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

Abstract

The application discloses a lower limb rehabilitation training exoskeleton control method, an electronic device and a storage medium. The method comprises the following steps: collecting standard gait information in the process of human walking; generating a hip joint angle change curve and a knee joint angle change curve according to the standard gait information; performing smoothing processing on the hip / knee joint angle change curve; generating a variable step frequency step length hip joint curve according to the hip joint angle change curve and a hip joint amplitude parameter and a step frequency parameter; generating a variable step frequency step length knee joint curve according to the knee joint angle change curve and a knee joint amplitude parameter and a step frequency parameter; and driving a hip / knee joint motor according to the variable step frequency step length hip / knee joint curve, and driving a human lower limb through an exoskeleton. The method is based on normal motion data of the human body for gait planning, is closer to the human motion mode, has strong adaptability, requires a small amount of data, has strong practicability, and solves the problem of speed mutation caused by non-smooth adjacent period curves.

Description

Technical Field

[0001] This invention belongs to the field of exoskeleton technology, specifically relating to a lower limb rehabilitation training exoskeleton control method, electronic device, and storage medium. Background Technology

[0002] Currently, gait planning for exoskeletons used in lower limb rehabilitation training mainly employs model-based gait planning methods and artificial intelligence algorithm-based gait planning methods. Model-based gait planning methods analyze and derive reasonable gait plans based on realistic physical models, but this method is complex, has poor adaptability, and suffers from significant model errors. Artificial intelligence algorithm-based gait planning methods primarily rely on learning algorithms to train gait parameters for robot control, but these methods require large amounts of data and computation, necessitating powerful hardware resources, increasing manufacturing costs, and limiting their practicality. Summary of the Invention

[0003] One objective of this invention is to provide a lower limb rehabilitation training exoskeleton control method, electronic device, and storage medium that can solve the technical problem of complex gait planning methods in the prior art.

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

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

[0006] A standard gait curve is generated based on the standard gait information, wherein the standard gait curve includes a hip joint angle change curve and a knee joint angle change curve;

[0007] The hip joint angle change curve is smoothed.

[0008] The knee joint angle change curve is smoothed.

[0009] Based on the smoothed hip joint angle change curve and the hip joint amplitude parameters and cadence parameters set by the host computer, a hip joint curve with variable cadence and stride is generated. The hip joint amplitude parameters include the maximum hip joint flexion value and the maximum hip joint extension value.

[0010] Based on the smoothed knee joint angle change curve, the knee joint amplitude parameters set by the host computer, and the step frequency parameters, a knee joint curve with variable step frequency and stride is generated. The knee joint amplitude parameters include the maximum value of knee flexion and the maximum value of knee extension.

[0011] The hip joint motor is driven according to the hip joint curve of the variable stride frequency and the knee joint motor is driven according to the knee joint curve of the variable stride frequency and the human lower limbs are moved by the exoskeleton.

[0012] Optionally, generating a standard gait curve based on the standard gait information includes:

[0013] The hip joint angle change curve is obtained by performing polynomial fitting based on the hip joint angle data.

[0014]

[0015] Where, θ hip For the hip joint angle, i h n represents the hip joint movement time. h p is the order of the polynomial. ho p h1 p h2 p hn These are the polynomial coefficients;

[0016] Based on the knee joint angle data, a polynomial fitting is performed to obtain the knee joint angle change curve;

[0017]

[0018] Where, θ knee For the knee joint angle, i k n represents the knee joint movement time. k p is the order of the polynomial. ko p k1 p k2 p kn These are the polynomial coefficients.

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

[0020] Piecewise interpolation fitting is performed 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 fitted hip joint angle change curve are both zero.

[0021] The fitted curve showing the change in hip joint angle is shown below:

[0022]

[0023] in, For the fitted i h The hip joint angle at time, f(i) h a hn b hnf(i) represents the first segment of the fitted hip joint angle change curve. h c hn d hn (a) represents the tail segment of the fitted hip joint angle change curve. hn b hn c hn d hn Ω represents the parameters to be identified for 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 last segment of the fitted hip joint angle change curve, and i4 is the end time of the last segment of the fitted hip joint angle change curve. hip The hip joint angular velocity is calculated based on the hip joint angle data, and Ω1 and Ω2 are preset hip joint angular velocity thresholds;

[0024] Based on the constraints satisfied by the first and last segments of the fitted hip joint angle change curve, the parameters to be identified for the hip joint angle change curve are calculated.

