JP2018018141A - Determination device, determination method, and determination program - Google Patents

Abstract

To accurately determine a user's operation proficiency level in real time.
A determination apparatus acquires information relating to an operation history of a mouse cursor that is moved by a user operation. Then, the determination apparatus 10 calculates a change in the speed of the mouse cursor per unit time using the acquired information. Subsequently, the determination device 10 determines the user's proficiency level according to the change in the calculated speed.
[Selection] Figure 1

Description

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

  2. Description of the Related Art Conventionally, a technique for determining a user's operation proficiency level in application operation is known for use in user level determination and user interface evaluation. For example, in the technique of presenting necessary support information each time according to the user's proficiency level, the operation proficiency level is determined in real time according to the operation.

  As a method for determining the user's operation proficiency level, for example, a method for determining the operation proficiency level from a variation in time between specific operations such as mouse down and key up is known (for example, Non-patent documents 1 and 2).

Yoshihiro Nakamura, Yasuyuki Kato, Yutaka Mannaga, “Proposal of Proficiency Index Focusing on User Thinking Time”, IEICE Transactions, A Vol. J79-A No. 2, February 1996, pp. 416-423 Minako Toba, Takao Sakurai, Masahide Mori, “Correlation analysis of PC operation log features and office worker stress”, IEICE Transactions, D Vol. J95-D No. 4, 2012, pp. 747-757

  However, the above-described conventional technology has a problem that the user's operation proficiency level may not be accurately determined in real time. For example, in the method of determining the operation proficiency from the time variability between specific operations, if the start and end of one operation unit cannot be determined due to the interface specifications, the user's operation proficiency is accurate. Cannot judge well. Although it is conceivable to determine the user's operation proficiency level using a statistical index, a large number of sample values are required, and real-time proficiency level determination is difficult.

  In order to solve the above-described problems and achieve the object, the determination device of the present invention includes an acquisition unit that acquires information related to an operation history of a mouse cursor that is moved by a user's operation, and information acquired by the acquisition unit. And a calculation unit that calculates a change in the speed of the mouse cursor per unit time, and a determination unit that determines the proficiency level of the user according to the change in the speed calculated by the calculation unit. It is characterized by.

  Further, the determination method of the present invention is a determination method executed by the determination device, the acquisition step of acquiring information related to the operation history of the mouse cursor that is moved by the user's operation, and the information acquired by the acquisition step A calculation step for calculating a change in the speed of the mouse cursor per unit time, and a determination step for determining the proficiency level of the user according to the change in the speed calculated by the calculation step. It is characterized by.

  In addition, the determination program of the present invention uses an acquisition step of acquiring information about an operation history of a mouse cursor that is moved by a user operation, and the speed of the mouse cursor per unit time using the information acquired by the acquisition step. The computer is caused to execute a calculation step for calculating a change in the number of steps and a determination step for determining the user's proficiency level according to the change in the speed calculated in the calculation step.

  According to the present invention, there is an effect that the user's operation proficiency level can be accurately determined in real time.

FIG. 1 is a configuration diagram illustrating an outline of a determination device according to the first embodiment. FIG. 2 is a diagram illustrating an example of information stored in the mouse cursor operation information storage unit. FIG. 3 is a diagram for explaining an example of calculation processing of an acceleration / deceleration index. FIG. 4 is a diagram illustrating a display example of support information according to a user's proficiency level. FIG. 5 is a diagram illustrating a display example of support information according to a user's proficiency level. FIG. 6 is a flowchart showing a flow of proficiency level determination processing in the determination apparatus according to the first embodiment. FIG. 7 is a diagram for explaining the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus according to the first embodiment. FIG. 8 is a diagram for explaining the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus according to the second embodiment. FIG. 9 is a diagram for explaining the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus according to the third embodiment. FIG. 10 is a diagram for explaining a weighting function in the determination apparatus according to the third embodiment. FIG. 11 is a diagram for explaining an example of an application screen for an experiment. FIG. 12 is a diagram for explaining an example of an experimental result. FIG. 13 is a diagram illustrating the threshold setting process. FIG. 14 is a diagram illustrating the threshold setting process. FIG. 15 is a diagram illustrating a computer that executes a determination program.

  Embodiments of a determination apparatus, a determination method, and a determination program according to the present application will be described below in detail with reference to the drawings. Note that the determination device, the determination method, and the determination program according to the present application are not limited by this embodiment.

[First embodiment]
In the following embodiments, the configuration of the determination apparatus 10 according to the first embodiment and the flow of processing of the determination apparatus 10 will be described in order, and finally the effects of the first embodiment will be described.

