Anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering

Abstract

The invention discloses an anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering, and the method comprises the following steps: S1, building a man-machine exoskeleton coupling model based on OpenSim simulation software, and obtaining kinematics data; s2, Kalman filtering processing is carried out on kinematics data of the man-machine exoskeleton coupling model; s3, constructing a training set, and performing Gaussian process regression fitting on residual dynamic items of the exoskeleton robot; s4, establishing and solving a human knee joint moment kinetic equation; according to the method, the problem of human joint torque estimation is effectively solved, the influence of noise, interference and the like is reduced, the cost is reduced, lightweight and portability of an exoskeleton technology are facilitated, and the use comfort of a user is improved.

Description

Technical Field

[0001] The present invention belongs to the field of exoskeleton robots, and more specifically, relates to an anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering. Background Art

[0002] An exoskeleton robot is a wearable device for human-machine interaction based on multiple technologies such as sensing, control, and mechatronics. It is widely used in medical rehabilitation, walking assistance, and earthquake relief. However, in the process of human-machine coupled interactive movement, both the exoskeleton's walking assistance control system and the compliance assistance control that affects the user experience need to know the human body's movement intention. Currently, the most popular way to estimate the human body's movement intention is to estimate the human body's joint torque by combining inverse dynamics with a multi-dimensional torque sensor, and then replace the human body's movement intention with the human body's joint torque.

[0003] However, the high cost of multi-dimensional torque sensors, the complex installation and calibration process, and the requirement for the sensor to fit the human body have limited its popularity in practical applications. At the same time, the use of multi-dimensional torque sensors may affect the structural design of the equipment and increase the weight and energy consumption of the exoskeleton system, which is not conducive to the development of lightweight and low-cost exoskeleton equipment. In addition, as one of the main joints that bear the weight of the human body and the impact of movement, the knee joint faces more challenges in torque estimation. When walking or exercising, the knee joint torque is affected by many factors, such as inertia, gravity, and ground reaction force, and the actual measured kinematic data such as joint angles are often accompanied by noise, which further increases the uncertainty of dynamic calculations.

[0004] In view of this, the present invention is proposed, which combines dynamic modeling and data-driven methods to effectively reduce the impact of noise on torque estimation, compensate for the residual dynamic terms, and improve the estimation accuracy of knee joint torque. Summary of the invention

[0005] The purpose of the present invention is to provide an anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering, which filters the noise of the measured kinematic data through Kalman filtering without using a force sensor, and compensates the residual dynamics term of the exoskeleton through offline training Gaussian process regression, thereby providing an efficient and low-cost solution for accurate estimation of the knee joint torque.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering includes the following steps: S1, build a human-machine exoskeleton coupling model based on OpenSim simulation software to obtain kinematic data; S2, Kalman filtering is performed on the kinematic data of the human-machine-exoskeleton coupling model; S3, constructing a training set and performing Gaussian process regression fitting on the residual dynamics term of the exoskeleton robot; S4, establish and solve the torque dynamic equation of the human knee joint.

[0007] Preferably, step S1 comprises: S1.1, Construction of human-machine exoskeleton coupling model: First, use OpenSim simulation software to build a human lower limb model. The single-leg model includes the thigh, calf, foot contact geometry, hip joint, knee joint and ankle joint, with a total of nine degrees of freedom; then design the exoskeleton model and add a fixed connection between the exoskeleton model and the human lower limb model, thereby completing the construction of the human-machine exoskeleton coupling model; S1.2, simulate the human motion process: load gait data and ground reaction force data into OpenSim simulation software, and jointly drive the human-machine exoskeleton coupling model for motion simulation; and run the inverse kinematics tool to obtain the kinematic data of the hip and knee joints, including the joint angles , angular velocity and angular acceleration ; S1.3, kinematic data storage and export: Save the kinematic data output by the simulation as data input for subsequent steps.

