Data processing method based on balance ability training and related device

Abstract

The invention provides a data processing method based on balance capability training and a related device, the method is applied to a server, and the method comprises the following steps: responding to a training starting instruction to obtain a latest balance capability score of a training user; determining a training difficulty level; controlling the six-degree-of-freedom motion platform and the three-dimensional audio-video system to execute balance ability training corresponding to the training difficulty level; and after a training termination instruction is detected, or the current balance ability training is detected to be executed, the six-degree-of-freedom motion platform is controlled to recover to the initial state, and the balance ability score is updated according to the current training data and the training difficulty level collected by the sensor equipment. Therefore, the balance ability training adaptive to the training user is executed through data interaction among multiple devices, and the balance ability score is determined, so that the accuracy of evaluating the balance ability of the training user by the server is improved, and the training effect and the completion degree of subsequent balance training are improved.

Description

Technical Field

[0001] The present application belongs to the general data processing technology field of the Internet industry, and specifically relates to a data processing method and related devices based on balance ability training. Background Art

[0002] At present, there are many rehabilitation medical institutions in China that can provide systematic training and treatment for users' balance abilities.

[0003] However, this type of training and treatment usually involves physical guidance and assistance from relevant personnel, and is unable to adaptively adjust the training intensity according to the individual conditions of the user to be trained, making it easy for the user to get injured during training, unable to effectively complete the training, and unable to make a comprehensive and effective assessment of the user's balance ability. Summary of the invention

[0004] The embodiments of the present application provide a data processing method and related devices based on balance ability training, which realize non-human balance ability training for training users through multi-device interaction, and can perform personalized training for the balance ability of training users, and evaluate the balance ability of training users after the training, thereby improving the practicality of the server's execution of ability evaluation and the flexibility of executing balance ability training.

[0005] In a first aspect, an embodiment of the present application provides a data processing method based on balance ability training, which is applied to a server in a human motion quantification training system, wherein the human motion quantification training system further includes a six-degree-of-freedom motion platform, a stereo audio-visual system, and a sensor device; the method includes:

[0006] When a training start instruction is detected and the training user has correctly worn the sensor device, the training user obtains the latest balance ability score of the training user in response to the training start instruction;

[0007] If the balance ability score fails to be obtained, or the balance ability score is less than the preset score, the training difficulty level is determined to be the first level, and the training difficulty level is determined by the change amplitude and change frequency of the body posture of the test subject when maintaining a balanced state during the training process of the corresponding balance ability training;

[0008] If the balance ability score is greater than or equal to the preset score, determining the training difficulty level to be a second level, the second level being higher than the first level;

[0009] According to the pre-stored platform motion data and audio and video playback data, the six-degree-of-freedom motion platform and the stereoscopic audio and video system are controlled to perform the balance ability training corresponding to the training difficulty level;

[0010] After a training termination instruction is detected, or after it is detected that the current balance ability training has been completed, the six-degree-of-freedom motion platform is controlled to return to its initial state, and the balance ability score is updated according to the current training data collected by the sensor device and the training difficulty level.

[0011] In a second aspect, an embodiment of the present application provides a data processing device based on balance ability training, which is applied to a server in a human motion quantification training system, wherein the human motion quantification training system further includes a six-degree-of-freedom motion platform, a stereo audio-visual system, and a sensor device; the device includes:

[0012] a response unit, configured to detect a training start instruction and, if the training user has correctly worn the sensor device, respond to the training start instruction to obtain the latest balance ability score of the training user;

[0013] a first determining unit, configured to determine the training difficulty level as a first level if the balance ability score fails to be obtained or the balance ability score is less than a preset score, wherein the training difficulty level is determined by a change amplitude and a change frequency of a body posture of the test subject when maintaining a balance state during the corresponding training process of the balance ability training;

[0014] A second determining unit, configured to determine that the training difficulty level is a second level if the balance ability score is greater than or equal to the preset score, and the second level is higher than the first level;

[0015] A control unit, for controlling the six-degree-of-freedom motion platform and the stereoscopic audio-visual system according to pre-stored platform motion data and audio-visual playback data, so as to perform the balance ability training corresponding to the training difficulty level;

[0016] The score updating unit is used to control the six-degree-of-freedom motion platform to return to its initial state after detecting a training termination instruction or detecting that the current balance ability training has been completed, and to update the balance ability score according to the current training data collected by the sensor device and the training difficulty level.

[0017] In a third aspect, an embodiment of the present application provides a server, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implement the steps in the first aspect of the embodiment of the present application.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement some or all of the steps described in the first aspect of the embodiment of the present application.

[0020] It can be seen that in the embodiment of the present application, the server performs balance ability training adapted to the training user by interacting with other training devices for data, and determines the user's balance ability score after each training to evaluate the user's balance ability, thereby improving the accuracy of the server's evaluation of the training user's balance ability, and improving the user's actual training effect and completion during the balance training process. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a structural block diagram of a human motion quantification training system provided in an embodiment of the present application;

[0023] Figure 2 It is a flowchart of a data processing method based on balance ability training provided in an embodiment of the present application;

[0024] Figure 3 is a scene diagram of a training scene provided in an embodiment of the present application;

[0025] Figure 4 It is a schematic diagram of a scenario of platform freedom for different training difficulty levels provided in an embodiment of the present application;

[0026] Figure 5 It is a schematic diagram of a scenario for personalized training of a training user provided in an embodiment of the present application;

[0027] Figure 6 It is a block diagram of the functional units of a data processing device based on balance ability training provided by an embodiment of the present application;

[0028] Figure 7It is a block diagram of the functional units of another data processing device based on balance ability training provided by an embodiment of the present application;

[0029] Figure 8 It is a structural block diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0031] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] See also Figure 1 , Figure 1 is a structural block diagram of a human motion quantification training system provided by an embodiment of the present application. Figure 1As shown, the human motion quantitative training system 100 includes a server 110, a six-degree-of-freedom motion platform 120, a stereoscopic audio-visual system 130, and a sensor device 140. The six-degree-of-freedom motion platform 120, the stereoscopic audio-visual system 130, and the sensor device 140 are all connected to the server 110 in communication. The server 110 can control the movement of the six-degree-of-freedom motion platform 120 through data interaction, and control the stereoscopic audio-visual system 130 to play the corresponding image. In addition, the server 110 can also obtain the data of the training user collected by the sensor device 140 in real time or at a fixed time. The specific data type is related to the device type of the sensor device 140 and the training needs of the balance ability training. The server 110 controls the six-degree-of-freedom motion platform 120 and the stereoscopic audio-visual system 130 to perform training adapted to the balance ability of the training user through the relevant information of the training user to improve the balance ability. In addition, the motion data collected by the sensor device 140 worn by the training user during the training process is obtained to perform the evaluation operation of the balance ability score. A human motion quantitative training system 100 can correspond to multiple servers 110 at the same time. One server 110 corresponds to one or more six-degree-of-freedom motion platforms 120, stereo audio-visual systems 130 and sensor devices 140. Usually, a set of training equipment includes a six-degree-of-freedom motion platform 120, a stereo audio-visual system 130 and a sensor device 140, which are used to perform balance ability training for a training user.

