CN112890808A - Human body limb joint axis calibration device based on MEMS sensor - Google Patents

Human body limb joint axis calibration device based on MEMS sensor Download PDF

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CN112890808A
CN112890808A CN202110056316.2A CN202110056316A CN112890808A CN 112890808 A CN112890808 A CN 112890808A CN 202110056316 A CN202110056316 A CN 202110056316A CN 112890808 A CN112890808 A CN 112890808A
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何峰
李柏寒
孟琳
明东
徐瑞
刘源
杜娟
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Abstract

The invention discloses a human body limb joint axis calibration device based on an MEMS sensor, which comprises: the IMU measuring unit takes a human motion joint as a hinge joint, establishes a human joint constraint condition, uses a method of least square estimation to search an optimal solution to establish a cost function and realizes optimal estimation of a local joint axis; a new joint constraint error condition is provided for the actual joint dynamic calibration condition, a new least square cost function is established by combining the angular velocity and the sensor azimuth, and an optimal solution is obtained, so that the calibration device is more suitable for actual joint axis calibration, and the dynamic calibration precision and the applicability of the joint axis are improved.

Description

Human body limb joint axis calibration device based on MEMS sensor
Technical Field
The invention relates to the field of human body limb joint axis calibration, in particular to a human body limb joint axis calibration device based on an MEMS (Micro-Electro-Mechanical system) sensor.
Background
With the rapid development of microelectronic technology, an Inertial Measurement Unit (IMU) has been widely used in wearable human motion analysis due to its advantages of small size, low cost, low power consumption, no environmental restriction on wireless transmission, etc., and generally, the IMU is used to collect human motion data and then drive an established human kinematics model, thereby being capable of approximately tracking the human motion posture. Therefore, a three-dimensional joint connection model conforming to human body behavior characteristics is established, as shown in fig. 1, (a) is a kinematic chain model which assumes each limb part of the human body as a rigid body connected by a hinge, the degree of freedom between each two adjacent parts is determined according to the rotational degree of freedom of the human joint, normally, the kinematic chain is composed of a plurality of rigid body parts, and is connected by joints with 1 to 3 degrees of freedom, therefore, the global direction and position of the root segment are determined in the first step, and then the global direction and position of each different body segment can be obtained according to the direction of the different body segment relative to the root segment; the diagram (b) is a free part model, the method assumes that the sensor has high precision and can independently estimate the posture of each body part, the model is usually used when the freedom degree of motion on some axes is limited during the estimation of the posture of the human body, the method needs to use random constraint (such as kinematic constraint) for estimation, the joint connection is not limited, the posture of each part can be independently estimated, and when the model of the elbow joint and the knee joint of the human body is established, the true direction of the rotating shaft is difficult to accurately position, so the method selects the free part model for modeling more appropriately.
However, in the practical application of the IMU, there is often a case that the coordinate system of the sensor is not consistent with the coordinate system of the limb, that is, it is very difficult to mount the IMU on the limb of the human body and make one of the local coordinate axes of the IMU precisely coincide with the joint axis, so a basic problem of the human motion analysis based on the IMU is that the local coordinate axes of the IMU are not aligned with the limb axis of the human body, in order to solve the problem that the placement position of the IMU is not consistent with the ideal posture, a human joint axis calibration device needs to be established, although the IMU can be approximately and precisely mounted on the predetermined joint axis facing the joint, or the position of the joint axis is manually measured, the measurement error can be finally reduced to about several centimeters, but the consistency of the mounting position can not be ensured every measurement, and when a plurality of sensors are used for posture measurement, the placement error of the IMU and the misalignment error between the plurality of sensors can seriously affect, therefore, the initial time must be calibrated using the human joint axis calibration device. A common method to do this is by calibrating the pose or motion, commonly referred to as IMU-to-segment (hereinafter I2S calibration). That is, during the study of limb movements using IMUs, the body is typically modeled as a set of rigid segments of known length, and assuming the IMUs are rigidly mounted on the relevant segments, they are connected together by frictionless joints of different degrees of freedom, thus requiring alignment of the orientation and position of each IMU with a segment of the body.
