Single-target-point motion artifact interference elimination method based on multiple reference points

Abstract

The invention relates to a method for eliminating motion artifact interference in a single-target motion data acquisition system, the acquisition system comprises a single target, a plurality of reference points and a terminal device, the terminal device is used for receiving and processing motion data, the method comprises the following steps: A1, a data acquisition and arrangement step: acquiring a motion data sequence comprising a single target point and a plurality of reference points; a2, analyzing the relevance between the single target point and the plurality of reference points: calculating a relevance index between each reference point and the single target point, and determining a time delay matching point and a relevance degree; a3, constructing a multi-reference-point weighting model: constructing the weighting model according to the relevance, introducing a stability index to correct a weighting coefficient, and constructing a single-target-point data estimation model based on multiple reference points; and A4, model optimization and artifact elimination: optimizing the weighted model by adopting a least square method to obtain an optimized single target point data sequence after motion artifact interference is eliminated.

Description

Technical Field

[0001] The present invention relates to the technical field of motion artifact processing, and in particular to a method for eliminating motion artifact interference of a single target point by using multiple reference points. Background Art

[0002] In many fields involving the collection of moving target data, such as wearable devices in biomedical monitoring collecting physiological signals and motion tracking systems recording object motion data, motion artifacts often seriously interfere with the accuracy of single target point data.

[0003] CN202011344388.9 discloses a gastrointestinal motility examination system, including a gastrointestinal motility capsule, a positioning device and a terminal device, wherein the gastrointestinal motility capsule includes a shell and a magnet, and the positioning device includes at least two magnetic sensors; the gastrointestinal motility capsule is used to generate a changing magnetic field when it moves with the peristalsis of the gastrointestinal tract in the body of the subject; the magnetic sensor is used to collect the intensity data of the magnetic field and send the intensity data to the terminal device. The terminal device determines the position, posture information and movement frequency of the gastrointestinal motility capsule in the gastrointestinal tract based on the intensity data.

[0004] However, the positioning device in this system is fixed on the abdomen of the subject, and the subject needs to wear it continuously for 24-72 hours. During this process, the change in the subject's body posture will cause the relative position and direction between the positioning device and the subject's body to change, which is motion artifact interference. As a result, the position, posture and movement frequency information of the gastrointestinal motility capsule estimated by the system includes not only the normal movement of the gastrointestinal motility capsule caused by gastrointestinal peristalsis, but also the relative movement caused by motion artifact interference. Therefore, the positioning data obtained in this way has high error and inaccurate precision.

[0005] Traditional motion artifact processing methods are mostly aimed at image data, while the means for processing simply collected data are relatively limited. Some existing methods are difficult to fully utilize the information of multiple reference points. When eliminating motion artifacts of single target points, they cannot effectively balance the requirements of data accuracy and algorithm complexity, resulting in large errors in the processed single target point data, which affects subsequent analysis and application. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a single target point motion artifact interference elimination algorithm based on multiple reference points, which can accurately eliminate the motion artifact interference of the single target point by effectively integrating multiple reference point information, thereby improving the accuracy and reliability of the data.

[0007] To this end, the present invention provides a method for eliminating motion artifact interference in a single target motion data acquisition system, the acquisition system comprising: a single target, multiple reference points, and a terminal device, wherein the terminal device is used to receive and process the collected motion data of the single target and the multiple reference points, characterized in that the method comprises the following steps:

[0008] A1. Data collection and sorting steps: Collect motion data sequences including single target points and multiple reference points;

[0009] A2. Analyze the correlation between a single target point and multiple reference points: calculate the correlation index between each reference point and the single target point, determine the time delay matching point and the degree of correlation;

[0010] A3. Construction of multi-reference point weighted model: A weighted model is constructed based on correlation, and the stability index is introduced to correct the weighted coefficient to construct a single target point data estimation model based on multiple reference points;

[0011] A4. Model optimization and artifact elimination: The weighted model is optimized using the least squares method to obtain an optimized single target point data sequence after eliminating motion artifact interference.

