Multi-dimensional lower limb rehabilitation training method and system based on double runways

Abstract

The invention relates to the field of rehabilitation training, in particular to a multi-dimensional lower limb rehabilitation training method and system based on double runways, and the method comprises the steps: firstly collecting plantar pressure data and heart rate data of a user, then carrying out the data analysis of the plantar pressure data and the heart rate data, obtaining an analysis result, and then selecting a training mode according to the analysis result, according to the training mode, the current inclination angle and / or the current speed of the double runways are / is adjusted, and finally, according to the current inclination angle and / or the current speed, a VR training scene of the VR equipment is generated in real time. Compared with the prior art, the method provided by the invention is higher in automation degree, has adaptive adjustment capability, can efficiently adjust equipment training parameters and switch VR training scenes, and can provide better user experience.

Description

Technical Field

[0001] The present invention relates to the field of rehabilitation training technology. More specifically, the present invention relates to a multi-dimensional lower limb rehabilitation training method and system based on dual runways. Background Art

[0002] Virtual reality (VR) technology uses computers to generate interactive three-dimensional environments, providing users with an immersive experience. It utilizes stereoscopic display technology, scene modeling technology, and natural interaction technology, combining multi-sensory simulations such as vision, hearing, and touch to create a highly realistic virtual world. In the field of medical rehabilitation, VR provides patients with engaging virtual sports training to enhance their motivation for recovery.

[0003] In the existing technology, there is an immersive upper and lower limb rehabilitation training system, which mainly consists of VR equipment, a training chair, and a lower limb pedal trainer. When patients use this rehabilitation training system, they need to be fixed to the chair during training, which makes the user experience less realistic. In addition, medical staff are required to accompany patients during rehabilitation training to assist in switching training parameters or virtual scenes of the training equipment. This method consumes human resources and has a low degree of automation, resulting in low efficiency of the overall rehabilitation training evaluation process. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems of poor user experience and low efficiency, the present invention provides solutions in the following aspects.

[0005] In a first aspect, the present invention discloses a multi-dimensional lower limb rehabilitation training method based on dual runways, which uses dual runways and VR equipment to perform rehabilitation training on users. The method of the present invention comprises:

[0006] Collect user's plantar pressure data and heart rate data;

[0007] Performing data analysis on plantar pressure data and heart rate data to obtain analysis results;

[0008] Select the training mode based on the analysis results;

[0009] Adjust the current inclination angle and / or current speed of the dual runways according to the training mode;

[0010] Generate a VR training scene for the VR device in real time based on the current tilt angle and / or current speed.

[0011] Beneficial Effects: The method of the present invention can perform real-time analysis of a user's plantar pressure and heart rate data, and select the user's training mode based on the real-time analysis results, adapting the inclination angle and current speed of the dual runways to the user's physical condition. Furthermore, the method of the present invention can automatically generate corresponding VR training scenarios based on the current inclination angle and current speed of the runways, thereby enhancing the authenticity of the user experience. Compared to existing technologies, the method of the present invention has a higher degree of automation and adaptive adjustment capabilities, enabling efficient adjustment of equipment training parameters and switching of VR training scenarios, providing a superior user experience.

[0012] Preferably, the analysis result includes whether one or more of the user's gait, foot load, user balance, target heart rate, heart rate variability and heart rate recovery rate are normal.

[0013] Beneficial effects: The method of the present invention can perform multi-dimensional health status analysis of users based on plantar pressure data and heart rate data, and can meet the remote monitoring needs of evaluators.

[0014] Preferably, performing data analysis on the plantar pressure data to obtain analysis results includes:

[0015] According to the plantar pressure data, the pressure distribution uniformity index is calculated, specifically:

[0016]

[0017] Where PUI is the pressure distribution uniformity index, σ is the standard deviation of multiple plantar pressure data, and μ is the mean of multiple plantar pressure data;

[0018] It is determined whether the pressure distribution uniformity index exceeds a first normal range. If so, the user's gait is determined to be abnormal; otherwise, the user's gait is determined to be normal.

