A floor feature analysis method, system and device

Abstract

The embodiments of the present application disclose a landing feature analysis method, system, and device, which belong to the technical field of human motion analysis. Among them, a landing feature analysis method includes: obtaining landing motion data of a user, the landing motion data including three-axis landing acceleration, three-axis landing angle, and three-axis landing angular velocity, the three-axis landing acceleration including Z-axis landing acceleration, Y-axis landing acceleration along the left and right direction of the user, and X-axis landing acceleration along the front and back direction of the user at multiple landing time points of the user in the landing stage, the three-axis landing angle including Z-axis landing angle, Y-axis landing angle, and X-axis landing angle at multiple landing time points of the user in the landing stage, and the three-axis landing angular velocity including X-axis landing angular velocity at multiple landing time points of the user in the landing stage; and determining the user's landing action type and the user's landing coordination characteristics based on the X-axis landing acceleration, X-axis landing angle, and X-axis landing angular velocity at multiple landing time points of the user.

Description

Technical Field

[0001] The present invention relates to the field of human motion analysis, and in particular to a landing feature analysis method, system and device. Background Art

[0002] No sport is solely about the activity of a single muscle. Therefore, when practicing, you can't focus solely on one specific part. For example, the key factors affecting standing long jump are leg power, waist and abdominal strength, overall coordination, and, of course, the technique itself. The main issues during the landing phase are: 1) Insufficient lower limb strength. Leg power plays an absolutely crucial role in standing long jump. Lower limb strength includes the strength of the major thigh muscles, knees, and ankles; 2) Weak waist and abdominal strength. The abdomen connects the upper and lower limbs, and both upper and lower limb strength events require the waist and abdomen to function. 3) Poor overall coordination. Coordination is also crucial for standing long jump. People with good coordination can sometimes jump farther than others even if their lower limb strength is inferior. Of course, there are also people with poor coordination who can jump farther without even swinging their arms; 4) Technical errors. Every sport has a theoretical foundation, and the analysis of technical movements is crucial.

[0003] Currently, most of the technologies used for standing long jump motion analysis focus on measuring the distance of the long jump, but there is no analysis of the technical movements during the landing process of the standing long jump.

[0004] Therefore, it is necessary to provide a landing feature analysis method, system and equipment to analyze the landing features of standing long jump. Summary of the Invention

[0005] One embodiment of the present specification provides a landing feature analysis method, including: obtaining landing motion data of a user, the landing motion data including three-axis landing acceleration, three-axis landing angle, and three-axis landing angular velocity, the three-axis landing acceleration including Z-axis landing acceleration along the user's up-down direction, Y-axis landing acceleration along the user's left-right direction, and X-axis landing acceleration along the user's front-back direction at multiple landing time points during the landing phase, the three-axis landing angle including Z-axis landing angle along the user's up-down direction, Y-axis landing angle along the user's left-right direction, and X-axis landing angle along the user's front-back direction at multiple landing time points during the landing phase, and the three-axis landing angular velocity including X-axis landing angular velocity along the user's front-back direction at multiple landing time points during the landing phase; and determining the user's landing action type and the user's landing coordination characteristics based on the Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity of the user at multiple landing time points.

[0006] Existing standing long jump motion analysis technologies focus on measuring the final results of the standing long jump, helping users intuitively obtain the results of the standing long jump, but cannot help correct landing movements. A landing feature analysis method can obtain the user's take-off motion data and analyze the landing motion data. Based on the Z-axis take-off angle, Y-axis take-off angle, and X-axis take-off angle, the user's landing movement type is calculated to improve standing long jump results.