[0034] Based on this, an embodiment of the present application provides a data processing method based on balance ability training, and the embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0035] See also Figure 2 , Figure 2 1 is a flow chart of a data processing method based on balance ability training provided by an embodiment of the present application. The method is applied to a server 110 in a human motion quantification training system 100. The human motion quantification training system 100 also includes a six-degree-of-freedom motion platform 120, a stereo audio-visual system 130, and a sensor device 140. The method includes:

[0036] Step S201: a training start instruction is detected, and the training user has correctly worn the sensor device, then the training user's latest balance ability score is obtained in response to the training start instruction.

[0037] Among them, before the server starts to respond to the training start instruction, two prerequisites need to be met. One is to detect the training start instruction sent by the training user based on device interaction, and the other is to determine that the training user has correctly worn the sensor device. It should be noted that there are two final results for obtaining the latest balance ability score of the training user in response to the training start instruction. One is that the server obtains the latest balance ability score of the training user, and the other is that the server does not obtain the score (that is, the training user is doing balance ability training for the first time). The balance ability score is a quantitative indicator used to evaluate the training user's ability to maintain body balance. This score is usually obtained through data collected during the balance ability training process involved in this application, and is used to measure the balance stability of an individual in different postures and movements.

[0038] Exemplarily, the device interaction methods involved in the training start instruction sent by the training user based on device interaction may include the following: 1. The training user touches the corresponding control on other terminal devices bound to the server to enable the terminal device to send the training start instruction to the server. The terminal device may be a computer, a mobile phone or other device; 2. The server obtains data collected by the sensor device, or analyzes the training user's image taken by the camera set in the stereoscopic audio and video system to obtain the specified action made by the training user. The specified action is a pre-set action of the user to trigger the training start instruction. Then the server detects the training start instruction by default based on the specified action; 3. The six-degree-of-freedom motion platform may be in a non-locked state before the training starts, and an inquiry pop-up window is displayed on the display screen in the stereoscopic audio and video system. The text on the inquiry pop-up window is used to ask the user whether he is ready to start training. After that, the training user controls the reply to the inquiry pop-up window by moving in a specific direction on the six-degree-of-freedom motion platform. If the training user determines that he is ready to start training, the server determines that the training start instruction is detected.

[0039] In one possible embodiment, before responding to a training start instruction to obtain the latest balance ability score of the training user, the method also includes: if a training start instruction is detected, controlling the stereoscopic audio and video system to play a first prompt message; obtaining warm-up action data corresponding to the warm-up action collected by the sensor device, a reference data type, and a reference data range corresponding to the reference data type; if the known data type in the warm-up action data is less than the reference data type, determining a target data type according to the reference data type and the known data type; and, according to the target data type, controlling the stereoscopic audio and video system to play a second prompt message; if the known data type is equal to the reference data type, and there is any known data range corresponding to the known data type that is not included in the corresponding reference data range, determining the known data type that is not included in the reference data range as the target data type; and, according to the target data type, controlling the stereoscopic audio and video system to play a second prompt message; if the known data type is equal to the reference data type, and the known data range corresponding to the known data type is not included in the corresponding reference data range, controlling the stereoscopic audio and video system to play a third prompt message, and determining that the training user has correctly worn the sensor.

[0040] The first prompt information is used to prompt the training user to wear the sensor device and complete the warm-up action, the second prompt information is used to prompt the training user that the sensor device corresponding to the target data type is not correctly worn, and the third prompt information is used to prompt the training user to prepare to start the current balance ability training. The specific prompting methods of the first prompt information, the second prompt information and the third prompt information can be displaying prompt text on the screen of the stereo audio-visual system, or the speakers in the stereo audio-visual system play pre-recorded prompt voice, or the sensor device vibrates for different positions, which is not limited here.

[0041] Among them, this embodiment is a specific implementation method of how the server determines whether the training user wears the sensor device correctly. Among them, when the server detects the training start instruction, the server will use the first prompt information to let the training user wear the sensor device by himself and perform the preset warm-up action to obtain the warm-up action data to perform the data analysis operation of whether the wearing is correct. Among them, the integrity of the action data is ensured by comparing the known data type and the reference data type. Furthermore, if the known data type is equal to the reference data type, the inclusion relationship between the known data range of each known data type and the corresponding reference data range is compared to determine the stability of the action data, and to determine whether there are abnormal data values, so as to determine whether the sensor of the corresponding part is worn correctly. And, by setting the second prompt information and the third prompt information, the training user can adjust the wearing of the sensor of the corresponding part in time, and prepare for the start of balance ability training, so as to improve the training experience of the training user and increase the interaction frequency between the user and the device.

[0042] It can be seen that in this example, the server ensures that the training user has correctly worn the sensor device before the formal training begins by analyzing the warm-up movement data and interacting with the training user's voice, thereby improving the stability of subsequent sensor data collection and the accuracy of subsequent balance ability score scoring.

[0043] Step S202: If the balance ability score fails to be obtained, or the balance ability score is less than a preset score, the training difficulty level is determined to be the first level.

[0044] The difficulty level of the training is determined by the amplitude and frequency of changes in the body posture of the test subjects when maintaining a balanced state during the corresponding balance ability training.

[0045] Among them, the first level can be understood as the basic training difficulty, and the second level can be understood as the advanced training difficulty. The preset score is used to indicate the balance ability score that the corresponding test personnel can obtain when they can maintain a balanced state to complete the first level of balance training difficulty. As long as the training user's latest score is greater than or equal to the score, the balance ability training of advanced training difficulty can be arranged for him to improve his balance ability. When the server fails to obtain the balance ability score, it means that the training user's training this time is his first balance ability training. In order to ensure the completion of the training and avoid injuries to the training user, a low-difficulty balance training is usually selected for him, that is, the first level of training difficulty level. Among them, the actual classification based on the training needs and the training difficulty of the designed balance ability training does not necessarily include only the first and second levels. Here it only means that the human motion quantification training system involved in this application can perform balance ability training of multiple difficulty levels to adapt to the balance ability of different users, provide personalized balance training for them, and ensure that the training user can improve his current balance ability through the training matched by the server, thereby achieving rehabilitation or achieving the purpose of intensive training, and improving the practicality of the server and the flexibility of data processing for executing training services.

[0046] Step S203: if the balance ability score is greater than or equal to the preset score, the training difficulty level is determined to be the second level.

[0047] Among them, the second level is higher than the first level.

