Method for collecting information on location of sports player in real time

Abstract

Provided is a method for collecting information on the location of a sports player in real time. The method comprises the steps of: acquiring information on the location of a player at a plurality of time points according to a predetermined time interval and at least one locationing state characteristic value on the basis of a locationing sensor, the locationing state characteristic value including information on the locationing state of the locationing sensor at a time point when the information on the location of the player is determined; and determining the validity of the information on the location of the player on the basis of the locationing state characteristic value obtained from the locationing sensor.

Description

A method for collecting information about the location of sports players in real time.

[0001] This disclosure relates generally to the analysis of sporting events, and more specifically, but not exclusively, to techniques for collecting information about the location of sports players in real time.

[0002] The explosive growth of the sports industry and the advancement of sports science are steadily increasing the importance of sports analytics. Amidst this trend, Electronic Performance Tracking Systems (EPTS) are increasingly being introduced, particularly in major sports like soccer, to track players during matches or training. In EPTS, the location and movements of players serve as crucial baseline data for providing various additional information. Therefore, various efforts are being made to facilitate the acquisition and improve the accuracy of tracking information on players' locations. For example, methods for determining object location based on the characteristics of signals measured by sensors corresponding to the object, such as the Global Positioning System (GPS), have been widely utilized.

[0003] Meanwhile, sports analysis and information provision have traditionally been primarily post-event analysis, collecting data obtained during a match, processing and analyzing it post-event, and providing information to users. However, recently, there has been a growing demand for real-time analysis of sports events and the provision of relevant information for various reasons, including to aid decision-making during a match or to control appropriate intensity during training.

[0004] The following provides a summary of specific embodiments disclosed in this disclosure. It should be understood that the aspects presented in the following summary are intended merely to provide a brief overview of specific embodiments and are not intended to limit the scope of this disclosure. Therefore, it should be noted in advance that this disclosure may include various aspects not presented below.

[0005] The present invention and the inventive concepts disclosed herein provide methods, devices, and computer-readable storage media for collecting information about the location of a sports player in real time.

[0006] An object of the present invention is to provide a method and apparatus for collecting information about the location of a sports player in real time to perform motion analysis in real time and provide relevant information.

[0007] In addition, one task of the present invention is to provide a procedure for collecting information on the location of a sports player in real time and determining the validity of the information on the location of the sports player based on information on the positioning status of the positioning sensor at the time of measuring the location information.

[0008] In addition, one task of the present invention is to provide a procedure for collecting information on the location of a sports player in real time, determining whether the target is moving based on an inertial sensor, and determining the validity of the information on the location of the sports player based on this.

[0009] In addition, one task of the present invention is to provide a procedure for obtaining real-time location information with improved accuracy by collecting information on the location of a sports player in real time and performing preprocessing using positioning information prior to the target time.

[0010] In addition, one task of the present invention is to provide a data processing procedure for post-analysis and / or real-time analysis that can ensure consistency between the information provided respectively according to post-analysis and real-time analysis.

[0011] However, the problem to be solved in this paper is not limited to this, and can be expanded in various ways without departing from the scope and idea of ​​this paper.

[0012] Embodiments include methods, devices, and computer-readable storage media for displaying player information in a motion video.

[0013] According to one embodiment, a method for collecting information about a location of a sports player in real time and determining the validity of the collected information, performed by a computing device, is provided. The method may include the steps of: obtaining, based on a positioning sensor, information about the location of the player at a plurality of points in time according to a predetermined time interval and at least one positioning state characteristic value, wherein the positioning state characteristic value includes information about the positioning state of the player at a point in time at which information about the location of the player is determined by the positioning sensor; and determining the validity of the information about the location of the player based on the positioning state characteristic value obtained from the positioning sensor.

[0014] According to another embodiment, a method for collecting information about a sports player in real time, performed by a computing device, and determining the validity of the data based on an inertial sensor is provided. The method may include the steps of collecting information about a sports player at a first time based on a first sensor; collecting information about whether there is movement of the inertial sensor at the first time based on the inertial sensor; and determining the validity of the information about the sports player based on the information about whether there is movement of the inertial sensor.

[0015] According to another embodiment, a method for collecting information about the location of a sports player in real time, performed by a computing device, is provided. The method may include: acquiring information about the location of the player at a plurality of points in time according to a predetermined time interval based on a positioning sensor; and determining processed location information for a target point in time based on information about the location of the player measured at a predetermined number of points in time prior to the target point in time.

[0016] The above exemplary embodiments and other exemplary embodiments will be explained or clarified by the detailed description set forth below of exemplary embodiments to be read in connection with the accompanying drawings.

[0017] The disclosed technology may have the following effects. However, this does not mean that a particular embodiment must include all or only the following effects, and therefore the scope of the disclosed technology should not be construed as being limited thereby.

[0018] According to an embodiment of the present disclosure, real-time movement analysis can be performed to provide relevant information by collecting information on the location of a sports player in real time.

[0019] In addition, by collecting information about the location of a sports player in real time and determining the validity of the information about the location of the sports player based on information about the positioning status of the positioning sensor at the time of measuring the location information, it is possible to filter out abnormal location information and improve the accuracy of the location information of the sports player.

[0020] Additionally, according to the present invention, the GPS ghosting problem, in which displacement is measured in a stationary device, can be solved by determining whether an object is moving based on an inertial sensor and determining the validity of information about the location of a sports player based on this.

[0021] In addition, according to the present invention, it is possible to obtain real-time location information with improved accuracy by performing pre-processing using positioning information prior to the target point in time.

[0022] In addition, according to the present disclosure, consistency between information provided by post-analysis and real-time analysis can be secured by appropriately designing data processing procedures for post-analysis and / or real-time analysis.

[0023] The above description of the invention is not intended to be an exhaustive list of all aspects of the invention. It should be understood that the invention encompasses all methods, devices, and systems capable of being implemented from all appropriate combinations of the various aspects disclosed in the detailed description and claims below, as well as those summarized above.

[0024] Figure 1 is an example of sensor-based location information acquisition.

[0025] FIG. 2 illustrates an exemplary system in which a method for collecting information about the location of a sports player in real time according to one embodiment of the present disclosure can be performed.

[0026] FIG. 3 is a block diagram of a sensor device that can be used to collect information about the location of a sports player according to one embodiment of the present disclosure.

[0027] Figure 4 is a block diagram of a server according to one embodiment of the present disclosure.

[0028] Figure 5 is a block diagram showing the configuration of an exemplary system for post-analysis of a sports event.

[0029] Figure 6 shows the configuration of an exemplary system for real-time analysis of a sports event.

[0030] FIG. 7 schematically illustrates a procedure for collecting information about the location of a sports player in real time according to one embodiment of the present disclosure.

[0031] Figure 8 is an example of missing value imputation according to one embodiment of the present invention.

[0032] Figure 9 is an example of preprocessing of data according to an embodiment of the present invention.

[0033] Figure 10 is an example of a Laplacian distribution.

[0034] FIG. 11 illustrates an exemplary structure of a live packet according to one embodiment of the present disclosure.

[0035] FIG. 12 is an example of preprocessing of speed information according to one embodiment of the present disclosure.

[0036] FIG. 13 is an example of preprocessing of acceleration information according to one embodiment of the present disclosure.

[0037] Figure 14 shows the results of position measurement of a stationary device using a conventional GPS device.

[0038] Fig. 15 shows the results of position measurement of a stationary device according to one embodiment of the present invention.

[0039] Figure 16 shows an example of abnormal measurements related to position determination.

[0040] Figure 17 illustrates an example of a step function.

[0041] Figure 18 illustrates an example of a Delta Function.

[0042] FIG. 19 illustrates a validity determination criterion for player location information according to one embodiment of the present disclosure.

[0043] FIG. 20 illustrates a threshold time length for determining validity of player location information according to one embodiment of the present disclosure.

[0044] FIG. 21 is a schematic flowchart of a procedure for determining the validity of information regarding the location of a sports player according to one embodiment of the present disclosure.

[0045] Figure 22 is a detailed flowchart of the validity determination step of Figure 21.

[0046] FIG. 23 is a schematic flowchart of a procedure for determining the validity of information regarding the position of a sports player based on an inertial sensor according to one embodiment of the present disclosure.

[0047] FIG. 24 is a schematic flowchart of a procedure for collecting information about the location of a sports player in real time based on preprocessing according to one embodiment of the present disclosure.

[0048] Figure 25 is a detailed flowchart of the processed location information determination step of the target point of Figure 24.

[0049] FIG. 26 is a block diagram showing an exemplary configuration of a computing system in which a method according to one embodiment of the present disclosure can be performed.

[0050] The present invention can be modified in various ways and has various embodiments, and specific embodiments are illustrated in the drawings and described in detail.

[0051] However, this is not intended to limit the present invention to a specific embodiment, but should be understood to include all modifications, equivalents, or substitutes included in the spirit and technical scope of the present invention.

[0052] While terms such as "first" and "second" may be used to describe various components, these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a "second component," and similarly, a second component may also be referred to as a "first component." The term "and / or" includes any combination of multiple related items described herein or any item among multiple related items described herein.

[0053] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0054] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0055] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0056] Hereinafter, with reference to the attached drawings, preferred embodiments of the present invention will be described in more detail. In order to facilitate an overall understanding in describing the present invention, identical reference numerals will be used for identical components in the drawings, and redundant descriptions of identical components will be omitted.

[0057] Since the embodiments described in this document are intended to clearly explain the idea of ​​the present invention to a person having ordinary skill in the art to which the present invention pertains, the present invention is not limited to the embodiments described in this document, and the scope of the present invention should be interpreted to include modified or altered examples that do not depart from the idea of ​​the present invention.

[0058] The terms used in this document are selected from commonly used terms in the technical field to which the present invention pertains. However, their meanings may vary depending on the intentions of those skilled in the art, customs, or the emergence of new technologies. However, if a specific term is defined and used with an arbitrary meaning, the meaning of that term will be described separately. Therefore, the terms used in this document should be interpreted based on their actual meaning and the overall content of this document, rather than simply their names.

[0059] The drawings attached to this document are intended to facilitate the explanation of the present invention, and the shapes depicted in the drawings may be exaggerated or abbreviated as necessary to help the understanding of the present invention, and therefore the present invention is not limited by the drawings.

[0060] In this document, if it is determined that a specific description of the composition or function of a public notice related to the present invention may obscure the gist of the present invention, a detailed description thereof will be omitted as necessary.

[0061]

[0062] A method for collecting information on the position of a sports player in real time according to one aspect of the present disclosure may include the steps of: obtaining, based on a positioning sensor, information on the position of the player at a plurality of points in time according to a predetermined time interval and at least one positioning state characteristic value, wherein the positioning state characteristic value includes information on the positioning state at a point in time at which information on the position of the player is determined by the positioning sensor; and determining the validity of the information on the position of the player based on the positioning state characteristic value obtained from the positioning sensor.

[0063] According to one aspect, the positioning sensor may be a Global Navigation Satellite System (GNSS) sensor.

[0064] According to one aspect, the positioning status characteristic value may include at least one of: information on the number of satellites used for positioning of the positioning sensor; information on the positioning mode of the positioning sensor; accuracy information on the vertical position of the positioning sensor; or information on the strength of a satellite signal received by the positioning sensor.

[0065] According to one aspect, the step of determining the validity of information about the position of the player may include: determining that a first condition for the positioning state characteristic value is not satisfied at a first time point; and, in response to a determination that the first condition is not satisfied continuously from the first time point to a second time point after a predetermined threshold time interval has elapsed, determining that information about the position of the player acquired after the second time point is invalid.

[0066] According to one aspect, the step of determining the validity of information about the player's location may further include a step of determining that information about the player's location obtained after a third time point when the first condition is satisfied for the first time after the second time point is valid.

[0067] According to one aspect, the first condition may be that all detailed conditions for the plurality of positioning state characteristic values ​​included in the first condition are satisfied.

[0068] According to one aspect, the detailed conditions may include whether the number of satellites used for positioning of the positioning sensor is 6 or more;

[0069] According to one aspect, the detailed conditions may further include whether the positioning mode of the positioning sensor is RTK-GPS mode or normal mode.

[0070] According to one aspect, the detailed conditions may further include whether the accuracy of the vertical position of the positioning sensor is 3 m or less;

[0071] According to one aspect, the detailed conditions may further include whether the strength of the satellite signal received by the positioning sensor is 20 dB or higher.

[0072] According to one aspect, the computing device may be a wearable device mounted on a sports player.

[0073] A device for collecting information on the location of a sports player in real time according to one aspect of the present disclosure comprises: a processor; and a memory; wherein the processor is configured to obtain, based on a positioning sensor, information on the location of the player at a plurality of points in time according to a predetermined time interval and at least one positioning status characteristic value, the positioning status characteristic value including information on the positioning status at a point in time at which the positioning sensor determines information on the location of the player; and determine the validity of the information on the location of the player based on the positioning status characteristic value obtained from the positioning sensor.