[0007] The motion data also includes the Z-axis height; obtaining the user's landing motion data includes: obtaining the user's full-process motion data during the standing long jump process, the full-process motion data including the X-axis angle, Y-axis angle and Z-axis angle, X-axis angular velocity, X-axis acceleration, Y-axis acceleration and Z-axis acceleration, and Z-axis height at multiple time points during the standing long jump process; determining the landing time point based on the Z-axis height; using the X-axis angle at the landing time point as the X-axis landing angle, the Y-axis angle as the Y-axis landing angle, the Z-axis angle as the Z-axis landing angle, the X-axis angular velocity as the X-axis landing angular velocity, the X-axis acceleration as the X-axis landing acceleration, the Y-axis acceleration as the Y-axis landing acceleration, and the Z-axis angle as the Z-axis landing acceleration.

[0008] Determining a landing time point based on the Z-axis height includes: for each time point, determining whether the Z-axis height is greater than a first preset Z-axis height value; if the Z-axis height is greater than the first preset Z-axis height value, using the time point as a take-off time point; determining whether the Z-axis height is equal to a second preset Z-axis height value; if the Z-axis height is equal to the second preset Z-axis height value, using the time point as a candidate landing time point; and sorting multiple candidate landing time points in chronological order, and selecting the first candidate landing time point after the take-off time point as the landing time point.

[0009] Determining the user's landing action type based on the user's Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity at multiple landing time points includes: judging the user's center of gravity transfer state based on the relationship between the Y-axis preset landing angle and the Y-axis landing angle, wherein if the Y-axis landing angle is less than the Y-axis preset landing angle, judging the user's center of gravity transfer state as backward; if the Y-axis landing angle is equal to or greater than the Y-axis preset landing angle, judging the relationship between the Y-axis landing angular velocity and the Y-axis preset landing angular velocity, wherein if the Y-axis landing angular velocity is greater than the Y-axis preset landing angular velocity, judging the user's center of gravity transfer state as forward leaning, and if the Y-axis landing angular velocity is less than or equal to the Y-axis preset landing angular velocity, judging the user's center of gravity transfer state as stable.

[0010] If the Y-axis landing angle is less than the Y-axis preset landing angle, the user's center of gravity transfer state is judged to be backward, including: obtaining the user's regression motion data, the regression motion data including the Y-axis regression angle along the front and back direction of the user, if the Y-axis regression angle is equal to the Y-axis preset landing angle, then the user's center of gravity transfer state is judged to be standing after backward, if the Y-axis regression angle is not equal to the Y-axis preset landing angle, then the user's center of gravity transfer state is judged to be slipping after backward.

[0011] Obtaining the user's regression motion data includes: obtaining the user's motion data at multiple time points throughout the standing long jump; sorting the multiple time points in chronological order, taking the last time point as the regression time point; and using the Y-axis angle of the regression time point as the Y-axis regression angle.

[0012] The user's landing coordination characteristics are determined based on the user's Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity at multiple landing time points, including: normalizing the Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity; and determining the landing coordination characteristics based on a weighted result of the normalized Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity.

[0013] The comprehensive coordination value is determined based on the weighted results of landing coordination characteristics, take-off angle, abdominal coordination value and standing long jump distance.

[0014] One embodiment of the present specification provides a landing feature acquisition system, including: a landing data acquisition module, used to acquire a user's landing motion data, the landing motion data including three-axis landing acceleration, three-axis landing angle and three-axis landing angular velocity, the three-axis landing acceleration including the Z-axis landing acceleration along the user's up-down direction, the Y-axis landing acceleration along the user's left-right direction and the X-axis landing acceleration along the user's front-back direction at multiple landing time points during the landing phase, the three-axis landing angle including the Z-axis landing angle along the user's up-down direction, the Y-axis landing angle along the user's left-right direction and the X-axis landing angle along the user's front-back direction at multiple landing time points during the landing phase, and the three-axis landing angular velocity including the X-axis landing angular velocity along the user's front-back direction at multiple landing time points during the landing phase; a landing type determination module, used to calculate the user's landing action type based on the Y-axis landing acceleration, the Y-axis landing angle and the Y-axis landing angular velocity; and a coordination feature determination module, used to calculate the user's landing coordination feature based on the Y-axis landing acceleration, the Y-axis landing angle and the Y-axis landing angular velocity.

