Information processing apparatus, computer program, and information management system

Abstract

To further enhance accuracy in a PDR method.SOLUTION: An information processing apparatus, which processes output from an acceleration sensor on a portable terminal, includes: an analysis section that segments a walking period during which a pedestrian walks into a plurality of step periods on the basis of a change in acceleration indicated by a series of acceleration data samples; and a determination section that determines a traveling direction in each step period by determining a principal direction that is a direction of dominant acceleration in a set of acceleration data samples corresponding to the step period, and selecting one of forward and backward directions in the principal direction. The determination section, when determining the traveling direction in the last step period in the walking period, selects one of forward and backward directions in the principal direction on the basis of acceleration indicated by samples belonging to an alternative step period that is earlier than the last step period instead of samples belonging to the last step period.SELECTED DRAWING: Figure 12A

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, a computer program, and an information management system. [Background technology]

[0002] RFID (Radio Frequency Identification) is a technology that enables information embedded in small devices, also known as tags, to be read by an external reader via short-range wireless communication. Passive RFID tags transmit information using the energy of electromagnetic waves emitted from a reader, and are widely used in a variety of situations, such as for managing and visualizing the location of items, because they do not require batteries and are therefore inexpensive to manufacture and can function semi-permanently.

[0003] Patent Document 1 discloses an invention that combines reading information from an RFID tag with a self-location estimation method in order to estimate the location of a managed object without relying on GPS (Global Positioning System) positioning, which is prone to being unstable in environments with many obstructions. In the invention disclosed in Patent Document 1, the location of a managed object is estimated based on the known location of a fixedly installed position tag and the relative movement amount of a reader calculated according to the self-location estimation method.

[0004] A typical example of a self-location estimation method is the PDR (Pedestrian Dead Reckoning) method, which estimates the amount of movement of a pedestrian or a mobile terminal based on data from a sensor provided in a mobile terminal carried by the pedestrian. In the PDR method, for example, a group of sensors including an acceleration sensor and a geomagnetic sensor may be used to identify each step of a pedestrian, determine the direction of travel for each step, and estimate the distance traveled for each step (also called stride length). Patent documents 2 and 3 disclose examples of such PDR technology. Non-patent document 1 discloses several examples of stride length estimation algorithms. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent Publication No. 2021-141415 [Patent Document 2] Patent No. 4126388 [Patent Document 3] Patent No. 5334131 [Non-patent literature]

[0006] [Non-Patent Document 1] Wenxia Lu, Fei Wu, Hai Zhu, and Yujin Zhang, "A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection", Journal of Sensors 2020, Article ID 8818130, November 26, 2020 Summary of the Invention [Problem to be solved by the invention]

[0007] As described in Patent Documents 2 and 3, many of the existing PDR-based technologies rely on the analysis of data output from an acceleration sensor in a time series. However, even if a pedestrian walks in a fixed direction, the direction of acceleration acting on a mobile device varies greatly. Patent Document 3 aims to improve the accuracy of the determination by determining the direction of travel of odd-numbered steps and the direction of travel of even-numbered steps based on a principal component analysis of acceleration, and adopting the bisector between these directions as the pedestrian's direction of travel. However, there is still room for improvement in the accuracy.

[0008] The present invention aims to further improve the accuracy of the PDR method. [Means for solving the problem]

[0009] According to one aspect, there is provided an information processing device that processes a series of acceleration data samples output in a time series manner from an acceleration sensor on a mobile terminal carried by a pedestrian, the information processing device comprising: a gait analysis unit that divides a walking period in which the pedestrian is walking into a plurality of step periods corresponding to each step of the pedestrian based on changes in acceleration indicated by the series of acceleration data samples; and a direction determination unit that determines the traveling direction of the pedestrian in each step period among the plurality of step periods by determining a main direction, which is the direction of acceleration that is predominant in a set of acceleration data samples corresponding to the step period, and selecting either forward or backward in the determined main direction as the traveling direction of the pedestrian, wherein, when determining the traveling direction of the pedestrian in a final step period within the walking period, the direction determination unit selects either forward or backward in the main direction determined for the final step period based on the acceleration indicated by acceleration data samples belonging to an alternative step period earlier than the final step period, instead of the acceleration data samples belonging to the final step period. Effect of the Invention

[0010] According to the present invention, the accuracy of the PDR method can be further improved. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic diagram showing an example of a configuration of an information management system according to an embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of the configuration of a tag reader according to an embodiment. [Diagram 3] FIG. 4 is a block diagram showing an example of a configuration of a management server according to an embodiment. [Figure 4] FIG. 2 is a block diagram showing an example of a specific configuration of a PDR processing unit according to an embodiment. [Diagram 5] FIG. 11 is a state transition diagram showing an example of transition conditions between three possible states that a pedestrian can take. [Figure 6]FIG. 4 is an explanatory diagram of an example of determination of a pedestrian's state by a gait analysis unit. [Figure 7A] FIG. 13 is an explanatory diagram showing a first example of determining a main direction based on a statistical analysis of acceleration. [Figure 7B] FIG. 11 is an explanatory diagram showing a second example of determining the main direction based on a statistical analysis of acceleration. [Figure 7C] FIG. 11 is an explanatory diagram showing a third example of determining the main direction based on a statistical analysis of acceleration. [Figure 8A] FIG. 11 is a first explanatory diagram for determining the direction of travel based on the center of gravity position of an acceleration vector during an ascending phase. [Figure 8B] FIG. 2 is a second explanatory diagram for determining the direction of travel based on the center of gravity position of the acceleration vector during the ascent phase. [Figure 9A] 4 is a first timing diagram for a first embodiment of a process associated with determining heading using sets of acceleration data samples over alternative step periods; [Figure 9B] 6 is a second timing diagram for a first embodiment of a process associated with determining heading using sets of acceleration data samples during an alternative step; [Figure 10A] 6 is a first timing diagram for a second embodiment of a process associated with determining heading using sets of acceleration data samples during an alternative step; [Figure 10B] 11 is a second timing diagram for a second embodiment of a process associated with determining heading using sets of acceleration data samples during an alternative step; [Figure 11] 10 is a timing diagram for a third embodiment of a process associated with determining heading using sets of acceleration data samples during an alternative step; [Figure 12A] 10 is a flowchart showing a first example of a flow of a movement amount measurement process according to an embodiment. [Figure 12B] 10 is a flowchart showing a second example of the flow of a movement amount measurement process according to an embodiment. [Figure 13] 10 is a flowchart showing an example of the flow of a data transmission process according to an embodiment. [Figure 14]10 is a flowchart showing an example of a flow of a position estimation process according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, the embodiments will be described in detail with reference to the attached drawings. Note that the following embodiments do not limit the invention according to the claims. Although the embodiments describe a number of features, not all of these features are essential to the invention, and the features may be combined in any manner. Furthermore, in the attached drawings, the same reference numbers are used for the same or similar configurations, and duplicated descriptions are omitted.

[0013] <1. System Overview> In this specification, an example in which the technology according to the present disclosure is applied to an information management system that manages location information of managed objects will be mainly described. However, the technology according to the present disclosure is not limited to the described example, and may be applied to any other type of system or device.

[0014] 1 is a schematic diagram showing an example of the configuration of an information management system 1 according to an embodiment. The information management system 1 is a system that tracks the location of a managed object, which may change daily, and visualizes the location information. The managed object may include at least one of an object located in the real space and a user who is active in the real space. The object may be an inanimate object (e.g., a machine, a device, an instrument, a material, a consumer good, a part, a vehicle, or a robot) or a living object (e.g., an animal or a plant).

[0015] In this embodiment, the information management system 1 estimates the location of each managed object and manages location information including the estimation result. The location of each managed object may be expressed by two-dimensional or three-dimensional position coordinates. In this embodiment, a plurality of zones are set in real space. Each managed object may be estimated to be located in one of the zones based on the estimation result of the location. In the example of FIG. 1, a plurality of zones 10a, 10b, and 10c are set in real space. For example, in the application of material management in construction work, these zones may correspond to construction sites in different locations. In the application of inventory management in a logistics warehouse, these zones may correspond to a warehouse or a storage shelf.

[0016] FIG. 1 also shows a user 20a carrying the portable system 100. The user 20a is a pedestrian who moves between multiple areas by walking in real space. In this specification, the expression that the user carries some object broadly includes various ways in which the user moves with the object (for example, moving while holding or wearing the object). FIG. 1 also shows items 30a, 30b, and 30c. These items are objects whose position information is managed by the information management system 1. In addition to the items, the user may also be a managed object.

[0017] The information management system 1 utilizes wireless devices, also called tags, to track the location of the managed object. The location tag is a wireless device (second wireless device) installed in each of a plurality of areas. Typically, a plurality of location tags are installed at different positions in the real space. In the figure, a location tag 40a is installed in the area 10a, a location tag 40b is installed in the area 10b, and a location tag 40c is installed in the area 10c. Note that two or more location tags may be installed in one area. Each location tag stores unique identification information (second identification information) associated with the corresponding installation area in an internal memory.

[0018] The target tag is a wireless device (first wireless device) attached to each managed object that can move in real space. Fig. 1 shows a target tag 50a attached to an item 30a, a target tag 50b attached to an item 30b, and a target tag 50c attached to an item 30c. Each target tag stores, in an internal memory, identification information (first identification information) for identifying the managed object to which the target tag is attached.

[0019] In the following description, when it is not necessary to distinguish between the areas 10a to 10c, the alphabet at the end of the reference numeral will be omitted and these will be collectively referred to as the area 10. The same applies to the items 30 (items 30a, 30b, ...), the position tags 40 (40a, 40b, ...), the target tags 50 (target tags 50a, 50b, ...), the users 20, and other elements.

[0020] The number of areas set in the real space, the number of managed objects under the management of the system, and the number of users or pedestrians involved in managing location information are not limited to the examples given above and may be any number.

