Muscle function rhythm analysis method and system based on unhealthy side cooperation and electronic equipment

Abstract

The invention discloses a muscle function rhythm analysis method and system based on unhealthy side cooperation and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring an uninjured side electromyographic signal of a patient, and a joint angle signal and a movement track signal of an affected side; based on the electromyographic signal, extracting an uninjured-side cooperative adjustment decline trajectory, and based on the joint angle signal and the motion trajectory signal, extracting an affected-side function take-over response trajectory; identifying a critical point of affected-side function takeover; calculating a first change curvature and a second change curvature, and taking a time point when the first change curvature is greater than the second change curvature for the first time as a motion stability starting point of the affected side; extreme point distribution is extracted, the motion rhythm period of the affected side is determined, and the time length change trend is calculated; and based on the time length change trend, determining a function autonomous maintenance score, and calculating a muscle function rhythm index of the upper limb of the affected side according to the function autonomous maintenance score. By implementing the technical scheme provided by the invention, the accuracy of analyzing the motion data of the limb on the affected side can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, system, and electronic device for analyzing muscle function rhythms based on healthy side synergy. Background Technology

[0002] In unilateral limb motor dysfunction (hemiplegia) caused by neurological damage, a complex neural synergistic relationship exists between the healthy and affected limbs. Studies have shown that during motor tasks, active movement of the healthy limb can produce a synergistic activation effect on the affected limb through the intrinsic connections of the central nervous system. This phenomenon forms the basis for understanding changes in motor ability of the affected limb.

[0003] Currently, analytical methods for this type of motor function mainly rely on feature extraction from biomechanical or bioelectrical signals during movement. For example, static or global indicators such as the range of motion of the affected limb, the smoothness of the movement trajectory, or the average intensity of muscle activation on the healthy side are used to quantify an individual's motor performance throughout the entire movement task.

[0004] However, these methods suffer from a fundamental technical flaw: they analyze the complex movement process as a whole, thus ignoring the inherent dynamic temporal characteristics of motor control. Specifically, the motor ability of the affected limb is not static but may undergo a dynamic transition from relying on the healthy side for synergistic drive to gradually achieving autonomous control. Traditional global indicators cannot accurately capture the critical time points of this control transfer, nor can they analyze the rhythmic, subtle movement fluctuations exhibited by the affected limb as it attempts to establish a stable movement pattern after the control transfer, resulting in insufficient accuracy in the analysis of motor data of the affected limb. Summary of the Invention

[0005] This application provides a method, system, and electronic device for analyzing muscle function rhythms based on healthy-side coordination, which can improve the accuracy of motion data analysis for the affected limb.

[0006] The first aspect of this application provides a method for analyzing muscle function rhythms based on unaffected side synergy, specifically including: Acquire electromyographic signals of the patient's unaffected upper limb during the execution of a preset functional movement, and joint angle signals and motion trajectory signals generated by the affected upper limb after the unaffected upper limb has performed the functional movement. Based on the electromyographic signals, the healthy side's coordinated regulation decreasing trajectory is extracted; based on the joint angle signals and motion trajectory signals, the affected side's functional takeover response trajectory is extracted. The critical point of functional takeover on the affected side is identified based on the numerical change trends of the decreasing trajectory of the healthy side's coordinated regulation and the response trajectory of the affected side's functional takeover. The first curvature and the second curvature of the healthy side's synergistic adjustment decreasing trajectory and the affected side's functional takeover response trajectory are calculated respectively during a preset time period before and after the critical point of the affected side's functional takeover. The time point when the first curvature is greater than the second curvature is taken as the starting point of the affected side's motion stability. Extract the distribution of extreme points of the functional takeover response trajectory of the affected side within the target time period between the critical point of functional takeover on the affected side and the starting point of motor stability on the affected side. Determine the time interval between adjacent extreme points as the motor rhythm cycle of the affected side, and calculate the trend of time length change between each motor rhythm cycle of the affected side. Based on the trend of the time length change, the functional self-maintenance score of the affected side within the target time period is determined, and the muscle function rhythm index of the affected upper limb is calculated based on the functional self-maintenance score.