[Configuration of judgment device]
First, the configuration of the determination apparatus 10 will be described with reference to FIG. FIG. 1 is a configuration diagram illustrating an outline of a determination device according to the first embodiment. As illustrated in FIG. 1, the determination device 10 includes a communication processing unit 11, an input unit 12, an output unit 13, a control unit 14, and a storage unit 15. The determination device 10 is described as a server device that is communicably connected to a user terminal (not shown) such as a PC (Personal Computer). However, the user terminal may have the function of the determination device 10. .

  The communication processing unit 11 controls communication related to various types of information exchanged with the connected user terminal. For example, the communication processing unit 11 receives information regarding an operation history of a mouse cursor that is moved by a user operation from the user terminal.

  The input unit 12 is an input device such as a keyboard or a mouse, and receives input operations for various information. The output unit 13 is an output device such as a display, and outputs various information.

  The storage unit 15 stores data and programs necessary for various processes performed by the control unit 14, and particularly includes a mouse cursor operation information storage unit 15a that is closely related to the present invention. For example, the storage unit 15 is a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

  The mouse cursor operation information storage unit 15a stores information related to an operation history of a mouse cursor that is moved by a user operation. For example, as illustrated in FIG. 2, the mouse cursor operation information storage unit 15a stores the x coordinate and the y coordinate on the screen of the mouse cursor in time-series order as information regarding the position of the mouse cursor. For example, it is assumed that the window size of the user terminal is 1200 × 1200 pixels, and the user terminal records the x coordinate and y coordinate of the mouse cursor as the operation history every 0.1 second. It is assumed that the mouse cursor operation information storage unit 15a stores the x and y coordinates of the mouse cursor in the operation history acquired from the user terminal by the acquisition unit 14a described later. In the example of FIG. 2, the mouse cursor operation information storage unit 15a stores the x coordinate “1150” and the y coordinate “378” as the mouse cursor position, and the x coordinate “1148” as the mouse cursor position 0.1 seconds later. ”And y coordinate“ 376 ”are stored.

  The control unit 14 has an internal memory for storing a program that defines various processing procedures and the necessary data, and performs various processes by using these programs. Particularly, as closely related to the present invention, It has the acquisition part 14a, the calculation part 14b, and the determination part 14c. Here, the control unit 14 is an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), or an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

  The acquisition unit 14a acquires information related to an operation history of a mouse cursor that is moved by a user operation. For example, the acquisition unit 14a acquires the x-coordinate and y-coordinate of the mouse cursor from the user terminal as the mouse cursor position as the mouse cursor operation history every predetermined time, and stores it in the mouse cursor operation information storage unit 15a. . The acquisition unit 14a may acquire the operation history of the mouse cursor at a timing when an operation history acquisition instruction is received, or the user terminal accumulates a certain amount or more of mouse cursor operation history data. You may acquire at the timing. Further, the acquisition unit 14a may acquire, for example, the movement distance of the mouse cursor, not the position of the mouse cursor, as the operation history of the mouse cursor. In this case, for example, it is assumed that the user terminal calculates the movement distance of the mouse cursor every predetermined time (for example, 0.1 seconds).

  Further, the acquisition unit 14a may extract only the mouse cursor operation history from the start to the end of a predetermined application from the acquired mouse cursor operation history, and store the extracted operation history in the mouse cursor operation information storage unit 15a. Here, the predetermined application is an application (for example, a game application with nerve weakness or a work application with a drawing action) that requires an operation of frequently moving the mouse cursor. That is, by extracting only the operation history of the mouse cursor at the time of starting the application as described above, only the operation history having a clearer characteristic of the user's operation of the mouse cursor is used, and the calculation unit 14b and the determination unit 14c described later. Processing can be performed.

  The calculation unit 14b calculates a change in the speed of the mouse cursor per unit time using the operation history acquired by the acquisition unit 14a. Specifically, the calculation unit 14b calculates the movement distance of the mouse cursor for each predetermined time interval from the information acquired by the acquisition unit 14a, and calculates the average speed in a predetermined time width based on the movement distance. The absolute value of the difference between the average speeds of different time widths is calculated as an acceleration / deceleration index. Here, the speed is the magnitude of the moving distance per unit time unless otherwise specified.

  For example, the calculation unit 14b calculates the movement distance r of the mouse cursor every certain time interval Δt using the operation history of the mouse cursor, and calculates the average speed Vave in a certain time width (n × Δt) therefrom. Then, the calculation unit 14b sets the magnitude of the speed change calculated as the absolute value | Vave2-Vave1 | of the average difference of speeds at different times as the index A representing the acceleration / deceleration in a certain time width T. It can be considered that as the value of A is larger, the movement of the mouse cursor in the time width T repeats deceleration and acceleration, and thus indicates an irregular change in speed. On the contrary, when the value of A is small, it is considered that the movement of the mouse cursor in the time width T indicates an unsharp movement with small changes in deceleration and acceleration.

