Joint motion range evaluation method for postoperative rehabilitation training of elderly patient

Abstract

The invention relates to the technical field of joint motion analysis, in particular to a joint motion range evaluation method for postoperative rehabilitation training of an elderly patient. The method comprises the following steps: acquiring a joint association anomaly degree according to an anomaly degree of a target joint point motion condition at each moment, and dividing an action period into similar sub-periods; a motion lag factor is obtained according to the difference of the movement angle changes of the target joint point and the adjacent joint point in the similar sub-time period, adjustment is conducted through the change degree of the movement angle range of the adjacent similar sub-time period, and a joint rigidity coefficient is obtained; according to the degree of correlation between the joint association abnormality and the joint rigidity coefficient, adjusting the joint rigidity coefficient and the joint association abnormality of the similar sub-period in the test period, and obtaining a dysfunction coefficient; and evaluating the postoperative rehabilitation training joint motion range of the elderly patient by using the dysfunction coefficient. According to the method, the joint dynamic cooperation mode and the space-time delay effect are considered, and the recognition sensitivity of the abnormal joint movement is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of joint motion analysis technology, specifically to a method for assessing joint range of motion during postoperative rehabilitation training in elderly patients. Background Technology

[0002] Joint range of motion refers to the arc of motion a joint can move. It is an important indicator for assessing the function of muscles, bones and the nervous system. It not only reflects the flexibility of the joint, but also serves as an important basis for diagnosing joint diseases and developing rehabilitation plans. Therefore, the recovery of joint range of motion is crucial for elderly patients during postoperative rehabilitation.

[0003] The prior art patent document CN116250831A discloses a method for calculating shoulder joint range of motion based on visual recognition. Specifically, it involves acquiring a frontal human image of the test subject performing a test action, extracting the first coordinates of the joint markers from the image, calculating a first length value and a second length value, and then obtaining the joint angle data for arm extension and elevation to obtain a shoulder joint range of motion score. However, postoperative nerve edema leads to delayed nerve signal transmission, manifested as a delayed response in the distal joint after the proximal joint movement command is issued. Furthermore, increased collagen deposition between joints in elderly patients after surgery increases joint viscous resistance, resulting in a decrease in joint range of motion. This significantly reduces the accuracy of identifying subtle abnormalities during postoperative rehabilitation, leading to low accuracy in assessing joint range of motion during postoperative rehabilitation training in elderly patients. Summary of the Invention

[0004] To address the technical problem of insufficient accuracy in identifying subtle abnormalities during postoperative rehabilitation due to the lack of analysis on the spatiotemporal delay effects induced by changes in kinetic chain biomechanical transmission efficiency and joint stiffness, this invention aims to provide a method for assessing joint range of motion during postoperative rehabilitation training in elderly patients. The specific technical solution adopted is as follows: This invention proposes a method for assessing joint range of motion during postoperative rehabilitation training in elderly patients, the method comprising: The tester obtains the three-dimensional coordinates and movement angles of the target joint and its adjacent joints at each moment during the test period; the test period includes different motion cycles. Based on the degree of abnormality of the target joint movement at each moment within the test period, the joint correlation anomaly degree at each moment is obtained; based on the joint correlation anomaly degree, each action cycle is divided into similar sub-time periods; Based on the difference in the change of the activity angle between the target joint and its adjacent joints in each similar sub-time period, the motion hysteresis factor of each similar sub-time period is obtained; based on the degree of change in the range of activity angle between adjacent similar sub-time periods in the test period, the motion hysteresis factor is adjusted to obtain the joint stiffness coefficient of each similar sub-time period. Based on the correlation between the joint correlation abnormality and the joint stiffness coefficient, the joint stiffness coefficient and joint correlation abnormality of similar sub-time periods within the test period are adjusted to obtain the functional impairment coefficient; the functional impairment coefficient is used to evaluate the joint mobility of elderly patients during postoperative rehabilitation training.

[0005] Furthermore, obtaining the joint association anomaly degree at each time step includes: Two adjacent joints of the target joint are designated as the first joint and the second joint, respectively. The direction from the first joint to the target joint is taken as the direction of the first vector, and the distance between the first joint and the target joint is taken as the magnitude of the first vector. The direction from the target joint to the second joint is taken as the direction of the second vector, and the distance between the second joint and the target joint is taken as the magnitude of the second vector. The angle between the first and second vectors at the same moment is obtained and recorded as the analysis angle at each moment. The rate of change of the analysis angle between any two adjacent moments is calculated and recorded as the joint linkage fluctuation at the next moment. The joint correlation anomaly degree at each moment is obtained based on the degree of deviation of the joint linkage fluctuation degree at each moment within the test period relative to the joint linkage fluctuation degree at all moments.