[0048] Among them, in order to improve the balance ability of the training user, if the balance ability score of the training user's latest training is greater than or equal to the preset score, it means that the training user's current balance ability can excellently complete the first level of balance ability training, then the second level of balance ability training will be arranged for him.

[0049] Step S204, controlling the six-degree-of-freedom motion platform and the stereoscopic audio-visual system according to the pre-stored platform motion data and audio-visual playback data, so as to perform balance ability training corresponding to the training difficulty level.

[0050] For example, see Figure 3 , Figure 3 Schematic diagram of a training scenario provided in an embodiment of the present application. Figure 3 As shown, Figure 3 The figure shows a human motion quantitative training system in an actual scenario, which includes a six-degree-of-freedom motion platform 120 and a stereo audio-visual system 130. In order to ensure the training user's experience, the stereo audio-visual system includes a circular audio-visual display screen and a built-in speaker 301. In addition, a high-definition motion capture system and an optical motion capture system can be optionally equipped according to actual needs. The six-degree-of-freedom motion platform 120 is a training venue provided to the training user 302. The training user 302 exercises on the degree-of-freedom motion platform 120 to achieve balance training. The degree-of-freedom motion platform 120 is moved by the device under the base to enable the training user 302 to perform corresponding balance movements for targeted training. It should be noted that the training user 302 also wears a sensor device 140 to collect the motion data of the training user 302 during the training process, so as to facilitate the server to perform subsequent determination of the balance ability score.

[0051] In one possible embodiment, according to pre-stored platform motion data and audio and video playback data, the six-degree-of-freedom motion platform and the stereo audio and video system are controlled to perform balance ability training corresponding to the training difficulty level, including: determining reference motion data adapted to the training difficulty level according to the platform motion data; determining reference audio and video data according to the reference motion data and the audio and video playback data; determining target motion data and target audio and video data adapted to the training user according to the reference motion data and the reference audio and video data; according to the correspondence between the platform motion parameters corresponding to the target motion data and the training time, controlling the six-degree-of-freedom motion platform to move based on the platform motion parameters to perform balance ability training for the training user; and, according to the target audio and video data, controlling the stereo audio and video system to play the corresponding simulated background images and simulated background sounds synchronously with the training time to perform balance ability training for the training user.

[0052] The reference motion data is used to indicate the platform motion parameters of the six-degree-of-freedom motion platform, as well as the corresponding relationship between the platform motion parameters and the training time. The number of degrees of freedom included in the platform motion parameters corresponding to the first level is less than the number of degrees of freedom included in the platform motion parameters corresponding to the second level. Each reference audio-visual data corresponds to a reference motion data in the same reference scene. The simulated background image and simulated background sound indicated by the reference audio-visual data are used to map the motion parameters of the same training time in the corresponding reference motion data. The platform motion parameters are used to indicate the type, direction, speed, and acceleration of the degree of freedom of the platform motion, and the number of degrees of freedom is the number of types of degrees of freedom.

[0053] Among them, the six-degree-of-freedom motion platform is a mechanical motion platform with six degrees of freedom (three translational degrees of freedom and three rotational degrees of freedom). It can realize accurate control and precise positioning of any posture in space. It is a high-precision, high-reliability mechanical motion platform that realizes the adjustment of the platform's arbitrary position and posture in space by controlling the movement of six independent actuators (such as servo electric cylinders). By controlling the extension and rotation of the electric cylinder, the upper platform produces translational and rotational motion in space. These movements are achieved through precise algorithms and control systems to ensure that the platform can move according to the preset trajectory. In this application, the six-degree-of-freedom motion platform can move according to the platform motion parameters provided by the reference motion data and the corresponding relationship between the platform motion parameters and the training time, that is, the server sends reference motion data to the six-degree-of-freedom motion platform, which can control it to move with a preset action at a preset time, so that the training user is immersed in the reference scene shaped by the simulated background image and simulated background sound played by the stereo audio-visual system based on the audio-visual playback data, so as to train the balance ability, provide a more realistic and exciting training experience for the training user, and ensure its safety.

[0054] Among them, for different training difficulty levels, the number of degrees of freedom involved in the corresponding platform motion parameters is different. Generally speaking, the more degrees of freedom involved, the more complex the training movements that the training user needs to make in order to maintain a balanced state during training, the higher the training difficulty, and the more the balance ability of the training user is tested. The specific number of degrees of freedom can be set as follows: if the training difficulty level only includes the first and second levels, the degrees of freedom of the motion parameters of the first level are 3, that is, three translational degrees of freedom. The degrees of freedom of the motion trajectory of the second level are 6, that is, three translational degrees of freedom and three rotational degrees of freedom. The translational degree of freedom refers to the ability of the platform to move independently in a straight line direction in space, which usually refers to movement along the X-axis, Y-axis and Z-axis directions. The translational degree of freedom enables the platform to move forward and backward, left and right, up and down in space, thereby changing its position. The rotational degree of freedom refers to the ability of an object to rotate independently around a certain axis in space. In three-dimensional space, this usually refers to rotation around the X-axis, Y-axis and Z-axis. The rotational degree of freedom enables an object to rotate at various angles in space, thereby changing its direction or posture.

[0055] For example, see Figure 4 , Figure 4 Schematic diagram of a scenario of platform freedom for different training difficulty levels provided by an embodiment of the present application. Figure 4 As shown, Figure 4 The scene demonstration diagrams of the platform degrees of freedom corresponding to the first and second levels are shown in the figure. After setting, when the training difficulty level is the first level, the platform motion parameters of the six-degree-of-freedom motion platform include only three degrees of freedom, namely the translational degree of freedom, specifically including forward thrust, lifting and lateral shifting, which are displacements in the directions of the X-axis, Y-axis, and Z-axis, respectively. When the training difficulty level is the second level, the platform motion parameters of the six-degree-of-freedom motion platform include only six degrees of freedom, namely the translational degree of freedom and the rotational degree of freedom, specifically including forward thrust, lifting, lateral shifting, roll, yaw, and pitch, which are displacements in the directions of the X-axis, Y-axis, and Z-axis, and rotations in the directions of the X-axis, Y-axis, and Z-axis, respectively. It can be seen that the difficulty of the second-level balance ability training is higher than that of the first-level balance ability training, so it is more difficult to improve the balance ability of the training user.

[0056] It can be seen that in this example, the server implements specific steps of balance ability training for different training difficulty levels based on the interaction with the six-degree-of-freedom motion platform and the stereo audio-visual system, thereby improving the efficiency of the server's training mode interaction and the practicality and flexibility of the server's execution of training services, so that training users can perform real and stimulating balance ability training in a safe training environment, thereby improving the training users' enthusiasm and concentration during the training process.

