Method, device, equipment and medium for determining effective counting axis

Abstract

The present application discloses a method, device, equipment and medium for determining a valid counting axis, which is applied to the field of intelligent device technology to solve the problem of low accuracy of rope skipping counting in the prior art. Specifically, the method comprises the following steps: obtaining valid data of the target counting axis in the rope skipping motion signal of the user; determining the valid features of the target counting axis in the rope skipping motion signal based on the valid data; and determining the valid counting axis for rope skipping counting in the target counting axis through a pre-trained counting axis determination model according to the valid features of the target counting axis. In this way, the valid counting axis is determined based on the valid data, thereby avoiding the influence of invalid data on the determination of the valid counting axis. The valid counting axis can be determined from the target counting axis through the pre-trained counting axis determination model, thereby avoiding the influence of noise interference caused by non-standard rope skipping movements in other counting axes on the counting accuracy, thereby improving the accuracy of counting.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent devices, and in particular to a method, apparatus, device and medium for determining an effective counting axis. Background Art

[0002] Rope skipping is a healthy aerobic exercise that requires full-body coordination. It can enhance the body's flexibility and coordination, and train a person's jumping speed, balance and endurance. Rope skipping often requires counting the number of jumps to determine the amount of exercise.

[0003] Currently, rope skipping can be counted using a wristband equipped with sensors. However, there may be irregular movements during rope skipping, which may cause abnormal waveforms of each counting axis detected by the sensor, affecting the accuracy of rope skipping counting. Summary of the Invention

[0004] The embodiments of the present application provide a method, device, equipment and medium for determining an effective counting axis, so as to solve the problem of low accuracy of rope skipping counting in the prior art.

[0005] The technical solutions provided in the embodiments of this application are as follows:

[0006] In one aspect, an embodiment of the present application provides a method for determining a valid counting axis, comprising:

[0007] Obtain valid data of the target counting axis in the user's rope skipping motion signal;

[0008] Determining effective features of a target counting axis in the rope skipping motion signal based on the effective data;

[0009] According to the effective features of the target counting axes, the effective counting axes for rope skipping counting in the target counting axes are determined by a pre-trained counting axis determination model.

[0010] On the other hand, an embodiment of the present application provides a device for determining a valid counting axis, comprising:

[0011] A data acquisition unit, used to acquire valid data of a target counting axis in a user's rope skipping motion signal;

[0012] a data processing unit, configured to determine effective features of a target counting axis in the rope skipping motion signal based on the effective data;

[0013] The counting axis determination unit is used to determine the valid counting axes for rope skipping counting in the target counting axes through a pre-trained counting axis determination model according to the valid features of the target counting axes.

[0014] On the other hand, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the effective counting axis determination method provided by the embodiment of the present application is implemented.

[0015] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the effective counting axis determination method provided by the embodiment of the present application is implemented.

[0016] The beneficial effects of the embodiments of the present application are as follows:

[0017] In an embodiment of the present application, by determining the effective features of the target counting axis in the rope skipping motion signal based on the effective data, the effective counting axis can be further determined based on the effective data, avoiding the influence of invalid data on the determination of the effective counting axis. According to the effective features of the target counting axis, the effective counting axis is determined from the target counting axis through a pre-trained counting axis determination model, avoiding the influence of noise interference in the counting axis due to non-standard rope skipping movements on the counting accuracy, thereby improving the counting accuracy.

[0018] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description or be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 Schematic diagram of an overview of the method for determining an effective counting axis in an embodiment of the present application;

[0021] Figure 2a This is a waveform diagram of a rope skipping motion signal in an embodiment of the present application;

[0022] Figure 2b This is a waveform diagram of the acceleration signal of the vector and counting axes in the embodiment of the present application;

[0023] Figure 3 Schematic diagram of an overview of the training method for the counting axis determination model in an embodiment of the present application;

[0024] Figure 4 This is a functional structure diagram of a device for determining an effective counting axis in an embodiment of the present application;

[0025] Figure 5 Schematic diagram of the hardware structure of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and beneficial effects of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] In order to facilitate those skilled in the art to better understand this application, the technical terms involved in this application are briefly introduced below.

