Lower limb rehabilitation training system for intelligent weight loss

Abstract

The invention provides a lower limb rehabilitation training system for intelligent weight loss. The lower limb rehabilitation training system can be applied to the technical field of artificial intelligence and the technical field of medical treatment. The system comprises a weight reduction device which comprises a weight reduction spring, a weight reduction motor, a floating motor and a floating spring; the weight reduction motor is used for adjusting the stretching length of the weight reduction spring according to the rotation angle of the weight reduction motor to obtain the spring stretching distance; the floating motor is used for finely adjusting the stretching length of the floating spring according to the rotating angle of the floating motor; the lower limb exoskeleton device is used for measuring the plantar pressure and the joint operation angle of the user in the walking process; the tension monitoring device is used for measuring the output tension of the weight reduction device; the control device is used for inputting the spring stretching distance, the plantar pressure and the output tension into a prediction model of the rotation angle of the weight reduction motor to obtain a prediction value of the rotation angle of the weight reduction motor; and inputting the joint operation angle into the floating rotation angle prediction model to obtain a floating rotation angle prediction value.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and medical technology, and more specifically to a lower limb rehabilitation training system for intelligent weight loss. Background Art

[0002] Stroke is a sudden central nervous system disease with a high disability rate, and the incidence of stroke is increasing year by year. Walking rehabilitation training is effective for the rehabilitation treatment of stroke, which can not only prevent muscle atrophy and maintain joint flexibility of patients, but also improve the degree of recovery of patients.

[0003] The lower limbs of stroke patients can only withstand limited pressure, and there is an urgent need for a device to assist patients in accurately reducing the pressure on their lower limbs during walking training, so that patients can walk smoothly and safely. Summary of the invention

[0004] In view of the above problems, the present invention provides a lower limb rehabilitation training system for intelligent weight loss.

[0005] According to a first aspect of the present invention, there is provided a lower limb rehabilitation training system for intelligent weight loss, comprising:

[0006] A weight reduction device connected to the body of a user wearing the lower limb exoskeleton device to reduce the force on the lower limbs of the user while walking on the treadmill device, the weight reduction device comprising a weight reduction spring, a weight reduction motor, a floating motor and a floating spring;

[0007] The weight-reducing motor is used to adjust the stretching length of the weight-reducing spring according to the rotation angle of the weight-reducing motor to obtain the spring stretching distance;

[0008] The floating motor is used to fine-tune the stretching length of the floating spring according to the rotation angle of the floating motor, so as to reduce the influence of the floating center of gravity of the user on the output tension of the weight reduction device during walking;

[0009] Lower limb exoskeleton device, used to measure the user's plantar pressure and joint operating angle during walking;

[0010] A tension monitoring device, used to measure the output tension of the weight reduction device;

[0011] The control device is used to input the spring stretching distance, plantar pressure and output pulling force into the prediction model of the weight-reducing motor rotation angle to obtain the predicted value of the weight-reducing motor rotation angle; and input the joint operation angle into the prediction model of the floating rotation angle to obtain the predicted value of the floating rotation angle.

[0012] According to an embodiment of the present disclosure, the control device includes a distance sensor, and the distance sensor is used to measure the stretching distance of the spring.

[0013] According to an embodiment of the present disclosure, a lower limb exoskeleton device includes a plantar pressure detector, which is used to measure the plantar pressure of a user during walking.

[0014] According to an embodiment of the present disclosure, the lower limb exoskeleton device further includes a joint module driving module, and the joint module driving module includes:

[0015] A drive motor, used to drive the joints of the user to move so that the user walks on the treadmill device;

[0016] Encoder detector, used to measure the user's joint running angles during walking.

[0017] According to an embodiment of the present disclosure, the joint module driving module also includes a brake, which is used to supply power to the driving motor when the lower limb exoskeleton device loses power.

