Method and system for processing training data of walking ability on uneven ground

Abstract

The present application discloses a method and system for processing non-flat ground walking ability training data, including determining personal tags according to the user's personal information; creating virtual walking scene data according to the personal tags, the virtual walking scene data including multiple walking path segmentation data; performing the following processing for multiple walking path segmentation data: obtaining the walking data of the user in the virtual walking sub-scene simulated by the currently running walking path segmentation data; determining the walking ability index of the user in the currently processed virtual walking sub-scene according to the walking data; updating the road surface undulation degree in the next walking path segmentation data according to the walking ability index, and running the updated walking path segmentation data to assist the user in completing the walking training task in the corresponding virtual walking sub-scene, until the multiple walking path segmentation data are all run. The present application can carry out targeted training on the user's walking ability on non-flat roads and improve the training effect.

Description

Technical Field

[0001] The present application relates to the technical field of walking ability training data processing, and in particular to a method and system for processing walking ability training data on uneven ground. Background Art

[0002] Due to reasons such as stroke or cerebellar damage, many patients have difficulty walking normally and need gait rehabilitation training to restore their walking ability.

[0003] The existing technology enables users to perform walking training in a fixed virtual scene based on virtual reality technology, and practice walking movements. However, in real life, it is often necessary to walk on uneven roads, such as stone brick paths, dirt roads, etc. When walking on such uneven roads, it is often necessary to adaptively adjust one's center of gravity and balance according to the ups and downs of the ground. However, simply practicing walking movements cannot enable users to gain targeted training in their ability to walk on such uneven ground, and it is very boring, resulting in poor training effects and poor user experience. Summary of the invention

[0004] The present application provides a method and system for processing training data of walking ability on uneven ground. The system determines the personalized label corresponding to the user according to the user's personal information, thereby determining the degree of undulation of the next virtual uneven path in real time according to the user's personalized label and the user's walking ability level detected in the current path segment, which is conducive to targeted training of the user's walking ability on uneven roads, reducing the boredom of training and improving the training effect.

[0005] In a first aspect, an embodiment of the present application provides a method for processing training data of walking ability on uneven ground, which is applied to a server of a human motion quantification training system, wherein the system further includes a six-axis motion platform, a six-dimensional force measurement and pressure distribution system, and an optical motion capture system; the method includes the following steps:

[0006] Determine a personal tag of the user according to the user's personal information;

[0007] Creating virtual walking scene data for the user according to the personal tag, the virtual walking scene data including a plurality of walking path segmentation data, the preset value of the road surface undulation index in the plurality of walking path segmentation data being a first value, and the first value being used to characterize the flatness of the road surface;

[0008] The following processing is performed on the plurality of walking path segment data:

[0009] Acquiring walking data of the user in a virtual walking sub-scene simulated by the currently running walking path segmentation data, wherein the walking data includes plantar pressure data and walking posture data, wherein the plantar pressure data is a collection of plantar motion signals collected by the six-axis motion platform and the six-dimensional force measurement and pressure distribution system, and the walking posture data is a collection of walking motion signals collected by the optical motion capture system; and,

[0010] Determining a walking ability index of the user in the virtual walking sub-scene currently being processed according to the walking data, the walking ability index including a balance index, a control index and an endurance index; and,

[0011] The road undulation degree in the next walking path segmentation data is updated according to the walking ability index, and the updated walking path segmentation data is run to assist the user in completing the walking training task in the corresponding virtual walking sub-scene until the multiple walking path segmentation data are all run.

[0012] In a second aspect, an embodiment of the present application provides a human motion quantitative training system, the system comprising a server, a six-axis motion platform, a six-dimensional force measurement and pressure distribution system, and an optical motion capture system, the server being used to execute instructions of the steps in the first aspect of the embodiment of the present application.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, 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.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program / instruction stored thereon, and the computer program / instruction is executed by a processor to implement the steps of the method described in the first aspect above.

[0015] It can be seen that in this embodiment, the server determines the personal tag according to the user's personal information; then creates virtual walking scene data for the user according to the personal tag, the virtual walking scene data including multiple walking path segmentation data, and the preset value of the road surface undulation index in the multiple walking path segmentation data is a first value; then the walking data of the user in the virtual walking sub-scene simulated by the currently running walking path segmentation data is obtained; secondly, the walking ability index of the user in the currently processed virtual walking sub-scene is determined according to the walking data; finally, the road surface undulation in the next walking path segmentation data is updated according to the walking ability index, and the updated walking path segmentation data is run to assist the user in completing the walking training task in the corresponding virtual walking sub-scene, until all the multiple walking path segmentation data are completed. Since the present application determines the walking ability index of the user in the currently running virtual walking sub-scene, and thus determines the degree of road undulation in the next walking path segment data according to the walking ability index, compared with the existing scheme of mainly training users to walk in fixed virtual scenes, the degree of road undulation in the virtual scene can be adjusted in time according to the user's walking ability, thereby realizing the function of targeted training of the user's walking ability in non-flat road scenes, which is conducive to improving the training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 any creative work.