[0008] Preferably, step S2 comprises: S2.1, establish the state transfer equation and observation equation, and discretize to obtain the discrete model: take the angles and angular velocities of the hip and knee joints of the lower limb exoskeleton human-machine coupling system as state variables, and the joint angular acceleration as input, and establish the following discretized state equation:

[0009] in, , is the hip joint angle, is the hip joint angular velocity, is the knee joint angle, is the knee joint angular velocity; , is the hip joint angular acceleration, is the knee joint angular acceleration; It is a discrete moment; is the sampling time; Observable quantities of lower limb exoskeleton coupling system For the angles and angular velocities of the hip and knee joints, the observation equations are established as follows:

[0010] S2.2, based on the state equation and observation equation, Kalman filtering is used to estimate the joint angle and angular velocity of the hip and knee joints in real time: by adjusting the process noise covariance and the measurement noise covariance , optimize the filtering effect, and the joint angles and angular velocities obtained after filtering are used as inputs for subsequent steps.

[0011] Preferably, step S3 comprises: S3.1, construct training set: knee joint angle , angular velocity , angular acceleration As input, the residual dynamics term As output; the residual dynamics term includes friction, noise, and model uncertainty terms; among them, the residual dynamics term It can be calculated by the following formula:

[0012] in, is the inertia matrix of the exoskeleton, is the Coriolis centrifugal force matrix, is the gravity matrix, is the joint rotation motor torque (which can be calculated from the current value), is the ground reaction force term; S3.2, Gaussian process regression model training: Select the square exponential kernel function:

[0013] The kinematic data after Kalman filtering in step S2 is input into the training Gaussian process regression model; S3.3, Residual Dynamics Prediction: Use the trained Gaussian process regression model to predict the unmodeled residual dynamics term ; At each moment, combined with kinematic data joint angle , angular velocity and angular acceleration The corresponding residual dynamics term is obtained to achieve anti-interference effect.

[0014] Preferably, in step S4, the human knee joint torque dynamics equation is:

[0015] in, is the inertia matrix of the human-machine-exoskeleton coupling model, is the Coriolis centrifugal force matrix, is the gravity matrix, is the joint rotation motor torque, is the ground reaction force term, is the predicted residual kinetic term, is the final estimated human knee joint torque.

[0016] 1. Use OpenSim software to build a human-machine exoskeleton coupling model. The simulation data can effectively replace the actual collected motion data, avoid complex experimental equipment and processes, and improve experimental efficiency; 2. By introducing Kalman filtering, the noise of measurement data is effectively reduced; 3. The residual dynamics term is fitted through Gaussian process regression, which effectively resists interference and improves the accuracy of human joint torque estimation; 4. A dynamic estimation model of human joint torque was constructed, providing core algorithm support for the optimization of control strategies for high-precision torque feedback exoskeleton robot systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of the method of the present invention is shown.

[0018] Figure 2 A diagram of the human-machine exoskeleton coupling model built using OpenSim software is shown.

[0019] Figure 3 A comparison diagram of the human knee joint torque estimated by the method of the present invention and the true value and measured value is shown. DETAILED DESCRIPTION

[0020] In order to explain the present invention more clearly, the present invention is further described below in conjunction with preferred embodiments and drawings. It should be understood by those skilled in the art that the content described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0021] The anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering provided in this embodiment is used for human joint torque estimation. Figure 1 As shown, the following steps are included: S1, build a human-machine exoskeleton coupling model based on OpenSim simulation software to obtain kinematic data; S2, Kalman filtering is performed on the kinematic data of the human-machine-exoskeleton coupling model; S3, constructing a training set and performing Gaussian process regression fitting on the residual dynamics term of the exoskeleton robot; S4, establish and solve the torque dynamic equation of the human knee joint.

