A hybrid active-passive driven flexible lower limb exoskeleton system and its coordinated control method

Abstract

This invention discloses a hybrid active-passive driven flexible lower limb exoskeleton system and its coordinated control method. The exoskeleton system includes an elastic energy storage element, a rope drive system, a flexible binding device, a sensing system, and a control system. The control system includes a gait recognition algorithm, an assist trajectory planning algorithm, and a force trajectory tracking algorithm. The gait recognition algorithm detects angles and angular velocities to detect the motion state. The assist trajectory planning algorithm calculates the rope drive assist. The force trajectory tracking algorithm converts the desired force into motor speed and corrects it using feedback information from the force sensor, achieving accurate tracking of the desired force trajectory. A finite state machine is used to divide the active and passive assist phases in the gait cycle. Based on the principles of passive energy storage and active assist, the assist magnitude and the switching between active and passive assist modes are coordinated and controlled during the active assist phase. This invention can coordinate and control the hybrid active-passive driven flexible lower limb exoskeleton, achieving high-precision, high-robustness, and high-response force control.

Description

Technical Field

[0001] This invention relates to exoskeleton robots and control methods, and particularly to a hybrid active-passive drive flexible lower limb exoskeleton system and a coordinated control method. Background Technology

[0002] Elderly people may experience lower limb muscle weakness, leading to difficulties in mobility and severely impacting their daily lives. As my country's population ages, this phenomenon will become increasingly prevalent.

[0003] Lower limb exoskeletons can assist walking, helping the elderly complete daily activities such as outdoor excursions. The development of lower limb exoskeletons is one of the effective methods to address the increasing aging population and the resulting mobility difficulties caused by lower limb muscle weakness in the elderly in my country. For those with lower limb dysfunction, lower limb exoskeletons can provide rehabilitation training to help restore lower limb motor function. They can also replace some of the work of rehabilitation therapists, effectively alleviating the severe shortage of rehabilitation therapists in my country.

[0004] Lower limb exoskeletons can be divided into rigid and flexible types. Rigid lower limb exoskeletons have some drawbacks, such as potential interference with the wearer's free movement when the exoskeleton joints are misaligned with the wearer's biological joints or when the exoskeleton's degrees of freedom do not meet the wearer's needs. Rigid structures located distally also have greater inertia, which can significantly increase the wearer's net metabolic rate.

[0005] Flexible lower limb exoskeletons, while compensating for the shortcomings of rigid lower limb exoskeletons, are lightweight, easy and comfortable to wear, and can provide assistive force, meeting the needs of different groups for enhanced lower limb motor function. Hybrid active-passive flexible lower limb exoskeletons can provide both active and passive assistance by absorbing the body's gravitational potential energy, improving the energy utilization efficiency of the exoskeleton system. The development of hybrid active-passive flexible lower limb exoskeletons is one of the important solutions for enhancing human lower limb motor function.

[0006] Therefore, those skilled in the art are dedicated to providing a coordinated control method for a hybrid active and passive drive flexible lower limb exoskeleton system, which provides hip joint assistance to the wearer, aids lower limb movement, and enhances the stability, safety, and comfort of the wearer's daily lower limb movements. Summary of the Invention

[0007] Purpose of the invention: This invention provides a hybrid active and passive actuation flexible lower limb exoskeleton system and a coordinated control method.

[0008] Technical solution: The active-passive hybrid drive flexible lower limb exoskeleton system and coordinated control method of the present invention include the following steps:

[0009] (1) When the inertial sensor detects that the hip joint angular velocity reaches the threshold, the active and passive hybrid drive flexible lower limb exoskeleton assistance system is activated. The gait recognition algorithm identifies the current motion state of the human hip joint by acquiring the angle and angular velocity information detected by the inertial sensor.

[0010] (2) Under the condition that the hip joint motion state meets the requirements, the assist trajectory required for the current motion state is planned. The assist trajectory planning algorithm plans the assist trajectory required for the current motion stage and outputs the assist size required for the rope drive.

[0011] (3) The force trajectory tracking algorithm tracks the assist trajectory output by the assist trajectory planning algorithm. The assist trajectory planning algorithm outputs force F. d (θ) and the force F measured by the tension sensor c The difference is calculated by the PID controller to determine the output force F. e Output force F e The admittance controller outputs the motor speed V. c The motor rotates and pulls the rope to provide assistance to the human body;

[0012] (4) The condition for stopping the operation is when the hip joint angular velocity is less than a certain set threshold that approaches zero.

