A method, medium, and apparatus for dynamically estimating a position of a center of rotation of a knee joint

Abstract

The application discloses a method, medium and device for dynamically estimating the position of the rotation center of a knee joint, and belongs to the technical field of motion analysis. The method comprises the following steps: collecting acceleration information and angular velocity information during joint motion by using a first inertial sensor and a second inertial sensor, the first inertial sensor and the second inertial sensor being arranged on the two sides of the joint at a set distance; taking the distance between the first inertial sensor and the second inertial sensor as a constraint condition, incorporating the constraint condition into an extended Kalman filter observation model for state estimation, and correcting the estimation error by using a Kalman gain, so as to obtain an estimation error covariance matrix of the first inertial sensor and the second inertial sensor; calculating an adaptive weight coefficient based on the estimation error covariance matrix, and fusing the estimation results of the first inertial sensor and the second inertial sensor according to the weight coefficient, so as to obtain an estimated knee joint center position. The application significantly improves the precision and stability of the estimation of the joint rotation center position.

Description

Technical Field

[0001] The present invention relates to the technical field of kinematic analysis, and more particularly to a method, medium and device for dynamically estimating the position of the rotation center of a knee joint. Background Art

[0002] Soft tissue artifact (STA) is a core issue hindering the accuracy of knee kinematic measurements. This is primarily due to the nonrigid coupling between skin markers or inertial sensors and the bone, leading to a deviation between the true and measured bone positions during dynamic motion. Studies comparing synchrotron X-rays with optical markers have shown that STA-induced errors in knee flexion angle during rapid motion can reach 8°-12° (particularly during sagittal plane motion), with the error increasing nonlinearly with motion speed. In related research, Sangeetha et al. found that STA can cause significant displacement of the knee joint center in various directions during knee flexion. During knee flexion, STA displacement can reach 13.2 mm for the lateral femoral condyle and 8.6 mm for the medial tibial condyle. This results in significant errors between the skin-marker-based knee center estimate and the actual bone center, with an average error of 9.4 mm and a maximum error of 15.6 mm, severely impacting the accuracy of knee center estimation.

[0003] Existing knee kinematic measurement solutions primarily include optical motion capture systems, inertial measurement unit (IMU) methods, and self-calibration techniques, all of which have significant limitations. Optical motion capture systems estimate joint motion by tracking skin landmarks with multiple cameras. However, soft tissue slip errors (e.g., knee joint angle errors of up to 8°-12%) and high equipment costs hinder real-time monitoring and widespread application. Inertial sensor methods rely on accelerometer and gyroscope data to calculate joint angles, but ignore dynamic offsets caused by skin slip, resulting in errors that increase significantly with increasing exercise intensity (e.g., adduction angle errors exceeding 7% during running). Self-calibration techniques optimize joint parameters through preset movements or algorithms, but dynamic calibration requires strict user cooperation, while static calibration ignores the complexity of dynamic motion and can significantly amplify errors during complex movements (e.g., errors as high as 16% when ascending or descending stairs). The closest existing technology is IMU-based joint axis optimization methods (e.g., magnetometer-assisted calibration). However, these rely on fixed biomechanical models and cannot adapt to nonlinear errors caused by STA in real time, further demonstrating the difficulty of traditional linear models in tackling multi-degree-of-freedom coupling.

[0004] In the prior art, patent application 202111203131.6 discloses a method for determining human joint centers based on wearable sensors. This solution involves attaching multiple inertial measurement units (IMUs) to the joints of the waist, thigh, and calf to collect three-axis angular velocity and acceleration values. This data is then processed using a data fusion algorithm, and the loss function is optimized using an optimal method to accurately determine the joint center position. Although this solution attempts to address the low-frequency and high-frequency noise issues encountered in the process of solving the angle by integrating angular velocity and acceleration through complementary filtering techniques, this method relies on fixed filter coefficients and is not applicable to all situations or individual differences, resulting in limited solution accuracy in some cases. Furthermore, this solution is primarily targeted at conventional movements such as walking, and its applicability to complex movements such as fast running and jumping is unclear. Patent application 202010117861.3 proposes a method for collecting human motion using inertial sensors that does not require standard corrective posture. The method includes: installing the IMU and establishing physiological kinematic constraints for the joints; when the number of IMU sampling points exceeds 100, running an optimization program based on the constraint equations and using the Gauss-Newton method to estimate the joint axes and joint position vectors; using the solved joint axes and joint position vectors, combined with acceleration information and angular velocity integrals, to calculate two sets of joint angles; and finally solving the joint angles by taking a weighted average of the two sets of joint angles through complementary filtering. However, this solution has specific requirements for the user population and cannot accurately obtain joint angle information for people with limited mobility or irregular limb movements. Furthermore, the complexity of multi-sensor deployment and algorithms not only increases the manufacturing and maintenance costs of the equipment, but also increases the requirements for computing resources, limiting the popularization and application of this technology.

