JP2012073105A - Navigation device, method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a navigation device capable of changing navigation system according to a user's individuality and state.SOLUTION: The navigation device comprises: a behavior recognition part; a first estimation part; a second estimation part; a user state estimation part; a user individuality estimation part; and a navigation system determination part. The behavior recognition part recognizes the user's behaviors by using one or more sensors. The first estimation part estimates a present behavior tendency based on a present behavior history recording the user's behaviors during a first period. The second estimation part estimates a regular behavior tendency based on a regular behavior history recording the user's behaviors during a second period longer than the first period. The user state estimation part estimates the user's state and its certainty factor based on the present behavior tendency. The user individuality estimation part estimates the user's individuality and its certainty factor based on the regular behavior tendency. The navigation system determination part determines a navigation system by comparing the user's individuality and the certainty factor of the individuality.

Description

  Embodiments described herein relate generally to a navigation apparatus, a method, and a program for changing a navigation method according to a user's personality and situation.

  Performing navigation in accordance with the user's personality is important for improving the convenience of the user and for dealing with the elderly and the disabled. A navigation device that changes the navigation method according to the user's personality set in advance is provided. is there.

In such a navigation device, it is desired to be able to realize navigation in consideration of the user's situation.

Japanese Patent No. 3697454

  However, the user personality necessary for navigation is diverse, and the user's situation changes. Therefore, it is difficult to preset all information necessary for navigation.

  The present disclosure has been made to solve the above-described problems, and provides a navigation device that automatically estimates a user's personality and situation and changes a navigation method according to the estimated user's personality and situation. With the goal.

  A navigation device according to an embodiment includes an action recognition unit, a first recording unit, a first estimation unit, a second recording unit, a second estimation unit, a user situation estimation unit, a user personality estimation unit, and a navigation method determination unit. The behavior recognition unit recognizes the user's behavior based on observation information including data observed by one or more sensors. The first recording unit holds a current behavior history in which behaviors recognized by the behavior recognition unit in the first period are recorded. The first estimation unit estimates a current behavior tendency indicating the behavior tendency of the user in the first period based on the current behavior history. The second recording unit holds a normal action history in which actions recognized by the action recognition unit in a second period longer than the first period are recorded. A 2nd estimation part estimates the normal action tendency which shows the user's action tendency in the said 2nd period based on the said normal action history. The user situation estimation unit estimates the user situation by comparing the current behavior tendency with the normal behavior tendency, and sets a first certainty factor indicating the certainty of the user situation. The user personality estimation unit estimates the personality of the user by comparing the normal behavior tendency with a general user behavior tendency indicating a behavior tendency of another user, and indicates a probability of the user's personality. Set confidence. The navigation method determination unit determines the navigation method by comparing the first certainty factor with the second certainty factor.

1 is a block diagram schematically showing a navigation device according to one embodiment. The figure which shows an example of the present action history currently recorded on the present action tendency recording part shown in FIG. 1, and a present action tendency. The figure which shows an example of the normal action history and normal action tendency which are recorded on the normal action tendency recording part shown in FIG. The figure which shows an example of the general user action history and the general user action tendency which are recorded on the general user action tendency table shown in FIG. The flowchart which shows an example of operation | movement of the navigation apparatus of FIG. The figure for demonstrating an example of the method in which the action recognition part shown in FIG. 1 recognizes a user's action. The figure for demonstrating an example of the method of estimating a user condition and user individuality. The figure which shows an example of the information obtained by the biometric sensor shown in FIG. The figure which shows an example of the information obtained by the acceleration sensor shown in FIG. The figure which shows an example of the vehicle information containing the information of a congestion degree attribute and a female exclusive vehicle attribute. The figure which shows an example of the information obtained using the vehicle information of FIG. The figure which shows the other example of the information obtained using the vehicle information of FIG. The figure which shows an example of the list | wrist of the navigation system which the navigation part shown in FIG. 1 hold | maintains. The figure which shows the navigation form by the navigation system shown in FIG. The figure for demonstrating the method of showing a some guide route information to a user. The figure for demonstrating the method of showing a some guide route information to a user. The figure for demonstrating the method of showing a some guide route information to a user.