[0021] In this embodiment, each of the tags such as the position tag 40 and the target tag 50 is a passive RFID tag (passive tag). The passive tag is composed of a small IC (Integrated Circuit) chip with built-in memory and an antenna, and stores unique identification information and other information for identifying the tag in the memory. In this specification, the identification information is also simply called ID, and the identification information for identifying the tag is also called tag ID. Note that the tag ID may be considered as information for identifying the object to which the tag is attached. The IC chip of the passive tag operates using the energy of electromagnetic waves emitted from the tag reader, modulates the tag ID and other information stored in the memory into an information signal, and transmits (returns) the information signal from the antenna.

[0022] In another embodiment, each tag may be an active RFID tag. When each tag actively (for example, periodically) transmits information to the surroundings using power from a built-in battery, the tag may be called a beacon tag. In yet another embodiment, each tag may be a wireless device that responds to a signal from a reader and returns information, for example, by the NFC (Near Field Communication) method or the Bluetooth (registered trademark) method. Each tag may be called by any name, such as an IC tag, an IC card, or a responder.

[0023] The information management system 1 includes a mobile system 100 and a management server 200. The mobile system 100 and the management server 200 are connected to a network 5. The network 5 may be a wired network, a wireless network, or any combination thereof. Examples of the network 5 may include the Internet, an intranet, and a cloud network.

[0024] In the example of FIG. 1, the mobile system 100 includes a user terminal 105 and a tag reader 110. The user terminal 105 and the tag reader 110 are information processing devices and are a type of mobile terminal. The user terminal 105 may be any type of information processing device, such as a notebook PC (Personal Computer), a tablet PC, a smartphone, or a smart watch. The user terminal 105 may be used for interaction with the user 20 by the information management system 1. The user terminal 105 typically includes a processor and a memory, an input device for accepting user input, a communication interface for communicating with other devices, and an output device for outputting information (e.g., a display and a speaker).

[0025] The tag reader 110 is a reading device capable of reading information stored in a wireless device such as an RFID tag. The tag reader 110 can detect a management target to which the target tag 50 is attached, for example, by reading a tag ID from the target tag 50. The tag reader 110 attempts to read periodically or in response to some trigger such as a user operation, and transmits the tag reading result to the management server 200. The tag reader 110 may be capable of communicating directly with the management server 200, or may be capable of communicating indirectly with the management server 200 via some relay device (for example, the user terminal 105). An example of a specific configuration of the tag reader 110 will be further described later.

[0026] The management server 200 is an information processing device that manages location information and other information of managed objects in a database. The management server 200 may be implemented as an application server, a database server, or a cloud server using, for example, a high-performance general-purpose computer. The management server 200 receives tag reading results from the tag reader 110 and updates the database based on the received tag reading results. For example, the management server 200 estimates the location of each managed object based on the tag reading results. An example of a specific configuration of the management server 200 will be further described later.

[0027] 1 shows an example in which portable system 100 includes user terminal 105 and tag reader 110, which are separate devices. However, portable system 100 is not limited to this example. For example, tag reader 110 may have some or all of the functions of user terminal 105, and user terminal 105 may have some or all of the functions of tag reader 110. Furthermore, the functions of management server 200 described in this embodiment may be realized in user terminal 105 or tag reader 110.

[0028] <2. Tag reader configuration example> Fig. 2 is a block diagram showing an example of the configuration of tag reader 110 according to an embodiment. Referring to Fig. 2, tag reader 110 includes control unit 111, storage unit 112, communication unit 113, measurement unit 114, operation unit 115, and reading unit 116.

[0029] The control unit 111 is composed of a memory that stores a computer program, and one or more processors (e.g., a CPU (Central Processing Unit)) that execute the computer program. The control unit 111 controls the overall functions of the tag reader 110 described in this specification. For example, the control unit 111 causes the reading unit 116 to execute reading of an RFID tag within a tag reading range, and causes the read information, the read time, and the signal reception level to be temporarily stored in the memory unit 112 as reading result data. In addition, the control unit 111 causes the measurement unit 114 to measure the amount of movement of the tag reader 110 in parallel with reading the RFID tag, and causes the measurement result to be stored in the memory unit 112. Then, the control unit 111 transmits the reading result data and the measurement result data stored in the memory unit 112 to the management server 200 via the communication unit 113, together with the reader identification information (also called a reader ID) of the own device.

[0030] The storage unit 112 may include any type of storage medium, such as a semiconductor memory such as a Read Only Memory (ROM) or a Random Access Memory (RAM), an optical disk, or a magnetic disk. In this embodiment, the storage unit 112 stores the above-mentioned reading result data, measurement result data, and the reader ID of the tag reader 110.

[0031] The communication unit 113 is a communication interface for the tag reader 110 to communicate with the management server 200. For example, the communication unit 113 may be a WLAN interface for communicating with a WLAN (Wireless Local Area Network) access point, or a cellular communication interface for communicating with a cellular base station. The communication unit 113 may also be a connection interface for connecting to a relay device (for example, a Bluetooth (registered trademark) interface or a USB (Universal Serial Bus) interface).

[0032] The measurement unit 114 is a unit capable of measuring the position of the tag reader 110. As shown in FIG. 2, the measurement unit 114 includes a sensor group 130 and a PDR processing unit 140. The sensor group 130 includes at least an acceleration sensor that measures a three-axis acceleration acting on the tag reader 110 in a device coordinate system specific to the tag reader 110 and outputs an acceleration data sample. The measurement unit 114 may further include a geomagnetic sensor that measures the absolute orientation of the tag reader 110 in the real space, and a gyro sensor that measures the angular velocity of the tag reader 110, i.e., a change in the attitude of the tag reader 110. The orientation and angular velocity of the tag reader 110 may be used for data conversion between the device coordinate system and the coordinate system of the real space. Furthermore, the measurement unit 114 may include a barometric pressure sensor that measures atmospheric pressure and outputs barometric pressure data. The barometric pressure data output from the barometric pressure sensor may be used to estimate the height of a point where the tag reader 110 is currently located. PDR processing unit 140 is a processing circuit that measures the relative position of tag reader 110, i.e., the relative amount of movement from a certain reference position, by processing a series of data samples output in time series from sensor group 130. PDR processing unit 140, for example, repeatedly measures the amount of movement of tag reader 110 and outputs the measured amount of movement to control unit 111. The reference position may be, for example, the position of tag reader 110 at the time tag reader 110 is started. The specific configuration of PDR processing unit 140 will be described in detail later.

[0033] As will be described later, in this embodiment, the position coordinates of the installation position of each position tag 40 are known and registered in a database. Therefore, the position coordinates of the point where the tag reader 110 is currently located can be estimated based on the amount of relative movement from the time when the tag reader 110 detected a certain position tag 40 to the current time and the known position coordinates of the position tag 40. In this embodiment, an example in which the management server 200 estimates the absolute position of the tag reader 110 will be mainly described, but the control unit 111 or measurement unit 114 of the tag reader 110 may access the database to estimate the absolute position of the tag reader 110.

[0034] Note that instead of tag reader 110 including measuring unit 114, mobile system 100 may include a measuring device (capable of measuring the amount of relative movement using the PDR method) separate from tag reader 110.

[0035] Operation unit 115 accepts operations by user 20. Operation unit 115 includes, for example, a physical input device such as a button, switch, or lever arranged on the housing of tag reader 110. Operation unit 115 accepts operations by user 20 via the input device, and outputs an operation signal to control unit 111. Operation unit 115 may also include a voice input interface such as a microphone.

[0036] The reading unit 116 is a unit capable of reading information stored in each of the position tags 40 and the target tags 50 under the management of the information management system 1. Referring to FIG. 2, the reading unit 116 includes an RF controller 120, a power amplifier 121, a filter 122, a first coupler 123, a second coupler 124, an antenna 125, a power detection unit 126, and a canceller 127. The RF controller 120 outputs a transmission signal (for example, a signal modulated in the UHF band) from the TX terminal to the power amplifier 121 in accordance with the control of the control unit 111. The power amplifier 121 amplifies the transmission signal input from the RF controller 120 and outputs it to the filter 122. The filter 122 may be, for example, a low-pass filter, and removes unnecessary frequency components of the transmission signal amplified by the power amplifier 121. The first coupler 123 distributes the transmission signal that has passed through the filter 122 to the second coupler 124 and the power detection unit 126. The second coupler 124 outputs the transmission signal input from the first coupler 123 to the antenna 125, and outputs the reception signal input from the antenna 125 to the RF controller 120. The antenna 125 transmits the transmission signal input from the second coupler 124 into the air as an electromagnetic wave. The antenna 125 also receives a signal returned from an RFID tag present within the reading range of the tag reader 110 in response to the transmission signal, and outputs the reception signal to the second coupler 124. The power detection unit 126 detects the power level of the signal input from the first coupler 123, and outputs a signal RF_DETECT indicating the detected power level to the control unit 111. The canceller 127 receives a signal CARRIER_CANCEL indicating the power level of the carrier wave from the control unit 111. Then, based on CARRIER_CANCEL, the canceller 127 cancels the carrier component of the transmission signal, thereby extracting a desired signal component of the reception signal to be output to the RX terminal of the RF controller 120. The RF controller 120 demodulates the signal input from the RX terminal, acquires the tag ID and other information returned from the RFID tag, and outputs the acquired information to the control unit 111. The RF controller 120 also measures the reception level (also called reception strength) of the signal input from the RX terminal, and outputs the measurement result to the control unit 111.

[0037] In this embodiment, the tag reading attempt by the reading unit 116 may be performed periodically (e.g., once per second) without requiring an explicit instruction from the user. Data transmission from the communication unit 113 to the management server 200 may also be performed periodically (e.g., once every few seconds) or each time a tag is read, without requiring an explicit instruction from the user. In order to reduce the communication load by omitting transmission of redundant data, the control unit 111 may exclude from the data to be transmitted a record that is the same as a record that has been transmitted within a recent predetermined period. Note that, in another embodiment, one or both of the tag reading attempt by the reading unit 116 and the transmission of data to the management server 200 may be performed in response to detection of a user input via the operation unit 115. When the communication unit 113 indirectly communicates with the management server 200 via a relay device, the transmission of data to the management server 200 may be performed only while the connection between the communication unit 113 and the relay device is valid.

