CN116327177A - Method and device for acquiring joint angle, storage medium and electronic equipment - Google Patents
Method and device for acquiring joint angle, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method and a device for acquiring joint angles, a storage medium and electronic equipment. Wherein the method comprises the following steps: acquiring a plurality of motion signal data of a target object acquired by angle acquisition equipment, wherein the motion signal data comprises: spatial position, velocity and acceleration; and processing the plurality of motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm so as to convert the plurality of motion signal data into the joint angle of the target object. The invention solves the technical problems of inconvenient use and poor universality of the acquisition method of the joint angle in the prior art.
Description
Technical Field
The invention relates to the technical field of angle acquisition, in particular to a method and device for acquiring joint angles, a storage medium and electronic equipment.
Background
In recent years, exoskeleton robots, which are wearable mechanical devices with powerful functions, are increasingly valued by a plurality of students and scientific researchers at home and abroad, and become a new research hotspot. Walking is one of the core tasks of the exoskeleton robot system, and the walking assisting gait effect of the exoskeleton robot is directly determined by the establishment and control of a gait model; in use, the exoskeleton system needs to quickly and accurately pre-judge the movement intention of the human body (such as walking, standing, sitting down or going up and down stairs) and judge the gait cycle phase; in the process of prejudging the movement intention of the human body, the joint angle identification of the human body is an indispensable circle.
However, in the prior art, most schemes identify joint angles through an angle sensor or an encoder of an exoskeleton joint module, and the angle sensor has the defects of inconvenient use, troublesome installation, complex result, easy damage and the like; the joint module encoder has the defects of high requirements on use conditions, poor universality, high cost and the like.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a storage medium and electronic equipment for acquiring a joint angle, which at least solve the technical problems of inconvenient use and poor universality of the joint angle acquisition method in the prior art.
According to an aspect of an embodiment of the present invention, there is provided a method of acquiring a joint angle, including: acquiring a plurality of motion signal data of a target object acquired by angle acquisition equipment, wherein the motion signal data comprises: spatial position, velocity and acceleration; and processing the plurality of motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm so as to convert the plurality of motion signal data into the joint angle of the target object.
Optionally, processing a plurality of the motion signal data using an adaptive time window cutting algorithm includes: pre-classifying the motion signal data to obtain periodic motion signal data and aperiodic motion signal data; and extracting a time window from the periodic active signal data by adopting the self-adaptive time window cutting algorithm, wherein the normalized autocorrelation function of the self-adaptive time window cutting algorithm has the same period as the initial signal of the motion signal data.
Optionally, pre-classifying the motion signal data to obtain periodic motion signal data and aperiodic motion signal data, including: performing an average value removing process on the plurality of motion signal data to obtain processed motion signal data, wherein the average value removing process is used for removing a non-zero average value in the plurality of motion signal data; and pre-classifying the processed active signal data to obtain the periodic active signal data and the aperiodic active signal data.
Optionally, the method further comprises: filtering the noise signals in the motion signal data by adopting a data filtering processing algorithm to obtain a filtering result; and carrying out optimal estimation processing on the filtering result by adopting a least mean square estimation algorithm to obtain optimal estimation of the motion signal data.
Optionally, when the angle acquisition device includes a first sensor disposed at a first joint and a second sensor disposed at a second joint, processing a plurality of the motion signal data using the angle feature extraction algorithm includes: respectively calculating the relative directions of a first sensor coordinate system and a second sensor coordinate system relative to a joint coordinate system, wherein the first sensor coordinate system is a coordinate system established based on the first sensor, the second sensor coordinate system is a coordinate system established based on the second sensor, the first sensor coordinate system and the second sensor coordinate system are dynamic coordinate systems, and the joint coordinate system is an initial reference coordinate system; controlling the first sensor coordinate system and the second sensor coordinate system to perform direction alignment processing with the joint coordinate system in the vertical direction and the horizontal direction based on the relative direction, so as to obtain the relative direction after the alignment processing; estimating the joint flexion and extension angles of the first joint and the second joint in a sagittal plane based on the relative directions after the alignment, wherein the joint flexion and extension angles are included angles of vertical components of the first sensor coordinate system and the second sensor coordinate system; the joint Qu Shenjiao thus estimated is taken as the joint angle.
Optionally, before calculating the relative directions of the first sensor coordinate system and the second sensor coordinate system with respect to the joint coordinate system, the method further includes: determining a first sensor coordinate system of the first sensor and a second sensor coordinate system of the second sensor; and determining the joint coordinate system constructed based on the first joint and the second joint.