[0057] In one possible embodiment, target motion data and target audio-visual data adapted to the training user are determined based on reference motion data and reference audio-visual data, including: obtaining the user occupation and training purpose of the training user; if there is a reference scene associated with the user occupation, determining the reference motion data corresponding to the associated reference scene as the target motion data, and determining the reference audio-visual data corresponding to the associated reference scene as the target audio-visual data; if the training purpose is rehabilitation training, determining the reference scene belonging to an outdoor travel scene or a traffic travel scene as the target reference scene; and, determining the reference motion data corresponding to the target reference scene as the target motion data, and determining the reference audio-visual data corresponding to the target reference scene as the target audio-visual data; if the training purpose is ability enhancement training, determining the reference scene belonging to a game scene or a sports scene as the target reference scene; and, determining the reference motion data corresponding to the target reference scene as the target motion data, and determining the reference audio-visual data corresponding to the target reference scene as the target audio-visual data.

[0058] Among them, the training objectives may include rehabilitation training and capacity enhancement training, among which rehabilitation training refers to helping the training user recover or improve his balance function through a series of training and treatment methods. Balance is the basic guarantee for the human body to maintain posture and position and complete various daily life activities, especially various transfer movements, walking, running, jumping and other complex movements. Capacity enhancement training refers to helping the training user improve his balance ability through a series of training methods, so that he can complete some difficult sports and the training requirements required by specific occupations, which helps to improve the function of motor limbs and vestibular organs and enhance the ability to adapt to complex environments. The user's occupation is mainly to distinguish whether the training user needs to achieve a higher balance ability to adapt to his occupational needs. For example, athletes, extreme sports enthusiasts and other occupations require a higher sports balance ability to ensure their safety.

[0059] Furthermore, first, the server will select a reference scene associated with the user's occupation as a reference scene based on the user's occupation. If there is no reference scene associated with the user's occupation, the corresponding reference scene is selected according to the training purpose. Among them, if the training purpose of the training user is rehabilitation training, the reference scene is determined to be a scene that may require balance in daily life, such as an outdoor travel scene or a traffic travel scene, which can specifically be riding a rocking boat, walking up and down hills, and various sports activities; if the training purpose of the training user is ability enhancement training, the reference scene is determined to be a scene that requires a higher balance ability, such as a game scene or a sports scene.

[0060] For example, see Figure 5 , Figure 5 Schematic diagram of a scenario for personalized training of users provided by an embodiment of the present application. Figure 5The examples shown are all reference scenes of simulated audio and video images displayed on the display screen in the stereo audio and video system. Scene 1 is a traffic scene. The training user will experience the travel process on the bus or subway from the first-person perspective of a passenger pulling the pull ring. It is suitable for training users whose training purpose is rehabilitation training. Scene 2 is a game scene. The training user will experience the hunting process in the game from the first-person perspective of a knight on horseback. It is suitable for training users whose training purpose is ability enhancement training. Scene 3 is a skiing scene. The training user will experience the real skiing process from the first-person perspective of a skier. It is suitable for training users whose profession is a skier.

[0061] It can be seen that in this example, by using the training user's occupation and training purpose, a reference scenario suitable for the training user is selected, thereby improving the flexibility and practicality of the training service provided by the server, so that the balance ability training can meet the user needs of the training user and improve the training user's enthusiasm and experience.

[0062] Step S205, after a training termination instruction is detected, or after it is detected that the current balance ability training has been completed, the six-degree-of-freedom motion platform is controlled to return to the initial state, and the balance ability score is updated according to the current training data and training difficulty level collected by the sensor device.

[0063] In one possible embodiment, if the training data is user motion data and plantar pressure data; the balance ability score is updated according to the current training data and training difficulty level collected by the sensor device, including: determining the user gait information of the current balance ability training according to the user motion data; determining the standard gait information according to the body shape data of the training user; determining the gait similarity between the user gait information and the standard gait information; and determining the number of falls of the training user in the current balance ability training according to the plantar pressure data and a preset pressure threshold; if the training difficulty level is the first level, determining the current balance ability score according to the first balance ability score, the second balance ability score and the preset score calculation formula to update the balance ability score.

[0064] Among them, the user action data includes the posture, speed and strength of each preset joint of the training user at the corresponding training time, the standard gait information is the gait information of the training user when maintaining a balance state in the current balance ability training when the server simulates the body shape data, and the pressure threshold includes the plantar pressure threshold and the pressure change rate threshold collected when the test person falls; the score calculation formula includes the corresponding relationship between the first balance ability score and the first weight, and the corresponding relationship between the second balance ability score and the second weight. The first balance ability score is determined by the gait similarity, and the second balance ability score is determined by the number of falls. Among them, the standard gait information can also be the gait information shown by a person or robot with body shape data similar to that of the training user completing the corresponding balance ability training on a six-degree-of-freedom platform.

[0065] Among them, the advantages of evaluating the balance ability of training users through balance ability scores are: 1. Reducing subjective bias: Compared with simple observation methods, scoring balance ability through scales or instruments can reduce the impact of the evaluator's personal experience and technical level on the evaluation results, making the evaluation results more objective. In addition, the scoring follows certain standardized processes and scoring standards, which helps to ensure the consistency and accuracy of the evaluation. 2. Clarifying quantitative indicators: The scoring method can convert balance ability into specific values ​​or levels, making the evaluation results clearer and more specific. This helps the person being evaluated understand his or her own balance ability level and changes in different time periods. 3. Dynamic monitoring: For people who need balance ability training, regular scoring of balance ability can dynamically monitor their training progress. By comparing the scoring results at different time points, you can intuitively see the quality of the training effect. 4. Adjusting the training plan: According to the balance ability score, the rehabilitation therapist or training user can adjust the training plan in time and formulate a more personalized training plan based on the specific situation of the training user.

[0066] Among them, by obtaining the user's motion data, the user's gait information can be determined, and the user's gait information is used to characterize the comprehensive information about the walking posture, behavioral characteristics and related parameters generated by the user during the walking process. Then, by obtaining the standard gait information corresponding to the training user's body shape data, by comparing the gait similarity between the gait information, the gait similarity can characterize the difference between the current gait of the training user and the normal gait. In addition, the plantar pressure threshold and the pressure change rate threshold are determined according to the preset pressure threshold, and then combined with the plantar pressure data of the training user in the current balance ability training, the number of falls that the training user may have is determined, and then the first balance ability score is determined based on the number of falls and the gait similarity.

[0067] It can be seen that in this example, the server conducts a quantitative assessment of the balance ability of the training user through multi-level data analysis, improves the accuracy of determining the balance ability score, and provides a reliable basis for subsequent training services for the training user, thereby improving the balance performance and training effect of the training user.