[0025] Optionally, 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:

[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] Where, θ hipθ is the hip joint angular velocity calculated based on the hip joint angle change curve. hip "α is the hip joint angular acceleration calculated based on the hip joint angle change curve." hip This refers to the hip joint angular acceleration calculated based on hip joint angle data.

[0033] Optionally, generating a variable cadence stride hip curve based on the smoothed hip joint angle change curve and the hip joint amplitude parameters and cadence parameters set by the host computer includes:

[0034] The fitted hip joint angle change curve is shown below:

[0035]

[0036] Based on the hip joint amplitude parameter and the cadence parameter, the hip joint curve with variable cadence and stride length is determined as shown below;

[0037]

[0038] Among them, e h1 e represents the maximum range of hip flexion. h2 f represents the maximum range of hip extension. h1 and f h2 This refers to the step frequency parameter.

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

[0040] Piecewise interpolation fitting is performed 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 fitted knee joint angle change curve are both zero.

[0041] The fitted curve showing the change in knee joint angle is shown below:

[0042]

[0043] in, For the fitted i k The knee joint angle at time, f(i) k a kn b kn f(i) represents the first segment of the fitted knee joint angle change curve. k c kn d kn ) represents the tail segment of the fitted knee joint angle change curve, a kn b kn c kn d knΩ represents the parameters to be identified for the knee joint angle change curve. i5 is the start time of the first segment of the fitted knee joint angle change curve, i6 is the end time of the first segment of the fitted knee joint angle change curve, i7 is the start time of the last segment of the fitted knee joint angle change curve, and i8 is the end time of the last segment of the fitted knee joint angle change curve. knee The knee joint angular velocity is calculated based on the knee joint angle data, and Ω3 and Ω4 are preset knee joint angular velocity thresholds;

[0044] Based on the constraints satisfied by the first and last segments of the fitted knee joint angle change curve, the parameters to be identified for the knee joint angle change curve are calculated.

[0045] Optionally, the first segment of the fitted knee joint angle change curve and the last segment 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] Where, θ knee θ is the knee joint angular velocity calculated based on the knee joint angle change curve. knee "α is the knee joint angular acceleration calculated based on the knee joint angle change curve." knee This refers to the knee joint angular acceleration calculated based on knee joint angle data.

[0053] Optionally, generating a variable cadence stride knee curve based on the smoothed knee joint angle change curve and the knee joint amplitude parameters and cadence parameters set by the host computer includes:

[0054] The fitted knee joint angle change curve is shown below:

[0055]

[0056] Based on the knee joint amplitude parameter and the cadence parameter, the knee joint curve with variable cadence and stride is determined as shown below;

[0057]

[0058] Among them, e k1 e represents the maximum range of knee flexion. k2 f represents the maximum range of knee extension. k1 and f k2 This refers to the step frequency parameter.

[0059] According to a second aspect of the present invention, an electronic device is provided, including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of a lower limb rehabilitation training exoskeleton control method as described in the first aspect of the present invention.

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

[0061] The beneficial effects of this invention are as follows: By collecting hip and knee joint angle data from healthy test subjects during normal walking, this invention generates hip and knee joint angle change curves and smooths them to avoid abnormal shaking, further improving gait training stability. Then, by combining hip, knee, and cadence parameters, it generates variable-cadence stride hip and knee curves to guide target patients in gait training. This allows them to train gait according to the gait of healthy individuals, based on normal human movement data, making gait planning more closely resemble human movement patterns. Furthermore, for patients with different physical conditions, different hip, knee, and cadence parameters can be set, making the variable-cadence stride hip and knee curves more consistent with the patient's actual physical condition, meeting the differentiated rehabilitation training needs of different patients, and demonstrating strong practicality. This method uses normal human movement data for gait planning, which is closer to the way humans move, has strong adaptability, requires less data, is highly practical, and solves the problem of sudden speed changes caused by the non-smoothness of adjacent period curves. Attached Figure Description

[0062] Figure 1 This is a flowchart of a lower limb rehabilitation training exoskeleton control method according to one embodiment of the present invention.