Optionally, the method further comprises: acquiring acceleration inertial data of the acquisition equipment in a static state when direction alignment processing is carried out in the vertical direction; and calculating an average gravity vector corresponding to a sensor coordinate system corresponding to the acquisition equipment based on the acceleration inertia data.
Optionally, the method further comprises: when a misalignment angle is detected during the direction alignment processing in the horizontal direction, a rotation matrix about the X-axis and the Y-axis of the joint coordinate system is calculated using the misalignment angle, and the second sensor coordinate system is controlled to rotate about the Z-axis of the joint coordinate system based on the rotation matrix so as to be aligned with the first sensor coordinate system.
Optionally, after controlling the rotation of the second sensor coordinate system about the Z-axis of the joint coordinate system based on the rotation matrix to align with the first sensor coordinate system, the method further comprises: determining the spatial positions of the first sensor and the second sensor by comparing the angular velocity vector differences of the first joint, the second joint and the joint coordinate system, respectively; and dynamically measuring and aligning the direction difference of the first joint and the second joint to obtain accurate motion data of the first joint and the second joint in a preset time.
According to another aspect of the embodiment of the present invention, there is also provided an apparatus for acquiring a joint angle, including: the acquisition module is used for acquiring a plurality of motion signal data of the target object acquired by the angle acquisition equipment, wherein the motion signal data comprise: spatial position, velocity and acceleration; and the processing module is used for processing the plurality of motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm so as to convert the plurality of motion signal data into the joint angle of the target object.
According to another aspect of an embodiment of the present invention, there is also provided a computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform any one of the above methods of acquiring a joint angle.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform any one of the methods for acquiring a joint angle.
In an embodiment of the present invention, a plurality of motion signal data of a target object acquired by an angle acquisition device is acquired, where the motion signal data includes: spatial position, velocity and acceleration; the self-adaptive time window cutting algorithm and the angle characteristic extraction algorithm are adopted to process a plurality of motion signal data so as to convert the motion signal data into the joint angles of the target object, thereby achieving the aim of acquiring accurate motion data through the angle acquisition equipment, realizing the technical effect of accurately identifying the joint angles of the human body, and further solving the technical problems of inconvenient use and poor universality of the joint angle acquisition method in the prior art.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method of acquiring joint angles according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an alternative fixed time window with quasi-periodicity according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an alternative joint coordinate system according to an embodiment of the present invention;
fig. 4 is a schematic structural view of a device for acquiring an angle of a joint according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided a method embodiment for obtaining joint angles, it being noted that the steps shown in the flowcharts of the figures may be performed in a computer system, such as a set of computer executable instructions, and, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in an order other than that shown or described herein.
Fig. 1 is a flowchart of a method for acquiring a joint angle according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:
step S102, acquiring a plurality of motion signal data of the target object acquired by the angle acquisition device, where the motion signal data includes: spatial position, velocity and acceleration;
step S104, processing the motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm to convert the motion signal data into the joint angle of the target object.
In the embodiment of the invention, the angle acquisition equipment acquires a plurality of motion signal data of the target object, and performs time window data cutting, data filtering and feature processing on the motion signal data so as to accurately identify the joint angle of the target object.
It should be noted that, the angle acquisition device may be a gyroscope (IMU) sensor, and the target object may be a joint of an exoskeleton, for example: leg joints, upper limb joints, etc.; the motion signal data includes: spatial position, velocity, acceleration, etc.
As an alternative embodiment, the exoskeleton robot and the wearer need to be integrated, and the exoskeleton is utilized for auxiliary work, so that protection and support functions are provided; ideally, an exoskeleton robot provides support and strength just like a bone in the body of a wearer. The proper exoskeleton is worn to help the wearer to walk normally or improve the upper limb movement performance, so that the limb function force of the wearer is greatly improved.
It should be noted that the mechanical exoskeleton system is worn outside the body of the operator, provides additional power or capability for the wearer on the basis of providing functions such as protection and body support for the operator, enhances the functions of the human body, and can complete certain functions and tasks under the control of the operator. Gait control of the exoskeleton needs to have high stability and robustness, and also needs to have "naturalness" and "adaptability"; "naturalness" means that the gait model must be established based on a normal human walking pattern such that gait approximates the normal human walking pattern, both in terms of muscle use and subjective perception of the walking process; the "adaptability" means that the gait model needs to be able to automatically complete gait matching and adjustment according to the physical condition and walking habit of different users.