[0006] Furthermore, the step of dividing each action cycle into similar sub-time periods based on the joint correlation anomaly degree includes: For each action cycle, the first moment in the action cycle constitutes the initial undetermined time period. The first moment in the action cycle is recorded as the initial undetermined moment. When the difference between the joint correlation abnormality degree between the undetermined moment and the next adjacent moment is less than a preset threshold, the next adjacent moment of the undetermined moment is added to the undetermined time period, the undetermined time period is updated, and the next moment of the undetermined moment is recorded as the new undetermined moment. When the difference between the joint correlation anomaly degree of the pending time and the next adjacent time is greater than or equal to the preset threshold, the pending time period is recorded as a similar sub-time period, the next adjacent time of the pending time is recorded as a new pending time, and the new pending time constitutes a new pending time period. It is then determined whether the difference between the joint correlation anomaly degree of the new pending time and the next adjacent time is less than the preset threshold. All times within the action cycle are traversed, and the action cycle is divided into different similar sub-time periods.

[0007] Further, obtaining the motion lag factor for each similar sub-time period includes: The proximal joint of two adjacent joints of the target joint is denoted as the analysis joint. For each similar sub-time period, the activity angle of the analysis joint at all times within the similar sub-time period is curve-fitted. The fitted function is differentiated twice to obtain the angular acceleration function. The absolute value of the difference between the angular velocity functions of the two analysis joints is taken to obtain the response efficiency function. Obtain the peak point of the autocorrelation function of the response efficiency function within the range from the beginning to the middle of a similar sub-period time delay, and use the value of the time delay corresponding to the peak point as the propagation delay coefficient; Based on the activity angle at the first moment within the similar sub-period and the transmission delay coefficient, the motion lag factor of the similar sub-period is obtained.

[0008] Further, obtaining the joint stiffness coefficient for each similar sub-time period includes: The range of the activity angles of the target key point at all times within each similar sub-time period is denoted as the activity range scale. Arrange the activity range scales of all similar sub-time periods within the test period in chronological order to obtain a scale sequence; calculate the difference between every two adjacent activity range scales in the scale sequence, and average all differences to obtain the joint activity attenuation coefficient; The joint stiffness coefficient for each similar sub-time period is obtained based on the joint activity attenuation coefficient and the motion hysteresis factor for each similar sub-time period.

[0009] Furthermore, the acquisition of the functional impairment coefficient includes: The mean of the joint association anomaly degree at all times within the similar sub-time periods is calculated and recorded as the overall smoothness; the overall smoothness of all similar sub-time periods within the test period and the joint stiffness coefficient are arranged in time sequence to obtain the smooth sequence and the stiffness sequence in turn; Using the sum of the correlation coefficient between the smooth sequence and the rigid sequence and the constant 1, the sum of the overall smoothness and the joint rigidity coefficient of similar sub-time periods is weighted to obtain the local obstacle coefficient; the mean of the local obstacle coefficients of all similar sub-time periods within the test period is normalized to obtain the functional impairment coefficient.

[0010] Furthermore, obtaining the joint association anomaly degree at each time step includes: The mean and standard deviation of the joint linkage fluctuation at all times during the test period are calculated respectively. The ratio of the joint linkage fluctuation at each time point as the numerator and the sum of the mean and twice the standard deviation as the denominator is normalized to obtain the joint linkage anomaly degree at each time point.

[0011] Furthermore, the method for curve fitting the activity angle of the analysis joint at all times within similar sub-time periods is the least squares method.

[0012] Furthermore, both the joint motion attenuation coefficient and the motion hysteresis factor are positively correlated with the joint stiffness coefficient.

[0013] Furthermore, the correlation coefficient is the Pearson correlation coefficient.