  Hereinafter, a navigation device, method, and program according to an embodiment will be described with reference to the drawings as necessary. In the present embodiment, an example will be described in which the navigation device is applied to a mobile terminal (that is, a computer device) such as a mobile phone or a PDA (Personal Digital Assistant). The navigation device of this embodiment performs navigation for a user having this portable terminal. The portable terminal includes a control unit, a storage unit, a communication control unit, a display unit, and an input unit, which are connected to each other via a bus so as to communicate with each other. The control unit includes a CPU (Central Processing Unit) and the like, and controls each unit in the mobile terminal. The storage unit includes a ROM (Read Only Memory) and a RAM that store various data and various programs. The communication control unit controls communication with the base station. The display unit displays information according to the control of the control unit. The input unit includes operation keys and operation buttons, and accepts user instruction input.

  FIG. 1 schematically shows a navigation device according to the present embodiment. As shown in FIG. 1, the navigation device includes a detection unit 110, a behavior recognition unit 120, a current behavior tendency recording unit 130, a current behavior tendency estimation unit 135, a user situation estimation unit 140, a normal behavior tendency recording unit 150, a normal behavior A behavior tendency estimation unit 155, a user personality estimation unit 160, a general user behavior tendency table 170, a navigation method determination unit 180, and a navigation unit 190 are provided.

  The detection unit 110 includes one or more sensors, for example, an acceleration sensor 112, a positioning sensor 114, a living body sensor 116, and the like, and outputs observation information including various data observed by these sensors 112, 114, and 116. Each sensor of the detection unit 110 may be a sensor provided in a mobile terminal, or may be a sensor prepared for a navigation device.

  The acceleration sensor 112 detects acceleration acting on the mobile terminal and outputs acceleration information. The acceleration sensor 112 may be a three-axis acceleration sensor that measures acceleration in each direction of three detection axes orthogonal to each other, and is a biaxial acceleration that measures acceleration in each direction of two detection axes orthogonal to each other. It may be a sensor.

  The positioning sensor 114 acquires the absolute position (latitude and longitude) of the mobile terminal and outputs position information. As an example, the positioning sensor 114 is a GPS receiver that receives GPS information from a GPS (Global Positioning System) satellite and calculates latitude and longitude from the GPS information.

  The biometric sensor 116 detects the heart rate, pulse rate, blood pressure, etc. of the user carrying the mobile terminal and outputs biometric information.

  The behavior recognition unit 120 recognizes the user's behavior based on observation information (for example, acceleration information, position information, biological information, etc.) from the detection unit 110. Examples of the behavior recognized by the behavior recognition unit 120 include a movement method, a walking pace, and a change in heart rate. As an example, the moving method includes stair walking indicating that the user has walked on the stairs, an elevator ride indicating that the user has got on the elevator, an escalator ride indicating that the user has taken the escalator, and the like.

  The current behavior trend recording unit (also referred to as a first recording unit) 130 records a history of behavior (current behavior history) recognized by the behavior recognition unit 120 during a predetermined period (for example, one day of the day). A current behavior tendency estimation unit (also referred to as a first estimation unit) 135 estimates a current behavior trend based on the current behavior history recorded in the current behavior trend recording unit 130. The current behavior tendency represents a behavior tendency of the user for a short period of time. The current behavior trend estimated by the current behavior trend estimation unit 135 is recorded in the current behavior trend recording unit 130.

  Note that the current behavior trend recording unit 130 is not limited to the example of recording the behavior history of the current day, and may record the behavior during a period longer or shorter than one day.