[0028] The rope skipping motion signal is a signal generated by the user skipping and detected by the sensor, including acceleration signals of multiple counting axes and / or angular velocity signals of multiple counting axes, wherein the acceleration signal is collected by the accelerometer sensor, and the angular velocity signal is collected by the gyroscope sensor, wherein the counting axes include the X-axis, the Y-axis and the Z-axis.

[0029] The target counting axis refers to the counting axis in the rope skipping motion signal that meets the preset threshold condition.

[0030] Valid data refers to data corresponding to the number of times the user jumps rope in the process of determining the user's jump-off.

[0031] Effective features are characteristic data representing the motion signals of corresponding counting axes. For the acceleration signals of multiple counting axes in a rope skipping motion signal, effective features for the counting axes include amplitude features and interval features. For the angular velocity signals of multiple counting axes in a rope skipping motion signal, effective features for the counting axes are mostly amplitude features.

[0032] It should be noted that the terms "first", "second", etc. mentioned in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "and / or" mentioned in this application describe the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0033] After introducing the technical terms involved in this application, the technical solutions provided by the embodiments of this application are described in detail.

[0034] The present application embodiment provides a method for determining an effective counting axis, see Figure 1 As shown, the general process of the effective counting axis determination method provided by the embodiment of the present application is as follows:

[0035] Step 101: Acquire valid data of a target counting axis in a user's rope skipping motion signal.

[0036] In actual applications, the rope skipping motion signal includes motion signals of multiple counting axes, wherein the counting axes include X-axis, Y-axis and Z-axis. Since the user may have irregular movements during exercise, after obtaining the rope skipping motion signal of the user, the target counting axis can be determined from the multiple counting axes included in the rope skipping motion signal, and the invalid data of the target counting axis in the motion signal of the user who did not jump can be filtered out to obtain the valid data of the target counting axis, wherein the valid data refers to the data corresponding to the number of times the user jumps rope in the process of determining the user's jumping, and the target counting axis refers to the counting axis in the rope skipping motion signal that meets the preset threshold conditions. Further valid counting axis determination is performed based on the valid data to avoid the influence of invalid data on the determination of the valid counting axis. Specifically, the determination of the valid data of the target counting axis in the rope skipping motion signal of the user can be determined by, but not limited to, the following methods:

[0037] First, obtain the user's rope skipping motion signal;

[0038] Then, the target counting axis and valid data of the target counting axis in the rope skipping motion signal are determined based on a preset threshold condition.

[0039] In actual applications, the rope skipping motion signal includes acceleration signals of multiple counting axes and / or angular velocity signals of multiple counting axes, wherein the acceleration signal is collected by an accelerometer sensor, and the angular velocity signal is collected by a gyroscope sensor. Correspondingly, the preset threshold conditions are divided into two categories, one is the amplitude condition and interval condition of acceleration, and the other is the amplitude condition of angular velocity. Each type of preset threshold condition can be set to multiple levels, and the grading standard can be the degree of force exerted by the user. Therefore, first, the threshold condition is selected according to the degree of force exerted by the user, and then the peak data and trough data of the motion signal of each axis of the rope skipping motion signal are determined by moving the sliding window according to the preset step size; the time interval data of the two adjacent peaks corresponding to the motion signal of each axis can be determined in the sliding window at adjacent positions. Next, the target counting axis and its valid data are determined according to the corresponding threshold conditions. It is worth mentioning that when the rope skipping motion signal includes acceleration signals of multiple counting axes, the valid data of the target counting axis are valid peak data, valid trough data and valid interval data; when the rope skipping motion signal includes angular velocity signals of multiple counting axes, the valid data of the target counting axis are valid peak data and valid trough data.

[0040] In specific implementation, taking the rope skipping motion signal including the acceleration signals of multiple counting axes as an example, the target counting axis and its valid data can be determined according to the corresponding threshold conditions in the following manner: if among the motion signals of each axis of the rope skipping motion signal, the amplitude data and interval data of the motion signal of one axis meet the amplitude condition and the interval condition, then start recording the valid data of the motion signal of this axis; if the amplitude data and interval data of the rope skipping motion signal of at least two axes both meet the amplitude condition and the interval condition, then determine that the user starts jumping, and record the valid data in the counting axis that meets the threshold condition before jumping, and use the counting axis that meets the threshold condition as the target counting axis. When the rope skipping motion signal includes the angular velocity signals of multiple counting axes, the difference in determining the target counting axis and its valid data according to the corresponding threshold conditions is only that the amplitude data of the motion signals of the multiple counting axes are judged according to the corresponding threshold conditions, without considering the interval data. The number of counting axes in the target counting axis includes the following two cases:

[0041] The first case: the target counting axes include two counting axes, that is, the motion signals of only two counting axes among the motion signals of the X-axis, Y-axis and Z-axis in the rope skipping motion signal meet the threshold condition.