[0007] By employing the aforementioned technical solution, firstly, the concordance-decrease trajectory of the healthy side is extracted based on the electromyographic signals generated during functional movements of the healthy upper limb. Then, the functional takeover response trajectory of the affected side is extracted based on the joint angle and motion trajectory signals generated by the affected upper limb. The critical point of functional takeover on the affected side is identified by the numerical trends of these two trajectories. Based on this, the curvature of the concordance-decrease trajectory and the functional takeover response trajectory of the affected side is calculated over a preset time period before and after the critical point of functional takeover on the affected side. The point at which the first curvature of change first exceeds the second curvature of change is determined as the starting point of motion stability on the affected side. This allows for accurate capture of the time node at which the affected limb begins to establish a stable movement pattern. Subsequently, within the target time period between the critical point of functional takeover on the affected side and the starting point of motion stability, the rhythmic cycle of motion on the affected side is obtained by extracting the distribution of extreme points of the functional takeover response trajectory. The trend of time length changes between adjacent cycles is analyzed, thereby quantitatively assessing the rhythmic fluctuation characteristics of the affected limb during the attempt to establish a stable movement pattern. Ultimately, by determining the functional autonomy maintenance score based on the trend of time length changes and calculating the muscle function rhythm index, a precise quantitative assessment of the recovery process of motor data of the affected limb was achieved, improving the accuracy of the analysis of motor data of the affected limb.

[0008] Optionally, identifying the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's coordinated regulation and the trajectory of the functional takeover response on the affected side includes: Calculate the numerical difference between the healthy side's collaborative adjustment decreasing trajectory and the affected side's functional takeover response trajectory at the same time point, and take the time point when the numerical difference is less than a preset trajectory difference threshold as the approach time point; Calculate the decreasing rate of the healthy side's coordinated adjustment trajectory and the increasing rate of the affected side's functional takeover response trajectory at the near time point; Within a preset observation period after the difference between the rising speed and the falling speed is greater than a preset difference, the cumulative value of the affected side functional takeover response trajectory exceeds the value of the healthy side collaborative adjustment decreasing trajectory. The moment when the accumulated value first reaches a preset multiple of the preset trajectory difference threshold is determined as the critical point of functional takeover on the affected side.

[0009] By employing the aforementioned technical solution, the real-time numerical difference between the healthy side's synergistic adjustment decreasing trajectory and the affected side's functional takeover response trajectory on the time axis is first calculated, and the approaching time point when the numerical difference first falls below a preset trajectory difference threshold is identified. Based on this, the descent rate of the healthy side's synergistic adjustment decreasing trajectory and the ascent rate of the affected side's functional takeover response trajectory at the approaching time point are calculated. Within a preset observation period after the speed difference exceeds a preset value, the cumulative exceedance value of the affected side's functional takeover response trajectory relative to the healthy side's synergistic adjustment decreasing trajectory is monitored. The moment when the cumulative value first reaches a preset multiple of the preset trajectory difference threshold is determined as the critical point of functional takeover on the affected side. This allows for accurate identification of the key time point when the affected limb transitions from reliance on healthy side synergistic drive to autonomous control. This solution not only considers the instantaneous numerical proximity of the two trajectories but also introduces two dimensions of judgment criteria: trajectory change rate and sustained exceedance performance. Thus, while eliminating interference from random fluctuations, it achieves robust identification of the critical point of functional takeover on the affected side, laying the foundation for accurate evaluation of subsequent affected side movement data.

[0010] Optionally, calculating the trend of time length changes between each of the affected side's motor rhythm cycles includes: Calculate the time difference between every two adjacent motor rhythm cycles on the affected side to form a sequence of cycle variation differences; The time point with the largest time length difference in the periodic variation difference sequence is taken as the mutation point, and the periodic variation difference sequence is divided into a pre-mutation segment and a post-mutation segment based on the mutation point. The first average cycle length of each of the affected side's motor rhythm cycles in the pre-mutation segment and the second average cycle length of each of the affected side's motor rhythm cycles in the post-mutation segment are calculated respectively. When the difference between the second average cycle length and the first average cycle length exceeds a preset difference threshold, the trend of change in time length is determined to be a step change trend; otherwise, it is determined to be a stable change trend.