  Here, an example of calculation processing of the acceleration / deceleration index will be described using the example of FIG. As illustrated in FIG. 3, the calculation unit 14b calculates the movement distances r1 to r4 of the mouse cursor for each of the time intervals Δt1 to Δt4. Then, the calculation unit 14b calculates the average speed Vave1 by dividing the moving distances r1 to r3 of the time widths Δt1 to Δt3 by the time widths Δt1 to Δt3, and calculates the average from the moving distances r2 to r4 of the time widths Δt2 to Δt4. The speed Vave2 is calculated. Then, the calculation unit 14b calculates an absolute value | Vave2-Vave1 | of the difference between the average speed Vave1 and the average speed Vave2, and sets the calculated value as the acceleration / deceleration index A in the time width T = 4Δ. .

  In the example of FIG. 3, the case where the acceleration / deceleration index A is calculated from the distances r1 to r4 of the time intervals Δt1 to Δt4 has been described. Thereafter, the calculation unit 14b performs, for example, every predetermined time interval. Then, the movement distance of the mouse cursor is calculated using the operation history of the mouse cursor, and the acceleration / deceleration index A is calculated in the same manner as described above. That is, for example, the calculation unit 14b calculates the distance r of the time interval Δt5 when the predetermined time interval elapses after calculating the acceleration / deceleration index A, and the interval Δt2 to Δt4 in the same manner as described above. The average speed Vave1 is calculated by dividing the moving distances r2 to r4 by the time widths Δt2 to Δt4, and the average speed Vave2 is calculated from the moving distances r3 to r5 of the time widths Δt3 to Δt5. Then, the calculation unit 14b calculates an absolute value | Vave2-Vave1 | of the difference between the average speed Vave1 and the average speed Vave2, and sets the calculated value as the acceleration / deceleration index A in the time width T = 4Δ. . When a plurality of acceleration / deceleration indexes A are calculated by repeating such processing, for example, an average value of the plurality of acceleration / deceleration indexes A is calculated, and the average value is used as the acceleration / deceleration index A, which will be described later. You may make it perform the process of the determination part 14c. In the following description, it is assumed that the process of the determination unit 14c described later is performed using the latest acceleration / deceleration index A.

  The determination unit 14c determines the user's proficiency level according to the change in speed calculated by the calculation unit 14b. For example, the determination unit 14c determines whether or not the change in speed calculated by the calculation unit 14b exceeds a predetermined value (threshold value). If it is determined that the value is equal to or less than the predetermined value, the user is determined to be an unskilled person.

  That is, the determination apparatus 10 determines that the user is a master during the time when the value of the acceleration / deceleration index A exceeds a certain threshold. Note that the user may be determined to be an expert when a time period in which the value of the acceleration / deceleration index A exceeds a certain threshold value continues for a certain period of time.

  The determination unit 14c may determine the user's proficiency level according to the acceleration / deceleration index A. For example, the determination unit 14c compares the four threshold values “0.1”, “0.2”, “0.3”, “0.4” with the acceleration / deceleration index A, and sets the user's proficiency level to “1”. The proficiency level may be determined from among the five-step evaluations “” to “5”. That is, for example, when the acceleration / deceleration index A is 0.1 or less, the user's proficiency level is determined as “1”, and the acceleration / deceleration index A exceeds “0.1” but “0.2”. ”Or less”, the user's proficiency level is determined to be “2”, and when the acceleration / deceleration index A exceeds “0.2” but is “0.3” or less, the user's proficiency level When the degree is determined to be “3” and the acceleration / deceleration index A exceeds “0.3” but is not more than “0.4”, the user's proficiency is determined to be “4”, and the acceleration / deceleration is determined. When the index A exceeds “0.4” but is not more than “0.5”, the user's proficiency level is determined as “5”.

  As described above, the determination apparatus 10 according to the first embodiment determines the proficiency level of the operation according to the magnitude of the acceleration / deceleration speed of the mouse cursor from the change in the speed of movement of the mouse cursor. In other words, for example, a person who is proficient in operation operates an operation component (for example, a button or a combo box) with a certain intention, so that the mouse cursor is moved to the target component and the target component is operated. It seems that there is a tendency to repeatedly stop the mouse cursor. On the other hand, those who are not familiar with the operation often move the mouse cursor without having a clear intention to operate, and are often confused about the menus and buttons to be operated. It seems that there is a tendency to show a movement with little change and no sharpness. For this reason, it is possible to accurately determine the user's operation proficiency in real time by determining the operation proficiency according to the mouse cursor acceleration / deceleration index from the change in the speed of movement of the mouse cursor.

