Lower limb rehabilitation training exoskeleton control method, electronic equipment and storage medium

Abstract

The invention discloses a lower limb rehabilitation training exoskeleton control method, electronic equipment and a storage medium. The method comprises the steps that standard gait information in the walking process of a human body is collected; generating a hip joint angle change curve and a knee joint angle change curve according to the standard gait information; smoothing the hip / knee joint angle change curve; according to the hip joint angle change curve, the hip joint amplitude parameter and the stride frequency parameter, generating a variable stride frequency stride hip joint curve; according to the knee joint angle change curve, the knee joint amplitude parameter and the stride frequency parameter, a variable stride frequency stride knee joint curve is generated; a hip / knee joint motor is driven according to the hip / knee joint curve with the variable stride frequency and stride, and the lower limbs of the human body are driven through the exoskeleton. According to the method, gait planning is carried out on the basis of normal motion data of the human body, the method is closer to the motion mode of the human body, the adaptive capacity is high, the needed data size is small, practicability is high, and the problem of speed sudden change caused by unsmooth adjacent periodic curves is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of exoskeletons, and in particular relates to a lower limb rehabilitation training exoskeleton control method, electronic equipment, and storage medium. Background Art

[0002] At present, the gait planning of lower limb rehabilitation training exoskeleton mainly adopts model-based gait planning methods, artificial intelligence algorithm-based gait planning methods, etc. The model-based gait planning method analyzes and obtains a reasonable gait planning based on a physical model that conforms to reality, but this method is complex, has poor adaptability, and has poor model error. The gait planning method based on artificial intelligence algorithm mainly relies on learning algorithms to train gait parameters for robot control, but such methods require a large amount of data and calculations, require strong hardware resources support, increase manufacturing costs, and are not very practical. Summary of the invention

[0003] An object of the present invention is to provide a lower limb rehabilitation training exoskeleton control method, electronic equipment, and storage medium, which can solve the complex technical problems of gait planning methods in the prior art.

[0004] According to a first aspect of the present invention, there is provided a method for controlling an exoskeleton for lower limb rehabilitation training, comprising:

[0005] Collecting standard gait information of a human body during walking, wherein the standard gait information includes hip joint angle data and knee joint angle data;

[0006] Generating a standard gait curve according to the standard gait information, wherein the standard gait curve includes a hip joint angle change curve and a knee joint angle change curve;

[0007] Smoothing the hip joint angle change curve;

[0008] Smoothing the knee joint angle change curve;

[0009] Generate a hip joint curve with variable step frequency and stride according to the smoothed hip joint angle change curve and the hip joint amplitude parameters and step frequency parameters set by the host computer, wherein the hip joint amplitude parameters include the maximum amplitude of hip joint flexion and the maximum amplitude of hip joint extension;

[0010] Generate a knee joint curve with variable step frequency and stride according to the smoothed knee joint angle change curve, the knee joint amplitude parameter set by the host computer, and the step frequency parameter, wherein the knee joint amplitude parameter includes the maximum knee joint flexion amplitude and the maximum knee joint extension amplitude;

[0011] The hip joint motor is driven according to the hip joint curve of the variable step frequency and stride, and the knee joint motor is driven according to the knee joint curve of the variable step frequency and stride, so as to drive the lower limbs of the human body through the exoskeleton.

[0012] Optionally, generating a standard gait curve according to the standard gait information includes:

[0013] Performing polynomial fitting according to the hip joint angle data to obtain the hip joint angle change curve;

[0014]

[0015] Among them, θ hip is the hip joint angle, i h is the hip joint movement time, n h is the polynomial order, p ho 、p h1 、p h2 、p hn are the polynomial coefficients;

[0016] Performing polynomial fitting according to the knee joint angle data to obtain the knee joint angle change curve;

[0017]

[0018] Among them, θ knee is the knee joint angle, i k is the knee joint movement time, n k is the polynomial order, p ko 、p k1 、p k2 、p kn are the polynomial coefficients.