[0042] The second case: the target counting axes include three counting axes, that is, the motion signals of the three counting axes in the motion signals of the X-axis, Y-axis and Z-axis in the rope skipping motion signal all meet the threshold condition.

[0043] Furthermore, in order to better eliminate the waveform noise interference caused by invalid movements during rope skipping, when the rope skipping motion signal includes acceleration signals of multiple counting axes, before obtaining the user's rope skipping motion signal, the vector and the acceleration signal of the counting axis can be added to the rope skipping motion signal. Specifically, but not limited to, the following methods can be used:

[0044] First, a vector sum of acceleration signals of a plurality of counting axes is determined, and the vector sum is used as the acceleration signal of the vector sum counting axis;

[0045] Then, the acceleration signals of the vector and counting axes are added to the rope skipping motion signal.

[0046] In a specific implementation, the vector sum of the acceleration signals of the X-axis, Y-axis, and Z-axis in the rope skipping motion signal can be obtained to obtain the acceleration signals of the vector and counting axes accordingly. The acceleration signals of the vector and counting axes are added to the rope skipping motion signal. Accordingly, the amplitude condition and interval condition corresponding to the vector and counting axes are additionally set in the threshold condition, thereby determining and recording the valid data of the third counting axis from the time when the motion signal of any counting axis meets the threshold condition to the time when the user starts jumping through the amplitude condition and interval condition corresponding to the vector and counting axes. It is worth mentioning that when the rope skipping motion signal includes the acceleration signals of multiple counting axes, the valid data also includes the valid peak data, valid trough data, and valid interval data of the vector and counting axes. Figure 2a The figure shows the acceleration signals of the X-axis, Y-axis, and Z-axis in the rope skipping motion signal. The acceleration signal of the X-axis has obvious noise interference, and there is a small peak at the arrow point. Figure 2b The figure shows the acceleration signals of the vector and counting axes, which effectively eliminate interference from the X-axis acceleration signal. By adding the acceleration signals of the vector and counting axes to the rope skipping motion signal, not only can noise interference in the waveform be eliminated, but waveform anomalies caused by non-standard rope skipping movements can also be largely eliminated, further improving counting accuracy.

[0047] Step 102: Determine the valid features of the target counting axis in the rope skipping motion signal based on the valid data.

[0048] In practical applications, the effective features of the counting axes corresponding to the acceleration signals of multiple counting axes in the rope skipping motion signal include amplitude features and interval features. The effective features of the counting axes corresponding to the angular velocity signals of multiple counting axes in the rope skipping motion signal are amplitude features. The interval features in the effective features can be determined based on the mean of the effective interval data in the effective data, and the amplitude features in the effective features can be determined based on the mean of the effective peak data and the effective trough data in the effective data. Specifically, the method for determining the effective features of the target counting axis in the rope skipping motion signal can be adopted, but not limited to, the following methods:

[0049] Calculate the average value of the valid data of the target counting axis and use the average value as the valid feature of the target counting axis.

[0050] In specific implementation, there may be multiple valid data for each counting axis in the target counting axis. Therefore, the average value of the valid data of each counting axis in the target counting axis can be calculated separately, and the average value can be used as the valid feature of the corresponding counting axis in the target counting axis, that is, the mean of the valid interval data of the corresponding counting axis in the target counting axis is used as the interval feature of the corresponding counting axis; the average value of the valid peak data and the average value of the valid trough data of each counting axis in the target counting axis are calculated separately, and the average value of the valid peak data and the average value of the valid trough data of the corresponding counting axis in the target counting axis are used as the amplitude feature of the corresponding counting axis.