[0074] A computer-readable storage medium comprising instructions executable by a processor according to one aspect of the present disclosure, wherein the instructions are for collecting information about the location of a sports player in real time, and are configured to be executed by the processor to cause the processor to obtain, based on a positioning sensor, information about the location of the player at a plurality of points in time according to a predetermined time interval and at least one positioning status characteristic value, the positioning status characteristic value including information about the positioning status of the positioning sensor at a point in time at which information about the location of the player is determined; and determine the validity of the information about the location of the player based on the positioning status characteristic value obtained from the positioning sensor.

[0075] According to one aspect of the present disclosure, a method for collecting information about a sports player in real time may include: a step of collecting information about a sports player at a first point in time based on a first sensor; a step of collecting information about whether there is movement of the inertial sensor at the first point in time based on an inertial sensor; and a step of determining the validity of information about the sports player based on the information about whether there is movement of the inertial sensor.

[0076] According to one aspect, the first sensor is a Global Navigation Satellite System (GNSS) sensor, the information about the sports player is information about a speed of the sports player, and the step of determining the validity of the information about the sports player may be configured to change the information about the speed of the sports player to 0 in response to a determination that there is no movement of the inertial sensor and that the speed of the sports player is below a predetermined threshold.

[0077] According to one aspect, the first sensor is a heart rate (HR) sensor, the information about the sports player is information about a heart rate of the sports player, and the step of determining the validity of the information about the sports player may be configured to change the information about the heart rate of the sports player to 0 in response to a determination that there is no movement of the inertial sensor.

[0078] A device for collecting information on the location of a sports player in real time according to one aspect of the present disclosure comprises: a processor; and a memory; wherein the processor is configured to collect information on the sports player at a first point in time based on a first sensor; collect information on whether there is movement of the inertial sensor at the first point in time based on an inertial sensor; and determine the validity of the information on the sports player based on the information on whether there is movement of the inertial sensor.

[0079] A computer-readable storage medium comprising instructions executable by a processor according to one aspect of the present disclosure, wherein the instructions are for collecting information about a sports player in real time and are configured to be executed by the processor to cause the processor to: collect information about a sports player at a first point in time based on a first sensor; collect information about whether there is movement of the inertial sensor at the first point in time based on an inertial sensor; and determine validity of information about the sports player based on the information about whether there is movement of the inertial sensor.

[0080]

[0081] According to one aspect of the present disclosure, a method for collecting information on the location of a sports player in real time may include: a step of acquiring information on the location of the player at a plurality of points in time according to a predetermined time interval based on a positioning sensor; and a step of determining processed location information for a target point in time based on information on the location of the player measured at each of a predetermined number of points in time prior to the target point in time.

[0082] According to one aspect, the positioning sensor may be a Global Navigation Satellite System (GNSS) sensor.

[0083] According to one aspect, the information about the position of the player may include a speed value of the player.

[0084] According to one aspect, the processed position information for the target point in time can be determined independently of information about the position of the player measured after the target point in time.

[0085] According to one aspect, the processed location information for the target point in time can be determined by multiplying different weights by information about the player's location measured at each of the predetermined number of points in time and adding them up.

[0086] According to one aspect, the weights can be determined based on a Laplacian distribution or a Gaussian distribution.

[0087] According to one aspect, the computing device may be a wearable device mounted on a sports player.

[0088] According to one aspect, the predetermined number may be determined by considering the sampling period of the positioning sensor and the data transmission period of the computing device.

[0089] According to one aspect, the predetermined number may be determined so as not to cause a waiting time according to the data transmission cycle of the computing device by processing information about the location according to the sampling cycle of the positioning sensor.

[0090] According to one aspect, the predetermined number may be determined to be equal to the number of information about locations included in a single packet according to the data transmission cycle of the computing device.

[0091] According to one aspect, the step of obtaining information about the position of the player at the plurality of points in time may be configured to replace information about the position of the player for a first point in time that is missing based on the predetermined time interval with information about the position of the player for a point in time previous to the first point in time.

[0092] According to one aspect, the above missing may be caused by a mismatch between the sampling period of the positioning sensor and the predetermined time interval.

[0093] According to one aspect, the step of determining processed position information for the target point of view may include: determining the processed position information for the target point of view as a first error value in response to a determination that information about the position of the player measured based on the positioning sensor for the target point of view is invalid;

[0094] According to one aspect, the step of determining processed position information for the target point in time may further include the step of determining the processed position information for the target point in time as a second error value in response to a determination that at least one of the pieces of information about the position of the player measured at each of a predetermined number of points in time prior to the target point in time is invalid.

[0095] According to one aspect, the step of determining processed position information for the target point in time may further include a step of determining the processed position information for the target point in time as a third error value in response to a determination that information about the position of the player measured at a point in time prior to the target point in time does not exist in the predetermined number.

[0096] A device for collecting information on the location of a sports player in real time according to one aspect of the present disclosure comprises: a processor; and a memory; wherein the processor is configured to acquire information on the location of the player at a plurality of points in time according to a predetermined time interval based on a positioning sensor; and determine processed location information for a target point in time based on information on the location of the player measured at a predetermined number of points in time prior to the target point in time.

[0097] A computer-readable storage medium comprising instructions executable by a processor according to one aspect of the present disclosure, wherein the instructions are for collecting information about the location of a sports player in real time and are configured to be executed by the processor to cause the processor to obtain information about the location of the player at a plurality of points in time according to a predetermined time interval based on a positioning sensor; and determine processed location information for a target point in time based on information about the location of the player measured at each of a predetermined number of points in time prior to the target point in time.

[0098]

[0099] Obtain location information

[0100] As previously discussed, the explosive growth of the sports industry and the advancement of sports science are steadily increasing the importance of sports analytics. Amidst this trend, Electronic Performance Tracking Systems (EPTS) are increasingly being introduced, particularly in major sports like soccer, to track players during matches and training. In EPTS, the location and movements of players serve as crucial baseline data for providing a variety of additional information. Therefore, various efforts are ongoing to facilitate the acquisition and increase the accuracy of tracking information on players' locations.

[0101]

[0102] More specifically, data science has become a crucial tool in the sports industry, as it has in many other industries. The data primarily utilized in sports can be divided into event data and tracking data. Event data may include information about ball-related events that may occur during a sporting event, while tracking data may include information collected about the positions of individual players over a specific time scale.

[0103] In dynamic team sports like soccer, basketball, or ice hockey, player tracking data can provide rich information, such as player interactions and off-the-ball movements that may be overlooked in event data. In soccer, for example, such player tracking data can be used for a variety of applications, including formation and role estimation, spatial control analysis, playing style identification, and even false predictions.

[0104]

[0105] In recent years, different types of tracking systems have been proposed and successfully adopted for soccer matches to obtain such tracking data, such as the Global Positioning System (GPS), the Local Positioning System (LPS), or the Optical Tracking System (OTS) based on multiple cameras.

[0106] GPS has the advantage of being low cost and easy to install compared to other methods, but it is known to be more sensitive to the measurement environment, such as weather and stadium conditions. On the other hand, OTS can provide more accurate tracking information when equipped with a sufficient number of high-definition cameras positioned to surround the stadium from different angles. However, it is not easy to install OTS equipment in all stadiums, and it is often difficult to install OTS equipment in training stadiums or away stadiums that are not home stadiums. Moreover, when trying to implement OTS with a small number of cameras, low-quality raw data is obtained and a manual modification process is required, making it difficult to secure reliable tracking data.

[0107] Meanwhile, as previously mentioned, unlike the past, where post-event analysis of sports events was primarily performed, there is a growing demand for real-time sports analysis and real-time information provision. In this regard, embodiments of the present disclosure provide procedures for collecting information on a sports player's position in real time with enhanced accuracy for performing real-time sports analysis, and further provide procedures for enhancing the consistency between real-time sports analysis results and post-event analysis results.

[0108] Although the embodiments of this disclosure are primarily described based on the use of sensor-based location information, they are not limited thereto, and it should be understood that they can also be applied to a positioning method using images, except for procedures that are applicable only to sensor-based location information.

[0109]

[0110] Figure 1 is an example of sensor-based position information acquisition. As illustrated in Figure 1, a positioning method that calculates the player's location using sensor-based positioning devices, such as a GPS module, can be utilized.

[0111] Specifically, the positioning method using a GPS module calculates the position of the athlete to be tracked based on signals transmitted from satellites (4a, 4b, 4c, 4d). In this regard, signals transmitted from satellites may be affected by structures surrounding the athlete to be tracked.

[0112] For example, GPS signals transmitted from some satellites (4b, 4c) of FIG. 1 can be transmitted into the stadium without being affected by structures surrounding the player to be tracked. However, GPS signals transmitted from some satellites (4a, 4d) of FIG. 1 may be affected by structures surrounding the player to be tracked and may not reach the stadium. In this case, if the GPS signals transmitted from some satellites (4a, 4d) are affected by structures surrounding the player, the position of the player calculated from the GPS signals may have an error.

[0113] In post-event analysis of sports events, various mechanisms have been applied to account for such errors. However, since real-time sports analysis requires corrections for errors to be performed in real time, the various mechanisms used in post-event analysis cannot be applied as-is. The embodiments of this disclosure disclose procedures for determining the validity of collected data while performing real-time analysis.

[0114] For example, according to an embodiment of the present disclosure, by collecting information on the location of a sports player in real time, a motion analysis is performed in real time to provide related information, and by determining the validity of the information on the location of the sports player based on information on the positioning status of the positioning sensor at the time of measuring the location information, abnormal location information can be filtered out, thereby improving the accuracy of the location information of the sports player.

[0115] Additionally, according to one embodiment of the present disclosure, the GPS ghosting problem, in which displacement is measured to have occurred in a stationary device, can be solved by determining whether there is movement of a target device based on an inertial sensor and determining the validity of information about the location of a sports player based on the same.

[0116] In addition, according to one embodiment of the present disclosure, it is possible to obtain real-time location information with improved accuracy by performing pre-processing using positioning information prior to the target time point.

[0117] Additionally, according to one embodiment of the present disclosure, consistency between information provided by post-analysis and real-time analysis can be secured by appropriately designing data processing procedures for post-analysis and / or real-time analysis.

[0118]

[0119] In this description, for convenience of explanation, a global positioning mechanism such as GPS may be disclosed as a sensor-based location information acquisition procedure; however, this is merely exemplary and it should be noted that information regarding the location of a player according to embodiments of this description is not limited thereto.

[0120]

[0121] Terminology

[0122] Certain technical terms may be used in this document, and the following provides a definition of the terms used in this document to establish definitional support for the terms used in this document.

[0123] The following is a summary of preferred definitions of some terms used in this document. The definitions provided below are provided for illustrative purposes only and are not intended to be exhaustive or limiting.

[0124]

[0125] The term "image-based location information" may refer to information about the location of an object determined using an image containing the object. The image-based location information may include information about the location of at least one object determined from a captured image of one or more objects. For example, the image-based location information may include information about the location of each player determined by analyzing video of a team sporting event involving multiple players, but is not limited thereto. The image-based location information should be interpreted in a comprehensive sense, including location information obtained from captured images of any type of object.

[0126] The term "sensor-based location information" may refer to information about the location of an object determined using signals from sensors corresponding to the object. The sensor-based location information may include information about the location or displacement of at least one object determined based on signals from sensors corresponding to each of one or more objects. For example, it may include, but is not limited to, location information acquired through positioning solutions such as a Global Navigation Satellite System (GNSS) such as GPS or a Local Positioning System (LPS), and should be interpreted in a comprehensive sense including location information of an object acquired through signals from sensors corresponding to a specific object.

[0127] For example, an acceleration-related signal can be measured from an inertial measurement unit (IMU) corresponding to an object, and by integrating such acceleration measurement values ​​to obtain information on velocity, and then integrating this again to obtain displacement, a form of measuring the movement path of the corresponding object can be implemented. It should be interpreted that information on the displacement of an object like this can also be included in sensor-based location information.

[0128] The term "positioning state characteristic value" may refer to a numerical value of information about the positioning state at the time of positioning, for example, when determining the position of an object such as a sports player. For example, it may include, but is not limited to, the number of satellites used when positioning was performed, the strength of the received signal, or whether the positioning was performed in which positioning mode. It should be interpreted inclusively that at least one of any conditions indicating the positioning state, such as the environment, characteristics, or measurement values ​​at the time of positioning, may be included in the positioning state characteristic value.

[0129] The term "processed location information" may refer to location information that has been processed to minimize measurement errors in determining location information for a target point in time by performing data processing based on measurements at other points in time as well as measurements at the target point in time, thereby improving accuracy and minimizing the impact of errors. Here, "processing" should be interpreted inclusively to include any computational procedure using any distribution or mechanism for determining location information for a target point in time.

[0130]

[0131] System

[0132] FIG. 2 illustrates an exemplary system in which a method for collecting information on the location of a sports player in real time according to an embodiment of the present disclosure can be performed. Hereinafter, a system (1000) for collecting information on the location of a sports player according to an embodiment of the present disclosure will be described with reference to FIG. 2. However, it should be understood that the procedure for collecting information on the location of a sports player according to the embodiments of the present disclosure is not limited to being performed only by the system configuration of FIG. 2, and that any hardware configuration and combination thereof for collecting and processing information on the location of a sports player in real time can be employed to implement the procedure for collecting information on the location of a sports player in real time according to the embodiments of the present disclosure.