[0011] By employing the above technical solution, a periodic variation difference sequence is first formed by calculating the time length difference between adjacent affected side motor rhythm cycles. By identifying the abrupt change point with the largest time length difference in the periodic variation difference sequence, the entire sequence is divided into a pre-abrupt and post-abrupt segment, and the average cycle length of each segment is calculated. When the difference between the second and first average cycle lengths exceeds a preset difference threshold, it can be determined that the affected side's motor rhythm exhibits a step change trend; otherwise, it is determined to be a stable change trend. This allows for precise differentiation between abrupt and gradual adjustments in the process of establishing a stable movement pattern on the affected side. By introducing abrupt change point analysis and threshold judgment mechanisms, this solution can identify the stability characteristics of the affected side's motor rhythm change process, thus providing richer and more accurate quantitative evidence for evaluating the analysis of affected side limb movement data.

[0012] Optionally, determining the functional autonomy maintenance score of the affected side within the target time period based on the trend of the time length variation includes: The value of the healthy side's synergistic adjustment decreasing trajectory at the critical point of the affected side's functional takeover is taken as the healthy side's synergistic contribution, and the value of the affected side's functional takeover response trajectory at the starting point of the affected side's motor stability is taken as the affected side's autonomous achievement. When the trend of the time length change is a stable trend, the ratio of the amount of autonomous achievement on the affected side to the amount of collaborative contribution on the healthy side is used as the progressive score; When the time length changes in a step-like trend, the ratio of the amount of autonomous achievement on the affected side minus the amount of collaborative contribution on the healthy side to the amount of collaborative contribution on the healthy side is used as the adaptation score. Calculate the number of affected-side motor rhythm cycles within the target time period, and multiply the progressive score or the adaptive score by the number of affected-side motor rhythm cycles to obtain the cycle adjustment score; The sum of the absolute values ​​of all differences in the periodic variation difference sequence is calculated as the overall variation amplitude. The periodic adjustment score is divided by the overall variation amplitude to obtain the functional autonomous maintenance score.

[0013] By employing the aforementioned technical solution, the system first extracts the healthy side's synergistic contribution at the critical point of functional takeover on the affected side, based on the decreasing trajectory of healthy side synergistic regulation, and the affected side's autonomous achievement at the starting point of stable movement on the affected side. Based on this, a differentiated scoring strategy is adopted according to different types of time-length trends: when a stable trend is observed, a progressive score is obtained by calculating the ratio of the affected side's autonomous achievement to the healthy side's synergistic contribution, reflecting the relative contribution of the affected limb in gradually establishing a stable movement pattern; when a step-like trend is observed, an adaptive score is obtained by calculating the relative degree to which the affected side's autonomous achievement exceeds the healthy side's synergistic contribution, reflecting the adaptive capacity of the affected limb during abrupt adjustments. Subsequently, a periodic adjustment score is obtained by multiplying the score by the number of affected side movement rhythm cycles within the target time period, and further divided by the sum of the absolute values ​​of all differences in the periodic change difference sequence to obtain a functional autonomy maintenance score. This achieves a comprehensive evaluation of the affected limb's movement data. This scheme not only considers the absolute degree of functional takeover on the affected side, but also introduces factors such as the stability and adaptability of motor rhythm, thus constructing a scoring system that can reflect both the level of functional recovery of the affected limb and the quality of its motor control.