[0038] <3. Management Server Configuration Example> <3-1. Basic configuration> 4 is a block diagram showing an example of a configuration of the management server 200 according to an embodiment. Referring to FIG. 4, the management server 200 includes a communication unit 210, a database 220, and a management unit 230.

[0039] The communication unit 210 is a communication interface for the management server 200 to communicate with other devices. The communication unit 210 may be a wired communication interface or a wireless communication interface. In this embodiment, the communication unit 210 communicates with the mobile system 100 (for example, one or both of the user terminal 105 and the tag reader 110). The database 220 is a database accessible from the management unit 230 that stores various data for estimating the location of the managed object and managing the location information. In this embodiment, the database 220 includes an object table 310, an area table 320, a location tag table 330, a reader table 340, a movement amount table 360, a tag detection table 370, and a location information table 380. The management unit 230 is a collection of multiple software modules that execute various processes related to location estimation and providing location information to the user. Each software module can be operated by one or more processors (not shown) of the management server 200 executing a computer program stored in a memory (not shown). In this embodiment, the management unit 230 includes a data management unit 231, a position estimation unit 232, and a display control unit 233.

[0040] <3-2. Data Management> The object table 310 of the database 220 is a table that stores data related to each managed object under the management of the information management system 1. The object table 310 may include, for example, one or more of the following data items: 'Tag ID' 'Target ID' 'name' 'Target type' 'Tag ID' is identification information that uniquely identifies the target tag 50 attached to each managed object. The value of 'tag ID' is the same as the tag ID value stored internally in the corresponding target tag 50. 'Target ID' is identification information that uniquely identifies each managed object. 'Name' represents the name of each managed object. 'Target Type' represents the type into which each managed object is classified.

[0041] (2) Area table The area table 320 is a table that stores data related to each area set in the real space. The area table 320 may include, for example, one or more of the following data items: 'Area ID' 'name' 'map' 'Scale' 'Direction' 'Area ID' is identification information that uniquely identifies each of the areas 10. 'Name' represents the name of each area. 'Map' is a data item for storing map data available for each area in association with the corresponding area when the map data is registered by a user. The map data includes at least image data representing a map image. In addition, the map data may include boundary data that defines the boundary positions of the areas. 'Scale' represents a ratio for converting a distance on a map image to a distance in real space when the map data is registered (e.g., how many meters in real space does one pixel of the image correspond to). 'Orientation' is a data item for storing orientation information indicating an orientation on a map image when the map data is registered. For example, the orientation information may include a vector that points to a specific direction (e.g., north) in the two-dimensional coordinates of the map image data.

[0042] (3) Location tag table The position tag table 330 is a table that stores data related to each of the position tags 40 installed in the real space. The position tag table 330 may include, for example, one or more of the following data items: 'Tag ID' 'Installation area' 'Tag position' 'Tag ID' is identification information that uniquely identifies each position tag 40. The value of 'tag ID' is the same as the tag ID value stored internally in the corresponding position tag 40. 'Installation area' identifies the area in which each position tag 40 is installed by the value of 'area ID' in area table 320. That is, the tag ID of each position tag 40 is associated with the installation area corresponding to that position tag 40 in position tag table 330. 'Tag position' represents the position coordinates of the installation position of each position tag 40 in the coordinate system of real space.

[0043] (4) Leader table Reader table 340 is a table that stores data related to each of tag readers 110, which may exist in information management system 1. Reader table 340 may include, for example, one or more of the following data items: 'Reader ID' 'name' 'Read range' 'User' 'Reader ID' is identification information that uniquely identifies each tag reader 110. 'Name' indicates the name of each reader. 'Read range' indicates the length of the read range as a performance of each reader (how far away a wireless device a tag ID can be read from). Note that if the read range length of all tag readers 110 is uniform, reader table 340 does not need to include 'read range' as a data item. 'User' is identification information that identifies user 20 who uses each tag reader 110.

[0044] (5) Data registration The data management unit 231 manages various data stored in the database 220 as described above. Data to be registered in each table of the database 220 may be generated by, for example, a user or an engineer. The data management unit 231 may receive a data file describing such data via the communication unit 210 and register the data in each table. The data management unit 231 may also provide, for example, the user terminal 105, with a user interface (UI) for accepting registration, modification, or deletion of data.

[0045] <3-3. Estimation of location> The position estimation unit 232 estimates the location of at least one managed object that is movable in real space. The position estimation unit 232 can estimate the location of the managed object to which the target tag 50 is attached based on, for example, the result of reading the tag ID from the target tag 50 by the tag reader 110 and the result of reading the tag ID from the position tag 40 by the same tag reader 110. The movement amount table 360 ​​and the tag detection table 370 of the database 220 are used for such location estimation.

[0046] (1) Movement amount table The movement amount table 360 ​​is a table for storing records of the measurement result data (hereinafter, referred to as measurement result records) received from the tag reader 110. The movement amount table 360 ​​may include, for example, one or more of the following data items: 'Measurement time' 'Reader ID' 'amount of movement' 'Measurement time' indicates the time when the measurement was performed for the measurement result indicated by each measurement result record. 'Reader ID' indicates tag reader 110 that performed the measurement for the measurement result indicated by each measurement result record, by the value of 'Reader ID' in reader table 340. 'Movement amount' indicates the relative movement amount as a measurement result (e.g., a two-dimensional or three-dimensional vector in the coordinate system of real space).

[0047] (2) Tag detection table Tag detection table 370 is a table for storing records of read result data (hereinafter referred to as read result records) received from tag reader 110. Tag detection table 370 may include, for example, one or more of the following data items: 'Read time' 'Tag ID' 'Reader ID' 'Reception strength' 'Read time' indicates the time when the tag ID was read for each read result record. 'Tag ID' indicates the tag ID read for each read result record. 'Reader ID' indicates the tag reader 110 that performed the tag reading for each read result record by the value of 'Reader ID' in reader table 340. 'Reception strength' indicates the reception level of the signal received by tag reader 110 when reading the tag for each read result record.

[0048] (3) Estimation of location Assume that a tag reader 110 reads a tag ID from a target tag 50 at a first read time, and further reads a tag ID from a position tag 40 at a second read time. The second read time may be before or after the first read time. The position estimation unit 232 can estimate the position coordinates of the point where the managed object to which the detected target tag 50 is attached is located, based on the amount of relative movement between the first read time and the second read time of this tag reader 110 and the known position of the detected position tag 40.

[0049] Specifically, the position estimation unit 232 adds each record of the measurement result data received from the portable system 100 via the communication unit 210 to the movement amount table 360 ​​as a measurement result record. In addition, the position estimation unit 232 adds each record of the reading result data received from the portable system 100 via the communication unit 210 to the tag detection table 370 as a reading result record. When the target tag 50 is detected by the tag reader 110, the position estimation unit 232 can estimate the two-dimensional position coordinates (u, v) in the horizontal plane of the point where the target tag 50 is located at that time according to the following equation (1):

number

[0050] When the same target tag 50 is detected multiple times within a certain period of time, the position estimation unit 232 may estimate the position coordinates of the corresponding managed object based on the relative movement amount of the tag reader 110 at the time when the signal reception strength was the highest. Furthermore, when the same target tag 50 is detected multiple times within a certain period of time, the position estimation unit 232 may estimate that the corresponding managed object is located at the center (for example, the center of gravity position) of the multiple detection positions derived using the above-mentioned calculation formula.

[0051] The location estimation unit 232 may select a reference location tag to be used for estimating the location of a certain managed object based on a correlation between the result of reading the tag ID from a target tag 50 of the managed object and the result of reading the tag ID from one or more position tags 40. The correlation here may include one or both of a temporal correlation and a spatial correlation. For example, the location estimation unit 232 may focus on each position tag 40 in order of the smallest difference in tag ID reading time with respect to a certain target tag 50, and first select a position tag 40 that satisfies both of the following conditions 1 and 2 as the reference location tag: Condition 1: The straight-line distance between the estimated positions of the tag readers at the two reading times is below a predetermined distance threshold (separate threshold determinations may be performed for the distance in the horizontal plane and the distance in the vertical direction). Condition 2: The cumulative movement amount of the tag reader between the two read times is below a predetermined movement amount threshold.

[0052] The location estimation unit 232 estimates that the corresponding managed object is located in the area associated with the tag ID of the reference location tag selected according to the above-mentioned conditions. That is, the value of the 'installation area' in the location tag table 330 of the reference location tag selected for the target tag 50 of a certain managed object identifies the area in which the managed object is located. Note that the location estimation unit 232 may determine that the location of a managed object for which a reference location tag cannot be selected because there is no location tag 40 that satisfies the above-mentioned conditions is unknown.

[0053] The location estimation unit 232 derives the location and other information for each managed object in this manner, and adds a location information record indicating the result to the location information table 380.

[0054] (4) Location information table The location information table 380 is a table for storing location information about various managed objects under the management of the information management system 1. The location information table 380 may include, for example, one or more of the following data items: 'Target ID' 'Location' 'Location area' 'Reference position tag' 'Read time' 'Leader' 'Target ID' indicates which managed object each record in the location information table 380 indicates its location information by using the identifier of the managed object registered in the target table 310. 'Location' indicates the location coordinates of the location of each managed object estimated by the location estimation unit 232. 'Location area' indicates the area in which each managed object is estimated to be located by the value of 'area ID' in the area table 320. 'Reference location tag' indicates the reference location tag selected when estimating the location of each managed object by the value of 'tag ID' in the position tag table 330. 'Read time' indicates the time (first read time) when the tag ID was read from the target tag 50 of each managed object. 'Reader' indicates the tag reader 110 that reads the tag ID from the target tag 50 of each managed object by the value of 'reader ID' in the reader table 340.