[0017] Figure 1 It is a system architecture diagram of a human motion quantification training system provided in an embodiment of the present application;

[0018] Figure 2 is a structural block diagram of an electronic device provided in an embodiment of the present application;

[0019] Figure 3 It is a flowchart of the steps of a method for processing uneven ground walking ability training data provided by an embodiment of the present application;

[0020] Figure 4 is a schematic diagram of a display interface of a human motion quantification training system provided in an embodiment of the present application;

[0021] Figure 5 This is an application scenario diagram of a method for processing non-flat ground walking ability training data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] 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.

[0023] 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.

[0024] 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.

[0025] Currently, the existing solution system only builds fixed virtual scenes for users based on virtual reality technology for users to conduct walking training. It is unable to process the user's walking data in real time, so as to timely adjust the degree of undulation of the user's walking road in the virtual scene. It is difficult to meet the needs of targeted training based on changes in the user's walking ability in the scenario of walking ability training on non-flat roads.

[0026] In response to the above problems, an embodiment of the present application provides a method and system for processing non-flat ground walking ability training data. The embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0027] See also Figure 1 , Figure 1 is a system architecture diagram of a human motion quantification training system provided in an embodiment of the present application, such as Figure 1 As shown, the human motion quantitative training system includes a server 101, a six-axis motion platform integrated motion walking system 102, a six-dimensional force measurement and pressure distribution system 103, a multi-channel annular (stereo) audio and video system 104, a dynamic weight reduction and fall protection system 105, an optical motion capture system 106 and a scientific research and training evaluation system 107.

[0028] Server 101 is used to obtain the user's personal information; and to determine the personal tag based on the personal information, and to create virtual walking scene data for the user based on the personal tag; and to process multiple walking path segmentation data included in the virtual walking scene data to obtain the user's walking ability index in the currently running virtual sub-scene, and to update the road undulation degree of the next walking path segment based on the walking ability index.

[0029] The six-axis motion platform integrated motion walking system 102 includes a Stewart six-degree-of-freedom motion platform, an embedded dual-track force measuring treadmill, and other fixed, linked, and attached structures for realistically simulating various scenarios in daily life.

[0030] The six-dimensional force measurement and pressure distribution system 103 includes a double-belt treadmill with a total track width of 1.5 meters. The double-belt treadmill contains six six-dimensional force measuring platforms and a plantar pressure distribution sensing system. The double-belt treadmill can perform unilateral or bilateral movement control and is used to collect dynamic balance and walking training data.

[0031] The multi-channel annular (stereo) audio-visual system 104 includes a 180° flexible annular LED screen, a visual interaction system and a 7.1.2-channel stereo surround sound system, and is used to display a virtual walking sub-scene corresponding to a user tag.

[0032] The dynamic weight loss and fall protection system 105 includes a dynamic weight loss system and a fall protection system, which can perform intelligent following and dynamic sensing compensation weight loss when the user is training, and can bear pulling force to prevent the user from losing balance and falling during walking training.

[0033] The optical motion capture system 106 includes a light capture lens, which is used to collect walking motion signals of the user during walking training to obtain walking posture data.

[0034] The scientific research and training evaluation system 107 includes a system for evaluating the data during the user's walking training to obtain a training report.

[0035] It can be seen that in this embodiment, the server 101 first determines the personal tag according to the user's personal information; then, creates virtual walking scene data for the user according to the personal tag; then, the plantar motion signal is collected through the six-axis motion platform integrated motion walking system 102 and the six-dimensional force measurement and pressure distribution system 103 to obtain plantar pressure data, and the walking motion signal is collected through the optical motion capture system 106 to obtain walking posture data; secondly, the walking ability index of the user in the currently processed virtual walking sub-scene is determined according to the plantar pressure data and the walking posture data, and finally, the road surface undulation degree in the next walking path segment data is updated according to the walking ability index, thereby realizing the function of conducting targeted training for the user according to the change of the user's walking ability in the non-flat road scene.

[0036] See also Figure 2 , Figure 2 is a structural block diagram of an electronic device provided in an embodiment of the present application, for executing Figure 1 Human motion quantization training system in Figure 2 As shown, the electronic device 200 may include one or more of the following components: a processor 201, a memory 202 coupled to the processor 201, wherein the memory 202 may store one or more computer programs, and the one or more computer programs may be configured to implement the method described in the above embodiment when executed by one or more processors 201. The electronic device may be a mobile phone terminal, a tablet computer, a laptop computer, and a wearable smart device.

