). Then, the mobile terminal 10, the treatment suitability judgment server 30, and the treatment suitability judgment server 50 download and install the treatment condition judgment trained model 14b from the server device or the like.

[0114] <Variation 3> In the above embodiments 1, 2 and 3, the cases where the mobile terminal 10 and the mobile terminal 20 have the acceleration sensor 12 and the angular velocity sensor 13 are described. However, in variant example 3, the case where the wearable device 70 has the acceleration sensor and the angular velocity sensor is described.

[0115] Fig. 21 is a diagram showing an overview of a treatment suitability determination system according to Modification 3. As shown in Fig. 21, a therapist wears a wearable device 70 equipped with an acceleration sensor and an angular velocity sensor on his / her body and performs treatment. A treatment condition determination learned model is downloaded and installed on a tablet terminal 80 from a predetermined server device or the like in order to determine whether or not treatment has been performed. In addition, treatment schedule data is downloaded from a predetermined server device or the like and stored in the tablet terminal 80 in order to determine whether or not treatment is appropriate.

[0116] The therapist wears the wearable device 70 on his / her body and performs treatment. Then, the wearable device 70 acquires acceleration and angular velocity data while the therapist is performing a predetermined treatment, and transmits the acquired data to the tablet terminal 80. The tablet terminal 80 receives the acceleration and angular velocity data from the wearable device 70 as acquired data, and inputs the received acquired data into the treatment condition determination learned model. Then, the tablet terminal 80 judges whether or not treatment has been performed based on the output from the treatment determination learned model.

[0117] Thereafter, the tablet terminal 80 compares the treatment schedule data with the treatment implementation data. The tablet terminal 80 then compares the planned treatment time included in the treatment schedule data with the treatment implementation time of the treatment implementation data, and if the difference between the planned treatment time and the treatment implementation time is within a predetermined threshold, it determines that the treatment was performed appropriately, and if the difference between the planned treatment time and the treatment implementation time is greater than or less than the predetermined threshold, it determines that the treatment was not performed appropriately. The tablet terminal 80 then displays the determination result data. After the treatment is completed, the therapist's administrator can check whether the therapist performed the treatment based on the treatment schedule data.

[0118] Although the treatment suitability determination system according to the third modification has been described as determining whether a treatment is appropriate based on the treatment implementation time, the treatment suitability determination may be performed based on sales information corresponding to the treatment implementation time. Also, the treatment suitability determination system according to the third modification has been described as obtaining acceleration and angular velocity data from the wearable device 70 using the tablet terminal 80, but the tablet terminal 80 may be replaced by a mobile terminal, a notebook PC, or the like.

[0119] <Relationship with hardware> Next, a description will be given of the correspondence between the treatment suitability determination server 30 according to the second embodiment and the main hardware configuration of a computer. Fig. 22 is a diagram showing an example of the hardware configuration.

[0120] Generally, a computer has a configuration in which a CPU 101, a ROM 102, a RAM 103, a non-volatile memory 104, etc. are connected via a bus 105. A hard disk device may be provided instead of the non-volatile memory 104. For convenience of explanation, only the basic hardware configuration is shown.

[0121] Here, the ROM 102 or the non-volatile memory 104 stores programs and the like required to start an operating system (hereinafter simply referred to as "OS"), and when the power is turned on, the CPU 101 reads and executes the OS program from the ROM 102 or the non-volatile memory 104.

[0122] Meanwhile, various application programs executed on the OS are stored in non-volatile memory 104, and the CPU 101 executes the application programs while using RAM 103 as the main memory, thereby executing processes corresponding to the applications.

[0123] The treatment management program of the treatment suitability judgment server 30 according to the second embodiment is stored in the non-volatile memory 104 or the like in the same manner as other application programs, and the CPU 101 loads and executes the treatment management program. In the case of the treatment suitability judgment server 30 according to the second embodiment, a treatment management program including routines corresponding to the data reception processing unit 35a, the treatment state judgment unit 15b, the treatment implementation time calculation unit 15c, the treatment suitability judgment unit 15d, and the judgment result data transmission processing unit 35b shown in FIG. 11 is stored in the non-volatile memory 104 or the like. The treatment judgment program is loaded and executed by the CPU 101, thereby generating an employment suitability judgment process corresponding to the data reception processing unit 35a, the treatment state judgment unit 15b, the treatment implementation time calculation unit 15c, the treatment suitability judgment unit 15d, and the judgment result data transmission processing unit 35b. The relationship with the above hardware is similar for the treatment management program of the treatment suitability judgment server 50 according to the third embodiment and the treatment suitability judgment server 90 according to the fourth embodiment.

[0124] In each of the above embodiments, the calculation of the treatment implementation time has been described based on the treatment start time and the treatment end time, but the present invention is not limited to this. If the treatment status determination unit 15b determines that the judgment result of the treatment status determination unit 15b is "treatment is being performed", it may start adding up the treatment time, and if the judgment result of the treatment status determination unit 15b is "treatment is not being performed", it may calculate the treatment implementation time as the treatment time accumulated up to that point.