[0051] Step 103: According to the effective features of the target counting axes, the effective counting axes for rope skipping counting in the target counting axes are determined by a pre-trained counting axis determination model.

[0052] In practical applications, the effective features of each counting axis in the target counting axis are characteristic data representing the motion signal of the corresponding counting axis. The effective features of each counting axis in the target counting axis are input into the counting axis determination model, and the counting axis determination model outputs the effective counting axis in the target counting axis, wherein the effective counting axis is the optimal counting axis for rope skipping counting in the rope skipping motion signal. Specifically, based on the effective features of the target counting axis, the effective counting axis for rope skipping counting is determined by a pre-trained counting axis determination model, which can be used in, but not limited to, the following ways:

[0053] First, according to the type of valid features, a counting axis determination model corresponding to the type of valid features is selected from multiple pre-trained counting axis determination models as a target model; wherein the type of valid features includes valid features of acceleration signals and valid features of angular velocity signals.

[0054] Then, the effective features of the target counting axis are input into the target model to obtain the effective counting axis for rope skipping counting.

[0055] In a specific implementation, the counting axis determination model includes a first counting axis determination model corresponding to the type of valid feature of the acceleration signal and a second counting axis determination model corresponding to the type of valid feature of the angular velocity signal. When obtaining the valid feature of the target counting axis, when the valid feature of the target counting axis is the valid feature of the acceleration signal, the valid feature of the target counting axis can be input into the first counting axis determination model to obtain the valid counting axis; and when the valid feature of the target counting axis is the valid feature of the angular velocity signal, the valid feature of the target counting axis can be input into the second counting axis determination model to obtain the valid counting axis. In this way, by determining the valid feature of the target counting axis in the rope skipping motion signal based on valid data, it is possible to further determine the valid counting axis using valid data, avoid the influence of invalid data on the determination of the valid counting axis, and determine the valid counting axis from the target counting axis based on the valid feature of the target counting axis through the pre-trained counting axis determination model, avoid the influence of noise interference caused by non-standard rope skipping movements in other counting axes on the counting accuracy, and improve the counting accuracy.

[0056] In one possible implementation, see Figure 3 As shown, the target counting axis determination model among the multiple counting axis determination models is trained in the following manner:

[0057] Step 301: Establish a training set; wherein the training set includes multiple groups of data consisting of valid features of target counting axes in rope skipping motion signals and corresponding valid counting axes, and the types of valid features of the target counting axes correspond to the counting axis determination model.

[0058] In practical applications, the counting axis determination model includes a first counting axis determination model and a second counting axis determination model. The first counting axis determination model and the second counting axis determination model are trained using different training sets. The training set for the first counting axis determination model includes multiple sets of data consisting of amplitude features and interval features of target counting axes in the acceleration signals of multiple counting axes, as well as corresponding valid counting axes; the training set for the second counting axis determination model includes multiple sets of data consisting of amplitude features of target counting axes in the angular velocity signals of multiple counting axes, as well as corresponding valid counting axes.

[0059] Step 302: Based on the training set, a counting axis determination model is obtained by training using a machine learning method.

[0060] In practical applications, both the first counting axis determination model and the second counting axis determination model can be trained by a machine learning method called a classification decision tree.

[0061] Based on the above embodiments, the present application provides a device for determining an effective counting axis. Figure 4As shown, the effective counting axis determination device 400 provided in the embodiment of the present application at least includes:

[0062] The data acquisition unit 401 is used to acquire valid data of the target counting axis in the rope skipping motion signal of the user;

[0063] A data processing unit 402 is used to determine the valid features of the target counting axis in the rope skipping motion signal based on the valid data;

[0064] The counting axis determining unit 403 is used to determine the valid counting axes for rope skipping counting in the target counting axes according to the valid features of the target counting axes by using a pre-trained counting axis determining model.

[0065] In a possible implementation, the data processing unit 402 is specifically configured to:

[0066] Calculate the average value of the valid data of the target counting axis and use the average value as the valid feature of the target counting axis.

[0067] In a possible implementation, the effective counting axis determination device 400 further includes:

[0068] A signal acquisition unit 404 is used to acquire a rope skipping motion signal of the user;

[0069] The signal processing unit 405 is configured to determine the target counting axis and valid data of the target counting axis in the rope skipping motion signal based on a preset threshold condition.