[0055] <3-4. Providing location information> The display control unit 233 can provide the user with location information of at least one managed object via a UI to assist the user in understanding the state of the managed object. For example, the display control unit 233 displays location information on the screen of the display of the user terminal 105 based on the result of estimation of the location of each managed object by the location estimation unit 232.

[0056] The display control unit 233 may obtain the latest location and location area of ​​the managed object selected by the user from the location information table 380, and display the obtained information on the screen. The display control unit 233 may also display history information indicating the history of the location of the managed object selected by the user in chronological order on the screen. The display of the location information on the screen may be performed in a simple text format or in a map format. The display in the map format may be performed, for example, by displaying a map image based on map data of the area selected by the user, and superimposing a display object indicating the location of one or more managed objects estimated to be located within the area on the map image.

[0057] The location information described herein may be provided to the user through voice instead of being displayed on a screen, or may be provided to the user in the form of a data file.

[0058] <4. Improving the accuracy of location information> In order to provide accurate position information based on the above-mentioned mechanism, it is required that the measurement accuracy of the relative movement amount by the measurement unit 114 of the tag reader 110 is sufficiently high. In particular, when the tag reader 110 is carried by a pedestrian, even if the pedestrian is walking in a fixed direction, the variation in the direction of acceleration acting on the portable terminal during the walk is extremely large. The existing method of measuring the relative movement amount by estimating the direction of travel and stride length for each step by statistically analyzing a series of acceleration data samples in a time series is advantageous in that it can absorb the variation of individual data samples and output a stable and highly accurate measurement result. Therefore, even in this embodiment, the PDR processing unit 140 of the measurement unit 114 adopts a method based on statistical analysis as the basic framework of measurement. However, the existing method still has the disadvantage that it is easily affected by the disturbance of acceleration at the end of the walking period and the accuracy is temporarily reduced. The embodiment described below is intended to improve such a drawback.

[0059] Fig. 4 is a block diagram showing an example of a specific configuration of the PDR processing unit 140 according to an embodiment. Referring to Fig. 4, the PDR processing unit 140 includes a data acquisition unit 141, a gait analysis unit 142, a stride length estimation unit 143, a direction determination unit 144, and a movement amount calculation unit 145.

[0060] <4-1. Data Acquisition Section> The data acquisition unit 141 acquires data samples of sensor data output in a time series manner from the sensor group 130 disposed in the tag reader 110. The data samples acquired by the data acquisition unit 141 include at least samples of acceleration data indicating three-axis acceleration in a device coordinate system measured by an acceleration sensor. The data samples acquired by the data acquisition unit 141 may further include samples of orientation data indicating an absolute orientation measured by a geomagnetic sensor and / or angular velocity data indicating an angular velocity measured by a gyro sensor. The data acquisition unit 141 may convert the coordinate system of the acceleration data from the device coordinate system to the coordinate system of the real space using the orientation data and / or the angular velocity data according to any known coordinate conversion method. The data acquisition unit 141 sequentially outputs the data samples that have been coordinate-converted as necessary to the gait analysis unit 142.

[0061] In the following description, the coordinate system of the real space is assumed to consist of an X-axis and a Y-axis that form a horizontal plane, and a Z-axis that is perpendicular to the X-axis and the Y-axis (i.e., extends vertically). The X-axis and the Y-axis may be associated with a specific geographical orientation, for example, the positive direction of the X-axis may be east, and the positive direction of the Y-axis may be north. When a constraint is introduced that the tag reader 110 is held by a pedestrian in a specific posture, it may be assumed that a specific coordinate axis of the device coordinate system coincides with any axis of the real space. For example, when a pedestrian walks with the tag reader 110 attached to his chest or waist, it may be assumed that the longitudinal axis of the tag reader 110 coincides with the Z-axis of the coordinate system of the real space (ignoring a slight error). Such constraints can avoid the complexity of the coordinate conversion algorithm and reduce the computation load.

[0062] <4-2. Gait Analysis Section> The gait analysis unit 142 analyzes a series of acceleration data samples to determine the state of the pedestrian, which may change from moment to moment. In this embodiment, the state of the pedestrian is assumed to be one of the following three possible states: ·Stopped Ascending phase (walking) -Descent phase (walking)

[0063] "Stopping" is a state where the pedestrian is not walking. "Upward phase" is a state where the pedestrian is walking and the acceleration acting on tag reader 110 is on an upward trend. "Downward phase" is a state where the pedestrian is walking and the acceleration acting on tag reader 110 is on a downward trend. Typically, each step of a pedestrian's walking consists of a period in which one foot is swung up from the ground until the pedestrian's center of gravity reaches the highest point, and a period in which one foot is swung down and lands, and then the other foot is swung up from the ground. In the former period, the inertial force of the center of gravity acts in the same direction as the gravitational acceleration, increasing the acceleration, so the pedestrian's state is the "upward phase". In the latter period, the inertial force of the center of gravity acts in the opposite direction to the gravitational acceleration, decreasing the acceleration, so the pedestrian's state is the "downward phase".

[0064] 5 is a state transition diagram showing an example of the transition conditions between the three state candidates described above. In the diagram, state S1 represents "stopping", state S2 represents "falling phase", and state S3 represents "rising phase".

[0065] When the state of the pedestrian is “stopping-S1”, the gait analysis unit 142 detects whether the acceleration indicated by the acceleration data sample (for example, the resultant acceleration of three axes) is greater than or equal to a first threshold value TH 1 When the first time length ΔT 1 time has elapsed and the acceleration indicated by the acceleration data samples exceeds a second threshold TH 2 When the state of the pedestrian falls below the first time length ΔT 1 time has elapsed and the acceleration indicated by the acceleration data samples exceeds a first threshold value TH 1 When the pedestrian's state exceeds the second time length ΔT 2After the second time length ΔT 2 is the first time length ΔT 1 longer than (ΔT 1 <ΔT 2 ).

[0066] In the following description, a period in which the state of the pedestrian thus determined is "stopped" is referred to as a stop period, and a period in which the state is an "upward phase" or a "downward phase" is referred to as a walking period. Typically, during a walking period, the state of the pedestrian repeats transitions from an "upward phase" to a "downward phase" and from a "downward phase" to an "upward phase". According to the transition conditions shown in FIG. 5, the beginning of a walking period is always a "downward phase". Therefore, in this embodiment, a pair of a "downward phase" and a subsequent "upward phase" is regarded as constituting one step of the pedestrian's walking, and a period including them is referred to as a step period. One walking period consists of one or more step periods.

[0067] As can be understood from the above explanation, the end of the walking period is determined by the second time length ΔT 2 The second time length ΔT 2 If a "downward period" ends due to the passage of time, then this "downward period" may be retroactively replaced with "stopped" since there is no corresponding "upward period" to pair with it.

[0068] 6 is an explanatory diagram of an example of the determination of a pedestrian's state by gait analysis unit 142. The horizontal axis of the illustrated graph 146 is the time axis, and the vertical axis represents the magnitude of resultant acceleration α indicated by acceleration data samples. Since a constant gravitational acceleration is always acting on tag reader 110, resultant acceleration α always contains a constant DC component, and fluctuations from the DC component occur due to walking movements.

[0069] The beginning of the graph 146 is the period D0. In the period D0, the state of the pedestrian is "stopping", that is, the period D0 is a stop period. 1When the first time length ΔT 1 After the time has elapsed, the magnitude of the resultant acceleration α exceeds the second threshold TH 2 When the walking distance falls below the first time length ΔT, the walking distance transitions to the “rising phase” and the period D1b begins. 1 After the time has elapsed again, the magnitude of the resultant acceleration α exceeds the first threshold TH 1 The step period of the first step of the walking period is made up of these periods D1a and D1b.

[0070] In the example of Fig. 6, the step period is followed by a second step period consisting of periods D2a and D2b, a third step period consisting of periods D3a and D3b, a fourth step period consisting of periods D4a and D4b, and a fifth step period consisting of periods D5a and D5b. 1 The process ends when the magnitude of the resultant acceleration α exceeds the second threshold TH 2 without falling below the second time length ΔT 2 Thus, the period D6 following the period D5b can be determined to be a stop period. As a result, the graph 146 shows a walking period consisting of a step period of five steps.

[0071] In other words, the analysis process executed by the gait analysis unit 142 can be said to be as follows: segmenting a walking period in which the pedestrian is walking into a number of step periods corresponding to respective steps of the pedestrian based on changes in acceleration indicated by the series of acceleration data samples; Based on the change in acceleration indicated by the series of acceleration data samples, each step period of the plurality of step periods is further divided into a descending period in which the acceleration is trending downward and an ascending period in which the acceleration is trending upward.

[0072] Upon completion of each step period, the gait analysis unit 142 passes a set of acceleration data samples corresponding to the step period to the stride estimation unit 143 to cause the stride estimation unit 143 to execute stride estimation. The gait analysis unit 142 also causes the direction determination unit 144 to determine the moving direction of the walker during each step period.

[0073] <4-3. Stride length estimation part> The stride estimating unit 143 estimates the stride of the pedestrian in each step period among a plurality of step periods constituting a walking period, based on a set of acceleration data samples corresponding to the step period. The stride estimating unit 143 may estimate the stride of the pedestrian in each step period according to any known stride estimating algorithm.

[0074] As an example, the stride estimation unit 143 calculates the stride L in the i-th step period according to the following calculation formula described in Non-Patent Document 1: STEP (i) can be inferred.

number

[0075] In equation (2), N represents the number of acceleration data samples acquired in the i-th step period, and k is the index of each acceleration data sample. zk is the vertical component (Z-axis component) of the k-th acceleration data sample. K is a coefficient that is determined in advance through experiments. The value of the coefficient K may be common within the system or may be different for each pedestrian.

[0076] The stride estimation unit 143 outputs the stride value estimated for each step period to the movement amount calculation unit 145.