[0037] The processor 201 may include one or more processing cores. The processor 201 uses various interfaces and lines to connect various parts of the entire electronic device 200, and executes various functions and processes data of the electronic device 200 by running or executing instructions, programs, code sets or instruction sets stored in the memory 202, and calling data stored in the memory 202. Optionally, the processor 201 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 201 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 201, and may be implemented separately through a communication chip.

[0038] The memory 202 may include a random access memory (RAM) or a read-only memory (ROM). The memory 202 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 202 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 electronic device 200 during use, etc.

[0039] It is understandable that the electronic device 200 may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, a Wi-Fi module, a speaker, a Bluetooth module, a sensor, etc., which are not limited here.

[0040] See also Figure 3 , Figure 3 This is a flowchart of a method for processing non-flat ground walking ability training data provided by an embodiment of the present application, which is applied to Figure 1 Server 101 in, such as Figure 3 As shown, the method comprises the following steps:

[0041] Step S301, determining the user's personal tag according to the user's personal information.

[0042] Among them, the user's personal information includes name, gender, contact information, age, basic physical data, place of origin, permanent residence, personal hobby information, etc.

[0043] In a possible embodiment, determining the user's personal tag according to the user's personal information includes:

[0044] Obtaining the user's permanent residence and personal hobby information from the user's personal information;

[0045] Extracting scene information from the personal hobby information;

[0046] The permanent residence and the scene information are used as personal tags of the user.

[0047] For example, the user's permanent residence is Shenzhen, and his personal hobby is visiting parks and appreciating garden landscapes. The scene information in the personal hobby information is parks and gardens, and the user's personal tags are determined to be Shenzhen, parks, and gardens.

[0048] It can be seen that in this embodiment, the server processes the user's personal information and determines the user's personal tag based on the user's permanent residence and the scene information in the personal hobby information, so that a virtual walking scene that suits the user's preferences can be created for the user based on the personal tag, which is conducive to reducing the boredom of training and improving the training effect.

[0049] Step S302, creating virtual walking scene data for the user according to the personal tag, wherein the virtual walking scene data includes a plurality of walking path segmentation data, wherein a preset value of a road surface undulation index in the plurality of walking path segmentation data is a first value, and the first value is used to characterize the flatness of the road surface.

[0050] In a possible embodiment, the creating virtual walking scene data for the user according to the personal tag includes:

[0051] Acquire a preset scene material library, wherein each scene material in the scene material library is marked with a material label, there is a corresponding relationship between the material label and each scene material, and each scene material is composed of a real scene;

[0052] Determine a plurality of scene materials in the scene material library whose material tags are identical or similar to the personal tags;

[0053] The virtual walking scene data is created according to the multiple scene materials.

[0054] Exemplarily, the scene material library includes Shenzhen's local landmark building materials, park-related materials and garden-related materials. The corresponding material labels may be Shenzhen, Ferris wheel, building, park, garden, beach, hillside, etc. By comparing the similarity between the material labels and the user's personal labels, scene modeling can be performed based on the scene materials corresponding to multiple material labels with higher similarity to create virtual walking scene data.

[0055] The first value is zero, and the value of the road surface undulation index is positively correlated with the undulation of the road surface.

[0056] It can be seen that in this embodiment, the server calls the preset scene material library, obtains the material label of each scene material in the scene material library, and compares it with the user's personal label to determine multiple scene materials corresponding to similar or identical scene labels, so that scene modeling can be performed based on the multiple scene materials to create virtual walking scene data, thereby realizing the creation of virtual walking scenes according to user preferences, avoiding the boredom of training, and helping to improve training effects.

[0057] Step S303, performing the following processing on multiple walking path segmentation data: obtaining the walking data of the user in the virtual walking sub-scene simulated by the currently running walking path segmentation data, the walking data including plantar pressure data and walking posture data; and, determining the walking ability index of the user in the currently processed virtual walking sub-scene based on the walking data, the walking ability index including balance index, control index and endurance index; and, updating the road surface undulation degree in the next walking path segmentation data based on the walking ability index, and running the updated walking path segmentation data to assist the user to complete the walking training task in the corresponding virtual walking sub-scene, until the multiple walking path segmentation data are all completed.

[0058] The plantar pressure data is a collection of plantar motion signals collected by the six-axis motion platform and the six-dimensional force measurement and pressure distribution system, and the walking posture data is a collection of walking motion signals collected by the optical motion capture system.

[0059] The optical motion capture system includes a light capture lens, which is arranged around the top of the 180° flexible annular LED screen and is used to capture the user's walking movements during training in high definition.