[0005] After analysis, the existing technology mainly has the following defects:

[0006] 1) While optical motion capture systems can accurately capture knee joint angles, they require specific laboratory environments. The equipment is expensive and complex to install, and the markers are susceptible to occlusion and interference, leading to data loss. The system also has high requirements for ambient conditions, requiring certain lighting and spatial conditions, otherwise the motion capture effect will be affected.

[0007] 2) Although the method based on electromyographic data fusion can estimate the knee joint angle in real time, the electromyographic signal is easily interfered by noise, and the feature extraction process is relatively complicated. In addition, this method requires wearing multiple electrodes, which is cumbersome and has a certain impact on the user's comfort.

[0008] 3) Model-based approaches require a mathematical model of the knee joint. The model creation and solution process requires high computing resources and makes real-time estimation difficult. Furthermore, model accuracy depends on the quality of the input data and the accuracy of the model parameters. Incorrect data or inaccurate parameters can lead to biased estimation results. Furthermore, the model's limited generalization makes it difficult to apply to diverse individuals and complex motion situations. Summary of the Invention

[0009] The purpose of the present invention is to overcome the above-mentioned defects of the prior art and provide a method, medium and device for dynamically estimating the position of the rotation center of the knee joint.

[0010] According to a first aspect of the present invention, a method for dynamically estimating the position of the rotation center of a knee joint is provided. The method comprises the following steps:

[0011] Using a first inertial sensor and a second inertial sensor to collect acceleration information and angular velocity information during joint movement, wherein the first inertial sensor and the second inertial sensor are arranged on both sides of the joint at a set distance;

[0012] The distance between the first inertial sensor and the second inertial sensor is incorporated into an extended Kalman filter observation model as a constraint condition for state estimation, and the estimation error is corrected using the Kalman gain to obtain an estimation error covariance matrix of the first inertial sensor and the second inertial sensor, wherein the state vector of the extended Kalman filter observation model is represented by a position vector of the joint center, a velocity vector of the joint center, an acceleration vector of the joint center, an angular velocity vector of the joint, and an angular acceleration vector of the joint;

[0013] Based on the estimation error covariance matrix, an adaptive weight coefficient is calculated, and the estimation results of the first inertial sensor and the second inertial sensor are fused according to the weight coefficient to obtain an estimated knee joint center position.

[0014] According to a second aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the method for dynamically estimating the rotation center position of the knee joint are implemented.

[0015] According to a third aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein a computer program that can be run on the processor is stored on the memory, wherein when the processor executes the computer program, the steps of the provided method for dynamically estimating the position of the rotation center of the knee joint are implemented.

[0016] Compared with the existing technology, the advantage of the present invention is that, in order to solve the problem of unstable and inaccurate measurements caused by the accumulation of soft tissue artifacts and kinematic parameter errors in existing knee joint center estimation technologies, the present invention can effectively reduce the impact of soft tissue artifacts on the joint center position by improving the extended Kalman filter algorithm, combining fixed geometric constraints and adaptive weight coefficients, thereby improving the stability and accuracy of joint center estimation and the accuracy and stability of knee joint angle estimation. Traditional methods have difficulty in real-time calibrating joint center positions in dynamic motion scenarios and have insufficient ability to handle nonlinear errors. The present invention improves the accuracy, robustness and adaptability of knee joint rotation center estimation and knee joint angle estimation, and also has good adaptability to joint center position estimation in non-fixed joints and complex motion scenarios.