In the prior art, most methods for acquiring the joint angle identify the joint angle through an angle sensor or an encoder of an exoskeleton joint module, and the angle sensor has the defects of inconvenient use, troublesome installation, complex result, easy damage and the like, and the encoder of the joint module has the defects of high requirements on use conditions, poor universality, high cost and the like.
According to the exoskeleton joint angle acquisition method based on the gyroscope (IMU) sensor, through time window data cutting, data filtering and feature processing of IMU sensor data, the purpose of acquiring accurate motion data through angle acquisition equipment is achieved, the technical effect of accurately identifying the joint angle of a human body is achieved, and the technical problems that the joint angle acquisition method in the prior art is inconvenient to use and poor in universality are solved.
In an alternative embodiment, the adaptive time window cutting algorithm is used to process a plurality of the motion signal data, including:
step S202, pre-classifying a plurality of motion signal data to obtain periodic motion signal data and aperiodic motion signal data;
step S204, the adaptive time window cutting algorithm is adopted to extract a time window from the periodic active signal data, wherein the normalized autocorrelation function of the adaptive time window cutting algorithm has the same period as the initial signal of the motion signal data.
In the embodiment of the invention, firstly, pre-classifying the acquired plurality of motion signal data to obtain periodic motion signal data and aperiodic motion signal data; and extracting a time window from the periodic active signal data by adopting the adaptive time window cutting algorithm.
It should be noted that most activities of the human body include, for example: walking, jogging, going up and down stairs, going up and down slopes, etc., all have quasi-periodicity, while some other activities, such as: such as sitting, standing, etc., are aperiodic, and thus the resulting motion signal data is divided into periodic activity signal data and aperiodic activity signal data.
It should be noted that, since the collected IMU sensor data is a discrete time series, it takes an indefinite time to complete a periodic activity, and thus the activity type cannot be directly calculated at the sampling point. A common approach is to divide the sequence into segments using a time window and take each segment as an example of human activity recognition. The method for dividing the time window mainly comprises three types of sliding window, event definition window and activity definition window, wherein the sliding window method divides the signal into fixed length segments, is the most common window method, but has the problems of low accuracy and poor universality. The event-defined window and the activity-defined window require preprocessing of the signal before the window length is obtained, and are often used for offline recognition due to the complexity of the algorithm.
As an alternative embodiment, an overlapping sliding window approach may be used to optimize the improvement of the sliding window, improving classification accuracy. Certain basic principles should be followed in selecting a sliding time window, such as: the length of the time window should be appropriate to prevent two or more activities from falling into one time window, or the data in the time window is too short to describe the activity. The instance is obtained by using a fixed length time window and moving the time window, the length of movement in the moving step determining the percentage of overlap between adjacent windows. The activity such as running and walking is quasi-periodic with a fixed time window schematic as shown in fig. 2, and if an instance is acquired using a fixed time window, the truncated positions of the data will occur at random positions for one period, and the number of periods in one time window will be unpredictable. To solve this problem, data having a quasi-periodicity may be processed by an adaptive time window method.
The cut-off position of the data is a position at a vertical dotted line shown in fig. 2.
In an alternative embodiment, the pre-classifying the motion signal data to obtain periodic motion signal data and non-periodic motion signal data includes:
step S302, performing average value removal processing on a plurality of motion signal data to obtain processed motion signal data, wherein,
step S304, pre-classifying the processed active signal data to obtain the periodic active signal data and the aperiodic active signal data.
In the embodiment of the invention, the average value removing process is performed on the plurality of motion signal data to obtain processed motion signal data, and the pre-classification process is performed on the processed motion signal data to obtain the periodic motion signal data and the aperiodic motion signal data.
As an alternative embodiment, the above method for adaptive time window uses an autocorrelation method to obtain the period from the motion data, because the average value other than 0 will result in high correlation, and therefore the average value needs to be removed from the motion data before the period is extracted, the periodic activity signal is separated from the non-periodic activity signal after the average value is removed, and then the periodic signal is extracted from the periodic activity by using an adaptive sliding window.
In the embodiment of the present application, the normalized autocorrelation function of the autocorrelation signal extraction method has the same period as that of the original signal, and when the autocorrelation method is used to extract the time window, the created time window length may be set to a length that can accommodate data of two to three periods.
The average removing process is used for removing non-zero average values in the plurality of motion signal data; the basic principle of the autocorrelation method is that the signal is normalized, the autocorrelation function has the same period as the original signal, the length between two maximum values of the autocorrelation method is the period length, the length between 0 and 1 st maximum value is the length of 1 st period, the length between 1 st maximum value and 2 nd maximum value is the length of 2 nd period, and so on, and the lengths of 3 rd and 4 th periods can be obtained until the length of all periods are similar to the period signal.