[0012] Preferably, the method further includes: A5. Data verification and quality assessment: using root mean square error and correlation coefficient indicators to perform quality assessment on the single target point data before and after processing, verifying the effectiveness of the algorithm and adjusting parameters or models as needed.

[0013] Preferably, in the method, step A1, collects a motion data sequence including a single target point and multiple reference points, wherein the collected single target point data sequence is S(t)={s1,s2,...,s n}, where t represents the time series, s i is the data value of the edible positioning marker (2) at time i; at the same time, the data sequence of m reference points is collected, and the data sequence of the jth reference point is recorded as R j (t) = {r j1 ,r j2 , ..., r jn}, j = 1, 2, ..., m; perform preliminary sorting on the collected data and remove obviously abnormal data points.

[0014] Preferably, in the method, the obviously abnormal data points are judged and eliminated by setting a data threshold range.

[0015] Preferably, in step A2: a cross-correlation function is used to measure the correlation. For the j-th reference point and the single target point, the cross-correlation function C s，Rj (τ) is calculated as follows:

[0016]

[0017] in is the mean of the single target point data sequence, is the mean value of the jth reference point data sequence, τ is the time delay parameter, and its value range is [-τ max ,τ max ], by analyzing the peak position and size of the cross-correlation function, the best matching point and the degree of correlation between each reference point and the single target point in time delay are determined.

[0018] Preferably, in step A3: let the weighting coefficient be ω j , and introduce the stability index St j , the stability index can be obtained by calculating the variance of the reference point data sequence , that is,

[0019] And the weighting coefficient is corrected, the corrected weighting coefficient ω' j The calculation formula is:

[0020]

[0021] Construct a single target point data estimation model based on multiple reference points:

[0022]

[0023] Where Δt j is the time delay compensation value between the jth reference point and the single target point determined according to the cross-correlation function.

[0024] Preferably, the initial value is preliminarily allocated according to the degree of association. The reference point with a higher degree of association has a higher weight coefficient ω. j The bigger.

[0025] Preferably, in step A4: the weighted model is optimized using the least squares method to minimize the estimated value S ′ (t) and the original single target point data S(t), where the error function is By calculating the weighting coefficient ω' j Find the partial derivative and set it equal to zero to get a set of equations. Solving the set of equations can get the optimized weighting coefficient ω" j , substitute the optimized weighting coefficient into the data estimation model to obtain the single target point data sequence S"(t) after eliminating motion artifact interference.

[0026] Preferably, in step A5: the root mean square error (RMSE) and correlation coefficient (CC) indicators are used to evaluate the quality of the single target point data before and after eliminating motion artifact interference, wherein the RMSE calculation formula is:

[0027]

[0028] The relevant CC coefficient calculation formula is:

[0029]

[0030] The effectiveness of the algorithm is verified based on the evaluation results. If the results do not meet the expected standards, return to step A2 or step A3 to adjust parameters or rebuild the model.

[0031] Advantages of the present invention:

[0032] 1. Utilization of multiple reference points: By making full use of the information of multiple reference points, compared with the traditional method that only relies on a single reference point or does not use reference points, it can more comprehensively capture the motion information related to a single target point, thereby more effectively eliminating motion artifact interference.

[0033] 2. Accuracy and reliability: Through correlation analysis, weighted model construction and optimization, the relationship between the reference point and the single target point is accurately determined, and the weighting coefficient is reasonably allocated, which improves the accuracy and reliability of the single target point data after processing, providing more powerful data support for subsequent analysis and decision-making based on the single target point data.

[0034] 3. Adaptability and flexibility: The selection of reference points, correlation analysis parameters, and weighted model parameters can be adjusted according to different data collection scenarios and requirements. It has strong adaptability and flexibility and can be widely used in a variety of single-target point data processing fields involving motion artifact interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0036] The invention discloses a motion artifact interference elimination method, which is used for eliminating motion artifact interference in a single target motion data acquisition system.

