Patent Application
20200082416
The present disclosure relates to an information processing apparatus, an information processing method, and a computer program.
As an example of a technique for identifying a user at a specific location, Patent Document 1 discloses a technique of identifying, in a case where a plurality of users has been recognized, a user having a short distance represented by a position detected by a person position detector and a position indicated by the user's personal characteristics behavior, as a user existing at the specific location. According to the technique described in Patent Document 1, it is possible to recognize that an individual is at a specific location with no special device carried by the individual. However, in a case where the individual does not carry a special device, it is necessary to install an infrared sensor or the like at a location where recognition of the user's presence is desired, and locations where the position of the user can be identified are limited.
Meanwhile, in a case where a user's position is to be identified by the sensor included in a terminal owned by the individual, short-range wireless communication is typically used. For example, Bluetooth (registered trademark) is standard equipment in mobile communication terminals or the like, and therefore has high versatility. In addition, a general-purpose terminal such as a mobile communication terminal similarly installs a Wi-Fi positioning sensor or the Global Positioning System (GPS) as standard equipment. Accordingly, it is possible to easily identify the position of the user on the basis of measurement results of these functions.
Here, accuracy of estimating a distance between two points using Bluetooth (registered trademark) is low in a case where users at same positions are to be identified. Moreover, it is difficult to determine the distance between the two points at difference times. In contrast, with the use of a Wi-Fi positioning sensor or GPS, it would be possible, in principle, to calculate the distance between two points even at different times. Measurement accuracy in these cases, however, largely depends on the environment, and thus, the methods lack stability.
In view of this, the present disclosure proposes a novel and enhanced information processing apparatus, an information processing method, and a computer program capable of performing positional sameness determination of users with high accuracy without depending on the environment.
According to the present disclosure, there is provided an information processing apparatus including a determination unit that determines similarity of positions of a plurality of users on the basis of time-series data that can identify a movement state of each of the plurality of users, obtained for each of the plurality of users.
Furthermore, according to the present disclosure, there is provided an information processing method including obtaining, by using a sensor, time-series data that can be used to identify a movement state of each of a plurality of users, for each of the users; and determining, by using a processor, similarity of positions of the users on the basis of time-series data.
Furthermore, according to the present disclosure, there is provided a computer program causing a computer to function as an information processing apparatus including a determination unit that determines similarity of positions of a plurality of users on the basis of time-series data that can be used to identify a movement state of each of the plurality of users, obtained for each of the plurality of users.
As described above, according to the present disclosure, it is possible to perform the user positional sameness determination with high accuracy without depending on the environment. Note that the above-described effect is not necessarily limited, and it is also possible to use any one of the effects illustrated in this specification together with the above-described effect or in place of the above-described effect, or other effects that can be assumed from this specification.
Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Note that same reference numerals are given to constituent elements having substantially a same functional configuration, and redundant description is omitted in the present specification and the drawings.
Note that the description will be given in the following order.
1. First embodiment (positional sameness determination processing and attribute identification processing)
1.1. Overview
(1) Positional sameness determination processing and attribute identification processing
(2) Positioning technology in positional sameness determination processing
1-2. Configuration of information processing system
(1) Device
(2) Server
1.3. Positional sameness determination processing
(1) Outline of processing
(2) Relative trajectory calculation processing
(3) Absolute position information acquisition processing
(4) Positional sameness determination processing
1.4. Social attribute identification processing
2. Second embodiment (realtime positional sameness determination)
3. Third embodiment (behavior identification based on location attribute table)
3-1. Configuration of information processing system
(1) Device
(2) Server
3.2. Behavior identification processing using location attribute table
(1) Area screening
(2) Positional sameness determination processing
(3) Location attribute identification processing and behavior identification processing
(4) Social attribute determination processing
4. Hardware configuration
5. Supplement
5.1. Feature amount other than relative trajectory
5.2. User agreement on information collection
(1.1 Overview)
(1) Positional Sameness Determination Processing and Attribute Identification Processing
First, description will follow, with reference to
The information processing apparatus according to the present embodiment performs positional sameness determination processing of determining that a plurality of users is present at a same location and attribute identification processing of identifying specific relevance between the plurality of users on the basis of a result of the positional sameness determination processing.
The positional sameness determination processing determines users at a same position on the basis of the time-series data that can be used to identify the movement state of the users. In the present embodiment, the time-series data that can be used to identify the movement state of the user is time-series data concerning the position of the user, and is information indicating a position change (that is, a movement) of the user within a certain period. Examples of such data include time-series data of original detection values of the inertial sensor, a movement trajectory of each of users obtained on the basis of the detection value of the inertial sensor, and time-series data of absolute position information by GPS, Wi-Fi positioning, or the like, for example. The positional sameness determination processing determines presence of users at a same position at a same time or different times on the basis of such time-series data and area information used to obtain the time-series data.
It is suspected that there is a common reason and a certain relevance to be at a specific position among users who have been confirmed to be at the same position by the positional sameness determination processing. Therefore, the positional sameness determination processing cannot merely identify the users present at the same positions but also identify users having relevance.
The attribute identification processing identifies a social attribute common to a plurality of users identified to be at a same position and identified to have a certain relevance by the positional sameness determination processing. In the present embodiment, social attributes refer to social relationships such as social affiliation, positioning, and behavior. For example, examples of the social attribute include a business person's destination (company), a student's destination (school), a favorite shop or place, a place to drop by (specific shelf in a supermarket, a place where a certain dish is placed in a buffet, and the like). Furthermore, in the present embodiment, as for the social attributes, for example, information representing simply the relevance such as working for a same company is regarded as a relative value of the social attribute, and specific information that indicates working for a company X, for example, is regarded as an absolute value.
In the attribute identification processing, social attribute of each of users is identified on the basis of the social attribute obtained from at least one user among a plurality of users identified to have some relevance to each other. The relative value of the user's social attribute may be identified by the area and time zone in which the user is located, by behavior recognition information, or the like. In contrast, the absolute value of the user's social attribute may be identified by absolute position information obtained by GPS, Wi-Fi positioning, or the like, or may be identified by absolute information revealed by a certain user on SNS or the like. Note that the absolute position information is one of absolute information. After identification of the absolute value of the social attribute is obtained for at least one user, the absolute value of the social attribute can also be identified for other users having a same relative value of the social attribute.
For example, as illustrated in
After identifying the relative value of the social attributes of the user A and the user B, obtaining an absolute value of the social attribute for either one of the user A or the user B would lead to acquisition of the absolute value of the social attribute for the other users as well. For example, as illustrated in
Similarly, as illustrated in
Furthermore, as illustrated in
In this manner, from the result of the positional sameness determination processing, it is possible to identify the fact that a plurality of users is at the same position, and that some relevance exists among the plurality of users. Furthermore, it is possible to identify relative values or absolute values of social attributes on the basis of time, area information, user's absolute information revealed in SNS regarding users having relevance, or the like. These results might be used to enable acquisition of useful information with data mining.