[0019] Optionally, the smoothing of the hip joint angle change curve comprises:

[0020] Performing piecewise interpolation fitting on the first and last segments of the curve corresponding to each step in the hip joint angle change curve, so that the angular velocity and angular acceleration at the first and last segments of the hip joint angle change curve after fitting are both zero;

[0021] The fitted hip joint angle change curve is shown as follows:

[0022]

[0023] in, is the i after fitting h The hip joint angle at the moment, f(i h , a hn , b hn) is the first curve of the hip joint angle change curve after fitting, f(i h , c hn , d hn ) is the tail curve of the fitted hip joint angle change curve, a hn 、b hn 、c hn d hn is the parameter to be identified of the hip joint angle change curve, i1 is the start time of the first segment of the fitted hip joint angle change curve, i2 is the end time of the first segment of the fitted hip joint angle change curve, i3 is the start time of the tail segment of the fitted hip joint angle change curve, i4 is the end time of the tail segment of the fitted hip joint angle change curve, Ω hip is the hip joint angular velocity calculated based on the hip joint angle data, Ω1 and Ω2 are the preset hip joint angular velocity thresholds;

[0024] According to the constraint conditions satisfied by the first segment curve of the fitted hip joint angle change curve and the last segment curve of the fitted hip joint angle change curve, the parameters to be identified of the hip joint angle change curve are calculated.

[0025] Optionally, the first segment curve of the fitted hip joint angle change curve and the last segment curve of the fitted hip joint angle change curve satisfy the following constraints:

[0026] θ hip (i1) = θ hip ′(i1)=θ hip "(i1) = 0;

[0027] θ hip (i4) = θ hip ′(i4)=θ hip "(i4) = 0;

[0028] θ hip ′(i2)=Ω hip (i2);

[0029] θ hip ″(i2)=α hip (i2);

[0030] θ hip ′(i3)=Ω hip (i3);

[0031] θ hip ″(i3)=α hip (i3);

[0032] Among them, θ hip′ is the hip joint angular velocity calculated according to the hip joint angle change curve, θ hip ″ is the hip joint angular acceleration calculated according to the hip joint angle change curve, α hip is the hip joint angular acceleration calculated based on the hip joint angle data.

[0033] Optionally, generating a hip joint curve with variable step frequency and stride according to the smoothed hip joint angle change curve and the hip joint amplitude parameter and the step frequency parameter set by the host computer includes:

[0034] The fitted hip joint angle change curve is expressed as follows:

[0035]

[0036] According to the hip joint amplitude parameter and the step frequency parameter, a hip joint curve with variable step frequency and step length as shown below is determined;

[0037]

[0038] Among them, e h1 is the maximum amplitude of hip flexion, e h2 is the maximum extension of the hip joint, f h1 and f h2 is the step frequency parameter.

[0039] Optionally, the smoothing of the knee joint angle change curve includes:

[0040] Performing piecewise interpolation fitting on the first and last segments of the curve corresponding to each step in the knee joint angle change curve, so that the angular velocity and angular acceleration at the first and last segments of the knee joint angle change curve after fitting are both zero;

[0041] The fitted knee joint angle change curve is shown as follows:

[0042]

[0043] in, is the i after fitting k The knee joint angle at the moment, f(i k , a kn , b kn ) is the first curve of the knee joint angle change curve after fitting, f(i k , c kn , d kn ) is the tail curve of the fitted knee joint angle change curve, a kn 、b kn 、c kn d knis the parameter to be identified of the knee angle change curve, i5 is the start time of the first segment of the knee angle change curve after fitting, i6 is the end time of the first segment of the knee angle change curve after fitting, i7 is the start time of the tail segment of the knee angle change curve after fitting, i8 is the end time of the tail segment of the knee angle change curve after fitting, Ω knee is the knee joint angular velocity calculated according to the knee joint angle data, Ω3 and Ω4 are the preset knee joint angular velocity thresholds;

[0044] According to the constraint conditions satisfied by the first segment curve of the fitted knee joint angle change curve and the last segment curve of the fitted knee joint angle change curve, the parameters to be identified of the knee joint angle change curve are calculated.

[0045] Optionally, the first segment curve of the fitted knee joint angle change curve and the last segment curve of the fitted knee joint angle change curve satisfy the following constraints:

[0046] θ knee (i5) = θ knee ′(i5)=θ knee "(i5) = 0;

[0047] θ knee (i8) = θ knee ′(i8)=θ knee "(i8) = 0;

[0048] θ knee ′(i6)=Ω knee (i6);

[0049] θ knee ″(i6)=α knee (i6);

[0050] θ knee ′(i7)=Ω knee (i7);

[0051] θ knee ″(i7)=αk nee (i7);

[0052] Among them, θ knee ′ is the knee joint angular velocity calculated according to the knee joint angle change curve, θ knee ″ is the knee joint angular acceleration calculated according to the knee joint angle change curve, α knee is the knee joint angular acceleration calculated based on the knee joint angle data.