[0037] A common single-target motion data acquisition system may be, for example, a gastrointestinal motility detection device, which includes an ingestible positioning marker that can move with the peristalsis of the gastrointestinal tract. The acquisition system also includes a plurality of auxiliary markers, which are fixed on parts of the human body and basically do not move, except for the displacement caused by changes in human posture. The ingestible positioning marker serves as a single target, and the auxiliary markers fixed on parts of the human body serve as multiple reference points. The system also includes a terminal device, such as a computer, a mobile device such as a mobile phone or an iPad, or a dedicated control instrument. The terminal device can be used to receive and process the collected motion data of the single target and multiple reference points, such as magnetic field strength or position and direction parameters determined according to the magnetic field strength, and process these motion data according to instructions.

[0038] like Figure 1 As shown, the method for eliminating motion artifact interference of the present invention includes the following steps.

[0039] Step A1. Data collection and sorting: collect motion data sequences including a single target point and multiple reference points, sort the data preliminarily, and remove abnormal data points;

[0040] Step A2. Correlation analysis between reference points and target points: Calculate the correlation index between each reference point and a single target point, and determine the time delay matching point and the degree of correlation;

[0041] Step A3. Construction of multi-reference point weighted model: construct a weighted model based on correlation, introduce stability index to correct weighted coefficient, and construct a single target point data estimation model based on multi-reference points;

[0042] Step A4. Model optimization and artifact elimination: The weighted model is optimized using the least square method to obtain a single target point data sequence after eliminating motion artifact interference;

[0043] Step A5. Data verification and quality assessment: Use indicators such as root mean square error and correlation coefficient to evaluate the quality of single target point data before and after processing, verify the effectiveness of the algorithm and adjust parameters or models as needed.

[0044] In step A1: collect motion data sequences including a single target point and multiple reference points, wherein the collected single target point data sequence is S(t)={s1,s2,...,s n}, where t represents the time series, s i is the data value of the edible positioning marker 2 at time i; at the same time, the data sequence of m reference points is collected, and the data sequence of the jth reference point is recorded as R j (t) = {r j 1,r j 2, ..., r jn}, j = 1, 2, ..., m; preliminarily organize the collected data and remove obviously abnormal data points (such as data mutations caused by sudden noise interference of abnormal data points caused by equipment jitter, electromagnetic interference, etc. during data collection). The obviously abnormal data points can be judged and removed by setting the data threshold range (which can be determined based on prior knowledge or previous experimental data).

[0045] In step A2: calculate the correlation index between each reference point and the single target point. The cross-correlation function is used to measure this correlation. For the jth reference point and the single target point, the cross-correlation function C s，Rj (τ) is calculated as follows:

[0046]

[0047] in is the mean of the single target point data sequence, is the mean value of the jth reference point data sequence, τ is the time delay parameter, and its value range is [-τ max ,τ max ], by analyzing the peak position and size of the cross-correlation function, the best matching point and correlation degree of each reference point and single target point in time delay are determined. For example, when calculating the cross-correlation function, τ max It is a certain proportion (such as 10%) of the data sequence length to comprehensively search for the best time delay matching point.

[0048] Step A3, construct a weighted model based on the correlation between the reference point and the single target point: let the weight coefficient be ω j , its initial value is preliminarily allocated according to the degree of association. The higher the degree of association, the higher the weight coefficient ω j At the same time, considering the stability of data at different reference points, the stability index St is introduced j , the stability index can be obtained by calculating the variance of the reference point data sequence , that is, And the weighting coefficient is corrected, the corrected weighting coefficient ω' j The calculation formula is:

[0049]

[0050] Construct a single target point data estimation model based on multiple reference points:

[0051]

[0052] Where Δt j is the time delay compensation value between the jth reference point and the single target point determined according to the cross-correlation function.