(2) Positioning Technology in Positional Sameness Determination Processing
In order to accurately identify a plurality of users having relevance by sharing a same position, the accuracy of the positioning technology for positioning the user has importance. With an ideal positioning technology enabling positioning with high accuracy and a small delay regardless of indoor or outdoor, it is possible to perform positional sameness determination of users by a simple technique of obtaining “whether or not positions of N persons are close to each other at a specific time”. However, there are various restrictions on the positioning technology that is generally utilized at present.
For example, as in the left illustration of
Meanwhile, Wi-Fi positioning or base station positioning can perform indoor positioning. However, Wi-Fi positioning or base station positioning generally includes large positioning errors as illustrated in
Meanwhile, in recent years, pedestrian dead reckoning (PDR) has attracted attention as indoor positioning technology. The PDR is a relative positioning technology that estimates a user's traveling direction and moving distance on the basis of a change in a detection value of inertial sensors such as an acceleration sensor, an angular velocity sensor, and a gyro sensor, mounted on a device worn by the user. Accordingly, the absolute position and the absolute orientation of the user are estimated on the basis of a positioning result of the PDR with reference to the absolute position and the absolute orientation obtained by GPS or the like. Normally, indoor positioning by the PDR is performed on the basis of GPS positioning information (absolute position and absolute orientation) obtained immediately before entrance of the user, and an absolute position is estimated on the basis of a positioning result measured by PDR after user's entrance indoors. That is, accuracy of the absolute position and the absolute orientation estimated from the positioning result of the PDR depends on the accuracy of the GPS positioning information obtained immediately before the user's entrance, and the orientation error depends, in particular, to the estimated absolute orientation.
However, the accuracy of the GPS before user's entrance indoors is low, and PDR positioning accuracy cannot be sufficiently achieved in some cases due to an error in GPS positioning information. For example, as illustrated in
In this manner, the positioning accuracy depends on the environment or the infrastructure in the positioning technology generally utilized at present. Accordingly, there is some limitation in executing positional sameness determination for a plurality of users simply and with high accuracy on the basis of the positioning results would have limitation. In view of this, the present embodiment proposes a method for performing positional sameness determination of users simply and with high accuracy without depending on the environment such as outdoor or indoor. Specifically, on the basis of time-series data of detection values obtained by sensors capable of identifying user's movement states and capable of achieving accuracy independent of the environment, positional sameness determination of a plurality of users is performed from the similarity of the time-series data. Alternatively, it is also possible to obtain relative trajectories for a fixed time from the time-series data and determine the similarity of the relative trajectories, and may thereby perform positional sameness determination on the plurality of users.
Examples of sensors capable of achieving accuracy independent of the environment include inertial sensors such as an acceleration sensor, an angular velocity sensor, and a gyro sensor. Note that, strictly speaking, sensor characteristics vary slightly with a temperature change, but, a large temperature change would not occur in a state where the device including an inertial sensor is worn on the human body, and fluctuation due to the environment is negligible. Furthermore, with the use of an inertial sensor with small temperature dependence, it is possible to suppress fluctuation in sensor characteristics due to environmental changes.
Inertial sensors are generally mounted on almost all devices that are held or worn by many users, such as smartphones and wearable terminals. Accordingly, there is no need to use a special device in order to perform the positional sameness determination by a plurality of users according to the present embodiment. In addition, GPS or Wi-Fi used to identify an user's absolute position is almost installed in these devices similarly to the inertial sensor. As illustrated in
Hereinafter, the present embodiment will describe positional sameness determination processing of a plurality of users performed on the basis of the relative trajectory of a fixed time based on time-series data of detection values of an inertial sensor obtained for each of users. Furthermore, the social attribute identification processing of a plurality of users based on the result obtained by the positional sameness determination processing will be described.
(1-2. Configuration of Information Processing System)
Prior to the explanation of positional sameness determination processing and social attribute identification processing according to the present embodiment, an information processing system 1 that performs the processing will be first described with reference to
As illustrated in
(1) Device
The device 100 is an information processing terminal held by a user, such as a smartphone and a wearable terminal. As illustrated in
The sensor unit 110 includes one or more sensors capable of detecting attitude and movement of the device 100 and includes, for example, inertial sensors such as an acceleration sensor 111 and a gyro sensor 113. Furthermore, the sensor unit 110 may include an environmental sensor such as a geomagnetic sensor 115. In addition to these sensors, the sensor unit 110 may include an angular velocity sensor or the like as an inertial sensor, and may include an atmospheric pressure sensor, a temperature sensor, a humidity sensor, and a wind velocity sensor as environmental sensors, a microphone, a camera, or the like. The detection value of each of the sensors of the sensor unit 110 is output to the trajectory calculation unit 120. Note that the sensor unit 110 may output time-series data of the detection value of each of the sensors to the transmission unit 160 so that the original time-series data can be transmitted from the transmission unit 160 to the server 200.
The trajectory calculation unit 120 calculates a relative trajectory of a user holding the device 100 on the basis of the detection value input from the sensor unit 110. When at least detection values of the acceleration sensor 111 and the gyro sensor 113 can be obtained among the sensors of the sensor unit 110, the trajectory calculation unit 120 can calculate the relative trajectory. The calculation processing of the relative trajectory by the trajectory calculation unit 120 will be specifically described later. The trajectory calculation unit 120 outputs the calculated relative trajectory to the transmission unit 160.
The absolute position information acquisition unit 130 obtains absolute position information of the device 100. The absolute position information acquisition unit 130 is, for example, a GPS receiver, a Wi-Fi reception unit, or the like. The absolute position information acquisition unit 130 outputs the obtained absolute position information of the device 100 to the area determination unit 140. Note that the absolute position information obtained by the absolute position information acquisition unit 130 may be directly output to the transmission unit 160.
The area determination unit 140 determines an area where the device 100 is located on the basis of the absolute position obtained by the absolute position information acquisition unit 130. The area to be identified by the area determination unit 140 is supposed to have been preliminarily set. In the present embodiment, the area where the user (the device 100 in practice) exists is utilized as information for avoiding erroneous determination in the positional sameness determination. For this reason, high determination accuracy is not required for area determination, and it is sufficient as long as an approximate position represented in the range of about several tens of meters can be identified. The area determination unit 140 outputs the determined area to the transmission unit 160.
