JP4520344B2 - Human error diagnosis system - Google Patents

Description

  The present invention relates to a human error diagnosis system for diagnosing the possibility of occurrence of a human error that is the cause of an accident caused by a human factor, and particularly to the possibility of occurrence of a specific human error classified based on a cognitive occurrence mechanism. The present invention relates to a human error diagnosis system for diagnosis.

  An accident occurs because there is an erroneous event that is different from the flow of events that should take place. The occurrence of this erroneous event is often caused by a human error, that is, a human error. However, human errors often involve many complicated causes, and it is very difficult to analyze the cause and formulate countermeasures.

  Conventionally, a safety management support system disclosed in the following Patent Document 1 is known as a system for analyzing such a human error and supporting the planning of measures for preventing recurrence.

  The safety management support system includes an accident database that stores accidents that are classified and stored according to time and place, degree of responsibility for accidents, and the like. In addition, for each accident, data indicating the error of the party (human error), direct factor data indicating the direct cause of the accident (eg, “ignore signal”), and background factor data indicating the background factor of the accident ( For example, a human error database storing “drinking driving”, “laziness”, and the like is provided. It also has a countermeasure database that stores countermeasures taken against past accidents.

The safety management support system having such a configuration uses the information in the database to analyze accidents by performing aggregations such as “previous year analysis” and “factor component ratio analysis”. It is configured to plan.
JP-A-10-105567

  In addition, the present applicant has developed a human error classification system disclosed in the following Patent Document 2 as a system capable of performing effective human error classification in order to plan effective prevention measures against human errors. .

This human error classification system is a system that classifies human errors generated by human factors into error types based on a model of a cognitive generation mechanism.
JP 2004-302742 A

  However, in order to effectively prevent human error, it is considered effective to diagnose the possibility of occurrence of human error for each individual and take measures in consideration of the diagnosis result. Then, such a diagnostic method is not disclosed.

  The present invention has been made in view of such problems, and an object thereof is to provide a human error diagnostic system for diagnosing the possibility of occurrence of a human error in order to effectively prevent the occurrence of a human error. .

  In order to solve the above problems, a human error diagnosis system according to the present invention performs various arithmetic processes in a human error diagnosis system that diagnoses the possibility of occurrence of a human error by causing a person to be diagnosed to execute a predetermined problem. An arithmetic device to perform, an input device used when the subject performs the task, a storage device that holds information for executing the task and information about the response of the subject to the task, and the task A display device for presenting the subject with the diagnosis, and the storage device is a long-term perspective that asks the subject for a predetermined response at a time that requires long-term attention as the predetermined subject. The information about the memory task is held, and the arithmetic unit records in the memory device the time when the diagnosed person responds to the prospective memory task via the input device. Both based on a difference between the predetermined time and the time which the diagnostician has reacted, and wherein the diagnosing the likelihood of human error in the type of the act or decision to be executed in the future is omitted.

  Further, the human error diagnosis method according to the present invention is a human error diagnosis system having a computing device, a storage device, an input device, and a display device. A long-term prospective memory in which the computing device stores a predetermined response at a predetermined time at intervals of 5 minutes or more stored in the storage device. A presenting step of presenting a problem on the display device, a recording step of recording in the storage device a time when the diagnosed person responds to the prospective memory task via the input device, and the arithmetic device, A diagnostic step for diagnosing the possibility of human error of a type in which actions or judgments to be performed in the future are omitted based on the difference between the predetermined time and the time when the person to be diagnosed reacts; Characterized by comprising a.

  The human error diagnostic program according to the present invention diagnoses the possibility of occurrence of a human error by causing a computer having a computing device, a storage device, an input device, and a display device to execute a predetermined task on a computer. A human error diagnostic program for executing a work, wherein the arithmetic device is stored in the storage device for a long period of time for a predetermined response to be diagnosed at a predetermined time at intervals of 5 minutes or more. A presenting step of presenting a prospective memory task on the display device, a recording step of recording in the memory device the time when the diagnosed responded to the prospective memory task via the input device, and the calculation Based on the difference between the predetermined time and the time when the diagnosed person reacts, the device can generate a human error that eliminates actions and judgments to be performed in the future Characterized in that to execute a diagnostic step of diagnosing, to computer.

  According to the human error diagnostic system of the present invention, it is possible to accurately diagnose the possibility of occurrence of a human error in order to effectively prevent the occurrence of a human error.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

(First embodiment)
First, a first embodiment of the present invention will be described. FIG. 1 is a diagram showing a schematic configuration of a human error diagnostic system 1 according to the first embodiment. As shown in the figure, a human error diagnosis system 1 according to the present embodiment includes a personal computer main body (hereinafter referred to as “PC main body”) 10, a display 20 as an output display device, a keyboard 30 as an input device, and It is comprised from the exclusive key 40 for using when a to-be-diagnosed person performs the prospective memory | storage task mentioned later. The dedicated key 40 includes two keys 40-1 and 40-2. The key A40-1 is a key for the diagnosis subject to press and react when a predetermined time has elapsed, and the key B40-2 is pressed to cause the diagnosis subject to display the elapsed time on the display 20. Is the key.

  In addition, the PC main body 10 measures an arithmetic unit 11 that performs various arithmetic operations, a storage unit 12 that holds information necessary for diagnosis of a human error, which will be described later, and information that the person to be diagnosed inputs through the keyboard 30 and time The timer 13 is included. The timer 13 can measure time in units of 1 ms.