[0013] The active-passive hybrid driven flexible lower limb exoskeleton includes an elastic energy storage element, a rope drive system, a flexible binding device, a sensing system, and a control system. The control system includes a gait recognition algorithm, an assist trajectory planning algorithm, and a force trajectory tracking algorithm. The gait recognition algorithm detects the angle and angular velocity information of the hip joint in the sagittal plane using an inertial sensor to detect the human hip joint movement state in real time. The assist trajectory planning algorithm calculates the magnitude of rope drive assistance in real time based on the hip joint movement state. The force trajectory tracking algorithm converts the desired force into motor speed through an admittance controller and corrects it in real time through feedback information from the force sensor to reduce tracking errors and achieve accurate tracking of the desired force trajectory.

[0014] The sensing system includes inertial sensors mounted on the front of the two thighs and a tension sensor mounted on the rope drive device.

[0015] In the above scheme,

[0016] Each sensor provides stable and effective physical signals to the control system and is used for recognizing the human body's motion state.

[0017] The data measured by each sensor is filtered by a low-pass filter to remove outliers and obtain smooth data. The low-pass filter formula is as follows:

[0018] Y(n) = αX(n) + (1-α)Y(n-1),

[0019] In the formula, α is the filter coefficient, X(n) is the current sample value, Y(n-1) is the previous filter output value, and Y(n) is the current filter output value.

[0020] Furthermore, the gait recognition algorithm obtains hip joint angle θ and angular velocity v information through data measured by inertial sensors, and divides the gait cycle using a finite state machine to obtain the current state. The states of the finite state machine are as follows:

[0021] First state: Gait cycle 0 to 30%, in which the rope is driven and assisted, and the force includes hip joint assistance and spring tension.

[0022] Second state: 30% to 50% of the gait cycle, in which the spring stores energy using the gravitational potential energy of the human body.

[0023] The third state: gait cycle 50% to 87%, in which the spring releases energy to assist the person.

[0024] Fourth state: Gait cycle 87% to 100%, in this state the rope is driven and assisted by the hip joint, and the spring uses the weight of the legs to store energy.

[0025] Furthermore, the trajectory planning algorithm is used to design the assist trajectory for each stage of the rope-driven system:

[0026] Phase 1: Gait cycle 0 to 10%, the rope-driven assist level during this phase is:

[0027]

[0028] Phase Two: Gait cycle 10% to 30%, during which the rope-driven assist is:

[0029]

[0030] Phase 3: Gait cycle from 30% to 87%, during this phase the rope remains in a warning state, and the force on the rope is:

[0031] F e ,

[0032] Phase 4: Gait cycle 87% to 100%, the rope-driven assist level during this phase is:

[0033]

[0034] In the formula, It is the expected peak joint assist, F p It is the rope's warning force, F s It represents the spring tension, and θ is the angle between the hip joint and the vertical direction. It is the maximum angle of the hip joint. It is the hip joint angle when peak force is reached.

[0035] Furthermore, based on the gait recognition algorithm, the current assist stage of the rope drive system is obtained, and the expected assist force F of the current rope drive system is calculated using the assist trajectory planning algorithm. d (θ).

[0036] Furthermore, the rope drive system expects an auxiliary force F d (θ) and the force F measured by the tension sensor c The difference is calculated by the PID controller to determine the output force F. e The PID algorithm calculation formula is as follows:

[0037]

[0038] In the formula, F e (t) is the output force of the PID controller, K p It is the proportional gain, T t It is the integration time constant, T D It is the differential time constant, and e(t) is the difference between the expected force and the measured force.

[0039] Furthermore, the PID algorithm calculates the output force and converts it into the required speed V of the motor via an admittance controller. c The admittance control model is as follows:

[0040]

[0041] In the formula, M d It is the coefficient of inertia, B d It is the damping coefficient, K d It is the stiffness coefficient, x r X is the reference position, F is the actual position. e It is contact force.

[0042] The admittance equation in the Laplace domain is defined as follows:

[0043]

[0044] In the formula, Y is the virtual admittance, and V c It is the rope velocity of the feedback term, M v It is virtual inertia, C v It is damping.