[0014] Optionally, calculating the muscle function rhythm index of the affected upper limb based on the functional self-maintenance score includes: The corresponding rhythm gain coefficient is retrieved from the preset rhythm gain coefficient table based on the autonomous maintenance score of the function. The difference between the amount of autonomous achievement on the affected side and the amount of collaborative contribution on the healthy side is calculated as the original functional difference. Multiplying the original functional difference by the rhythm gain coefficient yields the functional difference after gain adjustment. The ratio of the target time period to the number of cycles of the motor rhythm on the affected side is calculated as the rhythm frequency; The muscle function rhythm index is obtained by multiplying the functional difference after gain adjustment by the rhythm frequency.

[0015] By employing the above technical solution, the corresponding rhythmic gain coefficient is first retrieved from a pre-defined rhythmic gain coefficient table based on the functional autonomy maintenance score. Then, the original functional difference between the amount of autonomous achievement on the affected side and the amount of synergistic contribution on the healthy side is calculated and multiplied by the rhythmic gain coefficient to obtain the gain-adjusted functional difference, thus achieving adaptive adjustment of the functional recovery level of the affected limb. Subsequently, the rhythmic frequency is obtained by calculating the ratio of the target time period to the number of rhythmic cycles on the affected side. This frequency reflects the efficiency of the affected limb in establishing a stable movement pattern per unit time. Finally, the gain-adjusted functional difference is multiplied by the rhythmic frequency to obtain the muscle function rhythm index, thereby constructing a comprehensive evaluation index that considers both the degree of functional recovery and the characteristics of movement rhythm. This solution, by introducing a rhythmic gain mechanism and frequency weighting, not only achieves precise quantification of the functional state of the affected limb but also enables adaptive adjustment according to different movement rhythm patterns, thus providing a more scientific and reliable technical means for the analysis of movement data of the affected limb.

[0016] Optionally, after calculating the muscle function rhythm index of the affected upper limb based on the functional self-maintenance score, the method further includes: The importance of each extreme point is determined by analyzing the extreme points extracted within the target time period based on the muscle function rhythm index. The extreme points whose importance exceeds a preset importance threshold are marked as critical extreme points; Calculate the numerical gradient between adjacent critical extreme points; Based on the distribution of the numerical change gradients, the extreme point distribution results are generated; The trend of time length variation between the motor rhythm cycles of each affected side is updated based on the extreme point distribution results.

[0017] By employing the aforementioned technical solution, firstly, based on the calculated muscle function rhythm index, the importance of each extreme point extracted within the target time period is analyzed. Then, extreme points whose importance exceeds a preset importance threshold are marked as key extreme points, and the numerical change gradient between adjacent key extreme points is calculated, achieving accurate identification and quantitative representation of key moments in the movement of the affected limb. Subsequently, the distribution of these numerical change gradients is analyzed to generate extreme point distribution results, and the time length variation trend between the rhythmic cycles of the affected limb is updated accordingly, forming a dynamically optimized evaluation closed loop. This solution, by introducing an importance-based extreme point screening mechanism, not only improves the accuracy of identifying the rhythmic characteristics of the affected limb's movement but also enables dynamic correction of the movement data evaluation results through gradient analysis and distribution feedback, thus providing more refined and reliable technical support for the analysis of movement data of the affected limb.

[0018] Optionally, updating the trend of time length changes between each of the affected side's motor rhythm cycles based on the extreme point distribution results includes: Based on the extreme point distribution results, the number of data points contained in each of the affected side's motor rhythm cycles is counted, and the affected side's motor rhythm cycles with fewer than the preset minimum data volume threshold are marked as sparse cycles. For consecutive first sparse periods, the consecutive first sparse periods are merged; For the discontinuous second sparse period, based on the data density distribution characteristics of the adjacent affected side motor rhythm period of the second sparse period, the second sparse period is merged with the adjacent affected side motor rhythm period to obtain each merged affected side motor rhythm period. Calculate the new trend of time length changes among the combined affected-side motor rhythm cycles; The determination of the functional autonomy maintenance score of the affected side within the target time period based on the trend of the time length change includes: Based on the new trend in time length changes, the functional autonomy maintenance score of the affected side during the target time period is determined.