[0019] Beneficial Effects: The method of the present invention uses the pressure distribution uniformity index to determine whether a user's gait is abnormal. If the pressure distribution uniformity index exceeds a first normal range, it indicates that the user's gait is unstable and the abnormal gait is immediately determined. Compared with existing technologies, the method of the present invention achieves automatic identification of gait abnormalities without manual intervention, achieving a higher degree of automation.

[0020] Preferably, performing data analysis on the plantar pressure data to obtain analysis results includes:

[0021] identifying a maximum value among a plurality of plantar pressure data as a pressure peak;

[0022] The peak pressure ratio is calculated by taking the quotient of the peak pressure and the average of multiple plantar pressure data;

[0023] Determine whether the following conditions are met:

[0024] The pressure peak is in the second normal interval;

[0025] The peak pressure ratio is in the third normal range;

[0026] If any one of the conditions is not met, the foot load is judged to be abnormal, and if all the conditions are met, the foot load is judged to be normal.

[0027] Preferably, multiple plantar pressure data at the same time node are matched with a central position coordinate, and data analysis is performed on the plantar pressure data to obtain analysis results, including:

[0028] Calculate the trajectory length between the two center position coordinates corresponding to two adjacent plantar pressure data in time series as the pressure center trajectory;

[0029] According to the center position coordinates corresponding to a plurality of continuous time series plantar pressure data, the range of movement of the center position coordinates is calculated as the pressure swing area;

[0030] Determine whether the following conditions are met:

[0031] The trajectory of the center of pressure is in the fourth normal interval;

[0032] The pressure swing area is in the fifth normal interval;

[0033] If any of the conditions are not met, the user balance is judged to be abnormal; if all the conditions are met, the user balance is judged to be normal.

[0034] Preferably, if any one of the user's gait, foot load, user balance, target heart rate, heart rate variability and heart rate recovery rate is abnormal, the training mode adopts the passive training mode.

[0035] Preferably, adjusting the current inclination angle of the dual runways according to the training mode includes:

[0036] If the training mode is passive training mode and the foot load is abnormal;

[0037] Adjust the current inclination angle of the dual runways to a safe angle range.

[0038] Beneficial effect: When the user's foot load is abnormal, it means that the runway's inclination angle is too large, causing the local pressure on the user's foot to be too large, which is not conducive to the user's rehabilitation training. At this time, the inclination angle of the dual runways is automatically adjusted to ensure full contact between the user's feet and the runway, disperse local pressure, and improve user comfort.

[0039] Preferably, adjusting the current speed of the dual runways according to the training mode includes:

[0040] If the training mode is passive training mode and the target heart rate is abnormal;

[0041] Reduce current speed on dual runways.

[0042] Preferably, performing data analysis on the heart rate data to obtain analysis results includes:

[0043] Calculate the heart rate standard deviation and heart rate root mean square deviation of heart rate data;

[0044] Determine whether the following conditions are met:

[0045] The standard deviation of heart rate is in the sixth normal range;

[0046] The RMS deviation of heart rate was in the seventh normal interval;

[0047] If any of the conditions are not met, the user's heart rate variability is determined to be abnormal; if all the conditions are met, the user's heart rate variability is determined to be normal.

[0048] In a second aspect, the present invention further provides a dual-track multi-dimensional lower limb rehabilitation training device, which adopts the dual-track multi-dimensional lower limb rehabilitation training method described in the first aspect. The device of the present invention includes a treadmill, a plantar pressure collector, a VR device, a heart rate collector, a data analysis terminal, and two longitudinally spaced runways disposed on the top side of the treadmill.

[0049] The plantar pressure collector is set on the sole of the user's foot to collect the user's plantar pressure data;

[0050] The heart rate collector is set on the front of the user's chest and is used to collect the user's heart rate data;

[0051] The data analysis end is communicatively connected to the plantar pressure collector and the heart rate collector, and includes a data analysis module, a mode selection module, an adjustment module, and a scene generation module; the data analysis module is used to analyze the plantar pressure data and the heart rate data to obtain analysis results; the mode selection module is used to select a training mode based on the analysis results; the adjustment module is used to adjust the current inclination angle and / or current speed of the dual runways according to the training mode; and the scene generation module is used to generate a VR training scene for the VR device in real time based on the current inclination angle and / or current speed;

[0052] The treadmill is provided with an angle adjustment mechanism for adjusting the inclination angle of the runway, and the adjustment mechanism is communicatively connected to the data analysis terminal to receive angle adjustment instructions issued by the data analysis terminal;

[0053] The runway communication is connected to the data analysis terminal to receive the speed adjustment instruction issued by the data analysis terminal;

[0054] The VR device is communicatively connected to the data analysis end to receive scene generation instructions issued by the data analysis end.