[0133]

[0134]

[0135] *As illustrated in FIG. 2, the system (1000) may include a sensing platform (1100) and a server (1500). In one aspect, the system (1000) may further include a terminal (1700).

[0136] The system (1000) can detect information about an object, for example, a player (10), during a target time period through a sensing platform (1100), and determine information about the player from the detected information through a server (1500). In addition, information about the trajectory of the determined object can be displayed through a terminal (1700). In FIG. 2, the server (1500) and the terminal (1700) are illustrated as separate entities as an example, but it should be noted that the embodiments according to the present disclosure are not limited thereto. For example, a tablet PC in which the server (1500) and the terminal (1700) are implemented as an integrated unit can be used to provide information about the player in real time and also to display the player information together with a motion video in real time. In addition, for example, although not shown in FIG. 2, a relay device such as a live hub for receiving data to collect information from the sensing platform (1100) may be further provided in advance of the server (1500), so that information from a plurality of devices provided in the sensing platform is received by the relay device, and such information is transmitted again to the server (1500) or the terminal (1700). That is, the configuration of the system (1000) may be partially changed depending on whether post-analysis or real-time analysis is performed, and this will be described in more detail later in this description with reference to FIGS. 5 and 6.

[0137] Below, exemplary basic components of a system for collecting information on the location of a sports player according to an embodiment of the present disclosure are first described.

[0138]

[0139] Sensing Platform

[0140] The sensing platform (1100) can detect various information about an object (10), such as a player, for example. For example, the sensing platform (1100) can perform positioning of the object (10) or detect movement of the object (10).

[0141]

[0142] For example, the sensing platform (1100) can detect kinematic information about an object (10). Kinematic information is information about a location, posture, or movement of the object (10), and the kinematic information may include at least one of locational information, orientational information, and movement information, and the movement information may include at least one of velocity, acceleration, jerk, angular velocity, angular acceleration, angular jerk, magnitude thereof (e.g., in the case of velocity, speed), and direction thereof (e.g., in the case of velocity, direction of movement).

[0143] The result of the positioning performed on the object (10) by the sensing platform (1100) may be provided as the location information of the object, or the location of the object may be determined by processing at least one of the various kinematic information described above or a combination thereof, and provided as the location information of the object.

[0144]

[0145] The sensing platform (1100) may be provided in various forms for sensing the various types of information described above. For example, the sensing platform (1100) may be implemented as a sensor-based platform that utilizes sensor devices (1300) that are provided to correspond to each object (10) and provide signals regarding measurement results.

[0146]

[0147] Sensor-based platform

[0148] Below, we describe a sensor-based sensing platform.

[0149] A sensor-based sensing platform may include a sensor device (1300). The sensing platform may obtain activity information about an object (10) corresponding to the sensor device (1300) using a sensor mounted on the sensor device (1300). According to one aspect, the sensor device (1300) may be implemented in the form of an attachable device attached to each object (10), but is not limited thereto and may include any form of device configured to perform sensing on a corresponding object.

[0150] A sensor device (1300) according to an example may be attached to an object (10) and may be used to detect activity information of the object (10). For example, the sensor device (1300) may include a Global Positioning System (GPS) sensor and may be used to determine the position of the object (10) to which the sensor device (1300) is attached.

[0151] One or more sensor devices (1300) may be attached to one object (10). In particular, when it is difficult for a sensing platform to obtain all the information it wants to obtain through the sensor device (1300) with a single sensor device (1300), it may be necessary to attach multiple sensor devices (1300) to one object (10). For example, one attachable device including a GPS sensor and an IMU (Inertial Measurement Unit) sensor and another attachable device including a heart rate sensor may be attached to the torso and wrist of the object (10), respectively, and the attachable device attached to the torso may be configured to sense the position and movement of the object (10).

[0152]

[0153] For example, a sensor-based sensing platform can obtain kinematic information using a sensor device (1300).

[0154] Below, we describe several examples of sensor-based sensing platforms acquiring kinematic information.

[0155]

[0156] The sensing platform can obtain location information using the positioning module of the sensor device (1300).

[0157] For example, the sensor device (1300) includes a global positioning module (in other words, a satellite positioning module or a GNSS (Global Navigation Satellite System) module), and the sensing platform can measure the position of the object (10) by performing global positioning using the global positioning module. Specifically, the sensing platform can perform global positioning of the object (10) by having the GNSS module receive satellite signals from navigation satellites (20) and derive a global position (e.g., latitude and longitude) from the received satellite signals using a triangulation technique. Meanwhile, the sensing platform can additionally include a base station for performing RTK (Real-Time Kinematic) for more accurate positioning. The sensing platform can use the global position as activity information as it is, but can also process the global position into a local position defined by the pitch coordination system for the stadium and use this as activity information. Here, the stadium coordinate system may be a two-dimensional planar coordinate system with the longitudinal and transverse directions of the stadium as axes and a point on the stadium or its periphery (e.g., one of the corners or the center of the stadium) as the origin. It should be noted in advance that all location-related information processed below may be processed without limitation according to the stadium coordinate system.

[0158] For another example, the sensor device (1300) includes a local positioning module, and the sensing platform can measure the location of the object (10) by performing local positioning using a local positioning sensor network that includes the sensor device (1300). The local positioning sensor network includes a tag node that is attached to the object that is the target of positioning and moves, and an anchor node (30) that is fixedly installed in the positioning area, and can perform positioning using a local positioning system (LPS) signal that is transmitted and received between the tag node and the anchor node. The sensing platform can perform local positioning of the object (10) using the results of transmission and reception of LPS signals between the sensor device (1300) that includes a local positioning module and is attached to the object (10) that is the target of positioning and operates as a tag node, and the anchor node (30) that is fixedly installed in the playground or its surroundings.

[0159]

[0160] The sensing platform can obtain movement information and / or direction information using the motion sensing module of the sensor device (1300).

[0161] For example, the sensor device (1300) includes an IMU sensor (or an Attitude and Heading Reference System (AHRS) sensor), and the sensing platform can obtain movement information and / or direction information of the object (10) or a body part of the object (10) using the acceleration, angular velocity, and azimuth detected by the IMU sensor. When the sensor device (1300) is attached to the torso of the object (10), movement information according to the positional movement of the object (10) can be obtained, and when the sensor device (1300) is attached to the foot or leg of the object (10), movement information of the arm or leg of the object (10) can be obtained.

[0162]

[0163] Meanwhile, the sensing platform can produce other kinematic information using the measured kinematic information. For example, the sensing platform can output speed based on the global position continuously measured by the GPS module and its sampling interval. Since the production of kinematic information is possible through simple mathematical operations such as vector operations or calculus on the time axis, a detailed description thereof will be omitted. Here, the production of kinematic information can be performed internally in the sensor device (1300) or in a server (1500) or user terminal (1700) that receives information from the sensor device (1300). In the sensor device (1300), the sensing module can perform the production directly or the controller of the sensor device (1300) can perform the production based on the detection result of the sensing module.

[0164]

[0165] sensor devices

[0166] FIG. 3 is a block diagram of a sensor device that can be used to collect information about the location of a sports player according to one embodiment of the present disclosure.

[0167] As illustrated in FIG. 3, a sensor device (1300) according to one embodiment of the present disclosure may include a sensing module (1310), a communication module (1320), a controller (1330), a memory (1340), and a battery (1350).

[0168]

[0169] The sensing module (1310) can detect various signals to obtain activity information. The sensing module may be a positioning module or a motion sensing module.

[0170] The positioning module may be a satellite positioning module (1311) or a local positioning module (1313). The satellite positioning module (1311) may perform positioning using a navigation satellite system, i.e., GNSS. Here, the GNSS may include a global positioning system (GPS), GLONASS, BeiDou, Galileo, etc. Specifically, the global positioning module may include a satellite antenna that receives satellite signals and a positioning processor that performs positioning using the received satellite signals. For example, the satellite positioning module may be a GPS module that includes a GPS antenna that receives GPS signals and a GPS processor that performs positioning using the GPS signals. In this case, the sensor device (1300) may operate as a GPS receiver, receive GPS signals from satellites, and obtain latitude, longitude, altitude, speed, azimuth, diluition of precision (DOF), and time therefrom.

[0171] The local positioning module (1313) can perform positioning in collaboration with a sensor network. A sensor device (1300) including a local positioning module can operate as a tag node of a local positioning sensor network to transmit and receive LPS signals with surrounding anchor nodes, and positioning can be performed using the results of transmitting and receiving the LPS signals. For example, the sensor device (1300) can transmit LPS signals as a transmitter of a sensor network for local positioning and anchor nodes can receive the LPS signals, or anchor nodes can transmit LPS signals and the sensor device (1300) can receive LPS signals as a receiver of a sensor network for local positioning. At this time, the LPS signal can be periodically broadcast according to a communication method such as ultra-wideband (UWB) communication, Bluetooth, Wi-Fi, or RFID, and a time or device identifier can be embedded in the LPS signal. Position estimation can be performed based on the measurement results according to the transmission and reception of LPS signals, and can use the Received Signal Strength (RSS) technique, the Time-of-Arrival (ToA) technique, the Time Difference-of-Arrival (TDoA) technique, the Angle-of-Arrival (AOA) technique, triangulation technique, hyperbolic triangulation technique, etc. Position estimation can be performed by the master of the sensor network, and any one of the tag nodes, the anchor node, or the server can function as the master of the sensor network. If the sensing platform additionally includes an RTK base station, the base station can also be equipped with the position estimation function of the master.

[0172]

[0173] The motion sensing module (1315) may be referred to as a kinematic sensing module and can detect movement and / or posture. Here, the motion sensing module (1315) may include an IMU module or an AHRS module, and the IMU module and the AHRS module include sensors including an accelerometer and a gyroscope and optionally a magnetometer, and can measure movement and posture from the detection results of the sensors. According to one aspect, the inertial sensor may provide information on changes in the movement and / or posture of an object, as well as information on the presence or absence of movement. For example, a movement flag may be provided as a field having a minimum number of bits to indicate whether or not a movement has been detected by the inertial sensor.

[0174]

[0175] The biosensing module (1317) may be configured to collect information on various biosignals of an object associated with the sensor device (1300). For example, the biosensing module may include, but is not limited to, at least one of a heart rate sensor and an electrocardiogram sensor.

[0176]

[0177] Meanwhile, the above-described sensing modules do not necessarily have to be all equipped in one sensor device (1300), and multiple sensing modules of the same type may be equipped in one sensor device (1300), and the sensing modules equipped in each sensor device (1300) may be different. For example, the sensing platform may include two different sensor devices (1300), one of which includes a GPS sensor and an IMU (Inertial Measurement Unit) sensor to obtain location information and movement information about the object (10), and one of which includes one attached to the torso of the object (10) and the other attached to the wrist of the object (10).

[0178]

[0179] The sensor device (1300) can transmit and receive data with an external device such as a server (1500) or a user terminal (1700) or another sensor device (1300) through a communication module (1320). The communication module (1320) can perform wired communication and / or wireless communication. The sensor device (1300) can transmit and receive data with an external device using a wireless communication module that performs wireless communication of various standards such as a mobile communication network (e.g., LTE, 5G, etc.), Wi-Fi, Bluetooth, Zigbee, or other standards. For example, the wireless communication module (1320) can transmit information acquired by the sensor device (1300) using the sensing module (1310) to the server (1500) in real time so that the system can monitor the object (10) using activity information, or, when multiple sensor devices (1300) are attached to one object (10), data can be transmitted and received between the sensor devices (1300). In addition, the sensor device (1300) can transmit and receive data with an external device using a wired communication module that performs universal serial bus (USB) communication or wired local area network (LAN) communication. For example, the wired communication module can collectively transmit data collected by the sensor device (1300) to the server (1500) or a docking station that performs charging and / or data management of the sensor device (1300).

[0180]

[0181] The controller (1330) can control the overall operation of the sensor device (1300). The controller (1330) can be implemented as a hardware configuration, a software configuration, or a combination thereof. From a hardware perspective, the controller (1330) can be provided in various forms capable of performing operations or data processing, including electronic circuits, integrated circuits (ICs), microchips, and processors. In addition, since the physical configuration of the controller (1330) is not necessarily limited to a single physical entity, the controller (1330) can be provided as a single processor that comprehensively processes all processing of the sensor device (1300), a plurality of processors that each perform different functions, or can be provided in a form combined with some of the other components of the sensor device (1300). For example, the controller (1330) may be provided in a form that includes a GPS processor that processes GPS signals to perform positioning, an IMU processor that performs various operations using the results detected by the sensor of the IMU module, and a main processor that controls the operation and overall operation of the sensor device (1300).

[0182]

[0183] The memory (1340) can store various data related to operations in the sensor device (1300). For example, the memory (1340) can store firmware that manages the operation of the sensor device (1300) or detection results of sensors of the sensor device (1300). The memory (1340) can be provided as various volatile or non-volatile memories.

[0184] The battery (1350) can provide power necessary to drive the operation of the sensor device (1300). The battery (1350) can be provided as a built-in or detachable type, and can be charged by receiving power from an external power source.

[0185]

[0186] Video-based platform

[0187] Below, a video-based platform is described. The video-based platform may include, for example, a camera (1200).

