Coupling angle calculation method for quantifying and explaining joint coordination

Abstract

The invention discloses a coupling angle calculation method for quantifying and explaining joint coordination, and belongs to the technical field of lower limb rehabilitation. According to the coupling angle calculation method established by the invention, the dynamic interaction between different joints or segments can be captured, and detailed information about how the interaction affects the overall motion performance is provided, so that a reliable result can be provided when a complex motion mode is processed. The coupling angle calculation method provided by the invention can further promote the development of the biomechanical field, and provides a valuable tool for the application research of improving human health and athletic ability.

Description

Technical Field

[0001] The present invention relates to the technical field of lower limb rehabilitation, and specifically to a coupling angle calculation method for quantifying and interpreting joint coordination. Background Art

[0002] In the field of lower limb rehabilitation, especially in the branch of rehabilitation gait analysis, the coordination between joints / segments plays a crucial role in deeply understanding and accurately evaluating human movement. Currently, the identification of coordination patterns mainly relies on the method of inference through angle-angle diagrams. However, when it is necessary to comprehensively consider the synchronous movement of multiple joints or segments, this method has limitations in accurately quantifying the coordination relationship between them. Therefore, there is an urgent need to explore a more efficient strategy to achieve the precise quantification and reasonable interpretation of the coordination between joints / segments, providing new evaluation indicators for the assessment of the lower limb rehabilitation process.

[0003] In view of the limitations of the existing methods in dealing with the analysis of multi-joint / segment coordination, the present invention proposes an innovative and more effective method. The implementation of this method will help to deeply understand the complexity of human movement and provide new evaluation indicators for the assessment of the lower limb rehabilitation process. Summary of the Invention

[0004] 1. Technical problems to be solved by the present invention

[0005] The purpose of the present invention is to propose a coupling angle calculation method for quantifying and interpreting joint coordination to solve the problems raised in the background art, quantifying and interpreting the coordination between joints / segments; the present invention expands the existing vector coding technology and introduces a set of operation terms to summarize the coordination patterns between joints / segments, including in-phase, anti-phase, proximal phase, and distal phase. The present invention provides a systematic method to analyze and interpret the coordination between joints / segments, providing new evaluation indicators for deeply understanding the movement organization and gait assessment.

[0006] 2. Technical solutions

[0007] To solve the above problems, the present invention provides the following technical solutions:

[0008] A coupling angle calculation method for quantifying and interpreting joint coordination, comprising the following steps:

[0009] S1. Data preparation: Using a three-dimensional motion capture system to collect the joint motion data of the subject, the joint motion data including the joint position and orientation information of each bone sampled at multiple time points;

[0010] S2. Data processing: Performing smoothing, interpolation, and noise removal processing on the joint motion data obtained in S1 to generate high-quality joint motion data;

[0011] S3. Establishment of joint coordinate system: Establish a local coordinate system for each joint and standardize the position and orientation of each joint;

[0012] S4. Angle calculation: Use mathematical functions to calculate the included angles between joints, as well as the instantaneous coupling angle, average coupling angle, and variability;

[0013] S5. Result analysis and visualization: Analyze and display the absolute angles of each joint, the coordination relationship between joints, and the relative motion relationship at each time point;

[0014] S6. Result output: Output the analysis data results obtained in S5.

[0015] Preferably, the S3 specifically includes the following contents:

[0016] S3.1. Establish a local coordinate system for each joint and calculate the absolute angle between joints;

[0017] S3.2. Use the position of the joint as the origin of the local coordinate system;

[0018] S3.3. Use the direction information of the bone to define the axes of the local coordinate system. Assume that the defined variable is a structured data containing the joint positions and directions of each bone, and use the defined variable to obtain the joint positions and bone directions;

[0019] S3.4. After establishing the local coordinate system, standardize the position and orientation of each joint at each time point to ensure data consistency.

[0020] Preferably, the S4 specifically includes the following contents:

[0021] S4.1. Determine the parent joint of the target joint using the fields in the defined variable structure array;

[0022] S4.2. Use mathematical functions to calculate the included angles between joints;

[0023] S4.3. Calculate the instantaneous coupling angle:

[0024] At each instant i of the standardized gait cycle, calculate the coupling angle γ Pi ,θ P(i+1) ) and the consecutive distal segment angles (θ Di ,θ D(i+1) ) according to the consecutive proximal segment angles (θ i , and the formula is as follows:

[0025]

[0026] The following conditions apply:

[0027]

[0028] The coupling angle γ i is corrected to a value between 0° and 360° by the following formula:

[0029]