[0077] <4-4. Direction Determination Unit> The direction determination unit 144 determines the walking direction of the walker in each step period among the multiple step periods constituting the walking period based on a statistical analysis of the set of acceleration data samples corresponding to the step period. Here, the set of acceleration data samples corresponding to a certain step period typically means a set including the acceleration data samples belonging to the step period. However, an exception is applied to the final step period in the walking period, which will be described later.

[0078] In this embodiment, the direction determination process executed by the direction determination unit 144 is roughly divided into a main direction determination process and a forward / backward selection process. The main direction determination process is a process for determining a main direction, which is the direction of acceleration (in a horizontal plane) that is predominant in a set of acceleration data samples corresponding to each step period (hereinafter referred to as a corresponding sample set). The forward / backward selection process is a process for selecting either the forward or backward direction of the determined main direction as the traveling direction of the pedestrian.

[0079] (1) Main direction determination process In the main direction determination process, the direction determination unit 144 determines the main direction for each step period by performing principal component analysis on the X, Y components of the acceleration data samples in the corresponding sample set. At this time, by using a set of acceleration data samples belonging to two consecutive step periods as the corresponding sample set, the effect of repeated fluctuations in the direction of travel between odd-numbered steps and even-numbered steps can be offset. For example, in the main direction determination process for the i-th step period, the set of acceleration data samples belonging to the i-1-th step period and the i-th step period can be used as the corresponding sample set. Of course, more consecutive step periods may be considered.

[0080] When principal component analysis is performed on the two-dimensional acceleration vectors in the horizontal plane (XY plane) indicated by the samples in the corresponding sample set, the eigenvectors obtained are the first principal component, the second principal component, ..., etc., in descending order of eigenvalue. Among these, the eigenvector of the first principal component with the largest eigenvalue indicates the dominant acceleration direction, i.e., the main direction.

[0081] 7A to 7C respectively show examples of the determination of the main direction based on a statistical analysis of the acceleration. In these figures, the two-dimensional acceleration represented by the acceleration data samples in the corresponding sample set for the step period under determination is plotted in the form of a scatter plot. The horizontal axis of the scatter plot represents the acceleration α X , the vertical axis is the Y-axis acceleration α Y represents the origin of zero acceleration.

[0082] In the example of Fig. 7A, the acceleration mainly varies in a direction parallel to the X-axis. The eigenvector E1 of the first principal component obtained as a result of the principal component analysis forms an angle of 0° with respect to the positive direction of the X-axis. This means that during the step period to be determined, the acceleration mainly in the forward (forward direction) or backward (reverse direction) of the eigenvector E1 acted on tag reader 110 predominantly, and therefore either of these directions was the walking direction of the pedestrian. In the figure, a vector E1' in the reverse direction is shown together with the eigenvector E1.

[0083] In the example of Fig. 7B, the acceleration mainly varies in a direction intermediate between the X-axis and the Y-axis. The eigenvector E2 of the first principal component obtained as a result of the principal component analysis forms an angle of 45° with respect to the positive direction of the X-axis. This means that during the step period to be determined, the acceleration mainly in the forward (forward direction) or backward (reverse direction) of the eigenvector E2 acted on the tag reader 110 predominantly, and therefore either of these directions was the walking direction of the pedestrian. In the figure, the reverse direction vector E2' is shown together with the eigenvector E2.

[0084] In the example of Fig. 7C, the acceleration mainly varies in a direction parallel to the Y-axis. The eigenvector E3 of the first principal component obtained as a result of the principal component analysis forms an angle of 90° with respect to the positive direction of the X-axis. This means that during the step period to be determined, the acceleration mainly in the forward (forward direction) or backward (reverse direction) of the eigenvector E3 acted on tag reader 110 predominantly, and therefore either of these directions was the walking direction of the pedestrian. In the figure, a vector E3' in the reverse direction is shown together with the eigenvector E3.

[0085] In the forward / backward selection process described next, the direction determination unit 144 selects one of the two candidates for the traveling direction determined by the main direction determination process (forward and backward of the eigenvector) as the traveling direction of the pedestrian.

[0086] (2) Front and rear selection process During the ascending phase of a step, when the acceleration is increasing, the walker raises one leg from the ground toward the desired direction. Therefore, the direction of the acceleration in the horizontal plane during the ascending phase is considered to be toward the traveling direction with relatively little disturbance. Therefore, the direction determining unit 144 extracts acceleration data samples belonging to the ascending phase from the corresponding sample set, and selects either the forward or backward main direction as the traveling direction based on the acceleration indicated by the extracted sample.

[0087] For example, the direction determination unit 144 derives the position of the center of gravity of a two-dimensional acceleration vector indicated by a sample in the corresponding sample set (hereinafter referred to as the first center of gravity) and the position of the center of gravity of a two-dimensional acceleration vector indicated by a sample belonging to the rising phase (hereinafter referred to as the second center of gravity). In a one-dimensional perspective of the main direction, if a vector from the first center of gravity to the second center of gravity (hereinafter referred to as the inter-center vector) points in the same direction as the eigenvector obtained by the main direction determination process, the direction determination unit 144 selects the forward direction of the eigenvector as the moving direction of the pedestrian. Conversely, in a one-dimensional perspective of the main direction, if the inter-center vector points in the opposite direction to the eigenvector, the direction determination unit 144 selects the rear of the eigenvector as the moving direction of the pedestrian. Here, whether the two vectors point in the same direction or in opposite directions in a one-dimensional perspective can be determined by determining whether the inner product of the two vectors is positive or negative.

[0088] 8A and 8B are explanatory diagrams for determining the traveling direction based on the position of the center of gravity of the acceleration vector in the ascending phase. In FIG. 8A, a scatter diagram similar to that in FIG. 7A is shown again, except that the black circles in the figure represent samples in the descending phase in the corresponding sample set, and the white circles represent samples in the ascending phase in the corresponding sample set. The symbol G1 represents the position of the center of gravity of the acceleration vectors represented by all samples in the corresponding sample set, that is, the first center of gravity. The symbol G2 represents the position of the center of gravity of the acceleration vectors represented by samples belonging to the ascending phase in the corresponding sample set, that is, the second center of gravity. When the eigenvector E1 is placed on the XY plane with the first center of gravity G1 as the starting point, the second center of gravity G2 is on the positive side of the eigenvector E1. Therefore, in this case, the direction determination unit 144 selects the front of the eigenvector E1 as the traveling direction of the pedestrian. When the positive direction of the X-axis is used as a reference, the traveling direction θ can be determined to be θ=0°.

[0089] In the example of FIG. 8B, which samples in the corresponding sample set are samples in the rising phase differs from the example of FIG. 8A, and therefore the position of the second center of gravity G2 differs. When the eigenvector E1 is placed on the XY plane with the first center of gravity G1 as the starting point, the second center of gravity G2 is on the negative side of the eigenvector E1. Therefore, in this case, the direction determination unit 144 selects the rear of the eigenvector E1 (vector E1') as the traveling direction of the pedestrian. When the positive direction of the X axis is used as a reference, the traveling direction θ can be determined to be θ=180°.

[0090] The direction determining unit 144 outputs the value of the proceeding direction θ determined in this manner for each step period to the movement amount calculating unit 145.

[0091] (3) Exceptions during the final step The inventors have found that when the above-mentioned main direction determination process and forward / backward selection process are performed for the final step period at the end of a walking period, the probability of the pedestrian's moving direction being erroneously determined is significantly higher than when the moving direction is determined for other step periods. The reason why the probability of erroneous determination is high for the final step period is considered to be due to the disturbance of posture when the pedestrian tries to stop walking and the accompanying irregular change in acceleration. For example, if the moving direction that should be forward is erroneously selected as backward in the forward / backward selection process (or vice versa), a measurement error of the movement amount equivalent to twice the stride length in the final step period occurs.

[0092] Therefore, in this embodiment, when determining the moving direction of a walker in a final step period in a walking period, the direction determination unit 144 uses a set of acceleration data samples that does not belong to the final step period but belongs to an earlier alternative step period as a corresponding sample set. As an example, the corresponding sample set based on the alternative step period may be used only in the forward / backward selection process (selection of either forward or backward in the main direction) for the final step period. As another example, the corresponding sample set based on the alternative step period may be used in both the main direction determination process and the forward / backward selection process for the final step period.

[0093] The alternative step period may be, for example, two consecutive step periods immediately preceding the final step period. By using a set of acceleration data samples belonging to such an immediately preceding alternative step period as the corresponding sample set, the direction of travel can be approximately determined with good accuracy while avoiding the influence of disturbances in the pedestrian's posture and irregular changes in acceleration in the final step period. Note that, if a walking period consists of only one or two step periods, two step periods earlier than the final step period cannot be set as alternative step periods. In this case, the exceptions in the final step period described in this section do not need to be applied. The following description is based on the assumption that a walking period consists of three or more step periods.

[0094] Three different embodiments of the process associated with determining the heading using the acceleration data sample set during the substitution step will now be described with reference to the timing charts of FIGS. 9A to 11. FIG.

[0095] In the first embodiment, the direction determination unit 144 uses the following sets of acceleration data samples as corresponding sample sets in the main direction determination process and the front / rear selection process: -For non-final step periods -Main direction determination process... A set of acceleration data samples belonging to M consecutive step periods including the step period in question (hereinafter referred to as a normal corresponding sample set). Here, M is a predetermined integer equal to or greater than 2, and is advantageously an even number (for example, M=2). -Previous and next selection process...Normal corresponding sample set -About the final step period -Main direction determination process ... normal corresponding sample set -Previous / Next Selection Process... Set of acceleration data samples belonging to alternative step periods

[0096] The direction determination unit 144 executes the main direction determination process and the front / rear selection process for each even-numbered step period for non-final step periods. For the final step period, regardless of whether it is an odd-numbered step or an even-numbered step, the direction determination unit 144 executes the main direction determination process and the front / rear selection process after the final step period has elapsed.

[0097] Each time one step period elapses, the stride estimating unit 143 estimates the stride of the walker in that step period based on the acceleration indicated by the acceleration data sample belonging to that step period.