[0015] One of the embodiments of this specification provides a landing feature acquisition device, including a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that the processor implements the above-mentioned landing feature analysis method when executing the program. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] in:

[0018] Figure 1 This is a schematic diagram of an application scenario of a landing feature analysis system according to some embodiments of the present application;

[0019] Figure 2 is an exemplary block diagram of a landing feature analysis system according to some embodiments of the present application;

[0020] Figure 3 is an exemplary flow chart of a landing feature analysis method according to some embodiments of this specification;

[0021] Figure 4a is a schematic diagram for illustrating an X-axis acceleration change curve according to some embodiments of this specification;

[0022] Figure 4b is a schematic diagram for illustrating a Y-axis acceleration change curve according to some embodiments of this specification;

[0023] Figure 4c It is a schematic diagram for showing a Z-axis acceleration change curve according to some embodiments of this specification.

[0024] In the figure, 100 is a landing feature acquisition system; 110 is a processing device; 120 is a network; 130 is a sensor; 140 is a storage device; and 150 is a terminal device. DETAILED DESCRIPTION

[0025] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0026] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] Figure 1 It is a schematic diagram of an application scenario of a landing feature acquisition system 100 according to some embodiments of the present application.

[0028] like Figure 1 As shown, the landing feature acquisition system 100 may include a processing device 110 , a network 120 , a sensor 130 , a storage device 140 and a terminal device 150 .

[0029] The landing feature acquisition system 100 can provide assistance to students in sports. For example, it can be used to identify and analyze the landing features of students during standing long jump, effectively helping students correct irregular landing movements and improve their standing long jump performance. It should be noted that the landing feature acquisition system 100 can also be applied to other devices, scenarios, and applications that require sports action recognition. This is not a limitation here. Any device, scenario, and / or application that can use a landing feature analysis method included in this application is within the scope of protection of this application.

[0030] The processing device 110 can be used to process information and / or data related to landing feature identification. For example, the processing device 110 can be used to receive landing motion data of a user, wherein the landing motion data includes three-axis landing angles and three-axis landing accelerations, wherein the three-axis landing angles include an X-axis landing angle, a Y-axis landing angle, and a Z-axis landing angle, and the three-axis landing accelerations include an X-axis landing acceleration, a Y-axis landing acceleration, and a Z-axis landing acceleration, wherein the Z-axis is a vertically upward direction, the Y-axis is a direction from the left to the right of the user, and the X-axis is a direction from the back to the front of the user, and based on the X-axis landing acceleration, X-axis landing angle, and X-axis landing angular velocity of the user at multiple landing time points, the user's landing motion type and the user's landing coordination characteristics are determined.

[0031] Processing device 110 may be local or remote. For example, processing device 110 may access information and / or data stored in terminal device 150 and storage device 140 via network 120. Processing device 110 may directly connect to terminal device 150 and storage device 140 to access the information and / or data stored therein. Processing device 110 may be executed on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like.

[0032] The processing device 110 may include a processor. The processor may process data and / or information related to landing feature recognition to perform one or more functions described in this application. For example, the processor may receive the user's landing motion data. For another example, the processor may calculate the user's landing action type and the user's landing coordination characteristics based on the X-axis landing acceleration, the X-axis landing angle, and the X-axis landing angular velocity. The processor may include one or more sub-processors (for example, a single-core processing device or a multi-core multi-core processing device). As an example only, the processor may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic circuit (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc. or any combination thereof.

[0033] The network 120 can facilitate the exchange of data and / or information within the landing feature acquisition system 100. One or more components within the landing feature acquisition system 100 (e.g., the processing device 110, the sensor 130, the storage device 140, and the terminal device 150) can transmit data and / or information to other components within the landing feature acquisition system 100 via the network 120. For example, the processing device 110 can receive landing motion data of a user from the sensor 130 via the network 120. The network 120 can be any type of wired or wireless network. For example, the network 120 can include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, an internet network, a local area network (LAN), a wide area network (WAN), a wireless area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or any combination thereof. The network 120 can include one or more network entry and exit points. For example, the network 120 may include wired or wireless network access points, such as base stations and / or internet exchange points, through which one or more components of a ground feature acquisition system 100 may connect to the network 120 to exchange data and / or information.