[0053] Optionally, generating a knee joint curve with variable step frequency and stride according to the smoothed knee joint angle change curve and the knee joint amplitude parameter and the step frequency parameter set by the host computer includes:

[0054] The fitted knee joint angle change curve is expressed as follows:

[0055]

[0056] According to the knee joint amplitude parameter and the step frequency parameter, a knee joint curve with variable step frequency and step length as shown below is determined;

[0057]

[0058] Among them, e k1 is the maximum amplitude of knee flexion, e k2 is the maximum extension of the knee joint, f k1 and f k2 is the step frequency parameter.

[0059] According to a second aspect of the present invention, there is provided an electronic device comprising a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and when the programs or instructions are executed by the processor, the steps of a lower limb rehabilitation training exoskeleton control method as described in the first aspect of the present invention are implemented.

[0060] According to a third aspect of the present invention, there is provided a readable storage medium storing a program or instruction, which, when executed by a processor, implements the steps of a lower limb rehabilitation training exoskeleton control method as described in the first aspect of the present invention.

[0061] The beneficial effect of the present invention is that the present invention generates a hip joint angle change curve and a knee joint angle change curve by collecting the hip joint angle data and the knee joint angle data of the healthy test person during normal walking, and smoothes the hip joint angle change curve and the knee joint angle change curve to avoid abnormal shaking, thereby further improving the stability of gait training. Then, the hip joint amplitude parameter, the knee joint amplitude parameter and the step frequency parameter are combined to generate a hip joint curve with a variable step frequency stride and a knee joint curve with a variable step frequency stride, guide the target patient to perform gait training, and let the target patient perform gait training according to the gait of a healthy person in combination with the actual physical condition of the target patient, and perform gait planning based on the normal movement data of the human body itself, which is closer to the human body movement mode. In addition, for patients with different physical conditions, different hip joint amplitude parameters, knee joint amplitude parameters and step frequency parameters can be set, so that the hip joint curve with a variable step frequency stride and the knee joint curve with a variable step frequency stride are more in line with the actual physical condition of the patient, meet the differentiated rehabilitation training needs of different patients, and have strong practicality. This method performs gait planning based on the normal motion data of the human body, which is closer to the human body's motion mode, has strong adaptability, requires a small amount of data, is highly practical, and solves the problem of speed mutation caused by the unevenness of adjacent periodic curves. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flowchart of a method for controlling a lower limb rehabilitation training exoskeleton in one embodiment of the present invention.

[0063] Figure 2 This is a flow chart of a lower limb rehabilitation training exoskeleton control method in another embodiment of the present invention. DETAILED DESCRIPTION

[0064] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0065] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0066] The techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered as part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0067] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0068] In the specification and claims of the present invention, if the term "first" or "second" is involved, it may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally means that the related objects are in an "or" relationship.

[0069] like Figure 1 As shown, this embodiment introduces a lower limb rehabilitation training exoskeleton control method, including:

[0070] Step 1100: Collect standard gait information of a human body during walking, wherein the standard gait information includes hip joint angle data and knee joint angle data.

[0071] In the data collection experiment, a three-dimensional motion capture system and a pressure treadmill were used to collect hip joint angle data and knee joint angle data of multiple healthy test persons during normal walking.

[0072] Step 1200: Generate a standard gait curve according to the standard gait information, wherein the standard gait curve includes a hip joint angle change curve and a knee joint angle change curve.

[0073] Biomechanical analysis software is used to process and analyze the collected hip joint angle data and knee joint angle data to form a standard gait curve of the human body. By processing and analyzing the hip joint angle data, a hip joint angle change curve is obtained. By processing and analyzing the knee joint angle data, a knee joint angle change curve is obtained.

[0074] Step 1300: Smoothing the hip joint angle change curve.

[0075] In the data collection experiment, the tester will walk continuously, and each step of the tester is a cycle. If the hip joint angle change curve is discontinuous at the intersection of adjacent cycles, the angular velocity and angular acceleration at the intersection will change suddenly, causing abnormal jitter. The hip joint angle change curve is smoothed to avoid abnormal jitter.

[0076] Step 1400: Smoothing the knee joint angle change curve.