In an alternative embodiment, it is assumed that the motion signal data within a time window of length N is a c [n]A non-zero average value a within a time window of length i v [i]The processed active signal data a [ n ]]The method comprises the following steps:
it should be noted that the normalized autocorrelation function for the extraction period is: 
In an alternative embodiment, the method further comprises:
step S402, filtering noise signals in the motion signal data by adopting a data filtering processing algorithm to obtain a filtering result;
and step S404, carrying out optimal estimation processing on the filtering result by adopting a least mean square estimation algorithm to obtain optimal estimation of the motion signal data.
In the embodiment of the invention, the motion data signals acquired by the exoskeleton IMU sensor usually have certain errors, and can provide powerful guarantee for the subsequent joint recognition effect after pretreatment. Therefore, the data filtering processing algorithm is adopted to filter noise signals in the motion signal data, so that a filtering result is obtained; and after the data filtering processing algorithm is adopted to process a plurality of the motion signal data, adopting a least mean square estimation algorithm to perform optimal estimation processing on the filtering result, and obtaining optimal estimation of the motion signal data.
As an alternative embodiment, the noise signals mixed in the gait signals generally meet gaussian distribution, so that the noise signals are optimally estimated by adopting a least mean square estimation algorithm and are subjected to filtering processing. If s is used to represent the gait signal, x is the actual signal, and w is the noise: s=x+w; and under one constraint, estimating x such that w is 2 Minimum. I.e.
x∈span{e ikt K=1,..the n }, resulting +.>
I.e. the optimal estimate of the signal s.
In an alternative embodiment, when the angle acquisition device includes a first sensor disposed at a first joint and a second sensor disposed at a second joint, the processing the plurality of motion signal data using the angle feature extraction algorithm includes:
step S502, respectively calculating the relative directions of a first sensor coordinate system and a second sensor coordinate system relative to a joint coordinate system, wherein the first sensor coordinate system is a coordinate system established based on the first sensor, the second sensor coordinate system is a coordinate system established based on the second sensor, the first sensor coordinate system and the second sensor coordinate system are dynamic coordinate systems, and the joint coordinate system is an initial reference coordinate system;
step S504, controlling the first sensor coordinate system and the second sensor coordinate system to perform direction alignment processing with the joint coordinate system in the vertical direction and the horizontal direction based on the relative direction, so as to obtain the relative direction after the alignment processing;
step S506, estimating the joint flexion and extension angles of the first joint and the second joint in a sagittal plane based on the relative directions after the alignment processing, wherein the joint flexion and extension angles are included angles of vertical components of the first sensor coordinate system and the second sensor coordinate system;
In step S508, the joint Qu Shenjiao obtained by estimation is used as the joint angle.
In the embodiment of the invention, since the output data of each IMU sensor is the spatial position, the speed, the acceleration and the like of the current test point, the following steps are needed to be adopted to realize the extraction of the angle characteristics: alignment of leg IMU sensor coordinate systems, calculation of relative orientation of each IMU coordinate system, and estimation of joint flexion and extension angles. In the process of processing the motion signal data by using the angular feature extraction algorithm, as shown in the schematic diagram of the joint coordinate system structure in fig. 3, first, the relative directions of the first sensor coordinate system and the second sensor coordinate system with respect to the joint coordinate system are calculated, and the first sensor coordinate system and the second sensor coordinate system are controlled to perform direction alignment processing with respect to the joint coordinate system in the vertical direction and the horizontal direction based on the relative directions, so as to obtain the aligned relative directions; based on the relative direction after the alignment process, the joints Qu Shenjiao of the first and second joints in the sagittal plane are estimated, and the estimated joint Qu Shenjiao is used as the joint angle.
The first sensor coordinate system is a coordinate system established based on the first sensor, the second sensor coordinate system is a coordinate system established based on the second sensor, the first sensor coordinate system and the second sensor coordinate system are dynamic coordinate systems, and the joint coordinate system is an initial reference coordinate system; the joint flexion and extension angle is an included angle of a vertical component of the first sensor coordinate system and the second sensor coordinate system.
In an alternative embodiment, the method further comprises, prior to calculating the relative orientation of the first sensor coordinate system and the second sensor coordinate system with respect to the joint coordinate system, respectively:
step S602, determining a first sensor coordinate system of the first sensor and a second sensor coordinate system of the second sensor;
step S604, determining the joint coordinate system constructed based on the first joint and the second joint.