  The normal behavior trend recording unit (also referred to as a second recording unit) 150 is a history of behavior recognized by the behavior recognition unit 120 during a predetermined period (for example, one month) longer than the current behavior trend recording unit 130 (normal behavior history). Record. The normal behavior tendency estimation unit (also referred to as a second estimation unit) 155 estimates the normal behavior tendency based on the normal behavior history recorded in the normal behavior trend recording unit 150. The normal behavior history represents a long-term user behavior tendency. The normal behavior tendency estimated by the normal behavior tendency estimation unit 155 is recorded in the normal behavior trend recording unit 150.

  The normal behavior tendency recording unit 150 is not limited to the example of recording the history of behavior for one month, and may record the history of behavior for a period longer or shorter than one month.

  In the general user behavior tendency table 170, a history of general user behavior (general user behavior history) is recorded, and a general user behavior tendency obtained from the general user behavior history is recorded.

  Each of these current behavioral trends, normal behavioral trends and general user behavioral trends is the use of lifting equipment usage ratio indicating the ratio of riding on lifting equipment such as elevators and escalators when moving between floors, walking on stairs Various data such as an increase in the heart rate before and after moving with the.

  The user situation estimation unit 140 estimates the user situation by comparing the current behavior tendency with the normal behavior tendency, and sets the certainty of the user situation. The user personality estimating unit 160 compares the normal behavior tendency with the general user behavior tendency, estimates the user personality, and sets the certainty factor of the user personality. Thus, the user situation is estimated from the user's short-term behavior tendency, and the user personality is estimated from the user's long-term behavior tendency.

  The navigation method determination unit 180 determines the navigation method based on the comparison between the certainty of the user situation and the certainty of the user personality. As a navigation method, for example, there are “use of stairs” that presents a route that actively uses stairs, “use of elevator” that presents a route that actively uses elevators and escalators.

  The navigation unit 190 determines a route (guidance route) from the departure position to the destination position according to the navigation method determined by the navigation method determination unit 180, and displays the determined route on the display unit for presentation to the user. The starting position and the target position are specified by, for example, the user operating the input unit.

  In the present embodiment, an example in which the behavior recognition unit 120 detects stair walking, elevator boarding, and escalator boarding will be described. In this example, the current action history, the normal action history, and the general user action tendency table are shown in FIG. 2, FIG. 3, and FIG. 4, respectively, the number of times staircase walking is recognized, the number of times elevator ride is recognized, and the escalator. Holds the number of times the ride was recognized. In FIG. 2, FIG. 3, and FIG. 4, the lifting equipment usage ratio is calculated as the current behavior tendency, the normal behavior tendency, and the general user behavior tendency, respectively. The lifting equipment utilization ratio is obtained by dividing the total number of times that the escalator boarding and the elevator boarding are recognized by the total number of times that the stair walking, the elevator boarding and the escalator boarding are recognized (total number of times of lifting).

  In the current action history of FIG. 2, the number of stairs used is 3, the number of escalators used is 1, the number of elevators used is 1, and the elevator usage ratio is calculated to be 40%. In the normal action history of FIG. 3, the number of times of using the stairs is 660, the number of times of using the escalator is 60, the number of times of using the elevator is 12, and the elevator usage ratio is calculated to be 10%.

  The general user behavior tendency table 170 in FIG. 4 describes the lifting equipment usage ratios for general users in the same form as the current behavior tendency recording unit 130 and the normal behavior tendency recording unit 150. In FIG. 4, the number of stairs used is 4580, the number of escalators used is 1855, the number of elevators used is 252 and the elevator usage ratio is calculated to be 32%. The general user behavior tendency table 170 may be created by integrating data of all users who use the navigation device according to the present embodiment, and the number of uses within an average fixed period estimated from other statistical data. And a lifting equipment utilization ratio.

  The current behavior tendency, the normal behavior tendency, and the general user behavior tendency are not limited to the elevator usage ratio, but may include the usage ratios of stairs, escalators, and elevators. The elevator usage ratio calculated separately when moving to the floor may be used.