[0060] In a possible embodiment, determining the walking ability index of the user in the virtual walking sub-scene currently being processed according to the walking data includes:

[0061] Comparing the plantar pressure data with preset standard plantar pressure data to obtain multiple plantar pressure data scores, the plantar pressure data including plantar zone pressure, average peak pressure, average pressure, contact area, pressure-time integral, standing time and plantar pressure center trajectory;

[0062] Comparing the walking posture data with preset standard walking posture data to obtain a plurality of walking posture data scores, wherein the walking posture data includes step length, stride, step frequency, step speed, step width and walking cycle;

[0063] The walking ability index is determined according to the multiple plantar pressure data scores and the multiple walking posture data scores.

[0064] Among them, the plantar zoning can select nine parts of the plantar, including the big toe, the 2nd to 3rd toes, the 4th to 5th toes, the 1st toe bone, the 2nd toe bone, the 3rd to 4th toe bone, the 5th toe bone, the midfoot and the heel for zoning, so as to determine the plantar pressure value of each zone, the pressure average value, the average peak pressure, the contact area, the pressure time integral, the plantar pressure center trajectory and other data.

[0065] The average peak pressure of the nine plantar sites is used to represent the sum of the peak pressures of each phase / the number of phases within the preset collection time.

[0066] Among them, a phase is composed of a group of frames, and a frame represents the pressure value recorded by the pressure sensor at a certain moment. The server combines all the frames from the heel to the toe into a phase. After the frames are combined into a phase, each sensor unit displays the maximum pressure value in this phase, that is, the peak pressure. The preset collection time can be 8 seconds, and the server can divide the 8-second recording time into 6 phases. Therefore, the average peak pressure can be understood as the average value of the peak pressures of the 6 standing phases.

[0067] The pressure center trajectories of the nine sole positions represent the dynamic changes of the center of pressure acting on the sensor within a gait cycle.

[0068] The pressure-time integral represents the sum of the product of the pressure and the duration of each frame in a phase, and is expressed as follows:

[0069] Integral= + + … ;

[0070] Wherein, the Integral represents the pressure-time integral, Indicates the pressure of each frame in a phase, Indicates the duration of the pressure applied to an item in each frame.

[0071] In a possible embodiment, after determining the plantar pressure data and the walking posture data, the center of gravity distribution parameters of the gait can also be analyzed according to various graphs and indexes through a gravity analysis module. The gravity analysis module obtains four gait indexes through Fourier and harmonic wave analysis, thereby comparing the user's gait with the ideal gait parameters to determine the user's gait quality.

[0072] Among them, the gait index includes purity index, symmetry index, energy efficiency index and gait perfection index. The purity index is used to characterize the efficiency of converting kinetic energy into potential energy. The symmetry index is used to characterize the degree of symmetry between the left and right feet. The energy efficiency index is used to characterize the performance of energy preservation after the center of gravity is moved against the center of gravity. The gait perfection index is used to characterize the arithmetic integration of the purity index, the symmetry index and the energy efficiency index. The higher the gait perfection index, the closer the gait is to the ideal state.

[0073] In a possible embodiment, the plantar pressure data is compared with preset standard plantar pressure data to obtain multiple plantar pressure data scores, including:

[0074] Acquire multiple score interval tables corresponding to the preset standard plantar pressure data;

[0075] Determine the score interval to which each plantar pressure data in the plantar pressure data belongs according to the multiple score interval tables;

[0076] The plurality of plantar pressure data scores are determined according to the score intervals.

[0077] In a possible embodiment, the walking posture data is compared with preset standard walking posture data to obtain a plurality of walking posture data scores, including:

[0078] Acquire multiple score interval tables corresponding to the preset standard walking posture data;

[0079] Determine the score interval to which each walking posture data in the walking posture data belongs according to the multiple score interval tables;

[0080] The scores of the plurality of walking posture data are determined according to the score intervals.

[0081] In a possible embodiment, determining the walking ability index according to the multiple plantar pressure data scores and the multiple walking posture data scores includes:

[0082] Performing weighted average processing on the multiple plantar pressure data scores to obtain an average plantar pressure data score, and performing weighted average processing on the multiple walking posture data scores to obtain an average walking posture data score;

[0083] Determine a first score interval level corresponding to the average score of the plantar pressure data in a preset first score interval level table, and determine a second score interval level corresponding to the average score of the walking posture data in a preset second score interval level table;

[0084] The balance index is determined according to the first score interval level, and the control index is determined according to the second score interval level, the first score interval level is positively correlated with the value of the balance index, and the second score interval level is positively correlated with the value of the control index.

[0085] Exemplarily, the first score interval level table may be shown in the following table:

[0086]

[0087] First score range table

[0088] The score interval level is divided into five levels, from high to low, namely A, B, C, D, and E. After weighted average processing, the average score of the plantar pressure data can be obtained, and the corresponding score interval level is determined according to the average score of the plantar pressure data. The balance index value can be 5, 4, 3, 2, and 1 from high to low, corresponding to the score interval levels A, B, C, D, and E, respectively.