[0018] According to an embodiment of the present disclosure, the prediction model is a gray prediction model, and the spring stretching distance, plantar pressure and output tension are input into the prediction model of the weight-reducing motor rotation angle to obtain the predicted value of the weight-reducing motor rotation angle, including: inputting the spring stretching distance, plantar pressure and output tension into the gray prediction model to obtain the predicted weight-reducing weight of the weight-reducing device; and obtaining the predicted value of the weight-reducing motor rotation angle based on the error value between the predicted weight-reducing weight and the preset value.

[0019] According to an embodiment of the present disclosure, the control device further includes: a main control module, which is used to feedback control the operation of the weight reduction motor according to an error value between the predicted weight reduction weight and a preset value.

[0020] According to an embodiment of the present disclosure, the formula for the rotation angle of the weight-reducing motor is:

[0021]

[0022] Φ1 is the rotation angle of the weight-reducing motor, f z is the coupling function, the output tension is x, and the plantar pressure is y i , the spring stretch distance is xl, and the spring tension of the weight-reducing spring is x T , the weight reduction of the weight reduction device is m j .

[0023] According to an embodiment of the present disclosure, the coupling function f z The formula is:

[0024]

[0025] f is the control function between the output tension, plantar pressure and spring tension, f1 is the plantar pressure compensation function, f2 is the elastic force function, and f3 is the control function between the weight reduction motor and the spring stretching distance. The formulas of f, f1, f2 and f3 are as follows:

[0026]

[0027]

[0028]

[0029]

[0030] The user's weight is m r , k is the elastic coefficient of the weight-reducing spring.

[0031] According to an embodiment of the present disclosure, the formula for the rotation angle of the floating motor is as follows:

[0032]

[0033] Φ2(t) is the rotation angle of the floating motor at time t, and m is the spring tension x at time t. T The control function between (t) and the user's center of gravity height change value h(t), the formula of the center of gravity height change value h(t) is as follows:

[0034]

[0035] The length of the user's thigh is l0, the length of the user's calf is l1, and the joint operating angles include a hip joint angle θ0 and a knee joint angle θ1.

[0036] According to an embodiment of the present invention, the weight reduction device includes a weight reduction spring and a floating spring. The influence of the floating center of gravity on the output tension of the weight reduction device can be reduced by fine-tuning the stretching length of the floating spring. At the same time, the plantar pressure is combined with the spring stretching distance and the output tension of the weight reduction device to predict the rotation angle of the weight reduction motor, and the rotation angle of the floating motor is predicted using the joint operation angle. Since the influence of the floating center of gravity on weight reduction is taken into account, the user's supporting weight is accurately reduced during sports rehabilitation training to provide a smooth and safe walking environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0038] Figure 1 A scene diagram of user lower limb rehabilitation training according to an embodiment of the present invention is shown;

[0039] Figure 2 A structural block diagram of a lower limb rehabilitation training system for intelligent weight loss according to an embodiment of the present invention is shown;

[0040] Figure 3It shows a structural block diagram of a weight reduction device according to an embodiment of the present invention;

[0041] Figure 4 A schematic diagram showing the installation position of a distance sensor according to an embodiment of the present invention is shown;

[0042] Figure 5 A structural block diagram of a lower limb rehabilitation training system for intelligent weight loss according to another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0044] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0045] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0046] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0047] Stroke is a sudden central nervous system disease with a high disability rate and an increasing incidence rate. Patients often suffer from limb movement disorders and require long-term rehabilitation training and treatment, which brings great mental pain and heavy economic burden to the individual, family and society.

[0048] During the rehabilitation training and learning process of re-mastering limb motor functions, the central nervous system of the brain will undergo positive plastic changes, changing the functional connections and structures of brain regions to adapt to the needs of movement. Clinical practice has shown that exercise rehabilitation training is effective for the rehabilitation treatment of hemiplegia. It can not only prevent muscle atrophy and maintain the flexibility of the patient's joints, but also improve the patient's recovery. The lower limbs of stroke patients can only withstand limited pressure, and there is an urgent need for a device to assist patients in accurately reducing the pressure on their lower limbs during walking training, so that patients can walk smoothly and safely.