The I2S calibration problem is generally divided into two categories, one is static calibration of manually assigned positions, i.e. the subject is prescribed to do several simple actions, such as N-type posture, T-type posture or standing still, etc., and then the initial position data is subtracted from the data collected at this time using the IMU sensor in order to establish the relationship between the sensor and body segment coordinate systems, in which case the alignment difference between the sensor coordinate system and the body segment coordinate system is considered constant, which is the classical assumption in most studies on sensor-to-body segment calibration, however the accuracy of the experimental results of this method is limited by the accuracy with which the subject can perform the posture or movement, so the advantage of any motion calibration method (i.e. functional dynamic calibration) is reflected in that the specific orientation of the IMU and the orientation of the coordinate axes are not fixed, but rather an articulated axis calibration device is established, the device may use kinematic constraints (e.g., hinge constraints, spherical constraints, as shown in fig. 2) to identify position and attitude estimates from arbitrary motion data.
In the actual joint axis calibration process, the situation that the motion amplitudes of two limbs of the joint are inconsistent is avoided, when the knee joint is calibrated, the thigh is generally relatively static, the shank performs bending-stretching actions, and the traditional joint axis calibration is not applicable due to the problems of poor convergence of the optimal solution of the cost function and the like.
Disclosure of Invention
The invention provides a human body limb joint axis calibration device based on an MEMS sensor, which is characterized in that on the assumption that a human body motion joint is connected with a hinge joint, a tester is adopted to complete a group of data of dynamic actions and combine with a kinematics constraint equation to realize the calibration of a sensor-limb, and a new least square cost function is established by combining the angular velocity and the sensor orientation, so that the calibration is more suitable for the actual joint axis calibration condition to improve the dynamic calibration precision and the applicability of the joint axis, and the detailed description is as follows:
a MEMS sensor-based human limb joint axis calibration device, the device comprising: an IMU measurement unit for measuring the IMU,
the IMU measurement unit takes a human motion joint as a hinge joint, establishes a human joint constraint condition, uses a method of least square estimation to find an optimal solution to establish a cost function and realizes optimal estimation of a local joint axis;
a new joint constraint error condition is provided for the actual joint dynamic calibration condition, a new least square cost function is established by combining the angular velocity and the sensor azimuth, and an optimal solution is obtained, so that the calibration device is more suitable for actual joint axis calibration.
The device realizes the calibration of the sensor and the limbs by adopting a group of data of dynamic actions of a testee and combining a kinematic constraint equation.
Further, the device fuses 9-axis inertial sensor data for joint axis estimation.
Further, the human joint constraint conditions are as follows:
obtaining two joint axis vectors j based on dynamic joint calibration1、j2The projections in a fixed coordinate system should coincide with each other, i.e. the projections are projected in a fixed coordinate system
Figure BDA0002900989070000021
From its own coordinate system S2 down to relatively fixed
Figure BDA0002900989070000022
When in a coordinate system, should satisfy
Figure BDA0002900989070000023
And
Figure BDA0002900989070000024
the complete coincidence is that,
Figure BDA0002900989070000031
and
Figure BDA0002900989070000032
is a joint axis.
The method for finding the optimal solution by using least square estimation to create the cost function and realize the optimal estimation of the local joint axis comprises the following steps:
Figure BDA0002900989070000033
where C is the cost function, N is the number of samples, ω1、ω2Angular velocity information collected for sensors IMU1 and IMU2, respectively.
The new joint constraint error condition is recorded as
Figure BDA0002900989070000039
And
Figure BDA0002900989070000034
the included angle between:
wherein the optimal solution is:
Figure BDA0002900989070000035
wherein the content of the first and second substances,
Figure BDA0002900989070000036
joint axis information for the sensors IMU1 and IMU2 in their respective coordinate systems,
Figure BDA0002900989070000037
the relative attitude of the sensor IMU2 with respect to the IMU1,
Figure BDA0002900989070000038
is a multiplication of quaternions.
The technical scheme provided by the invention has the beneficial effects that:
1. the invention designs and utilizes kinematic constraint (hinge constraint) to determine the local joint axis coordinate position from the sensor to the human body section direction from the motion data collected by the human body joint axis calibration device, does not depend on the limit of an evaluator executing the preset use standard, reduces the error caused by installation calibration and calibration, and can greatly improve the stability of the follow-up result of the human body posture;
2. the invention improves the optimized estimation in the existing joint axis calibration by establishing a new least square cost function, integrates 9-axis inertial sensor data to estimate the joint axis, is suitable for the real joint dynamic calibration condition that the limb movement at one end of the joint is obviously smaller than the limb movement amplitude at the other end, and improves the practicability and the accuracy in the human joint movement calibration.
Drawings
FIG. 1 is a schematic representation of two different biomechanical models;
wherein, (a) is a kinematic chain model; (b) is a free segment model.
FIG. 2 is a schematic view of two joint constraint types;
wherein, (a) is hinge joint constraint; (b) is a spherical joint constraint.