[0030] S4.4. Calculate the average coupling angle and variability to reflect the characteristics of the data; Since the coupling angle has a direction, the average coupling angle is calculated using circular statistics based on the average value of the horizontal component x i and the vertical component y i at each instant:

[0031]

[0032] The average coupling angle is corrected to a value between 0° and 360° by the following formula:

[0033]

[0034] The length r of the average coupling angle i is calculated according to the following formula:

[0035]

[0036] The coupling angle variability CAV i is calculated according to the following formula:

[0037]

[0038] Preferably, S5 specifically includes the following:

[0039] S5.1. Record the absolute angles of each joint at each time point using a joint-angle matrix;

[0040] S5.2. Plot angle-angle diagrams and time series diagrams to show the coordination relationship between joints; Among them, the angle-angle diagram is used to display the relative movement trajectories of two joints during the entire movement cycle; The time series diagram is used to show the change of joint angles over time;

[0041] S5.3. Through the analysis results, identify the coordination patterns in the movement, judge the relative movement relationship between joints, and further understand the mechanical characteristics of the movement.

[0042] 3. Beneficial effects

[0043] (1) Accuracy: By using the coupling angle, the present invention can more accurately quantify the coordination relationship between joints / segments;

[0044] (2) Operability: The present invention introduces operation terms, making the summary of the coordination mode simpler and more intuitive;

[0045] (3) Wide application: The present invention is not only applicable to gait analysis, but also can be applied to other fields that require analysis of joint / segment coordination, such as rehabilitation medicine and sports training;

[0046] In summary, the coupling angle calculation method established by the present invention can capture the dynamic interactions between different joints or segments, and provide detailed information on how these interactions affect the overall motor performance, so as to provide reliable results when dealing with complex movement patterns. The coupling angle calculation method proposed by the present invention can further promote the development of the field of biomechanics, and provide a valuable tool for applied research to improve human health and motor ability. Brief Description of the Drawings

[0047] Figure 1 It is a method flow chart of a coupling angle calculation method for quantifying and interpreting joint coordination proposed in Embodiment 1 of the present invention;

[0048] Figure 2 It is an angular relative movement diagram, a polar coordinate diagram, a coupling angle time series diagram, and a statistical chart of the frequency of different patterns of the tibia and femur of a sagittal plane knee joint proposed in Embodiment 2 of the present invention;

[0049] Figure 3 It is an angular relative movement diagram, a polar coordinate diagram, a coupling angle time series diagram, and a statistical chart of the frequency of different patterns of the tibia and femur of a frontal plane knee joint proposed in Embodiment 2 of the present invention. Detailed Embodiment

[0050] The following combines the drawings and specific embodiments to describe in detail a coupling angle calculation method for quantifying and interpreting joint coordination provided by the present invention.

[0051] At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawing part is only for more specifically describing the embodiments, and is not intended to specifically limit the present invention.

[0052] The present invention proposes a coupling angle calculation method for quantifying and interpreting joint coordination, which is used to quantify and interpret the coordination between joints / segments, and particularly focuses on using the coupling angle to analyze and classify coordination modes. The present invention provides a powerful tool for gait researchers and provides new evaluation indicators for in-depth understanding of movement organization and gait assessment.

[0053] When using vector coding analysis to study movement coordination, a key challenge is how to handle the inherent circularity of the data. Vector coding analysis techniques can reveal the potential coordination mechanics of joints or segments, thus forming the overall movement organization. By quantifying the coupling angles that describe the relationship between joints or segments throughout the movement of interest, the measurement of movement coordination can be achieved. Specifically, the coupling angle is determined by calculating the movement of one segment relative to another. Then, using customized software and improved vector coding analysis techniques, the segment angle data relative to the experimental coordinate system is normalized to calculate the coordination between segments. This process involves creating an angle-angle plot between segment 1 and segment 2 and calculating the vectors created by each pair of consecutive time points on the angle-angle plot.

[0054] The calculation method proposed by the present invention is used to analyze and interpret the coordination between joints / segments, providing valuable insights into movement organization and gait. The following describes the coupling angle calculation method for quantifying and interpreting joint coordination proposed by the present invention in conjunction with specific drawings, including the following specific content.

[0055] Example 1:

[0056] Please refer to Figure 1 , the present invention proposes a coupling angle calculation method for quantifying and interpreting joint coordination, including the following content:

[0057] (1) Data preparation

[0058] Obtain the joint movement data of the subject. Assume that this data has been collected by a three-dimensional motion capture system and saved in a file. The file contains a defined variable that includes the joint positions and orientation information of each bone. This data typically includes samples at multiple time points to enable a comprehensive analysis of the subject's movement trajectory.