  The human error diagnosis system 1 having the above configuration is characterized by diagnosing the possibility of causing a specific error type based on a model of a cognitive generation mechanism by causing a subject to perform a prospective memory task. System. Here, the prospective memory task is a task of causing the diagnosis subject to perform a predetermined action or determination at a predetermined time, and will be described in detail later.

  In this way, by diagnosing the degree of possibility of causing a specific error type for each individual, it is possible to implement appropriate personnel assignment at the site, and for individuals who are likely to cause that error type. Therefore, it is possible to call attention so as not to cause the error, and effective prevention training can be performed for the error type.

  In describing the diagnosis processing by the human error diagnosis system 1, first, error types classified based on the above-described model of the cognitive occurrence mechanism will be described in detail.

  First, FIG. 2 is a diagram showing a range of human errors targeted by the human error diagnostic system 1. As shown in the figure, “human error” according to the present embodiment excludes “violation” that does not consciously do what should be done in a scene where it is known. In the present embodiment, the “human error” is an error that occurs when a user knows what to do, a “good definition error”, and an error that occurs when a user does not know what to do. It is divided into “definition errors”. The target of this human error separation system is this “good definition error”.

  Next, FIG. 3 is a diagram showing a model for explaining the occurrence of the good definition error. FIG. 3A is a diagram illustrating “when an action is correctly executed”, and FIG. 3B is a diagram illustrating “when an action is erroneously executed”. As shown in FIG. 5A, when attention is directed to the “correct schema”, the “correct schema” is stimulated and the “correct action” is performed. However, as shown in FIG. 5B, if attention is not directed to the “correct schema”, that is, the association strength between “stimulus” and “correct schema” is small, or “stimulus” and “wrong schema” If the strength of the association between the “right schema” and “wrong schema” conflicts and the “wrong schema” output wins, the “wrong schema” is stimulated, Will be performed. Here, the “schema” is a structure of knowledge or a cognitive framework, and refers to a mutual relationship (causal relationship or logical relationship) of knowledge possessed in large quantities by human beings.

  Next, FIG. 4 is a diagram showing a human error classification system based on the model of the cognitive generation mechanism according to the present embodiment. FIG. 5 is a diagram for explaining nine error types classified by this classification system.

  As shown in FIG. 4, in the classification system according to the present embodiment, first, in step 1 (hereinafter, “step” is referred to as “S”), “attention” is caused by a factor that lacks attention to the correct schema. Categorization into three categories: “switching delay (S)”, “distraction of attention (D)”, and “attenuation of persistent attention (V)”. Assuming that the correct flow is that attention shifts from the first schema (previous schema) to the second schema (correct schema), the “lag in attention shift” is the first schema to the second schema. This means that the shift to attention was made, but the timing of the change was late. Also, “disappointing attention” means that attention to the second schema has diverted to another (for example, an incorrect schema). In addition, “attenuation of persistent attention” means that attention to the second schema has been attenuated.

  Next, in S2, classification is performed based on the presence or absence of an erroneous schema. That is, the classification is made based on whether or not there is a schema that causes attention to be performed instead of the correct schema (second schema) and causes an erroneous action to be performed. If the wrong schema is “none”, it means that the wrong action is not executed by the wrong schema, and the correct action by the correct schema is simply not executed.

  In addition, in the case of the above “attention change delay”, the change from the first schema to the second schema (correct schema) is delayed, that is, attention has been directed to the first schema. Therefore, the first schema always exists as an erroneous schema and is “Yes”. In addition, “disappointment of attention” and “attenuation of persistent attention” are classified as “present” or “not present (NN)” depending on the presence or absence of an erroneous schema. In the case of “None”, the subsequent classification is not performed, and in the case of “Lack of attention (D)” − “None (NN)”, the error type “(5) DNN”, “Attenuation of persistent attention” If “(V)” − “None (NN)”, the error type is “(9) VNN”.

  Next, in S3, the association of false schemas and stimuli to which attention is directed instead of the correct schema (second schema) is classified by the enhanced factor. As a factor, “previous activity (P)” that the attention was directed to the erroneous schema immediately before, “high experience frequency ( F) ". In the case of the above “attention change delay”, since attention has been directed to the first schema which is an erroneous schema, all are “previous activity (P)”.

  Next, in S4, classification is performed according to the type of erroneous schema. In S <b> 3, if the schema is classified as “immediately active”, the erroneous schema is a schema to which attention has been paid immediately before, so that all are classified as “immediately active schema (P)”. However, in the case of “attention change delay” − “present” − “activate immediately before”, the erroneous schema is the first schema to which attention has been directed before, so it is not called the immediately preceding active schema. , “Previous schema (A)”. In the case of “attention change delay (S)” — “present” — “previous activity (P)” — “previous schema (A)”, an error type “(1) SPA”, “attention Error (D) "-" Yes "-" Previous activity (P) "-" Previous activity schema (P) ", the error type" (4) DPP "," Attenuation of persistent attention (V ) "-" Yes "-" Previous Active (P) "-" Previous Active Schema (P) ", it is classified as an error type" (8) VPP ".

  Further, in S3, when it is classified as “high experience frequency (F)”, it is usually “correct schema high schema (G)” which is a correct schema with a high probability, or an efficient schema. There are two types of “Efficient Error Schema (B)”.