[0077] Similarly, the intersections of adjacent cycles of the knee joint angle change curve are smoothed to avoid abnormal jitter.

[0078] Step 1500: Generate a hip joint curve with variable step frequency and stride according to the smoothed hip joint angle change curve and the hip joint amplitude parameters and step frequency parameters set by the host computer, wherein the hip joint amplitude parameters include the maximum amplitude of hip joint flexion and the maximum amplitude of hip joint extension.

[0079] During walking, the angle of the hip joint changes. The movement of the hip joint includes flexion and extension. The maximum amplitude of hip flexion is the maximum angle of the hip joint in flexion, and the maximum amplitude of hip extension is the maximum angle of the hip joint in extension. The amplitude parameters of the hip joint can be adjusted accordingly according to the actual physical condition of the target patient. Figure 2 As shown, the hip joint amplitude parameters include the maximum amplitude of hip flexion and the maximum amplitude of hip extension.

[0080] The smoothed hip angle change curve is obtained based on the hip angle data of healthy testers during walking. On this basis, the hip amplitude parameters and step frequency parameters are set according to the actual physical condition of the target patient, and combined with the smoothed hip angle change curve, the hip curve with variable step frequency and stride is obtained to guide the target patient in gait training.

[0081] Step 1600: Generate a knee joint curve with variable step frequency and stride according to the smoothed knee joint angle change curve and the knee joint amplitude parameters and the step frequency parameters set by the host computer, wherein the knee joint amplitude parameters include the maximum amplitude of knee flexion and the maximum amplitude of knee extension.

[0082] Similarly, during walking, the angle of the knee joint changes. The movement of the knee joint includes flexion and extension. The maximum amplitude of knee flexion is the maximum angle of the knee joint during flexion, and the maximum amplitude of knee extension is the maximum angle of the knee joint during extension. The amplitude parameters of the knee joint can be adjusted accordingly according to the actual physical condition of the target patient.

[0083] The smoothed knee angle change curve is obtained based on the knee angle data of healthy testers during walking. On this basis, the knee amplitude parameter and the step frequency parameter are set according to the actual physical condition of the target patient, and combined with the smoothed knee angle change curve, the knee curve with variable step frequency and stride length is obtained to guide the target patient in gait training.

[0084] Step 1700: driving the hip joint motor according to the hip joint curve of the variable step frequency and stride, and driving the knee joint motor according to the knee joint curve of the variable step frequency and stride, so as to drive the lower limbs of the human body through the exoskeleton.

[0085] The hip joint motor is a motor of the exoskeleton at the hip joint of the target patient, and is used to drive the exoskeleton at the hip joint to move. After obtaining the hip joint curve of the variable step frequency and stride, the hip joint motor is driven according to the hip joint curve of the variable step frequency and stride, and the exoskeleton at the hip joint is driven to make the hip joint move according to the hip joint curve of the variable step frequency and stride.

[0086] The knee joint motor is a motor of the exoskeleton at the knee joint of the target patient, and is used to drive the exoskeleton at the knee joint to move. After obtaining the knee joint curve of the variable step frequency and stride, the knee joint motor is driven according to the knee joint curve of the variable step frequency and stride, and the exoskeleton at the knee joint is driven to make the knee joint move according to the knee joint curve of the variable step frequency and stride.

[0087] The present invention generates a hip joint angle change curve and a knee joint angle change curve by collecting the hip joint angle data and the knee joint angle data of the healthy test personnel during normal walking, and smoothes the hip joint angle change curve and the knee joint angle change curve to avoid abnormal shaking, thereby further improving the stability of gait training. Then, the hip joint curve with variable step frequency and stride and the knee joint curve with variable step frequency and stride are generated by combining the hip joint amplitude parameter, the knee joint amplitude parameter and the step frequency parameter, and the target patient is guided to perform gait training. In combination with the actual physical condition of the target patient, the target patient is allowed to perform gait training according to the gait of a healthy person, and gait planning is performed based on the normal movement data of the human body itself, which is closer to the human body movement mode. In addition, for patients with different physical conditions, different hip joint amplitude parameters, knee joint amplitude parameters and step frequency parameters can be set, so that the hip joint curve with variable step frequency and stride and the knee joint curve with variable step frequency and stride are more in line with the actual physical condition of the patient, meet the differentiated rehabilitation training needs of different patients, and have strong practicality.