[0098] The timing chart of Fig. 9A shows a scenario in which the walking period consists of an even number of step periods (specifically, four step periods) in Example 1. The timing chart of Fig. 9B shows a scenario in which the walking period consists of an odd number of step periods (specifically, five step periods) in Example 1.

[0099] In each timing chart, the top row shows step periods constituting one walking period along the time axis. The second row shows pairs of descending and ascending periods constituting each step period. The third row shows the range of sample sets used for stride estimation in each step period with bars extending along the time axis. The fourth row shows the timing of the stride estimation process P1(i) performed by the stride estimation unit 143. Here, i is the index of the step period. The fifth row shows the range of corresponding sample sets used in the main direction determination process with bars extending along the time axis. The sixth row shows the timing of the main direction determination process P2(i) performed by the direction determination unit 144. The seventh row shows the range of corresponding sample sets used in the front / back selection process with bars extending along the time axis. Note that the shaded portion of the bar in the seventh row represents acceleration data samples belonging to the ascending period. The eighth row shows the timing of the front / back selection process P3(i) performed by the direction determination unit 144. The dashed arrows in the figure represent the correspondence between each process and the sample set referenced in that process.

[0100] As can be seen from FIG. 9A and FIG. 9B, in the first embodiment, in the main direction determination process P2(j) executed after the end of the jth step period, a set of acceleration data samples belonging to the j-1th and jth step periods is used as a corresponding sample set. Here, j is an even number or an index of the final step period. The same corresponding sample set is also usually used in the front-back selection process P3(j) executed following the main direction determination process P2(j). However, in the front-back selection process P3(j) of the final step period, the j-2th and j-1th step periods are set as alternative step periods. For example, in the scenario of FIG. 9A, when focusing on the final step period (j=4), the front-back selection process P3(4) uses a set 171 of data samples belonging to the second and third step periods as a corresponding sample set, not the normal corresponding sample set 170. 9B, focusing on the final step period (j=5), the front-back selection process P3(5) uses a set 173 of data samples belonging to the third and fourth step periods as the corresponding sample set, rather than the normal corresponding sample set 172. The cross marks in the figure indicate that the acceleration data samples belonging to the final step period are not used in the front-back selection process to derive at least the second center of gravity position.

[0101] In the second embodiment, the direction determination unit 144 uses the following sets of acceleration data samples as corresponding sample sets in the main direction determination process and the front / rear selection process: -For non-final step periods -Main direction determination process ... normal corresponding sample set -Previous and next selection process...Normal corresponding sample set -About the final step period -Main direction determination process ... set of acceleration data samples belonging to alternative step periods -Previous / Next Selection Process... Set of acceleration data samples belonging to alternative step periods

[0102] The timing at which the direction determination unit 144 executes the main direction determination process and the front / rear selection process may be the same as in Example 1. The timing at which the stride estimation unit 143 executes the stride estimation process and the sample set used in the stride estimation process may be the same as in Example 1.

[0103] The timing chart of Fig. 10A shows a scenario in which the walking period consists of an even number of step periods (specifically, four step periods) in Example 2. The timing chart of Fig. 10B shows a scenario in which the walking period consists of an odd number of step periods (specifically, five step periods) in Example 2.

[0104] In the second embodiment, in both the main direction determination process P2(j) and the front-rear selection process P3(j), the sets of samples belonging to the j-1th and jth step periods are usually used as the corresponding sample sets. However, in the final step period, the j-2th and j-1th step periods are set as the alternative step periods for the main direction determination process P2(j) and the front-rear selection process P3(j). For example, in the scenario of FIG. 10A, when the final step period (j=4) is focused on, the front-rear selection process P3(4) uses the exceptional corresponding sample set 171 based on the alternative step period, not the normal corresponding sample set 170. Similarly, the main direction determination process P2(4) also uses the exceptional corresponding sample set 181 based on the alternative step period, not the normal corresponding sample set 180. Also, in the scenario of FIG. 10B, when the final step period (j=5) is focused on, the front-rear selection process P3(5) uses the exceptional corresponding sample set 173 based on the alternative step period, not the normal corresponding sample set 172. Similarly, the main direction determination process P2(5) also uses an exceptional corresponding sample set 183 based on an alternative step period, rather than the normal corresponding sample set 182.

[0105] In the third embodiment, the direction determination unit 144 executes the main direction determination process and the front / rear selection process every time one step period elapses. In the main direction determination process and the front / rear selection process, the direction determination unit 144 uses the following sets of acceleration data samples as corresponding sample sets: -For non-final step periods -Main direction determination process ... normal corresponding sample set (however, for the first step period, the normal corresponding sample set includes only acceleration data samples belonging to that step period) -Previous and next selection process...Normal corresponding sample set -About the final step period -Main direction determination process ... normal corresponding sample set -Previous / Next Selection Process... Set of acceleration data samples belonging to alternative step periods As in the second embodiment, in the main direction determination process in the final step period, an exceptional corresponding sample set based on an alternative step period may be used instead of a normal corresponding sample set.

[0106] The timing at which the stride estimation unit 143 executes the stride estimation process and the sample set used in the stride estimation process may be similar to those in the first and second embodiments.

[0107] The timing chart of FIG. 11 shows a scenario in which the walking period consists of an even number of step periods (specifically, four step periods) in the third embodiment. As can be seen from FIG. 11, in the third embodiment, in the main direction determination process P2(i) executed after the end of the i-th step period, a set of acceleration data samples belonging to the i-1-th and i-th step periods is used as a corresponding sample set. Here, i is an integer index. The same corresponding sample set is also usually used in the front-back selection process P3(i) executed following the main direction determination process P2(i). However, in the front-back selection process P3(i) of the final step period, the i-2-th and i-1-th step periods are set as alternative step periods. For example, in the scenario of FIG. 11, when focusing on the final step period (j=4), the front-back selection process P3(4) uses a set 171 of data samples belonging to the second and third step periods as a corresponding sample set, not the normal corresponding sample set 170.

[0108] As in the above-mentioned embodiment, when determining the travel direction in the final step period, the probability of erroneously determining the travel direction can be reduced by referring to the data samples belonging to the alternative step period instead of the data samples belonging to the final step period. In particular, if an alternative step period is set and an accurate selection is made at least in the selection of forward or backward in the main direction, it is possible to avoid the occurrence of a measurement error of the movement amount equivalent to twice the stride length in the final step period. When an alternative step period is not set in the determination of the main direction as in the first and third embodiments, the possibility of capturing the actual change in the travel direction in the final step period is increased. On the other hand, when an alternative step period is set also in the determination of the main direction as in the second embodiment, it is possible to avoid the influence of the disturbance of the pedestrian's posture and the irregular change in acceleration on the determination in the final step period.

[0109] <4-5. Movement amount calculation section> The movement amount calculation unit 145 calculates the movement amount of the tag reader 110 during the walking period based on the stride estimated by the stride estimation unit 143 for each step period and the moving direction determined by the direction determination unit 144. For example, the stride estimated by the stride estimation unit 143 for the i-th step period is expressed as L STEP (i) The moving direction determined by the direction determination unit 144 is defined as θ(i). θ(i) may be an angle with respect to some reference direction in the horizontal plane (for example, the positive direction of the X-axis). Then, the moving amount vector A in the i-th step period is MOVE (i) can be derived according to the following formula:

number

[0110] The movement amount calculation unit 145 calculates the movement amount vector A MOVE By integrating (i) over the walking period, the relative movement amount over the walking period can be calculated. The movement amount calculation unit 145 may consider that the pedestrian is not moving during the stop period (i.e., the movement amount vector is zero). For example, the movement amount calculation unit 145 calculates the movement amount vector every time the walking analysis unit 142 detects the walking of the pedestrian, and updates the relative movement amount of the tag reader 110 from the reference position. Then, the PDR processing unit 140 outputs to the control unit 111, as a measurement result of the movement amount of the tag reader 110, a two-dimensional vector (or a three-dimensional vector including a height component based on the atmospheric pressure data) indicating the relative movement amount updated by the movement amount calculation unit 145.

[0111] <5. Processing flow> In this section, examples of the flow of several processes that can be executed in the information management system 1 will be described with reference to the flowcharts of Figures 12A to 14. In the following description, processing steps will be abbreviated as S (step).

[0112] <5-1. Measurement of movement amount> (1) First Example Fig. 12A is a flowchart showing a first example of the flow of a movement amount measurement process executed by PDR processing unit 140 of measurement unit 114 of tag reader 110. The first example shown in Fig. 12A corresponds to the first and third embodiments described above.

[0113] First, in S11, the data acquisition unit 141 acquires a data sample of sensor data output from the sensor group 130 including the acceleration sensor. The data acquisition unit 141 converts data in a device coordinate system specific to the tag reader 110 into data in a coordinate system of real space, as necessary. The data acquisition unit 141 outputs the acquired (converted) data sample to the gait analysis unit 142.

[0114] Next, in S12, gait analysis unit 142 determines the state of the pedestrian carrying tag reader 110 based on the changes in acceleration indicated by the series of acceleration data samples. The pedestrian's state is classified into one of stopped, descending phase, and ascending phase, as described above.

[0115] Next, in S13, the gait analysis unit 142 determines whether the timing to perform stride length estimation has arrived based on the determined state of the walker. For example, when one step period consisting of a descending phase and an ascending phase has elapsed, the gait analysis unit 142 determines that the timing to perform stride length estimation has arrived, and the process proceeds to S14. When the timing to perform stride length estimation has not arrived, the process proceeds to S15.

[0116] In S14, the stride estimation unit 143 estimates the stride of the walker in the latest step period based on the set of acceleration data samples corresponding to the step period. The estimation here can be performed, for example, according to the above-mentioned equation (2).

[0117] In S15, the gait analysis unit 142 judges whether the timing to perform direction determination has arrived based on the judged state of the pedestrian. For example, in the above-mentioned first embodiment, the gait analysis unit 142 judges that the timing to perform direction determination has arrived when two consecutive step periods have passed or when the pedestrian who was walking has stopped. In the above-mentioned third embodiment, the gait analysis unit 142 judges that the timing to perform direction determination has arrived when one step period has passed. If it is judged that the timing to perform direction determination has arrived, the process proceeds to S21. If not, the process returns to S11, and the above-mentioned S11 to S15 are repeated.