  Therefore, in the case of “distraction of attention (D)” — “present” — “high experience frequency (F)” — “positive schema rate high schema (G)”, the error type “(2) DFG”, “ In the case of "Deviation of attention (D)"-"Yes"-"High experience frequency (F)"-"Efficient error schema (B)", the error type of "(3) DFB" In the case of “Attenuation (V)” − “Yes” − “High experience frequency (F)” − “Positive schema rate high schema (G)”, the error type of “(6) VFG”, “Attenuation of persistent attention” In the case of (V) "-" Yes "-" High experience frequency (F) "-" Efficient erroneous schema (B) ", the error type is classified as" (7) VFB ".

  Next, each error type will be described in more detail based on FIG.

  As shown in the figure, first, (1) SPA is an error of the type “switching is delayed when it is necessary to rapidly switch from one work or judgment to another work or judgment”. For example, regarding an aircraft operation, a human error in which switching of an operation pattern is delayed in a situation in which an immediate operation pattern must be changed due to a sudden change in the state of the aircraft corresponds to (1) SPA.

  (2) DFG is an error of the type “normally correct work or judgment is interrupted when attention to correct work or judgment is diverted elsewhere”. For example, in a case where there is a delay in train operation, the braking force is weak (unusual) due to rain or a large number of passengers, so it must be braked at a different timing than usual. (2) DFG is a human error in which attention is diverted to a delay in the train, the brake is applied at a normal timing, and cannot be stopped.

  (3) DFB is an error of the type “Effective and incorrect work or judgment interrupts when attention to correct work or judgment is diverted elsewhere”. For example, in a case where there is a delay in train operation, it is necessary to confirm that the departure signal is blue when the train departs. An error corresponds to (3) DFB. Since the departure signal is mostly blue and it is more efficient not to confirm, such an error occurs.

  (4) DPP is an error of the type “when the attention to correct work or judgment is diverted elsewhere, the previous work or judgment interrupts”. For example, in a case where there is a delay in the operation of a train that is driven by a driver who has driven an express on the previous route, the driver's attention has diverted to the train delay, so the stopping pattern during the express operation has been interrupted. Therefore, a human error such as having passed a semi-stop station corresponds to (4) DPP.

  (5) DNN is an error of the type “correct work or judgment is not performed when attention to correct work or judgment is diverted elsewhere”. For example, in the case where a train becomes unable to move between stations due to a failure and a rescue train for towing is requested, even if the failure is corrected, wait until the rescue train arrives on site or notify the command center Although it is necessary, human error such as (5) DNN is that the train delays due to the failure, the attention is lost, the task of waiting at the site or contacting the command center is forgotten, and the train is moved.

  (6) VFG is an error of the type “normally correct work or judgment interrupts when persistent attention from correct work or judgment is attenuated”. For example, if there is a slow section due to track construction, the driver needs to drive while searching for the slow traffic light and its warning sign, that is, paying continuous attention to the warning sign. (6) In VFG, the driver's attention has been distracted, the driver has been driving according to a normal driving pattern that does not need to pay attention to the warning sign, and has overlooked the warning sign and the brake for slowing down has been delayed. Applicable.

  (7) VFB is an error of the “efficient work or judgment interrupts when persistent attention from correct work or judgment is attenuated”. For example, in the case of a train garage operation, in the case where the replacement signal has failed, the driver enters the garage in the back while checking the operator's replacement signal from the window, Although it is necessary to pay attention to the exchange signal continuously until the stop signal is issued, the position of the stop is roughly known, so that the continuous attention is distracted and the exchange signal is not confirmed. A human error, such as a delay in applying a brake when a stop signal is sent from a worker, is (7) VFB.

  (8) VPP is an error of the type “when the last attention is attenuated from the correct work or judgment, the previous work or judgment interrupts”. For example, a train driver must continue to drive while paying attention to the next stop, but the persistent attention is distracted and the express train pattern on the previous route has come to mind. The human error that the vehicle has passed through the stop station corresponds to (8) VPP.

  (9) VNN is an error of the type “no correct work or judgment is made when persistent attention from correct work or judgment is attenuated”. For example, when a rescue train is requested due to a train failure between stations, the rescue train operator must drive while paying continuous attention to the failed train. For example, a human error such as forgetting about this, delaying the discovery of a faulty train, and delaying braking could be considered.

  The error types classified based on the cognitive occurrence mechanism have been described above. However, the human error diagnosis system 1 according to the first embodiment is referred to as (5) DNN type error (hereinafter referred to as “DNN error”). ) Is a system for diagnosing the possibility of occurrence. Hereinafter, the diagnosis process will be described in detail.

  As a problem for diagnosing the possibility of DNN error occurrence according to the present embodiment, a prospective memory problem is used. Prospective memory is memory related to actions and judgments to be performed in the future, and is described in detail in Umeda Atsushi's “Forgetful Brain Mechanism” (Kitaoji Shobo, September 2003).

This prospective memory failure, that is, a prospective memory error that cannot be recalled when an action or judgment should be made and the action or judgment is omitted, pays sufficient attention to the action or judgment to be performed. Occurs when not directed. A prospective memory error that occurs because attention is directed to an event other than an action to be performed or judgment is equivalent to the above-mentioned (5) DNN error, and attention directed to an action or judgment to be performed lasts for a long time. The prospective memory error that occurs because it cannot be attenuated corresponds to the (9) VNN error described above.
The prospective memory task is a task for causing the subject to perform a predetermined action or determination at a predetermined time. The present specification provides a system for diagnosing the possibility of occurrence of a DNN error using a prospective memory task in the first embodiment, and also using a prospective memory task in the second embodiment. (9) A system for diagnosing the possibility of occurrence of a VNN error is provided.