Next, an example of the operation of the navigation device of the present embodiment will be described with reference to FIG.
In step S501 of FIG. 5, the behavior recognition unit 120 recognizes the user's behavior using at least one sensor of the detection unit 110. In the present embodiment, the action recognition unit 120 detects stair walking, elevator boarding, and escalator boarding based on acceleration information from the acceleration sensor 112. Specifically, time series patterns of acceleration detected by the acceleration sensor are prepared in advance for each movement method, and the action recognition unit 120 compares these patterns with acceleration information (acceleration pattern) from the acceleration sensor, A movement method having a similar pattern is recognized as a user's action.

  In addition, the action recognition part 120 may detect not only the acceleration sensor 112 but the stair walking, the elevator boarding, and the escalator boarding using the positioning sensor 114. When the positioning sensor 114 is used, for example, when the train arrives at the destination station and the user gets off the train, the action recognition unit 120 acquires a station premises map (map) regarding the destination station. The station premises map may be stored in advance in the storage unit of the mobile terminal, or may be acquired via the communication control unit. For example, as shown in FIG. 6, the station premises map holds the positions of the stairs 602, the escalator 603, and the elevator 604 on the station platform 601. In the station floor plan, the positions of the stairs 602, the escalator 603, and the elevator 604 on the station platform 601 are expressed by relative positions from a specific reference position (for example, the entrance of the station). The action recognition unit 120 converts the relative position into an absolute position, matches the position information from the positioning sensor 114, and detects whether the user has used a staircase, an elevator, or an escalator.

  In step S502, the behavior recognized by the behavior recognition unit 120 is accumulated in the current behavior trend recording unit 130 and the normal behavior trend recording unit 150, and the current behavior trend and the normal behavior trend are updated, that is, recalculated.

  In step S503, the user personality estimation unit 160 compares the normal behavior tendency and the general user behavior tendency to estimate the user personality, and sets the certainty factor of the estimated user personality. When the normal behavior tendency in FIG. 3 and the general user behavior tendency in FIG. 4 are compared, the user personality is estimated to be “staircase use priority” because the user has a lower lift equipment usage ratio than the general user. As shown in FIG. 7, user personality is estimated when the normal user behavior tendency is a predetermined value or more away from the reference value with the general user behavior tendency as the reference value.

  The certainty of the user individuality indicates the certainty of the estimated user individuality. In this embodiment, the certainty factor of the user personality is set according to the normal behavior tendency or the difference between the normal behavior tendency and the reference value. For example, when the elevator usage ratio as a normal behavior tendency is about 1, it is estimated that the user is a wheelchair user. In this case, in step S505, which will be described later, the certainty of user personality “elevator use priority” is selected so that the navigation method “wheelchair use” that presents a route that always uses the elevator is selected for movement between floors. Is set to the maximum value.

  In step S504, the user situation estimation unit 140 compares the current behavior tendency and the normal behavior tendency to estimate the user situation, and sets the certainty factor of the estimated user situation. In step S504, as shown in FIG. 7, the user behavior is estimated when the current behavior tendency is a predetermined value or more away from the reference value with the normal behavior tendency as the reference value. When the current behavior tendency in FIG. 2 and the normal behavior tendency in FIG. 3 are compared, this user has a higher lift / lower equipment utilization ratio than usual, so a user situation of “elevation / lower equipment priority” is estimated.

  Despite the fact that the user personality is estimated as “stairs use priority” in step S503, the reason that the user situation is estimated as “elevator use priority” in step S504 is that the physical condition is not excellent today or is large. It may be difficult to walk the stairs on foot due to reasons such as having luggage.