[0022] Specifically, step S1 includes: S1.1, Construction of human-machine exoskeleton coupling model: First, use OpenSim software to build a human lower limb model. The single-leg model mainly includes the thigh, calf, foot contact geometry, hip joint, knee joint and ankle joint, with a total of nine degrees of freedom, which can simulate normal walking, running or rehabilitation training of the human body; further design the exoskeleton model, and add a fixed connection between the exoskeleton model and the human lower limb model to complete the construction of the human-machine exoskeleton coupling model. See the model construction diagram for details. Figure 2 ; S1.2, simulate the human motion process: load gait data (such as .trc files) and ground reaction forces (such as .xml files and .mot files) in the OpenSim simulation software, and jointly drive the model for motion simulation; and run the Inverse Kinematics tool (IK) to obtain the kinematic data of the hip and knee joints, including the joint angles , angular velocity and angular acceleration ; S1.3, kinematic data storage and export: Save the kinematic data output by the simulation (such as .sto files or .mot files) as data input for subsequent steps; simulation data can effectively replace the actual collected motion data, avoiding complex experimental equipment and processes.

[0023] Specifically, step S2 includes: S2.1, establish the state transfer equation and observation equation, and discretize to obtain the discrete model: take the angles and angular velocities of the hip and knee joints of the lower limb exoskeleton human-machine coupling system as state variables, and the joint angular acceleration as input, and establish the following discretized state equation:

[0024] in, , is the hip joint angle, is the hip joint angular velocity, is the knee joint angle, is the knee joint angular velocity; , is the hip joint angular acceleration, is the knee joint angular acceleration; It is a discrete moment; is the sampling time; Observable quantities of lower limb exoskeleton coupling system For the angles and angular velocities of the hip and knee joints, the observation equations are established as follows:

[0025] S2.2, based on the state equation and observation equation, Kalman filtering is used to estimate the joint angle and angular velocity of the hip and knee joints in real time: by adjusting the process noise covariance and the measurement noise covariance , optimize the filtering effect, and the joint angles and angular velocities obtained after filtering are used as inputs for subsequent steps.

[0026] Specifically, step S3 includes: S3.1, construct training set: Since the joint torque of the human body cannot be measured directly, the kinematic data of the exoskeleton when it is unloaded (ensuring that the gait motion trajectory is the same as that when loaded) is selected for training, and the kinematic data is required to cover the entire gait cycle; the knee joint angle , angular velocity , angular acceleration As input, the residual dynamics term As output; the residual dynamics term includes friction, noise, and model uncertainty terms; among them, the residual dynamics term It can be calculated by the following formula:

[0027] in, is the inertia matrix of the exoskeleton, is the Coriolis centrifugal force matrix, is the gravity matrix, is the joint rotation motor torque (which can be calculated from the current value), is the ground reaction force term; the above information can be measured, so the residual dynamics term can be measured; S3.2, Gaussian process regression model training: Select the square exponential kernel function:

[0028] The kinematic data after Kalman filtering in step S2 is input into the training Gaussian process regression model; S3.3, Residual Dynamics Prediction: Use the trained Gaussian process regression model to predict the unmodeled residual dynamics term ; At each moment, combined with kinematic data joint angle , angular velocity and angular acceleration The corresponding residual dynamics term is obtained to achieve anti-interference effect.

[0029] Specifically, in step S4, a single-leg human-machine exoskeleton coupling dynamics model is established, and the Lagrangian method is used for dynamics modeling, and the torque dynamics equation of the human knee joint can be obtained as follows:

[0030] in, is the inertia matrix of the human-machine-exoskeleton coupling model, is the Coriolis centrifugal force matrix, is the gravity matrix, is the joint rotation motor torque, is the ground reaction force term, is the predicted residual kinetic term, This is the final estimated human knee joint torque.

[0031] Next, in order to verify the effectiveness of the anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering provided in this embodiment, a simulation experiment is carried out using MATLAB and a detailed description is given.

[0032] The single-leg human-machine exoskeleton model provided in this embodiment comprehensively considers the influence of noise, friction, model uncertainty, etc., and estimates the human knee joint torque more accurately without using a torque sensor, as follows: In the simulation experiment, the thigh mass , calf quality , thigh length , calf length , the distance from the center of mass of the thigh to the hip joint , the distance from the center of mass of the calf to the knee joint , thigh moment of inertia , calf moment of inertia , the acceleration due to gravity , process noise covariance , measurement noise covariance .