[0014] The present invention has the following beneficial effects: In this embodiment of the invention, the fluctuation of the target joint's motion state at adjacent time points reflects the asynchrony in the motion direction of adjacent joints. Abrupt changes in the linkage between adjacent joints exceeding the patient's normal fluctuation range can cause a sudden break in the linkage. The degree of linkage abrupt change between adjacent joints during motion can be analyzed to obtain the joint correlation abnormality degree. This degree can be used to divide similar sub-time periods, capturing minute local abnormalities and phase lag, achieving early warning and precise localization of joint linkage abnormalities. Postoperative nerve edema leads to delayed nerve signal transmission. By analyzing the difference in the change of the motion angle between the target joint and adjacent joints within similar sub-time periods, the instantaneous motion response efficiency from distal to proximal joints can be analyzed, yielding the motion lag factor and the degree of change in the range of motion angles between adjacent similar sub-time periods. This study comprehensively covers the spatial pathological characteristics of joint movement, quantifies spatial abnormalities during the movement maintenance phase, integrates two factors to construct a joint stiffness coefficient, and considers the spatiotemporal delay effect of joint movement. When joint stiffness and synergistic fracture are spatiotemporally coupled within a specific angular range, it indicates the existence of pathological kinetic chain failure, significantly underestimating the actual loss of joint range of motion. The degree of joint correlation abnormality and the joint stiffness coefficient reflect the degree of abnormal linkage between adjacent joints and the spatiotemporal delay effect during movement, respectively. The correlation between the two indicates the possibility of pathological kinetic chain failure of the joint, which can be used for adjustment. By considering the joint dynamic synergistic mode and the spatiotemporal delay effect, the sensitivity of joint movement abnormality identification is significantly improved, the accuracy of joint range of motion assessment in postoperative rehabilitation training of elderly patients is improved, rehabilitation programs are optimized, and the risk of missed detection is reduced. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the steps of a method for assessing joint range of motion in postoperative rehabilitation training for elderly patients, provided in one embodiment of the present invention. Figure 2 This is a flowchart illustrating a method for obtaining joint correlation anomaly degree according to an embodiment of the present invention. Figure 3 A flowchart illustrating a method for obtaining motion hysteresis factors according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a computer device for assessing joint mobility during postoperative rehabilitation training in elderly patients, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method for assessing joint mobility in postoperative rehabilitation training for elderly patients according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method for assessing joint mobility during postoperative rehabilitation training for elderly patients provided by this invention.

[0020] Example 1: This invention proposes a method for assessing joint range of motion during postoperative rehabilitation training in elderly patients. Please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a method for assessing joint mobility during postoperative rehabilitation training in elderly patients, provided by an embodiment of the present invention. The method includes: Step S1: Obtain the three-dimensional coordinates of the test subject's target joint and its adjacent joints at each moment during the test period; the test period includes different motion cycles.

[0021] Point the depth camera directly at the test subject, at a distance of 1.5 to 3 meters, to ensure the entire body is within the field of view. Keep the camera height level with the test subject's pelvis to minimize perspective distortion. Attach reflective markers to the target joint and its adjacent joints. Use the depth camera to capture images of the test subject as they perform the preset test actions. This process must ensure that the target joint and its adjacent joints remain visible at all times. Record the time interval corresponding to the test subject's performance of the preset test actions as the test period, obtaining the human image at each moment within that period. This solution uses a depth camera with a built-in skeletal tracking algorithm, which can directly provide the 3D coordinates of the reflective points (target joint and its adjacent joints) in each frame of the human image. It should be noted that the 3D coordinates are determined based on the depth map.

[0022] It should be noted that this plan uses the elbow joint as an example to assess the range of motion of the elbow joint during postoperative rehabilitation training. The target joint is the elbow joint, and the two adjacent joints of the elbow joint are the shoulder joint and the wrist joint. The preset test movement is to start with a 90-degree forward arm extension on one side and perform 5 repetitions of elbow flexion from 0 to 90 degrees. The time period corresponding to each elbow flexion movement is one movement cycle.

[0023] In one implementation of this invention, the frame rate of the depth camera is set to 120 frames per second.

[0024] Step S2: Based on the degree of abnormality of the target joint motion state fluctuation at adjacent moments within the test period, obtain the joint correlation anomaly degree at each moment; divide each motion cycle into similar sub-periods based on the joint correlation anomaly degree.

[0025] Postoperative joint stiffness in elderly patients can disrupt the coordination of multi-joint movements, leading to asynchrony in the movement directions of adjacent joints. Analysis can be performed by examining the fluctuations in the movement state of the target joint at adjacent time points. Postoperative joint stiffness in elderly patients often occurs intermittently. When significant synovial adhesions cause decreased joint mobility, abrupt changes in the linkage between adjacent joints can exceed the patient's normal fluctuation range, potentially resulting in a sudden break in the linkage. Therefore, the degree of abnormality in the linkage between adjacent joints during movement can be analyzed based on the abnormality of the fluctuations in the movement state of the target joint at adjacent time points, thus obtaining the degree of joint correlation abnormality.

[0026] Adjacent joints are normally linked under closed-loop nerve control, but joint adhesions or pain lead to open-loop control, that is, the patient's kinetic chain suddenly "gets stuck" at a specific angle, causing the linkage between adjacent joints to break pathologically. The moments with similar degrees of joint linkage abnormality can be classified to obtain similar sub-time periods, which can capture minute local abnormalities and phase lags, and achieve early warning and precise localization of joint linkage abnormalities.