[0118] In S21, the direction determination unit 144 acquires a set of acceleration data samples including samples belonging to the latest step period as a normal corresponding sample set from the gait analysis unit 142. Next, in S22, the direction determination unit 144 performs principal component analysis on the X and Y components of the acceleration data samples in the acquired normal corresponding sample set to determine a main direction indicating a dominant acceleration direction.

[0119] The process then branches in S23 depending on whether the latest step period is the final step period in the walking period. If the latest step period is the final step period, the process proceeds to S24. If the latest step period is not the final step period, S24 is skipped.

[0120] In S24, the direction determining unit 144 acquires a set of acceleration data samples belonging to an alternative step period not including the final step period as an exceptional corresponding sample set, instead of the normal corresponding sample set.

[0121] Next, in S26, the direction determination unit 144 selects either the forward or backward direction of the main direction determined in S22 as the pedestrian's traveling direction based on the center of gravity position of the ascending phase acceleration data samples in the corresponding sample set.

[0122] Next, in S27, the movement amount calculation unit 145 calculates a movement amount vector indicating the movement amount of the walker in the latest step period, based on the stride estimated by the stride length estimation unit 143 and the moving direction determined by the direction determination unit 144. The calculation of the movement amount here may be performed, for example, according to the above-mentioned formula (3). Note that, when the direction determination process is performed each time two consecutive step periods elapse, a movement amount vector may be calculated for each of those step periods.

[0123] Next, in S28, movement amount calculation section 145 uses the movement amount vector calculated in S27 to update the relative movement amount of tag reader 110 from the reference position. After that, the process returns to S11.

[0124] (2) Second Example Fig. 12B is a flowchart showing a second example of the flow of the movement amount measurement process executed by PDR processing unit 140 of measurement unit 114 of tag reader 110. The example shown in Fig. 12B corresponds to the second embodiment described above.

[0125] S11 to S15 are the same processing steps as S11 to S15 of the movement amount measurement processing described with reference to Fig. 12A, and therefore the description thereof will be omitted here. If it is determined in S15 that the timing for determining the direction has arrived, the processing proceeds to S20.

[0126] The movement amount measurement process of Fig. 12B branches depending on whether the latest step period is the final step period in the walking period in S20. If the latest step period is not the final step period, the process proceeds to S21. If the latest step period is the final step period, the process proceeds to S24.

[0127] In S21, the direction determining unit 144 acquires, from the gait analyzing unit 142, a set of acceleration data samples including samples belonging to the latest step period as a normal corresponding sample set.

[0128] On the other hand, in S24, the direction determining unit 144 acquires a set of acceleration data samples that belong to an alternative step period that does not include the final step period as an exceptional corresponding sample set.

[0129] Next, at S25, the direction determination unit 144 determines a principal direction that indicates the predominant acceleration direction by performing a principal component analysis on the X, Y components of the acceleration data samples in the corresponding sample set obtained at S21 or S24.

[0130] Next, in S26, the direction determination unit 144 selects either the forward or backward direction of the main direction determined in S25 as the pedestrian's traveling direction based on the center of gravity position of the ascending phase acceleration data samples in the corresponding sample set.

[0131] The subsequent steps S27 and S28 are the same processing steps as S27 and S28 of the movement amount measurement processing described with reference to FIG. 12A, and therefore description thereof will be omitted here.

[0132] <5-2. Data transmission process> FIG. 13 is a flowchart showing an example of the flow of a data transmission process executed by the mobile system 100.

[0133] First, in S31, reading unit 116 of tag reader 110 attempts to read a tag ID from a nearby RFID tag by emitting electromagnetic waves within the reading range. If the result of the tag reading attempt is that a tag ID is received from a nearby RFID tag using electromagnetic wave energy (S32-YES), the process proceeds to S33. On the other hand, if a tag ID is not received (S32-NO), the process proceeds to S35.

[0134] In S33, control unit 111 acquires the current time as the tag ID read time, for example by referring to an internal real-time clock. Next, in S34, control unit 111 transmits read result data including the read tag ID, read time, reception level, and reader ID of tag reader 110 to management server 200 via communication unit 113. Then, the process proceeds to S35.

[0135] In S35, the control unit 111 determines whether the user's walking has been detected by the measurement unit 114. If the user's walking has not been detected, the process returns to S31. If the user's walking has been detected by the measurement unit 114, the process proceeds to S36.

[0136] In S36, control unit 111 acquires the latest value of the relative movement amount of tag reader 110 measured by measurement unit 114. Next, in S37, control unit 111 acquires the current time as the measurement time. Next, in S38, control unit 111 transmits measurement result data including the relative movement amount measured by measurement unit 114, the measurement time, and the reader ID of tag reader 110 to management server 200 via communication unit 113.

[0137] Thereafter, the process returns to S31. Such data transmission process may be repeated while tag reading attempts are activated in the portable system 100.

[0138] <5-3. Location estimation process> Fig. 14 is a flowchart showing an example of the flow of the position estimation process executed by the management server 200. It is assumed that some measurement result records are stored in the movement amount table 360 ​​and some reading result records are stored in the tag detection table 370 at the time when the position estimation process of Fig. 14 is started.

[0139] First, in S41, the position estimation unit 232 of the management server 200 focuses on one management target and acquires a read result record for the target tag 50 attached to that management target from the tag detection table 370. Next, in S42, the position estimation unit 232 extracts read result records for one or more position tags 40 received from the same tag reader 110 as the acquired read result record from the tag detection table 370. Next, in S43, the position estimation unit 232 selects one reference position tag based on the correlation between the read result record for the target tag 50 and the read result records for one or more position tags 40.

[0140] Next, in S44, the position estimation unit 232 calculates the relative movement amount of the tag reader 110 between the read time (first read time) of the target tag 50 and the read time (second read time) of the reference position tag by referring to the measurement result record of the movement amount table 360. Next, in S45, the position estimation unit 232 estimates the location of the managed object of interest based on the calculated relative movement amount of the tag reader 110 and the known position of the reference position tag. The position estimation unit 232 can estimate the location of the managed object according to, for example, the above-mentioned formula (1).

[0141] Next, in S46, the location estimation unit 232 determines that the area associated with the reference location tag in the location tag table 330 is the location area of ​​the managed object of interest.

[0142] Then, in S47, the location estimation unit 232 adds a location information record indicating the target ID of the management target, the location coordinates of the estimated location, the estimation error, the location area, and related information to the location information table 380 of the database 220.

[0143] The location estimation unit 232 may repeat the above-mentioned process, focusing on each of one or more managed objects that may have moved within a certain period of time. By periodically executing such a process, location information reflecting the latest state of each managed object may be maintained in the database 220. The location information maintained in the database 220 may be provided to the user on the screen of the user terminal 105 by the display control unit 233, as described above.

[0144] <6. Variations> Various modifications of the above-described embodiment are conceivable. For example, the direction determination unit 144 may apply other analysis methods, such as regression analysis, to the corresponding sample set in the main direction determination process, instead of principal component analysis. The period covered by the corresponding sample set may be any number of step periods, which may be two or more. The conditions for determining the walking state of a pedestrian may be different from the conditions described using FIG. 5. One step period may be composed of an ascending period followed by a descending period, instead of being composed of a descending period followed by an ascending period.

[0145] In this specification, an example has been mainly described in which the PDR processing unit 140 of the measurement unit 114 of the tag reader 110 performs a calculation for PDR based on a series of acceleration data samples output from the sensor group 130, but the technology according to the present disclosure is not limited to such an example. For example, the communication unit 210 of the management server 200 may receive a series of acceleration data samples from the tag reader 110, and the processing circuit of the management unit 230 may perform a calculation for PDR. Similarly, the communication interface of the user terminal 105 may receive a series of acceleration data samples from the tag reader 110, and the processing circuit may perform a calculation for PDR. Regardless of whether the tag reader 110 performs the calculation for PDR locally or an external device communicating with the tag reader 110 performs the calculation for PDR, the calculation may be implemented by any combination of software and hardware. When software implementation is adopted, a computer program including a program code for causing an information processing device to perform the movement amount measurement process exemplified in FIG. 12A and FIG. 12B is stored in a computer-readable storage medium and executed by a processing circuit of the information processing device.

[0146] <7. Summary> Various embodiments, examples, and modifications of the technology according to the present disclosure have been described in detail above with reference to FIGS. 1 to 14. In the above-described embodiment, as part of the PDR method, the direction of travel of a pedestrian in each step period in a walking period is determined based on a series of acceleration data samples from an acceleration sensor on a mobile terminal. When determining the direction of travel, for a step period that is not the last in the walking period, a normal sample set including acceleration data samples belonging to the step period is used. On the other hand, for a final step period in the walking period, an exceptional sample set not including acceleration data samples belonging to the final step period is used. The exceptional sample set is a set of acceleration data samples belonging to an alternative step period earlier than the final step period. This configuration reduces the possibility that the direction of travel is erroneously determined due to disturbance in the pedestrian's posture and irregular changes in acceleration in the final step period, and improves the accuracy of position estimation by the PDR method. In addition, by adopting such a method of determining the direction of travel, it is possible to reduce errors in self-location estimation that does not rely on GPS positioning, and provide highly accurate position information for an object managed in an environment where external communication is difficult, such as indoors, underground, or inside a tunnel.

[0147] <8. Other embodiments> The above-mentioned embodiment can also be realized in the form of a process in which a program for realizing one or more functions is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., ASIC) that realizes one or more functions.