  The certainty of the user situation indicates the certainty of the estimated user situation. In the present embodiment, the certainty of the user situation is set in accordance with the number of data of the current action history (for example, the total number of ascending / descending times). For example, when the number of data of the current behavior history is small, such as in the morning, it cannot be said that the current behavior tendency estimated from the current behavior history accurately reflects the user behavior tendency. Therefore, in step S505, which will be described later, when the number of data in the current action history is small, the certainty factor of the current action tendency is set to a small value so that a navigation method corresponding to the normal action tendency is selected. When the number of data in the current action history is large, it is considered that the action tendency of the day is strongly reflected, and thus the certainty factor of the current action tendency is set to a large value.

  In step S505, the navigation method determination unit 180 compares the certainty factor of the user personality and the certainty factor of the user situation, determines the navigation method according to the higher certainty factor, and sends the determined navigation method to the navigation unit 190. Notice. The navigation unit 190 performs navigation according to the notified navigation method.

  Note that the navigation method determination unit 180 may determine the navigation method by giving priority to the user situation over the user personality without comparing the certainty factors. When the certainty levels are not compared, the setting of certainty levels can be omitted in steps S503 and S504. In this case, if the user personality is “stairs use priority” and the user situation is “elevator use priority”, the navigation method determination unit 180 selects “elevator use” according to the user situation “elevator use priority”. Is determined. Also, for example, in the morning, when the user situation is not estimated, the navigation method determination unit 180 determines the navigation method according to the user characteristics.

  As shown in step S506, steps S501 to S505 are repeated until the navigation is terminated, for example, when the user arrives at the destination.

  In this way, the navigation apparatus of FIG. 1 does not need to input data related to the user in advance, and can automatically identify the user's personality and situation and perform navigation suitable for the user.

  Note that the behavior history and behavior tendency recorded in the current behavior tendency recording unit 130 and the normal behavior tendency recording unit 150, and the user situation and user personality estimated from these are moved to different floors as described above ( It is not limited to actions related to (lifting).

  In one example, a user situation related to the degree of fatigue and the degree of fatigue may be estimated. As shown in FIG. 8, the fatigue level can be estimated by measuring the increase in heart rate before and after the user moves on the stairs on foot using the biological sensor 116. Alternatively, as shown in FIG. 9, the fatigue level may be estimated by measuring the user's walking pace with the acceleration sensor 112 and comparing it with the normal walking pace. The angle can be estimated by measuring the walking pace at a predetermined interval and detecting the variation in the walking pace. When it is determined that the degree of fatigue or sadness is high, a navigation method of “fatigue” or “酩酊” that preferentially uses a transportation means that is expensive, such as a taxi but has a low burden on the body, is selected.

  In another example, a characteristic that favors riding on a women-only vehicle may be estimated as the user personality. In this case, as shown in FIG. 11, the use frequency of the women-only vehicle is estimated as the behavior tendency. The frequency of use of women-only vehicles can be obtained by dividing the number of times of riding on women-only vehicles by the number of times of getting on the train. In the case of riding on a women-only vehicle, the vehicle number of the vehicle on which the user got on is identified by the position information acquired at the time when the user got on the train, and the time zone on which the user got on the train and the identified vehicle number are shown in FIG. It can discriminate | determine by collating with vehicle information as shown. In the same way, as shown in FIG. 12, the usage frequency of a vehicle with a low degree of congestion may be obtained, and a characteristic that favors a vehicle with a low degree of congestion may be estimated as the user personality.

  Furthermore, user personality that can be estimated by using a combination of map information and observation information includes wheelchair use characteristics and amblyopic characteristics. The wheelchair usage characteristic can be estimated from the riding behavior on the wheelchair vehicle, and the amblyopia characteristic can be estimated from the usage behavior of the non-touch panel ticket vending machine. Furthermore, by using a combination of train operation information and observation information, it is possible to estimate, as user personality, characteristics that favor the first train, characteristics that avoid rush hours, and characteristics that favor behavior with a margin.

  13 and 14 show an example of a list of navigation methods prepared in the navigation device of this embodiment. The navigation method determination unit 180 selects one or a plurality of navigation methods from the list of navigation methods based on the certainty of the user situation and the certainty of the user personality.