[0055] Beneficial effects: The present invention improves the training device, wherein the inclination angles of the dual runways can be adjusted individually, which is conducive to improving the realism of VR scene simulation and thus improving the user experience.

[0056] In the third aspect, the present invention also provides a multi-dimensional lower limb rehabilitation training system based on dual runways, comprising a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the multi-dimensional lower limb rehabilitation training method based on dual runways recorded in the first aspect is implemented.

[0057] The beneficial effects of the present invention are:

[0058] (1) Compared with the existing technology, the method of the present invention has a higher degree of automation and has adaptive adjustment capabilities. It can efficiently adjust equipment training parameters and switch VR training scenes, and can provide a better user experience.

[0059] (2) Compared with the existing technology, the method of the present invention can perform multi-dimensional health status analysis of users based on plantar pressure data and heart rate data, and can meet the remote monitoring needs of evaluators.

[0060] (3) Compared with the existing technology, the present invention improves the training device, wherein the inclination angles of the dual runways can be adjusted individually, which is conducive to improving the realism of VR scene simulation and thus improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0062] Figure 1 This is a flow chart of a multi-dimensional lower limb rehabilitation training method based on dual runways in Example 1 of the present invention;

[0063] Figure 2 Schematic diagram of the structure of the multi-dimensional lower limb rehabilitation training device based on dual runways in the second embodiment of the present invention;

[0064] Figure 3 This is a schematic diagram of the connection between the treadmill, the track, and the angle adjustment mechanism in the second embodiment of the present invention;

[0065] Figure 4 Schematic diagram of the structure of the multi-dimensional lower limb rehabilitation training system based on dual runways in the third embodiment of the present invention.

[0066] Description of reference numerals:

[0067] 100. Treadmill; 101. Angle adjustment mechanism; 102. Pulley; 200. Runway; 201. Limiting sliding groove. DETAILED DESCRIPTION

[0068] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0069] This embodiment discloses a multi-dimensional lower limb rehabilitation training method and system based on dual runways, which is used to solve the technical problems of poor user experience and low efficiency in the prior art. The specific implementation methods of the present invention are described in detail below with reference to the accompanying drawings.

[0070] Example 1

[0071] like Figure 1 As shown, this embodiment discloses a multi-dimensional lower limb rehabilitation training method based on dual runways, including:

[0072] S10: Collecting the user's plantar pressure data and heart rate data.

[0073] In this embodiment, plantar pressure data is collected by a plantar pressure collector installed on the soles of the user's feet, and heart rate data is collected by a heart rate collector installed on the front of the user's chest. During training, users need to enter their basic personal information (age, weight, recovery stage, etc.) into the corresponding system.

[0074] S20: Analyze the plantar pressure data and the heart rate data to obtain analysis results.

[0075] In this embodiment, the analysis results are mainly divided into normal data indicators and abnormal data indicators. The analysis results include whether one or more of the user's gait, foot load, user balance, target heart rate, heart rate variability, and heart rate recovery rate are normal.

[0076] S30: Select a training mode based on the analysis results.

[0077] In this embodiment, the training modes include active and passive training modes. If the user's data indicators are normal, the user will enter the active training mode, where the user can select the VR training scene, training speed, and training slope. If any data indicator is abnormal, the training mode will enter the passive training mode.

[0078] S40: Adjust the current inclination angle and / or current speed of the dual runways according to the training mode.

[0079] S50: Generate a VR training scene for the VR device in real time according to the current tilt angle and / or current speed.

[0080] It should be noted that, in the above steps S10 to S50, the user's feet are respectively located on two separate runways, and the user wears a VR device on his head.