[0063] Figure 2 This is a flowchart of a lower limb rehabilitation training exoskeleton control method according to another embodiment of the present invention. Detailed Implementation

[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, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

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

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

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

[0068] In the specification and claims of this invention, the terms "first" and "second" may explicitly or implicitly include one or more of those features. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0069] like Figure 1 As shown in the figure, this embodiment introduces a method for controlling an exoskeleton in lower limb rehabilitation training, including:

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

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

[0072] Step 1200: Generate a standard gait curve based on 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 was used to process and analyze the collected hip and knee joint angle data to generate standard human gait curves. Specifically, data processing and analysis of the hip joint angle data yielded hip joint angle variation curves, and data processing and analysis of the knee joint angle data yielded knee joint angle variation curves.

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

[0075] In the data acquisition experiment, the tester walks continuously, with each step constituting one cycle. If the transition between adjacent cycles of the hip joint angle change curve is discontinuous, the angular velocity and angular acceleration at the transition point will change abruptly, leading to abnormal shaking. Smoothing the hip joint angle change curve helps avoid this abnormal shaking.

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

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

[0078] Step 1500: Generate a hip joint curve with variable stride frequency based on the smoothed hip joint angle change curve and the hip joint amplitude parameters and stride frequency parameters set by the host computer. The hip joint amplitude parameters include the maximum value of hip joint flexion and the maximum value of hip joint extension.

[0079] During walking, the angle of the hip joint changes. Hip joint movements include flexion and extension. The maximum range of hip flexion is the maximum angle the hip joint reaches during flexion, and the maximum range of hip extension is the maximum angle the hip joint reaches during extension. These amplitude parameters can be adjusted according to the specific physical condition of the target patient. For example... Figure 2 As shown, the hip joint amplitude parameters include the maximum range of hip flexion and the maximum range of hip extension.

[0080] The smoothed hip joint angle change curve is obtained based on hip joint angle data of healthy test subjects during walking. Based on this, hip joint amplitude parameters and cadence parameters are set according to the actual physical condition of the target patient, and combined with the smoothed hip joint angle change curve to obtain a variable cadence stride length hip joint curve, which is used to guide the target patient in gait training.

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

[0082] Similarly, the angle of the knee joint changes during walking. Knee joint movements include flexion and extension. The maximum range of knee flexion is the maximum angle the knee joint reaches during flexion, and the maximum range of knee extension is the maximum angle the knee joint reaches during extension. These amplitude parameters can be adjusted according to the specific physical condition of the target patient.

[0083] The smoothed knee joint angle change curve is obtained based on knee joint angle data of healthy test subjects during walking. Based on this, knee joint amplitude parameters and cadence parameters are set according to the actual physical condition of the target patient, and combined with the smoothed knee joint angle change curve to obtain a knee joint curve with variable cadence and stride, which is used to guide the target patient in gait training.

[0084] Step 1700: Drive the hip joint motor according to the hip joint curve of the variable stride frequency and the knee joint motor according to the knee joint curve of the variable stride frequency and the human lower limbs are driven by the exoskeleton.

[0085] The hip joint motor is a motor for the exoskeleton at the hip joint of the target patient, used to drive the movement of the exoskeleton at the hip joint. After obtaining the hip joint curve with variable cadence and stride, the hip joint motor is driven according to the hip joint curve with variable cadence and stride, which drives the exoskeleton at the hip joint, so that the hip joint moves according to the hip joint curve with variable cadence and stride.

[0086] The knee joint motor is a motor for the exoskeleton at the knee joint of the target patient, used to drive the movement of the exoskeleton at the knee joint. After obtaining the knee joint curve with variable stride frequency, the knee joint motor is driven according to the knee joint curve with variable stride frequency, which drives the exoskeleton at the knee joint, allowing the knee joint to move according to the knee joint curve with variable stride frequency.