  Further, as described above, for example, display control of support information that assists the user's operation may be performed based on the result of determining the user as a master. Such a display control process may be performed by, for example, a user terminal, a display control device (not shown) different from the determination device 10, or the determination device 10. When the user terminal performs display control, the result determined by the determination unit 14c is notified to the user terminal. When the display control device different from the determination device 10 performs, the result determined by the determination unit 14c is displayed. Notify the device. Further, when the determination device 10 performs, for example, in addition to the function unit illustrated in FIG. 1, the determination device 10 further includes a display control unit, and notifies the display control unit of a result determined by the determination unit 14 c.

  Here, the display example of the support information according to a user's proficiency level is demonstrated using FIG. 4 and FIG. FIG. 4 and FIG. 5 are diagrams for explaining a display example of support information according to the user's proficiency level. 4 is an example of a screen when the screen illustrated in FIG. 4 is determined to be an expert, and an exemplary screen when the screen illustrated in FIG. 5 is determined to be an unskilled person.

  The screen examples illustrated in FIGS. 4 and 5 are input screens for applying for a service, and items for inputting the name, address, and telephone number of the user applying for the service and a check box for selecting the application service are displayed. Has been. As illustrated in FIG. 4, in the screen example when the user is determined to be an expert, the input screen for applying for the service is displayed as it is, and the support information is not displayed. Further, as illustrated in FIG. 5, in the screen example when the user is determined to be an unskilled person, the support information is displayed as an overlay on the input screen for applying for the service. In the example of FIG. 5, as support information, a message “name and address are required” and a message “select application service” are displayed.

[Example of processing of determination device]
Next, the flow of processing in the determination apparatus 10 will be described with reference to FIG. FIG. 6 is a flowchart showing a flow of proficiency level determination processing in the determination apparatus according to the first embodiment.

  As illustrated in FIG. 6, the acquisition unit 14a of the determination apparatus 10 acquires an operation history of the mouse cursor (Step S101). For example, the acquisition unit 14a acquires the x-coordinate and y-coordinate of the mouse cursor from the user terminal as the mouse cursor position as the mouse cursor operation history every predetermined time, and stores it in the mouse cursor operation information storage unit 15a. .

  Subsequently, the calculation unit 14b calculates an acceleration / deceleration index for mouse cursor operation (step S102). Specifically, the calculation unit 14b calculates the movement distance of the mouse cursor for each predetermined time interval from the information acquired by the acquisition unit 14a, and calculates the average speed in a predetermined time width based on the movement distance. The absolute value of the difference between the average speeds of different time widths is calculated as an acceleration / deceleration index.

  Then, the determination unit 14c compares the acceleration / deceleration index with a threshold (Step S103), and determines whether the acceleration / deceleration index exceeds the threshold (Step S104). As a result, when the determination unit 14c determines that the acceleration / deceleration index exceeds the threshold (Yes at Step S104), the determination unit 14c determines that the user is a master (Step S105). If the determination unit 14c determines that the acceleration / deceleration index does not exceed the threshold (No at Step S104), the determination unit 14c determines that the user is an unskilled person (Step S106).

[Effect of the first embodiment]
As described above, the determination apparatus 10 according to the first embodiment acquires information regarding the operation history of the mouse cursor that is moved by the user's operation. Then, the determination apparatus 10 calculates a change in the speed of the mouse cursor per unit time using the acquired information. Subsequently, the determination device 10 determines the user's proficiency level according to the change in the calculated speed. For this reason, the determination apparatus 10 according to the first embodiment can accurately determine the user's operation proficiency level in real time.

  In the determination apparatus 10 according to the first embodiment, for example, even when the definition of an operation unit by button click or the like is not clear, the user's operation proficiency level can be determined, and the user's proficiency level can be determined in real time. It becomes possible to judge. As a result, it becomes possible to determine the operation proficiency level in real time for the operation of an application composed of GUI (Graphical User Interface) parts having various functions. Therefore, an operation proficiency level determination function that is more generally applicable than the conventional technique is realized.

[Second Embodiment]
In the first embodiment described above, the case has been described in which the change in the speed of the mouse cursor, that is, the absolute value of the difference between the average speeds of different time widths, is used as the acceleration / deceleration index. The acceleration / deceleration index may be calculated using a weighting function in which the value decreases as the average speed increases.

  For example, when there is a large distance between specific buttons, even if the user is an unskilled person and the mouse cursor moves without sharpness, acceleration will follow, and the movement distance per unit time will increase. It is possible that the change in speed can be greatly estimated. As described above, depending on the structure of the user interface, a change in the speed of the mouse cursor may be large regardless of whether the user is an expert or an unskilled person.

  In the mouse cursor operation, as the moving distance becomes longer, the acceleration time for moving the cursor increases accordingly, and as a result, the cursor moving speed tends to increase. The acceleration / deceleration index introduced in the first embodiment changes linearly with respect to the change in speed, and if the acceleration time width T becomes longer, even if the mouse cursor moves with no sharpness. The acceleration / deceleration is greatly evaluated.