The attribute information acquisition unit 150 obtains information that can be used to identify a social attribute or a social attribute of the user. For example, the attribute information acquisition unit 150 obtains information regarding the relative value or the absolute value of the social attribute revealed by the user from, for example, information stored in the device 100, information input using the device 100, or the like. Alternatively, the attribute information acquisition unit 150 may identify the relative value or the absolute value of the user's social attribute from the behavior of the user. For example, it is allow to use the purchased item from the purchase history of the user's credit card settlement for this purpose. The relative value or absolute value of the user's social attribute obtained by the attribute information acquisition unit 150 is used to identify a social attribute of another user determined to have some relevance to the user. Note that absolute position information obtained by the absolute position information acquisition unit 130 can also be utilized as information to identify the social attribute of the user.
The transmission unit 160 transmits various types of information input from the sensor unit 110, the trajectory calculation unit 120, the absolute position information acquisition unit 130, the area determination unit 140, and the attribute information acquisition unit 150, to the server 200.
The reception unit 170 receives various types of information transmitted from the server 200. The information received by the reception unit 170 is processed as appropriately for individual purposes, for example, displayed on a display unit (not illustrated) provided in the device 100, recorded in a storage unit (not illustrated), or the like.
(2) Server
The server 200 performs positional sameness determination of determining whether or not users exist (existed) at a same position on the basis of information input from the device 100 of each of the users. Furthermore, the server 200 according to the present embodiment is assumed to be also capable of executing social attribute identification processing of identifying social attributes of a plurality of users on the basis of results of the positional sameness determination. As illustrated in
The data acquisition unit 210 receives various types of information from a plurality of the devices 100 that can communicate with the server 200. The data acquisition unit 210 obtains detection values of individual sensors of the sensor unit 110, a relative trajectory of a user calculated by the trajectory calculation unit 120, absolute position information obtained by the absolute position information acquisition unit 130, and area information identified by the area determination unit 140. These pieces of information are used for positional sameness determination processing and are output to the positional sameness determination unit 220. Furthermore, for the social attribute determination processing, the data acquisition unit 210 can also obtain the social attribute of the user, or information that can be used to identify the social attribute, obtained by the attribute information acquisition unit 150. Such information is output to the attribute identification unit 230. Furthermore, the data acquisition unit 210 may record the obtained various types of information in the obtained data storage unit 250.
The positional sameness determination unit 220 determines users existing at a same position. The positional sameness determination unit 220 according to the present embodiment determines the similarity of the relative trajectory of each of users and determines whether or not the users exist at a same position. The positional sameness determination unit 220 may function as a functioning unit that implements functions of: a synchronization processing unit that performs data synchronization of the positional sameness determination target; a deviation degree calculation unit that calculates a degree of deviation of the synchronized time-series data; and a determination processing unit that performs positional sameness determination of the users on the basis of calculated deviation degree. Note that specific positional sameness determination processing performed by the positional sameness determination unit 220 will be described later in detail. The positional sameness determination unit 220 outputs the determination result to the output unit 240. Note that the determination result may be output to the attribute identification unit 230.
The attribute identification unit 230 identifies social attribute of the users determined to have some relevance by their presence at a same position by the positional sameness determination processing. After acquisition of information that can be used for identifying the social attribute or the social attribute of at least one user, the attribute identification unit 230 identifies social attributes of all users having relevance on the basis of the information. The attribute identification unit 230 outputs the identified social attribute to the output unit 240.
The output unit 240 transmits information to the device 100 connected to the server 200. For example, the output unit 240 may output a result of determination by the positional sameness determination unit 220 and the user's social attribute identified by the attribute identification unit 230, to the device 100 of the user as a notification target. Furthermore, the output unit 240 may record the processing result of the server 200 in the analysis result storage unit 260.
The obtained data storage unit 250 is a storage unit that stores various pieces of information obtained from each of the devices 100. The analysis result storage unit 260 is a storage unit that stores the processing result in the server 200. The information held in these storage units can also be utilized for data mining or the like.
The configuration of the information processing system 1 according to the present embodiment has been described above. Note that while the present embodiment is an example in which the positional sameness determination processing and social attribute identification processing are executed by one server 200, the present disclosure is not limited to this example. The positional sameness determination processing and social attribute identification processing may be configured to be executed by different servers, or at least one of the processing may be configured to be executable by the device 100. Furthermore, although the area determination unit 140 is included in the device 100, the present disclosure is not limited to this example, and may be included in the server 200.
(1.3. Positional sameness determination processing)
Next, positional sameness determination processing according to the present embodiment will be described with reference to
(1) Outline of Processing
First, with reference to
After authentication of the authentication information, the device 100 executes processing of obtaining various types of information used for positional sameness determination processing and outputting the information to the server 200. More specifically, the relative trajectory acquisition processing (S20) and absolute position information acquisition processing (S30) are executed. Step S20 and step S30 are executed independently, executed at individual timings at which necessary information is obtained.
In the relative trajectory acquisition processing, the relative trajectory is calculated by the trajectory calculation unit 120 on the basis of the detection value of the sensor unit 110 of the device 100. The calculated relative trajectory is transmitted to the server 200 via the transmission unit 160 and recorded in the obtained data storage unit 250. Details of the relative trajectory calculation processing by the trajectory calculation unit 120 will be described later.
In the absolute position information acquisition processing, the device 100 transmits the absolute position information obtained by the absolute position information acquisition unit 130 to the server 200 via the transmission unit 160. The server 200 records the received absolute position information of the device 100 in the obtained data storage unit 250. Furthermore, in the positional sameness determination processing, the server 200 identifies an area to which the absolute position information belongs. Note that while
The relative trajectory acquisition processing (S20) and the absolute position information acquisition processing (S30) are executed between the devices 100 of the plurality of users and the server 200. Moreover, when information regarding the plurality of users is obtained for a predetermined period or more, the positional sameness determination processing (S40) of users by the server 200 can be executed. The positional sameness determination processing may be executed at a point where the processing can be executed or may be executed at predetermined cycle timing (for example, daily) on the premise that the processing can be executed. The server 200 may transmit the determination result of the positional sameness determination processing to each of the devices 100 as necessary. The device 100 that has received the determination result of the positional sameness determination processing may display the determination result on the display unit, and may notify the user, for example.
The above is the outline of the processing when the positional sameness determination processing is executed. Hereinafter individual processing will be described in detail.
(2) Relative Trajectory Calculation Processing
Calculation of the relative trajectory performed in the relative trajectory acquisition processing (S20) of
The trajectory calculation unit 120 of the device 100 according to the present embodiment calculates a movement trajectory of the user as a relative trajectory obtained on the basis of the detection value obtained by the inertial sensor. In the present embodiment, a case where the relative trajectory is calculated by PDR will be described. In acquisition of the relative trajectory, initial attitude to be the reference is first determined in the initial setting processing. Thereafter, the position at each of times is calculated and the moving distance and the moving direction from the initial attitude are identified.