[0081] Through steps S10-S50, the present method first analyzes the user's plantar pressure and heart rate data in real time, obtaining multi-dimensional analysis results. Based on these results, the method then selects the user's training mode, adapting the inclination angle and current speed of the dual-tracks to the user's physical condition. Compared to existing technologies, the present method offers a higher degree of automation and adaptive adjustment capabilities, enabling efficient adjustment of device training parameters and switching between VR training scenarios, providing a superior user experience.

[0082] Furthermore, in order to analyze the pressure distribution on the user's sole to judge the user's gait, balance ability and lower limb load condition, the above step S20 includes:

[0083] S201: Calculate the pressure distribution uniformity index based on the plantar pressure data, specifically:

[0084]

[0085] Where PUI is the pressure distribution uniformity index, σ is the standard deviation of multiple plantar pressure data, and μ is the mean of multiple plantar pressure data.

[0086] S202: Determine whether the pressure distribution uniformity index exceeds a first normal range. If so, determine that the user's gait is abnormal; if not, determine that the user's gait is normal.

[0087] Specifically, the sole of the foot is generally divided into regions such as the forefoot, arch, and heel. The above-mentioned multiple plantar pressure data refers to multiple plantar pressure data collected from the forefoot, arch, and heel regions. The first normal range is 0.8-1.0, where the closer to 1, the more uniform the pressure distribution and the more standard the user's gait. Through the above steps S201 and S202, the method of the present invention realizes automatic quantitative identification of abnormal gait of the user, is less affected by subjective factors, and has higher accuracy and efficiency in identification.

[0088] Furthermore, the above step S20 further includes:

[0089] S211: Identify the maximum value among the plurality of plantar pressure data as a pressure peak value.

[0090] S212: Calculate the quotient of the peak pressure value and the average of the plurality of plantar pressure data to obtain a peak pressure ratio.

[0091] S213: Determine whether the following conditions are met:

[0092] Condition 1: The pressure peak is in the second normal range;

[0093] Condition 2: The pressure peak ratio is in the third normal range;

[0094] S214: If any one of the conditions is not met, the foot load is determined to be abnormal; if all the conditions are met, the foot load is determined to be normal.

[0095] In this embodiment, the second normal range is less than 1.2 times the user's own weight, and the second normal range can be adaptively adjusted according to the user's own weight. The third normal range is 0.0-2.0.

[0096] Through steps S211-S214, the method of the present invention can detect the peak pressure in each area of ​​the user's foot (forefoot, arch, and heel) and determine whether there is localized overload in each area of ​​the user's foot. Compared with existing technologies, the method of the present invention has higher judgment efficiency and more accurate judgment results.

[0097] It should be explained that the above-mentioned plantar pressure collector collects multiple plantar pressure data on a foot surface area. In the time series, multiple plantar pressure data at the same time node need to be matched with a unique center position coordinate.

[0098] Furthermore, the above step S20 further includes:

[0099] S221: Calculate the trajectory length between two center position coordinates corresponding to two adjacent plantar pressure data in time series as the pressure center trajectory.

[0100] S222: Calculate the moving range of the center position coordinates according to the center position coordinates corresponding to the plurality of continuous plantar pressure data in time series as the pressure swing area.

[0101] S223: Determine whether the following conditions are met:

[0102] The trajectory of the center of pressure is in the fourth normal interval;

[0103] The pressure swing area is in the fifth normal interval;

[0104] S224: If any of the conditions are not met, the user balance is determined to be abnormal; if all the conditions are met, the user balance is determined to be normal.

[0105] In this embodiment, the reference system of the center position coordinates is the ground, and the preset point on the ground is taken as the coordinate origin. Considering that when the user is moving forward, the track moves backward while the user's foot moves forward, and the user's stride length varies during movement, the fourth normal interval is 0-10cm to conform to the law of relative movement between the track and the sole of the foot. Similarly, the fifth normal interval is 0-5cm. 2 .

[0106] Through the above steps S221-S224, the method of the present invention can accurately and efficiently analyze the user's balance ability.