[0049] An embodiment of the present invention provides a lower limb rehabilitation training system for intelligent weight loss, comprising: a weight loss device connected to the body of a user wearing a lower limb exoskeleton device to reduce the force on the lower limbs of the user while walking on a treadmill device, the weight loss device comprising a weight loss spring, a weight loss motor, a floating motor and a floating spring; the weight loss motor is used to adjust the stretching length of the weight loss spring according to the rotation angle of the weight loss motor to obtain the spring stretching distance; the floating motor is used to fine-tune the stretching length of the floating spring according to the rotation angle of the floating motor to reduce the influence of the floating center of gravity of the user during walking on the output tension of the weight loss device; the lower limb exoskeleton device is used to measure the plantar pressure and joint operating angle of the user during walking; the tension monitoring device is used to measure the output tension of the weight loss device; the control device is used to input the spring stretching distance, plantar pressure and output tension into a prediction model of the weight loss motor rotation angle to obtain a predicted value of the weight loss motor rotation angle; the joint operating angle is input into the prediction model of the floating rotation angle to obtain a predicted value of the floating rotation angle.

[0050] Figure 1 A scene diagram of user lower limb rehabilitation training according to an embodiment of the present invention is shown.

[0051] like Figure 1 As shown, the weight reduction device 110 is connected to the body of the user wearing the lower limb exoskeleton device 120 to reduce the force on the lower limbs of the user when walking on the treadmill device 130. The support device 140 is used to fix the treadmill device 130 to prevent the treadmill device 130 from moving due to the user's walking.

[0052] According to an embodiment of the present invention, the user may be a stroke patient, and the sole of the foot of the lower limb exoskeleton device 120 can measure the plantar pressure value, and can measure the user's lower limb support weight in real time. The lower limb exoskeleton device 120 also has a sensor for detecting the joint running angle in real time, and can monitor the user's training status in real time.

[0053] According to an embodiment of the present invention, the weight reduction device 110 is connected to the user's body, and the reduced force can be measured by the tension monitoring device. The weight reduction device can provide an upward supporting force or a tension device.

[0054] The weight lost by the weight-reducing device 110 measured by the tension monitoring device and the plantar pressure value measured at the sole of the foot of the lower limb exoskeleton device 120 can provide real-time feedback on the stress condition of the user's lower limbs, and then adjust the parameters of the weight-reducing device 110 to achieve precise weight loss, which can effectively protect the user's lower limb joints and muscles and provide a healthy, effective and safe rehabilitation training environment.

[0055] The following will be based on Figure 1 The scene described by Figure 2~Figure 5 The lower limb rehabilitation training system for intelligent weight loss according to an embodiment of the invention is described in detail.

[0056] Figure 2 A structural block diagram of a lower limb rehabilitation training system for intelligent weight loss according to an embodiment of the present invention is shown.

[0057] like Figure 2 As shown, the lower limb rehabilitation training system 200 for intelligent weight loss in this embodiment includes a weight loss device 110 , a lower limb exoskeleton device 120 , a tension monitoring device 150 and a control device 160 .

[0058] The weight reduction device 110 is connected to the body of a user wearing the lower limb exoskeleton device 120 to reduce the force on the lower limbs of the user when walking on the treadmill device 130.

[0059] Figure 3 A structural block diagram of a weight reduction device according to an embodiment of the present invention is shown.

[0060] like Figure 3 As shown, the weight reduction device 110 includes a weight reduction spring 111 , a weight reduction motor 112 , a floating spring 113 and a floating motor 114 .

[0061] The weight reduction motor 112 is used to adjust the stretching length of the weight reduction spring 111 according to the rotation angle of the weight reduction motor to obtain the spring stretching distance.

[0062] According to an embodiment of the present invention, the weight reduction motor rotation angle represents the angle at which the weight reduction motor 112 adjusts the weight reduction spring 111. For example, the weight reduction motor rotation angle is the rotation angle of the weight reduction motor output shaft, which can be determined by measuring the angle of one rotation of the weight reduction motor.

[0063] The floating motor 114 is used to fine-tune the stretching length of the floating spring 113 according to the rotation angle of the floating motor, so as to reduce the influence of the floating center of gravity of the user on the output tension of the weight reduction device 110 during walking.