[0017] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 is a flow chart of a method for dynamically estimating the position of the rotation center of the knee joint according to one embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of the overall process of a method for dynamically estimating the position of the rotation center of a knee joint according to one embodiment of the present invention;

[0021] Figure 3 FIG. 4 is a schematic diagram of knee joint angle estimation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0023] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0024] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0025] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0026] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0027] This paper focuses on the measurement accuracy and stability issues of knee joint center estimation caused by soft tissue artifacts. It innovatively proposes a dynamic estimation method that integrates dual inertial measurement units (IMUs, or inertial sensors) with an improved extended Kalman filter (EKF) algorithm. By introducing fixed geometric constraints between the dual IMUs and adaptive weighted data fusion technology, the impact of soft tissue artifacts on the estimation of the knee center of rotation (COR) position is effectively reduced, significantly improving the accuracy and reliability of joint center position estimation in dynamic motion scenarios. As the key reference point for knee angle calculation, its accurate estimation provides a reliable reference coordinate system for knee angle calculation, laying the foundation for in-depth research on the kinematic and dynamic characteristics of the knee joint. This method has important clinical applications in the diagnosis of related diseases, rehabilitation monitoring, and motion analysis.

[0028] Combine Figure 1 and Figure 2 As shown, the provided method for dynamically estimating the position of the knee joint rotation center includes the following steps:

[0029] Step S110 : using two inertial sensors to collect acceleration information and angular velocity information of the knee joint during movement.

[0030] For example, the acceleration information and angular velocity information of the knee joint during movement are collected using a first inertial sensor and a second inertial sensor, wherein the first inertial sensor and the second inertial sensor are arranged on both sides of the knee joint at a set distance.

[0031] In one embodiment, two MPU9250 inertial sensors (including accelerometers and gyroscopes) are used, one on each side of the knee joint, to synchronously collect three-axis acceleration and angular velocity data at a sampling rate of 100 Hz or higher to ensure time consistency. The high-frequency sampling of the MPU9250 (e.g., 200 Hz) can capture movement details. By installing the inertial sensors on each side of the knee joint, acceleration and angular velocity information during knee joint movement can be captured. The sensors should be installed as close to the skin as possible to reduce the impact of soft tissue artifacts, and the dual IMUs synchronously collect three-axis acceleration and angular velocity signals at a high sampling rate (typically 100 Hz or higher) to ensure time consistency of the data.

[0032] In step S120 , the distance constraint between the two inertial sensors is incorporated into the extended Kalman filter observation model for state estimation, and the estimation error is corrected using the Kalman gain to obtain the estimation error covariance matrix of the two inertial sensors.

[0033] In this step, the error covariance matrix of the two inertial sensors is obtained through state modeling and the improved extended Kalman filter (EKF) algorithm. The overall process includes: initializing the state vector and covariance matrix, predicting the state at the next moment based on the state transfer equation, integrating the geometric constraints between the dual IMUs, using the Kalman gain to correct the state estimation, adaptive weight data fusion, and updating the fusion result by correcting the data error.

[0034] For example, the state vector includes the position, velocity, linear acceleration, angular velocity, and angular acceleration of the joint center. The kinematic relationship is described by the state transfer equation, assuming that the acceleration and angular acceleration are constant in the short term.

[0035] In order to enhance the geometric consistency of position estimation, geometric constraints are integrated and the dual IMU fixed distance constraint is incorporated into the EKF observation model.

[0036] The covariance adaptive update is to adjust the state covariance matrix by the prediction and update steps, combining the process noise ( ) and observation noise ( ) optimizes the Kalman gain to correct the estimation error.

[0037] In the following, the state vector, state transfer equation, observation model, geometric constraint integration, state prediction process and update process of the improved extended Kalman filter (EKF) observation model will be introduced in detail.

[0038] 1. Define variables

[0039] First, define the state vector and related variables, where the state vector is expressed as:

[0040] (1)

[0041] in, is the position vector of the joint center, which indicates the position of the joint center in three-dimensional space. is the velocity vector of the joint center, which represents the linear velocity of the joint center. is the acceleration vector of the joint center, which represents the linear acceleration of the joint center. is the angular velocity vector of the joint, which represents the angular velocity of the joint rotation. is the angular acceleration vector of the joint, which represents the angular acceleration of the joint rotation, k Represents the time step.

[0042] 2. State transfer equation

[0043] The state transition equation is used to describe the state vector at time step k arrive k A change of +1, expressed as:

[0044] (2)

[0045] These state transition equations describe how the state vector changes from k Evolving to k +1 moment. Specifically, Indicates the next moment k +1 Position vector of the joint center. Indicates the current time k The position vector of the joint center. Indicates the current time k The linear velocity vector of the joint center. Indicates the current time k Linear acceleration vector of the joint center. express k Moment and k +1 The time interval between moments. Indicates the next moment k +1 Linear velocity vector of the joint center. Indicates the next moment k +1 Linear acceleration vector of the joint center (assumed to remain constant for a short period of time). Indicates the next moment k +1 The angular velocity vector of the joint. Indicates the current time k The angular velocity vector of the joint. Indicates the current time k The angular acceleration vector of the joint. Indicates the next moment k +1 The angular acceleration vector of the joint (assumed to remain constant over a short period of time).