[0107] It should be further explained that, in addition to analyzing the user's gait, balance ability and lower limb load condition, step S20 of the method of the present invention can also analyze the user's target heart rate, heart rate variability and heart rate recovery rate based on the user's heart rate data.

[0108] The specific process of data analysis for target heart rate is as follows:

[0109] S230: Define the user's standard heart rate range based on the user's age, specifically:

[0110] THR = (220 - age) × (60% to 80%)

[0111] S231: Determine whether the user's heart rate data is always within the user's standard heart rate range. If so, the user's target heart rate is normal; otherwise, the user's target heart rate is abnormal.

[0112] The specific process of data analysis for heart rate variability is as follows:

[0113] S240: Calculate the heart rate standard deviation and the heart rate root mean square deviation of the heart rate data.

[0114] S241: Determine whether the following conditions are met:

[0115] The standard deviation of heart rate is in the sixth normal range;

[0116] The RMS deviation of heart rate was in the seventh normal interval;

[0117] S242: If any of the conditions are not met, the user's heart rate variability is determined to be abnormal; if all the conditions are met, the user's heart rate variability is determined to be normal.

[0118] In this embodiment, the sixth normal interval is greater than or equal to 50ms, and the seventh normal interval is greater than or equal to 30ms. If the heart rate standard deviation is within the sixth normal interval, it indicates that the user's heart rate variability is good. If the heart rate root mean square deviation is within the seventh normal interval, it indicates that the user's parasympathetic nervous system activity is good.

[0119] The specific process of data analysis for heart rate recovery rate is as follows:

[0120] S250: Extracting a heart rate peak value from the plurality of heart rate data.

[0121] S251: Collecting recovery heart rate data for a preset time period after the heart rate peak.

[0122] S252: Calculate the difference between the peak heart rate and the recovery heart rate data as the heart rate recovery rate.

[0123] S253: Determine whether the heart rate recovery rate is in the eighth normal range. If so, determine that the user's heart rate recovery rate is normal. If not, determine that the user's heart rate recovery rate is abnormal.

[0124] In this embodiment, the eighth normal interval is an interval greater than or equal to 12 bpm.

[0125] Through steps S230-S253, the present method can perform real-time analysis of the user's heart rate data, including target heart rate, heart rate variability, and heart rate recovery rate, to help determine whether the user's cardiovascular fitness is normal. Compared to existing technologies, the present method offers a higher degree of automation and more accurate analysis results.

[0126] Furthermore, in order to improve user experience and ensure safe training, the above step S40 includes:

[0127] S401: If the training mode is the passive training mode and the foot load is abnormal.

[0128] S402: Adjust the current inclination angle of the dual runways to a safe angle range.

[0129] It should be noted that the above-mentioned safe angle range is between -15 and +15 degrees. The above-mentioned dual runways can adjust the angles of the dual runways according to the adaptive adjustment parameters of the training mode to achieve the effects of forward tilt, horizontal tilt, or backward tilt of the dual runways. When the foot load is abnormal, the dual runways automatically adjust the current tilt angle of the dual runways to the safe angle range (tending to horizontal) to ensure the comfort and safety of the user's soles.

[0130] More specifically, if the pressure on the forefoot is too great, the track tilts backward to simulate a downhill slope and reduce the pressure on the forefoot. If the pressure on the user's heel is too great, the track tilts forward to simulate an uphill slope and reduce the pressure on the heel.

[0131] Furthermore, in order to maintain the user's heart rate normal during training, the above step S40 further includes:

[0132] S411: If the training mode is the passive training mode and the target heart rate is abnormal.

[0133] S412: Reduce the current speed of the dual runway.

[0134] It should be noted that if the user's target heart rate is below the user's standard heart rate range, the method of the present invention can gradually increase the exercise speed. If the user's heart rate is above the user's standard heart rate range, the method of the present invention needs to reduce the current speed of the dual runways to ensure safety. If the user's heart rate suddenly increases or decreases, the method of the present invention should immediately adjust the exercise speed and issue an alarm.

[0135] In addition, for the adaptive adjustment of dual runways, the method of the present invention further includes:

[0136] If the pressure distribution on the user's foot is stable, the dual track can gradually increase the speed. If the pressure distribution on the foot is unstable (e.g., large pressure fluctuations), the dual track needs to reduce the speed to ensure patient safety. If the pressure peak in a certain area of ​​the foot is too high, the dual track needs to reduce the speed to avoid local overload.