  The DNN error simulation task for diagnosing the possibility of occurrence of a DNN error according to the first embodiment is performed by combining a prospective memory task as a main task with a numerical recitation task as a sub task. The prospective memory task according to the present embodiment is a task that causes the subject to be tested by pressing the key A40-1 and responding every 15 minutes. The numerical recitation task is a five-digit numerical value consisting of numbers 1 to 9 that is presented to the person to be diagnosed by voice from a speaker (not shown) connected to the PC main body 10, and the keyboard 30 is used after 4 to 6 seconds. This is a task that allows an attempt to input a numerical value in parallel while the prospective memory task is being performed.

  In the diagnosis of the possibility of occurrence of DNN error, there is a moderate variation in the results of the subject's main task, and the time interval between trials is too long, so that it does not cause fatigue etc. resulting from sustained attention. Considering the two, the time interval of the prospective memory task is set to 2 minutes. This is a result of empirically appropriate diagnosis results obtained by actually setting several time intervals and executing a task. Of course, the time interval and the number of trials may be adjusted according to the age and characteristics of the person to be diagnosed, other conditions, etc., but as a rule of thumb, a time interval of 1 to 3 minutes was appropriate.

  Next, the flow of processing of the DNN error simulation task according to the present embodiment will be described in detail with reference to the drawings. FIG. 6 is a flowchart showing the flow of the DNN error simulation task according to the first embodiment. FIG. 7 is a diagram showing the contents of the screen presented on the display 20 when the DNN error simulation task is executed. FIG. 8 is a diagram illustrating information recorded in the storage device 12 regarding the DNN error simulation task.

  In S10, when the diagnosed person operates the input device such as the keyboard 30 to start diagnosis, the process proceeds to S20, the prospective memory task as the main task is started, and the process proceeds to S30, where the numerical recitation as the sub task is performed. Tasks are started in parallel. The main task and the sub task are executed by the arithmetic device 11 controlling the timer 13 and the display 20 based on information held in the storage device 12.

  First, the prospective memory task will be described. When the prospective memory task is started (S20), in S21, the arithmetic unit 11 resets the timer 13 and starts measuring the elapsed time. In S22, the arithmetic unit 11 monitors the depression of the key B40-2 by the person to be diagnosed. In S22, when the key B40-2 is pressed, the arithmetic unit 11 refers to the elapsed time measured by the timer 13 and displays the elapsed time on the display 20 for 2 seconds as shown in FIG. The person to be diagnosed can confirm whether or not it is time to react by pressing the key A40-1 by pressing the key B40-2 to display the elapsed time. In other words, the person to be diagnosed cannot refer to the elapsed time unless the user presses the key B40-2 voluntarily. If the key B40-2 is not pressed in S22, S23 is skipped and the process proceeds to S24.

  In S24, the arithmetic unit 11 monitors the depression of the key A40-1 by the diagnosed person. When it is determined that 2n minutes have elapsed in the nth trial, which is the nth trial and the key A40-1 is pressed, the arithmetic unit 11 refers to the elapsed time of the timer 13 in S25, and FIG. As shown, the reaction time is displayed on the display 20 for 2 seconds. If the key A40-1 is not pressed, the process returns to S22, and the pressing of the key B40-2 is monitored.

In S25, as shown in FIG. 8, the arithmetic unit 11 records the diagnosis time of the diagnosis subject in the storage device 12 together with the time to react (2n minutes = 120n seconds).
In S <b> 25, the arithmetic unit 11 performs display / recording processing by regarding the first key A <b> 40-1 pressed after 2n minutes, which is the time to react, as a reaction by the diagnosed person. The key press before the first 2 minutes and the second and subsequent key presses after 2n minutes are regarded as erroneous reactions, and the number of erroneous reactions is recorded in the storage device 12 separately.

  For example, taking the case of the first trial as an example, even if the key A40-1 is pressed between 0 seconds and 119 seconds, it is recorded as an erroneous reaction, and the key A40-1 is pressed in 123 seconds. Then, a display / recording process is performed as a response by the diagnosed person (see the first trial in FIG. 8). Furthermore, even if the key A40-1 is pressed during the subsequent 124 seconds to 239 seconds, the arithmetic unit 11 records as an erroneous reaction, and after 240 seconds, which is the time at which the second trial should react, has elapsed. The first key press is regarded as the reaction of the diagnosis subject, and display / recording processing is performed (257 seconds of the second trial in FIG. 8).

  Further, when the subject does not react, that is, when the key A40-1 is not pressed during the nth to (n + 1) th minutes, the arithmetic unit 11 treats it as non-responsive, and the nth The reaction time is not recorded for the trial (see the third trial in FIG. 8). If there is an unreactive trial, it is recorded in the storage device 12 as an erroneous reaction for one time.