As an alternative embodiment, still as shown in fig. 3, the above mentioned joint coordinate system JCS is determined based on a first sensor IJK at the thigh and a second sensor IJK at the calf.
As shown in fig. 3, the generalized joint coordinate e 1 、e 2 、e 3 Three rotary systems, e, respectively representing lower limbs of the human body 1 Representing flexion and extension rotations of the leg knee joint, e 2 Indicating the adduction and abduction rotation of the thigh, e 3 Representing the pronation and supination of the leg. Setting two sensor coordinate systems formed by IMU sensors attached to thighs and calves as a first sensor coordinate system UVW and a second sensor coordinate system UVW respectively; the directions of the first sensor coordinate system and the second sensor coordinate system on the base coordinate system XYZ can be represented by two four-dimensional vectors Q A And Q B To represent.
In an alternative embodiment, the method further comprises:
step S702, acquiring acceleration inertial data of the acquisition equipment in a static state when direction alignment processing is performed in the vertical direction;
step S704, calculating an average gravity vector corresponding to the sensor coordinate system corresponding to the acquisition device based on the acceleration inertia data.
As an alternative embodiment, as also shown in fig. 3, the orientation of the sensor coordinate systems UWV and uwv relative to JCS is calculated using a tracking algorithm that fuses the gyroscope and accelerometer signals to calculate the optimal orientation for each sample. And calculates the direction and position values of the sensor coordinate system of the thigh and the calf relative to the base coordinate system XYZ by using two four-dimensional vectors Q' A And Q' B And (3) representing.
It should be noted that, compared with JCS models of thighs and calves proposed by other solutions in the prior art, in the embodiment of the present application, the sensor coordinate system is aligned in a vertical and horizontal direction; in the vertical direction, inertial data of the acceleration of the IMU at rest is required to be tested in order to align with the direction of the JCS; in the resting state, the IMU gravitational acceleration signal is very prominent, so that the average gravitational vector of the thigh and calf sensor coordinate system can be calculated.
In an alternative embodiment, the method further comprises:
in step S802, when a misalignment angle is detected during the direction alignment processing in the horizontal direction, a rotation matrix about the X-axis and the Y-axis of the joint coordinate system is calculated using the misalignment angle, and the second sensor coordinate system is controlled to rotate about the Z-axis of the joint coordinate system based on the rotation matrix so as to be aligned with the first sensor coordinate system.
As an alternative embodiment, when the alignment process is performed in the horizontal direction, by straightening the leg and lifting it up and down from the side for about 30 seconds, the effect on calculating the angular velocity vector of the thigh and calf sensor coordinate system is remarkable; misalignment angles can be easily detected according to the above procedure, and by using the misalignment angles, a rotation matrix about the X-axis and the Y-axis can be calculated that rotates the second sensor coordinate system at the calf IMU about the aligned Z-axis to align with the first sensor coordinate system at the thigh IMU.
In an alternative embodiment, after controlling the rotation of the second sensor coordinate system about the Z-axis of the joint coordinate system based on the rotation matrix to align with the first sensor coordinate system, the method further comprises:
Step S902 of comparing angular velocity vector differences between the first joint, the second joint and the joint coordinate system to determine spatial positions of the first sensor and the second sensor;
step S904, dynamically measuring and aligning the direction difference between the first joint and the second joint to obtain accurate motion data of the first joint and the second joint within a predetermined time.
As an alternative embodiment, the spatial position of each IMU sensor is obtained by comparing the angular velocity vector differences of the first joint thigh and the second joint calf based on the joint coordinate system JCS by the individual movements of the hip joints. The tracking algorithm used in conjunction with the functional alignment procedure can obtain accurate thigh to calf movement data over a period of time.
It should be noted that the tracking system can dynamically measure the directional difference of IMUs at the thigh and the shank, and through the functional alignment procedure, the directional description on the three rotating systems is realized.
In the embodiment of the invention, the movement direction of the joint can be determined according to the directions of the first joint coordinate system and the second joint coordinate system, and the description of the movement direction of the joint is performed based on the Euler angle of the base coordinate system XYZ. The IMU sensor coordinate system at any instant can be converted to a rotation matrix of the initial coordinate system, then the rotation matrix about each axis can be defined as:
Wherein the method comprises the steps of
θ and ψ are the rotation angles around the U, V and W axes, respectively, in the respective sensor coordinate systems.