[0137] It should be further explained that the speed range of the above-mentioned dual runways is 0.5km / h-6km / h. The speed of the dual runways is matched with the corresponding user abnormal conditions through a preset database table. When a certain user abnormality occurs, the table lookup method is used to change the speed of the dual runways.

[0138] Furthermore, in order to realize automatic switching of VR scenes, the above step S50 is specifically as follows:

[0139] The simulation mode of the VR scene is determined according to the inclination angle of the dual runways.

[0140] According to the current speed of the dual runways, the movement speed of the perspective in the VR scene is determined.

[0141] In this embodiment, the VR scene simulation modes include flatland mode, uphill mode, and downhill mode. Flatland mode corresponds to a virtual scene such as a city street or park, uphill mode to a mountain or forest, and downhill mode to a downhill road. This technical solution provides users with an immersive experience.

[0142] After the above step S60, in order to assist doctors in training evaluation, after the user's training is completed, the method of the present invention will generate a report containing multiple indicators based on the user's stress data and heart rate data, and provide training suggestions based on an intelligent model, a semantic understanding model or an AI model.

[0143] After the user finishes the training, the method of the present invention will store and archive the analysis data and initial collection data during the user training process.

[0144] Example 2

[0145] like Figure 2 As shown, the present invention also provides a multi-dimensional lower limb rehabilitation training device based on dual runways, which adopts the multi-dimensional lower limb rehabilitation training method based on dual runways recorded in Example 1. The device of the present invention includes a treadmill 100, a plantar pressure collector, a VR device, a heart rate collector, a data analysis terminal and two runways 200 spaced longitudinally and arranged on the top side of the treadmill 100.

[0146] The plantar pressure collector is set on the sole of the user's foot to collect the user's plantar pressure data.

[0147] The heart rate collector is set at the front of the user's chest and is used to collect the user's heart rate data.

[0148] The data analysis terminal is communicatively connected to the plantar pressure collector and the heart rate collector and includes a data analysis module, a mode selection module, an adjustment module, and a scene generation module. The data analysis module is used to analyze the plantar pressure data and heart rate data to obtain analysis results. The mode selection module is used to select a training mode based on the analysis results. The adjustment module is used to adjust the current inclination angle and / or current speed of the dual runway 200 according to the training mode. The scene generation module is used to generate VR training scenes for the VR device in real time based on the current inclination angle and / or current speed.

[0149] The treadmill 100 is provided with an angle adjustment mechanism 101 for adjusting the inclination angle of the runway 200. The adjustment mechanism is communicatively connected to the data analysis terminal to receive an angle adjustment instruction issued by the data analysis terminal.

[0150] The runway 200 is communicatively connected to the data analysis terminal to receive a speed adjustment instruction issued by the data analysis terminal.

[0151] The VR device is communicatively connected to the data analysis end to receive scene generation instructions issued by the data analysis end.

[0152] Among them, such as Figure 2 and Figure 3 As shown, the runway 200 adopts a crawler runway 200, and the angle adjustment mechanism 101 can adopt an electric cylinder or a hydraulic cylinder. The telescopic end of the angle adjustment mechanism 101 is provided with a pulley 102. At least four angle adjustment mechanisms 101 are configured on the left and right sides of each runway 200 to maintain the stability of the runway 200. The pulley 102 is set in a limiting sliding groove 201 provided on the outer side of the runway 200 shell. When a runway 200 tilts forward, the telescopic ends of the two angle adjustment mechanisms 101 located on the front side are retracted; when a runway 200 leans back, the telescopic ends of the two angle adjustment mechanisms 101 located on the rear side are retracted; when a runway 200 maintains a horizontal state, the telescopic ends of the four angle adjustment mechanisms 101 remain at the same height.

[0153] Example 3

[0154] like Figure 4 As shown, this embodiment discloses a multi-dimensional lower limb rehabilitation training system based on dual runways, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, the multi-dimensional lower limb rehabilitation training method based on dual runways recorded in Example 1 is implemented.