[0027] Step S3: Based on the degree of lag in motion transmission of the target joint in each similar sub-time period, obtain the motion lag factor for each similar sub-time period; adjust the motion lag factor based on the degree of change in the range of activity angles of adjacent similar sub-time periods in the test period, and obtain the joint stiffness coefficient for each similar sub-time period.

[0028] Postoperative nerve edema leads to delayed nerve signal transmission, manifesting as a delayed response in the distal joint after a proximal joint movement command is issued. To capture this lag in joint movement initiation, the difference in the change of motion angle between the target joint and its adjacent joints within each similar sub-time period is analyzed to assess the instantaneous movement response efficiency from the distal to the proximal joint. This reflects the degree of initiation lag of the target joint during movement within similar sub-time periods, yielding a motion lag factor. The motion lag factor directly reflects the delay in neuromuscular control or joint structural abnormalities and can serve as an early warning indicator of the risk of joint linkage fracture.

[0029] Typically, postoperative collagen deposition in the joints of elderly patients increases joint viscous resistance, leading to a decrease in joint range of motion. Motion lag factors quantify the temporal abnormalities of the joint movement initiation phase. The degree of variation in the range of motion angles between adjacent similar sub-time periods within the test period covers the spatial pathological characteristics of joint movement. By analyzing the degree of variation in the range of motion angles between adjacent similar sub-time periods within the test period, spatial abnormalities in the movement maintenance phase are quantified. A joint stiffness coefficient is constructed by integrating the degree of variation in the range of motion angles between adjacent similar sub-time periods with the motion lag factor, taking into account the spatiotemporal delay effect of joint movement.

[0030] Step S4: Based on the correlation between joint correlation abnormality and joint stiffness coefficient, adjust the joint stiffness coefficient and joint correlation abnormality for similar sub-time periods within the test period to obtain the functional impairment coefficient; use the functional impairment coefficient to assess the joint mobility of elderly patients during postoperative rehabilitation training.

[0031] When joint stiffness and synergistic fracture are spatiotemporally coupled within a specific angular range, it indicates the presence of pathological kinetic chain failure, i.e., structural biomechanical dysfunction. Traditional static range of motion calibration methods cannot capture this dynamic coupling, significantly underestimating the actual loss of joint range of motion. Joint correlation anomaly reflects the degree of abnormal linkage between adjacent joint points during movement, while the joint stiffness coefficient reflects the spatiotemporal delay effect of joint movement. The correlation between joint correlation anomaly and joint stiffness coefficient reveals the possibility of pathological kinetic chain failure. By adjusting the joint stiffness coefficient and joint correlation anomaly using these parameters, and considering both dynamic synergistic patterns and spatiotemporal delay effects, the sensitivity for identifying joint movement anomalies is significantly improved. This provides high-precision, full-cycle coverage three-dimensional range of motion calibration results, optimizing rehabilitation programs and reducing the risk of missed detections.

[0032] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the joint association anomaly degree is described in [reference needed]. Figure 2 The diagram illustrates a flowchart of a method for obtaining joint correlation anomaly degree according to an embodiment of the present invention. The method includes: Step S211: Denote the two adjacent joints of the target joint as the first joint and the second joint, respectively; denote the direction from the first joint to the target joint as the direction of the first vector and the distance between the first joint and the target joint as the magnitude of the first vector; denote the direction from the target joint to the second joint as the direction of the second vector and the distance between the second joint and the target joint as the magnitude of the second vector; obtain the angle between the first vector and the second vector at the same moment and denote it as the analysis angle at each moment; calculate the rate of change of the analysis angle between any two adjacent moments and denote it as the joint linkage fluctuation degree at the next moment.

[0033] It should be noted that in this protocol, the patient's upper limb is held at a 90-degree angle before performing elbow flexion, meaning the analysis angle before elbow flexion is 0 degrees. The analysis angle reflects the consistency of the joint's movement direction. The ratio of the absolute value of the difference between the analysis angles of two adjacent moments to the time interval is used as the rate of change to quantify the instantaneous rate of change in the movement direction of adjacent joints, reflecting the severity of the asynchrony in the movement directions of adjacent joints. The greater the joint linkage fluctuation at each moment, the less smooth the linkage between the elbow / shoulder joint structure and the elbow / wrist joint structure, and the more severe the stiffness of the patient's joints or abnormal nerve control at that moment. In this embodiment, the joint linkage fluctuation at the first moment within the test period is directly set to a constant of 0.

[0034] Step S212: Based on the deviation of the joint linkage fluctuation degree at each moment during the test period from the joint linkage fluctuation degree at all moments, obtain the joint association anomaly degree at each moment.