[0064] According to an embodiment of the present invention, the floating motor rotation angle represents the angle at which the floating motor 114 adjusts the floating spring 113. For example, the floating motor rotation angle is the rotation angle of the floating motor output shaft, which can be determined by measuring the angle of one rotation of the floating motor.

[0065] For example, the rotation angle of the weight-reducing motor and the rotation angle of the floating motor can be measured using a motor rotation angle encoder or a photoelectric sensor.

[0066] The lower limb exoskeleton device 120 is used to measure the plantar pressure and joint operating angle of the user during walking.

[0067] According to an embodiment of the present invention, the lower limb exoskeleton device 120 can be a lower limb exoskeleton robot equipped with numerous precision sensors that can measure the plantar pressure, center of gravity changes, joint operating angles, etc. of the user during walking, so as to adjust the stretching length of the weight-reducing spring and the floating spring in the weight-reducing device, thereby allowing the user's body to be balanced and reducing the stress on the lower limbs.

[0068] The tension monitoring device 150 is used to measure the output tension of the weight reduction device 110 .

[0069] According to an embodiment of the present invention, the output pulling force represents the reduced weight of the weight reducing device 110 .

[0070] According to the embodiment of the present invention, the tension monitoring device 150 is mainly used to measure the weight reduced by the weight reducing device, and then feedback control the output of the weight reducing device 110. The tension monitoring device 150 is generally a pressure sensor.

[0071] The control device 160 is used to input the spring stretching distance, plantar pressure and output tension into the prediction model of the weight-reducing motor rotation angle to obtain the predicted value of the weight-reducing motor rotation angle; and input the joint operation angle into the prediction model of the floating rotation angle to obtain the predicted value of the floating rotation angle.

[0072] The control device 160 may be a server that provides various services.

[0073] According to an embodiment of the present invention, the prediction model may be a machine learning algorithm, for example, the prediction model may be a neural network algorithm, a random forest algorithm, a support vector machine algorithm, a grey prediction algorithm, etc.

[0074] For example, when the amount of data on spring stretch distance, plantar pressure, output tension, and joint operating angle is huge, the prediction model can be a neural network algorithm. By training the prediction model with a large amount of data, the prediction model parameters can be updated by continuously acquiring new data to continuously improve the prediction model performance. The prediction model can be trained based on the historical spring stretch distance, historical plantar pressure, and historical output tension of other users.

[0075] The prediction model can be used to extract features of spring stretching distance, plantar pressure and output tension to obtain state features, which can characterize the force intensity on the user's plantar; the predicted output tension of the weight reduction device is predicted based on the state features; based on the formula of spring tension (for example, spring tension = elastic coefficient * spring stretching distance), the predicted spring stretching distance is calculated according to the predicted output tension; and then the required rotation angle of the weight reduction motor is inferred based on the predicted spring stretching distance to obtain the predicted value of the rotation angle of the weight reduction motor.

[0076] For example, due to the different recovery stages of stroke patients, the force standards of the lower limbs of patients' walking training may also be different. In order for the lower limb rehabilitation training system for intelligent weight loss to adapt to users at different recovery stages, the prediction model can be a random forest algorithm. There are multiple decision trees in the random forest algorithm, and each decision tree corresponds to a different recovery stage. For example, a user who has performed lower limb rehabilitation training 1 to 10 times can be a decision tree corresponding to the previous period. The decision tree corresponding to the previous period can be obtained by using the historical spring stretching distance, historical plantar pressure and historical output tension training of other users for 1 to 10 times.

[0077] The user's recovery phase can be set in advance, and the spring stretch distance, plantar pressure and output tension are input into the decision tree corresponding to the recovery phase in the random forest algorithm of the weight-reducing motor rotation angle, so as to accurately predict the weight-reducing motor rotation angle. The joint running angle is input into the decision tree corresponding to the recovery phase in the random forest algorithm of the floating rotation angle, so as to accurately predict the floating rotation angle.