[0088] In this embodiment, step 1200 includes:

[0089] Performing polynomial fitting according to the hip joint angle data to obtain the hip joint angle change curve;

[0090]

[0091] Among them, θ hip is the hip joint angle, i h is the hip joint movement time, n h is the polynomial order, p ho 、p h1 、p h2 、p hnare the polynomial coefficients;

[0092] Performing polynomial fitting according to the knee joint angle data to obtain the knee joint angle change curve;

[0093]

[0094] Among them, θ knee is the knee joint angle, i k is the knee joint movement time, n k is the polynomial order, p ko 、p k1 、p k2 、p kn are the polynomial coefficients.

[0095] The hip joint angle data and the knee joint angle data are fitted by a polynomial fitting method to obtain a hip joint angle change curve and a knee joint angle change curve. The polynomial coefficients can be calculated by the least squares method.

[0096] In this embodiment, step 1300 includes steps 1310-1330.

[0097] Step 1310: Perform piecewise interpolation fitting on the first and last segments of the curve corresponding to each step in the hip joint angle change curve, so that the angular velocity and angular acceleration at the first and last segments of the hip joint angle change curve after fitting are both zero.

[0098] During gait training, the patient needs to pause between two gait cycles to allow the patient's body to adapt. Therefore, by performing segmented interpolation fitting on the first and last segments, the angular velocity and angular acceleration at the first and last segments are set to zero, ensuring that the patient pauses between two gait cycles during gait training.

[0099] Step 1320: The fitted hip joint angle change curve is shown as follows:

[0100]

[0101] in, is the i after fitting h The hip joint angle at the moment, f(i h , a hn , b hn ) is the first curve of the hip joint angle change curve after fitting, f(i h , c hn , d hn ) is the tail curve of the fitted hip joint angle change curve, a hn 、b hn 、c hn d hnis the parameter to be identified of the hip joint angle change curve, i1 is the start time of the first segment of the fitted hip joint angle change curve, i2 is the end time of the first segment of the fitted hip joint angle change curve, i3 is the start time of the tail segment of the fitted hip joint angle change curve, i4 is the end time of the tail segment of the fitted hip joint angle change curve, Ω hip is the hip joint angular velocity calculated based on the hip joint angle data, and Ω1 and Ω2 are the preset hip joint angular velocity thresholds.

[0102] The fitted hip joint angle change curve is divided into three sections. The first section f(i h , a hn , b hn ) is the first curve, and the second curve f(i h , c hn , d hn ) is the tail curve, and the third segment is the curve obtained by polynomial fitting based on the hip joint angle data.

[0103] Step 1330: Calculate the parameters to be identified of the hip joint angle change curve according to the constraints satisfied by the first segment curve of the fitted hip joint angle change curve and the last segment curve of the fitted hip joint angle change curve.

[0104] In this embodiment, the first segment of the fitted hip joint angle change curve and the last segment of the fitted hip joint angle change curve satisfy the following constraints:

[0105] θ hip (i1) = θ hip ′(i1)=θ hip "(i1) = 0;

[0106] θ hip (i4) = θ hip ′(i4)=θ hip "(i4) = 0;

[0107] θ hip ′(i2)=Ω hip (i2);

[0108] θ hip ″(i2)=α hip (i2);

[0109] θ hip ′(i3)=Ω hip (i3);

[0110] θ hip ″(i3)=α hip (i3);

[0111] Among them, θ hip ′ is the hip joint angular velocity calculated according to the hip joint angle change curve, θ hip ″ is the hip joint angular acceleration calculated according to the hip joint angle change curve, α hip is the hip joint angular acceleration calculated based on the hip joint angle data.

[0112] By performing differential calculation on the original collected hip joint angle data, the hip joint angular velocity Ω can be obtained. hip . For the hip joint angular velocity Ω hip By performing differential calculation, we can get the hip joint angular acceleration α hip .

[0113] By bringing the above constraints into the fitted hip joint angle change curve, the parameters to be identified of the hip joint angle change curve can be calculated.

[0114] In this embodiment, generating a hip joint curve with variable step frequency and stride according to the smoothed hip joint angle change curve and the hip joint amplitude parameter and the step frequency parameter set by the host computer includes:

[0115] The fitted hip joint angle change curve is expressed as follows:

[0116]

[0117] According to the hip joint amplitude parameter and the step frequency parameter, a hip joint curve with variable step frequency and step length as shown below is determined;

[0118]

[0119] Among them, e h1 is the maximum amplitude of hip flexion, e h2 is the maximum extension of the hip joint, f h1 and f h2 is the step frequency parameter.