[0068] In one possible embodiment, determining the gait similarity between user gait information and standard gait information includes: performing a gait recognition operation according to the standard gait information to determine a reference feature parameter set; performing a gait recognition operation according to the user gait information to determine a user feature parameter set; determining the feature similarity between the reference feature parameters and the user feature parameters corresponding to each preset gait feature; obtaining a similarity threshold corresponding to each preset gait feature; and determining the gait similarity based on a feature similarity less than the corresponding similarity threshold and a preset similarity calculation formula.

[0069] Among them, the reference feature parameter set includes multiple reference feature parameters, each reference feature parameter corresponds to a preset gait feature, and the preset gait feature corresponds to any of the following feature types: step length, step width, step frequency, joint movement angle, center of gravity offset, and foot support area. Among them, step length and step width: the step length is the horizontal distance between the left and right heels (or toes) when walking, and the step width is the minimum horizontal distance between the inner edges of the two feet. An unbalanced gait may show characteristics such as different step lengths and increased step widths. Step frequency and step speed: the step frequency is the number of steps completed per unit time, and the step speed is the distance walked per unit time. An unbalanced gait may be accompanied by phenomena such as reduced step frequency and slowed step speed. Joint angle and motion trajectory: analyze the angle changes and motion trajectories of joints such as hips, knees, and ankles during walking. Unbalanced gaits may show characteristics such as abnormal joint angles and irregular motion trajectories. Dynamic stability indicators: such as center of gravity offset, support area, etc., these indicators can reflect the stability and balance ability of the body during walking. The user feature parameter set includes multiple user feature parameters, each of which corresponds to a preset gait feature, and the similarity calculation formula includes a corresponding relationship between a parameter weight corresponding to each preset gait feature and a feature similarity. The numerical value of the parameter weight is used to characterize the importance of the corresponding preset gait feature relative to the balance ability.

[0070] Among them, the gait similarity is determined only based on the feature similarity less than the corresponding similarity threshold and the similarity calculation formula. The reason is that the feature similarity greater than the corresponding similarity threshold can be classified as a high-similarity gait feature. If the gait features are the same, it is not necessary to determine the gait similarity based on this feature similarity, which reduces the amount of data to be processed, thereby improving the efficiency of the server in performing data processing to determine the gait similarity. The similarity threshold corresponding to each preset gait feature is used to filter out gait features with small differences. The value of the similarity threshold is associated with the type of gait feature. If the value difference corresponding to the gait features of different users of the same feature type is greater, the value of the similarity threshold is larger, and vice versa.

[0071] It can be seen that in this example, the server determines multiple gait features from the gait information for further feature similarity comparison, and then determines the gait similarity, thereby improving the accuracy and comprehensiveness of the server's determination of gait similarity and improving the accuracy of subsequent determination of the balance ability score.

[0072] In a possible embodiment, if the score calculation formula also includes the correspondence between the third balance ability score and the first weight, and the correspondence between the fourth balance ability score and the second weight; the method also includes: if the training difficulty level is the second level, then according to the degree of freedom type of the platform motion parameter corresponding to the first level and the degree of freedom type of the platform motion parameter corresponding to the second level, determine the newly added degree of freedom type as the target degree of freedom; determine the platform motion parameter with the target degree of freedom as the advanced motion parameter, and determine the training time corresponding to the advanced motion parameter as the target time point; determine the action data set corresponding to each target time point according to the user action data; perform action analysis operations for each action data set to determine the response time of the training user to the advanced motion parameter; obtain the total training time and expected completion time of the current balance ability training; determine the current balance ability score according to the first balance ability score, the second balance ability score, the third balance ability score, the fourth balance ability score and the score calculation formula to update the balance ability score.

[0073] Among them, the action data set is user action data with the corresponding target time point as the starting point and the total duration as the preset duration. The response time is used to characterize the time taken for the training user to recover to a balanced state from the corresponding target time point. The third balance ability score is determined by the average value of the response time, and the fourth balance ability score is determined by the ratio of the total training duration to the expected completion time.

[0074] Among them, when the training difficulty level increases, the server determines the newly added degree of freedom in the platform motion parameters as the target degree of freedom, performs data analysis on the target degree of freedom and its platform motion parameters, and determines the training user's sports performance under the advanced motion parameters corresponding to the newly added degree of freedom, that is, determines the response time. The third balance ability score is used to characterize the training user's sports performance when facing high-difficulty balance training, and the fourth balance ability score is used to characterize the completion of the training user's current balance ability training. Among them, in the setting of weights, the third weight and the fourth weight can be set higher, so that the score of the second level of balance ability training is higher, thereby improving the confidence of the training user and increasing the possibility of the training user completing advanced training. In addition, it is also possible to determine the fifth balance ability score based on the degree of pushing during the training process, such as the number, amplitude, and strength of simulated pushing, which affect the balance, so as to achieve more levels and accurately judge the balance ability of the training user.

[0075] It can be seen that in this example, under the balance ability training of the second level of training difficulty, the server determines the additional balance ability score by determining the user's motion performance corresponding to the advanced motion parameters of the newly added degree of freedom, as well as the user's completion of the training, thereby evaluating the user's balance ability from data at more levels and more aspects, thereby improving the comprehensiveness and accuracy of the server's balance ability evaluation.

[0076] visible, Figure 2 This is a flow chart of a data processing method based on balance ability training provided by an embodiment of the present application. The server controls the six-degree-of-freedom motion platform and the stereo audio-visual system to allocate corresponding balance ability training to the training user based on the previous balance ability score of the training user, so as to avoid injury to the training user during the training process, improve the training effect and completion, and improve the flexibility and practicality of the server in making personalized training adjustments for the user. In addition, by obtaining the motion data of the training user during the training process, the accuracy of the balance ability score assessment for the training user can be improved, which is convenient for the subsequent training difficulty adjustment, thereby gradually improving the balance ability of the training user.

[0077] The following is an embodiment of the device of the present application. The embodiment of the device of the present application and the embodiment of the method of the present application are of the same concept and are used to execute the method described in the embodiment of the present application. For the convenience of explanation, the embodiment of the device of the present application only shows the part related to the embodiment of the device of the present application. For the specific technical details not disclosed, please refer to the description of the embodiment of the method of the present application, which will not be repeated here one by one.

[0078] The present invention provides a data processing device based on balance ability training, which is applied to Figure 1The server 110 in the human motion quantification training system 100 shown in the figure, the human motion quantification training system 100 also includes a six-degree-of-freedom motion platform 120, a stereo audio-visual system 130 and a sensor device 140. Specifically, the data processing device based on balance ability training is used to execute the steps executed by the server in the above data processing method based on balance ability training. The data processing device based on balance ability training provided in the embodiment of the present application may include modules corresponding to the corresponding steps.