[0034] Sensor 130 can be used to obtain full-process motion data of the user during the standing long jump, and transmit the full-process motion data of the user during the standing long jump to processing device 110. Processing device 110 can then obtain landing motion data from the full-process motion data. The full-process motion data obtained by sensor 130 may include the user's X-axis angle, Y-axis angle, and Z-axis angle, X-axis angular velocity, X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and Z-axis height at multiple time points during the standing long jump. Sensor 130 can be a device for obtaining the user's X-axis angle, Y-axis angle, and Z-axis angle, X-axis angular velocity, X-axis acceleration, Y-axis acceleration, and Z-axis height, such as a ten-axis sensor, a nine-axis sensor, a six-axis sensor, or a three-axis sensor. In actual applications, the sensor is worn on the user's ankle.

[0035] The storage device 140 can be connected to the network 120 to enable communication with one or more components of the landing feature acquisition system 100 (e.g., the processing device 110, the terminal device 150, etc.). One or more components of the landing feature acquisition system 100 can access data or instructions stored in the storage device 140 through the network 120. The storage device 140 can be directly connected to or communicate with one or more components of the landing feature acquisition system 100 (e.g., the processing device 110, the terminal device 150). The storage device 140 can be part of the processing device 110. The processing device 110 can also be located in the terminal device 150.

[0036] The terminal device 150 can obtain information or data from a landing feature acquisition system 100. A user (for example, a student or a teacher) can obtain the landing feature type through the terminal device 150. The terminal device 150 may include one of a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. The mobile device may include a wearable device, a smart action device, a virtual reality device, an augmented reality device, etc., or any combination thereof. The wearable device may include a smart bracelet, smart shoes and socks, smart glasses, smart helmets, smart watches, smart clothing, smart backpacks, smart accessories, smart handles, etc., or any combination thereof. The smart action device may include a smart phone, a personal digital assistant (PDA), a gaming device, a navigation device, a POS device, etc., or any combination thereof. The virtual reality device and / or the augmented reality device may include a virtual reality helmet, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, etc., or any combination thereof.

[0037] It should be noted that the above description is provided for illustrative purposes only and is not intended to limit the scope of this application. For those skilled in the art, various changes and modifications can be made under the guidance of the contents of this application. The features, structures, methods and other features of the exemplary embodiments described in this application can be combined in various ways to obtain additional and / or alternative exemplary embodiments. For example, the storage device 140 can be a data storage device including a cloud computing platform, such as a public cloud, a private cloud, a community and a hybrid cloud. However, these changes and modifications will not deviate from the scope of this application.

[0038] Figure 2 It is an exemplary block diagram of a landing feature acquisition system 100 according to some embodiments of the present application.

[0039] like Figure 2 As shown, the landing feature acquisition system 100 may include a landing data acquisition module, a landing type determination module, and a coordination feature determination module. The landing data acquisition module, the landing type determination module, and the coordination feature determination module may be implemented on the processing device 110.

[0040] The landing data acquisition module can be used to obtain the user's landing motion data. The landing motion data includes three-axis landing acceleration, three-axis landing angle, and three-axis landing angular velocity. The three-axis landing acceleration includes the user's Z-axis landing acceleration along the user's up and down direction, the Y-axis landing acceleration along the user's left and right direction, and the X-axis landing acceleration along the user's front and back direction at multiple landing time points during the landing phase. The three-axis landing angle includes the user's Z-axis landing angle along the user's up and down direction, the Y-axis landing angle along the user's left and right direction, and the X-axis landing angle along the user's front and back direction at multiple landing time points during the landing phase. The three-axis landing angular velocity includes the user's X-axis landing angular velocity along the user's front and back direction at multiple landing time points during the landing phase.