[0046] The state transition equations are used to: Position update: The position of the joint center at the next time step is determined by the current position, velocity, and acceleration. Velocity update: The velocity of the joint center is determined by the current acceleration. Acceleration preservation: The acceleration is assumed to remain constant over a short period of time. Angular velocity update: The angular velocity of the joint is determined by the current angular acceleration. Angular acceleration preservation: The angular acceleration is assumed to remain constant over a short period of time.

[0047] 3. Observation model

[0048] Linear acceleration of each IMU It can be expressed as:

[0049] (3)

[0050] in, i Used to identify the IMU, such as i =1 means the first IMU, i =2 indicates the second IMU. is the linear acceleration of the joint center, is the angular acceleration, is the angular velocity, is the vector from the IMU to the joint center. It is the linear acceleration caused by angular acceleration. When the rotation speed of the joint changes, the IMU will be affected by this acceleration. is the centrifugal acceleration. This is the acceleration caused by the rotational motion itself, and its direction points to the vertical direction of the rotation axis.

[0051] 4. Geometric Constraint Integration

[0052] Fixed distance between dual IMUs d It can be added as a constraint to the Extended Kalman Filter (EKF):

[0053] (4)

[0054] Expressed as:

[0055] (5)

[0056] in, express k The position vector of the first IMU relative to the joint center at this moment, express k The position vector of the second IMU relative to the joint center at this moment, d is the distance between the two IMUs, and || || represents the Euclidean norm (the modulus of the vector). By fixing the distance constraint, we can ensure that the distance between the two IMUs remains unchanged, providing additional geometric constraints.

[0057] 5. Prediction Process

[0058] The prediction process uses the state transition equation to predict the state at the next moment and updates the uncertainty (covariance) of the state estimate. The prediction process includes state prediction and covariance prediction.

[0059] State prediction is based on the current state and state transition equation to predict the state at the next moment, which is expressed as:

[0060] (6)

[0061] in, Indicates k The predicted state vector at time t is based on k −1 time information, Indicates the last moment k A state vector of −1, f is the nonlinear state transfer function, It's the last moment k −1 control input.

[0062] The covariance matrix P represents the uncertainty of the state estimate. The formula for the predicted covariance matrix is:

[0063] (7)

[0064] in, represents the covariance matrix of the predicted state error, express k The state error covariance matrix at time −1, express k The state transition matrix at time ( f about x Jacobian matrix), express k The noise covariance matrix at time t. is the state transfer matrix, which is the Jacobian matrix of the state transfer equation to the state vector and is used to linearize the nonlinear state transfer model. is the process noise covariance matrix, which reflects the impact of process noise on state estimation.

[0065] 6. Update process

[0066] The updating process uses the actual observations to correct the predicted state and update the covariance matrix to improve the estimation accuracy.

[0067] 1) Residual calculation

[0068] residuals is the difference between the observed and predicted values ​​and is used to measure the accuracy of the forecast:

[0069] (8)

[0070] in, yes k The residual at time (the difference between the actual observation and the predicted observation), represents the actual measurement value (observation value) at time k, h (.) represents a nonlinear observation function.

[0071] 2) Covariance Update

[0072] Update the covariance matrix to reflect the new uncertainty:

[0073] (9)

[0074] in, Represents the covariance matrix of the residuals. yes k The observation matrix at time ( h about x The Jacobian matrix of the observation equation with respect to the state vector is used to linearize the nonlinear observation model. yes k The observation noise covariance matrix at time t reflects the statistical characteristics of the observation noise.

[0075] 3) Kalman gain calculation

[0076] Kalman gain Determines the degree to which the observation value corrects the state estimate:

[0077] (10)

[0078] in, yes k The Kalman gain at time t, represents the inverse of the residual covariance matrix.

[0079] 4) Status Update

[0080] The state estimate is modified using the Kalman gain and residual:

[0081] (11)

[0082] 5) Covariance Update

[0083] Finally, the covariance matrix is ​​updated to reflect the new estimation uncertainty:

[0084] (12)

[0085] in, I Represents the identity matrix.