[0079] The embodiment of the present application can divide the functional modules of the data processing device based on balance ability training according to the above method example. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. The division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0080] In the case of dividing each functional module into corresponding functional modules, Figure 6 It is a functional unit composition block diagram of a data processing device based on balance ability training provided in an embodiment of the present application; the data processing device 60 based on balance ability training is applied to the server 110 in the human motion quantification training system 100, and the human motion quantification training system 100 also includes a six-degree-of-freedom motion platform 120, a stereo audio-visual system 130 and a sensor device 140; the data processing device 60 based on balance ability training includes: a response unit 601, which is used to detect a training start instruction, and if the training user has correctly worn the sensor device, then respond to the training start instruction to obtain the latest balance ability score of the training user; a first determination unit 602, which is used to determine that the training difficulty level is the first level if the balance ability score acquisition fails, or the balance ability score is less than the preset score, and the training difficulty is The level is determined by the amplitude and frequency of changes in the body posture of the test subject when maintaining a balanced state during the corresponding balance ability training; the second determination unit 603 is used to determine that the training difficulty level is the second level if the balance ability score is greater than or equal to the preset score, and the second level is higher than the first level; the control unit 604 is used to control the six-degree-of-freedom motion platform and the stereo audio-visual system according to the pre-stored platform motion data and audio-visual playback data to perform balance ability training corresponding to the training difficulty level; the score updating unit 605 is used to control the six-degree-of-freedom motion platform to return to the initial state after detecting a training termination instruction or detecting that the current balance ability training has been completed, and to update the balance ability score according to the current training data and training difficulty level collected by the sensor device.

[0081] In one possible embodiment, in terms of controlling the six-degree-of-freedom motion platform and the stereo audio-visual system according to pre-stored platform motion data and audio-visual playback data to perform balance ability training corresponding to the training difficulty level, the control unit 604 is specifically used to: determine reference motion data adapted to the training difficulty level according to the platform motion data, the reference motion data being used to indicate the platform motion parameters of the six-degree-of-freedom motion platform, and the corresponding relationship between the platform motion parameters and the training time, the number of degrees of freedom included in the platform motion parameters corresponding to the first level being less than the number of degrees of freedom included in the platform motion parameters corresponding to the second level; determine reference audio-visual data according to the reference motion data and the audio-visual playback data, each reference audio-visual data corresponding to a reference motion data in the same reference scene, the simulated background image and the simulated background sound indicated by the reference audio and video data are used to map the motion parameters under the same training time in the corresponding reference motion data; according to the reference motion data and the reference audio and video data, the target motion data and the target audio and video data adapted to the training user are determined; according to the correspondence between the platform motion parameters corresponding to the target motion data and the training time, the six-degree-of-freedom motion platform is controlled to move based on the platform motion parameters to perform balance ability training for the training user; and, according to the target audio and video data, the stereo audio and video system is controlled to play the corresponding simulated background image and simulated background sound synchronously with the training time to perform balance ability training for the training user.

[0082] In one possible embodiment, if the training data is user motion data and plantar pressure data; in terms of updating the balance ability score according to the current training data and training difficulty level collected by the sensor device, the score updating unit 605 is specifically used to: determine the user gait information of the current balance ability training according to the user motion data, the user motion data includes the posture, speed and strength of each preset joint of the training user at the corresponding training time; determine the standard gait information according to the body shape data of the training user, the standard gait information is the gait information performed by the server when the training user maintains a balance state in the current balance ability training based on the body shape data; determine the user gait information and the standard gait information gait similarity between them; and, determining the number of falls of the training user in the current balance ability training according to the plantar pressure data and a preset pressure threshold, the pressure threshold including the plantar pressure threshold and the pressure change rate threshold collected when the test subject falls; if the training difficulty level is the first level, determining the current balance ability score to update the balance ability score according to the first balance ability score, the second balance ability score and a preset score calculation formula, the score calculation formula including the corresponding relationship between the first balance ability score and the first weight, and the corresponding relationship between the second balance ability score and the second weight, the first balance ability score is determined by the gait similarity, and the second balance ability score is determined by the number of falls.

[0083] In a possible embodiment, in terms of determining the gait similarity between the user gait information and the standard gait information, the score updating unit 605 is specifically used to: perform a gait recognition operation according to the standard gait information to determine a reference feature parameter set, the reference feature parameter set includes multiple reference feature parameters, each reference feature parameter corresponds to a preset gait feature, and the preset gait feature corresponds to any one of the following feature types: step length, step width, step frequency, joint movement angle, center of gravity offset, foot support area; perform a gait recognition operation according to the user gait information to determine a user feature parameter set, the user feature parameter set includes multiple user feature parameters, each user feature parameter corresponds to a preset gait feature; determine the feature similarity between the reference feature parameters and the user feature parameters corresponding to each preset gait feature; obtain the similarity threshold corresponding to each preset gait feature; determine the gait similarity according to the feature similarity less than the corresponding similarity threshold and the preset similarity calculation formula, the similarity calculation formula includes the corresponding relationship between the parameter weight corresponding to each preset gait feature and the feature similarity.

[0084] In a possible embodiment, if the score calculation formula also includes the corresponding relationship between the third balance ability score and the first weight, and the corresponding relationship between the fourth balance ability score and the second weight; the score updating unit 605 is also specifically used for: if the training difficulty level is the second level, then according to the degree of freedom type of the platform motion parameter corresponding to the first level and the degree of freedom type of the platform motion parameter corresponding to the second level, determining the newly added degree of freedom type as the target degree of freedom; determining the platform motion parameter with the target degree of freedom as the advanced motion parameter, and determining the training time corresponding to the advanced motion parameter as the target time point; determining the action data set corresponding to each target time point according to the user action data, the action data set is based on the corresponding target time point a point as a starting point and a total duration as a preset duration; performing a motion analysis operation on each set of motion data to determine the response time of the training user to the advanced motion parameters, the response time being used to characterize the time taken for the training user to recover to a balanced state from the corresponding target time point; obtaining the total training duration and the estimated completion duration of the current balance ability training; determining the current balance ability score to update the balance ability score according to the first balance ability score, the second balance ability score, the third balance ability score, the fourth balance ability score and the score calculation formula, the third balance ability score being determined by the average value of the response time, and the fourth balance ability score being determined by the ratio of the total training duration to the estimated completion duration.

[0085] In a possible embodiment, in terms of determining the target motion data and target audio-visual data adapted to the training user based on the reference motion data and the reference audio-visual data, the control unit 604 is specifically used to: obtain the user occupation and training purpose of the training user; if there is a reference scene associated with the user occupation, determine the reference motion data corresponding to the associated reference scene as the target motion data, and determine the reference audio-visual data corresponding to the associated reference scene as the target audio-visual data; if the training purpose is rehabilitation training, determine the reference scene belonging to the outdoor travel scene or the traffic travel scene as the target reference scene; and, determine the reference motion data corresponding to the target reference scene as the target motion data, and determine the reference audio-visual data corresponding to the target reference scene as the target audio-visual data; if the training purpose is ability enhancement training, determine the reference scene belonging to the game scene or the sports scene as the target reference scene; and, determine the reference motion data corresponding to the target reference scene as the target motion data, and determine the reference audio-visual data corresponding to the target reference scene as the target audio-visual data.