[0041] The sensor 130 can obtain the full-process motion data of the user during the standing long jump, wherein the full-process motion data includes the X-axis angle, Y-axis angle and Z-axis angle, X-axis angular velocity, X-axis acceleration, Y-axis acceleration and Z-axis acceleration, and Z-axis height of the user at multiple time points during the standing long jump. The interval between two adjacent time points is consistent. The interval between two adjacent time points can be 0.1s, 0.5s, etc. The landing data acquisition module can determine the landing time point based on the Z-axis height, and use the X-axis angle at the landing time point as the X-axis landing angle, the Y-axis angle as the Y-axis landing angle, the Z-axis angle as the Z-axis landing angle, the X-axis angular velocity as the X-axis landing angular velocity, the X-axis acceleration as the X-axis landing acceleration, the Y-axis acceleration as the Y-axis landing acceleration, and the Z-axis angle as the Z-axis landing acceleration.

[0042] The landing data acquisition module determines the landing time point based on the Z-axis heights of multiple time points, which may include: for each time point, judging whether the Z-axis height is greater than a first preset Z-axis height value; if the Z-axis height is greater than the first preset Z-axis height value, using the time point as the take-off time point; judging whether the Z-axis height is equal to a second preset Z-axis height value; if the Z-axis height is equal to the second preset Z-axis height value, using the time point as a candidate landing time point; sorting the multiple candidate landing time points in chronological order, and selecting the first candidate landing time point after the take-off time point as the landing time point.

[0043] The landing type determination module can also generate an X-axis acceleration change curve based on the full-process motion data, in which the X-axis acceleration changes with the time point; based on the full-process motion data, generate a Y-axis acceleration change curve in which the Y-axis acceleration changes with the time point; based on the full-process motion data, generate a Z-axis acceleration change curve in which the Z-axis acceleration changes with the time point.

[0044] The landing type determination module can be used to calculate the user's landing action type based on the Y-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity.

[0045] The landing type determination module can calculate the user's landing trend based on the Y-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity; and determine the landing action type based on the user's landing trend. The landing type determination module can also determine the landing action type based on the user's landing trend using a first neural network model.

[0046] The coordination feature determination module can be used to calculate the user's landing coordination feature based on the Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity. The coordination feature determination module can normalize the Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity; and determine the landing coordination feature based on the weighted result of the normalized Y-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity.

[0047] Figure 3 FIG. 1 is an exemplary flow chart of a landing feature analysis method according to some embodiments of this specification, which can be executed by a landing feature analysis system 100. Figure 3 As shown, the landing feature analysis method includes the following steps:

[0048] Step 310: Acquire the user's landing motion data. Step 310 may be executed by a landing data acquisition module.

[0049] Landing motion data includes three-axis landing acceleration, three-axis landing angle and three-axis landing angular velocity. The three-axis landing acceleration includes the Z-axis landing acceleration along the user's up and down direction, the Y-axis landing acceleration along the user's left and right direction and the X-axis landing acceleration along the user's front and back direction at multiple landing time points during the landing phase. The three-axis landing angle includes the Z-axis landing angle along the user's up and down direction, the Y-axis landing angle along the user's left and right direction and the X-axis landing angle along the user's front and back direction at multiple landing time points during the landing phase. The three-axis landing angular velocity includes the X-axis landing angular velocity along the user's front and back direction at multiple landing time points during the landing phase.

[0050] Acquiring the user's landing motion data may include the following steps 311-313.

[0051] Step 311: Obtain motion data for the entire process of the user's standing long jump. This motion data includes the user's X-axis angle, Y-axis angle, and Z-axis angle, X-axis angular velocity, X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and Z-axis height at multiple time points during the standing long jump. While performing the standing long jump, the user may wear sensor 130, which can obtain motion data for the entire process.