[0087] This invention collects hip and knee joint angle data from healthy individuals during normal walking, generating hip and knee joint angle variation curves. These curves are then smoothed to prevent abnormal shaking, further improving gait training stability. By combining hip, knee, and cadence parameters, variable cadence stride hip and knee curves are generated to guide gait training for target patients. This allows patients to train their gait according to the gait of healthy individuals, using data from normal human movement for gait planning that more closely resembles natural human movement. Furthermore, different hip, knee, and cadence parameters can be set for patients with varying physical conditions, making the variable cadence stride hip and knee curves more closely match their individual needs and meeting the diverse rehabilitation training requirements of different patients, demonstrating strong practicality.

[0088] In this embodiment, step 1200 includes:

[0089] The hip joint angle change curve is obtained by performing polynomial fitting based on the hip joint angle data.

[0090]

[0091] Where, θ hip For the hip joint angle, i h n represents the hip joint movement time. h p is the order of the polynomial. ho p h1 p h2 p hnThese are the polynomial coefficients;

[0092] Based on the knee joint angle data, a polynomial fitting is performed to obtain the knee joint angle change curve;

[0093]

[0094] Where, θ knee For the knee joint angle, i k n represents the knee joint movement time. k p is the order of the polynomial. ko p k1 p k2 p kn These are the polynomial coefficients.

[0095] Polynomial fitting was used to fit the hip and knee joint angle data, resulting in curves showing the changes in hip and knee joint angles. The polynomial coefficients can be calculated using 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 fitted hip joint angle change curve are both zero.

[0098] During gait training, patients need to pause between two gait cycles to allow their bodies to adapt. Therefore, by performing piecewise interpolation fitting on the first and last segments, the angular velocity and angular acceleration at both ends are made zero, ensuring that patients pause between two gait cycles during gait training.

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

[0100]

[0101] in, For the fitted i h The hip joint angle at time, f(i) h a hn b hn f(i) represents the first segment of the fitted hip joint angle change curve. h c hn d hn (a) represents the tail segment of the fitted hip joint angle change curve. hn b hn c hn d hnΩ represents the parameters to be identified for 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 last segment of the fitted hip joint angle change curve, and i4 is the end time of the last segment of the fitted hip joint angle change curve. hip The hip joint angular velocity is calculated based on the hip joint angle data, and Ω1 and Ω2 are preset hip joint angular velocity thresholds.

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

[0103] Step 1330: Calculate the parameters to be identified for the hip joint angle change curve based on the constraints satisfied by the first segment of the fitted hip joint angle change curve and the last segment 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] Where, θ hip θ is the hip joint angular velocity calculated based on the hip joint angle change curve. hip "α is the hip joint angular acceleration calculated based on the hip joint angle change curve." hip This refers to the hip joint angular acceleration calculated based on hip joint angle data.

[0112] By performing differential calculations on the original 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 calculations, the hip joint angular acceleration α can be obtained. hip .

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

[0114] In this embodiment, generating a variable cadence stride curve based on the smoothed hip joint angle change curve and the hip joint amplitude parameters and cadence parameters set by the host computer includes:

[0115] The fitted hip joint angle change curve is shown below:

[0116]

[0117] Based on the hip joint amplitude parameter and the cadence parameter, the hip joint curve with variable cadence and stride length is determined as shown below;

[0118]

[0119] Among them, e h1 e represents the maximum range of hip flexion. h2 f represents the maximum range of hip extension. h1 and f h2 This refers to the step frequency parameter.

[0120] Hip joint movements include flexion and extension. During hip flexion, the hip joint angle is greater than 0°. During hip extension, the hip joint angle is less than 0°. The fitted hip joint angle variation curve is adjusted based on hip joint amplitude and cadence parameters to obtain a hip joint curve with variable cadence and stride length.

[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 fitted knee joint angle change curve are both zero.