[0148] The disclosure of this specification includes at least the following information processing device, computer program, and information management system. (Item 1) An information processing device that processes a series of acceleration data samples output in a time series manner from an acceleration sensor on a mobile terminal carried by a pedestrian, a gait analysis unit that divides a walking period during which the pedestrian is walking into a plurality of step periods corresponding to each of the pedestrian's steps based on a change in acceleration indicated by the series of acceleration data samples; A traveling direction of the pedestrian in each step period among the plurality of step periods is determining a dominant direction that is the direction of acceleration that predominates in the set of acceleration data samples that corresponds to the step period; and Selecting either the forward direction or the backward direction in the determined main direction as the traveling direction of the pedestrian; a direction determining unit for determining the direction of the Equipped with the direction determination unit, when determining the traveling direction of the pedestrian in a final step period within the walking period, selects either forward or backward in the main direction determined for the final step period based on the acceleration indicated by the acceleration data sample belonging to an alternative step period earlier than the final step period, instead of the acceleration data sample belonging to the final step period. Information processing device. (Item 2) 2. The information processing device according to item 1, wherein the direction determination unit determines the main direction for each step period based on acceleration indicated by acceleration data samples belonging to two consecutive step periods. (Item 3) 3. The information processing device according to item 2, wherein the direction determination unit determines the main direction for each step period by performing a principal component analysis on acceleration data samples belonging to the two consecutive step periods. (Item 4) 4. The information processing device according to item 2 or 3, wherein the alternative step periods are two consecutive step periods immediately preceding the final step period. (Item 5) The information processing device according to any one of items 2 to 4, wherein the direction determination unit determines the main direction for the final step period based on acceleration indicated by acceleration data samples belonging to two consecutive step periods including the final step period. (Item 6) 5. The information processing device according to any one of items 2 to 4, wherein the direction determination unit determines the main direction for the final step period based on an acceleration indicated by an acceleration data sample belonging to the alternative step period. (Item 7) The gait analysis unit further divides each step period among the plurality of step periods into a descending period in which acceleration is in a descending trend and an ascending period in which acceleration is in an ascending trend based on the change in acceleration indicated by the series of acceleration data samples, the direction determination unit, when determining the traveling direction of the pedestrian in each step period, selects either the forward direction or the backward direction in the main direction determined for the step period based on the acceleration indicated by the acceleration data sample belonging to an upturn period; 7. The information processing device according to any one of items 1 to 6. (Item 8) The information processing device includes: a stride estimation unit that estimates a stride of the pedestrian in each step period of the plurality of step periods based on a set of acceleration data samples corresponding to the step period; a movement amount calculation unit that calculates a movement amount of the portable terminal during the walking period based on the traveling direction determined by the direction determination unit for each of the plurality of step periods and the stride estimated by the stride estimation unit; 8. The information processing device according to any one of items 1 to 7, further comprising: (Item 9) 9. The information processing device according to item 8, wherein the stride estimation unit estimates the stride of the pedestrian in the final step period based on the acceleration indicated by an acceleration data sample belonging to the final step period. (Item 10) 10. The information processing device according to any one of items 1 to 9, wherein the information processing device is a mobile terminal. (Item 11) The information processing device includes: a communication unit for receiving the series of acceleration data samples from the mobile device; 10. The information processing device according to any one of items 1 to 9, further comprising: (Item 12) When executed by a processing circuit of an information processing device, the information processing device Obtaining a series of acceleration data samples output in a time series manner from an acceleration sensor on a mobile device carried by a pedestrian; segmenting a walking period during which the pedestrian is walking into a number of step periods corresponding to respective steps of the pedestrian based on changes in acceleration indicated by the series of acceleration data samples; A traveling direction of the pedestrian in each step period among the plurality of step periods is determining a dominant direction that is the direction of acceleration that predominates in the set of acceleration data samples that corresponds to the step period; and Selecting either the forward direction or the backward direction in the determined main direction as the traveling direction of the pedestrian; and a computer program including a program code for causing the information processing device to: when determining the direction of travel of the pedestrian in a final step period within the walking period, selecting either forward or backward in the main direction determined for the final step period based on acceleration indicated by acceleration data samples belonging to an alternative step period earlier than the final step period, instead of acceleration data samples belonging to the final step period; A computer program that performs the following: (Item 13) An information management system for managing position information of a movable management object in a real space, comprising: a first wireless device that is assigned to the management target and stores first identification information; a second wireless device installed at a known position in the real space and storing second identification information; A reading device capable of reading identification information stored in a wireless device from the wireless device, the reading device including the information processing device according to item 8; a location estimation unit that estimates a location of the managed object based on a result of reading the first identification information from the first wireless device at a first read time by the reader and a result of reading the second identification information from the second wireless device at a second read time by the reader; Including, the reading device is the portable terminal, the location estimation unit estimates the location of the managed object based on a relative movement amount of the reader between the first read time and the second read time and the known location of the second wireless device. Information management system.

[0149] The invention is not limited to the above-described embodiments, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, the following claims are appended to apprise the public of the scope of the invention. [Explanation of symbols]

[0150] 1: information management system, 5: network, 20 (20a): user, 30 (30a, 30b, 30c): item, 40 (40a, 40b, 40c): position tag (second wireless device), 50 (50a, 50b, 50c): target tag (first wireless device), 100: mobile system, 105: user terminal (information processing device / mobile terminal), 110: tag reader (information processing device / mobile terminal), 114: measurement unit, 130: sensor group, 140: PDR processing unit, 141: data acquisition unit, 142: gait analysis unit, 143: stride length estimation unit, 144: direction determination unit, 145: movement amount calculation unit, 200: management server (information processing device), 210: communication unit, 232: position estimation unit

Claims

1. An information processing device that processes a series of acceleration data samples output in a time series manner from an acceleration sensor on a mobile terminal carried by a pedestrian, a gait analysis unit that divides a walking period during which the pedestrian is walking into a plurality of step periods corresponding to each of the pedestrian's steps based on a change in acceleration indicated by the series of acceleration data samples; A traveling direction of the pedestrian in each step period among the plurality of step periods is determining a dominant direction that is the direction of acceleration that predominates in the set of acceleration data samples that corresponds to the step period; and Selecting either the forward direction or the backward direction in the determined main direction as the traveling direction of the pedestrian; a direction determining unit for determining the direction of the Equipped with the direction determination unit, when determining the traveling direction of the pedestrian in a final step period within the walking period, selects either forward or backward in the main direction determined for the final step period based on the acceleration indicated by the acceleration data sample belonging to an alternative step period earlier than the final step period, instead of the acceleration data sample belonging to the final step period. Information processing device.

2. The information processing device of claim 1 , wherein the direction determining unit determines the main direction for each step period based on accelerations indicated by acceleration data samples belonging to two consecutive step periods.

3. The information processing device of claim 2 , wherein the direction determining unit determines the main direction for each step period by performing a principal component analysis on acceleration data samples belonging to the two consecutive step periods.

4. The information processing apparatus according to claim 2 , wherein the alternative step periods are two consecutive step periods immediately preceding the final step period.

5. The information processing device according to claim 2 , wherein the direction determining unit determines the main direction for the final step period based on accelerations indicated by acceleration data samples belonging to two consecutive step periods including the final step period.

6. The information processing device of claim 2 , wherein the direction determining unit determines the main direction for the final step period based on acceleration indicated by acceleration data samples belonging to the alternative step period.

7. The gait analysis unit further divides each step period among the plurality of step periods into a descending period in which acceleration is in a descending trend and an ascending period in which acceleration is in an ascending trend based on the change in acceleration indicated by the series of acceleration data samples, the direction determination unit, when determining the traveling direction of the pedestrian in each step period, selects either the forward direction or the backward direction in the main direction determined for the step period based on the acceleration indicated by the acceleration data sample belonging to an upturn period; The information processing device according to claim 1 .

8. The information processing device includes: a stride estimation unit that estimates a stride of the pedestrian in each step period of the plurality of step periods based on a set of acceleration data samples corresponding to the step period; a movement amount calculation unit that calculates a movement amount of the portable terminal during the walking period based on the traveling direction determined by the direction determination unit for each of the plurality of step periods and the stride estimated by the stride estimation unit; The information processing device according to claim 1 , further comprising:

9. The information processing device according to claim 8 , wherein the stride estimating unit estimates the stride of the walker in the final step period based on the acceleration indicated by an acceleration data sample belonging to the final step period.

10. The information processing device according to claim 1 , wherein the information processing device is a mobile terminal.

11. The information processing device includes: a communication unit for receiving the series of acceleration data samples from the mobile device; The information processing device according to claim 1 , further comprising:

12. When executed by a processing circuit of an information processing device, the information processing device Obtaining a series of acceleration data samples output in a time series manner from an acceleration sensor on a mobile device carried by a pedestrian; segmenting a walking period during which the pedestrian is walking into a number of step periods corresponding to respective steps of the pedestrian based on changes in acceleration indicated by the series of acceleration data samples; A traveling direction of the pedestrian in each step period among the plurality of step periods is determining a dominant direction that is the direction of acceleration that predominates in the set of acceleration data samples that corresponds to the step period; and Selecting either the forward direction or the backward direction in the determined main direction as the traveling direction of the pedestrian; and a computer program including a program code for causing the information processing device to: when determining the direction of travel of the pedestrian in a final step period within the walking period, selecting either forward or backward in the main direction determined for the final step period based on acceleration indicated by acceleration data samples belonging to an alternative step period earlier than the final step period, instead of acceleration data samples belonging to the final step period; A computer program that performs the following:

13. An information management system for managing position information of a movable management object in a real space, comprising: a first wireless device that is assigned to the management target and stores first identification information; a second wireless device installed at a known position in the real space and storing second identification information; A reading device capable of reading identification information stored in a wireless device from the wireless device, the reading device including the information processing device according to claim 8; a location estimation unit that estimates a location of the managed object based on a result of reading the first identification information from the first wireless device at a first read time by the reader and a result of reading the second identification information from the second wireless device at a second read time by the reader; Including, the reading device is the portable terminal, the location estimation unit estimates the location of the managed object based on a relative movement amount of the reader between the first read time and the second read time and the known location of the second wireless device. Information management system.

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