[0089] In a possible embodiment, the method further includes:

[0090] Obtaining the user's blood pressure, blood oxygen and heart rate in real time through a cardiopulmonary blood oxygen and blood pressure monitoring device;

[0091] Determining an average walking speed of the user in a virtual walking sub-scene simulated by the currently running walking path segmentation data;

[0092] The average walking speed, the blood pressure value, the blood oxygen value and the heart rate value are input into a pre-trained endurance index calculation model to determine the endurance index of the user in the virtual walking sub-scene currently being processed.

[0093] Among them, the cardiopulmonary blood oxygen and blood pressure monitoring equipment includes a sphygmomanometer and a blood oximeter. The sphygmomanometer is worn on the user's left arm, at the same height as the heart, and is used to monitor the user's blood pressure value in real time. The blood oximeter is worn on the user's index finger or thumb, and is used to monitor the user's blood oxygen value and heart rate value in real time.

[0094] Among them, determining the average walking speed of the user in the virtual walking sub-scene simulated by the currently running walking path segmentation data includes: obtaining the total walking distance and walking time of the user in the current virtual walking sub-scene, and expressing the average walking speed by the ratio of the total walking distance and the walking time.

[0095] Among them, the endurance index calculation model mainly evaluates the user's endurance index with reference to the Borg index. The Borg index is used to score the degree of dyspnea of ​​the user after walking within six minutes. The endurance index calculation model integrates the Borg index and can determine the user's dyspnea and fatigue level according to the user's average speed in each walking path segment, combined with changes in the user's blood pressure, blood oxygen and heart rate values, thereby obtaining the endurance index value. The higher the endurance index value, the better the user's endurance.

[0096] In a possible embodiment, the road surface undulation degree includes undulation density and undulation severity, the undulation density is the density of potholes and protrusions on the road surface, and the undulation severity is the height difference between the potholes and protrusions on the road surface and the plane; updating the road surface undulation degree in the next walking path segmentation data according to the walking ability index includes:

[0097] Determine the latest fluctuation density adapted to the walking ability of the user according to the control force index, wherein the value of the control force index is positively correlated with the fluctuation density; and

[0098] Determine the latest fluctuation severity level adapted to the user's walking ability according to the balance index, wherein the value of the balance index is positively correlated with the fluctuation severity level;

[0099] The road surface undulation degree in the next walking path segmentation data is determined and updated according to the latest undulation density degree and the latest undulation severity degree.

[0100] Exemplarily, the control force index value from high to low can be 5, 4, 3, 2, 1, the balance force index value from high to low can be 5, 4, 3, 2, 1, the fluctuation density index value can include 5, 4, 3, 2, 1, and the higher the value, the greater the fluctuation density; the fluctuation intensity index value can include 5, 4, 3, 2, 1, and the higher the value, the greater the fluctuation intensity.

[0101] The control force index value is positively correlated with the value of the undulation density index, and the balance force index value is positively correlated with the value of the undulation severity index. Specifically, when the control force index value is 5, the value of the latest undulation density index is also 5, and when the balance force index value is 4, the value of the latest undulation severity index is also 4, so that the road undulation in the next walking path segment data can be updated with the corresponding latest undulation density index value and the latest undulation severity index value, so that the user can perform the next training.

[0102] In a possible embodiment, the user participates in the training for the first time; the method further includes:

[0103] If it is determined according to the personal information of the user that this is not the first time for the user to participate in the training, obtaining a historical walking ability index database;

[0104] Determine the latest historical walking ability index in the historical walking ability index database;

[0105] The latest historical walking ability index is determined as the walking ability index of the user in the virtual walking sub-scene simulated by the first walking path segment data in this training event.

[0106] Among them, the user's personal information is manually input and stored in the server. If the user participates in the training for the first time, it is necessary to manually enter the user's name, gender, contact information, age, basic physical data, native place, permanent residence, personal hobby information and other personal information; and, if the user is not participating in the training for the first time, the user's historical training records can be queried by entering the name or contact information.

[0107] The historical training records include historical training items, historical training evaluation data, historical walking ability index database, historical training reports, etc.

[0108] It can be understood that in this embodiment, when it is determined that this is not the first time that the user has participated in the walking training, the latest historical walking ability index in the historical walking ability index database is used as the walking ability index of the user in the virtual walking sub-scene simulated by the first walking path segment data in this training event. There is no need to determine the walking ability index based on the plantar pressure data and walking posture data obtained in the first walking path segment, which can reduce the amount of calculation of the server and perform continuous targeted training according to the user's training situation.