[0053] Step A4, model optimization and artifact elimination: the weighted model is optimized using the least squares method to minimize the estimated value S ′ (t) and the original single target point data S(t), where the error function is By calculating the weighting coefficient ω' j Find the partial derivative and set it equal to zero to get a set of equations. Solving the set of equations can get the optimized weighting coefficient ω" j , substitute the optimized weighting coefficient into the data estimation model to obtain the single target point data sequence S"(t) after eliminating motion artifact interference.

[0054] In step A5, data verification and quality assessment: the root mean square error (RMSE) and correlation coefficient (CC) indicators are used to evaluate the quality of the single target point data before and after eliminating motion artifact interference, where the RMSE calculation formula is:

[0055]

[0056] The relevant CC coefficient calculation formula is:

[0057]

[0058] The effectiveness of the algorithm is verified based on the evaluation results. If the results do not meet the expected standards, return to step A2 or step A3 to adjust parameters or rebuild the model.

[0059] In the embodiment of the above method, during the data collection and sorting stage, the accuracy and stability of the data collection equipment are ensured, and the data of a single target point and multiple reference points are collected according to a predetermined sampling frequency. For example, in biomedical monitoring, a single target point may be a monitoring point for the displacement of dissolved matter in the colon, and a reference point may be a monitoring point in other relatively stable parts of the body, but the present invention is not limited to this. For the collected data, by setting a reasonable data threshold (which can be determined based on prior knowledge or previous experimental data), abnormal data points caused by equipment jitter, electromagnetic interference, etc. are removed.

[0060] In the step of analyzing the correlation between the reference point and the target point, for each reference point, the cross-correlation function between it and the single target point at different time delays is calculated. For example, when calculating the cross-correlation function, τ max is a certain proportion of the data sequence length (such as 5-15%) to comprehensively search for the best time delay matching point. By analyzing the peak value of the cross-correlation function, Δt j and the degree of association, providing a basis for constructing a weighted model.

[0061] When constructing a multi-reference point weighted model, first roughly assign the weight coefficient ω according to the degree of association jFor example, the initial weight coefficient of the reference point with the highest correlation is set to 0.5, and other reference points are allocated between 0 and 0.5 according to their relative correlation. Then the stability index St of the reference point is calculated. j , and correct the weighting coefficient ω' according to the formula j Substitute the corrected weighting coefficient and time delay compensation value into the data estimation model to obtain a preliminary single target point data estimate.

[0062] In the model optimization and artifact elimination stage, the least squares method is used to solve the optimized weight coefficient ω" j By constructing the error function and finding the partial derivative, we get a set of equations, which can be solved by matrix operations and other methods. Substituting the optimized weighted coefficients into the model, we get the final single target point data sequence after eliminating motion artifacts.

[0063] Finally, in the data verification and quality assessment stage, calculate indicators such as RMSE and CC. If RMSE is too large or CC is too small, it means that the processing effect is not ideal. At this time, check whether the reference point selection is reasonable, adjust the parameters in the correlation analysis (such as τ max ), rebuild the weighted model or optimize the model parameters until a satisfactory processing result is obtained.

[0064] The above content is only used as an example, and the present invention is not limited thereto, but can be applied to any motion artifact interference of single target motion data based on multiple reference points. The following are other application scenarios of single target motion data collection based on multiple reference points.

[0065] -Cardiovascular disease monitoring

[0066] In Holter monitoring, the electrode patch is fixed at a relatively stable position on the patient's chest as a reference point, and the heart is used as a single target point. When the patient is engaged in daily activities, the artifacts generated by body movement will interfere with the ECG signal and affect the accurate diagnosis of heart disease. This algorithm can use the signals collected by multiple electrode patches as a reference to eliminate the motion artifact interference of the single target point (heart electrical activity signal) caused by body movement (such as walking, arm swinging, etc.), and more accurately analyze cardiovascular disease conditions such as arrhythmia.