[0019] By adopting the above technical solution, the number of data points within each affected side's motor rhythm cycle is first counted based on the extreme point distribution results. This number is then compared with a preset minimum data volume threshold to identify sparse motor cycles. For consecutive first sparse cycles, a direct merging strategy is employed; for discontinuous second sparse cycles, intelligent merging is performed by analyzing the data density distribution characteristics of adjacent cycles, thus achieving adaptive optimization of the motor rhythm cycle. Based on this, the new time length variation trend between the merged affected side's motor rhythm cycles is calculated, and the affected side's functional self-maintenance score is updated accordingly, forming a data-driven assessment and optimization closed loop. This solution, by introducing a data density-based cycle merging mechanism, not only solves the problem of inaccurate assessment caused by data sparsity but also enables intelligent cycle optimization based on data distribution characteristics, thereby improving the reliability and accuracy of affected limb motor function assessment. This data feature-based adaptive optimization strategy provides more scientific and robust technical support for the analysis of affected limb motor data.

[0020] A second aspect of this application provides a muscle function rhythm analysis system based on unaffected side synergy, specifically including: The data acquisition module is used to acquire electromyographic signals of the patient's unaffected upper limb during the execution of a preset functional movement, as well as joint angle signals and motion trajectory signals generated by the affected upper limb after the unaffected upper limb performs the functional movement. The trajectory extraction module is used to extract the healthy side's coordinated adjustment decreasing trajectory based on the electromyographic signals, and to extract the affected side's functional takeover response trajectory based on the joint angle signals and motion trajectory signals. The critical point calculation module is used to identify the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's coordinated adjustment and the functional takeover response trajectory on the affected side. The starting point calculation module is used to calculate the first and second curvatures of the healthy side's coordinated adjustment decreasing trajectory and the affected side's functional takeover response trajectory respectively during a preset time period before and after the affected side's functional takeover critical point, and to take the time point when the first curvature is greater than the second curvature as the starting point of the affected side's motion stability. The rhythm cycle calculation module is used to extract the extreme point distribution of the functional takeover response trajectory of the affected side within the target time period between the critical point of functional takeover on the affected side and the starting point of motor stability on the affected side, determine the time interval between adjacent extreme points as the rhythm cycle of motor movement on the affected side, and calculate the time length variation trend between each rhythm cycle of motor movement on the affected side. The rhythm index calculation module is used to determine the functional self-maintenance score of the affected side within the target time period based on the trend of the time length change, and to calculate the muscle function rhythm index of the affected upper limb based on the functional self-maintenance score.

[0021] By employing the aforementioned technical solution, firstly, the concordance-decrease trajectory of the healthy side is extracted based on the electromyographic signals generated during functional movements of the healthy upper limb. Then, the functional takeover response trajectory of the affected side is extracted based on the joint angle and motion trajectory signals generated by the affected upper limb. The critical point of functional takeover on the affected side is identified by the numerical trends of these two trajectories. Based on this, the curvature of the concordance-decrease trajectory and the functional takeover response trajectory of the affected side is calculated over a preset time period before and after the critical point of functional takeover on the affected side. The point at which the first curvature of change first exceeds the second curvature of change is determined as the starting point of motion stability on the affected side. This allows for accurate capture of the time node at which the affected limb begins to establish a stable movement pattern. Subsequently, within the target time period between the critical point of functional takeover on the affected side and the starting point of motion stability, the rhythmic cycle of motion on the affected side is obtained by extracting the distribution of extreme points of the functional takeover response trajectory. The trend of time length changes between adjacent cycles is analyzed, thereby quantitatively assessing the rhythmic fluctuation characteristics of the affected limb during the attempt to establish a stable movement pattern. Ultimately, by determining the functional autonomy maintenance score based on the trend of time length changes and calculating the muscle function rhythm index, a precise quantitative assessment of the recovery process of motor data of the affected limb was achieved, improving the accuracy of the analysis of motor data of the affected limb.

[0022] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the foregoing.