[0109] It can be seen that in this embodiment, the server obtains the walking data of the user in the virtual walking sub-scene simulated by the currently running walking path segmentation data; then, the walking ability index of the user in the currently processed virtual walking sub-scene is determined according to the walking data; finally, the road surface undulation degree in the next walking path segmentation data is updated according to the walking ability index, and the updated walking path segmentation data is run to assist the user to complete the walking training task in the corresponding virtual walking sub-scene until the multiple walking path segmentation data are all run to completion, thereby realizing timely adjustment of the road surface undulation degree in the virtual sub-scene according to the changes in the user's walking ability during the walking training process, which is conducive to targeted training for users and improving training effects.

[0110] See also Figure 4 , Figure 4 is a schematic diagram of a display interface of a human motion quantification training system provided in an embodiment of the present application, such as Figure 4 As shown, the display interface is a training setting interface.

[0111] Among them, the training setting page includes training scenarios and training parameters, the training scenarios are used to determine the training scenarios for users to perform different training items, the training scenarios include balance training, step training, gait training, transportation and environment training; and the training parameters are used to perform relevant data and equipment settings for the training scenarios selected by the user, the training parameters include training duration, training difficulty, pressure plate selection, trajectory parameter adjustment and walking environment.

[0112] Among them, the training duration indicates the duration of each training of the user in the selected training scene, which is between 1 and 30 minutes, specifically 10 minutes; the training difficulty includes elementary difficulty, intermediate difficulty and advanced difficulty. The difference in training difficulty at different levels lies in the complexity of the road conditions in the virtual scene. The complexity of the road conditions is mainly characterized by the number of slopes, the density of slopes, the size of slopes, and the number of stairs; the pressure plate selection is used to select at least one of the left pressure plate and the right pressure plate to obtain the user's foot walking data; the trajectory parameter adjustment is used to adjust parameters related to the motion trajectory, such as speed, acceleration, steering, etc. The trajectory parameter adjustment can affect the user's movement mode. There is no trajectory parameter adjustment under environmental training; the walking environment includes multiple specific walking environments and corresponding training difficulties. The number of star ratings of the training difficulty is represented. The more star ratings, the greater the training difficulty.

[0113] Among them, the multiple specific walking environments may include supermarket training, Century Park, outdoor hiking, Lixiang Park, Sports Park, stone brick paths, Bund stroll and Central Avenue. The multiple specific walking environments correspond to different walking road conditions, and users can choose according to personal preferences and their own physical conditions.

[0114] Specifically, the supermarket training scene includes flat roads and escalators in a supermarket environment; the Century Park scene includes flat roads and wooden bridges; the outdoor hiking scene includes non-flat roads and uphill and downhill slopes in the wild; the Lixiang Park scene includes flat roads and uphill and downhill slopes; the sports park scene includes flat roads and uphill and downhill stairs; the stone brick path scene includes non-flat roads; the Bund stroll scene includes flat roads; and the Central Avenue scene includes flat roads and steep stairs.

[0115] Exemplarily, after creating virtual walking scene data for the user based on the personal tag, the multiple specific walking scenes can be obtained, and then the user can be prompted to select one of the walking scenes for training on the display interface of the system, or the system can automatically select one of the walking scenes for training for the user based on the user's non-flat road walking ability training requirements.

[0116] Before making the training settings, the user's personal information can also be entered or queried in the system. If it is determined that the user is participating in the training for the first time, a training assessment is performed on the user's physical condition so as to make targeted training settings based on the user's physical condition. The training assessment includes posture assessment, balance assessment, step assessment, gait assessment and endurance assessment.

[0117] Among them, the posture assessment requires the user to stand on one side of the treadmill in a natural standing posture for training assessment; the balance assessment requires the user to stand barefoot and perform training assessment according to the assessment conditions pre-set according to the user's physical condition, the assessment conditions are eyes open for 60 seconds on both feet or eyes closed for 60 seconds on both feet or eyes open for 10 seconds on one foot or eyes closed for 10 seconds on one foot; the step assessment requires the user to stand barefoot in the test position and move according to the task requirements displayed by the system for training assessment; among them, the gait assessment requires the user to stand barefoot on one side of the treadmill and move according to the task requirements displayed by the system for training assessment; the endurance assessment needs to be determined by referring to the Borg index comparison table, the Borg index is used to characterize the degree of dyspnea and fatigue of the user after walking as far as possible within six minutes. During the assessment process, the user needs to wear an oximeter and a blood pressure monitor throughout the process to obtain the user's blood pressure, blood oxygen value and heart rate in real time.

[0118] Among them, the Borg index comparison table is shown in the following table:

[0119]

[0120] Borg Index Comparison Table

[0121] Among them, the user's endurance evaluation score can be determined according to the Borg index comparison table, and the user's endurance level can be determined according to the score. The lower the score, the better the user's endurance.