As an alternative embodiment, an Extended Kalman Filter (EKF) may be used to describe the roll and pitch directions of two sensor coordinate systems, in this embodiment the EKF used has a vector of eight rows of state components:
wherein three-dimensional acceleration including an IMU sensor
And three-dimensional angular velocity->
Both in three axes of the sensor coordinate system and in the rotation angle +.>
And the tilt angle θ. The rotation between the dynamic and the initial coordinate system of the sensor at time step k is made up of three euler angles +.>
θ and ψ, so that only the sitting position needs to be calculated when calculating the flexion and extension angles of the jointRotation angle +.>
And an inclination angle θ.
As an alternative embodiment, the dynamic system f can be modeled linearly as:
where k denotes the time steps, Δt denotes the time interval between each time step,
and->
Is the noise vector and angular velocity on acceleration, < +.>
And->
Representation->
And the time derivative of θ. In calculating knee joint angles at IMU angular velocity using the euler equation, IMU angular velocity vectors have a bias component, mainly due to drift in EKF output. Thus, in calculating azimuth angle, ω is set assuming that the yaw axis of the IMU offset component is zero ψ =0, f 7 And f 8 The estimation is:
f 7 ：
f 8 ：
as an alternative embodiment, the measurement results of the dynamic system are the three-dimensional acceleration and the three-dimensional angular velocity of the IMU test points at the thigh and the calf, which are measured in the sensor coordinate system of the thigh and the calf. The measured values are described as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the measurement noise, the output of the IMU sensor is represented by the angular velocity vector +.>
And a deviation component b; on the premise that the exercise data is periodic data, b is set to be constant in each test, and +.>
Is the average value of the angular velocity. The covariance matrix of measuring and processing noise can be expressed as:
R＝rId 6×6
wherein Id 3 Representing the identity matrix, R is the overall rotation matrix,
and r is a parameter in the algorithm.
As an alternative embodiment, the joint Qu Shenjiao is defined in the sagittal plane of the thigh and calf for representing the angle of the vertical component in the coordinate system of the first sensor at the thigh and the second sensor at the calf. The relative direction of the coordinate system over time is estimated by an extended kalman filter EKF, where the direction is referenced to the initial coordinate system.
It is necessary to say thatIt is clear that the transformation matrix of the sensor dynamic coordinate system at any time step relative to the initial coordinate system is R i . Using EKF to estimate roll and pitch components, the vertical components of the sensor coordinate system with time step k are calculated based on the initial coordinate system, where the vertical components of the thigh and calf are:
alternatively, a vertical and horizontal alignment matrix R is used Z1 、R Z2 And R is XY The sensor vertical component represented by the initial coordinate system is further converted on the joint coordinate system JCS as follows:
wherein by taking the leg vertical component r 1 And r 2 Projected onto the plane of the base coordinate system XY, the joint Qu Shenjiao with time step k can be calculated:
wherein the sign function is rotated
Through the steps, the motion data acquired by the IMU are converted into leg joint angles in real time based on the acquisition of the joint motion signal data of the exoskeleton sensor and by adopting a self-adaptive time window cutting algorithm, an angle feature extraction algorithm and the like; the method has the characteristics of small algorithm consumption, high instantaneity, good stability, reliable output data, high universality and high expandability, small delay, large data volume, high accuracy and the like; and the structure improvement and treatment at the joint are not needed, and the method has the characteristics of simplicity in implementation, low cost, convenience in maintenance, good scene applicability and the like.
It should be noted that, the exoskeleton joint angle obtaining method based on the IMU provided by the embodiment of the application not only can be used for detecting and identifying the leg joint angle, but also can be used for identifying the upper limb joint angle, has good universality, can meet the identification requirements of most exoskeleton joint angles, and has a wide application range.
Example 2
According to an embodiment of the present invention, there is further provided an embodiment of an apparatus for implementing the method for acquiring a joint angle, and fig. 4 is a schematic structural diagram of an apparatus for acquiring a joint angle according to an embodiment of the present invention, as shown in fig. 4, where the apparatus for acquiring a joint angle includes: an acquisition module 40 and a processing module 42, wherein:
an acquiring module 40, configured to acquire a plurality of motion signal data of the target object acquired by the angle acquisition device, where the motion signal data includes: spatial position, velocity and acceleration;
a processing module 42, configured to process the motion signal data by using an adaptive time window cutting algorithm and an angle feature extraction algorithm, so as to convert the motion signal data into the joint angle of the target object.
It should be noted that each of the above modules may be implemented by software or hardware, for example, in the latter case, it may be implemented by: the above modules may be located in the same processor; alternatively, the various modules described above may be located in different processors in any combination.
Here, it should be noted that the above-mentioned obtaining module 40 and processing module 42 correspond to step S102 to step S104 in embodiment 1, and the above-mentioned modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1 above. It should be noted that the above modules may be run in a computer terminal as part of the apparatus.