[0120] Hip joint movement includes flexion and extension. When the hip joint is flexed, the hip joint angle is greater than 0. When the hip joint is extended, the hip joint angle is less than 0. The fitted hip joint angle change curve is adjusted according to the hip joint amplitude parameter and the step frequency parameter to obtain the hip joint curve with variable step frequency and stride.

[0121] In this embodiment, step 1400 includes steps 1410-1430.

[0122] Step 1410: Perform piecewise interpolation fitting on the first and last segments of the curve corresponding to each step in the knee joint angle change curve, so that the angular velocity and angular acceleration at the first and last segments of the knee joint angle change curve after fitting are both zero.

[0123] During gait training, the patient needs to pause between two gait cycles to allow the patient's body to adapt. Therefore, by performing segmented interpolation fitting on the first and last segments, the angular velocity and angular acceleration at the first and last segments are set to zero, ensuring that the patient pauses between two gait cycles during gait training.

[0124] Step 1420: The fitted knee joint angle change curve is shown as follows:

[0125]

[0126] in, is the i after fitting k The knee joint angle at the moment, f(i k , a kn , b kn ) is the first curve of the knee joint angle change curve after fitting, f(i k , c kn , d kn ) is the tail curve of the fitted knee joint angle change curve, a kn 、b kn 、c kn d kn is the parameter to be identified of the knee angle change curve, i5 is the start time of the first segment of the knee angle change curve after fitting, i6 is the end time of the first segment of the knee angle change curve after fitting, i7 is the start time of the tail segment of the knee angle change curve after fitting, i8 is the end time of the tail segment of the knee angle change curve after fitting, Ω knee is the knee joint angular velocity calculated based on the knee joint angle data, and Ω3 and Ω4 are the preset knee joint angular velocity thresholds.

[0127] The fitted knee joint angle change curve is divided into three sections. The first section f(i k , a kn , b kn ) is the first curve, and the second curve f(i k , c kn , d kn ) is the tail curve, and the third segment is the curve obtained by polynomial fitting based on the knee joint angle data.

[0128] Step 1430: Calculate the parameters to be identified of the knee joint angle change curve according to the constraints satisfied by the first segment curve of the fitted knee joint angle change curve and the last segment curve of the fitted knee joint angle change curve.

[0129] In this embodiment, the first segment curve of the fitted knee joint angle change curve and the last segment curve of the fitted knee joint angle change curve meet the following constraints:

[0130] θ knee (i5) = θ knee ′(i5)=θ knee "(i5) = 0;

[0131] θ knee (i8) = θ knee ′(i8)=θ knee "(i8) = 0;

[0132] θ knee ′(i6)=Ω knee (i6);

[0133] θ knee ″(i6)=α knee (i6);

[0134] θ knee ′(i7)=Ω knee (i7);

[0135] θ knee ″(i7)=α knee (i7);

[0136] Among them, θ knee ′ is the knee joint angular velocity calculated according to the knee joint angle change curve, θ knee ″ is the knee joint angular acceleration calculated according to the knee joint angle change curve, α knee is the knee joint angular acceleration calculated based on the knee joint angle data.

[0137] By performing differential calculation on the original collected knee joint angle data, the knee joint angular velocity Ω can be obtained. knee . For knee joint angular velocity Ω knee By performing differential calculation, we can get the knee joint angular acceleration α knee .

[0138] By bringing the above constraints into the fitted knee joint angle change curve, the parameters to be identified of the knee joint angle change curve can be calculated.

[0139] In this embodiment, the knee joint curve with variable step frequency and stride is generated according to the smoothed knee joint angle change curve and the knee joint amplitude parameter and the step frequency parameter set by the host computer, including:

[0140] The fitted knee joint angle change curve is expressed as follows:

[0141]

[0142] According to the knee joint amplitude parameter and the step frequency parameter, a knee joint curve with variable step frequency and step length as shown below is determined;

[0143]

[0144] Among them, e k1 is the maximum amplitude of knee flexion, e k2 is the maximum extension of the knee joint, f k1 and f k2 is the step frequency parameter.

[0145] Knee joint movement includes flexion and extension. When the knee joint is flexed, the knee joint angle is greater than 0. When the knee joint is extended, the knee joint angle is less than 0. The fitted knee joint angle change curve is adjusted according to the knee joint amplitude parameter and the step frequency parameter to obtain the knee joint curve with variable step frequency and stride.