[0023] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions. Attached Figure Description

[0024] Figure 1 This is an exemplary system architecture diagram of a muscle function rhythm analysis system based on healthy side synergy provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for analyzing muscle function rhythms based on healthy side synergy, provided in an embodiment of this application. Figure 3 This is a schematic diagram of a process for updating the trend of time length changes provided in an embodiment of this application; Figure 4 This is a schematic diagram of a muscle function rhythm analysis system based on healthy side synergy provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0025] Explanation of reference numerals in the attached figures: 901, processor; 902, communication bus; 903, user interface; 904, network interface; 905, memory. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0027] In the description of the embodiments in this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0029] Figure 1 An architecture for a muscle function rhythm analysis system based on healthy side synergy is presented.

[0030] like Figure 1 As shown, the system architecture may include a healthy side monitoring device 011, a network 012, an electronic device 013, and a affected side monitoring device 014. The network 012 serves as the medium for providing a communication link between the healthy side monitoring device 011, the electronic device 013, and the affected side monitoring device 014. The network 012 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0031] Electronic device 013 can receive electromyographic signals collected by the healthy side monitoring device 011 and joint angle signals and motion trajectory signals collected by the affected side monitoring device 014 via network 012, and analyze these monitoring data to determine the functional recovery assessment results of the affected side. The healthy side monitoring device 011 can be equipped with an electromyographic signal acquisition application, and the affected side monitoring device 014 can be equipped with a joint angle measurement application and a motion trajectory tracking application.

[0032] The healthy side monitoring device 011 is hardware, which can be any type of device with electromyography (EMG) signal acquisition function, including but not limited to surface EMG sensors, EMG signal acquisition devices, etc. The affected side monitoring device 014 is hardware, which can be any type of device with joint angle and motion trajectory monitoring function, including but not limited to angle sensors, inertial measurement units, and motion trajectory capture devices, etc.

[0033] Electronic device 013 can be a server that provides various muscle function rhythm analysis services, such as a central control server that analyzes data from monitoring devices. The central control server can perform collaborative analysis, rhythm feature extraction, and muscle function rhythm index calculation on the received electromyographic signals from the healthy side and the motor parameters from the affected side.

[0034] It should be noted that a server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0035] It should be understood that Figure 1 The number of healthy-side monitoring devices 011, network 012, electronic devices 013, and affected-side monitoring devices 014 in the diagram is merely illustrative. Depending on the implementation requirements, any number of healthy-side monitoring devices 011, network 012, electronic devices 013, and affected-side monitoring devices 014 may be included. Specifically, if the target data does not need to be acquired remotely, the above system architecture may exclude network 012 and include only healthy-side monitoring devices 011, electronic devices 013, or affected-side monitoring devices 014.

[0036] The following section uses an electronic device as an example to illustrate a method for analyzing muscle function rhythms based on healthy side synergy provided in this application.

[0037] This application provides a method for analyzing muscle function rhythms based on unaffected side synergy, referencing... Figure 2 , Figure 2 This is a flowchart illustrating a method for analyzing muscle function rhythms based on healthy-side synergy, as provided in an embodiment of this application. The method includes steps S101 to S106, as follows: S101: Acquire electromyographic signals of the patient's unaffected upper limb during the execution of a preset functional movement, as well as joint angle signals and motion trajectory signals generated by the affected upper limb after the unaffected upper limb has performed the functional movement.

[0038] In the embodiments of this application, the preset functional movements refer to the standardized movements set for upper limb functional rehabilitation training, including but not limited to arm raising movements, elbow flexion movements, fist clenching movements, object picking movements, and other common functional movements in the rehabilitation training process.

[0039] Specifically, the surface electromyography (EMG) sensor in the healthy side monitoring device 011 collects EMG signals from the patient's healthy upper limb. During the patient's performance of a preset functional movement, the bioelectrical signals generated by the healthy upper limb are collected. These bioelectrical signals are amplified by an amplification circuit, filtered for noise by a bandpass filter, and finally converted into analyzable digital EMG signals by an analog-to-digital converter. After the healthy upper limb completes the preset functional movement, the affected side monitoring device 014 acquires the joint angle and motion trajectory signals generated by the affected upper limb. The affected side monitoring device 014 includes an angle sensor and an inertial measurement unit. The angle sensor collects the angle change signals during joint rotation; the inertial measurement unit collects the position change signals of the affected upper limb in three-dimensional space using an accelerometer and a gyroscope, thereby obtaining the motion trajectory signal of the affected upper limb.