[0052] Step 312: Determine the landing time based on the Z-axis height.

[0053] For each time point, a determination can be made as to whether the Z-axis height is greater than a first preset Z-axis height value. If so, the time point is considered the take-off time point. A determination can then be made as to whether the Z-axis height is equal to a second preset Z-axis height value. If so, the time point is considered a candidate landing time point. For example, if the first preset Z-axis height value is set to 10 cm above the ground, it can be determined that the user is in a take-off state when the user's vertical height is greater than 10 cm above the ground. If the second preset Z-axis height value is set to 0 cm above the ground, the time point at which the user makes contact with the ground is considered a candidate landing time point. Furthermore, the landing time point can be determined by analyzing the Z-axis acceleration curve and the amount of change in the sensor's Z-axis acceleration per unit time. Multiple candidate landing time points are sorted in chronological order, and the first candidate landing time point after the take-off time point is selected as the landing time point. For example, given three candidate time points: 10 seconds 11, 10 seconds 24, and 10 seconds 33, 10 seconds 11 is selected as the take-off time point.

[0054] Step 313: Determine the user's landing motion data based on the landing time point.

[0055] The Y-axis angle at the landing time point can be used as the Y-axis landing angle, the Y-axis angle as the Y-axis landing angle, the Z-axis angle as the Z-axis landing angle, the Y-axis angular velocity as the Y-axis landing angular velocity, the Y-axis acceleration as the Y-axis landing acceleration, the Y-axis acceleration as the Y-axis landing acceleration, and the Z-axis angle as the Z-axis landing acceleration.

[0056] Step 320: Calculate the user's landing action type based on the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity. Step 320 may be performed by a landing type determination module.

[0057] The user's center of gravity transfer state is judged based on the relationship between the Y-axis preset landing angle and the Y-axis landing angle. If the Y-axis landing angle is less than the Y-axis preset landing angle, the user's center of gravity transfer state is judged to be backward; if the Y-axis landing angle is equal to or greater than the Y-axis preset landing angle, the relationship between the Y-axis landing angular velocity and the Y-axis preset landing angular velocity is judged. If the Y-axis landing angular velocity is greater than the Y-axis preset landing angular velocity, the user's center of gravity transfer state is judged to be forward leaning; if the Y-axis landing angular velocity is less than or equal to the Y-axis preset landing angular velocity, the user's center of gravity transfer state is judged to be stable.

[0058] The user's center of gravity transfer state can also be judged based on the Y-axis regression angle. If the Y-axis regression angle is equal to the Y-axis preset landing angle, the user's center of gravity transfer state is judged to be standing after backing up. If the Y-axis regression angle is not equal to the Y-axis preset landing angle, the user's center of gravity transfer state is judged to be slipping after backing up.

[0059] The landing action type can also be determined by the first neural network model based on the Z-axis landing acceleration, the Y-axis landing angle, the Y-axis landing angular velocity, the Y-axis preset landing angle, the Y-axis regression angle and the Y-axis preset landing angular velocity.

[0060] The first neural network model may include, but is not limited to, a deep neural network model, a recurrent neural network model, a custom model structure, etc. The input of the first neural network model may be the Z-axis landing acceleration, the Y-axis landing angle, the Y-axis landing angular velocity, the Y-axis preset landing angle, the Y-axis regression angle, and the Y-axis preset landing angular velocity, and the output of the first neural network model may be the user's landing action type.

[0061] Step 330 calculates the user's landing coordination characteristics based on the user's Z-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity at multiple landing time points. A comprehensive coordination value is determined based on a weighted combination of the landing coordination characteristics, take-off angle, abdominal coordination value, and standing long jump distance. Step 330 can be performed by a coordination characteristic determination module.