[0086] Through the above steps, the improved EKF can accurately estimate the center position of the knee joint in real time and effectively cope with various challenges in complex motion scenarios.

[0087] Step S130 , calculating an adaptive weight coefficient based on the estimation error covariance matrix of the two inertial sensors, and then fusing the estimation results of the two inertial sensors according to the weight coefficient to obtain an estimated knee joint center position.

[0088] In order to further improve the accuracy and robustness of the estimation, weights are dynamically assigned according to the estimated error covariance matrix trace (trace value) of the dual IMUs, and then the joint center position estimation results of the two are weighted fused.

[0089] For example, the estimated error covariance matrix of each IMU is calculated as and Then, the weight coefficient is dynamically adjusted according to the trace of the covariance matrix, which is expressed as:

[0090] (13)

[0091] in, express k The trace of the first IMU estimated error covariance matrix at time t. The trace is the sum of the elements on the main diagonal of the matrix and is a measure of the overall uncertainty. express k Trace of the second IMU estimated error covariance matrix at time t. Indicates time k The weight coefficient of .

[0092] In one embodiment, the estimation results of the two IMUs are fused by weighted averaging to obtain a more accurate knee joint center position, which is expressed as:

[0093] (14)

[0094] in, express k More accurate joint center position vector after moment fusion, express k The joint center position vector estimated by the first IMU at the moment, express k The joint center position vector estimated by the second IMU at time .

[0095] Step S140: Apply the estimated knee joint center position to motion analysis.

[0096] The knee joint center position is a key reference point in knee joint kinematic analysis, and its determination is crucial for the accurate estimation of the knee joint angle. In sports biomechanics research, the calculation of the knee joint angle relies on the relative motion analysis of the femur and tibia, and the knee joint center position provides an accurate reference coordinate system for this relative motion. Figure 3As shown, based on the angle between these vectors, the vector dot product formula can be used to determine the size and variation of the knee joint angle. This precise calculation method is crucial for in-depth research on the kinematic and dynamic characteristics of the knee joint. It not only helps to assess the normality and abnormality of lower limb movement, but also provides a reliable quantitative analysis basis for optimizing exercise training, formulating rehabilitation treatment plans, and diagnosing and planning surgical procedures for knee joint diseases. Therefore, it has broad application value in the fields of sports biomechanics and clinical medicine.

[0097] In one embodiment, based on the estimated knee joint center position, thigh segment (hip-knee) and shank segment (knee-ankle) vectors are defined, and the knee joint angle is calculated by vector dot product.

[0098] For example, the thigh segment vector Defined as:

[0099] (15)

[0100] Calf segment vector Defined as:

[0101] (16)

[0102] in, represents the position vector of the knee joint center, represents the position vector of the hip joint center, The position vector representing the center of the ankle joint.

[0103] knee angle Calculated according to the following formula:

[0104] (17)

[0105] In summary, the advantages of the present invention are mainly reflected in the following aspects:

[0106] 1) This invention innovatively integrates the linear and angular displacements of the knee joint center and their corresponding derivatives (velocity, acceleration, angular velocity, and angular acceleration) into a multidimensional motion state vector. This multi-motion state fusion modeling approach overcomes the limitations of focusing solely on single-dimensional motion characteristics and accurately captures the complex biomechanical properties of the knee joint. Furthermore, by establishing highly accurate state transition equations, dynamic mapping of the knee joint's motion state is achieved, providing a solid theoretical foundation for joint center and angle estimation.

[0107] 2) The observation model constructed in this paper deeply binds the linear acceleration of each IMU to the kinematic parameters of the knee joint center by introducing kinematic geometric constraints. This precise association maximizes the extraction of useful information from the IMU measurement data, effectively reducing observation errors even in complex motion scenarios, ensuring high fidelity of the observed data to the actual motion state of the knee joint, thereby improving the observability and stability of the overall estimation.

[0108] 3) During the data fusion process, this invention introduces an adaptive algorithm that dynamically adjusts fusion weights based on the trace of the covariance matrix. This algorithm calculates the estimated error covariance matrix for each IMU measurement and uses its trace as a key indicator of data reliability, thereby optimizing the allocation of fusion weights in real time. This unique covariance adjustment algorithm ensures that dual IMU data is appropriately weighted under different motion states, thereby improving the accuracy and reliability of the fused data.