FIG. 3 is a schematic view of a knee joint alignment;
FIG. 4 is a simplified schematic diagram of a knee joint being modeled to approximate a hinge joint;
FIG. 5 is a schematic view of a lower extremity exoskeleton hinge connection;
FIG. 6 is a schematic diagram of the creation of a magnetic interference environment using two notebook computers;
fig. 7 is a schematic diagram of the angle error between the joint principal axis j2 and the reference joint axis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
Example 1
The embodiment of the invention designs a limb joint axis calibration device suitable for the actual joint axis calibration condition, which uses a Micro Electro-Mechanical System (MEMS), namely an IMU (Inertial Measurement Unit, IMUs) consisting of an accelerometer, a gyroscope and a magnetometer and human body joint constraint. It is often the case that when the two IMUs and the hinge joint formed body joint axis alignment means are attached to the joint, i.e., may be used to calculate the hinge joint angle, in most robotic and mechanical applications, the IMU may be mounted in such a manner, i.e. one of the local coordinate axes coincides with the hinge axis, however, a very important difference between the setup of a human limb and most robots, is that it is difficult to attach the IMU correctly to the limb, one local coordinate axis is completely consistent with the human body joint axis, so that the IMU is more prone to be placed at any position, handling arbitrary sensor-to-limb segment installations is therefore a major challenge for motion analysis using IMUs, manual measurement and calibration poses and motions are often solutions for IMU sensor-to-limb segment alignment, another major challenge in IMU applications in the field of motion analysis is that the accuracy of joint calibration depends on the amplitude of motion of the two connected limbs.
Therefore, the embodiment of the present invention provides a joint calibration apparatus combining 9-axis IMU direction information under a specific motion to adapt to a real joint axis calibration situation.
The technical process comprises the following steps: firstly, a human motion joint is assumed to be a hinge joint, and a human joint axis calibration device is established by combining two IMUs. And then, aiming at the real joint dynamic calibration condition that the limb movement of one end is obviously smaller than the limb movement amplitude of the other end, a new joint constraint error condition is provided, and a new least square cost function is established by combining the angular velocity and the sensor orientation, so that the calibration device is more suitable for the real joint axis calibration condition.
Example 2
The scheme of example 1 is further described below with reference to fig. 2 to 7, and specific calculation formulas, which are described in detail below:
first, kinematic constraint
The purpose of using the joint axis dynamic calibration device is to automatically identify the local joint axis coordinates of the sensors connected to the two ends of the limb without the need for precise IMU placement or precise calibration movements in a more realistic and convenient installation direction environment. Joint axis dynamic calibration joint axis estimation is performed using a non-linear least squares method, assuming that a human limb segment passes through a hinge joint (as shown in fig. 2 (a)), using the kinematic constraints imposed by this type of joint. The advantage of this approach is that it does not need to consider the mounting direction and position of the IMU, while not being limited by the accuracy with which the subject performs the predetermined posture or motion, while avoiding the assumption of sensor-to-body segment coincidence when estimating the human motion pose using inertial sensors.
The principle of the knee joint axis calibration device is described as an example. Based on the kinematic chain model, the apparatus embodiment of the present invention assumes that the thigh and calf are each attached with an IMU (IMU 1 and IMU2, respectively) in any position and orientation, as shown in fig. 3. A cartesian coordinate system (sensor coordinate system S) fixed at the three-dimensional center of the sensor is defined on each IMU separately1And S2) And defining a relatively fixed inertial coordinate system U. The subscript i e 1,2 is used to denote a particular sensor. Defining an acquisition dataset s (i) { ω ═ ω1(t),ω2(t) }, where ω is1(t)，
Figure BDA0002900989070000051
In order to be the angular velocity value,
Figure BDA0002900989070000052
represents omegaiThe value ranges of the three coordinates are all arbitrary real numbers.
Based on joint axis calibration theory, the knee joint was considered approximately as a hinge joint and the model was simplified, as shown in fig. 4. Theoretically, ω1(t) and ω2(t) only the difference between the joint angular velocity and the (time-varying) rotation matrix. Thus, in the thigh-and-calf-end sensor local coordinate system, ω is for each sampling instant1(t) and ω2(t) projection onto respective joint planes (joint axes)
Figure BDA0002900989070000053
And
Figure BDA0002900989070000054
a geometric plane that is a normal vector) all have the same length, the joint constraint must then satisfy:
Figure BDA0002900989070000055
wherein | · | purple sweet2Representing the euclidean norm.