[0059] (2) Data processing

[0060] Data processing is a key step to ensure the quality and accuracy of the data. The raw data collected usually contains noise and outliers, so smoothing, interpolation, and noise removal are required. Common smoothing methods include the moving average method and low-pass filters. Interpolation methods can use spline interpolation or linear interpolation. Noise removal can be achieved through techniques such as wavelet transform or Kalman filter. The goal of this step is to generate high-quality joint movement data for subsequent angle calculation and analysis.

[0061] (3) Establishment of joint coordinate system

[0062] a. In order to calculate the absolute angles between joints, a local coordinate system needs to be established for each joint.

[0063] b. Use the position of the joint as the origin of the local coordinate system.

[0064] c. Use the orientation information of the bone to define the axes of the coordinate system. Assuming the defined variable is a structure array containing the joint positions and orientations of each bone, this variable can be used to obtain the joint positions and bone orientations.

[0065] d. After establishing the local coordinate system, we can standardize the positions and orientations of each joint at each time point to ensure data consistency.

[0066] (4) Angle calculation

[0067] a. Determine the parent joint of the target joint using the fields in the defined variable structure array.

[0068] b. Use mathematical functions such as "atan2" to calculate the angle between joints. For example, the "atan2" function can calculate the angle between two vectors, and the result is between -π and π. This ensures that the calculated angles have a unified standard for subsequent analysis.

[0069] c. Calculate the instantaneous coupling angle

[0070] At each instant i of the standardized gait cycle, calculate the coupling angle based on the consecutive proximal segment angles (θ Pi , θ P(i+1) ) and consecutive distal segment angles (θ Di , θ D(i+1) ) using the following formula:

[0071]

[0072] The following conditions apply:

[0073]

[0074] The coupling angle (γ i ) is corrected to a value between 0° and 360° using the following formula:

[0075]

[0076] d. Calculate the average coupling angle and variability to reflect the characteristics of the data.

[0077] Since the coupling angle has a direction, the average coupling angle is calculated using circular statistics based on the average of the horizontal (x i ) and vertical (y i ) components at each instant:

[0078]

[0079] The following formula is used to correct the average coupling angle which is a value between 0° and 360°:

[0080]

[0081] The length (r i ) of the average coupling angle is calculated according to the following formula:

[0082]

[0083] The coupling angle variability (CAV i ) is calculated according to the following formula:

[0084]

[0085] (5) Result analysis and visualization

[0086] a. Use the joint-angle matrix to record the absolute angles of each joint at each time point.

[0087] b. Plot the angle-angle diagram and the time series diagram to visually display the coordination relationship between joints. The angle-angle diagram can show the relative movement trajectories of two joints during the entire movement cycle, while the time series diagram can show the change of joint angles over time.

[0088]

[0089] c. By analyzing the data results, the coordination patterns in the movement can be identified, the relative movement relationship between joints can be judged, and the mechanical characteristics of the movement can be further understood.

[0090] (6) Output the results.

[0091] Example 2:

[0092] Based on Example 1 but different in that, please refer to Figures 2-3 , by analyzing the coupling angles of the femur and the tibia in the sagittal plane and the coronal plane (also called the frontal plane), the dynamic coupling patterns between the two in different movement states are revealed. This analysis method provides a scientific basis for gait optimization, motion analysis, and rehabilitation therapy through the display of four subgraphs. The following is a specific description of the functions of the four subgraphs, and the coupling analysis between the sagittal plane and the coronal plane is discussed in detail.

[0093] 1. Absolute angle-angle diagram ( Figure 2 , 3 upper left corner)

[0094] This sub - figure shows the angular variation relationship between the femur and the tibia in the sagittal plane (anteroposterior direction). During the gait cycle, the flexion and extension angles of the femur and the tibia change over time. The sagittal plane angle - angle diagram helps analyze the relationship between the flexion and extension angles of the femur and the tibia, revealing their coordinated movements during the stance and swing phases of gait. Through this diagram, the flexion and extension synchronization of the femur and the tibia during the gait cycle can be evaluated, as well as their dynamic adjustments in the sagittal plane.

[0095] 2. Coupling Angle Polar Coordinate Diagram ( Figure 2 、 3 upper right corner)

[0096] This diagram shows the variation of the coupling angle between the femur and the tibia in the sagittal and coronal (frontal) planes, presented in polar coordinate form. In the polar coordinate diagram, the coupling angle between the femur and the tibia is presented as a combination of angle and amplitude, thus showing the coupling pattern between the two during different movement stages. By analyzing the distribution of the coupling angle, the polar coordinate diagram helps researchers understand the cooperative action between the femur and the tibia during the gait cycle, especially how different coupling characteristics are manifested during different stages of gait (such as walking and running).