[0045] Furthermore, the operation of the control system includes the following steps:

[0046] At the point of maximum hip flexion, the cable-driven system begins to assist, ceasing assistance at 30% of the gait cycle. At the point of maximum hip flexion, the elastic energy storage element begins to store energy, ending energy storage at the point of maximum hip extension and beginning to release elastic potential energy. At the next point of maximum hip flexion, the release of elastic potential energy ends, and energy storage begins again.

[0047] The gait recognition algorithm detects the current hip joint angle θ, and the assist trajectory planning algorithm calculates the magnitude F of the cable-driven assist force corresponding to the current hip joint angle θ. d , to the desired auxiliary force F d The force F on the rope detected by the tension sensor c After the difference is calculated, the force F is output through the PID controller. e (t), the force trajectory tracking algorithm uses an admittance controller to control the output force F of the PID controller. e (t) is converted to the speed V required by the motor. c The motor pulls the rope to output auxiliary force to do work on the human body.

[0048] Furthermore, when the hip joint angle or angular velocity exceeds a set threshold, the active-passive hybrid drive flexible lower limb exoskeleton stops working.

[0049] Furthermore, when the rope drive system does not provide assistance, the rope maintains a low preload, keeping it taut at all times. This allows the rope to remain in close contact with the chute and respond quickly when assistance is provided in the next cycle.

[0050] Furthermore, the aforementioned active-passive hybrid actuation flexible lower limb exoskeleton system includes a battery, a soft outer garment, a rope drive system, a sensing system, an elastic energy storage element, a flexible binding device, a Bowden cable drive device, and a control system.

[0051] The battery is mounted on the chest and provides power to the control system.

[0052] The soft outer garment is worn on the human body, and the battery, rope drive system, and elastic energy storage element are mounted on the soft outer garment. The rope drive system is installed on the back of the human body to provide power to the control system.

[0053] The sensing system includes inertial sensors mounted on the front of both thighs and a tension sensor mounted on the rope drive device.

[0054] The elastic energy storage element is installed on the front of the thigh; it stores energy when it extends and releases energy when it contracts.

[0055] The flexible binding device is installed on the upper and lower sides of the knee joint. Its main function is to fix the connection points of the elastic energy storage element and Bowden wire drive device to the human body, while ensuring that the force is evenly distributed on the human body.

[0056] Bowden's cable drive is installed on the back of the thigh to provide assist force to the hip joint.

[0057] The control system collects human motion information through a sensor system and controls the rope drive system to drive the Bowden cable drive device to provide assistance to the human lower limbs.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The active-passive hybrid drive flexible lower limb exoskeleton system coordination control method provided by the present invention includes a gait recognition algorithm, an assist trajectory planning algorithm, and a force trajectory tracking algorithm. The gait recognition algorithm detects the angle and angular velocity information of the hip joint in the sagittal plane through an inertial sensor to detect the human hip joint movement state in real time. The assist trajectory planning algorithm calculates the magnitude of the rope drive assist in real time based on the hip joint movement state. The force trajectory tracking algorithm converts the desired force into motor speed through an admittance controller. The motor pulls the rope to output force to the human leg. The force sensor feedback information is used for real-time correction to reduce tracking error and achieve accurate tracking of the desired force trajectory.

[0060] 2. The active-passive hybrid drive flexible lower limb exoskeleton system coordination control method provided by the present invention uses a gait recognition algorithm to collect hip joint angle and angular velocity information by a single inertial sensor installed on the front of the thigh, and identifies the hip joint motion state through a finite state machine. The gait recognition algorithm has the characteristics of high precision and high real-time performance. At the same time, single sensor acquisition is more friendly to wearable exoskeletons and has good generalization ability and adaptability. Attached Figure Description

[0061] Figure 1 This is a side view of the wearing effect of the active and passive hybrid driven flexible lower limb exoskeleton according to an embodiment of the present invention;

[0062] Figure 2 This is an overall schematic diagram of the coordinated control method for the active-passive hybrid drive flexible lower limb exoskeleton system provided in this embodiment of the invention;

[0063] Figure 3 This is a flowchart of the coordinated control method for a hybrid active-passive driven flexible lower limb exoskeleton system provided in this embodiment of the invention.