[0035] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the joint linkage anomaly degree includes: calculating the mean and standard deviation of the joint linkage fluctuation at all times during the test period, and normalizing the ratio obtained by taking the joint linkage fluctuation at each time as the numerator and the sum of the mean and twice the standard deviation as the denominator to obtain the joint linkage anomaly degree at each time. It should be noted that the joint linkage anomaly degree measures the degree of abnormality of the joint linkage fluctuation at each time by analyzing the deviation of the joint linkage fluctuation at each time from the overall distribution of joint linkage fluctuation. If the joint linkage fluctuation at each time is larger, but the overall joint linkage fluctuation during the test period is smaller and more stable, it indicates that the structural linkage change between the target joint point and its adjacent joint points at each time exceeds the patient's normal fluctuation range, and the possibility of sudden joint linkage breakage is greater, such as stunting caused by synovial adhesion.

[0036] In this embodiment of the invention, the normalization process is performed using max-min normalization. Alternatively, normalization methods such as the Sigmoid function or function transformation can be selected, and no limitation is made here.

[0037] It should be noted that, since the target joints move continuously during the patient's testing period, the mean and standard deviation of the joint linkage fluctuation at all times will not be zero simultaneously.

[0038] In other embodiments of the present invention, the Z-score of the joint linkage fluctuation at each moment during the test period can also be obtained and denoted as the joint linkage anomaly degree.

[0039] Preferably, in some possible implementations of the embodiments of the present invention, the method for dividing similar sub-time periods includes: for each action cycle, the first moment within the action cycle constitutes an initial undetermined time period, the first moment within the action cycle is recorded as the initial undetermined moment, when the difference in joint association anomaly degree between the undetermined moment and the adjacent next moment is less than a preset threshold, the adjacent next moment of the undetermined moment is added to the undetermined time period, the undetermined time period is updated, and the next moment of the undetermined moment is recorded as a new undetermined moment; when the difference in joint association anomaly degree between the undetermined moment and the adjacent next moment is greater than or equal to the preset threshold, the undetermined time period is recorded as a similar sub-time period, the adjacent next moment of the undetermined moment is recorded as a new undetermined moment, and the new undetermined moments constitute a new undetermined time period, iterating through all moments within the action cycle to divide the action cycle into different similar sub-time periods. Here, difference refers to the absolute value of the difference.

[0040] It should be noted that the greater the difference in joint correlation anomaly between the pending time and the next adjacent time, the greater the likelihood of abnormality in the linkage of adjacent joints, resulting in instantaneous motion lag. Within the same similar sub-time period, the absolute value of the difference in joint correlation anomaly between two adjacent time points is less than a preset threshold.

[0041] In one implementation of this invention, the preset threshold is set to 0.3.

[0042] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the motion hysteresis factor is described in [reference needed]. Figure 3 The diagram illustrates a flowchart of a method for obtaining a motion hysteresis factor according to an embodiment of the present invention, the method comprising: Step S221: Denote the proximal joint of the two adjacent joints of the target joint as the analysis joint; for each similar sub-time period, perform curve fitting on the activity angle of the analysis joint at all times within the similar sub-time period, and perform second derivative on the fitted function to obtain the angular acceleration function; take the absolute value of the difference between the angular velocity functions of the two analysis joints to obtain the response efficiency function.

[0043] Based on the kinetic chain theory, proximal joints need to be activated first to provide a stable motion foundation for distal joints. Among the shoulder and wrist joints, the shoulder joint is the proximal joint, and its stability directly affects the elbow joint's motion efficiency. An electronic goniometer was used to measure the range of motion of the two analytical joints. Taking the elbow and shoulder joints as examples, when measuring the range of motion of the shoulder joint, the goniometer axis was aligned with the humeral head, and the fixed arm was parallel to the sagittal plane of the trunk. When measuring the range of motion of the elbow joint, the goniometer axis was placed at the lateral epicondyle of the humerus, and the moving arm moved with the forearm. During the testing period, the goniometer was used to measure the range of motion of the analytical joints at each moment.

[0044] It should be noted that this embodiment uses the least squares method for curve fitting, but cubic spline interpolation or other methods can also be used. Since natural joint movements such as elbow flexion usually involve variable acceleration, an angular acceleration function is determined. The motion response functions of the distal and proximal joints are constructed using the angular acceleration functions of two analyzed joint points. The smaller the absolute value of the difference between the two angular acceleration functions, the higher the instantaneous motion response efficiency from the distal joint (elbow) to the proximal joint (shoulder).

[0045] Step S222: Obtain the peak point of the autocorrelation function of the response efficiency function within the range from the beginning to the middle of the time delay in similar sub-periods, and use the value of the time delay corresponding to the peak point as the propagation delay coefficient.