[0040] S102: Extract the healthy side's collaborative regulation decreasing trajectory based on electromyography signals, and extract the affected side's functional takeover response trajectory based on joint angle signals and motion trajectory signals.

[0041] In this embodiment, the decreasing trajectory of healthy-side synergistic regulation refers to the trend of the healthy upper limb's synergistic regulation of the affected upper limb gradually weakening over time. The decreasing trajectory of healthy-side synergistic regulation reflects the process of the healthy upper limb gradually reducing its synergistic regulation of the affected side from the initial strong synergistic regulation of the affected side, demonstrating the change in the degree of dependence of the affected side on the healthy side. As the synergistic regulation of the healthy upper limb on the affected upper limb gradually weakens over time, the voluntary motor ability of the affected upper limb gradually improves under the synergistic regulation of the healthy side, and the functional takeover response trajectory of the affected side reflects the effect of the gradual improvement of voluntary motor ability.

[0042] Specifically, the acquired electromyographic (EMG) signals were first analyzed in the time-frequency domain. Energy features were extracted using wavelet transform and normalized to obtain a curve showing the change in the intensity of synergistic regulation between the healthy and affected sides over time. By smoothing this curve and detecting inflection points, a decreasing trajectory of synergistic regulation on the healthy side, representing a gradual decrease in the synergistic effect between the healthy and affected sides, was extracted. Simultaneously, the rate of change of joint angles in the affected upper limb was calculated to obtain angle change features; the motion trajectory signal was differentiated to obtain the displacement, velocity, and acceleration of the affected upper limb. The angle change features and the displacement, velocity, and acceleration features of the motion trajectory were weighted and fused to form a feature vector. This feature vector was then arranged in a time series to obtain the functional takeover response trajectory on the affected side.

[0043] S103: Identify the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's synergistic regulation and the functional takeover response trajectory of the affected side.

[0044] In this embodiment, the critical point of functional takeover on the affected side refers to the turning point when the affected upper limb changes from relying on the coordinated regulation of the healthy side to voluntary movement, marking the time node when the function of the affected side begins to have the ability to maintain itself.

[0045] Specifically, firstly, the numerical difference between the healthy side's synergistic adjustment decreasing trajectory and the affected side's functional takeover response trajectory on the time axis is calculated. When the numerical difference decreases below a preset trajectory difference threshold, this moment is marked as an approach time point. At the approach time point, the decreasing rate of the healthy side's synergistic adjustment decreasing trajectory and the increasing rate of the affected side's functional takeover response trajectory are calculated respectively. When the difference between the increasing and decreasing rates exceeds a preset difference, the cumulative amount by which the affected side's functional takeover response trajectory value exceeds the healthy side's synergistic adjustment decreasing trajectory value is counted within a preset observation period. When this cumulative amount first reaches a preset multiple of the preset trajectory difference threshold, this moment is determined as the affected side's functional takeover critical point.

[0046] Based on the above embodiments, as an optional embodiment, S103: the step of identifying the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's coordinated regulation and the trajectory of the functional takeover response on the affected side may specifically include the following steps: S201: Calculate the numerical difference between the healthy side's synergistic adjustment decreasing trajectory and the affected side's functional takeover response trajectory at the same time point, and take the time point when the numerical difference is less than the preset trajectory difference threshold as the approach time point.