[0078] At the same time, the spring stretching distance, plantar pressure, output tension and joint running angle can be input into the random forest algorithm to first identify the user's recovery stage, and then the spring stretching distance, plantar pressure and output tension are input into the random forest algorithm of the weight-reducing motor rotation angle and the decision tree corresponding to the recovery stage, and the joint running angle is input into the random forest algorithm of the floating rotation angle and the decision tree corresponding to the recovery stage.

[0079] For example, when the amount of data on spring stretch distance, plantar pressure, output tension, and joint running angle is small, the prediction model can be a gray prediction algorithm. The gray prediction algorithm does not require a clear mathematical assumption model and can make effective predictions under conditions of incomplete information and small sample data. Common gray prediction algorithms such as GM (1,1) have a relatively simple model structure, few parameters, and are easy to implement.

[0080] The weight reduction device 110, the lower limb exoskeleton device 120, the tension monitoring device 150 and the control device 160 may be connected via a network, such as a wired or wireless communication link or a fiber optic cable.

[0081] According to an embodiment of the present invention, the weight reduction device includes a weight reduction spring and a floating spring. The influence of the floating center of gravity on the output tension of the weight reduction device can be reduced by fine-tuning the stretching length of the floating spring. At the same time, the plantar pressure is combined with the spring stretching distance and the output tension of the weight reduction device to predict the rotation angle of the weight reduction motor, and the rotation angle of the floating motor is predicted using the joint operation angle. Since the influence of the floating center of gravity on weight reduction is taken into account, the user's supporting weight is accurately reduced during sports rehabilitation training to provide a smooth and safe walking environment.

[0082] According to an embodiment of the present invention, a lower limb exoskeleton device includes a plantar pressure detector, which is used to measure the plantar pressure of a user during walking.

[0083] According to an embodiment of the present invention, the lower limb exoskeleton device also includes a joint module driving module, which includes: a driving motor for driving the user's joints to move so that the user can walk on the treadmill device; an encoder detector for measuring the joint operating angle of the user during walking.

[0084] According to an embodiment of the present invention, the drive motor can be used to drive the joint to move in a specific direction. The drive motor can be composed of a DC motor, a planetary reduction unit, and a torque sensor.

[0085] According to an embodiment of the present invention, the encoder detector can be used to measure the joint running angle, and then feedback control the rotation of the motor. For ease of installation, the encoder detector can be a Hall encoder.

[0086] According to an embodiment of the present invention, the joint module driving module also includes a brake, which is used to supply power to the driving motor when the lower limb exoskeleton device loses power.

[0087] According to an embodiment of the present invention, the brake can be used to maintain the state of the driving motor in the power-off state, and then provide support force to prevent the user from falling and causing secondary injuries.

[0088] According to an embodiment of the present invention, the control device comprises a distance sensor, and the distance sensor is used to measure the stretching distance of the spring.

[0089] For example, the weight-reducing device is implemented using a weight-reducing spring and a floating spring structure. During use, the weight-reducing motor is used to stretch or compress the weight-reducing spring to achieve different pulling forces. The floating amplitude of the lower limb exoskeleton device is calculated according to the gait of the lower limb exoskeleton device, and the floating motor is controlled in real time to compress and stretch the floating spring to compensate for the changes in the output pulling force of the weight-reducing device caused by the up and down floating during walking, thereby achieving constant force output.

[0090] Figure 4 A schematic diagram of the installation position of a distance sensor according to an embodiment of the present invention is shown.

[0091] like Figure 4 As shown, the weight reduction device in the support device 170 is pulled up by the pulley 410, and the weight reduction motor 112 is fixed to a relative position on the first spring fixing frame by the first lead screw 420. At the same time, the floating motor 114 is fixed to a relative position on the second spring fixing frame by the second lead screw 430. The two ends of the weight reduction spring 111 are connected to the first spring fixing frame and the second spring fixing frame respectively. The first distance sensor 440 and the second distance sensor 450 are respectively installed on the first spring fixing frame and the second spring fixing frame.