It should be noted that, the optional or preferred implementation manner of this embodiment may be referred to the related description in embodiment 1, and will not be repeated here.
The device for acquiring the joint angle may further include a processor and a memory, wherein the device for acquiring the joint angle and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, the kernel fetches corresponding program units from the memory, and one or more of the kernels can be arranged. The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
According to an embodiment of the present application, there is also provided an embodiment of a computer-readable storage medium. Optionally, in this embodiment, the computer readable storage medium includes a stored program, where the program controls a device in which the computer readable storage medium is located to execute any one of the methods for acquiring the joint angle.
Alternatively, in this embodiment, the computer readable storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group, where the computer readable storage medium includes a stored program.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: acquiring a plurality of motion signal data of a target object acquired by angle acquisition equipment, wherein the motion signal data comprises: spatial position, velocity and acceleration; and processing the plurality of motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm so as to convert the plurality of motion signal data into the joint angle of the target object.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: pre-classifying the motion signal data to obtain periodic motion signal data and aperiodic motion signal data; and extracting a time window from the periodic active signal data by adopting the self-adaptive time window cutting algorithm, wherein the normalized autocorrelation function of the self-adaptive time window cutting algorithm has the same period as the initial signal of the motion signal data.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: performing an average value removing process on the plurality of motion signal data to obtain processed motion signal data, wherein the average value removing process is used for removing a non-zero average value in the plurality of motion signal data; and pre-classifying the processed active signal data to obtain the periodic active signal data and the aperiodic active signal data.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: filtering the noise signals in the motion signal data by adopting a data filtering processing algorithm to obtain a filtering result; performing optimal estimation processing on the filtering result by adopting a least mean square estimation algorithm to obtain optimal estimation of the motion signal data
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: respectively calculating the relative directions of a first sensor coordinate system and a second sensor coordinate system relative to a joint coordinate system, wherein the first sensor coordinate system is a coordinate system established based on the first sensor, the second sensor coordinate system is a coordinate system established based on the second sensor, the first sensor coordinate system and the second sensor coordinate system are dynamic coordinate systems, and the joint coordinate system is an initial reference coordinate system; controlling the first sensor coordinate system and the second sensor coordinate system to perform direction alignment processing with the joint coordinate system in the vertical direction and the horizontal direction based on the relative direction, so as to obtain the relative direction after the alignment processing; estimating the joint flexion and extension angles of the first joint and the second joint in a sagittal plane based on the relative directions after the alignment, wherein the joint flexion and extension angles are included angles of vertical components of the first sensor coordinate system and the second sensor coordinate system; the joint Qu Shenjiao thus estimated is taken as the joint angle.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: determining a first sensor coordinate system of the first sensor and a second sensor coordinate system of the second sensor; and determining the joint coordinate system constructed based on the first joint and the second joint.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: the method further comprises the following steps: acquiring acceleration inertial data of the acquisition equipment in a static state when direction alignment processing is carried out in the vertical direction; and calculating an average gravity vector corresponding to a sensor coordinate system corresponding to the acquisition equipment based on the acceleration inertia data.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: when a misalignment angle is detected during the direction alignment processing in the horizontal direction, a rotation matrix about the X-axis and the Y-axis of the joint coordinate system is calculated using the misalignment angle, and the second sensor coordinate system is controlled to rotate about the Z-axis of the joint coordinate system based on the rotation matrix so as to be aligned with the first sensor coordinate system.
Optionally, the computer readable storage medium is controlled to perform the following functions when the program is run: determining the spatial positions of the first sensor and the second sensor by comparing the angular velocity vector differences of the first joint, the second joint and the joint coordinate system, respectively; and dynamically measuring and aligning the direction difference of the first joint and the second joint to obtain accurate motion data of the first joint and the second joint in a preset time.
According to an embodiment of the present application, there is also provided an embodiment of a processor. Optionally, in this embodiment, the processor is configured to run a program, where any one of the methods for acquiring the joint angle is executed when the program runs.
According to an embodiment of the present application, there is also provided an embodiment of an electronic device, including a memory, in which a computer program is stored, and a processor configured to run the computer program to perform any one of the above methods of acquiring a joint angle.
According to an embodiment of the present application, there is also provided an embodiment of a computer program product adapted to perform a program initializing any of the above-mentioned method steps of acquiring a joint angle when executed on a data processing device.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a computer-readable storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (12)
1. A method of obtaining a joint angle, comprising:
acquiring a plurality of motion signal data of a target object acquired by angle acquisition equipment, wherein the motion signal data comprises: spatial position, velocity and acceleration;
and processing the plurality of motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm so as to convert the plurality of motion signal data into joint angles of the target object.