[0146] By modifying the hip joint amplitude parameter and the knee joint amplitude parameter, the joint angle amplitude can be adjusted to achieve the purpose of changing the stride length during gait training. By modifying the step frequency parameter, the step frequency can be changed during gait training.

[0147] This embodiment introduces an electronic device, including a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of a lower limb rehabilitation training exoskeleton control method as described in any embodiment of the present invention are implemented.

[0148] The electronic device introduced in the present invention may be a medical exoskeleton.

[0149] This embodiment introduces a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of a lower limb rehabilitation training exoskeleton control method as described in any embodiment of the present invention are implemented.

[0150] Although some specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will be appreciated by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

[0151] Those of ordinary skill in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0152] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0153] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules 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 modules, which can be electrical, mechanical or other forms.

[0154] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0155] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0156] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0157] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

[0158] It should be understood that the size of the sequence number of each step in the content of the invention and the embodiments of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. For the purpose of example and description, the foregoing description of the implementation of the present disclosure has been given. The foregoing description is not exhaustive and is not intended to limit the present disclosure to the exact form disclosed. Various deformations and modifications may exist according to the above teachings, or various deformations and modifications may be obtained from the practice of the present disclosure. These embodiments are selected and described to illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can use the present disclosure in various embodiments and various modifications suitable for the specific purpose conceived.

Claims

1. A lower limb rehabilitation training exoskeleton control method, characterized in that: include: Collecting standard gait information of a human body during walking, wherein the standard gait information includes hip joint angle data and knee joint angle data; Generating a standard gait curve according to the standard gait information, wherein the standard gait curve includes a hip joint angle change curve and a knee joint angle change curve; Smoothing the hip joint angle change curve; Smoothing the knee joint angle change curve; Generate a hip joint curve with variable step frequency and stride length according to the smoothed hip joint angle change curve and the hip joint amplitude parameters and step frequency parameters set by the host computer, wherein the hip joint amplitude parameters include the maximum amplitude of hip joint flexion and the maximum amplitude of hip joint extension; Generate a knee joint curve with variable step frequency and stride according to the smoothed knee joint angle change curve, the knee joint amplitude parameter set by the host computer, and the step frequency parameter, wherein the knee joint amplitude parameter includes the maximum knee joint flexion amplitude and the maximum knee joint extension amplitude; The hip joint motor is driven according to the hip joint curve of the variable step frequency and stride, and the knee joint motor is driven according to the knee joint curve of the variable step frequency and stride, so as to drive the lower limbs of the human body through the exoskeleton.

2. The method according to claim 1, characterized in that Generating a standard gait curve according to the standard gait information includes: Performing polynomial fitting according to the hip joint angle data to obtain the hip joint angle change curve; Among them, θ hip is the hip joint angle, i h is the hip joint movement time, n h is the polynomial order, p ho 、p h1 、p h2 、p hm are the polynomial coefficients; Performing polynomial fitting according to the knee joint angle data to obtain the knee joint angle change curve; Among them, θ knee is the knee joint angle, i k is the knee joint movement time, n k is the polynomial order, p ko 、p k1 ,px2,p kn are the polynomial coefficients.

3. The method according to claim 2, characterized in that The step of smoothing the hip joint angle change curve comprises: Performing piecewise interpolation fitting on the first and last segments of the curve corresponding to each step in the hip joint angle change curve, so that the angular velocity and angular acceleration at the first and last segments of the hip joint angle change curve after fitting are both zero; The fitted hip joint angle change curve is shown as follows: in, is the i after fitting h The hip joint angle at the moment, f(i h , a hn , b hn ) is the first curve of the hip joint angle change curve after fitting, f(i h , c hn , d hn ) is the tail curve of the fitted hip joint angle change curve, a hn 、b hn 、c hn d hn is the parameter to be identified of the hip joint angle change curve, i1 is the start time of the first segment of the fitted hip joint angle change curve, i2 is the end time of the first segment of the fitted hip joint angle change curve, i3 is the start time of the tail segment of the fitted hip joint angle change curve, i4 is the end time of the tail segment of the fitted hip joint angle change curve, Ω hip is the hip joint angular velocity calculated based on the hip joint angle data, Ω1 and Ω2 are the preset hip joint angular velocity thresholds; According to the constraint conditions satisfied by the first segment curve of the fitted hip joint angle change curve and the last segment curve of the fitted hip joint angle change curve, the parameters to be identified of the hip joint angle change curve are calculated.