[0064] Figure 4 This is a schematic diagram of the finite state machine used in the gait recognition algorithm provided in this embodiment of the invention;

[0065] Figure 5 This is a schematic diagram of the energy storage and release stages of the elastic energy storage element provided in the embodiment of the present invention;

[0066] Figure 6 This is a schematic diagram of the rope-driven work stage provided in an embodiment of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be further described below.

[0068] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0069] This embodiment provides a coordinated control method for a hybrid active-passive actuation flexible lower limb exoskeleton system, used for controlling such a system.

[0070] like Figure 1 As shown, the active-passive hybrid driven flexible lower limb exoskeleton of the present invention includes a battery 1, a soft outer garment 2, a drive system 3, a sensing system 4, an elastic energy storage element 5, a binding device 6, and a Bowden wire drive device 7. The drive system 3 is driven by a motor and provides a power source for the exoskeleton. The sensing system 4 includes an inertial sensor and a tension sensor. The inertial sensor detects hip joint angle and angular velocity information, and the tension sensor detects tension on the Bowden wire.

[0071] The active-passive hybrid drive flexible lower limb exoskeleton system coordination control method of this embodiment includes a gait recognition algorithm, an assist trajectory planning algorithm, and a force trajectory tracking algorithm. The gait recognition algorithm detects the angle and angular velocity information of the hip joint in the sagittal plane through an inertial sensor to detect the human hip joint movement state in real time. The assist trajectory planning algorithm calculates the magnitude of the rope drive assist in real time based on the hip joint movement state. The force trajectory tracking algorithm converts the desired force into motor speed through an admittance controller. The motor pulls the rope to output force to the human leg. The force sensor feedback information is used for real-time correction to reduce tracking error and achieve accurate tracking of the desired force trajectory.

[0072] The specific implementation method is as follows:

[0073] An inertial sensor acquires the angle and angular velocity information of hip joint motion in the sagittal plane. The acquired data is then filtered using a low-pass filter, the formula of which is as follows:

[0074] Y(n) = aX(n) + (1-α)Y(n-1),

[0075] In the formula, α is the filter coefficient, X(n) is the current sample value, Y(n-1) is the previous filter output value, and Y(n) is the current filter output value.

[0076] Gait recognition algorithms determine the current motion state using filtered angle and angular velocity information. The motion states are classified as follows:

[0077] First state: Gait cycle 0 to 30%, in which the rope is driven and assisted, and the force includes hip joint assistance and spring tension.

[0078] Second state: 30% to 50% of the gait cycle, in which the spring stores energy using the gravitational potential energy of the human body.

[0079] The third state: gait cycle 50% to 87%, in which the spring releases energy to assist the person.

[0080] Fourth state: Gait cycle 87% to 100%, in this state the rope is driven and assisted by the hip joint, and the spring uses the weight of the legs to store energy.

[0081] The rope-driven assist trajectory planning algorithm plans the rope-driven assist trajectory based on the human motion information detected by the gait recognition algorithm. The assist trajectory at each stage is as follows:

[0082] Phase 1: Gait cycle 0 to 10%, the rope-driven assist level during this phase is:

[0083]

[0084] Phase Two: Gait cycle 10% to 30%, during which the rope-driven assist is:

[0085]

[0086] Phase 3: Gait cycle from 30% to 87%, during this phase the rope remains in a warning state, and the force on the rope is:

[0087] F p ,

[0088] Phase 4: Gait cycle 87% to 100%, the rope-driven assist level during this phase is:

[0089]

[0090] In the formula, It is the expected peak joint assist, F p It is the rope's warning force, F s It represents the spring tension, and θ is the angle between the hip joint and the vertical direction. It is the maximum angle of the hip joint. It is the hip joint angle when peak force is reached.

[0091] The force trajectory tracking algorithm tracks the force trajectory output by the assist trajectory planning algorithm, and detects the tension F on the Bowden line using a tension sensor. c The desired auxiliary force F of the rope drive system d (θ) and the force F measured by the tension sensor cThe difference is calculated by the PID controller to determine the output force F. e The PID algorithm calculation formula is as follows:

[0092]

[0093] In the formula, F e (t) is the output force of the PID controller, K p It is the proportional gain, T t It is the integration time constant, T D It is the differential time constant, and e(t) is the difference between the expected force and the measured force.