[0092] According to the set weight reduction requirements, the weight reduction motor 112 is started to pull the weight reduction spring 111 to stretch, and the spring stretching distance is obtained. The first distance sensor 410 and the second distance sensor 420 are used to measure the spring stretching distance. The weight reduction motor rotation angle of the weight reduction motor 112 and the plantar pressure of the lower limb exoskeleton device are combined and adjusted so that the output tension of the weight reduction device meets the requirements. During the floating process of the center of gravity, the floating motor 114 is used to fine-tune the extension of the floating spring to compensate for the change in the output tension caused by the up and down floating of the lower limbs during walking, and then achieve constant force output.

[0093] For example, the distance sensor may be a laser distance sensor.

[0094] According to an embodiment of the present invention, the formula for the rotation angle of the weight-reducing motor is:

[0095] (1)

[0096] Φ1 is the rotation angle of the weight-reducing motor, f z is the coupling function, the output tension is x, and the plantar pressure is y i , the spring stretch distance is xl, and the spring tension of the weight-reducing spring is x T , the weight reduction of the weight reduction device is m j .

[0097] According to an embodiment of the present invention, the coupling function f z The formula is:

[0098] (2)

[0099] f is the control function between the output tension, plantar pressure and spring tension, f1 is the plantar pressure compensation function, f2 is the elastic force function, and f3 is the control function between the weight reduction motor and the spring stretching distance. The formulas of f, f1, f2 and f3 are as follows:

[0100] (3)

[0101] (4)

[0102] (5)

[0103] (6)

[0104] The user's weight is m r , k is the elastic coefficient of the weight-reducing spring.

[0105] According to an embodiment of the present invention, a grey prediction algorithm may be used to predict and control the operation of a weight-reducing motor.

[0106] For example, let X1, X2, ..., X6 represent the weight reduction of the weight reduction device m j , output tension x, spring tension x of the weight-reducing spring T , spring stretching distance xl, plantar pressure of the left foot y0, plantar pressure of the right foot y1.

[0107] (7)

[0108] like , where n is the number of collected data points. Obtained by one-time cumulative production.

[0109] (8)

[0110] in, .

[0111] The established whitening equations are as follows:

[0112] (9)

[0113] For example, the least squares method is used to solve , and then solve the obtained differential equation formula (10), so according to m j ,x,x T , xl, y0, y1, and their respective differential equation formulas are used to generate a prediction function of the rotation angle of the weight-reducing motor (such as formula 1), and the predicted value of the rotation angle of the weight-reducing motor can be obtained.

[0114] (10)

[0115] Among them, t=2, 3…n.

[0116] By inverting the column and generating the algorithm, the predicted value is obtained. For example, the weight loss m is obtained. j The predicted value is .

[0117] (11)

[0118] For example, the weight reduction using the weight reduction device is m j , output tension x, spring tension x of the weight-reducing spring T , spring stretching distance xl, left foot plantar pressure y0, right foot plantar pressure y1, and the predicted value of the weight-reducing motor rotation angle can also be obtained.

[0119] According to an embodiment of the present invention, the prediction model is a gray prediction model, and the spring stretching distance, plantar pressure and output tension are input into the prediction model of the weight-reducing motor rotation angle to obtain the predicted value of the weight-reducing motor rotation angle, including: inputting the spring stretching distance, plantar pressure and output tension into the gray prediction model to obtain the predicted weight-reducing weight of the weight-reducing device; and obtaining the predicted value of the weight-reducing motor rotation angle based on the error value between the predicted weight-reducing weight and the preset value.

[0120] According to the error value between the predicted weight reduction weight and the preset value, the control device can be used to perform feedback control to obtain the adjustment length of the weight reduction spring. According to the adjustment length of the weight reduction spring, the predicted value of the rotation angle of the weight reduction motor is determined.

[0121] According to an embodiment of the present invention, the control device further comprises: a main control module for feedback controlling the operation of the weight reduction motor according to an error value between the predicted weight reduction weight and a preset value.

[0122] For example, according to The error between the predicted value and the preset value is used to control the operation of the weight-reducing motor using PID (Proportional-Integral-Derivative) feedback.