4. The method according to claim 3, characterized in that The first segment curve of the fitted hip joint angle change curve and the last segment curve of the fitted hip joint angle change curve meet the following constraints: i hip (i1)=θ hip ′(i1)=θ hip ″(i1)=0; i hip (i4)=θ hip ′(i4)=θ hip ″(i4)=0; i hip ′(i2)=Ω hip (i2); i hip ″(i2)=a hip (i2); θ hip ′(i3)=Ω hip (i3); θ hip ″(i3)=α hip (i3); Among them, θ hip ′ is the hip joint angular velocity calculated according to the hip joint angle change curve, θ hip ″ is the hip joint angular acceleration calculated according to the hip joint angle change curve, α hip is the hip joint angular acceleration calculated based on the hip joint angle data.

5. The method according to claim 4, characterized in that The step of generating a hip joint curve with a variable step frequency and stride according to the smoothed hip joint angle change curve and the hip joint amplitude parameter and the step frequency parameter set by the host computer comprises: The fitted hip joint angle change curve is expressed as follows: According to the hip joint amplitude parameter and the step frequency parameter, a hip joint curve with variable step frequency and step length as shown below is determined; Among them, e h1 is the maximum amplitude of hip flexion, e h2 is the maximum extension of the hip joint, f h1 and f h2 is the step frequency parameter.

6. The method according to claim 2, characterized in that The step of smoothing the knee joint angle change curve comprises: Performing piecewise interpolation fitting on the first and last segments of the curve corresponding to each step in the knee joint angle change curve, so that the angular velocity and angular acceleration at the first and last segments of the knee joint angle change curve after fitting are both zero; The fitted knee joint angle change curve is shown as follows: in, is the i after fitting k The knee joint angle at the moment, f(i k , a kn , b kn ) is the first curve of the knee joint angle change curve after fitting, f(i k , c kn , d kn ) is the tail curve of the fitted knee joint angle change curve, a kn 、b kn 、c kn ,d kn is the parameter to be identified of the knee angle change curve, i5 is the start time of the first segment of the knee angle change curve after fitting, i6 is the end time of the first segment of the knee angle change curve after fitting, i7 is the start time of the tail segment of the knee angle change curve after fitting, i8 is the end time of the tail segment of the knee angle change curve after fitting, Ω knee is the knee joint angular velocity calculated according to the knee joint angle data, Ω3 and Ω4 are the preset knee joint angular velocity thresholds; According to the constraint conditions satisfied by the first segment curve of the fitted knee joint angle change curve and the last segment curve of the fitted knee joint angle change curve, the parameters to be identified of the knee joint angle change curve are calculated.

7. The method according to claim 6, characterized in that The first segment curve of the fitted knee joint angle change curve and the last segment curve of the fitted knee joint angle change curve meet the following constraints: i knee (i5)=θ knee ′(i5)=θ knee ″(i5)=0; i knee (i8)=θ knee ′(i8)=θ knee ″(i8)=0; i knee ′(i6)=Ω knee (i6); i knee ″(i6)=a knee (i6); θ knee ′(i7)=Ω knee (i7); θ knee ″(i7)=a knee (i7); Among them, θ knee ′ is the knee joint angular velocity calculated according to the knee joint angle change curve, θ knee ″ is the knee joint angular acceleration calculated according to the knee joint angle change curve, α knee is the knee joint angular acceleration calculated based on the knee joint angle data.

8. The method according to claim 7, characterized in that The method of generating a knee joint curve with a variable step frequency and stride according to the smoothed knee joint angle change curve and the knee joint amplitude parameter and the step frequency parameter set by the host computer comprises: The fitted knee joint angle change curve is expressed as follows: According to the knee joint amplitude parameter and the step frequency parameter, a knee joint curve with variable step frequency and step length as shown below is determined; Among them, e k1 is the maximum amplitude of knee flexion, e k2 is the maximum extension of the knee joint, f k1 and f k2 is the step frequency parameter.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of a lower limb rehabilitation training exoskeleton control method as described in any one of claims 1 to 8 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the steps of a lower limb rehabilitation training exoskeleton control method as described in any one of claims 1 to 8 are implemented.

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