[0067] For patients with implanted cardiac pacemakers, the pacemaker itself can be used as a single target point, and the surrounding tissues or micro-sensors fixed to the ribs and other locations can be used as reference points. When the patient's body posture changes or exercises, the pacemaker and surrounding tissues may be relatively displaced, thus affecting the accuracy of the pacemaker's working status monitoring data. The algorithm processes the collected data and can effectively remove motion artifacts, ensuring that doctors can accurately assess the pacemaker function and the patient's heart condition, and promptly identify potential problems and adjust treatment plans.

[0068] -Sports training and rehabilitation assessment

[0069] During the training process of athletes, in order to analyze the joint movement trajectory and muscle force of athletes, sensors are usually pasted on key parts of the athletes' bodies (such as joints, muscle attachment points, etc.). Among them, a specific joint (such as the knee joint) is used as a single target point, and other sensors pasted on relatively stable parts (such as the pelvis, torso, etc.) are used as reference points. When athletes perform high-intensity training movements, the overall movement and vibration of the body will cause motion artifacts to interfere with the motion data collection of the target joint. Using this algorithm, the real movement of the target joint can be accurately restored. Coaches can optimize training plans and improve training effects accordingly, which also helps prevent sports injuries.

[0070] In the field of rehabilitation therapy, for example, for limb rehabilitation training for stroke patients, a single target point sensor is set on the patient's rehabilitation limb (such as the arm), and reference point sensors are set on other parts of the body (such as the shoulder, back, etc.). When patients perform rehabilitation training movements, limb shaking caused by uncoordinated muscle control and other reasons will produce motion artifacts, affecting the accurate assessment of rehabilitation progress. With the help of this algorithm, more accurate limb movement data can be obtained, and rehabilitation therapists can adjust the intensity and method of rehabilitation training based on this data to provide patients with more personalized and effective rehabilitation treatment plans.

[0071] -Virtual reality (VR) and augmented reality (AR) interaction

[0072] In VR / AR systems, the user's handheld interactive device (such as a handle, controller, etc.) is used as a single target point, and the head-mounted display device (HMD) or a sensor fixed in the surrounding environment is used as a reference point. When the user uses a VR / AR device for an immersive experience or interactive operation, the natural movement of the body (such as head rotation, body translation, etc.) will cause artifacts in the motion data of the interactive device. Applying this algorithm to process data can enable the system to more accurately track the user's interactive actions, improve the matching degree between the virtual environment and the real action, and enhance the realism and smoothness of the user experience. For example, more accurate aiming and operation feedback can be achieved in VR games, or more accurate operation guidance can be provided in AR-assisted maintenance.

[0073] In VR / AR-based motion capture applications, such as dance teaching and sports motion analysis, the limb joints of dancers or athletes are used as single target points, and cameras or sensors arranged around the venue are used as reference points. Due to the dancers' rapid limb movements and complex body posture changes, the collected motion data is easily affected by motion artifacts. This algorithm can restore more realistic and accurate motion postures, which can be used for teaching evaluation, motion correction or performance effect optimization, providing more valuable data support for professionals.

[0074] Those skilled in the art will appreciate that the foregoing examples are illustrative and not restrictive. It will be apparent to those skilled in the art upon reading the specification and studying the drawings that all permutations, enhancements, equivalents, and improvements are included within the true spirit and scope of the present invention. Therefore, the appended claims are intended to include all such modifications, permutations, and equivalents that fall within the true spirit and scope of these teachings.

Claims

1. A method for eliminating motion artifact interference in a single target motion data acquisition system, the acquisition system comprising: A single target, multiple reference points, and a terminal device, wherein the terminal device is used to receive and process the collected motion data of the single target and the multiple reference points, characterized in that the method comprises the following steps: A1. Data collection and sorting steps: Collect motion data sequences including single target points and multiple reference points; A2. Analyze the correlation between a single target point and multiple reference points: calculate the correlation index between each reference point and the single target point, and determine the time delay matching point and the degree of correlation; A3. Construction of multi-reference point weighted model: A weighted model is constructed based on correlation, and the stability index is introduced to correct the weighted coefficient to construct a single target point data estimation model based on multiple reference points; A4. Model optimization and artifact elimination: The weighted model is optimized using the least squares method to obtain an optimized single target point data sequence after eliminating motion artifact interference.