[0125] The configurations illustrated in the above embodiments are merely functional schematics, and are not necessarily physically configured as illustrated. In other words, the form of distribution and integration of each device is not limited to that illustrated, and all or part of the devices can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc. [Industrial Applicability]

[0126] The treatment suitability determination system, treatment suitability determination device, treatment suitability determination method, and treatment suitability determination program according to the present invention are suitable for efficiently determining the suitability of a treatment performed by a therapist on a customer. [Explanation of symbols]

[0127] 10 Mobile devices 11 Operation display section 12 Acceleration Sensor 13 Angular rate sensor 14 Storage section 14a Acquired data 14b Treatment status judgment trained model 14c Treatment implementation data 14d Treatment schedule data 15 Control section 15a Operation information acquisition unit 15b Treatment status determination unit 15c Treatment Time Calculation Department 15d Treatment Suitability Assessment Section 15e Display control unit 20, 20a, 20b, 20c Mobile terminal 23 Communication I / F section 24 Memory section 25 Control Unit 25a Data transmission processing unit 30 Treatment suitability determination server 31 Display section 32 Input section 33 Communication I / F section 34 Storage section 34a Judgment result data 35 Control section 35a Data receiving processing unit 35b Judgment result data transmission processing unit 40 Administrator terminal 44 Storage section 44a Treatment schedule data 45 Control section 45a Judgment result data receiving processing unit 45b Display control unit 50 Treatment suitability determination server 54 Storage section 54a Treatment schedule data 55 Control section 55a POS data receiving processing unit 60 POS terminals 70 Wearable Devices 80 Tablets 90 Treatment suitability determination server 94 Memory section 94a Treatment fee data 95 Control Unit 95a Treatment Fee Calculation Department 95b Treatment Suitability Assessment Department 101 CPU 102 ROM 103 RAM 104 Non-volatile memory 105 Bus A. Teacher data M trained models N Network R correct answer data

Claims

1. A treatment suitability determination system for determining the suitability of a treatment by an employee of a store who performs a predetermined treatment on a customer, A motion information acquisition unit that acquires motion information of the employee during treatment; a treatment state determination unit that determines whether or not the treatment state is being performed by the employee based on the operation information acquired by the operation information acquisition unit; A treatment time calculation unit that calculates a treatment time by the employee based on the judgment result by the treatment state judgment unit; a treatment suitability determination unit that determines suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation unit and a scheduled treatment time to be performed by the employee; A treatment suitability determination system equipped with

2. The treatment state determination unit is The treatment suitability determination system of claim 1 , which determines whether or not the treatment is being performed by the employee based on a learned model machine-learned based on teacher data including teacher motion information and correct answer data.

3. A treatment suitability determination system for determining the suitability of a treatment by an employee of a store who performs a predetermined treatment on a customer, A motion information acquisition unit that acquires motion information of the employee during treatment; a treatment state determination unit that determines whether or not the treatment state is being performed by the employee based on the operation information acquired by the operation information acquisition unit; A treatment time calculation unit that calculates a treatment time by the employee based on the judgment result by the treatment state judgment unit; a treatment suitability determination unit that determines the suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation unit and sales information corresponding to the treatment implementation time; A treatment suitability determination system equipped with

4. The treatment suitability determination system according to any one of claims 1 to 3, further comprising a notification means for notifying a predetermined administrator terminal of the determination result by the treatment suitability determination unit.

5. The motion information acquisition unit is The treatment suitability determination system according to any one of claims 1 to 3, wherein the operation information includes an output value of an acceleration sensor that detects the acceleration associated with the treatment operation of the employee and an output value of an angular velocity sensor that detects the angular velocity associated with the treatment operation of the employee.

6. The treatment suitability determination system according to any one of claims 1 to 3, further comprising a display control unit that controls the display of a determination result by the treatment suitability determination unit on a predetermined display unit.

7. The predetermined treatment is The treatment suitability determination system according to any one of claims 1 to 3, wherein the treatment is a private treatment or a non-medical treatment given to the customer.

8. A treatment suitability determination device that determines the suitability of a treatment by an employee of a store that performs a predetermined treatment on a customer, A motion information acquisition unit that acquires motion information of the employee during treatment; a treatment state determination unit that determines whether or not the treatment state is being performed by the employee based on the operation information acquired by the operation information acquisition unit; A treatment time calculation unit that calculates a treatment time by the employee based on the judgment result by the treatment state judgment unit; a treatment suitability determination unit that determines suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation unit and a scheduled treatment time to be performed by the employee; A treatment suitability determination device equipped with the device.

9. A method for determining whether a treatment is appropriate for a customer by an employee of a store that performs a predetermined treatment, comprising: A motion information acquisition step of acquiring motion information of the employee during treatment; a treatment state determination step of determining whether or not the treatment state is being performed by the employee based on the motion information acquired by the motion information acquisition step; A treatment time calculation step of calculating a treatment time by the employee based on the result of the treatment state determination step; a treatment suitability determination step for determining suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation step and a planned implementation time to be performed by the employee; A method for determining whether treatment is appropriate.

10. A treatment suitability determination program executed by a treatment suitability determination device that determines the suitability of a treatment by an employee of a store that performs a predetermined treatment on a customer, A motion information acquisition step for acquiring motion information of the employee during treatment; a treatment state determination step of determining whether or not the treatment is being performed by the employee based on the operation information acquired by the operation information acquisition step; A treatment time calculation procedure for calculating a treatment time performed by the employee based on a result of the treatment state determination procedure; a treatment suitability determination procedure for determining suitability of the treatment by the employee based on the treatment implementation time calculated by the treatment implementation time calculation procedure and the planned implementation time to be performed by the employee; A treatment suitability determination program that causes a computer to execute the above.

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