Specifically, in order to identify the initial attitude, the stop time, the position and the orientation at the start of the initial setting processing illustrated in
Next, the trajectory calculation unit 120 refers to the detected value of the buffered acceleration sensor 111 (S106) and determines whether or not the user is stopped from the change in the detected value (S108). Since the initial attitude is set on the basis of the acceleration at the time of stop, it is necessary to confirm that the user is stopped. The user stop determination may be performed, for example, by whether or not the acceleration is obtained for a predetermined time (for example, one second) or more and the variance value of the obtained acceleration is a predetermined value or less. In a case where the variance value is larger than the predetermined value and does not satisfy the stop determination condition, the processing returns to step S106 and the processing from step S106 is executed again. In contrast, in a case where it is determined in step S108 that the variance value is the predetermined value or less, the trajectory calculation unit 120 determines that the user is stopped, sets the moving speed of the user at that time to zero (S110), and then, starts counting stop time (S112).
Thereafter, it is determined whether or not the stop time has reached a predetermined time (for example, five seconds) or more (S114). In a case where the stop time is shorter than the predetermined time, the processing returns to step S106, and the processing from step S106 is executed again. In contrast, in a case where the stop time is the predetermined time or more in step S114, gravity is calculated from a time average value of acceleration obtained by the acceleration sensor 111 during stoppage (S116), and initial attitude of the device 100 is calculated from the calculated gravity (S118). When the initial attitude of the device is determined in step S118, processing of calculating the position of the device 100 at each of times and obtaining the relative trajectory is executed in step S120.
In the position calculation processing, as illustrated in
(3) Absolute Position Information Acquisition Processing
The acquisition of the absolute position information performed in the absolute position information acquisition processing (S30) in
The absolute position information is obtained by the absolute position information acquisition unit 130 of the device 100 (S130). Examples of the absolute position information include positioning information by GPS, positioning information by Wi-Fi, positioning information by communication with a mobile phone base station, or the like. The absolute position information can be obtained as long as the device 100 is connectable to their own network.
Furthermore, the area determination unit 140 may identify the area where the device 100 is located on the basis of the absolute position information (S132). The area may be identified on the basis of correspondence information between a preliminarily set absolute position and an area, for example.
The absolute position information obtained in step S130 and the area obtained in step S132 are output to the server 200 via the transmission unit 160. As described above, the area identified by the absolute position information in the present embodiment is utilized as information for avoiding erroneous determination of the positional sameness determination. For this reason, high determination accuracy is not required for area determination, and it is sufficient as long as an approximate position represented in the range of about several tens of meters can be identified. Therefore, the absolute position information acquisition processing may be executed and the information obtained by this processing may be used even in an environment where the measurement error of the absolute position information is large.
(4) Positional sameness determination processing
The positional sameness determination processing (S40) in
After acquisition of the relative trajectories of two or more persons in the same area for a predetermined time by the relative trajectory acquisition processing (S20) and the absolute position information acquisition processing (S30) in
The present embodiment determines the similarity in relative trajectories obtained by using the detection value of the inertial sensor independent of the environment, thereby determining that the users are present at a same position. Here, the relative trajectories obtained by the devices 100 of individual users might have different scales of the relative trajectory or the entire orientation of the trajectory, and thus cannot be compared in original states, in some cases. As for the scale, an error occurs due to an error in the stride of the user, and there is usually an error of about 5%. As for orientation, since the orientation of the relative trajectory is a relative orientation, there is no match in absolute orientation in typical cases. Furthermore, in a case where the acquisition time of the relative trajectory is too short, information obtained is often not enough to determine the similarity. Therefore, it is necessary to obtain the relative trajectory over a certain length of time. Moreover, defining similar trajectories obtained in completely different locations as the trajectories obtained at the same position would lead to incorrect positional sameness determination. Accordingly, the present embodiment performs the positional sameness determination by handling the relative trajectory as follows so as to be able to perform comparison of relative trajectories with high accuracy.
First, the scale of the relative trajectory or the deviation of the orientation of the entire trajectory is to be handled by scale correction and rotation of the trajectory. For example, as in the left illustration of
Furthermore, in order to enhance the accuracy of the positional sameness determination, it takes some time to obtain the relative trajectory as described above. For example, as in the left illustration of
Furthermore, erroneous determination in a case where a similar trajectory is obtained in another location can be managed by using area information, for example. For example, as in the upper illustration of
Alternatively, it is allowable to suppress occurrence of erroneous determination in a case where a similar trajectory is obtained at another location by extending the time for obtaining the relative trajectory. For example, as in the upper illustration of
Step S140 of
When it is allowed to execute the positional sameness determination processing in step S140, the positional sameness determination unit 220 calculates a trajectory deviation degree with respect to the relative trajectories of arbitrary two of N users as targets of the positional sameness determination (S141). The trajectory deviation degree is an index of the similarity of the relative trajectory of the positional sameness determination target. Hereinafter, an example of a method of calculating the trajectory deviation degree will be described with reference to
As illustrated in
First, the positional sameness determination unit 220 performs data synchronization of the relative trajectory in which relative positions of the individual relative trajectories are associated with each other. As illustrated in
Note that the relative trajectories of the positional sameness determination target are supposed to have been synchronized so as to be comparable with each other. For example, data synchronization of the relative trajectory can be performed onto time-series data obtained as the detection value of the sensor unit 110 of each of the devices 100 on the basis of the time-series correlation in the following process.
For example, here is an exemplary case where the time-series data of the detection value of the gyro sensor 113 as illustrated in
Scale ratio (time axis)=period average speed of user/prescribed speed
Scale ratio (detection value axis)=1/scale ratio (time axis)
For example, here is an example of time-series data of detection values obtained by the gyro sensor 113 of each of the devices 100 of the users A and B as illustrated in
Here, normalizing the time axis changes the sampling intervals that used to be the same, and thus, resampling is performed with sampling intervals of preliminarily set prescribed interval. Resampling may be performed using linear interpolation or the like. For example, in a case where the original waveform is extended in the time axis direction as in the user A in
Thereafter, time shift correction of correcting deviation of the absolute time length of each of the relative trajectories is performed. For example, as illustrated in
Waveform deviation degree=Σ((sensor detection valueuser A)−(sensor detection valueuser B))2/m
where, m: number of detection values included in the scale unit after resampling
Note that the minimum value of the waveform deviation degree is a small value in a case where the user A and the user B exist at the same position, whereas the value is large in a case where the user A and the user B exist at different positions. That is, it is also possible to perform the positional sameness determination on users on the basis of the waveform deviation degree.