[0097] 3. Coupling Angle Diagram ( Figure 2 、 3 lower left corner)

[0098] This sub - figure shows the variation of the coupling angle between the femur and the tibia over time or the gait cycle. During the stance and swing phases of gait, the coupling angle between the femur and the tibia may exhibit different dynamic changes, reflecting their coordination and stability. By analyzing the coupling angle between the femur and the tibia in the sagittal and coronal planes, this diagram can evaluate the interaction of the lower limb joints during movement, especially the coupling changes between the two during different phases of gait. The coupling angle diagram helps analyze the dynamic coordination between the femur and the tibia during gait and identify the differences between normal and abnormal gaits.

[0099] 4. Coupling Pattern Statistical Diagram ( Figure 2 、 3 lower right corner)

[0100] This sub - figure identifies the coupling patterns between the femur and the tibia under different movement states through statistics on a large amount of movement data. By statistically analyzing the distribution of the coupling angles in different gait patterns (such as normal gait, abnormal gait, or gait under sports injury conditions), it helps discover the differences in the coupling patterns between the femur and the tibia among different populations. This diagram is of great significance for analyzing gait abnormalities and the recovery process of sports injuries. Especially in the fields of rehabilitation therapy and sports medicine, by identifying and classifying the coupling patterns, it provides a theoretical basis for individualized treatment and sports intervention.

[0101] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and its improved concept of the present invention, making equivalent substitutions or changes should be covered by the protection scope of the present invention.

Claims

1. A coupling angle calculation method for quantifying and explaining joint coordination, characterized in that: The following steps are involved: S1. Data preparation: using a three-dimensional motion capture system to collect the subject's joint motion data, the joint motion data including the joint position and direction information of each bone obtained by sampling at multiple time points; S2, data processing: smoothing, interpolation and noise removal are performed on the joint motion data obtained in S1 to generate high-quality joint motion data; S3, establishment of joint coordinate system: establish a local coordinate system for each joint and standardize the position and orientation of each joint; S4, angle calculation: use mathematical functions to calculate the angles between joints as well as the homeotropic coupling angle, average coupling angle and variability; S5. Result analysis and visualization: Analyze and display the absolute angles of each joint, the coordination relationship between joints, and the relative motion relationship at each time point; S6. Result output: output the analysis data results obtained in S5.

2. A coupling angle calculation method for quantifying and explaining joint coordination according to claim 1, characterized in that: The S3 specifically includes the following contents: S3.

1. Establish a local coordinate system for each joint and calculate the absolute angles between the joints. S3.2, use the position of the joint as the origin of the local coordinate system; S3.3, using the orientation information of the bones to define the axis of the local coordinate system, assuming that the defined variable is a structure data containing the joint position and orientation of each bone, use the defined variable to obtain the position of the joint and the orientation of the bone; S3.

4. After establishing the local coordinate system, the position and orientation of each joint are standardized at each time point to ensure data consistency.

3. A coupling angle calculation method for quantifying and explaining joint coordination according to claim 1, characterized in that: The S4 specifically includes the following contents: S4.

1. Determine the parent joint of the target joint using the fields in the defined variable structure array; S4.

2. Use mathematical functions to calculate the angles between joints; S4.

3. Calculate the instantaneous coupling angle: At each instant i of the standardized gait cycle, the continuous proximal segment angle (θ Pi ,θ P(i+1) ) and continuous distal segment angles (θ Di ,θ D(i+1) ) Calculate the coupling angle γ i , the formula is as follows: The following conditions apply: Set the coupling angle γ i Corrected to a value between 0° and 360°, the formula is as follows: S4.

4. Calculate the average coupling angle and variability to reflect the characteristics of the data. Since the coupling angle is directional, the average coupling angle γ i Based on the horizontal component x at each moment i and the vertical component y i The mean of is calculated using the circular statistics method: The average coupling angle is corrected by the following formula For values ​​between 0° and 360°: The length of the average coupling angle r i Calculated according to the following formula: Coupling Angle Variability CAV i Calculated according to the following formula:

4. A coupling angle calculation method for quantifying and explaining joint coordination according to claim 1, characterized in that: The S5 specifically includes the following contents: S5.

1. Use the joint-angle matrix to record the absolute angle of each joint at each time point. S5.

2. Draw angle-angle diagrams and time series diagrams to show the coordination relationship between joints; the angle-angle diagram is used to show the relative motion trajectory of two joints in the entire motion cycle; the time series diagram is used to show the change of joint angles over time; S5.

3. By analyzing the results, identify the coordination patterns in the movement, determine the relative movement relationship between joints, and further understand the mechanical characteristics of the movement.

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