However, the accuracy of the joint calibration in the above joint constraint (TA constraint for short above) depends on the motion amplitude of the two connected limbs. In order to make the calibration method more suitable for the real joint axis calibration situation, the present study further optimizes the joint constraints. Theoretically, two joint axis vectors j are obtained based on dynamic joint calibration1、j2The projections in a fixed coordinate system should coincide with each other, i.e. the projections are projected in a fixed coordinate system
Figure BDA0002900989070000056
From its own coordinate system S2 down to relatively fixed
Figure BDA0002900989070000057
When in a coordinate system, should satisfy
Figure BDA0002900989070000058
And
Figure BDA0002900989070000059
and (3) completely coinciding:
Figure BDA00029009890700000510
that is, the above joint axis constraints (1), (2) are true under ideal conditions regardless of the installation position and orientation of the sensor on the body segment.
Second, joint axis dynamic calibration optimization model
The knee joint flexion-extension axis j under the reference coordinate system can be automatically identified by using the collected data set S (i), i belongs to {1,2}UUnit length direction vector under respective sensor coordinate systems S1, S2
Figure BDA0002900989070000061
And
Figure BDA0002900989070000062
(both are generally constant and depend only on the mounting direction of the sensor relative to the joint), thereby limiting joint axis estimation to unit length.
At this time, the position information of the joint axis is changed into four dimensions, and the position coordinates of the joint axis are calculated for convenience
Figure BDA0002900989070000063
And
Figure BDA0002900989070000064
parameterization with spherical coordinates, where θiAnd
Figure BDA0002900989070000065
the dip and azimuth angles of the IMU1 and IMU2, respectively, i.e.:
Figure BDA0002900989070000066
the method for finding the optimal solution by using least square estimation can establish a cost function (4) for TA constraint so as to realize the optimal estimation of the local joint axis:
Figure BDA0002900989070000067
however, the above TA constraint cost function is not suitable for the real joint dynamic calibration case where the limb motion at one end is significantly smaller than the limb motion amplitude at the other end, as shown in fig. 4, it can be seen that the actual joint axis
Figure BDA0002900989070000068
And
Figure BDA0002900989070000069
are not parallel to each other.
Thus, a new joint constraint error condition e is proposed2：
Figure BDA00029009890700000610
Wherein the three-dimensional vector
Figure BDA00029009890700000611
The quaternion multiplication (6) can be executed to be converted from the coordinate system S2 of the sensor to the coordinate system S1, namely the quaternion multiplication is obtained
Figure BDA00029009890700000612
Figure BDA00029009890700000613
Relative attitude of sensor IMU2 with respect to IMU1
Figure BDA00029009890700000614
This can be derived from equation (7):
Figure BDA00029009890700000615
the method for finding the optimal solution by using the least square estimation can establish a new cost function (OA constraint) for the two joint constraint error conditions and obtain the joint axis coordinates
Figure BDA00029009890700000616
The optimal solution of (2):
Figure BDA00029009890700000617
third, data acquisition
In order to verify the effectiveness of the joint axis calibration optimization device, a mechanical simulation experiment of the joint axis calibration device is developed. The motion sensing data of thighs and cruses in a joint axis calibration experiment are collected by IMU wireless inertial sensors attached to different body sections of the lower-limb exoskeleton robot. The nine-axis sensor includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. Wherein, the range of the accelerometer is set to be +/-16G, the range of the gyroscope is +/-500 degrees/s, the range of the magnetometer is +/-1 Ga, and the sampling frequency is 51.2 Hz.
As shown in fig. 5, the alignment apparatus of IMU1 and IMU2, respectively, is attached to a lower extremity exoskeleton robot body segment, wherein the thigh and calf are articulated by hinges.
In order to compare the differences of the joint axis calibration device under the influence of different experimental environments, a magnetic interference environment was created by using 2 notebook computers (as shown in fig. 6).
2 groups of experiments are designed in total, namely a Non-magnetic interference environment (Non-Mag) and a magnetic interference environment (Mag). The lower limb exoskeleton robot controls the left knee joint and the right knee joint to repeatedly perform bending-stretching movement at a certain fixed speed through software, and the experimental time of each group is 1 minute.
As shown in FIG. 7, during the flexion-extension movement of the knee joint of the lower-limb exoskeleton robot, the value of the joint main axis j2 is consistent with the Z-axis direction of the sensor, so the reference joint axis is set to be[0 0 1]T. To evaluate the device accuracy, the present study calculated the angle between joint axis j2 and the reference joint axis as the estimated angle error, with the results of the T-test shown in table 1.