After the data synchronization of the relative trajectory is performed by this processing, the relative positions of the relative trajectories are associated with each other, and correspondence relationships of the relative positions as illustrated in
Δθ=Σ(θ2(n)−θ1(n))/m
After the orientation correction is performed, scale correction of the relative trajectory is performed next. In the scale correction, distances D_1(n) and D_1(2) between the reference position and each of the relative positions are first calculated. For example, as illustrated in
ScaleRatio=Σ(D_1(n)/D_2(n))/m
After performing orientation correction and scale correction, the positional sameness determination unit 220 corrects the position of the relative trajectory. Although the positions of the start point (time 0) of the relative trajectory are already aligned, the average position of the entire trajectories might be misaligned in some cases by this alignment alone. Therefore, the center of gravity of the relative trajectory is calculated from the relative position of each of the relative trajectories 1 and 2. Next, the difference of the center of gravities is defined as a position correction amount ΔPOS of the relative trajectory, and then, either one of the relative trajectories is entirely shifted by the position correction amount ΔPOS. The center of gravity of the relative trajectory may be represented by an average value of individual relative positions included in the relative trajectory. For example, as illustrated in
As described above, performing data synchronization (ΔPOS), orientation correction (Δθ), and scale correction (ScaleRatio) on the relative trajectory minimizes the distance difference between the relative trajectories. In this state, as illustrated in
Error=Σ((Pos_2_x[n]−Pos_1_x[n])2+(Pos_2_y[n]−Pos_1_y[n])2)/m
Returning to the explanation of
In contrast, in a case where the positional sameness determination is completed for all user combinations, the positional sameness determination unit 220 confirms whether or not the positional sameness determination processing is executable in other areas (S146). When there is an area for which determination has not been completed, the area number set for each of areas is updated (S147), and execution of processing from step S140 is repeated. Thereafter, when there is no more information for which the positional sameness determination processing is to be implemented, the positional sameness determination unit 220 finishes the processing illustrated in
(1.4. Social Attribute Identification Processing)
Social attribute identification processing of identifying social attributes of a plurality of users determined to have some relevance in the positional sameness determination processing will be described with reference to
The social attribute identification processing is started from user authentication processing by the device communicating with the server 200 (S10). For example, authentication information is transmitted from a device 100A of the user A to the server 200, the server 200 executes authentication processing on the basis of the authentication information and transmits an authentication result to the device 100A. Although
Here it is assumed that, after the authentication information is authenticated, the device 100A of the user A has obtained absolute position information by the absolute position information acquisition unit 130 and the absolute trajectory of the user A has been obtained. The absolute trajectory is output to the server 200 via the transmission unit 160. The server 200 having received the absolute trajectory records the information in the obtained data storage unit 250. Furthermore, the attribute identification unit 230 identifies the social attribute of the user B, having relevance to the user A, on the basis of the absolute trajectory of the user A. For example, as illustrated in
Thereafter, the server 200 may transmit a result of the social attribute identification processing to the device 100B of the user B as necessary. The device 100B that has received the result of the social attribute identification processing may display the determination result on the display unit, for example, and notify the user of the result.
The above has described the information processing system 1 according to the first embodiment and the positional sameness determination processing of a plurality of users by this information processing system 1 and the social attribute identification processing of identifying social attributes of the users determined to have some relevance by the positional sameness determination processing. According to the present disclosure, the positional sameness determination processing is performed on the basis of a relative trajectory obtained on the basis of an inertial sensor independent of the environment. This enables implementation of the positional sameness determination with high accuracy without depending on the environment. Furthermore, the detection value used for the positional sameness determination can be easily obtained from a sensor mounted on a general-purpose device. Moreover, regarding the social attributes of the plurality of users determined to have some relevance by the positional sameness determination processing, it is possible to identify the social attributes of other users from identified social attributes of a certain user, making it possible to easily identify the absolute value of the user's social attribute.
Next, a second embodiment of the present disclosure will be described with reference to
In the positional sameness determination in real time, as illustrated in
Specifically, the positional sameness determination processing is performed in real time by the processing as illustrated in
First, the positional sameness determination unit 220 calculates a trajectory deviation degree with respect to the relative trajectories of arbitrary two of N users as targets of the positional sameness determination (S200). As described in the first embodiment, the trajectory deviation degree is an index of the similarity of the relative trajectories of the positional sameness determination targets and is represented by the least squared error of individual relative positions of the relative trajectories to be compared, for example. The positional sameness determination unit 220 determines whether or not the trajectory deviation degree is a predetermined value or less (S202). In a case where it is determined in step S202 that the deviation degree of the trajectory is larger than the predetermined value, it is determined that the users exist at different positions (S210).
In contrast, in a case where it is determined in step S202 that the trajectory deviation degree is the predetermined value or less, the difference average value of the absolute times of the respective relative positions with respect to the two users is calculated (S204). In the present embodiment, it is important to be present at a same position at a same time. Therefore, it is determined that similar trajectories are obtained at a same timing on the basis of a difference average value of the absolute time. In step S206, in a case where the difference average value of the absolute time is a predetermined time or less, it is determined that the two users exist at the same position at the same time (S208). In contrast, in a case where it is determined in step S206 that the difference average value of the absolute time is larger than the predetermined time, it is determined that the users exist at different positions (S210).
Subsequently, when the positional sameness determination for the current two users is completed, the positional sameness determination unit 220 confirms whether or not the positional sameness determination processing has been completed for all combinations of all users who are the positional sameness determination targets (S212). In a case where there is a combination of users for which determination has not been completed, next combination will be set (S214), and the processing from step S200 is repeated. In a case where the positional sameness determination is completed for all user combinations, the positional sameness determination unit 220 finishes the processing illustrated in
The above has described the positional sameness determination processing in real time according to the present embodiment. The positional sameness determination processing in real time makes it possible to learn whether or not a plurality of users exists at the same place. Note that, in view of the idea that it is desirable to enable early identification of the child straying from the parent in parent-child positional sameness determination processing in
Next, a third embodiment of the present disclosure will be described with reference to
<3.1. Configuration of Information Processing System>
As illustrated in
(1) Device
The device 100 is an information processing terminal held by a user, such as a smartphone and a wearable terminal. As illustrated in
(2) Server
The server 200 performs positional sameness determination of determining whether or not users exist (existed) at a same position on the basis of information input from the device 100 of each of the users. Furthermore, the server 200 according to the present embodiment executes the processing of identifying behavior of users determined to exist at same position by the positional sameness determination processing. The server 200 may also be capable of executing social attribute identification processing of identifying social attributes of the user. As illustrated in
The data acquisition unit 210 receives various types of information from a plurality of the devices 100 that can communicate with the server 200. The data acquisition unit 210 obtains detection values of individual sensors of the sensor unit 110, a relative trajectory of a user calculated by the trajectory calculation unit 120, absolute position information obtained by the absolute position information acquisition unit 130, and area information identified by the area determination unit 140. These pieces of information are used for positional sameness determination processing and are output to the positional sameness determination unit 220. Furthermore, the data acquisition unit 210 can also obtain the social attribute of the user, or information that can be used to identify the social attribute, obtained by the attribute information acquisition unit 150. Such information is output to the attribute identification unit 230. Furthermore, the data acquisition unit 210 may record the obtained various types of information in the obtained data storage unit 250.