[0070] In a possible implementation, the rope skipping motion signal includes acceleration signals of multiple counting axes and / or angular velocity signals of multiple counting axes.

[0071] In a possible implementation, when the rope skipping motion signal includes acceleration signals of multiple counting axes, the valid counting axis determination device 400 further includes:

[0072] A vector sum determining unit 406 is configured to determine a vector sum of acceleration signals of a plurality of counting axes, and use the vector sum as an acceleration signal of a vector sum counting axis;

[0073] The signal updating unit 407 is used to add the acceleration signal of the vector and counting axis to the rope skipping motion signal.

[0074] In a possible implementation, the counting axis determination unit 403 is specifically configured to:

[0075] According to the type of the effective feature, a counting axis determination model corresponding to the type of the effective feature is selected from a plurality of pre-trained counting axis determination models as a target model; wherein the type of the effective feature includes the effective feature of the acceleration signal and the effective feature of the angular velocity signal;

[0076] The valid features of the target counting axis are input into the target model to obtain the valid counting axis for rope skipping counting.

[0077] In a possible implementation, the effective counting axis determination device 400 further includes:

[0078] A training set establishing unit 408 is configured to establish a training set; wherein the training set includes a plurality of sets of data consisting of valid features of target counting axes in the rope skipping motion signal and corresponding valid counting axes, wherein the types of the valid features of the target counting axes correspond to the counting axis determination model;

[0079] The model training unit 409 is configured to obtain a counting axis determination model through training using a machine learning method based on a training set.

[0080] It should be noted that the principle of solving the technical problem by the effective counting axis determination device 400 provided in the embodiment of the present application is similar to the effective counting axis determination method provided in the embodiment of the present application. Therefore, the implementation of the effective counting axis determination device 400 provided in the embodiment of the present application can refer to the implementation of the effective counting axis determination method provided in the embodiment of the present application, and the repeated parts will not be repeated.

[0081] After introducing the effective counting axis determination method and device provided by the embodiments of the present application, the electronic device provided by the embodiments of the present application is briefly introduced next.

[0082] See Figure 5 As shown, the electronic device 500 provided in the embodiment of the present application includes at least: a processor 501, a memory 502 and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, the effective counting axis determination method provided in the embodiment of the present application is implemented.

[0083] It should be noted that Figure 5 The electronic device 500 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0084] The electronic device 500 provided in the embodiment of the present application may further include a bus 503 connecting different components (including the processor 501 and the memory 502). The bus 503 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and the like.

[0085] The memory 502 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 5021 and / or a cache memory 5022 , and may further include a read-only memory (ROM) 5023 .

[0086] The memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to: an operating subsystem, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0087] The electronic device 500 may also communicate with one or more external devices 504 (e.g., keyboards, remote controls, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 500 (e.g., mobile phones, computers, etc.), and / or any device that enables the electronic device 500 to communicate with one or more other electronic devices 500 (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 505. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 506. Figure 5 As shown, the network adapter 506 communicates with other modules of the electronic device 500 via the bus 503. Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk arrays (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, and data backup storage subsystems.

[0088] The following describes the computer-readable storage medium provided in the embodiments of the present application. The computer-readable storage medium provided in the embodiments of the present application stores computer instructions that, when executed by a processor, implement the method for determining valid count axes provided in the embodiments of the present application. Specifically, the computer instructions may be built into or installed in the electronic device 500. Thus, the electronic device 500 can implement the method for determining valid count axes provided in the embodiments of the present application by executing the built-in or installed computer instructions.

[0089] In addition, the effective counting axis determination method provided in the embodiment of the present application can also be implemented as a program product, which includes a program code. When the program product can be run on the electronic device 500, the program code is used to enable the electronic device 500 to execute the effective counting axis determination method provided in the embodiment of the present application.

[0090] The program product provided in the embodiments of the present application may adopt any combination of one or more readable media, wherein the readable medium may be a readable signal medium or a readable storage medium, and the readable storage medium may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above. Specifically, more specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, RAM, ROM, Erasable Programmable Read Only Memory (EPROM), optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0091] The program product provided in the embodiments of the present application may be a CD-ROM and include program code, and may also be run on a computing device. However, the program product provided in the embodiments of the present application is not limited thereto. In the embodiments of the present application, the readable storage medium may be any tangible medium containing or storing a program, and the program may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0092] It should be noted that although several units or subunits of the device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, depending on the embodiment of the application, the features and functions of two or more units described above can be embodied in a single unit. Conversely, the features and functions of a single unit described above can be further divided and embodied by multiple units.