  For this reason, the increase in the acceleration / deceleration index introduced in the first embodiment may be caused not only by the purpose of moving the mouse cursor but also by the increase in the movement distance of the mouse cursor. The acceleration / deceleration index introduced in the embodiment does not completely eliminate the influence of the user interface structure as described above.

  On the other hand, a method of weighting the acceleration / deceleration index according to the latest average speed as well as the average speed difference is considered. For example, weighting that estimates the acceleration / deceleration smaller as the most recent speed average is larger can suppress an increase in the acceleration / deceleration accompanying the increase in the movement distance of the mouse cursor. With the method as described above, the dependency of the acceleration / deceleration index on the structure of the user interface can be suppressed.

  In the second embodiment described below, the calculation method of the acceleration / deceleration index A uses not only a change in the speed of the mouse cursor but also a weighting function that decreases as the average speed of the latest mouse cursor increases. The case where the acceleration / deceleration index A is calculated will be described. In addition, description is abbreviate | omitted suitably about the structure and process similar to 1st embodiment.

The calculation unit 14b of the determination device according to the second embodiment calculates an acceleration / deceleration index using a weighting function that decreases as the average speed of the latest mouse cursor increases. For example, the calculation unit 14b calculates the movement distance r of the mouse cursor every certain time interval Δt using the operation history of the mouse cursor, and calculates the average speed Vave in a certain time width (n × Δt) therefrom. Then, the calculation unit 14b calculates the magnitude of the change in speed calculated as the absolute value | Vave2-Vave1 | of the average difference in speed at different times. Further, the calculation unit 14b calculates the weight W ₂ = 1 / Vave2 as a weight function. Then, the calculation unit 14b calculates the acceleration / deceleration index A by multiplying the absolute value | Vave2-Vave1 | of the average difference of the mouse cursor speeds by the weight W ₂ = 1 / Vave2.

  Here, using FIG. 7 and FIG. 8, the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus 10 according to the first embodiment, and the acceleration / deceleration index in the determination apparatus according to the second embodiment The relationship between the weighting functions will be described. FIG. 7 is a diagram for explaining the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus according to the first embodiment. FIG. 8 is a diagram for explaining the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus according to the second embodiment.

As shown in FIG. 7, in the determination apparatus 10 according to the first embodiment, the absolute value | Vave2-Vave1 | of the average difference of the mouse cursor speeds at different times and the acceleration / deceleration index A are the same. Therefore, the value of the real weight W ₁ is “1”. That is, the magnitude of the acceleration / deceleration index A is proportional to the magnitude of the absolute value | Vave2-Vave1 | of the average difference of the mouse cursor speeds.

On the other hand, as shown in FIG. 8, in the determination apparatus 10 according to the second embodiment, the acceleration / deceleration index A is set to the absolute value | Vave2−Vave1 | of the average difference of the mouse cursor speeds and the weight W ₂ = Assume that 1 / Vave2 is multiplied. That is, when the speed of the mouse cursor is too high, it is assumed that the long distance movement of the cursor is reflected, and the estimation as the acceleration / deceleration index A is reduced. Conversely, when the speed is low, the estimate as the acceleration / deceleration index A is increased.

  As described above, in the second embodiment, the calculation method of the acceleration / deceleration index A is not only a change in the speed of the mouse cursor, but also the weight that the change in speed becomes smaller as the average speed of the latest mouse cursor is larger. Since the acceleration / deceleration index is calculated using a weighting function, it is possible to accurately determine the user's operation proficiency even when the acceleration / deceleration index increases due to an increase in travel distance per unit time regardless of the proficiency level It is.

[Third embodiment]
In the second embodiment described above, a case has been described in which the acceleration / deceleration index A is calculated using a weighting function that decreases as the average speed of the most recent mouse cursor increases. However, the present invention is not limited to this. Instead, the weighting function uses a weighting function that decreases as the average speed of the mouse cursor increases, and decreases when the average speed of the mouse cursor is smaller than a certain value. The acceleration / deceleration index A may be calculated.

  In the second embodiment, the dependency on the mouse cursor movement distance can be solved. On the other hand, when Vave2 is small, an index of acceleration / deceleration is greatly estimated. Therefore, the acceleration / deceleration index introduced in the first embodiment or the second embodiment is a fine movement of the mouse cursor due to hand shake or noise of an IO (Input Output) device, or unpurposed and exploratory. Even when the neighboring components are operated at random, the evaluation can be performed with the same size as the mouse cursor movement with the purpose, or can be evaluated more greatly.