[0046] It should be noted that the detailed steps for obtaining the peak point of the autocorrelation function are as follows: Obtain the autocorrelation function of the response efficiency function within similar sub-time periods, with the time delay ranging from the start to the middle of the similar sub-time periods; calculate all values ​​of the autocorrelation function within this range, and select the time corresponding to the maximum value as the conduction delay coefficient. A larger conduction delay coefficient indicates a more significant lag in joint initiation within the similar sub-time periods, suggesting abnormal joint mobility in the patient during these periods; conversely, a smaller coefficient indicates better coordination between the two joints, resulting in more accurate joint mobility for the patient during similar sub-time periods. The expression for the autocorrelation function is well-known to those skilled in the art and will not be elaborated upon here.

[0047] In this embodiment of the invention, by constraining the time delay range to the first 50% of the similar sub-period time window, edge effect interference is avoided, and the dominant dynamic mode of the system response is characterized by the maximum autocorrelation time delay.

[0048] Step S223: Obtain the motion lag factor of the similar sub-period based on the activity angle and transmission delay coefficient of the first moment within the similar sub-period.

[0049] Considering the increased tendon pretension at large angles, requiring additional time to overcome static friction, and the lag intensifying with increasing starting angle, the analysis of joint movement initiation lag incorporates the activity angle at the first moment within a similar sub-period for angle compensation. A larger activity angle results in greater tendon pretension and more severe joint initiation lag; a larger conduction delay coefficient indicates a more significant lag in joint initiation within the similar sub-period. Therefore, both the activity angle at the first moment within the similar sub-period and the conduction delay coefficient are positively correlated with the motion lag factor. In this embodiment, the product of the sine of the activity angle at the first moment within the similar sub-period and the conduction delay coefficient is normalized to obtain the motion lag factor for the similar sub-period. The motion lag factor reflects the degree of initiation lag of the joint during movement within the similar sub-period. A larger motion lag factor indicates poorer joint coordination during postoperative rehabilitation, with a more significant delay between movement initiation and movement.

[0050] It should be noted that the range of the active angle is from 0 degrees to 90 degrees.

[0051] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the joint stiffness coefficient includes: recording the range of the activity angles of the target joint point at all times within each similar sub-time period as the activity range scale; arranging the activity range scales of all similar sub-time periods within the test period in chronological order to obtain a scale sequence; calculating the difference between every two adjacent activity range scales in the scale sequence, averaging all differences to obtain the joint activity attenuation coefficient; and obtaining the joint stiffness coefficient of each similar sub-time period based on the joint activity attenuation coefficient and the motion hysteresis factor of each similar sub-time period. Wherein, the difference between two adjacent activity range scales in the sequence is equal to the difference between the preceding and following activity range scales.

[0052] It should be noted that a larger joint mobility attenuation coefficient indicates a faster decline in joint mobility within similar sub-time periods, and a more pronounced spatial abnormality in joint movement. This is due to the increased nonlinearity of viscous resistance caused by increased collagen deposition, which exacerbates the impact on joint mobility. When both the motion lag factor and the joint mobility attenuation coefficient are larger within similar sub-time periods, it indicates more severe biomechanical dysfunction of the joint caused by the combined effects of delayed nerve conduction and viscous resistance, resulting in a larger joint stiffness coefficient. Therefore, both the motion lag factor and the joint mobility attenuation coefficient are positively correlated with the joint stiffness coefficient. In this embodiment of the invention, the product of the motion lag factor and the joint mobility attenuation coefficient for each similar sub-time period is normalized to obtain the joint stiffness coefficient.

[0053] In this embodiment of the invention, the Sigmoid function is used for normalization. Other normalization methods such as function transformation and max-min normalization can also be selected, and no limitation is made here.

[0054] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the functional impairment coefficient includes: The mean of joint correlation anomaly degree at all times within similar sub-time periods is calculated and recorded as the overall smoothness. The overall smoothness and joint stiffness coefficients of all similar sub-time periods within the test period are arranged in chronological order to obtain the smooth sequence and the stiff sequence. The sum of the correlation coefficient between the smooth sequence and the stiff sequence and the constant 1 is used to weight the sum of the overall smoothness and joint stiffness coefficients of similar sub-time periods to obtain the local impairment coefficient. The mean of the local impairment coefficients of all similar sub-time periods within the test period is normalized to obtain the functional impairment coefficient.

[0055] It should be noted that when joint stiffness and synergistic fracture are spatiotemporally coupled within a specific angular range—that is, when the degree of joint correlation anomaly and the joint stiffness coefficient are greater—the more abnormal the linkage between adjacent joint points during the test subject's movement and the more pronounced the spatiotemporal delay effect of joint movement. This indicates the presence of pathological kinetic chain failure in similar sub-time periods, i.e., structural biomechanical dysfunction. The correlation between joint correlation anomaly and the joint stiffness coefficient reveals the possibility of pathological kinetic chain failure in the joint. Adjusting this correlation can further improve the accuracy of structural biomechanical dysfunction analysis.