[0122] It is understandable that after the training evaluation of the user, the various indicators corresponding to the user's physical condition can be determined; then the training settings are made according to the user's physical condition, and for the training of the user's ability to walk on non-flat roads, the environmental training in the training scene can be selected to conduct targeted walking training for the user in a virtual environment built based on the real environment; then the training parameters in the environmental training are set, and the training duration, training difficulty and single-sided or double-sided pressure plates for standing can be selected according to the user's physical condition, and the stone brick path training scene can be selected according to the user's non-flat road walking ability training needs, thereby realizing the determination of the virtual walking scene according to the user's personal preferences and the adjustment of the corresponding training parameters according to the user's physical condition, so as to conduct targeted training for the user, which is conducive to improving the training effect.

[0123] See also Figure 5 , Figure 5 is an application scenario diagram of a method for processing non-flat ground walking ability training data provided by an embodiment of the present application, such as Figure 5 As shown, this scene is a scene for training walking ability on uneven ground.

[0124] Among them, the user performs walking training on the Stewart six-degree-of-freedom motion platform 501, the user walks on the double-belt treadmill 502, the user's body is connected to the weight-reducing suspension device 504 above through a safety rope 503, and there is a 180° flexible annular LED screen 505 directly in front of the user. A plurality of light-capturing lenses 506 are arranged around the top of the 180° flexible annular LED screen 505. During the training process, the electric door 507 remains closed, and there is a central control console 508 on the right side of the electric door 507.

[0125] Among them, the dual-belt treadmill 502 contains six six-dimensional force platforms and a plantar pressure distribution sensing system. The dual-belt treadmill 502 can perform unilateral or bilateral movement control and is used to collect dynamic balance and plantar pressure data. The plantar pressure data includes plantar zone pressure, average peak pressure, average pressure, contact area, pressure-time integral, standing time and plantar pressure center trajectory.

[0126] The weight-reducing suspension device 504 can provide 80kg of weight reduction, and has intelligent following and dynamic sensing compensation functions. In addition, the weight-reducing suspension device 504 can bear a maximum pulling force of 200kg, which can prevent the user from falling when losing balance.

[0127] Among them, the 180° flexible annular LED screen 505 can display a virtual walking sub-scene corresponding to the user tag through a visual interaction system and a 7.1.2-channel stereo surround sound system.

[0128] The light capture lens 506 is used to capture the user's walking movements during training in high definition to collect walking movement signals, thereby obtaining walking posture data, which includes step length, stride, step frequency, step speed, step width and walking cycle.

[0129] Among them, buttons are arranged on the doorposts on both sides of the electric door 507, which can control the opening and closing of the electric door 507.

[0130] In a possible embodiment, the scene further includes barrier-free facilities, which include ramps and barrier-free lifting platforms. The barrier-free lifting platform can support wheelchair users to enter and exit, and can also support equipment transportation. The specifications of the barrier-free lifting platform need to be adjusted according to actual conditions. The typical load value of the barrier-free lifting platform can be 400kg. The barrier-free lifting platform can support an operator and a wheelchair user to rise and fall together, or support an operator to push a device not exceeding 250kg to rise and fall together.

[0131] It can be understood that when the user performs walking training on the dual-belt treadmill 502, the six six-dimensional force platforms and the plantar pressure distribution sensor system built into the dual-belt treadmill 502 can collect plantar pressure signals to obtain plantar pressure data, and the light capture lens 506 can collect walking motion signals to obtain walking posture data; then the server determines the user's walking ability index in the currently processed virtual walking sub-scene based on the plantar pressure data and the walking posture data; then the server updates the road surface undulation in the next walking path segmentation data based on the walking ability index, and runs the updated walking path segmentation data to assist the user in completing the walking training task in the corresponding virtual walking sub-scene, until multiple walking path segmentation data are all completed, thereby realizing timely adjustment of the road surface undulation of the walking road surface according to changes in the user's walking ability, so as to conduct targeted training for the user's walking ability on non-flat roads, which is conducive to improving the training effect.

[0132] In addition, an embodiment of the present application also provides a computer storage medium, which stores a computer program that can be loaded by a processor and executes the above-mentioned processing method for training data of walking ability on non-flat roads. The computer-readable storage medium includes, for example: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0133] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0134] 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 only 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. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, the functional units in various embodiments of the present invention can be integrated into a processing unit, or each unit can be physically included separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0135] 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 random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DRRAM), etc., various media that can store program code.

[0136] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0138] Although the present application is disclosed as above, the present application 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 application, 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 application.