[0123] When the user walks, the center of gravity of the body changes, causing the traction to float up and down, which in turn causes the output tension to change, causing discomfort to the user during rehabilitation training; if the rope is tightened in real time, the feet will not be able to touch the ground, forming the so-called space walk, which is not conducive to rehabilitation.

[0124] According to an embodiment of the present invention, the formula for the rotation angle of the floating motor is as follows:

[0125] (12)

[0126] Φ2(t) is the rotation angle of the floating motor at time t, and m is the spring tension x at time t. T The control function between (t) and the user's center of gravity height change value h(t), the formula of the center of gravity height change value h(t) is as follows:

[0127] (13)

[0128] The length of the user's thigh is l0, the length of the user's calf is l1, and the joint operation angles include the hip joint angle θ0 and the knee joint angle θ1. For example, the gray prediction algorithm is used to solve the functional relationship of the floating motor rotation angle (such as formula (12)).

[0129] The up and down floating during walking causes the output tension of the weight-reducing device to change. By predicting the rotation angle of the floating motor, the output tension of the weight-reducing device can be kept constant, while allowing users to walk smoothly.

[0130] Figure 5 A structural block diagram of a lower limb rehabilitation training system for intelligent weight loss according to another embodiment of the present invention is shown.

[0131] like Figure 5 As shown, the lower limb rehabilitation training system 500 for intelligent weight loss in this embodiment includes a weight loss device 110, a lower limb exoskeleton device 120, a treadmill device 130, a support device 140, a tension monitoring device 150, a control device 160 and a human-computer interaction device 170.

[0132] The treadmill device 130 may include a treadmill track, a pressure sensor, a displacement sensor, and a rolling motor, which are connected to the support device 140 and installed on a flat ground. A pressure sensor may also be installed inside the treadmill device 130. The displacement sensor and the DC power supply circuit; the treadmill device 130 may be provided with a free walking mode and a set speed mode. In the free walking mode, the user's walking will not change the relative position between the user and the treadmill. The installation direction of the handrails of the treadmill device 130 and the support device 140 is fixed, and the handrails should be slidably arranged on both sides of the support device 140 in the vertical direction.

[0133] The weight reduction device 110 is connected to the body of the user wearing the lower limb exoskeleton device 120 to reduce the force on the lower limbs of the user when walking on the treadmill device 130. Figure 3 shown.

[0134] The lower limb exoskeleton device 120 is used to measure the plantar pressure and joint operating angle of the user during walking.

[0135] The tension monitoring device 150 is used to measure the output tension of the weight reduction device 110 .

[0136] The control device 160 is used to input the spring stretching distance, plantar pressure and output tension into the prediction model of the weight-reducing motor rotation angle to obtain the predicted value of the weight-reducing motor rotation angle; and input the joint operation angle into the prediction model of the floating rotation angle to obtain the predicted value of the floating rotation angle.

[0137] The control device 160 also includes an exoskeleton robot motion control unit, a weight reduction unit control unit, a treadmill control unit and a main control unit.

[0138] The exoskeleton robot motion control unit not only pre-processes the plantar pressure sensor data but also performs signal amplification, analog-to-digital conversion, and filtering. The exoskeleton robot motion control unit mainly controls the length adjustment subunit, the joint module drive subunit, the waist adjustment subunit, etc. The waist adjustment subunit and the length adjustment subunit both include an adjustment motor, a reducer, a rolling gear device, a laser rangefinder, etc. The exoskeleton robot motion control unit is connected to the lower limb exoskeleton device 120.

[0139] The weight reduction unit control unit is used to detect and adjust the weight reduction spring length in real time using laser ranging. The weight reduction unit control unit is connected to the weight reduction device 110, and uses PID technology feedback control to adjust the weight reduction motor operation. It can also be used to detect and adjust the floating spring length in real time using laser ranging, and use PID technology feedback control to adjust the floating motor operation.

[0140] The treadmill control unit is used to control the running speed of the treadmill according to the required parameters of the main control motor.