In order to remove such hand shake and IO device noise, a method of estimating the acceleration / deceleration index small when the mouse cursor moves at a low speed is conceivable. As a specific method, for example, a method of assigning weighting W ₂ as in the _second embodiment and not evaluating if the value of W ₂ exceeds a certain value can be considered. Alternatively, such acceleration is estimated to be smaller when Vave2 is small, it is conceivable approach using a new weighting W _3.

  In the following third embodiment, regarding the calculation method of the acceleration / deceleration index A, the value decreases as the average speed of the mouse cursor increases, and the speed increases when the average speed of the mouse cursor is smaller than a certain value. A case will be described in which the acceleration / deceleration index is calculated using a weighting function in which the value becomes smaller as the average is smaller. In addition, description is abbreviate | omitted suitably about the structure and process similar to 1st embodiment and 2nd embodiment.

The calculation unit 14b of the determination apparatus according to the third embodiment has a smaller value as the average speed of the mouse cursor is larger, and the average speed is smaller when the average speed of the mouse cursor is smaller than a certain value. An acceleration / deceleration index is calculated using a weighting function that decreases as the value decreases. For example, the calculation unit 14b calculates the movement distance r of the mouse cursor every certain time interval Δt using the operation history of the mouse cursor, and calculates the average speed Vave in a certain time width (n × Δt) therefrom. Then, the calculation unit 14b calculates the magnitude of the change in speed calculated as the absolute value | Vave2-Vave1 | of the average difference in speed at different times. In addition, the calculation unit 14b calculates weight W ₃ = (tanh (5Vave2-3) +1) / 2Vave2 as a weight function. Then, the calculation unit 14b calculates the acceleration / deceleration index A by multiplying the absolute value | Vave2-Vave1 | of the average difference of the mouse cursor speeds by the weight W ₃ = (tanh (5Vave2-3) +1) / 2Vave2. .

  Here, the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus 10 according to the third embodiment will be described with reference to FIG. FIG. 9 is a diagram for explaining the relationship between the acceleration / deceleration index and the weighting function in the determination apparatus according to the third embodiment.

As shown in FIG. 9, in the determination apparatus according to the third embodiment, the acceleration / deceleration index A is weighted to the absolute value | Vave2−Vave1 | of the average difference of the mouse cursor speeds with a weight “W ₃ = (tanh). It is assumed that (5Vave2-3) +1) / 2Vave2 "is multiplied. That is, when the speed of the mouse cursor is too high (when Vave2 is high), it is considered that the long-distance movement of the cursor is reflected, and the estimation as the acceleration / deceleration index A is reduced. Similarly, when the speed of the mouse cursor is too high (when Vave2 is high), the estimation of the acceleration / deceleration index A is reduced as noise during mouse cursor operation.

Here, the weighting function in the determination apparatus 10 according to the third embodiment will be described. FIG. 10 is a diagram for explaining a weighting function in the determination apparatus according to the third embodiment. As shown in FIG. 10, in the first embodiment, the value of the weight W ₁ is “1”, and in the second embodiment, the weight is obtained by “W ₂ = 1 / Vave ₂ ”. In the third embodiment, the weight is obtained by “W ₃ = (tanh (5Vave2−3) +1) / 2Vave2”. For this reason, when the speed of the mouse cursor is out of a specific range, the weighting is evaluated to be small and the acceleration / deceleration evaluation is made small.

  As described above, in the third embodiment, regarding the calculation method of the acceleration / deceleration index A, the change in the speed decreases as the mouse cursor speed average increases, and the value increases as the mouse cursor speed average increases. When the mouse cursor speed is smaller and the mouse cursor speed average is smaller than a certain value, the acceleration / deceleration index is calculated using a weighting function that decreases as the speed average decreases. At that time, the acceleration / deceleration index is reduced. As a result, the acceleration / deceleration index can be reduced in the case of hand shake, IO device noise, and the like, so that the user's operation proficiency can be accurately determined.

[Experimental result]
An experiment was conducted to determine whether the above-described acceleration / deceleration index of the mouse cursor is an index of the proficiency level of the user's operation. For the experimental data, the subject was made to operate a nerve weakening application as illustrated in FIG. There are two modes of the nervous breakdown app. One is the Hide mode, and as shown in FIG. 11A, the nerve is weakened in the original meaning with all the numbers hidden. The other is the Open mode, which performs nerve weakness in a state where all the numbers are visible, as illustrated in FIG. 11B. The former operation in Hide mode that requires exploratory behavior can be regarded as an unfamiliar operation, the latter Open mode operation in which the operation procedure can be grasped, and the subject performs tasks in both modes. Compare the histograms of the acceleration / deceleration indices in both cases.