  The “wheelchair use” navigation method is a navigation method that presents a route for using an elevator. The “wheelchair use” navigation method is selected when the certainty set in the user personality of “elevator use priority” is large. The “walking difficulty” navigation method is a navigation method that presents a route using elevator equipment such as an elevator and an escalator. The navigation method of “difficulty walking” is selected when the certainty factor set in the user personality of “elevating device use priority” is large.

  The “heavy baggage” navigation method is a navigation method that preferentially presents a route using the lifting device. The “heavy luggage” navigation method is used when the user personality is “stairs use priority” and the user situation is “elevator use priority”, and the certainty of the user situation is greater than the certainty of the user personality. Selected.

  The “fatigue” navigation method is a navigation method that preferentially presents a route using the lifting device. The “fatigue” navigation method is selected when it is estimated that the user's fatigue level is high. The “酩酊” navigation method is a navigation method that preferentially presents a route using a taxi. The “酩酊” navigation method is selected when it is estimated that the user's degree of accuracy is high.

  The “female” navigation method is a navigation method that preferentially presents a route using a female-only vehicle. The “female” navigation method is selected when the frequency of use of women-only vehicles is high. In the “rapid walk” navigation method, the required time to the target position is presented by adjusting the required time for walking short. The “fast walk” navigation method is selected when the walking pace is estimated to be fast. The navigation method of “preferred vehicle preference” is a navigation method that presents a route that leads to a vehicle with a low degree of congestion. The navigation method of “preferred vehicle preference” is selected when the user personality is estimated to be a characteristic that favors a vehicle with a low degree of congestion.

  Next, referring to FIG. 15, FIG. 16, and FIG. 17, when three navigation methods of “fast walk”, “fatigue”, and “酩酊” are selected, guide route information is presented to the user. An example of the method of performing will be described. When a plurality of navigation methods are selected, the navigation unit 190 compares the certainty factors of the user situation or the user personality that caused each navigation method to be selected, and is determined according to each navigation method in order of the certainty factor. Present the route.

  The guide route information is displayed in a tab (or toggle button) format, for example, as shown in FIGS. In FIG.15, FIG16 and FIG.17, the schematic of the station platform is displayed on the map. There are two ticket gates under the stairs and under the escalator. As shown in FIG. 15, the “fast walk” tab is displayed in a state selected from the beginning, and the route to the bus stop via the stairs is displayed. When the user selects the “fatigue” tab, the route determined according to the “fatigue” navigation method is displayed. In the “fatigue” navigation method, as shown in FIG. 16, a route to the bus stop via the escalator is displayed. When the “酩酊” tab is selected, the route determined according to the “酩酊” navigation method is displayed. In the “酩酊” navigation system, as shown in FIG. 17, a route to the taxi rank via the escalator is displayed.

  As described above, the navigation device according to the present embodiment automatically determines various personalities and situations of the user without inputting user personality information, and performs navigation suitable for the personality and situation of the user. Can do.

  As described above, the portable device to which the present embodiment is applied includes a control unit (CPU), a storage unit (ROM and RAM), a communication control unit, an input unit, and a display unit. The RAM stores a current action history, a current action tendency, a normal action history, a normal action tendency, a general user action tendency table 170, and the like. The ROM holds a control program for executing the series of processes described above, and this control program is read by the CPU to operate the navigation apparatus. In addition, the RAM can store various data generated in accordance with the execution of the control program. The mobile device is communicably connected to a communication network such as the Internet by the communication control unit, and the general user behavior tendency table 170 is periodically updated.