[0155] In the present invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible or connectable to a device. Any application or module described in the present invention can be implemented using computer-readable / executable instructions that can be stored or otherwise retained by such a computer-readable medium.

[0156] In the description of this specification, “a plurality of” means at least two, for example, two, three or more, etc., unless otherwise clearly defined.

[0157] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

Claims

1. A multi-dimensional lower limb rehabilitation training method based on dual runways, characterized in that: A dual runway (200) and VR equipment are used to perform rehabilitation training on a user, the method comprising: Collect user's plantar pressure data and heart rate data; Performing data analysis on the plantar pressure data and the heart rate data to obtain analysis results; selecting a training mode according to the analysis results; According to the training mode, adjusting the current inclination angle and / or current speed of the dual runway (200); A VR training scene for the VR device is generated in real time according to the current tilt angle and / or the current speed.

2. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 1, characterized in that: The analysis result includes whether one or more of the user's gait, foot load, user balance, target heart rate, heart rate variability, and heart rate recovery rate are normal.

3. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 2, characterized in that: Performing data analysis on the plantar pressure data to obtain analysis results includes: According to the plantar pressure data, the pressure distribution uniformity index is calculated, specifically: Where PUI is the pressure distribution uniformity index, σ is the standard deviation of multiple plantar pressure data, and μ is the mean of multiple plantar pressure data; It is determined whether the pressure distribution uniformity index exceeds a first normal range. If so, the user's gait is determined to be abnormal; if not, the user's gait is determined to be normal.

4. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 2, characterized in that: Performing data analysis on the plantar pressure data to obtain analysis results includes: identifying a maximum value among a plurality of plantar pressure data as a pressure peak; Taking the quotient of the peak pressure value and the average of the plurality of plantar pressure data to calculate a peak pressure ratio; Determine whether the following conditions are met: The pressure peak is located in the second normal interval; The pressure peak ratio is within the third normal range; If any one of the conditions is not met, the foot load is determined to be abnormal; if all the conditions are met, the foot load is determined to be normal.

5. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 2, characterized in that: Multiple plantar pressure data at the same time node are matched with a central position coordinate, and data analysis is performed on the plantar pressure data to obtain analysis results, including: Calculating the trajectory length between two center position coordinates corresponding to two adjacent plantar pressure data in time series as the pressure center trajectory; Calculating the moving range of the center position coordinates corresponding to a plurality of continuous plantar pressure data as the pressure swing area; Determine whether the following conditions are met: The pressure center trajectory is located in the fourth normal interval; The pressure swing area is located in the fifth normal range; If any one of the conditions is not met, the user balance is determined to be abnormal; if all the conditions are met, the user balance is determined to be normal.

6. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 2, characterized in that: If any one of the user's gait, the foot load, the user's balance, the target heart rate, the heart rate variability, and the heart rate recovery rate is abnormal, the training mode adopts a passive training mode.

7. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 6, characterized in that: According to the training mode, adjusting the current inclination angle of the dual runway (200) comprises: If the training mode is the passive training mode and the foot load is abnormal; The current inclination angle of the dual runway (200) is adjusted to a safe angle range.

8. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 2, characterized in that: According to the training mode, adjusting the current speed of the dual runway (200) comprises: If the training mode is the passive training mode and the target heart rate is abnormal; The current speed of the dual runway (200) is reduced.

9. The multi-dimensional lower limb rehabilitation training method based on dual runways according to claim 2, characterized in that: Performing data analysis on the heart rate data to obtain analysis results, including: Calculate the heart rate standard deviation and heart rate root mean square deviation of heart rate data; Determine whether the following conditions are met: The heart rate standard deviation is in the sixth normal interval; The heart rate root mean square difference is in the seventh normal interval; If any of the conditions are not met, the user's heart rate variability is determined to be abnormal; if all the conditions are met, the user's heart rate variability is determined to be normal.

10. A multi-dimensional lower limb rehabilitation training system based on dual runways, characterized in that: It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the multi-dimensional lower limb rehabilitation training method based on dual runways according to any one of claims 1 to 9 is implemented.

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