[0094] The PID algorithm calculates the output force, which is then converted into the required speed V of the motor by the admittance controller. c The admittance control model is as follows:

[0095]

[0096] In the formula, M d It is the coefficient of inertia, B d It is the damping coefficient, K d It is the stiffness coefficient, X r X is the reference position, F is the actual position. e It is contact force.

[0097] The admittance equation in the Laplace domain is defined as follows:

[0098]

[0099] In the formula, Y is the virtual admittance, and V c It is the rope velocity of the feedback term, M v It is virtual inertia, C v It is damping.

[0100] like Figure 3 As shown, the specific workflow of the coordinated control method for the active-passive hybrid actuation flexible lower limb exoskeleton system is as follows:

[0101] The working process of the rope-driven active assist is as follows: When the inertial sensor in the sensing device 4 detects that the hip joint angular velocity has reached a threshold, the active-passive hybrid drive flexible lower limb exoskeleton assist system is activated. The assist trajectory planning algorithm outputs the amount of assistance required for the current movement state of the human body based on the angle and angular velocity information detected by the inertial sensor in the sensing device 4. The force trajectory tracking algorithm controls the Bowden cable drive device 7 to assist the human lower limbs through the rope drive system 3 at 0-30% and 87%-100% of the gait cycle. First, the assist force is converted into the required speed of the motor in the rope drive system 3 by the admittance controller. The speed controller controls the motor speed, and the motor rotation drives the actuator, namely the Bowden cable drive device 7, to output assistance to the human body. The Hall sensor in the motor feeds back the motor speed to the speed controller, which corrects the motor speed in real time. The tension sensor in the sensing device 4 feeds back the tension in the Bowden cable drive device 7, and the PID controller corrects the required assistance of the Bowden cable drive device 7 in real time. When the hip joint angle or angular velocity exceeds the set threshold, the active-passive hybrid drive flexible lower limb exoskeleton stops working.

[0102] The passive assist function of the elastic energy storage element is as follows: The elastic energy storage element 5 stores energy using gravitational potential energy during human movement at 30% to 50% and 87% to 100% of the gait cycle; it stores energy using energy provided by the Bowden wire drive device 7 at 0% to 30% of the gait cycle; and it releases energy to provide passive assistance to the lower limbs at 50% to 87% of the gait cycle. The elastic energy storage element 5 is a passive drive device; its state changes with the movement of the lower limbs. It is at its original length when the hip joint reaches maximum flexion, lengthens during hip extension, and shortens during hip flexion, without requiring control from a control system.

[0103] The coordinated control method of the active and passive hybrid drive flexible lower limb exoskeleton system divides the active and passive assistance phases in the gait cycle using a finite state machine. During the gait cycle from 0 to 30% and from 87% to 100%, the control rope drive system 3 provides assistance to the human lower limbs, while during the gait cycle from 50% to 87%, the elastic energy storage element 5 provides assistance to the human lower limbs.

[0104] The above are merely preferred embodiments of the present invention and do not constitute any limitation on the present invention. Any equivalent substitutions or modifications made by those skilled in the art to the technical solutions and content disclosed in the present invention without departing from the scope of the present invention shall be deemed to have remained within the protection scope of the present invention.

Claims

1. A coordinated control method for a hybrid active-passive actuation flexible lower limb exoskeleton system, characterized in that, Includes the following steps: (1) When the inertial sensor detects that the hip joint angular velocity reaches the threshold, the active and passive hybrid drive flexible lower limb exoskeleton assistance system is activated. The gait recognition algorithm identifies the current motion state of the human hip joint by acquiring the angle and angular velocity information detected by the inertial sensor. (2) Under the condition that the hip joint motion state meets the requirements, the assist trajectory required for the current motion state is planned. The assist trajectory planning algorithm plans the assist trajectory required for the current motion stage and outputs the assist size required for the rope drive. (3) The force trajectory tracking algorithm tracks the assist trajectory output by the assist trajectory planning algorithm. The assist trajectory planning algorithm outputs the force. Force measured by a tension sensor The difference is calculated by the PID controller to determine the output force. Output force The motor speed is output through the admittance controller. The motor rotates and pulls the rope to provide assistance to the human body; (4) The condition for stopping operation is when the hip joint angular velocity is less than a certain set threshold approaching zero. The gait recognition algorithm divides the stages of rope-driven assistance, spring energy storage, and spring energy release into a finite state machine. The states of the finite state machine in the gait recognition algorithm are as follows: First state: Gait cycle 0 to 30%, in this state the rope is driven and the force includes hip joint assistance and spring tension force; Second state: Gait cycle 30% to 50%, in which the spring uses the gravitational potential energy of the human body to store energy; Third state: Gait cycle 50% to 87%, in which the spring releases energy to assist the person; Fourth state: Gait cycle 87% to 100%, in this state the rope is driven and assisted by the hip joint, and the spring uses the weight of the legs to store energy.