Claims

1. A method for processing training data of walking ability on uneven ground, characterized in that: A server applied to a human motion quantitative training system, the system further comprising a six-axis motion platform, a six-dimensional force measurement and pressure distribution system and an optical motion capture system; the method comprising: Determine a personal tag of the user according to the user's personal information; Creating virtual walking scene data for the user according to the personal tag, the virtual walking scene data including a plurality of walking path segmentation data, the preset value of the road surface undulation index in the plurality of walking path segmentation data being a first value, and the first value being used to characterize the flatness of the road surface; The following processing is performed on the plurality of walking path segment data: Acquiring walking data of the user in a virtual walking sub-scene simulated by the currently running walking path segmentation data, wherein the walking data includes plantar pressure data and walking posture data, wherein the plantar pressure data is a collection of plantar motion signals collected by the six-axis motion platform and the six-dimensional force measurement and pressure distribution system, and the walking posture data is a collection of walking motion signals collected by the optical motion capture system; and, Determining a walking ability index of the user in the virtual walking sub-scene currently being processed according to the walking data, the walking ability index including a balance index, a control index and an endurance index; and, The road undulation degree in the next walking path segmentation data is updated according to the walking ability index, and the updated walking path segmentation data is run to assist the user in completing the walking training task in the corresponding virtual walking sub-scene until the multiple walking path segmentation data are all run.

2. The method according to claim 1, characterized in that The road surface undulation degree includes undulation density and undulation severity. The undulation density refers to the density of potholes and bumps on the road surface, and the undulation severity refers to the height difference between the potholes and bumps on the road surface and the plane. The updating of the road surface undulation degree in the next walking path segmentation data according to the walking ability index comprises: Determine the latest fluctuation density adapted to the walking ability of the user according to the control force index, wherein the value of the control force index is positively correlated with the fluctuation density; and Determine the latest fluctuation severity level adapted to the user's walking ability according to the balance index, wherein the value of the balance index is positively correlated with the fluctuation severity level; The road surface undulation degree in the next walking path segmentation data is determined and updated according to the latest undulation density degree and the latest undulation severity degree.

3. The method according to claim 2, characterized in that The determining, according to the walking data, a walking ability index of the user in the virtual walking sub-scene currently being processed includes: Comparing the plantar pressure data with preset standard plantar pressure data to obtain multiple plantar pressure data scores, the plantar pressure data including plantar zone pressure, average peak pressure, average pressure, contact area, pressure-time integral, standing time and plantar pressure center trajectory; Comparing the walking posture data with preset standard walking posture data to obtain a plurality of walking posture data scores, wherein the walking posture data includes step length, stride, step frequency, step speed, step width and walking cycle; The walking ability index is determined according to the multiple plantar pressure data scores and the multiple walking posture data scores.

4. The method according to claim 3, characterized in that The step of determining the walking ability index according to the plurality of plantar pressure data scores and the plurality of walking posture data scores comprises: Performing weighted average processing on the multiple plantar pressure data scores to obtain an average plantar pressure data score, and performing weighted average processing on the multiple walking posture data scores to obtain an average walking posture data score; Determine a first score interval level corresponding to the average score of the plantar pressure data in a preset first score interval level table, and determine a second score interval level corresponding to the average score of the walking posture data in a preset second score interval level table; The balance index is determined according to the first score interval level, and the control index is determined according to the second score interval level, the first score interval level is positively correlated with the value of the balance index, and the second score interval level is positively correlated with the value of the control index.

5. The method according to claim 4, characterized in that The method further comprises: Obtaining the user's blood pressure, blood oxygen and heart rate in real time through a cardiopulmonary blood oxygen and blood pressure monitoring device; Determining an average walking speed of the user in a virtual walking sub-scene simulated by the currently running walking path segmentation data; The average walking speed, the blood pressure value, the blood oxygen value and the heart rate value are input into a pre-trained endurance index calculation model to determine the endurance index of the user in the virtual walking sub-scene currently being processed.

6. The method according to claim 5, characterized in that The user participates in the training for the first time; the method further includes: If it is determined according to the personal information of the user that this is not the first time for the user to participate in the training, obtaining a historical walking ability index database; Determine the latest historical walking ability index in the historical walking ability index database; The latest historical walking ability index is determined as the walking ability index of the user in the virtual walking sub-scene simulated by the first walking path segment data in this training event.

7. The method according to claim 6, characterized in that The step of creating virtual walking scene data for the user according to the personal tag comprises: Acquire a preset scene material library, wherein each scene material in the scene material library is marked with a material label, there is a corresponding relationship between the material label and each scene material, and each scene material is composed of a real scene; Determine a plurality of scene materials in the scene material library whose material tags are identical or similar to the personal tags; The virtual walking scene data is created according to the multiple scene materials.

8. A human motion quantification training system, characterized in that: The method comprises a server, a six-axis motion platform, a six-dimensional force measurement and pressure distribution system and an optical motion capture system, wherein the server is used to execute the step instructions in the method as described in any one of claims 1 to 7.

9. An electronic device, 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 any one of the methods of claims 1 to 7.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: The computer program / instructions are executed by a processor to implement the steps of the method according to any one of claims 1 to 7.

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