[0093] Furthermore, although the operations of the method of the present application are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in this particular order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0094] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0095] Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include such modifications and variations.

Claims

1. A method for determining an effective counting axis, characterized in that: include: Obtaining valid data of a target count axis in the rope skipping motion signal of the user; wherein the target count axis is a count axis in the rope skipping motion signal that meets a preset threshold condition, wherein the threshold condition includes an acceleration amplitude condition, an acceleration interval condition, and an angular velocity amplitude condition; and the valid data is data corresponding to the number of rope skipping times of the user during the process of determining the user's jump; Determining effective features of a target counting axis in the rope skipping motion signal based on the effective data; According to the effective features of the target counting axes, determining the effective counting axes for rope skipping counting in the target counting axes by using a pre-trained counting axis determination model includes: According to the type of the effective feature, a counting axis determination model corresponding to the type of the effective feature is selected from a plurality of pre-trained counting axis determination models as a target model; wherein the type of the effective feature includes effective features of acceleration signals and effective features of angular velocity signals; The effective features of the target counting axis are input into the target model to obtain the effective counting axis for rope skipping counting.

2. The effective counting axis determination method according to claim 1, wherein: The step of determining the effective features of the target counting axis in the rope skipping motion signal based on the effective data comprises: An average value of the effective data of the target counting axis is calculated, and the average value is used as the effective feature of the target counting axis.

3. The effective counting axis determination method according to claim 1, wherein: Before obtaining valid data of the target counting axis in the rope skipping motion signal of the user, the method further includes: Obtain the user's rope skipping motion signal; A target counting axis in the rope skipping motion signal and valid data of the target counting axis are determined based on a preset threshold condition.

4. The method for determining a valid counting axis according to any one of claims 1 to 3, wherein: The rope skipping motion signal includes acceleration signals of multiple counting axes and / or angular velocity signals of multiple counting axes.

5. The effective counting axis determination method according to claim 4, characterized in that: When the rope skipping motion signal includes acceleration signals of multiple counting axes, before obtaining the rope skipping motion signal of the user, the method further includes: determining a vector sum of acceleration signals of a plurality of counting axes, and using the vector sum as the acceleration signal of the vector sum counting axis; The vector and the acceleration signal of the counting axis are added to the rope skipping motion signal.

6. The effective counting axis determination method according to claim 4, characterized in that: The steps of training a target counting axis determination model among the multiple counting axis determination models include: Establishing a training set; wherein the training set includes multiple groups of data consisting of valid features of target counting axes in the rope skipping motion signal and corresponding valid counting axes, and the types of the valid features of the target counting axes correspond to the counting axis determination model; Based on the training set, the counting axis determination model is obtained by training using a machine learning method.

7. An effective counting axis determination device, characterized in that: include: a data acquisition unit, configured to acquire valid data of a target count axis in the rope skipping motion signal of the user; wherein the target count axis is a count axis in the rope skipping motion signal that meets a preset threshold condition, wherein the threshold condition includes an acceleration amplitude condition, an acceleration interval condition, and an angular velocity amplitude condition; and the valid data is data corresponding to the number of rope skipping times of the user during the process of determining the user's take-off; a data processing unit, configured to determine effective features of a target counting axis in the rope skipping motion signal based on the effective data; a counting axis determination unit, configured to determine, based on the effective features of the target counting axis, an effective counting axis for rope skipping counting among the target counting axes using a pre-trained counting axis determination model, and select, based on the type of the effective features, a counting axis determination model corresponding to the type of the effective features from a plurality of pre-trained counting axis determination models as a target model; wherein the types of the effective features include effective features of acceleration signals and effective features of angular velocity signals; The effective features of the target counting axis are input into the target model to obtain the effective counting axis for rope skipping counting.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the effective counting axis determination method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the effective counting axis determination method according to any one of claims 1 to 6 is implemented.

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