  Subsequently, in S26, the arithmetic unit 11 determines whether or not all 15 trials have been completed. In other words, the arithmetic unit 11 presses the key A40-1 after 30 minutes (time when the fifteenth trial should respond) after the prospective memory task is started, or the key A40-1 is not pressed. When 32 minutes have passed (no response), it is determined that 15 trials have been completed, and the process proceeds to S40 to diagnose the possibility of occurrence of a DNN error. If all 15 trials have not been completed, the process returns to S22, and the next trial is repeated in the same manner.

  Next, the numerical recitation task will be described. When the numerical recitation task is started (S30), in S31, the computing device 11 randomly determines a five-digit numerical value consisting of numbers 1 to 9 to be recited by the examinee, and from a speaker (not shown) by voice. Present. When 4 to 6 seconds elapse after the end of S31, the process proceeds to S32, and an input screen for inputting the recitation value to the diagnosis subject is displayed on the display 20 by the computing unit 11.

  The time interval between S31 and S32 is randomly determined between 4 and 6 seconds, and is set so that the average of all 15 trials is 5 seconds. The numerical recitation task is a subtask for distracting attention from the prospective memory task, which is the main task, and distracts attention from the main task after a certain period of time from the voice presentation of the recitation value to the patient being answered. The role grows. On the other hand, when the time is long, the difficulty of the sub-task increases, which is not preferable. Therefore, although the time interval of S31 to S32 needs to be set appropriately in view of conditions such as the number of digits of the recitation value, the age and characteristics of the person to be diagnosed, in this embodiment, it is 4 to 6 seconds (average 5 Second).

  As shown in FIG. 7, the numerical value input screen in S <b> 32 presents a five-digit numerical value input field together with a message “Please input memorized numerical value”. This numeric input screen is presented for 15 seconds. The person to be diagnosed must input the recited numerical value using the keyboard 30 while the numerical value input screen is presented.

  Subsequently, the process proceeds to S <b> 33, where the arithmetic unit 11 determines whether the numerical value input by the diagnosis subject is correct and records the result in the storage device 12. At this time, if the five-digit recitation value presented by the voice and the numerical value entered by the person to be diagnosed completely match, the answer is correct, if not, and during the 15 seconds when the numerical value input screen is displayed. If the diagnostician does not enter a numerical value, it is treated as an incorrect answer.

  Then, it progresses to S34 and it is determined whether all the 15 trials of a prospective memory | storage task are complete | finished similarly to S26, and when not complete | finished, it returns to S31 and a numerical recitation task is repeated. . If the prospective storage task has been completed, the process proceeds to S40, and the possibility of occurrence of a DNN error is diagnosed based on the information recorded in the storage device 12 shown in FIG. Thus, the numerical recitation task of the sub task is performed in parallel as a task for diverting attention from the main task while the prospective memory task as the main task is performed.

  Next, the diagnosis process of the possibility of DNN error occurrence at S40 will be described in detail. FIG. 9 is a diagram showing the diagnostic criteria. As shown in the figure, in the diagnostic process according to the present embodiment, three diagnostic indicators are used: average reaction error, number of erroneous reactions, and numerical recitation correct answer rate.

  The average reaction error is the average of the differences (reaction errors) between the reaction time of each trial recorded in the storage device 12 and the reaction time. In the example shown in FIG. 8, the response error of the first trial is 0 seconds, the reaction error of the second trial is 17 seconds, and the reaction error of the fourth trial is 1 second. In determining the average reaction error, no reaction (third trial in FIG. 8) or erroneous reaction is not taken into consideration.

  In S <b> 40, the arithmetic device 11 calculates an average reaction error from the information recorded in the storage device 12, and determines that there is a high possibility of DNN error if the average reaction error is 15 seconds or longer.

  The number of erroneous reactions is the number recorded in the storage device 12 as erroneous reactions (including no reactions) in S25 described above. In S40, if the number of erroneous responses recorded in the storage device 12 is 5 or more, the arithmetic unit 11 determines that the prospective storage task that is the attention division scene has not been appropriately performed and cannot be determined. To do.

  The numerical recitation correct answer rate can be calculated from the correctness determination result of the numerical recitation task recorded in the storage device 12 in S33 described above. In S <b> 40, the arithmetic device 11 cannot determine that the prospective memory task that is the attention division scene has not been appropriately performed even when the correct answer rate recorded in the memory device 12 is less than 0.7. And

  Each threshold value in S40 is set based on experience by performing test diagnosis in consideration of each condition of the DNN error simulation task according to the present embodiment. These threshold values are appropriately set according to the situation and conditions. It can be changed.

  The diagnosis results may be displayed on the display 20, collected in a server via a network (not shown), aggregated, or printed on paper by an output device such as a printer and output.

  Here, the reason why the possibility of occurrence of a DNN error can be diagnosed by the DNN error simulation task according to the present embodiment will be described in detail. As described with reference to FIGS. 4 and 5, the DNN error refers to an action or determination that should be executed in the future (in this embodiment, a key press every 2n minutes), and other events ( In the present embodiment, this is an error of omission of actions to be performed and judgments that occur due to deviation from numerical recitation.

  FIG. 10 is a diagram illustrating a model for explaining the occurrence of a DNN error. FIG. 10A is a model when actions and judgments that should be executed in the future are correctly executed, and FIG. 10B shows actions and judgments that must be executed in the future because attention is lost. It is a figure which shows a model when not made.

  As shown in FIG. 10A, if attention can be continuously given to a schema to be executed in the future, the schema to be executed is correctly stimulated (activated) when the time to be executed is reached and executed. Actions and judgments to be performed will be executed correctly.