[0086] In one possible embodiment, before responding to a training start instruction to obtain the latest balance ability score of the training user, the response unit 601 is specifically used to: if a training start instruction is detected, control the stereoscopic audio and video system to play a first prompt message, the first prompt message is used to prompt the training user to wear the sensor device and complete the warm-up action; obtain the warm-up action data corresponding to the warm-up action collected by the sensor device, the reference data type and the reference data range corresponding to the reference data type; if the known data type in the warm-up action data is less than the reference data type, determine the target data type according to the reference data type and the known data type; and, according to the target data type, control the stereoscopic audio and video system to play a second prompt message, the second prompt message is used to prompt The training user does not correctly wear the sensor device at the position corresponding to the target data type; if the known data type is equal to the reference data type, and there is any known data range corresponding to the known data type that is not included in the corresponding reference data range, then the known data type that is not included in the reference data range is determined to be the target data type; and, according to the target data type, the stereoscopic audio and video system is controlled to play a second prompt message; if the known data type is equal to the reference data type, and the known data range corresponding to the known data type is not included in the corresponding reference data range, then the stereoscopic audio and video system is controlled to play a third prompt message, and it is determined that the training user has correctly worn the sensor, and the third prompt message is used to prompt the training user to prepare to start the current balance ability training.

[0087] In the case of integrated units, such as Figure 7 As shown, Figure 7 This is a block diagram of the functional units of another data processing device based on balance ability training provided by an embodiment of the present application. Figure 7In the embodiment, the data processing device 60 based on balance ability training includes: a processing module 702 and a communication module 701. The processing module 702 is used to control and manage the actions of the data processing device based on balance ability training, for example, the steps of the response unit 601, the first determination unit 602, the second determination unit 603, the control unit 604 and the score update unit 605, and / or other processes for executing the technology described herein. The communication module 701 is used to support the interaction between the data processing device based on balance ability training and other devices. Figure 7 As shown, the data processing device based on balance ability training may include a storage module 703, and the storage module 703 is used to store program codes and data of the data processing device based on balance ability training.

[0088] Among them, the processing module 702 can be a processor or a controller, for example, a central processing unit (CPU), a general processor, a digital signal processor (DSP), an ASIC, an FPGA or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like. The communication module 701 can be a transceiver, an RF circuit or a communication interface, and the like. The storage module 703 can be a memory.

[0089] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here. The above data processing device 60 based on balance ability training can execute the above Figure 2 The data processing method based on balance ability training is shown.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0091] Figure 8 is a structural block diagram of a server provided in an embodiment of the present application. Figure 8 As shown, the server 110 may include one or more of the following components: a processor 801, a memory 802 coupled to the processor 801, wherein the memory 802 may store one or more computer programs 803, and the one or more computer programs 803 may be configured to implement the methods described in the above embodiments when executed by one or more processors 801. The server described here is the server 110 in the above embodiments.

[0092] The processor 801 may include one or more processing cores. The processor 801 uses various interfaces and lines to connect various parts of the entire server 110, and executes various functions and processes data of the server 110 by running or executing instructions, programs, code sets or instruction sets stored in the memory 802, and calling data stored in the memory 802. Optionally, the processor 801 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 801 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing display content; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 801, and it can be implemented separately through a communication chip.

[0093] The memory 802 may include a random access memory (RAM) or a read-only memory (ROM). The memory 802 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 802 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the server 110 during use, etc.

[0094] It is understandable that the server 110 may include more or fewer structural elements than those in the above structural block diagram, which is not limited here.

[0095] An embodiment of the present application also provides a computer storage medium, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, part or all of the steps of any method recorded in the above method embodiments are implemented.

[0096] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0097] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are merely schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0101] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a volatile memory or a non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM) and direct memory bus random access memory (directrambus RAM, DR RAM) and other media that can store program code.

[0102] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A data processing method based on balance ability training, characterized in that: A server applied to a human motion quantitative training system, wherein the human motion quantitative training system further comprises a six-degree-of-freedom motion platform, a stereoscopic audio-visual system and a sensor device; the method comprises: When a training start instruction is detected and the training user has correctly worn the sensor device, the training user obtains the latest balance ability score of the training user in response to the training start instruction; If the balance ability score fails to be obtained, or the balance ability score is less than the preset score, the training difficulty level is determined to be the first level, and the training difficulty level is determined by the change amplitude and change frequency of the body posture of the test subject when maintaining a balanced state during the training process of the corresponding balance ability training; If the balance ability score is greater than or equal to the preset score, determining the training difficulty level to be a second level, the second level being higher than the first level; According to the pre-stored platform motion data and audio and video playback data, the six-degree-of-freedom motion platform and the stereoscopic audio and video system are controlled to perform the balance ability training corresponding to the training difficulty level; After a training termination instruction is detected, or after it is detected that the current balance ability training has been completed, the six-degree-of-freedom motion platform is controlled to return to its initial state, and the balance ability score is updated according to the current training data collected by the sensor device and the training difficulty level.

2. The method according to claim 1, characterized in that The controlling the six-degree-of-freedom motion platform and the stereoscopic audio-visual system according to the pre-stored platform motion data and audio-visual playback data to perform the balance ability training corresponding to the training difficulty level includes: Determining reference motion data adapted to the training difficulty level according to the platform motion data, the reference motion data being used to indicate platform motion parameters of the six-degree-of-freedom motion platform, and a corresponding relationship between the platform motion parameters and training time, wherein the number of degrees of freedom included in the platform motion parameters corresponding to the first level is less than the number of degrees of freedom included in the platform motion parameters corresponding to the second level; Determine reference audio-visual data according to the reference motion data and the audio-visual playback data, each of the reference audio-visual data corresponds to a reference motion data in the same reference scene, and the simulated background image and simulated background sound indicated by the reference audio-visual data are used to map the motion parameters in the same training time in the corresponding reference motion data; Determining target motion data and target audio and video data adapted to the training user according to the reference motion data and the reference audio and video data; According to the correspondence between the platform motion parameters corresponding to the target motion data and the training time, the six-degree-of-freedom motion platform is controlled to move based on the platform motion parameters to perform the balance ability training for the training user; and according to the target audio and video data, the stereo audio and video system is controlled to play the corresponding simulated background image and the simulated background sound synchronously with the training time to perform the balance ability training for the training user.