[0188] A camera (1200) may be positioned on or around a sports field to capture images of objects (10) moving within the field or sports field, for example, for sports analysis applications. The camera (1200) may be installed semi-permanently (e.g., installed on an auxiliary facility of the sports field), temporarily installed (installed on a movable pole), or carried by a cameraman. The sports video captured by the camera (1200) may be a tactical view, a broadcast view, or a player-focused view. The tactical view is an image generally used for sports tactical analysis, and may be an image captured so that most of the objects (10) are captured in the image for team tactical analysis, and the horizontal axis of the tactical view may correspond to the longitudinal direction or the width direction of the field. The broadcast view is a video mainly used for sports broadcasting and can be captured with a smaller angle of view than the tactical view, and the player-focused view is a video captured along a specific object (10) and can be mainly used to analyze the individual capabilities of the object (10). In addition, the camera (1200) may not necessarily have a fixed field of view, and the field of view direction adjustment and zoom adjustment may be performed manually or automatically.

[0189] According to one aspect of the present disclosure, the video-based platform may include one or more cameras. The multiple cameras may be provided in a multi-camera configuration capable of capturing a panoramic view from a single spot, distributed across multiple spots, or a combination of the two.

[0190] The video-based platform can be configured to capture various types of motion images via a camera (1200).

[0191]

[0192] Meanwhile, according to one aspect, image-based location information may be obtained separately from sensor-based location information. For example, the sensing platform may analyze an image captured by a camera (1200) to obtain activity information therefrom. For example, the sensing platform may perform object recognition on an object (10) in an image, extract the position (e.g., pixel coordinates) of the object (10) within the image, and project the position within the image onto the ground using the pose information of the camera (1200), thereby obtaining location information on the object (10). Here, a deep learning algorithm may be used for image processing such as object detection. Specifically, for object detection (e.g., object (10) detection), various deep learning algorithms for object detection ranging from Region-based Convolutional Neural Network (R-CNN) to You-Only-Look-Once (YOLO) can be used, and for coordinate transformation, top-view transformation using camera parameters (e.g., installation location, shooting posture, etc.) can be used. For example, the server (1500) obtains a sports image from a camera (1200) positioned to capture a tactical view, obtains a bounding box for a sports object such as a ball or an object (10) in the sports image through an artificial neural network model that performs object detection, determines a representative pixel (e.g., the lower center of the bounding box) for the sports object considering the bounding box, and performs top-view transformation using a coordinate transformation metric between a pixel coordinate system and a reference coordinate system in the sports image generated considering the camera parameters for the determined representative pixel, thereby obtaining position data for the sports object. Here, object detection using artificial neural networks can be replaced with object segmentation.Accordingly, the server (1500) can obtain location data for a sports object in a sports video captured by the camera (1200) through video analysis.

[0193]

[0194] As mentioned in the above description, the sensing platform (1100) may optionally further include a base station for RTK correction and an anchor node for building a local positioning sensor network as needed.

[0195] In addition, some of the various operations performed in the sensing platform (1100) may be processed in the processor or controller mounted on the sensor device (1300), while others may be processed in the server (1500) or the user terminal (1700). For example, object detection for an image may be processed by the server (1500), location calculation using the results of LPS signal transmission and reception between each node in a local positioning sensor network may be processed by the server (1500), and various processing related to image analysis may be processed by the server (1500). Therefore, at this time, it may also be understood that the server (1500) is included in the sensing platform (1100).

[0196]

[0197] Server

[0198] The server (1500) may be configured to receive location information from the sensing platform (1100) or to collaborate with the sensing platform (1100) to obtain location information and use the information to track the trajectory of an object.

[0199] According to one aspect, the server (1500) may be provided as a local server located near the playground and / or a web server connected via the web. In addition, the server (1500) does not necessarily have to be implemented as a single entity. For example, the web server may be implemented as including a main server for processing operations and a data server for storing various data. Meanwhile, the local server may be provided as an independently operated device that performs only the original function of the server, but may also be provided as a device having a composite function combined with other components of the exercise load information provision system. For example, the server may be provided in the form of a docking station, which is a container for accommodating, storing, and managing the sensor device (1300). Here, the docking station may have an internal space for accommodating multiple sensor devices (1300), including a docking unit for accommodating the sensor device (1300), and may store the sensor device (1300) when it is not in use. Furthermore, the docking station can provide various convenient functions necessary for using the sensor device (1300) in addition to simply storing the sensor device (1300). For example, the docking station can perform functions such as charging the sensor device (1300), displaying the battery status, updating firmware, and collecting data.

[0200] Additionally, as described above, the server (1500) may be implemented as an integral part of the terminal (1700), and may be a mobile computing device such as a tablet PC, for example.

[0201]

[0202] Figure 4 is a block diagram of a server according to one embodiment of the present disclosure.

[0203] The server (1500) may include a communication module (1510), a controller (1520), and memory (1530).

[0204] The communication module (1510) can transmit and receive data between the server (1500) and other components of the object tracking system or external devices. For example, the server (1500) can collect data from a sensor device (1300), receive images from a camera (1200), or transmit various types of information to a terminal (1700) via the communication module (1510) via the web or wired / wireless communication.

[0205] The controller (1520) can control the overall operation of the server (1500). Like the controller of the sensor device (1300), the controller (1520) of the server can be implemented as a hardware configuration, a software configuration, or a combination thereof, and from a hardware perspective, the controller can be provided in various forms capable of performing operations or data processing, including electronic circuits, integrated circuits (ICs), microchips, and processors, and its physical configuration is not necessarily limited to a single physical entity. Meanwhile, specific operations performed by the server (1500) will be described below, and methods or operations according to the embodiments of the present disclosure described below can be understood to be performed by the controller (1520) of the server unless otherwise mentioned, but it should be noted in advance that they are not limited thereto.

[0206] However, as examined in this description, the embodiments according to this description are not necessarily limited to being performed by the controller (1520) of the server, and it should be understood that the embodiments according to this description may be performed by an operable processor, such as the controller (1330) of the sensor device (1300), or at least some of the procedures may be performed by the server, and at least some of the procedures may be performed by another processor. In particular, when collecting information on the location of a sports player in real time according to the embodiments of this description, more procedures may be directly performed by the sensor device (1300) or its controller (1330) compared to when only post-hoc analysis is considered.

[0207]

[0208] Terminal

[0209] A terminal (or terminal device, or user device) (1700) may function as a user interface that provides various data or information collected or produced by the system to a user or receives user input from a user. The terminal (1700) may be a smart device such as a smart phone or tablet, a personal computer such as a laptop or desktop, or any other electronic device having an input interface for receiving user input and an output interface such as a display. In particular, in performing real-time sports analysis according to the embodiments of the present disclosure, the user device (1700) may be configured to directly perform metric calculations based on collected information on the location of a sports player and provide the sports analysis results to the user.

[0210]

[0211] System configuration for post-mortem analysis

[0212] Figure 5 is a block diagram illustrating the configuration of an exemplary system for post-event analysis of a sports event. Below, with reference to Figure 5, the configuration of an exemplary system for post-event analysis after the end of a sports event will be described in more detail.

[0213] As illustrated in FIG. 5, according to one aspect of the present disclosure, the system may include a plurality of sensor devices (1300-1, 1300-2, 1300-3) corresponding to a plurality of sports players, respectively. Each sensor device may be configured to collect kinematic information and / or bio-signal information for the corresponding sports player. Here, in case of performing a post-analysis, the sensing information from the sensor devices may be collectively transmitted to a processing device, for example, a docking station (510), after the end of the sports event. Accordingly, the sensing information acquired by the sensor devices during the sports event may be optionally subjected to analog-to-digital conversion (step 501) and stored in a storage means included in the sensor devices. The sensing information stored in this manner may have, for example, a file format (540) of a specific format as illustrated in FIG. 5. The sensed information may be messages collected by time and device, including, for example, time information of Coordinated Universal Time (UTC), which is a standard agreed time, positioning information such as GPS values, movement information such as IMU sensing values, and biometric information such as HR values.

[0214] When a sporting event is over, a plurality of sensor devices may be connected to an interface device configured to enable charging of the devices and / or data transmission and reception with the devices, such as a docking station (510), and sensing information stored in a file format (540) of a specific format may be transmitted to a computing device, such as a personal computer (520), via the docking station (510). The transmission and reception of information between the docking station (510) and the personal computer (520) may include, but is not limited to, a wired connection, such as a USB or serial port, and any of various wireless communication architectures, such as Bluetooth, Wi-Fi, and cellular communication, may be applied.

[0215] The sensing information transmitted to a personal computer can be transmitted via a network, for example, to a cloud service (520) or a dedicated server, and post-event analysis of a sports event can be performed based on such sensing information. The sports analysis results can be transmitted to a user terminal (1700) via wired or wireless communication at the user's request, allowing the user to view the desired analysis results.

[0216]

[0217] System configuration for real-time analysis

[0218] Figure 6 illustrates the configuration of an exemplary system for real-time analysis of a sports event. Below, with reference to Figure 6, the configuration of an exemplary system for real-time analysis of a sports event or training session while it is in progress will be described in more detail.

[0219] As exemplarily illustrated in FIG. 6, real-time analysis of a sports event or training session may be performed by, for example, but not limited to, a user terminal (1700). While real-time analysis may be performed by any computing device equipped with a processor, it is noted below that real-time analysis is performed by, but not limited to, a user terminal that can be easily carried to the scene of a sports event, such as a tablet PC.

[0220] According to one aspect of the present disclosure, prior to performing real-time sports analysis, the user terminal (1700) may first connect to a remote entity, such as a cloud server (620). The cloud server (620) may store basic information for performing sports analysis, and the user terminal (1700) may obtain basic information corresponding to the corresponding event from the cloud. The basic information may include, for example, setting values ​​for real-time analysis. For example, the setting values ​​may include identification numbers of sensor devices used for sports analysis and corresponding player matching information. In addition, the setting values ​​may be setting values ​​for the team being analyzed. The values ​​that serve as the basis for calculating metrics for sports analysis may vary depending on the team or individual seeking sports analysis. For example, in sports event analysis, analysis of the number and frequency of sprints is very important, and the standards for such sprints may vary depending on the team. For example, a specific team may define a sprint as maintaining a speed of 25.4 km / h or more for 0.5 seconds or more, while another team may define the speed standard here as 20 / 25 / 30 km / h or more, and thus each team may set different analysis criteria. The user terminal (1700) may be configured to receive basic analysis information, such as sensor device-related information or basic information for sports analysis, by switching the connection status to the cloud (620) to online prior to real-time analysis. After receiving the basic analysis information, the user terminal (1700) may be configured to perform real-time analysis while transmitting and receiving information with elements within the stadium without maintaining the connection status to the cloud (620).

[0221]

[0222] As illustrated in FIG. 6, according to one aspect of the present disclosure, the system may include a plurality of sensor devices (1300-1, 1300-2, 1300-3) each corresponding to a plurality of sports players. Each sensor device may be configured to collect kinematic information and / or bio-signal information for the corresponding sports player.

[0223] Here, when performing real-time sports analysis, sensing information from sensor devices can be transmitted to the user terminal (1700) during the progress of the sports event. Accordingly, the sensing information acquired by the sensor device during the sports event can be processed into a real-time packet (Live Packet) for transmission to the user terminal (1700) through optional analog-to-digital conversion (step 601). The sensing information in the real-time packet generated in this way can have a packet configuration (640) having a specific format, for example, as illustrated in FIG. The sensed information can be messages collected by time and device, including, for example, time information of the standard coordinated time UTC (Coordinated Universal Time), positioning information such as GPS values, movement information such as IMU sensing values, and biometric information such as HR values. The transmission cycle of the real-time packet transmitted from the sensor devices to the user terminal (1700) can be, for example, 2 Hz, but is not limited thereto. Additionally, the number of measurement points of sensing information included in one packet may be determined by considering at least one of the transmission cycle of the real-time packet and / or the sensing cycle of the sensor device, but is not limited thereto.

[0224] Additionally, the number of sensing points included in a single packet may vary depending on the information transmission and reception policy. For example, according to embodiments of the present disclosure, sensor devices may be configured to transmit information using the UDP protocol. Compared to the TCP / IP protocol, which determines whether transmission is successful based on whether an ACK is received for the transmitted information, the UDP protocol does not require a separate ACK procedure, and thus can have a significant advantage in terms of battery efficiency of the sensor device.

[0225] In this regard, in an environment for performing real-time sports analysis, various obstacles exist, particularly in performing sensor-based positioning such as GPS, as described above. For example, as described with reference to FIG. 1, there may be problems such as satellite signals being reflected by stadium structures, problems such as the signal coverage of a sensor device not covering the entire stadium, and problems such as signal interference caused by wireless communication devices carried by numerous spectators. To address these problems, according to embodiments of the present disclosure, the signal strength of a sensor device can be set as high as possible within a range that satisfies realistic legal regulations. Accordingly, the coverage of a signal transmitted from a sensor device can be ensured to include the entire stadium, and a radio transmission and reception environment that is robust against signal interference caused by spectators or player equipment can be established. Accordingly, in the transmission and reception of data for sports analysis, shadow areas can be minimized, transmission failures can be minimized, and there can be no data loss (No Shadow Are, No Signal Failure, No Data Loss).