[0123] During gait training, patients need to pause between two gait cycles to allow their bodies to adapt. Therefore, by performing piecewise interpolation fitting on the first and last segments, the angular velocity and angular acceleration at both ends are made zero, ensuring that patients pause between two gait cycles during gait training.

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

[0125]

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

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

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

[0129] In this embodiment, the first segment of the fitted knee joint angle change curve and the last segment of the fitted knee joint angle change curve satisfy 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] Where, θ knee θ is the knee joint angular velocity calculated based on the knee joint angle change curve. knee "α is the knee joint angular acceleration calculated based on the knee joint angle change curve." knee This refers to the knee joint angular acceleration calculated based on knee joint angle data.

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

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

[0139] In this embodiment, generating a knee joint curve with variable stride frequency based on the smoothed knee joint angle change curve and the knee joint amplitude parameters and stride frequency parameters set by the host computer includes:

[0140] The fitted knee joint angle change curve is shown below:

[0141]

[0142] Based on the knee joint amplitude parameter and the cadence parameter, the knee joint curve with variable cadence and stride is determined as shown below;

[0143]

[0144] Among them, e k1 e represents the maximum range of knee flexion. k2 f represents the maximum range of knee extension. k1 and f k2 This refers to the step frequency parameter.

[0145] Knee joint movements include flexion and extension. During knee flexion, the knee angle is greater than 0°. During knee extension, the knee angle is less than 0°. The fitted knee angle variation curve is adjusted based on knee joint amplitude and cadence parameters to obtain a knee joint curve with variable cadence and stride length.

[0146] By modifying the hip and knee joint amplitude parameters, the joint angle amplitude can be adjusted to change stride length during gait training. Similarly, by modifying the cadence parameter, cadence can be changed during gait training.

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

[0148] The electronic device described in this invention can be a medical exoskeleton.

[0149] This embodiment introduces a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of a lower limb rehabilitation training exoskeleton control method as described in any embodiment of the present invention.

[0150] While specific embodiments of the invention have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.

[0151] Those skilled in the art will recognize 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

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

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

[0156] If the aforementioned functions are implemented as software functional 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 this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0157] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0158] It should be understood that the sequence numbers of the steps in the invention's content and embodiments do not absolutely imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The foregoing description of embodiments of this disclosure has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this disclosure to the exact form disclosed. Various modifications and variations may exist based on the foregoing teachings, or various modifications and variations may be derived from the practice of this disclosure. These embodiments were chosen and described to illustrate the principles of this disclosure and its practical application, so that those skilled in the art can utilize this disclosure in various implementations and modifications suitable for the specific purpose of the concept.

Claims

1. A lower limb rehabilitation training exoskeleton, characterized in that, The system includes a processor and a memory, the memory storing programs or instructions executable on the processor, the programs or instructions being executed by the processor to implement the steps of a lower limb rehabilitation training exoskeleton control method, the lower limb rehabilitation training exoskeleton control method comprising: Collect standard gait information during human walking, wherein the standard gait information includes hip joint angle data and knee joint angle data; A standard gait curve is generated based on the standard gait information, wherein the standard gait curve includes a hip joint angle change curve and a knee joint angle change curve; The hip joint angle change curve is smoothed. The knee joint angle change curve is smoothed. Based on the smoothed hip joint angle change curve and the hip joint amplitude parameters and cadence parameters set by the host computer, a hip joint curve with variable cadence and stride is generated. The hip joint amplitude parameters include the maximum hip joint flexion value and the maximum hip joint extension value. Based on the smoothed knee joint angle change curve, the knee joint amplitude parameters set by the host computer, and the step frequency parameters, a knee joint curve with variable step frequency and stride is generated. The knee joint amplitude parameters include the maximum value of knee flexion and the maximum value of knee extension. The hip joint motor is driven according to the hip joint curve of the variable stride frequency and the knee joint motor is driven according to the knee joint curve of the variable stride frequency, thereby driving the human lower limbs through the exoskeleton. The step of generating a standard gait curve based on the standard gait information includes: The hip joint angle change curve is obtained by performing polynomial fitting based on the hip joint angle data. ； wherein is the hip joint angle, is the hip joint movement time, is the polynomial order, , , , is the polynomial coefficient; Based on the knee joint angle data, a polynomial fitting is performed to obtain the knee joint angle change curve; ； in, For knee joint angle, The duration of knee joint movement. Let be the order of the polynomial. , , , These are the polynomial coefficients; The process of generating a variable cadence stride hip curve based on the smoothed hip joint angle change curve and the hip joint amplitude parameters and cadence parameters set by the host computer includes: The fitted hip joint angle change curve is shown below: ； Based on the hip joint amplitude parameter and the cadence parameter, the hip joint curve with variable cadence and stride length is determined as shown below; ； in, This represents the maximum range of hip flexion. This represents the maximum range of hip extension. and This refers to the step frequency parameter; The process of generating a variable cadence stride knee curve based on the smoothed knee joint angle change curve and the knee joint amplitude parameters and cadence parameters set by the host computer includes: The fitted knee joint angle change curve is shown below: ； Based on the knee joint amplitude parameter and the cadence parameter, the knee joint curve with variable cadence and stride is determined as shown below; ； in, This represents the maximum range of knee flexion. This represents the maximum range of knee extension. and This refers to the step frequency parameter.