[0062] The Z-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity can be normalized; the landing coordination feature can be determined based on the weighted results of the normalized Z-axis landing acceleration, Y-axis landing angle, and Y-axis landing angular velocity. The landing velocity feature can be calculated based on the following formula:

[0063] V=x1*A1+y1*B1+z1*C1;

[0064] Among them, A1 is the normalized Z-axis landing acceleration value, x1 is the weight of A1, B1 is the normalized Y-axis landing angle value, y1 is the weight of B1, C1 is the normalized Y-axis landing angular velocity value, and z1 is the weight of C1.

[0065] A landing coordination feature can also be determined using a second neural network model based on the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity. The second neural network model can include, but is not limited to, a deep neural network model, a recurrent neural network model, a custom model structure, etc. The input of the second neural network model can be the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity, and the output of the second neural network model can be the landing coordination feature.

[0066] Existing standing long jump motion analysis technologies focus on measuring the final results of the standing long jump, helping users to intuitively obtain the results of the standing long jump, but cannot help correct the take-off action. A take-off feature analysis method can obtain the user's take-off motion data and analyze the take-off motion data, calculate the user's take-off motion type based on the Z-axis take-off angle, Y-axis take-off angle and X-axis take-off angle; calculate the user's take-off speed characteristics based on the Z-axis take-off acceleration, Y-axis take-off acceleration and X-axis take-off acceleration; calculate the user's lower limb strength characteristics based on the Z-axis take-off angle, Y-axis take-off angle, X-axis take-off angle, Z-axis take-off acceleration, Y-axis take-off acceleration and X-axis take-off acceleration, which can help users improve their take-off movements and improve their standing long jump results.

[0067] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0071] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A landing feature analysis method, characterized in that: include: Acquire landing motion data of the user, the landing motion data including three-axis landing acceleration, three-axis landing angle, and three-axis landing angular velocity. The three-axis landing acceleration includes the Z-axis landing acceleration along the user's up-down direction, the Y-axis landing acceleration along the user's left-right direction, and the X-axis landing acceleration along the user's front-back direction at multiple landing time points during the landing phase. The three-axis landing angle includes the Z-axis landing angle along the user's up-down direction, the Y-axis landing angle along the user's left-right direction, and the X-axis landing angle along the user's front-back direction at multiple landing time points during the landing phase. The three-axis landing angular velocity includes the X-axis landing angular velocity along the user's front-back direction at multiple landing time points during the landing phase. Determining the landing action type of the user and the landing coordination characteristics of the user based on the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity of the user at the multiple landing time points further includes: The user's center of gravity transfer state is determined based on the relationship between the preset Z-axis landing angle and the Y-axis landing angle, wherein if the Y-axis landing angle is less than the preset Y-axis landing angle, the user's center of gravity transfer state is determined to be backward, wherein regression motion data of the user is obtained, the regression motion data including the Y-axis regression angle along the front-back direction of the user, if the Y-axis regression angle is equal to the preset Y-axis landing angle, the user's center of gravity transfer state is determined to be standing after backward, and if the Y-axis regression angle is not equal to the preset Y-axis landing angle, the user's center of gravity transfer state is determined to be slipping after backward; If the Y-axis landing angle is equal to or greater than the Y-axis preset landing angle, the relationship between the Y-axis landing angular velocity and the Y-axis preset landing angular velocity is determined. If the Y-axis landing angular velocity is greater than the Y-axis preset landing angular velocity, the user's center of gravity transfer state is determined to be forward leaning; if the Y-axis landing angular velocity is less than or equal to the Y-axis preset landing angular velocity, the user's center of gravity transfer state is determined to be stable.

2. The method according to claim 1, characterized in that The motion data also includes Z-axis height; The acquiring of the user's landing motion data includes: Acquiring full-process motion data of the user during the standing long jump, the full-process motion data including X-axis angle, Y-axis angle, and Z-axis angle, Y-axis angular velocity, X-axis acceleration, Y-axis acceleration, and Z-axis acceleration, and Z-axis height of the user at multiple time points during the standing long jump; Determining a landing time point based on the Z-axis height; The X-axis angle at the landing time point is used as the X-axis landing angle, the Y-axis angle is used as the Y-axis landing angle, the Z-axis angle is used as the Z-axis landing angle, the X-axis angular velocity is used as the X-axis landing angular velocity, the X-axis acceleration is used as the X-axis landing acceleration, the Y-axis acceleration is used as the Y-axis landing acceleration, and the Z-axis angle is used as the Z-axis landing acceleration.