3. The method according to claim 2, characterized in that If the training data is user motion data and plantar pressure data; updating the balance ability score according to the current training data collected by the sensor device and the training difficulty level includes: Determine the user gait information of the current balance ability training according to the user motion data, wherein the user motion data includes the posture, speed and strength of each preset joint of the training user at the corresponding training time; Determine standard gait information according to the body shape data of the training user, wherein the standard gait information is gait information performed by the server when the training user maintains the balance state in the current balance ability training based on the body shape data; Determine the gait similarity between the user gait information and the standard gait information; and determine the number of falls of the training user in the current balance ability training according to the plantar pressure data and a preset pressure threshold, wherein the pressure threshold includes a plantar pressure threshold and a pressure change rate threshold collected when the test person falls; If the training difficulty level is the first level, the current balance ability score is determined according to the first balance ability score, the second balance ability score and a preset score calculation formula to update the balance ability score, the score calculation formula including the correspondence between the first balance ability score and the first weight, and the correspondence between the second balance ability score and the second weight, the first balance ability score is determined by the gait similarity, and the second balance ability score is determined by the number of falls.

4. The method according to claim 3, characterized in that: The determining the gait similarity between the user gait information and the standard gait information includes: Performing a gait recognition operation according to the standard gait information to determine a reference feature parameter set, wherein the reference feature parameter set includes a plurality of reference feature parameters, each of which corresponds to a preset gait feature, and the preset gait feature corresponds to any one of the following feature types: step length, step width, step frequency, joint movement angle, center of gravity offset, and foot support area; Performing a gait recognition operation according to the user gait information to determine a user feature parameter set, wherein the user feature parameter set includes a plurality of user feature parameters, each of which corresponds to one of the preset gait features; Determining a feature similarity between the reference feature parameter corresponding to each of the preset gait features and the user feature parameter; Obtaining a similarity threshold corresponding to each of the preset gait features; The gait similarity is determined according to the feature similarity that is less than the corresponding similarity threshold and a preset similarity calculation formula, wherein the similarity calculation formula includes a corresponding relationship between a parameter weight corresponding to each of the preset gait features and the feature similarity.

5. The method according to claim 3, characterized in that: If the score calculation formula also includes a corresponding relationship between the third balance ability score and the first weight, and a corresponding relationship between the fourth balance ability score and the second weight; the method further includes: If the training difficulty level is the second level, determining the newly added degree of freedom type as the target degree of freedom according to the degree of freedom type of the platform motion parameter corresponding to the first level and the degree of freedom type of the platform motion parameter corresponding to the second level; Determining a platform motion parameter having the target degree of freedom as an advanced motion parameter, and determining a training time corresponding to the advanced motion parameter as a target time point; Determine, according to the user action data, an action data set corresponding to each of the target time points, wherein the action data set is user action data starting from the corresponding target time point and having a total duration of a preset duration; Performing a motion analysis operation for each of the motion data sets to determine a response time of the training user to the advanced motion parameter, wherein the response time is used to represent the time taken for the training user to recover to the balance state from the corresponding target time point; Obtaining the total training duration and estimated completion duration of the current balance ability training; The current balance ability score is determined according to the first balance ability score, the second balance ability score, the third balance ability score, the fourth balance ability score and the score calculation formula to update the balance ability score, the third balance ability score is determined by the average value of the response time, and the fourth balance ability score is determined by the ratio of the total training time to the expected completion time.

6. The method according to claim 3 or 5, characterized in that: The step of determining target motion data and target audio and video data adapted to the training user according to the reference motion data and the reference audio and video data comprises: Acquire the user occupation and training purpose of the training user; If the reference scene associated with the user's occupation exists, determining the reference motion data corresponding to the associated reference scene as the target motion data, and determining the reference audio-visual data corresponding to the associated reference scene as the target audio-visual data; If the training purpose is rehabilitation training, the reference scene belonging to an outdoor travel scene or a traffic travel scene is determined as a target reference scene; and the reference motion data corresponding to the target reference scene is determined as the target motion data, and the reference audio and video data corresponding to the target reference scene is determined as the target audio and video data; If the training purpose is ability enhancement training, the reference scene belonging to the game scene or the sports scene is determined as the target reference scene; and the reference motion data corresponding to the target reference scene is determined as the target motion data, and the reference audio and video data corresponding to the target reference scene is determined as the target audio and video data.

7. The method according to any one of claims 1 to 5, characterized in that: Before responding to the training start instruction to obtain the latest balance ability score of the training user, the method further includes: If the training start instruction is detected, the stereoscopic audio-visual system is controlled to play a first prompt message, where the first prompt message is used to prompt the training user to wear the sensor device and complete warm-up movements; Acquire warm-up action data corresponding to the warm-up action collected by the sensor device, a reference data type, and a reference data range corresponding to the reference data type; If the known data type in the warm-up action data is less than the reference data type, determining a target data type according to the reference data type and the known data type; and, according to the target data type, controlling the stereoscopic audio-visual system to play a second prompt message, wherein the second prompt message is used to prompt the training user that the sensor device at the position corresponding to the target data type is not correctly worn; If the known data type is equal to the reference data type, and there is a known data range corresponding to any one of the known data types that is not included in the corresponding reference data range, then determining that the known data type whose known data range is not included in the reference data range is the target data type; and, according to the target data type, controlling the stereoscopic audio-visual system to play the second prompt information; If the known data type is equal to the reference data type, and the known data range corresponding to the known data type is not included in the corresponding reference data range, the stereoscopic audio and video system is controlled to play a third prompt message, and it is determined that the training user has correctly worn the sensor. The third prompt message is used to prompt the training user to prepare to start the current balance ability training.

8. A data processing device based on balance ability training, characterized in that: A server used in a human motion quantitative training system, wherein the human motion quantitative training system further comprises a six-degree-of-freedom motion platform, a stereo audio-visual system and a sensor device; the device comprises: a response unit, configured to detect a training start instruction and, if the training user has correctly worn the sensor device, respond to the training start instruction to obtain the latest balance ability score of the training user; a first determining unit, configured to determine the training difficulty level as a first level if the balance ability score fails to be obtained or the balance ability score is less than a preset score, wherein the training difficulty level is determined by a change amplitude and a change frequency of a body posture of the test subject when maintaining a balance state during the corresponding training process of the balance ability training; A second determining unit, configured to determine that the training difficulty level is a second level if the balance ability score is greater than or equal to the preset score, and the second level is higher than the first level; A control unit, for controlling the six-degree-of-freedom motion platform and the stereoscopic audio-visual system according to pre-stored platform motion data and audio-visual playback data, so as to perform the balance ability training corresponding to the training difficulty level; The score updating unit is used to control the six-degree-of-freedom motion platform to return to its initial state after detecting a training termination instruction or detecting that the current balance ability training has been completed, and to update the balance ability score according to the current training data collected by the sensor device and the training difficulty level.

9. A server, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program causes a computer to execute the method according to any one of claims 1 to 7.

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