[0056] In the embodiments of the present invention, the overall smoothness, joint stiffness coefficient, correlation coefficient between smooth sequences and stiff sequences, and functional impairment coefficient can also be constructed through other basic mathematical operations, which are not limited or elaborated here.

[0057] In one implementation of this invention, the correlation coefficient is the Pearson correlation coefficient, but it can also be the Spearman correlation coefficient, Kendall rank correlation coefficient, canonical correlation coefficient, etc.

[0058] In this embodiment of the invention, an inertial measurement unit (IMU) is used to collect the range of motion of the elbow joint during each elbow flexion movement, and the average range of motion of all elbow flexion movements is recorded as the final range of motion. The functional impairment coefficient of the test subject is input into a pre-trained machine learning model, and multimodal features are fused through a multilayer perceptron branch to output the final range of motion. The model establishes a mapping relationship between the functional impairment coefficient and the range of motion through supervised learning, resulting in a trained machine learning model. The functional impairment coefficient of elderly patients is obtained and input into the trained machine learning model, which outputs an estimated value of the range of motion of the elbow joint of the elderly patients. It should be noted that the machine learning module can be a gradient boosting decision tree, a temporal convolutional network, or a neural network, etc. At the same time, rehabilitation stage parameters of elderly patients are obtained as auxiliary features. These rehabilitation stage parameters include postoperative days, pain index, and muscle strength level, etc., to further improve the reliability of the range of motion of the elbow joint of elderly patients.

[0059] The joint mobility data in this solution is based on objective feature annotations generated from big data analysis. It does not provide a direct diagnostic conclusion, but rather provides doctors with objective quantitative data for reference. This allows doctors to focus more quickly on the pathological features of the target joint and improves their efficiency in judging joint mobility during postoperative rehabilitation training. The final medical judgment still needs to be made by a professional doctor.

[0060] This invention is now complete.

[0061] Example 2: This invention also provides a schematic diagram of a computer device for assessing joint mobility during postoperative rehabilitation training in elderly patients. Please refer to [link / reference]. Figure 4 The computer device includes a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and running on the processor 502. When the processor 502 executes the computer program 503, the computer device can perform any of the aforementioned methods for assessing joint mobility in postoperative rehabilitation training for elderly patients.

[0062] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a method for assessing joint mobility in postoperative rehabilitation training for elderly patients provided in embodiments of this application.

[0063] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0064] When each module is divided according to its function, the device may also include a communication module, a signal analysis module, a complexity analysis module, and a positioning module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0065] It should be understood that the device provided in this embodiment is used to perform the above-described method for assessing joint range of motion in postoperative rehabilitation training for elderly patients, and therefore can achieve the same effect as the above-described implementation method.

[0066] When using integrated units, the device may include a processing module and a storage module. When applied to a workpiece, the processing module can be used to control and manage the workpiece's operations. The storage module can be used to support the execution of program code by the workpiece.

[0067] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits contained in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0068] Example 3: This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described related method steps to implement the method for assessing joint mobility in postoperative rehabilitation training for elderly patients provided in the above embodiment.

[0069] Example 4: This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the method for assessing joint mobility during postoperative rehabilitation training for elderly patients provided in the above embodiment.

[0070] In this embodiment, the apparatus, computer-readable storage medium, or computer program product chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0071] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0072] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for assessing joint range of motion during postoperative rehabilitation training in elderly patients, characterized in that, The method includes: The tester obtains the three-dimensional coordinates and movement angles of the target joint and its adjacent joints at each moment during the test period; the test period includes different motion cycles. Based on the degree of abnormality of the target joint movement at each moment within the test period, the joint correlation anomaly degree at each moment is obtained; based on the joint correlation anomaly degree, each action cycle is divided into similar sub-time periods; Based on the difference in the change of the activity angle between the target joint and its adjacent joints in each similar sub-time period, the motion hysteresis factor of each similar sub-time period is obtained; based on the degree of change in the range of activity angle between adjacent similar sub-time periods in the test period, the motion hysteresis factor is adjusted to obtain the joint stiffness coefficient of each similar sub-time period. Based on the correlation between the joint correlation abnormality and the joint stiffness coefficient, the joint stiffness coefficient and joint correlation abnormality of similar sub-time periods within the test period are adjusted to obtain the functional impairment coefficient; the functional impairment coefficient is used to evaluate the joint mobility of elderly patients during postoperative rehabilitation training.

2. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 1, characterized in that, The process of obtaining the joint correlation anomaly degree at each time step includes: Two adjacent joints of the target joint are designated as the first joint and the second joint, respectively. The direction from the first joint to the target joint is taken as the direction of the first vector, and the distance between the first joint and the target joint is taken as the magnitude of the first vector. The direction from the target joint to the second joint is taken as the direction of the second vector, and the distance between the second joint and the target joint is taken as the magnitude of the second vector. The angle between the first and second vectors at the same moment is obtained and recorded as the analysis angle at each moment. The rate of change of the analysis angle between any two adjacent moments is calculated and recorded as the joint linkage fluctuation at the next moment. The joint correlation anomaly degree at each moment is obtained based on the degree of deviation of the joint linkage fluctuation degree at each moment within the test period relative to the joint linkage fluctuation degree at all moments.

3. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 1, characterized in that, The process of dividing each motion cycle into similar sub-time periods based on the joint correlation anomaly degree includes: For each action cycle, the first moment in the action cycle constitutes the initial undetermined time period. The first moment in the action cycle is recorded as the initial undetermined moment. When the difference between the joint correlation abnormality degree between the undetermined moment and the next adjacent moment is less than a preset threshold, the next adjacent moment of the undetermined moment is added to the undetermined time period, the undetermined time period is updated, and the next moment of the undetermined moment is recorded as the new undetermined moment. When the difference between the joint correlation anomaly degree of the pending time and the next adjacent time is greater than or equal to the preset threshold, the pending time period is recorded as a similar sub-time period, the next adjacent time of the pending time is recorded as a new pending time, and the new pending time constitutes a new pending time period. It is then determined whether the difference between the joint correlation anomaly degree of the new pending time and the next adjacent time is less than the preset threshold. All times within the action cycle are traversed, and the action cycle is divided into different similar sub-time periods.

4. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 1, characterized in that, The process of obtaining the motion lag factor for each similar sub-time period includes: The proximal joint of two adjacent joints of the target joint is denoted as the analysis joint. For each similar sub-time period, the activity angle of the analysis joint at all times within the similar sub-time period is curve-fitted. The fitted function is differentiated twice to obtain the angular acceleration function. The absolute value of the difference between the angular velocity functions of the two analysis joints is taken to obtain the response efficiency function. Obtain the peak point of the autocorrelation function of the response efficiency function within the range from the beginning to the middle of a similar sub-period time delay, and use the value of the time delay corresponding to the peak point as the propagation delay coefficient; Based on the activity angle at the first moment within the similar sub-period and the transmission delay coefficient, the motion lag factor of the similar sub-period is obtained.

5. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 1, characterized in that, The process of obtaining the joint stiffness coefficient for each similar sub-time period includes: The range of the activity angles of the target key point at all times within each similar sub-time period is denoted as the activity range scale. Arrange the activity range scales of all similar sub-time periods within the test period in chronological order to obtain a scale sequence; calculate the difference between every two adjacent activity range scales in the scale sequence, and average all differences to obtain the joint activity attenuation coefficient; The joint stiffness coefficient for each similar sub-time period is obtained based on the joint activity attenuation coefficient and the motion hysteresis factor for each similar sub-time period.

6. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 1, characterized in that, The acquisition of the functional impairment coefficient includes: The mean of the joint association anomaly degree at all times within the similar sub-time periods is calculated and recorded as the overall smoothness; the overall smoothness of all similar sub-time periods within the test period and the joint stiffness coefficient are arranged in time sequence to obtain the smooth sequence and the stiffness sequence in turn; Using the sum of the correlation coefficient between the smooth sequence and the rigid sequence and the constant 1, the sum of the overall smoothness and the joint rigidity coefficient of similar sub-time periods is weighted to obtain the local obstacle coefficient; the mean of the local obstacle coefficients of all similar sub-time periods within the test period is normalized to obtain the functional impairment coefficient.

7. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 2, characterized in that, The process of obtaining the joint correlation anomaly degree at each time step includes: The mean and standard deviation of the joint linkage fluctuation at all times during the test period are calculated respectively. The ratio of the joint linkage fluctuation at each time point as the numerator and the sum of the mean and twice the standard deviation as the denominator is normalized to obtain the joint linkage anomaly degree at each time point.

8. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 4, characterized in that, The method for curve fitting the activity angles of the analysis joints at all times within similar sub-time periods is the least squares method.

9. The method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 5, characterized in that, The joint motion attenuation coefficient and the motion hysteresis factor are both positively correlated with the joint stiffness coefficient.

10. A method for assessing joint range of motion in postoperative rehabilitation training of elderly patients according to claim 6, characterized in that, The correlation coefficient mentioned is the Pearson correlation coefficient.

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