2. The method of claim 1, wherein processing a plurality of the motion signal data using an adaptive time window cutting algorithm comprises:
pre-classifying the motion signal data to obtain periodic motion signal data and aperiodic motion signal data;
and extracting a time window from the periodic active signal data by adopting the self-adaptive time window cutting algorithm, wherein a normalized autocorrelation function of the self-adaptive time window cutting algorithm has the same period as an initial signal of the motion signal data.
3. The method of claim 2, wherein pre-classifying the plurality of motion signal data to obtain periodic activity signal data and non-periodic activity signal data, comprises:
performing average value removing processing on the plurality of motion signal data to obtain processed motion signal data, wherein the average value removing processing is used for removing non-zero average values in the plurality of motion signal data;
and pre-classifying the processed active signal data to obtain the periodic active signal data and the aperiodic active signal data.
4. The method according to claim 1, wherein the method further comprises:
filtering noise signals in the motion signal data by adopting a data filtering processing algorithm to obtain a filtering result;
and carrying out optimal estimation processing on the filtering result by adopting a least mean square estimation algorithm to obtain optimal estimation of the motion signal data.
5. The method of claim 1, wherein processing a plurality of the motion signal data using the angular feature extraction algorithm when the angular acquisition device includes a first sensor disposed at a first joint and a second sensor disposed at a second joint comprises:
Respectively calculating the relative directions of a first sensor coordinate system and a second sensor coordinate system relative to a joint coordinate system, wherein the first sensor coordinate system is a coordinate system established based on the first sensor, the second sensor coordinate system is a coordinate system established based on the second sensor, the first sensor coordinate system and the second sensor coordinate system are dynamic coordinate systems, and the joint coordinate system is an initial reference coordinate system;
controlling the first sensor coordinate system and the second sensor coordinate system to perform direction alignment processing with the joint coordinate system in the vertical direction and the horizontal direction based on the relative direction, so as to obtain the relative direction after the alignment processing;
estimating the joint flexion and extension angles of the first joint and the second joint in a sagittal plane based on the relative directions after the alignment processing, wherein the joint flexion and extension angles are included angles of vertical components of the first sensor coordinate system and the second sensor coordinate system;
the joint Qu Shenjiao obtained by estimation is taken as the joint angle.
6. The method of claim 5, wherein prior to calculating the relative orientation of the first sensor coordinate system and the second sensor coordinate system with respect to the joint coordinate system, respectively, the method further comprises:
Determining a first sensor coordinate system of the first sensor and a second sensor coordinate system of the second sensor;
the joint coordinate system constructed based on the first joint and the second joint is determined.
7. The method of claim 5, wherein the method further comprises:
acquiring acceleration inertial data of the acquisition equipment in a static state when direction alignment processing is carried out in the vertical direction;
and calculating and obtaining an average gravity vector corresponding to a sensor coordinate system corresponding to the acquisition equipment based on the acceleration inertia data.
8. The method of claim 5, wherein the method further comprises:
when the misalignment angle is detected during the direction alignment processing in the horizontal direction, a rotation matrix around the X axis and the Y axis of the joint coordinate system is calculated by using the misalignment angle, and the second sensor coordinate system is controlled to rotate around the Z axis of the joint coordinate system based on the rotation matrix so as to be aligned with the first sensor coordinate system.
9. The method of claim 8, wherein after controlling the second sensor coordinate system to rotate about the Z-axis of the joint coordinate system based on the rotation matrix to align with the first sensor coordinate system, the method further comprises:
Determining the spatial positions of the first sensor and the second sensor by comparing the angular velocity vector differences of the first joint, the second joint and the joint coordinate system, respectively;
and carrying out dynamic measurement and alignment treatment on the direction difference of the first joint and the second joint to obtain accurate motion data of the first joint and the second joint in a preset time.
10. An apparatus for obtaining an angle of a joint, comprising:
the acquisition module is used for acquiring a plurality of motion signal data of the target object acquired by the angle acquisition equipment, wherein the motion signal data comprise: spatial position, velocity and acceleration;
and the processing module is used for processing the plurality of motion signal data by adopting an adaptive time window cutting algorithm and an angle characteristic extraction algorithm so as to convert the plurality of motion signal data into joint angles of the target object.
11. A computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of acquiring joint angles according to any one of claims 1 to 10.
12. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of acquiring joint angles according to any of claims 1 to 9.
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