[0033] Based on the above parameters, a simulation experiment was conducted to compare the human knee joint torque estimated by the method of the present invention with the true value and the measured value. Figure 3 It can be seen that the method of the present invention has good estimation effect and significant filtering effect.

[0034] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. An anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering, characterized in that: The steps include: S1, build a human-machine exoskeleton coupling model based on OpenSim simulation software to obtain kinematic data; S2, Kalman filtering is performed on the kinematic data of the human-machine-exoskeleton coupling model; S3, constructing a training set and performing Gaussian process regression fitting on the residual dynamics term of the exoskeleton robot; S4, establish and solve the torque dynamic equation of the human knee joint.

2. The anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering according to claim 1 is characterized in that: Step S1 includes: S1.1, Construction of human-machine exoskeleton coupling model: First, use OpenSim simulation software to build a human lower limb model. The single-leg model includes the thigh, calf, foot contact geometry, hip joint, knee joint and ankle joint, with a total of nine degrees of freedom; then design the exoskeleton model and add a fixed connection between the exoskeleton model and the human lower limb model, thereby completing the construction of the human-machine exoskeleton coupling model; S1.2, simulate the human motion process: load gait data and ground reaction force data into OpenSim simulation software, and jointly drive the human-machine exoskeleton coupling model for motion simulation; and run the inverse kinematics tool to obtain the kinematic data of the hip and knee joints, including the joint angles , angular velocity and angular acceleration ; S1.3, kinematic data storage and export: Save the kinematic data output by the simulation as data input for subsequent steps.

3. The anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering according to claim 2 is characterized in that: Step S2 includes: S2.1, establish the state transfer equation and observation equation, and discretize to obtain the discrete model: take the angles and angular velocities of the hip and knee joints of the human-machine exoskeleton coupling model as state variables, and the joint angular acceleration as input, and establish the following discretized state equation: , in, , is the hip joint angle, is the hip joint angular velocity, is the knee joint angle, is the knee joint angular velocity; , is the hip joint angular acceleration, is the knee joint angular acceleration; It is a discrete moment; is the sampling time; Observable quantities of lower limb exoskeleton coupling system For the angles and angular velocities of the hip and knee joints, the observation equations are established as follows: , S2.2, based on the state equation and observation equation, Kalman filtering is used to estimate the joint angle and angular velocity of the hip and knee joints in real time: by adjusting the process noise covariance and the measurement noise covariance , optimize the filtering effect, and the joint angles and angular velocities obtained after filtering are used as inputs for subsequent steps.

4. The anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering according to claim 3 is characterized in that: Step S3 includes: S3.1, construct training set: knee joint angle , angular velocity , angular acceleration As input, the residual dynamics term As output; the residual dynamics term includes friction term, measurement noise term and model uncertainty term; among them, the residual dynamics term It is calculated by the following formula: , in, is the inertia matrix of the exoskeleton, is the Coriolis centrifugal force matrix, is the gravity matrix, is the joint rotation motor torque, is the ground reaction force term; S3.2, Gaussian process regression model training: Select the square exponential kernel function: , The kinematic data after Kalman filtering in step S2 is input into the training Gaussian process regression model; S3.3, Residual Dynamics Prediction: Use the trained Gaussian process regression model to predict the unmodeled residual dynamics term ; At each moment, combined with kinematic data joint angle , angular velocity and angular acceleration The corresponding residual dynamics term is obtained to achieve anti-interference effect.

5. The anti-interference exoskeleton human knee joint torque estimation method based on Kalman filtering according to claim 4 is characterized in that: In step S4, the human knee joint torque dynamics equation is: , in, is the inertia matrix of the human-machine-exoskeleton coupling model, is the Coriolis centrifugal force matrix, is the gravity matrix, is the joint rotation motor torque, is the ground reaction force term, is the predicted residual kinetic term, is the final estimated human knee joint torque.

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