  If applied to this embodiment, if the subject can continue to pay attention to the schema (prospective memory task) of pressing key A every 2 minutes, every 2 minutes The act of pressing the key A can be executed correctly.

  Note that the schema to be executed in the present embodiment must be executed every two minutes, so that the person to be diagnosed needs to continue to check the time (elapsed time) by himself / herself. In other words, in the present embodiment, a subschema that checks the time (elapsed time) when the schema is executed is also imposed. However, this sub-schema needs to check the time (elapsed time) as appropriate if attention is continuously directed to the schema to be executed finally (key press every 2 minutes). It is what is done.

  On the other hand, as shown in FIG. 10B, when attention is diverted to other events and attention cannot be correctly directed to the schema to be executed in the future, the schema to be executed at the time to be executed. Are not stimulated (activated) correctly, and actions or judgments to be performed are not performed or are delayed.

  If this embodiment is applied, attention will shift to the numerical recitation task unless it continues to be directed to the schema (prospective memory task) of pressing the key A every 2 minutes, and when 2 minutes have passed. The key A is not pressed or is pressed with a delay.

  Thus, in the DNN error simulation task, which is a combination of the prospective memory task and the numerical recitation task according to the present embodiment, no reaction or reaction delay is the DNN error itself. Therefore, it is possible to diagnose the possibility of DNN error occurrence based on the average reaction error, the number of erroneous responses, and the numerical recitation correct answer rate described above.

  Here, the above-described diagnosis function of the human error diagnosis system 1 according to the present embodiment is realized by installing a program for realizing these functions in the PC main body 10.

  As described above in detail, according to the human error diagnosis system according to the present embodiment, it is possible to accurately diagnose the possibility of occurrence of DNN errors classified based on the model of the cognitive generation mechanism. In addition, based on the results of this diagnosis, individuals with a high possibility of DNN error can be alerted to DNN errors and DNN error prevention training can be implemented, effectively preventing the occurrence of human errors. can do. In addition, it is possible to effectively prevent the occurrence of a human error even by performing the personnel assignment in consideration of the possibility of the occurrence of a DNN error.

In addition, this embodiment is not limited to the said structure, A various deformation | transformation is possible within the range which does not deviate from the main point of this invention.
For example, in this embodiment, only pressing of the key A after 2n minutes has been handled as a response of the diagnosed person, and the diagnosed person is impatient, etc. before 2n minutes (for example, 1, 2 seconds ago) Although the so-called agitation reaction such as reaction is ignored, it may be configured to take this into account.

  Further, in this embodiment, as a numerical recitation task, a five-digit number consisting of numbers 1 to 9 is used, but each digit number may be a number including 0, and the number of digits of the number is also five. Not limited to digits, it may be three digits or six digits.

  In the present embodiment, the elapsed time is displayed on the display 20 for 2 seconds by pressing the key B 40-2. However, the displayed time is not limited to 2 seconds. What is necessary is just enough time for a to-be-diagnosed person to confirm elapsed time.

  Further, instead of displaying the elapsed time on the display, a clock arranged around may be used. However, in this case, it is necessary to accurately synchronize the surrounding clock and the timer 13. Further, in order to check the time spontaneously, it is desirable to turn the watch over or place the watch in a place outside the field of view of the person being diagnosed.

  The elapsed time may be displayed on the display 20 at all times. However, in this case, since the subject is always presented with a few clues indicating that it is time to execute the prospective memory task, the difficulty of the task is reduced and the subject is diagnosed. It is necessary to pay attention so that the variation among persons does not decrease.

  Moreover, in this embodiment, since the prospective memory task and the numerical recitation task are performed in parallel, the execution of the prospective memory task and the execution of the numerical recitation task are requested at the same timing for the diagnosis subject. There is a possibility that. Even in such a case, the prospective memory task is a simple operation of pressing a key and has almost no effect. However, if both tasks are likely to overlap, the voice presentation of the recitation numerical value of the numerical recitation task (S31) And adjusting the time (4 to 6 seconds) between the input screen presentations (S32) so that they do not overlap.

(Second Embodiment)
Next, a second embodiment of the present invention will be described. Since the human error diagnostic system according to the second embodiment has substantially the same configuration as the human error diagnostic system according to the first embodiment, the description of the same configuration is omitted, and only the different configuration is described. This will be described in detail.

  The human error diagnosis system 1 according to the second embodiment is a system for diagnosing the possibility of occurrence of the above-mentioned (9) VNN type error (hereinafter referred to as “VNN error”). Is different. Therefore, in the present embodiment, as a problem for diagnosing the possibility of occurrence of a VNN error, a long-time interval that is longer than that of the first embodiment is required to request the subject to maintain caution for a long time. A VNN error simulation task composed of prospective memory tasks is employed.

  The long-term prospective memory task according to the second embodiment is a task for making a total of 15 trials that require the subject to respond by pressing the key A40-1 every 7 minutes. . In the diagnosis of the possibility of occurrence of a VNN error, it is necessary to cause a moderate variation in the results of the subject to the prospective memory task, and it is necessary to request the subject to maintain attention for a long time.

  In the present embodiment, after actually setting several time intervals and executing the task, it is set to 7 minutes when an appropriate diagnostic result is obtained empirically. Of course, the time interval and the number of trials may be adjusted according to the age and characteristics of the patient to be diagnosed, other conditions, etc., but as a rule of thumb, a time interval of 5 minutes or more was appropriate. In addition, if the time interval is too long, the diagnosis time also becomes longer. Therefore, a time interval of 5 minutes to 15 minutes is more preferable for efficient diagnosis.