3. The method according to claim 2, characterized in that Determining the landing time point based on the Z-axis height includes: For each of the time points, Determining whether the Z-axis height is greater than a first preset Z-axis height value; If the Z-axis height is greater than the first preset Z-axis height value, the time point is used as the take-off time point; Determining whether the Z-axis height is equal to a second preset Z-axis height value; If the Z-axis height is equal to the second preset Z-axis height value, the time point is used as a candidate landing time point; The plurality of candidate landing time points are sorted in chronological order, and the first candidate landing time point after the take-off time point is selected as the landing time point.

4. The method according to claim 1, wherein The obtaining of the user's regression motion data includes: Acquiring the motion data of the user at multiple time points throughout the standing long jump process; Sort the multiple time points in chronological order, and take the last time point as the regression time point; The Y-axis angle at the regression time point is used as the Y-axis regression angle.

5. The method according to any one of claims 1 to 3, characterized in that The determining of the user's landing coordination feature based on the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity of the user at the multiple landing time points includes: Normalizing the X-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity; The landing coordination feature is determined based on a weighted result of the normalized Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity.

6. The method according to claim 5, characterized in that include: The comprehensive coordination value is determined based on a weighted result of the landing coordination characteristics, the take-off angle, the abdominal coordination value and the standing long jump distance.

7. A landing feature acquisition system, characterized in that: include: A landing data acquisition module is used to acquire the user's landing motion data, wherein the landing motion data includes three-axis landing acceleration, three-axis landing angle, and three-axis landing angular velocity. The three-axis landing acceleration includes the user's Z-axis landing acceleration along the user's up-down direction, the Y-axis landing acceleration along the user's left-right direction, and the X-axis landing acceleration along the user's front-back direction at multiple landing time points during the landing phase. The three-axis landing angle includes the user's Z-axis landing angle along the user's up-down direction, the Y-axis landing angle along the user's left-right direction, and the X-axis landing angle along the user's front-back direction at multiple landing time points during the landing phase. The three-axis landing angular velocity includes the user's X-axis landing angular velocity along the user's front-back direction at multiple landing time points during the landing phase. a landing type determination module, configured to calculate the landing action type of the user based on the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity; a coordination feature determination module, configured to calculate the user's landing coordination feature based on the Z-axis landing acceleration, the Y-axis landing angle, and the Y-axis landing angular velocity, and further configured to determine the user's center of gravity transfer state based on the magnitude relationship between the Z-axis preset landing angle and the Y-axis landing angle, wherein if the Y-axis landing angle is less than the Y-axis preset landing angle, the user's center of gravity transfer state is determined to be backward, wherein regression motion data of the user is obtained, the regression motion data including the Y-axis regression angle along the front-back direction of the user, if the Y-axis regression angle is equal to the Y-axis preset landing angle, the user's center of gravity transfer state is determined to be standing after backward, and if the Y-axis regression angle is not equal to the Y-axis preset landing angle, the user's center of gravity transfer state is determined to be slipping after backward; If the Y-axis landing angle is equal to or greater than the Y-axis preset landing angle, the relationship between the Y-axis landing angular velocity and the Y-axis preset landing angular velocity is determined. If the Y-axis landing angular velocity is greater than the Y-axis preset landing angular velocity, the user's center of gravity transfer state is determined to be forward leaning; if the Y-axis landing angular velocity is less than or equal to the Y-axis preset landing angular velocity, the user's center of gravity transfer state is determined to be stable.

8. A landing feature acquisition device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the landing feature analysis method according to any one of claims 1 to 6 is implemented.

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