[0226] In this regard, an increase in signal strength may cause a disadvantage in the battery efficiency of the sensor device, which can be solved by adopting a UDP protocol that omits repeated signal transmission and reception for ACK, according to one embodiment of the present disclosure. However, an aspect that data measured at a specific point in time is not transmitted to the user terminal (1700) must be considered, and according to one embodiment of the present disclosure, this can be solved by including not only the measurement data immediately before the transmission point in time but also all measurement data for a predetermined period of time in a real-time packet transmitted from the sensor device to the user terminal (1700). For example, the real-time packet may include a history for 1 minute, and for example, when the sampling rate for sensing of the sensor device is 10 Hz, measurement information for 600 points in time can be transmitted for each transmission cycle. The transmission cycle of real-time packets can be set to, for example, transmit twice per second. Therefore, if a user terminal successfully receives information from a specific sensor device even once during a one-minute period, it can secure all data for that sensor device without any loss. Therefore, the information loss issue can be resolved even while using the UDP protocol.

[0227] As illustrated in FIG. 6, a relay device for data transmission, such as a live hub (610), may be provided between the sensor devices and the user terminal (1700). That is, the live hub (610) may be configured to receive information from the sensor devices, amplify the information, and transmit it to the user terminal (1700). Accordingly, the reliability of data transmission and reception can be further improved.

[0228] The user terminal (1700) can perform real-time analysis of a sports event or training session based on the information received in this manner. That is, the user terminal (1700) can perform metric calculations and simultaneously provide analysis results to the user.

[0229] After the sports event has ended, the user terminal may switch its connection status to the cloud (620) back online and transmit at least some of the collected information and / or analysis results to the cloud.

[0230]

[0231] Real-time player information collection

[0232] FIG. 7 schematically illustrates a procedure for collecting information about the location of a sports player in real time according to one embodiment of the present disclosure. As illustrated in FIG. 7, in order to collect information about the location of a sports player, information about the sports player may first be sensed (step 710). Such information sensing may be performed, for example, by the sensor device (1300) illustrated in FIGS. 2 to 6 .

[0233]

[0234] According to one aspect of the present disclosure, missing value imputation (step 720) can be performed on data measured by the sensor device (1300) as illustrated in FIG. 7. In this regard, FIG. 8 is an exemplary diagram for missing value imputation (Imputation) according to one embodiment of the present disclosure. As illustrated in FIG. 8, according to embodiments of the present disclosure, information on the position of a player at multiple points in time can be collected at predetermined time intervals during a target time interval, which is, for example, a time interval during which a sports event is in progress.

[0235] As illustrated in FIG. 8, the predetermined time interval may be, for example, a 0.1 second interval, as indicated in the timestamp entry. That is, the sampling rate of the expected information may be, for example, 10 Hz. The measured data may be a speed according to the measurement interval, as illustrated in FIG. 8, and a value regarding the speed of the target player may be measured at each point in time.

[0236] In relation to this, while the expected time interval between multiple data is 0.1 seconds, there may be cases where the actual sampling rate of the positioning sensor equipped in the sensor device sensing the data is less than exactly 10 Hz, such as 9.97 Hz, thereby sensing data. According to one aspect of the present disclosure, due to a mismatch between the sampling period of the positioning sensor and the expected predetermined time interval of the information about the player's position, for missing data (810) occurring at a specific point in time, that is, a first point in time (e.g., 12:55:11.5 in FIG. 8), the information about the player's position for the missing first point in time can be replaced with information about the player's position for a point in time previous to the first point in time (e.g., 12:55:11.4) (820). Therefore, even if a missing value occurs due to the sampling rate of the sensor device at any point among a plurality of points in time according to predetermined time intervals, it is possible to ensure that no point in time is missing data in the subsequent data processing procedure. That is, as illustrated on the right side of Fig. 8, in the case of the imputed speed where the missing value is replaced, it is possible to have corresponding sensed values ​​for all points in time without any missing values ​​at a plurality of points in time according to predetermined time intervals.

[0237]

[0238] Referring again to FIG. 7, a validation procedure (step 730) may be performed on data measured by the sensor device (1300) as illustrated in FIG. 7. As described above, in embodiments of the present disclosure, GPS may be used as an exemplary positioning mechanism. In this case, abnormal measurements may be included in the sensing results due to various causes. If analysis is performed without processing such abnormal measurements, significant errors may exist in the analysis results.

[0239] In relation to this, Fig. 16 illustrates an example of an abnormal measurement value related to positioning. An abnormal measurement value for a positioning result may take various forms, but for example, as illustrated in Fig. 16, an example may be a case where an excessively high or excessively low value appears momentarily (1610) or an example may be a case where a measurement value abnormally fluctuates, for example, a case where a measurement value changes in a stepwise manner over a certain period of time (1620).

[0240] Abnormal numerical values ​​may be measured, for example, as shown in Fig. 16, where the instantaneous speed of a sports player is measured as 800 km / h, which is not a value that can be observed with common sense. Such abnormal values ​​may be due to, but are not limited to, at least one of the following: the reflective properties of the steel structure used in the stadium, the triangulation and time-of-flight (ToF)-based measurement mechanism of GPS, and the velocity calculation mechanism using the Doppler effect based on phase shift.

[0241] In the past, when sports analysis was performed through post-analysis, processing of such abnormal measurements was performed on data that had been acquired in a time series after the end of the sports event. For example, Fig. 17 shows an example of a step function. Fig. 18 shows an example of a delta function. That is, for example, a step function or a delta function as shown in Fig. 17 or Fig. 18 has been effectively utilized to detect abnormal measurements as shown in Fig. 16. However, in performing sports analysis in real time as in the embodiments of the present disclosure, since data after the present point in time is not acquired, there is a problem that various procedures that were previously utilized in post-analysis cannot be fully utilized.

[0242] According to one aspect of the present disclosure, when positioning is performed based on GPS, for example, as illustrated in FIG. 7, the validity of each piece of information about the position of a player obtained based on GPS characteristic values ​​can be verified. As an example, FIG. 19 illustrates a criterion for determining the validity of player position information according to one embodiment of the present disclosure, and FIG. 20 illustrates a threshold time length for determining the validity of player position information according to one embodiment of the present disclosure. As illustrated in FIG. 19, for example, with respect to position information about a specific player at a specific point in time, validity can be verified by setting criteria using various metrics for the positioning status, such as at least one or all of the positioning status at the time the information is obtained, for example, how many satellites were used to obtain the information, in what measurement mode the information was measured, the accuracy of the speed for the information, and the strength of the signal received when the information was measured. Furthermore, according to one aspect, as illustrated in FIG. 20, a threshold time may be set for determining whether the acquired data is valid or invalid based on whether the conditions are satisfied. If the invalid condition is satisfied for a predetermined period of time, the acquired data from that point onward may be treated as invalid. If the invalid condition is lifted, the acquired data from that point onward may be treated as valid. The procedure for verifying the validity of information regarding a player's location according to embodiments of the present disclosure will be described in more detail later in this disclosure.

[0243]

[0244] Referring again to FIG. 7, a preprocessing and filtering procedure (step 740) can be performed based on information about the player's position according to the missing value replacement procedure (step 720) and the validation procedure (step 730).

[0245] For a sequence of continuous data, data processing using a moving average can be utilized. That is, for a measurement value at a specific first point in time, a measurement value processed based on measurement values ​​at that point in time and / or points around that point in time can be used for subsequent metric calculations, instead of the measurement value at that point in time.

[0246] Meanwhile, an outlier filtering procedure can also be performed on invalid values ​​for such data sequences. As described above, an invalidity verification procedure can be performed on each sensing data point at a specific time point. In cases where a moving average is utilized using data from multiple time points, a separate filtering procedure can be performed on each moving average value.

[0247] In relation to this, in the case of existing post-analysis of sports events, such moving averages or filtering procedures have been mainly performed on the server or cloud where the data was collected and transmitted, or on the data analysis device that received the data therefrom. However, when real-time analysis according to the embodiments of the present disclosure must be performed, there is a problem in that many of the procedures performed in existing post-analysis cannot be utilized. According to the embodiments of the present disclosure, pre-processing and / or filtering procedures for data that are distinct from conventional post-analysis may be employed, and furthermore, such pre-processing and / or filtering procedures may be configured to be performed directly on an individual device, such as a sensor device, and the processed information may be transmitted to a server or user terminal.

[0248] In relation to this, FIG. 9 is an example of preprocessing for data according to an embodiment of the present disclosure, and FIG. 10 is an example of a Laplacian distribution. As illustrated in FIG. 9, according to one aspect of the present disclosure, for example, information regarding speed may be subject to preprocessing and filtering. For example, a moving average value for speed values ​​in a time interval (910) from 12:55:11.3 to 12:55:11.7 may become a processed speed value (915) at the time point 12:55:11.7. The processed speed value may be determined by multiplying previous values ​​by different weights and summing them, for example, using a Laplacian distribution as illustrated in FIG. 10.

[0249] Considering the nature of real-time analysis, in order to determine the processed information (915) at a specific point in time, information at that point in time and / or points in time (910) prior to that point in time may be used, and data after that point in time may not be utilized to determine the processed value regarding the speed at that point in time. In addition, as shown in the preprocessed and filtered data on the right side of FIG. 9, for specific processing results such as V1, V2, and V3, the data may be filtered by setting them as error values ​​instead of moving average values. The preprocessing and / or filtering procedures according to the embodiments of the present disclosure will be described in more detail later in the present disclosure.

[0250]

[0251] Referring back to FIG. 7, the processed data (Proc.Data) can be processed into a real-time packet (Live Packet) for real-time analysis or a file in a specific format (File Format) for post-analysis through a packetizing step. For example, as described with reference to FIG. 5, for post-analysis, it can be processed into a file in a specific format and transmitted to a storage (510), for example, a computing device for performing sports analysis through a docking station, or as described with reference to FIG. 6, it can be processed into a real-time packet and directly transmitted to a live hub (610) or a user terminal (1700) for real-time analysis. In this regard, FIG. 11 illustrates an exemplary structure of a real-time packet (Live Packet) according to one embodiment of the present disclosure. As exemplarily illustrated in FIG. 11, a packet for real-time analysis may include, but is not limited to, at least some of sensor-based measured velocity (SOG), information about positioning mode (posMode), number of satellites used for positioning, average signal strength (Average C / N0), validation information about GPS values ​​(GPS_Valid), and filtered velocity data (Filtered SOG data).

[0252]

[0253] Validate information about the player's location

[0254] Below, a method for collecting information on the location of a sports player in real time according to an embodiment of the present invention is described, particularly a procedure for determining the validity of the acquired information. In the following description, the real-time player information acquisition procedure may be exemplarily described as being performed by the aforementioned system (1000). However, this is merely for convenience of explanation and is not limited to the procedures being performed by the system.

[0255]

[0256] FIG. 21 is a schematic flowchart of a procedure for determining the validity of information regarding a sports player's location according to one embodiment of the present disclosure, and FIG. 22 is a detailed flowchart of the validity determination step of FIG. 21. Hereinafter, with reference to FIGS. 21 and 22, a more specific description will be given of a procedure for determining the validity of information regarding a sports player's location according to one embodiment of the present disclosure.

[0257]

[0258] As an example, a method according to an embodiment of the present disclosure may be performed by a computing device. Hereinafter, for convenience of explanation, the methods according to the embodiments of the present disclosure may be described as being performed by a computing device. Here, the computing device may be, for example, at least one of a sensor device (1300), a server (1500), or a terminal (1700), but is not limited thereto, and it should be understood that it includes at least one of any computing device capable of performing calculations, including a processor. However, in an embodiment according to the present disclosure, in collecting information about the location of a sports player in real time, specific procedures may be performed directly by the sensor device (1300), and will be described later in the present disclosure.

[0259]

[0260] A method for collecting information on the location of a sports player in real time according to embodiments of the present disclosure may include a step of obtaining information on the location of the player and at least one positioning status characteristic value (step 2110), as illustrated in FIG. 21, and a step of determining the validity of the information on the location of the player based on the obtained information (step 2120).

[0261]

[0262] More specifically, but not exclusively, as illustrated in FIG. 21, the computing device may first obtain, based on a positioning sensor, information about the player's position at multiple points in time according to a predetermined time interval and at least one positioning status characteristic value (Step 2110).

[0263] According to one aspect of the present disclosure, the positioning sensor may be, for example, a Global Navigation Satellite System (GNSS) sensor. More specifically, but not necessarily limited to, the positioning sensor may be, for example, a GPS sensor included in the sensor device (1300), but is not limited thereto. Furthermore, the predetermined time interval may be, but is not limited to, a 0.1 second interval, as exemplified above with reference to FIG. 8.

[0264] Here, the positioning status characteristic value may include information about the positioning status at the time when the positioning sensor determines information about the player's location. For example, GPS characteristic values ​​as exemplified in FIG. 7 may be included in the exemplary positioning status characteristic value. That is, at the time when information about the player's location is determined, various indicators may be included in the positioning status characteristic value to indicate under what environment and / or conditions the information about the player's location was determined.