  With reference to the drawings, the processing flow of the VNN error simulation task according to the present embodiment will be described in detail. FIG. 11 is a flowchart showing a flow of a VNN error simulation task according to the second embodiment. The long-term prospective memory task that constitutes the VNN error simulation task is the same processing as the prospective memory task that is the main task of the DNN error simulation task, except for the time interval of each trial.

  In S50, when the person to be diagnosed operates the input device such as the keyboard 30 to start diagnosis, the process proceeds to S60, and a long-term prospective memory task is started. In S61, the timer 13 is reset and the elapsed time is started. In S62, the key B40-2 pressed for time confirmation by the diagnosis subject is monitored, and when the key B40-2 is pressed, the elapsed time is presented to the diagnosis subject for 2 seconds in S63.

  In S64, the key A40-1 pressed by the diagnosed person is monitored. When the diagnosed person who has determined that 7n minutes (the nth trial) has elapsed presses the key A40-1, the reaction time is determined in S65. It is displayed on the display 20 and recorded in the storage device 12. The timing at which a key press is treated as a response of the diagnosed person, the determination method of no response / error response, and recorded information are the same as in the first embodiment.

  Subsequently, in S66, it is determined whether or not all 15 trials have been completed. That is, 105 minutes (time when the fifteenth trial should respond) has elapsed since the prospective memory task started, or key A40-1 has been pressed, or 112 minutes have elapsed without key A40-1 being pressed If it is (no response), it is determined that 15 trials have been completed, and the process proceeds to S70. In S70, the possibility of occurrence of a VNN error is diagnosed. If all 15 trials have not been completed, the process returns to S62, and then the next trial is repeated.

  The diagnosis processing of the possibility of occurrence of the VNN error in S70 is performed using the diagnostic index of the average reaction error and the number of erroneous reactions as in the first embodiment. However, in the second embodiment, since the numerical recitation task as a sub-task is not imposed, the numerical recitation correct answer rate is not used as a diagnostic index. The determination criteria in S70 are the same as in S40 of the first embodiment (see FIG. 9). If the average reaction error is 15 seconds or more, it is determined that the possibility of occurrence of a VNN error is high. Moreover, if the number of erroneous reactions is 5 or more, it is determined that the prospective memory task, which is a attention division scene, has not been appropriately performed, and the determination is impossible.

  Each threshold value in S70 is also set empirically in consideration of each condition of the VNN error simulation task according to the present embodiment, but each value can be appropriately changed according to the situation and conditions.

  Here, the reason why the possibility of occurrence of a VNN error can be diagnosed by the VNN error simulation task according to the present embodiment will be described in detail. As described with reference to FIGS. 4 and 5, the VNN error is caused by a decrease in attention to be directed to actions or judgments to be performed in the future (in this embodiment, key press every 7 n minutes). This is an error to omit actions or judgments to be performed.

  FIG. 12 is a diagram illustrating a model for explaining the occurrence of a VNN error. FIG. 12A shows a model when an action or judgment that should be executed in the future is correctly executed, and FIG. 12B shows an action or judgment that needs to be executed in the future because attention is attenuated. It is a figure which shows a model when not made.

  As shown in FIG. 12A, if attention can be continuously given to a schema to be executed in the future, the schema to be executed is stimulated (activated) correctly when the time to be executed is reached and executed. Actions and judgments to be performed will be executed correctly.

  If applied to the present embodiment, if the subject can continue to pay attention to the schema (long-term prospective memory task) of pressing the key A every 7 minutes, 7 The act of pressing the key A every minute can be executed correctly.

  On the other hand, as shown in FIG. 12 (b), if the attention is attenuated due to tiredness of continuing attention, etc., and the attention cannot be correctly directed to the schema to be executed in the future, the processing is executed. The schema to be executed at the correct time is not stimulated (activated) correctly, and the action or judgment to be executed is not performed or is delayed.

  If this embodiment is applied, the attention will be attenuated unless the key A is pressed every time 7 minutes elapse, and the key will be attenuated when 7 minutes elapse. A will not be pushed or will be pushed late.

  As described above, in the VNN error simulation task configured by the long-term prospective memory task according to the present embodiment, no response or reaction delay is the VNN error itself. Therefore, it is possible to diagnose the possibility of occurrence of a VNN error based on the average reaction error and the number of erroneous reactions described above.

  Here, the above-described diagnosis function of the human error diagnosis system 1 according to the present embodiment is realized by installing a program for realizing these functions in the PC main body 10.

  As described above in detail, according to the human error diagnosis system according to the present embodiment, it is possible to accurately diagnose the possibility of occurrence of VNN errors classified based on the model of the cognitive generation mechanism. Also, based on the diagnosis results, individuals with a high possibility of VNN error can be alerted to VNN errors and VNN error prevention training can be implemented to effectively prevent human errors. can do. In addition, it is possible to effectively prevent the occurrence of a human error by performing a human resource allocation considering the possibility of occurrence of a VNN error.

  As mentioned above, although this invention was demonstrated in detail based on 1st and 2nd embodiment, embodiment of this invention is not limited to these, Various in the range which does not deviate from the main point of this invention. Can be modified.