2. The lower limb rehabilitation training exoskeleton according to claim 1, characterized in that, The smoothing process for the hip joint angle change curve includes: Piecewise interpolation fitting is performed 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 fitted hip joint angle change curve are both zero. The fitted curve showing the change in hip joint angle is shown below: ； in, For the fitted Hip joint angle at any given moment This is the first segment of the fitted hip joint angle change curve. This is the tail segment of the fitted hip joint angle change curve. , , , The parameters to be identified in the hip joint angle change curve are as follows: This represents the start time of the first segment of the fitted hip joint angle change curve. This represents the end time of the first segment of the fitted hip joint angle change curve. This represents the start time of the tail segment of the fitted hip joint angle change curve. This represents the end time of the tail segment of the fitted hip joint angle change curve. The hip joint angular velocity is calculated based on hip joint angle data. and The preset hip joint angular velocity threshold; Based on the constraints satisfied by the first and last segments of the fitted hip joint angle change curve, the parameters to be identified for the hip joint angle change curve are calculated.

3. The lower limb rehabilitation training exoskeleton according to claim 2, characterized in that, 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: ； ； ； ； ； ； in, The hip joint angular velocity is calculated based on the hip joint angle change curve. The hip joint angular acceleration is calculated based on the hip joint angle change curve. This refers to the hip joint angular acceleration calculated based on hip joint angle data.

4. The lower limb rehabilitation training exoskeleton according to claim 1, characterized in that, The smoothing process for the knee joint angle change curve includes: Piecewise interpolation fitting is performed 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 fitted knee joint angle change curve are both zero. The fitted curve showing the change in knee joint angle is shown below: ； in, For the fitted The knee joint angle at any given moment, This is the first segment of the fitted knee joint angle change curve. This is the tail segment of the fitted knee joint angle change curve. , , , The parameters to be identified in the knee joint angle change curve are... This represents the start time of the first segment of the fitted knee joint angle change curve. This represents the end time of the first segment of the fitted knee joint angle change curve. This represents the start time of the tail segment of the fitted knee joint angle change curve. This represents the end time of the tail segment of the fitted knee joint angle change curve. The knee joint angular velocity is calculated based on knee joint angle data. and The preset knee joint angular velocity threshold; Based on the constraints satisfied by the first and last segments of the fitted knee joint angle change curve, the parameters to be identified for the knee joint angle change curve are calculated.

5. The lower limb rehabilitation training exoskeleton according to claim 4, characterized in that, The first segment of the fitted knee joint angle change curve and the last segment of the fitted knee joint angle change curve satisfy the following constraints: ； ； ； ； ； ； in, The knee joint angular velocity is calculated based on the knee joint angle change curve. The knee joint angular acceleration is calculated based on the knee joint angle change curve. This refers to the knee joint angular acceleration calculated based on knee joint angle data.

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