[0265] As a non-limiting example, when a positioning sensor senses information about a player's location based on signals from satellites according to an aspect of an embodiment of the present disclosure, for example, the positioning status characteristic value may include at least one of information about the number of satellites used for positioning by the positioning sensor, information about a positioning mode of the positioning sensor, accuracy information about a vertical position of the positioning sensor, or information about the strength of a satellite signal received by the positioning sensor. In this regard, the computing device may receive not only information about the player's location from the positioning sensor, but also various additional information related to positioning.

[0266] For example, as exemplarily illustrated in the real-time packet of FIG. 11, the positioning device can basically sense information about the positioning point in time for the target, as well as information about latitude, altitude, and height. In addition, the positioning device can provide information about the ground direction speed of the target at the positioning point in time, and can also provide information about the vertical speed. In addition, the positioning device can provide a value regarding posMode indicating in which mode the positioning at that point in time was performed. In relation to this, as described above, RTK-GPS with improved accuracy can be used in performing the positioning according to the embodiment of the present disclosure, and in this case, the RTK-GPS can distinguish the positioning mode at the positioning point in time, for example, positioning in R / A / D / N mode. For example, R mode indicates that the positioning was performed using RTK-GPS mode, A mode indicates that the positioning was performed in a mode that has a positioning accuracy corresponding to the degree when there are no surrounding obstacles, such as Adaptive Mode, D mode indicates that the positioning was performed in a mode that provides normal results with an appropriate level of accuracy even when some obstacles are present, such as the Half Dome stadium, for example, Dynamic mode, and N mode can indicate that the positioning was performed in a mode that has a high error, such as near a steel structure or indoors, as Noise mode. In addition, the positioning sensor can provide various additional information in addition to the positioning information, such as information about whether the positioning was performed based on signals from several satellites and information about the strength of the signal from the satellites received at the time of positioning.

[0267] According to one aspect of the present disclosure, in order to determine the validity of positioning data according to a positioning sensor at a specific point in time, among the additional information as described above, information indicating a state and / or environment for positioning at the point in time of positioning, for example, a positioning state characteristic value, may be used, and the positioning state characteristic value may include at least one of information on the number of satellites used for positioning by the positioning sensor, information on a positioning mode of the positioning sensor, accuracy information on a vertical position of the positioning sensor, or information on the strength of a satellite signal received by the positioning sensor, but is not limited thereto.

[0268]

[0269] Referring again to FIG. 21, the computing device may determine the validity of information regarding the player's location based on the positioning status characteristic value acquired from the positioning sensor (step 2120). That is, the computing device may be configured to determine whether the information regarding the player's location at a specific point in time is valid information using the positioning status characteristic value at that point in time.

[0270] According to one aspect of the present disclosure, a computing device may set a first condition to determine validity. As a non-limiting but more specific example, the first condition may be, but is not limited to, a combination of values ​​for various positioning states.

[0271] In the exemplary embodiment in which the positioning sensor performs positioning based on satellite signals as discussed above, the first condition may include a combination of various state values ​​associated with the signals from the satellite.

[0272] More specifically, but not exclusively, FIG. 19 illustrates an example of a validity determination criterion for player location information according to one embodiment of the present disclosure, and FIG. 20 illustrates a threshold time length for a validity determination for player location information according to one embodiment of the present disclosure.

[0273] For example, as exemplarily illustrated in FIG. 19, the first condition may be that all detailed conditions for multiple positioning state characteristic values ​​included in the first condition are satisfied. For example, the detailed conditions may include, but are not limited to, conditions 1 to 4 as illustrated in FIG. 19.

[0274] According to one aspect, the detailed condition may include whether the number of satellites used for positioning of the positioning sensor is 6 or more (condition 1). In addition, according to one aspect, the detailed condition may further include whether the positioning mode of the positioning sensor is RTK-GPS mode or normal mode (condition 2). For example, the normal mode may indicate a case in which posMode is A mode or higher as discussed above, but is not limited thereto. In addition, according to one aspect, the detailed condition may further include whether the accuracy of the vertical position of the positioning sensor is 3 m or less (condition 3). Alternatively, the detailed condition may further include whether the accuracy of the velocity of the positioning sensor is less than or equal to a predetermined threshold. Meanwhile, according to one aspect, the detailed condition may further include whether the strength of a satellite signal received by the positioning sensor is 20 dB or more (condition 4).

[0275] That is, in a non-limiting example, the first condition may be set to be satisfied only when positioning is performed based on signals from 6 or more satellites, the positioning mode is performed in RTK-GPS mode or normal mode, the accuracy for vertical position is 3 m or less, and the strength of the received satellite signal is 20 dB or more.

[0276]

[0277] As exemplarily illustrated in FIG. 19, if information about a player's location at a specific point in time is determined to be valid, location information validity information, such as a GPS_Valid field, may be set to have a value of, for example, 1, and if it is determined to be invalid, it may be set to have a value of 0.

[0278]

[0279] In relation to this, according to one aspect of the present disclosure, the validity of information about a player's position at a specific point in time can be determined based on the relationship between the duration of satisfaction or dissatisfaction with the first condition and the threshold time. For example, if the first condition remains unsatisfied for a predetermined time, such as 3 seconds in the example of FIG. 19, an alert state is entered, and an arming flag is set to 1, so that information about the player's position acquired after that point in time can be determined to be invalid. On the other hand, at the moment when a point in time that satisfies the first condition occurs in the alert state, the alert state is released, the disarming flag is set to 1, and information about the player's position acquired after that point in time can be determined to be valid.

[0280] More specifically, according to one aspect, the computing device, as illustrated in FIG. 22, first determines that the first condition for the positioning state characteristic value is not satisfied at a first time point (step 2121), and then continuously determines whether the first condition is satisfied, and in response to the determination that the first condition is not satisfied continuously from the first time point to a second time point after a predetermined threshold time interval has elapsed (step 2122), determines that information about the player's position acquired after the second time point is invalid (step 2123). For example, the threshold time interval may be 3 seconds, and as illustrated in FIG. 20, if it is determined that the first condition is not satisfied because condition 1 is not satisfied for, for example, 0 to 3 seconds, the Arming flag may be set to 1, and information about the player's position acquired after 3 seconds may be determined to be invalid.

[0281]

[0282] Referring back to FIG. 22, the computing device may determine that information about the player's position acquired after the third time point when the first condition is satisfied for the first time after the second time point is valid (step 2124). For example, as illustrated in FIG. 20, at the time point t = 9 s, if all the detailed conditions are satisfied again and it is determined that the first condition is satisfied, the Disarming flag may be set to 1, so that information about the player's position acquired after that time point may be determined to be valid.

[0283]

[0284] As described above, when real-time sports analysis is performed according to the embodiments of the present disclosure, the validity of the collected information regarding the player's location can be determined through procedures that are somewhat different from those of post-analysis. Meanwhile, according to one aspect, the computing device that performs real-time data collection and validation according to the embodiments of the present disclosure may be a wearable device mounted on a sports player. That is, unlike post-analysis, the data collection and validation procedures described above may be performed by a processor provided in a device for acquiring location information regarding the player, such as a sensing device.

[0285]

[0286] Motion detection-based validation

[0287] Fig. 14 shows the results of position measurement of a stationary device by a conventional GPS device. As illustrated in Fig. 14, in a conventional GPS device, even if a sensor device equipped with a GPS module is not used at all for a predetermined period of time and is fixedly located at a specific location, movement of a predetermined distance may be detected due to an error occurring in the GPS sensor. As illustrated in Fig. 14, in two-dimensional movement, a device that actually did not move at all may be observed to move in a random direction, and in altitude change, a device that actually did not change its position may be observed to have a change in height. Errors due to such GPS characteristics may be referred to as so-called GPS ghosting, and even if the error due to such errors does not have a large value, there is a problem that it may cause a significant decrease in reliability when the total movement distance for a specific device is calculated.

[0288] According to one aspect of the present disclosure, the above problem can be solved by ignoring the displacement detected by the positioning sensor, for example, based on the inertial sensor, to reflect whether a movement has been performed on the actual device. In this regard, FIG. 15 shows the position measurement result of a stationary device according to one embodiment of the present disclosure. As illustrated in FIG. 15, according to one embodiment of the present disclosure, it is possible to observe that a stationary device exists at a constant position, such that no displacement is detected in a two-dimensional direction or in a height direction.

[0289]

[0290] Hereinafter, a method for collecting information on the position of a sports player in real time according to an embodiment of the present disclosure will be described, particularly a procedure for determining the validity of the acquired information based on whether movement has occurred. In the following description, the procedure for acquiring real-time player information may be exemplarily described as being performed by the aforementioned system (1000). However, this is merely for convenience of explanation and is not limited to the procedures being performed by the system.

[0291]

[0292] FIG. 23 is a schematic flowchart of a procedure for determining the validity of information regarding the position of a sports player based on an inertial sensor according to one embodiment of the present disclosure. Hereinafter, with reference to FIG. 23, the procedure for determining the validity of information regarding the position of a sports player based on an inertial sensor according to one embodiment of the present disclosure will be described in more detail.

[0293]

[0294] As an example, a method according to an embodiment of the present disclosure may be performed by a computing device. Hereinafter, for convenience of explanation, the methods according to the embodiments of the present disclosure may be described as being performed by a computing device. Here, the computing device may be, for example, at least one of a sensor device (1300), a server (1500), or a terminal (1700), but is not limited thereto, and it should be understood that it includes at least one of any computing device capable of performing calculations, including a processor. However, in an embodiment according to the present disclosure, in collecting information about the location of a sports player in real time, specific procedures may be performed directly by the sensor device (1300), and will be described later in the present disclosure.

[0295]

[0296] A method for collecting information on a location of a sports player in real time according to embodiments of the present disclosure may include, as illustrated in FIG. 23, a step of collecting information on a sports player at a first point in time based on a first sensor (step 2310), a step of collecting information on whether there is movement of an inertial sensor at the first point in time based on an inertial sensor (step 2320), and a step of determining the validity of information on the sports player based on the information on whether there is movement of the inertial sensor (step 2330).

[0297]

[0298] Hereinafter, with reference to FIG. 23, an inertial sensor-based procedure according to an embodiment of the present disclosure will be described in more detail. More specifically, but not limitingly, as illustrated in FIG. 23, a computing device may first collect information about a sports player at a first point in time based on a first sensor (step 2310).

[0299] Here, according to one aspect of the present disclosure, the first sensor may be a Global Navigation Satellite System (GNSS) sensor, and the information about the sports player may be information about the speed of the sports player. For example, as illustrated in FIGS. 15 and 16, information about the location of the sports player may be sensed based on a positioning sensor, and, for example, the information about the location may be information about the speed of the sports player. Here, the speed information may be a value calculated based on measured location information, or may be a value provided by a satellite-based positioning mechanism such as GPS, for example.

[0300] In addition, according to another aspect of the present disclosure, the first sensor may be a heart rate (HR) sensor, and the information about the sports player may be information about the heart rate of the sports player. For example, as illustrated in FIG. 3, the first sensor may be provided in a biosensing module (1317) included in a sensor device (1300). Such a biosensing module (1317) may be, for example, a heart rate sensor configured to measure the heart rate of a player corresponding to the sensor device. Although a heart rate sensor is described as an example, it should be understood that various biosignal measuring devices, such as, for example, an electrocardiogram sensor, are included in the technical spirit of the present invention.

[0301]

[0302] Referring again to FIG. 23, the computing device can collect information on whether there is movement of the inertial sensor at a first point in time based on the inertial sensor (step 2320). The inertial sensor can be provided in a motion sensing module (1315) included in a sensor device (1300) as exemplarily illustrated in FIG. 3, for example. In one aspect, the inertial sensor can be, but is not limited to, an IMU sensor.

[0303] For example, a motion sensor, such as an IMU sensor, can be configured to provide not only a measurement value regarding the degree of motion, but also flag information indicating whether motion exists at a specific point in time. Such motion flag information can be set to have the smallest possible bit size. For example, the motion flag information can be set to have a value of 1 when motion is detected by the inertial sensor, and a value of 0 when no motion is detected, but this is only an exemplary setting and is not limited thereto.

[0304]

[0305] Referring again to FIG. 23, the computing device may determine the validity of information about a sports player based on information about the presence or absence of movement of the inertial sensor (step 2330). According to one aspect of the present disclosure, if no movement is detected by the inertial sensor based on the aforementioned movement flag information, the information about the player collected based on the first sensor may be determined to be invalid. Furthermore, according to one aspect, if no movement is detected by the inertial sensor and the size of the collected information about the player is below a threshold value, the size of the collected information about the player may be set to be changed to 0.

[0306] As a non-limiting but more specific example, when the first sensor is a positioning sensor, the computing device may change information about the speed of the sports player to 0 in response to a determination that there is no movement from the inertial sensor and that the speed of the sports player is below a predetermined threshold. That is, when movement or speed is detected due to an error in the GPS sensor, such as a GPS sensing value as illustrated in FIG. 14, information about the presence of movement from the inertial sensor may be obtained, and when it is determined that there is no movement from the inertial sensor, the measured speed value may be changed to 0 if the information measured by the positioning sensor, for example, information about the speed of the player, is below a predetermined threshold. Thus, the GPS ghosting problem may be solved by changing displacement or speed detected to 0 due to a GPS error even when the device is not actually moving.