2. The coordinated control method for the active-passive hybrid actuation flexible lower limb exoskeleton system according to claim 1, characterized in that, The assist trajectory planning algorithm uses a piecewise function to design the rope drive assist magnitude, and the functions for each stage are as follows: Phase 1: Gait cycle 0 to 10%, the rope-driven assist level during this phase is: ， Phase Two: Gait cycle 10% to 30%, during which the rope-driven assist is: ， Phase 3: Gait cycle 30% to 87%, during this phase the rope remains in a warning state, and the force on the rope is: ， Phase 4: Gait cycle 87% to 100%, the rope-driven assist level during this phase is: ， In the formula, It is the expected peak joint assist. It is the rope's early warning capability. It is the spring tension. It is the angle between the hip joint and the vertical direction. It is the maximum angle of the hip joint. It is the hip joint angle when peak force is reached.

3. The coordinated control method for the active-passive hybrid actuation flexible lower limb exoskeleton system according to claim 2, characterized in that, The force trajectory tracking algorithm employs an admittance control strategy to achieve human-machine force interaction control. The admittance model of the active-passive hybrid driven flexible lower limb exoskeleton in the joint space during the gait cycle is as follows: ， In the formula, It is the coefficient of inertia. It is the damping coefficient. It is the stiffness coefficient. This is a reference position. It is the actual location. It is contact force. The admittance equation in the Laplace domain is defined as follows: ， In the formula, It is virtual admittance. It is the rope speed of the feedback item. It is virtual inertia. is the damping, and s is the complex frequency variable in the Laplace transform.

4. The coordinated control method for the active-passive hybrid actuation flexible lower limb exoskeleton system according to claim 3, characterized in that, The PID algorithm is used to quickly and accurately achieve the desired joint assist peak value as described in claim 2. The PID algorithm formula is as follows: ， In the formula, It is the output force of the PID controller. It is proportional gain. It is the integration time constant. It is the differential time constant. It is the difference between the expected force and the measured force.

5. The coordinated control method for the active-passive hybrid actuation flexible lower limb exoskeleton system according to claim 4, characterized in that, The angle and angular velocity detected by the inertial sensor are filtered using a low-pass filter algorithm. The calculation formula for the low-pass filter algorithm is as follows: ， In the formula, These are the filter coefficients. This is the value of this sample. This is the output value from the previous filter. This is the output value of this filter.

6. The system used to implement the method as described in any one of claims 1-5, characterized in that, Includes a battery (1), a soft outer garment (2), a rope drive system (3), a sensing system (4), an elastic energy storage element (5), a flexible binding device (6), a Bowden cable drive device (7), and a control system. The battery (1) provides energy to the control system; The soft outer garment (2) is used to install the battery (1), the rope drive system (3), and the elastic energy storage element (5); The rope drive system (3) is installed on the back of the human body to provide power to the control system; The sensing system (4) includes an inertial sensor installed on the front of the two thighs and a tension sensor installed on the rope drive system (3); The elastic energy storage element (5) is installed on the front of the thigh, which stores energy when it extends and releases energy when it contracts. The flexible binding device (6) is installed on the upper and lower sides of the knee joint. Its main function is to fix the connection point between the elastic energy storage element (5) and the Bowden wire drive device (7) and the human body, while making the human body evenly stressed. The Bowden line drive device (7) is installed on the back of the thigh to provide auxiliary force to the hip joint; The control system collects human motion information through the sensor system (4) and controls the rope drive system (3) to drive the Bowden line drive device (7) to provide assistance to the lower limbs of the human body.

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