The positional sameness determination unit 220 determines users existing at a same position. The positional sameness determination unit 220 according to the present embodiment determines the similarity of the relative trajectory of each of users and determines whether or not the users exist at a same position. The positional sameness determination unit 220 may function as a functioning unit that implements functions of: a synchronization processing unit that performs data synchronization of the positional sameness determination target; a deviation degree calculation unit that calculates a degree of deviation of the synchronized time-series data; and a determination processing unit that performs positional sameness determination of the users on the basis of calculated deviation degree. Note that the positional sameness determination processing by the positional sameness determination unit 220 is the same as that in the first embodiment, and thus, description thereof will be omitted here. The positional sameness determination unit 220 outputs the determination result to the output unit 240. The positional sameness determination unit 220 may output a determination result to the attribute identification unit 230.
The attribute identification unit 230 identifies behavior of the users determined to have some relevance by their presence at a same position by the positional sameness determination processing. In the present embodiment, the attribute identification unit 230 calculates location attribute information including information regarding the staying time at the location where these users stay. Thereafter, the attribute identification unit 230 identifies the behavior of the user on the basis of the calculated location attribute information and the location attribute table recorded in the location attribute table storage unit 270 described later. Furthermore, the attribute identification unit 230 may identify the social attribute of the user on the basis of the social attribute table recorded in the social attribute table storage unit 280 described later.
Furthermore, similarly to the first embodiment, the attribute identification unit 230 can identify behavior of all users having relevance on the basis of the behavior details or information that can be used to identify the behavior, obtained for at least one user. In a similar manner, the attribute identification unit 230 can identify social attributes of all users having relevance on the basis of the social attribute or information that can be used to identify the social attribute, obtained for at least one user. The attribute identification unit 230 can also update the location attribute table or update the social attribute table on the basis of the information obtained from the user. The attribute identification unit 230 may output the identified user's behavior or social attribute to the output unit 240.
The output unit 240 transmits information to the device 100 connected to the server 200. For example, the output unit 240 may output a result of determination by the positional sameness determination unit 220 and the user's behavior or social attribute identified by the attribute identification unit 230, to the device 100 of the user as a notification target. Furthermore, the output unit 240 may record the processing result of the server 200 in the analysis result storage unit 260.
The obtained data storage unit 250 is a storage unit that stores various pieces of information obtained from each of the devices 100. The analysis result storage unit 260 is a storage unit that stores the processing result in the server 200. The information held in these storage units can also be utilized for data mining or the like.
The location attribute table storage unit 270 stores a location attribute table in which information regarding the staying time is set in accordance with a location or behavior. The location attribute table sets, for example, a staying time in which certain behavior of a user is performed and a staying time length during which the user stays for performing the behavior, as the information regarding the staying time. For example, in the location attribute table concerning lunch, setting of the table is such that the staying time is from 12:00 to 13:00 and the staying time length is 10 to 30 minutes. The attribute identification unit 230 refers to the location attribute table storage unit 270 and identifies a location attribute tables having user's location attribute information matching each other. This makes it possible to identify the behavior at a user's staying location.
The social attribute table storage unit 280 stores a social attribute table set on the basis of the staying time and the staying time length. In a case where there are users with different staying time lengths or staying times at a same location, the social attributes of these users are considered to be different. For example, a user at a restaurant having staying time from 12:00 to 13:00 and the staying time length of 10 to 30 minutes can be estimated to be a customer who has lunch at a restaurant. In contrast, a user at a restaurant having staying time from approximately 9:00 to 15:00 and the staying time length of about six hours can be estimated to be a shop clerk of the restaurant. The social attribute table sets general information for identifying social attributes of users from their staying times and staying time lengths. The attribute identification unit 230 refers to the social attribute table storage unit 280 and identifies a social attribute tables having user's location attribute information matching each other. This makes it possible to identify the social attribute of the user.
The configuration of the information processing system 1 according to the present embodiment has been described above. Note that while the present embodiment is an example in which the positional sameness determination processing and attribute identification processing are executed by one server 200, the present disclosure is not limited to this example. The positional sameness determination processing and attribute identification processing may be configured to be executed by different servers, or at least one of the processing may be configured to be executable by the device 100.
(3.2. Behavior Identification Processing Using Location Attribute Table)
The behavior identification processing using the location attribute table according to the present embodiment will be described with reference to
(1) Area Screening
First, in order to identify the users staying at the same position, screening of the user staying area is performed (S300). By area screening, users having similar trajectories but existing in different areas are excluded so as to enhance the accuracy of the positional sameness determination processing described later. The area screening may be performed, for example, by executing the processing of step S132 in
(2) Positional Sameness Determination Processing
Next, among the users determined to be present in the same area by the area screening, the positional sameness determination unit 220 identifies the users existing at a same position (S310). The positional sameness determination unit 220 determines the similarity of the relative trajectory of each of users and determines whether or not the users exist at a same position. The positional sameness determination processing is implemented by using a relative trajectory corresponding to a predetermined time of the user calculated on the basis of PDR, inertial navigation calculation processing, or the like. Similarly to the first embodiment, the relative trajectory is supposed to have been calculated on the basis of the relative trajectory calculation processing illustrated in
Note that when a highly accurate communication technology capable of positioning the user's absolute position information at once becomes generally available, the positional sameness determination processing may be performed on the basis of the absolute position information obtained by the communication technology, other than performed on the basis of the similarity of the relative trajectories. Furthermore, the positional sameness determination can be performed on the basis of a result of positioning by combining PDR and map matching.
Moreover, an atmospheric pressure sensor is provided in the sensor unit 110 of the device 100 in acquisition of the relative trajectory, it is possible to estimate the altitude, enabling detection of a three-dimensional trajectory including user's movement across floors of a building. For example, as illustrated in
(3) Location Attribute Identification Processing and Behavior Identification Processing
When the users existing at a same position are identified, the attribute identification unit 230 calculates the location attribute information of the users existing at the same position (S320) and identifies behavior of the users (S330). The attribute identification unit 230 refers to the location attribute table storage unit 270, identifies the location attributes of the users on the basis of the information regarding the staying times of the users, and then, identifies the behavior of the users. In the present embodiment, information to be obtained using the location attribute table is not absolute position information of the users, but context information associated with location, such as “shopping”, “having a meal”, or “staying in a cafe”. Therefore, the location attribute table is not limited to the purpose of identifying that the user is staying at a specific location, but also is used for the purpose of identifying the user's behavior or the location where the behavior is performed.