2. The method for eliminating motion artifact interference according to claim 1, characterized in that: Also includes: A5. Data verification and quality assessment: Use root mean square error and correlation coefficient indicators to evaluate the quality of single target point data before and after processing, verify the effectiveness of the algorithm and adjust parameters or models as needed.

3. The method for eliminating motion artifact interference according to claim 1 or 2, characterized in that: Step A1, collecting a motion data sequence including a single target point and multiple reference points, wherein the collected single target point data sequence is S(t)={s1,s2,...,s n }, where t represents the time series, s i is the data value of the edible positioning marker (2) at time i; at the same time, the data sequence of m reference points is collected, and the data sequence of the jth reference point is recorded as R j (t) = {r j1 ,r j2 , ..., r jn }, j = 1, 2, ..., m; perform preliminary sorting on the collected data and remove obviously abnormal data points.

4. The method for eliminating motion artifact interference according to claim 3, characterized in that: The data points with obvious abnormality are judged and eliminated by setting the data threshold range.

5. The method for eliminating motion artifact interference according to claim 1, characterized in that: In step A2: the cross-correlation function is used to measure the correlation. For the jth reference point and the single target point, the cross-correlation function C s，Rj (η) is calculated as follows: in is the mean of the single target point data sequence, is the mean value of the jth reference point data sequence, τ is the time delay parameter, and its value range is [-τ max ,τ max ], by analyzing the peak position and size of the cross-correlation function, the best matching point and the degree of correlation between each reference point and the single target point in time delay are determined.

6. The method for eliminating motion artifact interference according to claim 1, characterized in that: τ max 5-15% of the length of the entire data sequence.

7. The method for eliminating motion artifact interference according to claim 1, characterized in that: In step A3: let the weighting coefficient be ω j , and introduce the stability index St j , the stability index can be obtained by calculating the variance of the reference point data sequence , i.e. And the weighting coefficient is corrected, the corrected weighting coefficient ω' j The calculation formula is: Construct a single target point data estimation model based on multiple reference points: Where Δt j is the time delay compensation value between the jth reference point and the single target point determined according to the cross-correlation function.

8. The method for eliminating motion artifact interference according to claim 7, characterized in that: Its initial value is preliminarily allocated according to the degree of association. The higher the degree of association, the higher the weight coefficient ω j The bigger.

9. The method for eliminating motion artifact interference according to claim 1, characterized in that: In step A4: the weighted model is optimized using the least squares method to minimize the estimated value S ′ (t) and the original single target point data S(t), where the error function is By calculating the weighting coefficient ω' j Find the partial derivative and set it equal to zero to get a set of equations. Solving the set of equations can get the optimized weighting coefficient ω" j , substitute the optimized weighting coefficient into the data estimation model to obtain the single target point data sequence S"(t) after eliminating motion artifact interference.

10. The method for eliminating motion artifact interference according to claim 2, characterized in that: In step A5: the root mean square error (RMSE) and correlation coefficient (CC) indicators are used to evaluate the quality of the single target point data before and after eliminating motion artifact interference, where the RMSE calculation formula is: The relevant CC coefficient calculation formula is: The validity of the calculation results is verified based on the evaluation results. If the results do not meet the expected standards, return to step A2 or step A3 to adjust the parameters or rebuild the model.

11. The method for eliminating motion artifact interference according to claim 1, characterized in that: The single target and the multiple reference points are magnetic points containing magnetic substances, and the motion data are magnetic field intensity or position and direction parameters determined according to the magnetic field intensity.

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