  The control program executed by the portable device of the present embodiment is not limited to an example stored in advance in the ROM, and may be provided by downloading via a communication network such as the Internet. The control program can also be provided by being stored in a computer-readable storage medium. The storage medium is a storage medium that can store a control program and can be read by a computer, such as a magnetic disk, an optical disk (CD-ROM, CD-R, DVD, etc.), a magneto-optical disk (MO, etc.), and a semiconductor memory. If there is, the storage format may be any form.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 110 ... Detection part, 112 ... Acceleration sensor, 114 ... Positioning sensor, 116 ... Biometric sensor, 120 ... Action recognition part, 130 ... Current action tendency recording part, 135 ... Current action tendency estimation part, 140 ... User condition estimation part, 150 ... normal behavior tendency recording unit, 155 ... normal behavior tendency estimation unit, 160 ... user personality estimation unit, 170 ... general user behavior tendency table, 180 ... navigation method determination unit, 190 ... navigation unit.

Claims (5)

An action recognition unit for recognizing a user's action based on observation information including data observed by one or more sensors;
A first recording unit for holding a current behavior history that records the behavior recognized by the behavior recognition unit in a first period;
A first estimation unit that estimates a current behavior tendency indicating the behavior tendency of the user in the first period based on the current behavior history;
A second recording unit for storing a normal behavior history in which behaviors recognized by the behavior recognition unit in a second period longer than the first period are recorded;
A second estimation unit that estimates a normal behavior tendency indicating the behavior tendency of the user in the second period based on the normal behavior history;
By comparing the current behavior tendency with the normal behavior tendency, a user situation estimation unit that estimates the user situation and sets a first certainty factor indicating the certainty of the user situation;
A user who estimates the individuality of the user by comparing the normal behavior tendency with a general user behavior tendency indicating a behavior tendency of another user, and sets a second certainty factor indicating the certainty of the individuality of the user. A personality estimator;
A navigation method determination unit that determines a navigation method for the user by comparing the first certainty factor with the second certainty factor;
A navigation device comprising:   The navigation determining unit determines a plurality of navigation methods, and the order of presenting the guide route information to the user determined according to the navigation method is determined based on the first and second certainty factors. The navigation device according to claim 1.   The user personality estimation unit estimates a user personality related to use of an elevator device including an elevator and an escalator, and the user situation estimation unit estimates a user situation related to use of the elevator device. The navigation device described. Recognizing user behavior based on observation information including data observed by one or more sensors;
Estimating a current behavior tendency indicating a behavior tendency of the user in the first period based on a current behavior history in which the behavior recognized by the behavior recognition unit in the first period is recorded;
Estimating a normal behavior tendency indicating the behavior tendency of the user in the second period based on a normal behavior history in which the behavior recognized by the behavior recognition unit in a second period longer than the first period is recorded;
Estimating the user's situation by comparing the current behavior tendency with the normal behavior tendency, and setting a first certainty factor indicating the certainty of the user's situation;
Estimating the individuality of the user by comparing the normal behavior tendency with a general user behavior tendency indicating the behavior tendency of another user, and setting a second certainty factor indicating the certainty of the individuality of the user. When,
Determining a navigation method by comparing the first certainty factor with the second certainty factor;
A navigation method comprising: In a computer with one or more sensors,
A function of recognizing user behavior based on observation information including data observed by the sensor;
A function of estimating a current behavior tendency indicating a behavior tendency of the user in the first period, based on a current behavior history in which the behavior recognized by the behavior recognition unit in the first period is recorded;
A function for estimating a normal behavior tendency indicating a behavior tendency of the user in the second period, based on a normal behavior history in which a behavior recognized by the behavior recognition unit in a second period longer than the first period is recorded;
A function of estimating the user's situation by comparing the current behavior tendency with the normal behavior tendency, and setting a first certainty factor indicating the certainty of the user's situation;
A function of estimating the individuality of the user by comparing the normal behavior tendency with a general user behavior tendency indicating a behavior tendency of another user, and setting a second certainty factor indicating the certainty of the individuality of the user. When,
A function of determining a navigation method by comparing the first certainty factor with the second certainty factor;
A navigation program to achieve this.

Images