  In addition, as a condition of the experiment, a total of 36 cards of 6 × 6 in nerve weakness (a total of 18 pairs of 0 to 17 and a window size of 1200 × 1200 pixels. And the number of subjects is two. Each subject was asked to perform a total of 4 tasks, 2 times in each of the Hide mode and the Open mode, repeatedly clicking the same number twice in succession, and all pairs of numbers. If you select a single element in the Hide mode task, the number of that element will be displayed, and the next time you select another element, the number will be the same as the previous selection. It is considered that the pair has been successfully matched, and those elements can no longer be selected.If the number is different from the previous selection, the pair is applied. The numbers of the two elements are no longer displayed again, and in the Open mode task, the task of selecting a pair was performed with all the element numbers visible. In addition, subjects were instructed to complete the task with as few trials as possible.

  The result of the experiment conducted under such conditions is illustrated in FIG. The graph illustrated in FIG. 12 is a histogram in which the frequency is counted for each width of 0.03 in the interval of 0 to 0.6 with respect to the acceleration / deceleration value. The calculation of the acceleration / deceleration value is weighted as in the third embodiment. The horizontal axis shows the acceleration / deceleration index calculated by the method in the third embodiment, and the vertical axis shows the frequency (frequency). As shown in FIG. 12, the Open mode task (learning operation) Tend to take larger acceleration / deceleration index values.

For each subject's data, only the first half of the total required time is extracted from the data for two tasks for each mode. This is because there are fewer elements to be selected in the second half of the task, and the operation becomes like a master even in the Hide mode. Then, an acceleration / deceleration index for each time zone is calculated for these data, and 150 samples are extracted by random sampling. In order to compare the distribution of the acceleration / deceleration index of the Hide mode and the Open mode, the test was performed by the Welch's t test (one-sided test). As a result, for subject 1, “p = 6.13 × 10 ⁻⁴ , t = −3.27, degree of freedom 254.5”, and for subject 2 “p = 1.13 × 10 ⁻⁶ , t = −4.85, The degree of freedom is 234.3 ”, and it can be said that the average value of the acceleration / deceleration index distribution is significantly larger in the Open mode at the significance level p <0.01 and | t |> 2.

[Threshold setting]
As an example of the threshold determination method, the following method using information entropy can be considered. For example, as described above, the number of samples greater than a certain value α (Nhide_right and Nopen_right respectively) with respect to the samples of acceleration / deceleration index values in the Hide mode and Open mode (respectively, Nhide and Nopen). And the number of samples lower than α (Nhide_left and Nopen_left), respectively. For example, as illustrated in FIG. 13, the number of samples Nright = Nside_right + Nopen_right when α = 0.1 and the number of samples Nleft = Nside_left + Nopen_left below α are obtained.

  Then, the information entropy H is calculated using the following formula (1) and the following formula (2) for each of the sample set equal to or larger than α and the set lower than α. However, “LR” contains either left or right, and “mode” contains either hide or open. As a result, Hleft and High are obtained respectively. The information entropy H is an indicator of the degree of deviation of the number of samples in both modes in the region of interest. If the value is large, it indicates that the same number of samples in both modes is included, and conversely the value is small. Indicates that the number of samples in either mode is dominant.

  With respect to the entropy of the left and right regions, an expected value Hexp of entropy when the sample is divided at a certain threshold value α is calculated by the following equation (3).

  FIG. 14 illustrates a graph in which 300 samples are randomly acquired from the samples of both subjects, and the horizontal axis is α and the vertical axis is the above Heexp (log base is 2). Hex takes a minimum value when α = 0.1, and therefore when the sample is divided at α = 0.1, the samples in the open mode and the hide mode are distributed unevenly in the left and right regions. Indicated. From the results of statistical tests, the average value of the acceleration / deceleration index is significantly larger in the open mode. Therefore, when α = 0.1, the sample in the hide mode is on the left and the open mode is on the right. The sample is most clearly divided and distributed. Therefore, in this experiment task, it is conceivable to set the threshold for determining the operation proficiency level to α = 0.1.

  In this way, using the acceleration / deceleration index values of a plurality of mouse cursors calculated using the open mode sample, which is information related to an operation history with a high level of proficiency, and the hide mode, which is information related to an operation history with a low level of proficiency Among the calculated acceleration / deceleration index values of the plurality of mouse cursors, the number of threshold values α or more and the number less than the threshold value α are counted, respectively. A value that is most biased between the sample and sampling in the open mode is set as a threshold value. Thereby, an optimal threshold value can be set for the determination apparatus 10, and the proficiency level can be determined with high accuracy.

[System configuration, etc.]
Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. Further, all or any part of each processing function performed in each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic. For example, the acquisition unit 14a and the calculation unit 14b may be integrated.

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

[program]
It is also possible to create a program in which the processing executed by the determination apparatus described in the above embodiment is described in a language that can be executed by a computer. For example, a determination program in which processing executed by the determination apparatus 10 according to the embodiment is described in a language that can be executed by a computer can be created. In this case, when the computer executes the determination program, the same effect as in the above embodiment can be obtained. Further, the same processing as in the above embodiment may be realized by recording such a determination program on a computer-readable recording medium, and causing the computer to read and execute the determination program recorded on the recording medium.