The location attribute table stored in the location attribute table storage unit 270 is set for each of location where certain behavior is performed on the basis of information based on the staying times of a plurality of users. For example, the location attribute table for identifying the fact that the user is having lunch may be set on the basis of past user behavior data, or may be set as appropriate by a system setting engineer. Specifically, as illustrated in
For example, the location attribute table identifying a fact that the user is having lunch has a setting that the population distribution over time has a peak from 12:00 to 13:00 and the staying time length is 10 to 30 minutes. Furthermore, for example, since the tendency of the population distribution over time and the staying time length differs between male and female in the location attribute table identifying the fact that the user is in a hair salon, the location attribute table may be set for each of male/female groups. Regarding the population distribution over times, for example, in a case where there is a difference in the trend between weekdays and holidays, as indicated in a location attribute table that identifies the fact that the user is in a hair salon or in a location attribute table that identifies the fact that the user is at a conference, illustrated as
In step S320, the population distribution over time and the population distribution over staying time length on the basis of the staying times at the position of the users determined in step S310 to exist at the same position are calculated. As illustrated in
Thereafter, the attribute identification unit 230 refers to the location attribute table storage unit 270 and identifies the location attribute table 271 that matches the calculated location attribute information, and then, identifies the behavior of these users (S330). The attribute identification unit 230 may identify a peak value of the location attribute information and may perform matching with the location attribute table. For example, according to the location attribute information of the user illustrated in
In this manner, the location attribute table 271 can be used to identify the behavior of the users existing at the same position. The attribute identification unit 230 may output the identified behavior to the output unit 240.
In step S330, the behavior of the users is identified by comparing the location attribute information of the user and the location attribute table. Alternatively, similarly to the first embodiment, for example, the behavior of the users may be identified on the basis of information revealed by one of the users existing at the same position by SNS or the like. Furthermore, the information regarding the behavior revealed by the user is more accurate than the user's behavior estimated on the basis of the location attribute table. Accordingly, the location attribute table for identifying the corresponding behavior may be updated on the basis of the information revealed by the user.
Specifically, for example, instead of steps S320 and S330 of
Thereafter, when the device 100 detects the user's start of movement from the position of stay, the device 100 transmits the movement start time at the user's location of stay to the server 200 (S343). Movement start determination processing may be performed on the basis of a detection value of the acceleration sensor 111, for example, similarly to the steps S106 and S108 in
The updating of the location attribute table 271 may be performed on the basis of the information of one user. Still, updating performed on the basis of the statistical result of the information of a plurality of users would make it possible to reduce the influence of variation of behavior of users, enabling updating to the location attribute table 271 closer to the actual situation. As illustrated in
(4) Social Attribute Determination Processing
In behavior identification processing using the location attribute table 271 illustrated in
In a case where there is a user, for example, determined to be in a conference by the location attribute table that identifies the fact that the user is in a conference out of the location attribute tables 271 stored in the location attribute table storage unit 270 of
Moreover, in another case where a user determined to be eating and drinking by a location attribute table that identifies the fact that the user is in a bar restaurant, for example, is often at a place different from the location at the time of determination of that fact that the user is eating and drinking, from the position information of the user, the eating and drinking is estimated to be a client dinner. It is also possible to determine that such a user works in a business division involving dealing with individual customers. Note that when the users are determined to be eating and drinking at the same place, it is estimated that the restaurant is their favorite place.
Alternatively, a user who is determined to be on duty, for example, by a location attribute table identifying the fact that the user is in office can be estimated to be an in-service worker such as a researcher or a receptionist, on the basis of their long staying time length in the office.
Furthermore, it is also conceivable that the users staying at the same location but having different social attributes. For example, as in the left illustration of
In this manner, in a case where the users' staying times or the staying time lengths are mutually different, the social attribute of the users can be estimated. Similarly to the location attribute table, the social attribute table also includes setting of information regarding the staying times. For example, as in right illustration of
With reference to
As illustrated in
Furthermore, the device 100 includes a communication apparatus which is a communication interface including a communication device or the like for connecting to a communication network, in order to obtain absolute position information. The communication apparatus can be, for example, a communication card, etc., for local area network (LAN), Bluetooth (registered trademark), Wi-Fi, or a wireless USB (WUSB). Furthermore, the communication apparatus may be a router for optical communication, a router for asymmetric digital subscriber line (ADSL), a modem for various communication, or the like. Furthermore, the device 100 may include a GPS receiver that receives a global navigation satellite system(s) (GNSS) signal and measures the latitude, longitude, and altitude of the device.
The control unit 910 includes a central processing unit (CPU) 911, a read only memory (ROM) 912, and a random access memory (RAM) 913, and a nonvolatile memory 914. The CPU 911 functions as an arithmetic processing unit and a control apparatus, and controls all or part of operation in the device 100 in accordance with various programs recorded in the ROM 912, the RAM 913, and the nonvolatile memory 914. The ROM 912 stores programs, calculation parameters, or the like, used by the CPU 911. The RAM 913 primarily stores programs to be used in the execution by the CPU 911 or parameters, or the like, appropriately changing in execution of the programs. The CPU 911, the ROM 912, the RAM 913, and the nonvolatile memory 914 are mutually connected via a host bus 915. Note that the device 100 may include a processing circuit such as a digital signal processor (DSP), an application specific integrated circuit (ASIC), or a field-programmable gate array (FPGA) in place of or in addition to the CPU 911.
Furthermore, the host bus 915 is connected to an external bus (not illustrated) such as a peripheral component interconnect/interface (PCI) bus via a bridge (not illustrated). The device 100 may include an operation unit 921, an output unit 922, an external I/F923, or the like connected via the external bus. The operation unit 921 is a device that is operated by the user, such as a mouse, a keyboard, a touch panel, buttons, a switch, and a lever, for example. The operation unit 921 may be, for example, a remote control device utilizing infrared rays or other radio waves. The operation unit 921 includes an input control circuit that generates an input signal on the basis of the information input by the user and outputs the generated input signal to the CPU 911. The user operates the operation unit 921, thereby inputting various data to the device 100 or giving an instruction on processing operation to the device 100.
The output unit 922 includes a device capable of notifying the user of obtained information using a sense such as visual sense, auditory sense, or tactile sense. The output unit 922 can be, for example, a display device including a nonvolatile display such as an electronic paper or a memory liquid crystal display and a MEMS display, an audio output device such as a speaker or a headphone, a vibrator, or the like. The output unit 922 outputs a result obtained by processing of the device 100 as a video including a text or an image, sound such as voice or sound, vibration, or the like.