  For example, the number of trials in the first and second embodiments can be changed as appropriate, and more accurate diagnosis can be performed by increasing the number of trials. Regarding the prospective memory task, the time interval of each trial is set to a constant interval of 2 minutes in the first embodiment and 7 minutes in the second embodiment, but the time interval is made different for each trial. May be. However, it is necessary to pay attention so that the difficulty of the task becomes too high and the possibility of Newman error cannot be properly diagnosed.

  In addition, the human error classification system according to the present embodiment includes the dedicated key 40 as a dedicated input device for the diagnosis subject to react to the problem. However, a general-purpose mouse or keyboard is used for the reaction. It may be used as an input device.

  In the first and second embodiments, the possibility of occurrence of DNN errors and VNN errors classified based on the error classification system shown in FIGS. 4 and 5 is diagnosed. The target error types are not limited to these two error types. That is, according to the prospective memory task according to the first and second embodiments, actions and judgments to be performed that are caused by insufficient attention directed to actions and judgments that must be performed in the future are omitted. It is possible to diagnose the possibility of occurrence of types of errors. Then, adjust the time interval of each trial according to the error type, or add another task such as a numerical recitation task as a sub task, and diagnose the possibility of error occurrence according to the error type more be able to.

FIG. 1 is a diagram showing a schematic configuration of a human error diagnostic system according to the first embodiment of the present invention. FIG. 2 is a diagram showing a range of human errors targeted by the human error diagnostic system according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating a model for explaining the occurrence of a good definition error according to the first embodiment of the present invention. FIG. 4 is a diagram illustrating a human error classification system based on a model of a cognitive occurrence mechanism according to the first embodiment of the present invention. FIG. 5 is a diagram for explaining details of error types according to the first embodiment of the present invention. FIG. 6 is a flowchart showing a flow of processing for diagnosing the possibility of occurrence of a DNN error according to the first embodiment of the present invention. FIG. 7 is a diagram for explaining the contents of the DNN error simulation task according to the first embodiment of the present invention. FIG. 8 is a diagram for explaining information recorded in the DNN error simulation task according to the first embodiment of the present invention. FIG. 9 is a diagram showing diagnostic criteria for the DNN error simulation task according to the first embodiment of the present invention. FIG. 10 is a diagram illustrating a model for explaining the occurrence of a DNN error according to the first embodiment of the present invention. FIG. 11 is a flowchart showing a flow of processing for diagnosing occurrence of a VNN error according to the second embodiment of the present invention. FIG. 12 is a diagram illustrating a model for explaining the occurrence of a VNN error according to the second embodiment of the present invention.

Explanation of symbols

1 Human Error Diagnosis System 10 PC Main Body 11 Arithmetic Unit 12 Storage Device 13 Timer 20 Display 30 Keyboard 40 Dedicated Key

Claims (5)

In a human error diagnostic system that diagnoses the possibility of human error by causing a person to be diagnosed to perform a predetermined task,
An arithmetic device that performs various arithmetic processes, an input device that is used when the subject performs the task, and a memory that stores information for executing the task and information on the response of the subject to the task A device, and a display device for presenting the subject to the diagnosis subject,
The storage device holds, as the predetermined task, information on a long-term prospective memory task that asks the patient for a predetermined response at a time that requires long-term attention.
The arithmetic unit records in the storage device the time when the diagnosed person responds to the prospective memory task via the input device, and the difference between the predetermined time and the time when the diagnosed person reacts Based on the above, a human error diagnosis system characterized by diagnosing the possibility of occurrence of a human error of a type in which actions or judgments to be executed in the future are omitted.   The human error diagnosis system according to claim 1, wherein the long-term prospective memory task is a task for obtaining a predetermined response at intervals of 5 minutes or more.   3. The human error diagnosis system according to claim 1, wherein the computing device displays the time on the display device for a predetermined time when a time confirmation request is received from the person to be diagnosed via the input device. . A human error diagnostic method for diagnosing the possibility of occurrence of a human error by causing a person to be diagnosed to execute a predetermined problem by a human error diagnostic system including an arithmetic device, a storage device, an input device, and a display device,
A presentation step in which the arithmetic device presents a long-term prospective memory task for the subject to be diagnosed at a predetermined time at an interval of 5 minutes or more, which is stored in the storage device, on the display device When,
A recording step of recording, in the storage device, a time when the diagnosis subject has reacted to the prospective storage task via the input device;
A diagnostic step of diagnosing the possibility of occurrence of a human error of a type in which an action or determination to be performed in the future is omitted based on a difference between the predetermined time and a time when the diagnosis subject reacts; A human error diagnosis method comprising: A human error diagnostic program for causing a computer equipped with an arithmetic device, a storage device, an input device, and a display device to perform a task for diagnosing the possibility of occurrence of a human error by causing a person to be diagnosed to execute a predetermined task. And
The presenting step of presenting the display device with a long-term prospective memory task in which the computing device is stored in the storage device and asks the subject for a predetermined response at a predetermined time at intervals of 5 minutes or more. When,
A recording step of recording, in the storage device, a time when the diagnosis person responds to the prospective storage task via the input device;
A diagnostic step of diagnosing the possibility of occurrence of a human error of a type in which an action or judgment to be performed in the future is omitted based on a difference between the predetermined time and a time when the diagnosis subject reacts; A human error diagnosis program which is executed by a computer.

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