[0307] As another non-limiting but specific example, when the first sensor is, for example, a heart rate sensor or an electrocardiogram sensor, the computing device may change information about the heart rate of the sports player to 0 in response to a determination that there is no movement of the inertial sensor. For example, when the sensor device is taken out of a docking station and placed in a state having certain conditions, such as having a certain component or having moisture, a heart rate or an electrocardiogram may be measured even when the sensor device is not worn by the player. According to one embodiment of the present disclosure, by obtaining information about the presence of movement from the inertial sensor, if it is determined that there is no movement of the inertial sensor, the value of the biosignal measured by the heart rate sensor or the electrocardiogram sensor may be changed to 0. More specifically, the sensing value may be configured to be changed to 0 only when it is determined that there is no movement of the inertial sensor and the measured sensing value is less than or equal to a threshold value. Accordingly, the problem of a heart rate or an electrocardiogram being measured and recorded even when the sensor device is not worn by the player can be prevented.

[0308]

[0309] As described above, when real-time sports analysis is performed according to the embodiments of the present disclosure, the validity of the collected player-related information may be determined through procedures that are somewhat different from those of post-analysis. Meanwhile, according to one aspect, the computing device performing real-time data collection and validation according to the embodiments of the present disclosure may be a wearable device mounted on a sports player. That is, unlike post-analysis, the data collection and validation procedures described above may be performed by a processor provided in a device for acquiring location information about the player, such as a sensing device.

[0310]

[0311] Preprocessing and filtering for real-time analysis

[0312] As described above with reference to FIG. 7, according to one embodiment of the present disclosure, a preprocessing and filtering procedure (step 740) can be performed on sensing information obtained from a sensor device.

[0313] For a sequence of continuous data, data processing using a moving average can be utilized. That is, for a measurement value at a specific first point in time, a measurement value processed based on measurement values ​​at that point in time and / or points around that point in time can be used for subsequent metric calculations, instead of the measurement value at that point in time.

[0314] Meanwhile, an outlier filtering procedure can also be performed on invalid values ​​for such data sequences. As described above, an invalidity verification procedure can be performed on each sensing data point at a specific time point. In cases where a moving average is utilized using data from multiple time points, a separate filtering procedure can be performed on each moving average value.

[0315] In relation to this, in the case of existing post-analysis of sports events, such moving averages or filtering procedures have been mainly performed on the server or cloud where the data was collected and transmitted, or on the data analysis device that received the data therefrom. However, when real-time analysis according to the embodiments of the present disclosure must be performed, there is a problem in that many of the procedures performed in existing post-analysis cannot be utilized. According to the embodiments of the present disclosure, pre-processing and / or filtering procedures for data that are distinct from conventional post-analysis may be employed, and furthermore, such pre-processing and / or filtering procedures may be configured to be performed directly on an individual device, such as a sensor device, and the processed information may be transmitted to a server or user terminal.

[0316]

[0317] Hereinafter, a method for collecting information on the location of a sports player in real time according to an embodiment of the present disclosure will be described, particularly regarding preprocessing and / or filtering procedures for information acquired by a sensor device. In the following description, the real-time player information acquisition procedure may be exemplarily described as being performed by the aforementioned system (1000). However, this is merely for convenience of explanation and is not limited to the procedures being performed by the system.

[0318]

[0319] FIG. 24 is a schematic flowchart of a procedure for collecting information on a sports player's location in real time based on preprocessing according to one embodiment of the present disclosure, and FIG. 25 is a detailed flowchart of a step for determining processed location information at a target time point of FIG. 24. Hereinafter, with reference to FIGS. 24 and 25, a procedure for collecting information on a sports player's location in real time based on preprocessing according to one embodiment of the present disclosure will be described in more detail.

[0320]

[0321] As an example, a method according to an embodiment of the present disclosure may be performed by a computing device. Hereinafter, for convenience of explanation, the methods according to the embodiments of the present disclosure may be described as being performed by a computing device. Here, the computing device may be, for example, at least one of a sensor device (1300), a server (1500), or a terminal (1700), but is not limited thereto, and it should be understood that it includes at least one of any computing device capable of performing calculations, including a processor. However, in one embodiment according to the present disclosure, when collecting information about the location of a sports player in real time, certain procedures may be directly performed by the sensor device (1300).

[0322]

[0323] A method for collecting information about a sports player's location in real time based on preprocessing according to embodiments of the present disclosure may include a step of obtaining player location information at multiple points in time (step 2410), and a step of determining processed location information at a target point in time (step 2330), as illustrated in FIG. 24.

[0324]

[0325] Hereinafter, with reference to FIGS. 24 and 25, a method for collecting information on the location of a sports player in real time based on preprocessing according to an embodiment of the present disclosure will be described in more detail. More specifically, but not limitingly, as illustrated in FIG. 24, a computing device can first obtain information on the location of a player at multiple points in time according to predetermined time intervals based on a positioning sensor (step 2410).

[0326] According to one aspect of the present disclosure, the positioning sensor may be, for example, a Global Navigation Satellite System (GNSS) sensor. More specifically, but not necessarily, the positioning sensor may be, for example, a GPS sensor included in the sensor device (1300). The predetermined time interval may be, but is not limited to, a 0.1 second interval indicated in the timestamp item, as exemplarily illustrated in FIG. 8. That is, the sampling rate of the expected information may be, for example, 10 Hz. The measured data may be a speed according to the measurement interval, as exemplified in FIG. 8, and a value relating to the speed of the target player at each point in time may be measured.

[0327] According to one aspect, the step of obtaining information about the player's position at multiple points in time (step 2410) may be configured to replace information about the player's position at a first point in time, which is missing based on a predetermined time interval, with information about the player's position at a point in time prior to the first point in time. Such missing information may be caused by, but is not limited to, a mismatch between the sampling period of the positioning sensor and the predetermined time interval. For example, the missing value replacement procedure described above with reference to FIG. 8 may be performed.

[0328]

[0329] Referring again to FIG. 24, the computing device can determine processed position information for the target point in time based on information about the player's position measured at each of a predetermined number of points in time prior to the target point in time (step 2420).

[0330] According to one aspect of the present disclosure, the processed position information for the target point in time described above can be determined independently of information regarding the player's position measured after the target point in time. Therefore, real-time analysis can utilize only information prior to the current point in time, without using information after the current point in time, while still obtaining processed position information with improved accuracy and robustness against error.

[0331]

[0332] In addition, according to one aspect of the present disclosure, the processed position information for the target time point can be determined by multiplying different weights by information about the player's position measured at each of a predetermined number of time points and adding them up, and more specifically, but not limited to, such weights can be determined based on a Laplacian distribution or a Gaussian distribution, but are not limited thereto.

[0333] In this regard, FIG. 9 is an example of preprocessing of data according to an embodiment of the present disclosure, and FIG. 10 is an example of a Laplacian distribution. Hereinafter, processed velocity information will be described as an example of processed position information, but it should be noted that the technical idea of ​​the present invention is not limited thereto.

[0334] As illustrated in FIG. 9, according to one aspect of the present disclosure, for example, information regarding speed can be subject to preprocessing and filtering. For example, a weighted average value of speed values ​​in a time interval (910) from 12:55:11.3 to 12:55:11.7 can be a processed speed value (915) at the time point 12:55:11.7. The processed speed value can be determined by multiplying each of the previous values ​​by different weights and adding them up, for example, using a Laplacian distribution as illustrated in FIG. 10.

[0335] An exemplary weight value (940) is illustrated in FIG. 9. That is, for example, when the predetermined number of measurement values ​​of previous time points for a weighted average for a specific time point is five, as illustrated in FIG. 9, the five weights can be determined based on, for example, a Laplacian distribution, and, in order to calculate the processed velocity value at the first time point (915) illustrated in FIG. 9, a value obtained by sequentially multiplying and summing the velocity values ​​of the five time points during the first time interval (910) by the five weights can be utilized.

[0336] In a similar spirit, in order to calculate the processed speed value at the second time point (925) illustrated in FIG. 9, a value obtained by sequentially multiplying the speed values ​​at five time points during the second time interval (920) by five weights and adding them up can be utilized, and in order to calculate the processed speed value at the third time point (965) illustrated in FIG. 9, a value obtained by sequentially multiplying the speed values ​​at five time points during the third time interval (960) by five weights and adding them up can be utilized.

[0337]

[0338] According to one aspect of the present disclosure, a predetermined number of points in time preceding the target point in time that can be utilized to determine processed location information of the target point in time can be determined in consideration of a sampling period of a positioning sensor for sensing location information and a data transmission period of a computing device. Non-limitingly, but more specifically, the predetermined number of points in time preceding the target point in time that can be utilized to determine processed location information of the target point in time can be determined so as not to cause a waiting time according to the data transmission period of the computing device by processing location information according to the sampling period of the positioning sensor. As a non-limiting but more specific example, for example, the sensing device and / or the GPS module provided therein can be configured to sense location information at a sampling rate of 10 Hz, and the computing device can be configured to transmit data to another device at a data transmission period of 2 Hz. Accordingly, while the computing device transmits data once, the sensing device and / or the GPS module can sense location information at 5 points in time. If the processed location information of the target time point is determined by referring to more than 5 time points, data may not be transmitted even when the data transmission cycle has arrived to secure location information for the additional time points, and a time delay may occur in securing additional location information. Therefore, according to one aspect of the present disclosure, a predetermined number of time points preceding the target time point that can be utilized to determine the processed location information of the target time point is determined by considering the sampling cycle of the positioning sensor for sensing the location information and the data transmission cycle of the computing device, thereby preventing a waiting time from occurring in data transmission. According to one aspect of the present disclosure, the predetermined number may be determined to be the same as the number of location information included in a single packet according to the data transmission cycle of the computing device.

[0339] FIG. 12 is an exemplary diagram of preprocessing for velocity information according to an embodiment of the present disclosure, and FIG. 13 is an exemplary diagram of preprocessing for acceleration information according to an embodiment of the present disclosure. As illustrated in FIGS. 12 and 13, different preprocessing mechanisms may be applied to velocity information and acceleration information. As illustrated in FIG. 12, for velocity information, for example, information at five points in time may be used, but only measurement information for points in time prior to a target point in time may be subject to weighting in preprocessing. On the other hand, as illustrated in FIG. 13, for acceleration, information at eleven points in time may be used, but not only measurement information for points in time prior to a target point in time, but also measurement information for points in time after the target point in time may be subject to weighting. This may be determined based on the accuracy of the moving average calculation of velocity and acceleration, but is not limited thereto. When performing real-time analysis, if you want to utilize measurement information from points in time after the target point in time, such as acceleration, analysis can be performed that is delayed by, for example, five time intervals.

[0340]

[0341] As described above, considering the characteristics of real-time analysis, in order to determine the processed information (915) at a specific point in time, information at that point in time and / or at points in time (910) prior to that point in time may be used, and data after that point in time may not be utilized to determine the processed value regarding the speed at that point in time. Meanwhile, as shown in the preprocessed and filtered data on the right side of Fig. 9, for specific processing results such as V1, V2, and V3, the data may be filtered by setting them as error values ​​instead of moving average values.

[0342] In relation to this, as illustrated in FIGS. 24 to 25, the step of determining processed location information for a target point of view according to one aspect of the present disclosure (step 2420) may include a step of determining processed location information for the target point of view as a first error value (step 2421), a step of determining processed location information for the target point of view as a second error value (step 2423), and a step of determining processed location information for the target point of view as a third error value (step 2425).

[0343] More specifically, but not exclusively, the computing device may determine the processed location information for the target time point as a first error value in response to a determination that the information about the player's location measured based on the positioning sensor for the target time point is invalid (step 2421). As described above, the validity of each piece of information about the player's location for each time point may be determined according to an aspect of the present disclosure. It should be noted that, according to one aspect, the validity may be determined based on the positioning status characteristic value as described above, but is not limited thereto. In this regard, if the information about the player's location at the target time point itself is invalid, the processed location information determined based on information from time points prior to the target time point may be determined as the first error value. As illustrated in FIG. 9, for invalid location information in which the validity (Validity) of the location information at the corresponding time point is indicated as 0, the processed location information may be determined as the first error value (V1) (e.g., 995).

[0344] Furthermore, according to one aspect, in response to a determination that at least one of the pieces of information about the player's position measured at each of a predetermined number of points in time prior to the target point in time is invalid, the computing device may determine the processed position information for the target point in time as a second error value (step 2423). That is, as exemplarily illustrated in FIG. 9, since the measurement information from the point in time 12:57:25.1 to the point in time 12:59:02.3 is invalid, the measurement information itself as the target point in time is valid, but the processed position information from the point in time 12:59:02.3 to the point in time 12:59:02.7, which are included as targets for weighting, may be determined as the second error value (V2).

[0345] Meanwhile, according to one aspect, the computing device may determine the processed position information for the target time point as a third error value in response to a determination that a predetermined number of pieces of information regarding the player's position measured at a time point prior to the target time point do not exist (step 2425). That is, from the time point 12:55:11.3 to the time point 12:55:11.6, the processed position information may be determined as the third error value (V3) because the predetermined number of pieces of measurement information for the time points prior to the target time point to which the weighting is applied do not exist.