[0141] The main control unit is used to generate control commands according to the setting parameters of the human-machine interaction device 170 and the operating parameters of each device, so as to control the normal operation of each device.

[0142] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0143] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention may be combined and / or combined in various ways. All of these combinations and / or combinations fall within the scope of the present invention.

[0144] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A lower limb rehabilitation training system for intelligent weight loss, characterized in that: The system comprises: A weight reduction device connected to the body of a user wearing the lower limb exoskeleton device to reduce the force on the lower limbs of the user when walking on the treadmill device, the weight reduction device comprising a weight reduction spring, a weight reduction motor, a floating motor and a floating spring; The weight reduction motor is used to adjust the stretching length of the weight reduction spring according to the rotation angle of the weight reduction motor to obtain the spring stretching distance; The floating motor is used to fine-tune the stretching length of the floating spring according to the rotation angle of the floating motor, so as to reduce the influence of the floating center of gravity of the user during the walking process on the output tension of the weight reduction device; The lower limb exoskeleton device is used to measure the plantar pressure and joint operating angle of the user during the walking process; A tension monitoring device, used to measure the output tension of the weight reduction device; The control device is used to input the spring stretching distance, the plantar pressure and the output tension into the prediction model of the weight-reducing motor rotation angle to obtain the predicted value of the weight-reducing motor rotation angle; and input the joint operation angle into the prediction model of the floating rotation angle to obtain the predicted value of the floating rotation angle.

2. The system according to claim 1, characterized in that The control device comprises a distance sensor, and the distance sensor is used to measure the stretching distance of the spring.

3. The system according to claim 1, characterized in that The lower limb exoskeleton device includes a plantar pressure detector, which is used to measure the plantar pressure of the user during the walking process.

4. The system according to claim 3, characterized in that The lower limb exoskeleton device further includes a joint module driving module, and the joint module driving module includes: A driving motor, used for driving the joints of the user to move so that the user can walk on the treadmill device; An encoder detector is used to measure the joint operation angle of the user during the walking process.

5. The system according to claim 4, characterized in that The joint module driving module also includes a brake, which is used to supply power to the driving motor when the lower limb exoskeleton device loses power.

6. The system according to claim 1, characterized in that The prediction model is a grey prediction model, and the prediction model of the rotation angle of the weight-reducing motor is input with the spring stretching distance, the plantar pressure and the output tension, to obtain the prediction value of the rotation angle of the weight-reducing motor, including: Inputting the spring stretching distance, the plantar pressure and the output tension into the grey prediction model to obtain the predicted weight loss weight of the weight loss device; The predicted value of the rotation angle of the weight-reducing motor is obtained according to the error value between the predicted weight-reducing weight and the preset value.

7. The system according to claim 6, characterized in that The control device also includes: The main control module is used to feedback control the operation of the weight reduction motor according to the error value between the predicted weight reduction weight and the preset value.

8. The system according to claim 1, characterized in that The formula for the rotation angle of the weight-reducing motor is: Φ1 is the rotation angle of the weight-reducing motor, f z is a coupling function, the output tension is x, and the plantar pressure is y i , the spring stretching distance is xl, and the spring tension of the weight-reducing spring is x T The weight reduction of the weight reduction device is m j .

9. The system according to claim 8, characterized in that The coupling function f z The formula is: f is the control function between the output tension, the plantar pressure and the spring tension, f1 is the plantar pressure compensation function, f2 is the elastic force function, f3 is the control function between the weight reduction motor and the spring stretching distance, and the formulas of f, f1, f2 and f3 are as follows: The weight of the user is m r , k is the elastic coefficient of the weight-reducing spring.

10. The system according to claim 1, characterized in that The formula for the floating motor rotation angle is as follows: Φ2(t) is the rotation angle of the floating motor at time t, and m is the spring tension x at time t. T (t) and the center of gravity height change value h(t) of the user, the formula of the center of gravity height change value h(t) is as follows: The length of the user's thigh is l0, the length of the user's calf is l1, and the joint operation angles include a hip joint angle θ0 and a knee joint angle θ1.

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