The external I/F 923 is a port for connecting equipment to the device 100. The external I/F 923 can be, for example, a universal serial bus (USB) port, an IEEE 1394 port, a small computer system interface (SCSI) port, or the like. Furthermore, the external I/F 923 may be an RS-232C port, an optical audio terminal, a High-Definition Multimedia Interface (HDMI) (registered trademark) port, or the like. Connecting externally connected equipment to the external I/F 923 enables various data to be exchanged between the device 100 and the externally connected equipment.
An example of the hardware configuration of the device 100 has been described above. Each of the above-described constituents may use general-purpose members, or may use hardware specialized for the function of each of the constituents. Such a configuration can be appropriately changed in accordance with the technical level at the time of implementation. Furthermore, the server 200 included in the information processing system 1 of the present embodiment can be implemented by an apparatus having an equivalent hardware configuration.
(5.1. Feature Amount Other than Relative Trajectory)
The above embodiment has described a case where the positional sameness determination is performed on the basis of the relative trajectory identified by the detection value obtained by the inertial sensor. Alternatively, however, the present disclosure can execute the positional sameness determination processing in a similar manner even with a feature amount other than the relative trajectory.
For example, the positional sameness determination may be performed by using original time-series data of the detection value of the sensor. For example, as illustrated in
Alternatively, as illustrated in
(5.2. User Agreement on Information Collection)
In the information processing system 1 according to the above embodiment, it is necessary to collect information to identify the user's behavior obtained by the device 100. In operation of the information processing system 1, it is desirable to obtain agreement from the user on collection of information for identifying behavior, such as position information and time information, for example. For example, as illustrated in
Furthermore, in the above embodiment, the absolute time (date and time) is not necessarily needed for identification of the absolute position information of the user. Still, acquisition of the absolute time would make it possible to distinguish whether or not a user is during lunch or dinner in a case where the user is in a restaurant, for example, leading to an increase in information amount of behavior to be recognized. Accordingly, as illustrated in
While
In a case where the travel time and the staying time length are transmitted to the server 200, there is a case where the user's behavior can be estimated even without transmission of the absolute time. For example, here is an exemplary case where a user stays in the company for three hours, thereafter travels to a restaurant over five minutes, and stayed in a restaurant for 15 minutes. At this time, for example, in a case where the user behavior in the restaurant is estimated to be eating lunch by the method of the third embodiment, the staying time in the restaurant is estimated to be around 12:00. Subsequently, it is also possible to estimate that the user was in the company in the morning by calculating back from the estimated staying time.
Hereinabove, the preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, while the technical scope of the present disclosure is not limited to the above examples. A person skilled in the art in the technical field of the present disclosure may find it understandable to reach various alterations and modifications within the technical scope of the appended claims, and it should be understood that they will naturally come within the technical scope of the present disclosure.
In addition, the effects described in this specification are merely illustrative or exemplary, and are not limiting. That is, the technology according to the present disclosure can exhibit other effects obvious to those skilled in the art from the description of the present specification together with the above effects or in place of the above effects.
Note that the following configuration should also be within the technical scope of the present disclosure.
(1)
An information processing apparatus including a determination unit that determines similarity of positions of a plurality of users on the basis of time-series data that can identify a movement state of each of the plurality of users, obtained for each of the plurality of users.
(2)
The information processing apparatus according to (1),
in which the determination unit determines similarity of the positions of the users on the basis of the time-series data in a same area.
(3)
The information processing apparatus according to (1) or (2),
in which the determination unit performs positional sameness determination on the plurality of users on the basis of a relative trajectory obtained on the basis of the time-series data.
(4)
The information processing apparatus according to any one of (1) to (3),
in which the determination unit includes:
a synchronization processing unit that achieves synchronization between each of pieces of the time-series data with the two pieces of time-series data as determination targets;
a deviation degree calculation unit that calculates a deviation degree of the synchronized time-series data; and
a determination processing unit that determines similarity of positions of the users on the basis of the calculated deviation degree.
(5)
The information processing apparatus according to (4),
in which the synchronization processing unit
segments each of pieces of time-series data by a predetermined scale unit,
normalizes a scale of each of pieces of the time-series data for each of the segmented periods, and
performs synchronization while aligning data start positions of one of the pieces of time-series data as a reference with the other of the pieces of time-series data for each of the periods.
(6)
The information processing apparatus according to any one of (1) to (5),
in which the time-series data is a detection value of an inertial sensor.
(7)
The information processing apparatus according to any one of (1) to (5),
in which the time-series data is a detection value of an environmental sensor.
(8)
The information processing apparatus according to any one of (1) to (7), further including an absolute position information acquisition unit that obtains absolute position information indicating an absolute position of the user.
(9)
The information processing apparatus according to any one of (1) to (8), further including an attribute identification unit that decides attributes of one user and another user on the basis of absolute information of at least the one user among the plurality of users determined to have similar positions.
(10)
The information processing apparatus according to (1), further including an attribute identification unit that identifies an attribute of the user,
in which the attribute identification unit
identifies behavior of the user
on the basis of location attribute information based on a staying time at the position calculated for the users determined to have similar positions and on the basis of a location attribute table including setting of information regarding the staying time indicating the behavior of the user.
(11)
The information processing apparatus according to (10),
in which the location attribute table includes setting of the staying time and staying time length of the user, implemented at the location.
(12)
The information processing apparatus according to (10) or (11),
in which the attribute identification unit updates the location attribute table on the basis of position information and behavior-related information obtained from at least one user among the users determined to have similar positions.
(13)
The information processing apparatus according to any one of (10) to (12),
in which the attribute identification unit identifies a social attribute of the user on the basis of a result of comparison between the location attribute information of the user and the identified location attribute table.
(14)
The information processing apparatus according to any one of (10) to (13),
in which the attribute identification unit identifies the social attribute of the user on the basis of the location attribute information of the user and a social attribute table representing the social attribute of the user.
(15)
An information processing method including:
obtaining, by using a sensor, time-series data that can be used to identify a movement state of each of a plurality of users, for each of the users; and determining, by using a processor, similarity of positions of the users on the basis of the time-series data.
(16)
A computer program causing a computer to function as an information processing apparatus including a determination unit that determines similarity of positions of a plurality of users on the basis of time-series data that can be used to identify a movement state of each of the plurality of users, obtained for each of the plurality of users.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2017-009693 | Jan 2017 | JP | national |
| 2017-213650 | Nov 2017 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2017/044436 | 12/11/2017 | WO | 00 |