[0109] In summary, compared to the existing technology, the present invention innovatively introduces fixed geometric constraints and adaptive weight data fusion technology between the dual inertial measurement units (IMUs) by fusing data from the dual IMUs with the improved extended Kalman filter (EKF) algorithm. This can effectively reduce the impact of soft tissue artifacts in dynamic motion scenarios and significantly improve the accuracy and stability of the knee joint rotation center position estimation. The improved EKF algorithm can more comprehensively and accurately capture the complex motion characteristics of the knee joint by defining a multi-dimensional state vector that includes the knee joint center position, velocity, acceleration, angular velocity, and angular acceleration, as well as an observation model that closely links the linear acceleration of each IMU with the kinematic parameters of the joint center. In addition, the introduction of adaptive weight data fusion technology achieves optimal fusion of the two IMU estimation results by dynamically adjusting the weight coefficients, further improving the adaptability to different motion scenarios.

[0110] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0111] A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure within a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0112] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0113] The computer program instructions for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C" or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the state information of the computer-readable program instructions is used to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), so that the electronic circuit can execute the computer-readable program instructions, thereby implementing various aspects of the present invention.

[0114] Various aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0115] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0116] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0117] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and the module, program segment or part of the instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes 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 and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0118] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for dynamically estimating the center of rotation of a knee joint, characterized in that: The method comprises the following steps: Using a first inertial sensor and a second inertial sensor to collect acceleration information and angular velocity information of the knee joint during movement, wherein the first inertial sensor and the second inertial sensor are arranged on both sides of the knee joint at a set distance; The distance between the first inertial sensor and the second inertial sensor is incorporated into an extended Kalman filter observation model as a constraint condition for state estimation, and the estimation error is corrected using the Kalman gain to obtain an estimation error covariance matrix of the first inertial sensor and the second inertial sensor, wherein the state vector of the extended Kalman filter observation model is represented by a position vector of the knee joint center, a velocity vector of the knee joint center, an acceleration vector of the knee joint center, an angular velocity vector of the knee joint, and an angular acceleration vector of the knee joint; Based on the estimation error covariance matrix, an adaptive weight coefficient is calculated, and the estimation results of the first inertial sensor and the second inertial sensor are fused according to the weight coefficient to obtain an estimated knee joint center position.

2. The method according to claim 1, characterized in that The estimated knee joint center position is expressed as: in, Indicates time k The estimated knee joint center position vector, Indicates time k The weight coefficient of Indicates time k The knee joint center position vector estimated by the first inertial sensor, Indicates time k The knee joint center position vector estimated by the second inertial sensor.

3. The method according to claim 2, characterized in that The weight coefficient of the moment k Calculated according to the following formula: in, Indicates time k The trace of the error covariance matrix of the first inertial sensor estimate, Indicates time k Trace of the error covariance matrix of the second inertial sensor estimate.

4. The method according to claim 1, wherein The constraints are set as: in, Indicates time k The position vector of the first inertial sensor relative to the center of the knee joint, Indicates time k The position vector of the second inertial sensor relative to the center of the knee joint, d is the distance between the first inertial sensor and the second inertial sensor.

5. The method according to claim 1, wherein Also includes: Calculate the knee joint angle based on the estimated knee joint center position , expressed as: Among them, || || represents the Euclidean norm, is the thigh segment vector, is the calf segment vector, which are set as: in, represents the estimated knee joint center position vector, represents the position vector of the hip joint center, The position vector representing the center of the ankle joint.

6. The method according to claim 1, characterized in that The first inertial sensor and the second sensor both include an accelerometer and a gyroscope, which synchronously collect three-axis acceleration information and angular velocity information during knee joint movement at a sampling rate of 100 Hz or above.

7. The method according to claim 1, characterized in that The use of Kalman gain to correct the estimation error is based on the following formula: in, It's time k The Kalman gain, Indicates based on the deadline k The moment predicted by the state of −1 k The state vector of It's time k The residual.

8. The method according to claim 1, characterized in that For the extended Kalman filter observation model, the linear acceleration of the first inertial sensor and the second inertial sensor is expressed as: in, i Used to identify inertial sensors, i =1 indicates the first inertial sensor, i =2 indicates the first inertial sensor, is the linear acceleration of the knee joint center, is the angular acceleration, is the angular velocity, It is an inertial sensor i The vector to the center of the knee joint.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory and a processor, wherein a computer program capable of being run on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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