TABLE 1 OA and TA constraints joint axis estimation error and T test result under magnetic interference-free and magnetic interference-free environment
Figure BDA0002900989070000071
As can be seen from table 1, the angular error of OA constraint under no magnetic interference environment (3.71 ° ± 1.96 °) is significantly lower than TA constraint (6.19 ° ± 3.97 °). Because TA constraint does not adopt magnetic induction vector data, the algorithm is small in magnetic field environment, and the error of the algorithm angle has no significant difference under the Non-Mag condition and the Mag condition.
On the other hand, the constraint is optimized by using the quaternion position information of the sensor in the OA constraint, which is influenced by magnetic interference more than the TA constraint, but the OA constraint has an angular error (4.65 ° ± 2.04 °) in the magnetic interference environment, which is significantly smaller than the TA constraint (6.46 ° ± 3.05 °).
Therefore, the dynamic optimization device for the joint axis provided by the embodiment of the invention effectively improves the calibration precision of the joint axis.
The embodiment of the invention provides a limb joint axis calibration device suitable for the actual joint axis calibration condition. The method firstly assumes that a human motion joint is a hinge joint, establishes joint constraint conditions, uses least square estimation to find an optimal solution, creates a cost function and realizes optimal estimation of a local joint axis. Then, aiming at the real joint dynamic calibration condition that the limb movement amplitude at one end is obviously smaller than that at the other end, a new joint constraint error condition is provided, and a new least square cost function is established by combining the angular velocity and the sensor direction, so that the calibration method is more suitable for the real joint axis calibration condition, can be applied to the fields of rehabilitation, life, entertainment and the like of disabled people, and can greatly obtain better social and economic values.
In the embodiment of the present invention, except for the specific description of the model of each device, the model of other devices is not limited, as long as the device can perform the above functions.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A MEMS sensor based human limb joint axis calibration device, the device comprising: an IMU measurement unit for measuring the IMU,
the IMU measurement unit takes a human motion joint as a hinge joint, establishes a human joint constraint condition, uses a method of least square estimation to find an optimal solution to establish a cost function and realizes optimal estimation of a local joint axis;
a new joint constraint error condition is provided for the actual joint dynamic calibration condition, a new least square cost function is established by combining the angular velocity and the sensor azimuth, and an optimal solution is obtained, so that the calibration device is more suitable for actual joint axis calibration.
2. The device of claim 1, wherein the device uses data of a set of dynamic actions performed by a subject in combination with kinematic constraint equations to achieve sensor-limb calibration.
3. The MEMS sensor-based human limb joint axis calibration device of claim 1, wherein the device fuses 9-axis inertial sensor data for joint axis estimation.
4. The MEMS sensor based human limb joint axis calibration device of claim 1, wherein the human joint constraints are:
obtaining two joint axis vectors j based on dynamic joint calibration1、j2The projections in a fixed coordinate system should coincide with each other, i.e. the projections are projected in a fixed coordinate system
Figure FDA0002900989060000011
When the coordinate system is transferred from the self coordinate system S2 to the relatively fixed S1 coordinate system
Figure FDA0002900989060000012
And
Figure FDA0002900989060000013
the materials are completely overlapped with each other,
Figure FDA0002900989060000014
and
Figure FDA0002900989060000015
direction vectors of the joint axis in the S1 and S2 coordinate systems, respectively.
5. The device for calibrating joint axes of human limbs based on MEMS sensor as claimed in claim 4, wherein the method for finding the optimal solution using least square estimation to create the cost function and realize the optimal estimation of local joint axes comprises:
Figure FDA0002900989060000016
where C is the cost function, N is the number of samples, ω1、ω2Angular velocity information collected for sensors IMU1 and IMU2, respectively.
6. The MEMS sensor-based human limb joint axis calibration device of claim 4, wherein the new joint constraint error condition is recorded as
Figure FDA0002900989060000021
And
Figure FDA0002900989060000022
the included angle between:
Figure FDA0002900989060000023
7. the MEMS-sensor based human limb joint axis calibration device of claim 6, wherein the optimal solution is:
Figure FDA0002900989060000024
wherein the content of the first and second substances,
Figure FDA0002900989060000025
joint axis information for the sensors IMU1 and IMU2 in their respective coordinate systems,
Figure FDA0002900989060000026
the relative attitude of the sensor IMU2 with respect to the IMU1,
Figure FDA0002900989060000027
is a multiplication of quaternions.
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