[0346] According to embodiments of the present disclosure, preprocessing procedures and / or filtering procedures as discussed above may be performed.

[0347] Furthermore, as described above, when real-time sports analysis is performed according to embodiments of the present disclosure, preprocessing of collected player information may be performed through procedures that are somewhat different from those of post-analysis. Meanwhile, according to one aspect, the computing device that performs real-time data collection and preprocessing and / or filtering procedures according to the embodiments of the present disclosure may be a wearable device mounted on a sports player. That is, unlike post-analysis, the preprocessing and filtering procedures described above may be performed by a processor provided in a device for obtaining location information about the player, such as a sensing device.

[0348]

[0349] Ensuring consistency between post-mortem and real-time analysis

[0350] At least some of the validation or preprocessing or filtering procedures for data as described herein may result in improved accuracy of information regarding player positions for real-time analysis, which may have the beneficial effect of improving consistency between post-hoc and real-time analysis of sporting events.

[0351] In addition, embodiments of the present disclosure may employ at least some of various features to improve consistency between post-hoc and real-time analysis of a sporting event.

[0352] For example, the problem of floating point differences may be considered due to the difference in the operating language of the computing device when performing post-analysis and the operating language of the computing device when performing real-time analysis. For example, various standards may be set when performing sports analysis. For example, the speed standard for classifying a specific action may be set to 10 m / s, where 10 m / s may be converted to 36 km / h. However, depending on the operating language of the computing device, a floating point error may be reflected when converting 10 m / s to km / h through calculation. That is, while in a certain operating language, as an example, when converting 10 m / s to km / h, a result of 36.0001 km / h is produced, in another operating language, a result of 36.00002 km / h is produced, so there may be a difference between the two.

[0353] In other words, floating point issues may exist. For example, if you use / 3.6 to convert the HSR threshold of 19.8 km / h to SI units in an if statement, the result may be different values, such as 5.5000000034 on a computing device for real-time analysis, such as a tablet PC, and 5.5000000023 on a computing device for post-analysis, such as a web server.

[0354] Accordingly, instead of sending the phenotypic threshold and changing it to a calculation threshold for each, when synchronizing the threshold value on the live side on the web, the phenotypic value and the calculation value can be sent separately to perform metric calculation. For example, in order to secure consistency between real-time analysis and post-analysis according to one aspect of the present disclosure, unit conversion for a specific criterion can be performed not separately in the computing device for real-time analysis or the computing device for post-analysis, but by transmitting the converted criterion value from either device to perform metric calculation. For example, by presetting the criterion values ​​for each of the m / s unit and the km / h unit in the computing device for post-analysis and loading and utilizing the saved setting values ​​in the computing device for real-time analysis, the consistency between real-time analysis and post-analysis can be improved.

[0355]

[0356] FIG. 26 is a block diagram showing an exemplary configuration of a computing system in which a method according to one embodiment of the present disclosure can be performed.

[0357] Referring to FIG. 26, the computing system (800) may include flash storage (3010), a processor (3020), RAM (3030), an input / output device (3040), and a power supply (3050). In addition, the flash storage (3010) may include a memory device (3011) and a memory controller (3012). Meanwhile, although not shown in FIG. 26, the computing system (3000) may further include ports for communicating with a video card, a sound card, a memory card, a USB device, or the like, or for communicating with other electronic devices.

[0358] The computing system (3000) may be implemented as a personal computer or a portable electronic device such as a laptop computer, a mobile phone, a personal digital assistant (PDA), or a camera.

[0359] The processor (3020) can perform specific calculations or tasks. Depending on the embodiment, the processor (3020) can be a microprocessor, a central processing unit (CPU). The processor (3020) can communicate with the RAM (3030), the input / output device (3040), and the flash storage (3010) via a bus (3060), such as an address bus, a control bus, and a data bus.

[0360] According to one embodiment, the processor (3020) may also be connected to an expansion bus, such as a Peripheral Component Interconnect (PCI) bus.

[0361] RAM (3030) can store data required for the operation of the computing system (3000). For example, any type of random access memory including DRAM, mobile DRAM, SRAM, PRAM, FRAM, MRAM, and RRAM can be used as RAM (3030).

[0362] The input / output device (3040) may include input means such as a keyboard, keypad, mouse, etc. and output means such as a printer, display, etc. The power supply (3050) may supply an operating voltage necessary for the operation of the computing system (3000).

[0363]

[0364] The method according to the present invention described above can be implemented as computer-readable code on a computer-readable recording medium. Computer-readable recording media include all types of recording media that store data that can be deciphered by a computer system. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices. Furthermore, the computer-readable recording medium can be distributed across computer systems connected to a computer communications network, and stored and executed as readable code in a distributed manner.

[0365] Although the present invention has been described with reference to the drawings and embodiments, it does not mean that the scope of protection of the present invention is limited by the drawings or embodiments, and it will be understood that a person skilled in the art can modify and change the present invention in various ways without departing from the spirit and scope of the present invention as described in the following claims.

[0366] Specifically, the described features may be implemented within digital electronic circuitry, or within computer hardware, firmware, or combinations thereof. The features may be implemented, for example, in a computer program product embodied within a storage device within a machine-readable storage device for execution by a programmable processor. And the features may be implemented by a programmable processor executing a program of instructions for performing the functions of the described embodiments by operating on input data and generating output. The described features may be implemented within one or more computer programs executable on a programmable system comprising at least one programmable processor, at least one input device, and at least one output device coupled to receive data and instructions from a data storage system, and to transmit data and instructions to the data storage system. A computer program comprises a set of instructions that can be used directly or indirectly within a computer to perform a particular operation for a given result. A computer program may be written in any programming language, including compiled or interpreted languages, and may be used in any form, including as a module, component, subroutine, or other unit suitable for use in another computing environment, or as a standalone program.

[0367] Suitable processors for executing the program of instructions include, for example, both general-purpose and special-purpose microprocessors, and either a single processor or multiple processors of another type of computer. Also suitable storage devices for implementing the computer program instructions and data implementing the described features include, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices, magnetic devices such as internal hard disks and removable disks, magneto-optical disks, and all forms of non-volatile memory including CD-ROM and DVD-ROM disks. The processor and memory may be integrated within or added to application-specific integrated circuits (ASICs).

[0368] Although the present invention described above is described based on a series of functional blocks, it is not limited to the above-described embodiments and the attached drawings, and it will be apparent to those skilled in the art to which the present invention pertains that various substitutions, modifications, and changes are possible within a scope that does not depart from the technical spirit of the present invention.

[0369] The combination of the above-described embodiments is not limited to the above-described embodiments, and various combinations may be provided in addition to the above-described embodiments depending on implementation and / or needs.

[0370] In the above-described embodiments, the methods are described based on a flowchart as a series of steps or blocks. However, the present invention is not limited to the order of the steps, and some steps may occur in a different order or simultaneously with other steps described above. Furthermore, those skilled in the art will understand that the steps depicted in the flowchart are not exclusive, and other steps may be included, or one or more steps in the flowchart may be deleted without affecting the scope of the present invention.

[0371] The above-described embodiments include examples of various aspects. While not all possible combinations to illustrate the various aspects can be described, those skilled in the art will recognize that other combinations are possible. Accordingly, the present invention is intended to encompass all other alterations, modifications, and variations within the scope of the following claims.

Claims

1. A method for collecting information on the location of a sports player in real time, performed by a computing device, A step of obtaining, based on a positioning sensor, information about the position of the player at a plurality of points in time according to a predetermined time interval and at least one positioning state characteristic value, wherein the positioning state characteristic value includes information about the positioning state at a point in time at which the positioning sensor determines information about the position of the player; and A step of determining the validity of information about the position of the player based on the positioning status characteristic value acquired from the positioning sensor; including; A method for collecting information about the location of a sports player in real time.

2. In paragraph 1, The above positioning sensor, A Global Navigation Satellite System (GNSS) sensor, A method for collecting information about the location of a sports player in real time.

3. In paragraph 2, The above positioning status characteristic values ​​are, Information on the number of satellites used for positioning of the above positioning sensor; Information about the positioning mode of the above positioning sensor; Accuracy information about the vertical position of the above positioning sensor; or Information on the strength of a satellite signal received by the positioning sensor; including at least one of: A method for collecting information about the location of a sports player in real time.

4. In paragraph 3, Information about the above positioning mode is: The above positioning sensor, RTK-GPS (Realtime-kinematic GPS) mode; Normal mode; Contains information that it operates in one of the Noise modes; A method for collecting information about the location of a sports player in real time.

5. In paragraph 2, The step of determining the validity of information about the position of the above player is: A step of determining that a first condition for the positioning state characteristic value is not satisfied at a first point in time; and In response to a determination that the first condition is not satisfied continuously from the first time point to the second time point after a predetermined threshold time interval has elapsed, a step of determining that information about the player's location acquired after the second time point is invalid; including; A method for collecting information about the location of a sports player in real time.

6. In paragraph 5, The step of determining the validity of information about the position of the above player is: A step of determining that information about the player's location acquired after the third time point when the first condition is satisfied for the first time after the second time point is valid; further comprising; A method for collecting information about the location of a sports player in real time.

7. In paragraph 5, The first condition above is, All detailed conditions for multiple positioning status characteristic values ​​included in the above first condition are satisfied. A method for collecting information about the location of a sports player in real time.

8. In paragraph 7, The above detailed conditions are, Whether the number of satellites used for positioning of the above positioning sensor is 6 or more; including; A method for collecting information about the location of a sports player in real time.

9. In paragraph 8, The above detailed conditions are, Further including whether the positioning mode of the above positioning sensor is RTK-GPS mode or normal mode; A method for collecting information about the location of a sports player in real time.

10. In paragraph 9, The above detailed conditions are, Whether the accuracy of the vertical position of the above positioning sensor is 3 m or less; further including; A method for collecting information about the location of a sports player in real time.

11. In Article 10, The above detailed conditions are, Whether the strength of the satellite signal received by the positioning sensor is 20 dB or higher; further including; A method for collecting information about the location of a sports player in real time.

12. In paragraph 1, The above computing device, A wearable device that is mounted on a sports player, A method for collecting information about the location of a sports player in real time.

13. A device for collecting information on the location of a sports player in real time, the device comprising: a processor; and a memory; The above processor, Based on a positioning sensor, information about the position of the player at a plurality of points in time according to a predetermined time interval and at least one positioning state characteristic value (Characteristic value), wherein the positioning state characteristic value includes information about the positioning state at a point in time at which the positioning sensor determines information about the position of the player; and Determine the validity of information about the position of the player based on the positioning status characteristic value acquired from the positioning sensor; A device for collecting information about the location of sports players in real time.

14. A computer-readable storage medium containing instructions executable by a processor, said instructions being for collecting information about the location of a sports player in real time and being executed by said processor to cause said processor to: Based on a positioning sensor, information about the position of the player at a plurality of points in time according to a predetermined time interval and at least one positioning state characteristic value (Characteristic value), wherein the positioning state characteristic value includes information about the positioning state at a point in time at which the positioning sensor determines information about the position of the player; and Determine the validity of information about the position of the player based on the positioning status characteristic value acquired from the positioning sensor; Computer readable storage medium.

15. A method for collecting information about a sports player in real time, performed by a computing device, A step of collecting information about a sports player at a first point in time based on a first sensor; A step of collecting information on whether there is movement of the inertial sensor at a first point in time based on the inertial sensor; and A step of determining the validity of information about the sports player based on information about the presence or absence of movement of the inertial sensor; comprising; A method for collecting information about sports players in real time.

16. In paragraph 15, The above first sensor is a Global Navigation Satellite System (GNSS) sensor, The information about the above sports player is information about the speed of the above sports player, The step of determining the validity of the information about the sports player comprises changing the information about the speed of the sports player to 0 in response to a determination that there is no movement of the inertial sensor and that the speed of the sports player is below a predetermined threshold. A method for collecting information about sports players in real time.

17. In paragraph 15, The above first sensor is a heart rate (HR) sensor, The information about the above sports player is information about the heart rate of the above sports player, The step of determining the validity of the information about the sports player comprises changing the information about the heart rate of the sports player to 0 in response to a determination that there is no movement of the inertial sensor. A method for collecting information about sports players in real time.

18. A device for collecting information on the location of a sports player in real time, the device comprising: a processor; and a memory; The above processor, Collect information about a sports player at a first point in time based on a first sensor; Collecting information on whether there is movement of the inertial sensor at a first point in time based on the inertial sensor; and Determine the validity of information about the sports player based on information about the presence or absence of movement of the inertial sensor; A method for collecting information about sports players in real time.

19. A computer-readable storage medium containing instructions executable by a processor, said instructions being for collecting information about a sports player in real time and being executed by said processor to cause said processor to: Collect information about a sports player at a first point in time based on a first sensor; Collecting information on whether there is movement of the inertial sensor at a first point in time based on the inertial sensor; and Determine the validity of information about the sports player based on information about the presence or absence of movement of the inertial sensor; Computer readable storage medium.

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