  FIG. 15 is a diagram illustrating a computer 1000 that executes a determination program. As illustrated in FIG. 15, the computer 1000 includes, for example, a memory 1010, a CPU 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These units are connected by a bus 1080.

  The memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012 as illustrated in FIG. The ROM 1011 stores a boot program such as BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to the hard disk drive 1090 as illustrated in FIG. The disk drive interface 1040 is connected to the disk drive 1100 as illustrated in FIG. For example, a removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to, for example, a mouse 1110 and a keyboard 1120 as illustrated in FIG. The video adapter 1060 is connected to a display 1130, for example, as illustrated in FIG.

  Here, as illustrated in FIG. 15, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, the above determination program is stored in, for example, the hard disk drive 1090 as a program module in which a command to be executed by the computer 1000 is described.

  In addition, various data described in the above embodiment is stored as program data in, for example, the memory 1010 or the hard disk drive 1090. Then, the CPU 1020 reads the program module 1093 and the program data 1094 stored in the memory 1010 and the hard disk drive 1090 to the RAM 1012 as necessary, and executes various processing procedures.

  Note that the program module 1093 and the program data 1094 related to the determination program are not limited to being stored in the hard disk drive 1090, but may be stored in, for example, a removable storage medium and read out by the CPU 1020 via the disk drive or the like. Good. Alternatively, the program module 1093 and the program data 1094 related to the determination program are stored in another computer connected via a network (LAN (Local Area Network), WAN (Wide Area Network), etc.), and via the network interface 1070. May be read by the CPU 1020.

DESCRIPTION OF SYMBOLS 10 Determination apparatus 11 Communication processing part 12 Input part 13 Output part 14 Control part 14a Acquisition part 14b Calculation part 14c Determination part 15 Storage part 15a Mouse cursor operation information storage part

Claims (8)

An acquisition unit that acquires information about an operation history of a mouse cursor that is moved by a user operation;
Using the information acquired by the acquisition unit, a calculation unit that calculates a change in the speed of the mouse cursor per unit time;
A determination device comprising: a determination unit that determines a proficiency level of the user according to a change in speed calculated by the calculation unit.   The determination unit determines whether or not a change in speed calculated by the calculation unit exceeds a predetermined value, and determines that the user is an expert when determining that the change exceeds the predetermined value. The determination apparatus according to claim 1, wherein when it is determined that the value is equal to or less than the predetermined value, the user is determined to be an unskilled person.   The calculation unit calculates a movement distance of the mouse cursor for each predetermined time interval from the information acquired by the acquisition unit, calculates an average speed in a predetermined time width based on the movement distance, and different time widths The determination apparatus according to claim 1, wherein an absolute value of a difference between average speeds of the two is calculated as a change in the speed. The calculation unit calculates an acceleration / deceleration index from the change in speed using a weighting function that decreases as the average speed of the mouse cursor is increased.
The determination device according to claim 1, wherein the determination unit determines the proficiency level of the user according to the acceleration / deceleration index calculated by the calculation unit. The calculation unit weights such that the value becomes smaller as the speed average of the mouse cursor is larger, and the value becomes smaller as the speed average is smaller when the speed average of the mouse cursor is smaller than a certain value. Using the function, calculate the acceleration / deceleration index from the change in speed,
The determination device according to claim 1, wherein the determination unit determines the proficiency level of the user according to the acceleration / deceleration index calculated by the calculation unit.   A change in speed of a plurality of mouse cursors calculated using information related to an operation history having a high level of proficiency and a change in speed of a plurality of mouse cursors calculated using information related to an operation history having a low level of proficiency Of these, the number greater than or equal to the predetermined value and the number less than the predetermined value are counted, and the number greater than or equal to the predetermined value and the number less than the predetermined value are calculated using information related to the operation history having a high skill level. 3. The predetermined value is defined as a value that is most biased between a change in mouse cursor speed and a change in mouse cursor speed calculated using information related to an operation history having a low proficiency level. Judgment device. A determination method executed by a determination device,
An acquisition step of acquiring information about an operation history of a mouse cursor that is moved by a user operation;
A calculation step of calculating a change in the speed of the mouse cursor per unit time using the information acquired by the acquisition step;
And a determination step of determining the user's proficiency level according to a change in speed calculated by the calculation step. An acquisition step of acquiring information about an operation history of a mouse cursor that is moved by a user operation;
A calculation step of calculating a change in the speed of the mouse cursor per unit time using the information acquired by the acquisition step;
A determination program for causing a computer to execute a determination step of determining the proficiency level of the user according to a change in speed calculated in the calculation step.

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