[0047] Specifically, the healthy side's synergistic regulation decreasing trajectory and the affected side's functional takeover response trajectory are first aligned on the time axis to ensure that both trajectories have the same time sampling points. Then, at each time sampling point, the absolute numerical difference between the values ​​of the healthy side's synergistic regulation decreasing trajectory and the affected side's functional takeover response trajectory is calculated, resulting in a difference sequence reflecting the degree of difference between the two trajectories. By traversing this difference sequence, when the difference at a certain time point is detected to be less than a preset trajectory difference threshold for the first time, that time point is marked as a near-term time point.

[0048] S202: Calculate the rate of decrease of the healthy side's synergistic regulation decline trajectory and the rate of increase of the affected side's functional takeover response trajectory near the time point.

[0049] Specifically, firstly, near the designated time point, a time window centered on that point is selected, and the data of the declining trajectory of healthy-side synergistic regulation and the functional takeover response trajectory of the affected side within this time window are differentiated. By calculating the ratio of the numerical change between adjacent sampling points on the declining trajectory of healthy-side synergistic regulation within the time window to the time interval, the rate of decline, representing the weakening of the healthy-side synergistic regulation effect, is obtained. Similarly, by calculating the ratio of the numerical change between adjacent sampling points on the functional takeover response trajectory of the affected side within the time window to the time interval, the rate of increase, representing the speed of functional recovery on the affected side, is obtained.

[0050] S203: The cumulative value of the affected side's functional takeover response trajectory exceeding the healthy side's synergistic adjustment decreasing trajectory within a preset observation period after the difference between the rising and falling speeds exceeds the preset difference.

[0051] Specifically, the difference between the rising rate of the affected side's functional takeover response trajectory and the falling rate of the healthy side's synergistic adjustment decreasing trajectory is first calculated. When this difference first exceeds a preset value, that moment is marked as the starting point of the observation, and data is statistically analyzed over the subsequent preset observation period. Within the preset observation period, the values ​​of the affected side's functional takeover response trajectory and the healthy side's synergistic adjustment decreasing trajectory are compared point by point. When the value of the affected side's functional takeover response trajectory is greater than the value of the healthy side's synergistic adjustment decreasing trajectory, the difference between the two values ​​is calculated. All the differences where the value of the affected side's functional takeover response trajectory is greater than the value of the healthy side's synergistic adjustment decreasing trajectory within the preset observation period are accumulated to obtain a cumulative value characterizing the degree to which the affected side's function surpasses the healthy side's synergistic adjustment.

[0052] S204: The moment when the cumulative value first reaches a preset multiple of the preset trajectory difference threshold is determined as the critical point of functional takeover on the affected side.

[0053] Specifically, a preset trajectory difference threshold is multiplied by a preset factor to obtain the target threshold for determining the functional takeover critical point on the affected side. By monitoring the cumulative values ​​in real time, the moment when the cumulative value first exceeds the target threshold is marked as the functional takeover critical point on the affected side. The determination of this critical point indicates that the affected upper limb has achieved stable voluntary movement ability. This judgment mechanism based on a preset factor ensures the accuracy of the critical point judgment and avoids misjudgments that may be caused by data fluctuations, providing a reliable time point basis for assessing the degree of functional recovery on the affected side.

[0054] S104: Calculate the first and second curvatures of the healthy side's synergistic adjustment decreasing trajectory and the affected side's functional takeover response trajectory within a preset time period before and after the affected side's functional takeover critical point. The time point when the first curvature of change first exceeds the second curvature of change is taken as the starting point of motion stability on the affected side.

[0055] In the embodiments of this application, the curvature variation refers to the degree of curvature of the trajectory at a certain moment, which is used to characterize the degree of change of the healthy side synergistic regulation decreasing trajectory and the affected side functional takeover response trajectory in a local time period. The first curvature variation reflects the stability of the affected side functional takeover response trajectory, and the second curvature variation reflects the volatility of the healthy side synergistic regulation decreasing trajectory.

[0056] Specifically, data is collected for preset time periods before and after the critical point of functional takeover on the affected side. Within these two preset time periods, the curvature changes of the decreasing trajectory of the healthy side's synergistic regulation and the functional takeover response trajectory on the affected side are calculated, respectively. By calculating the ratio of the change in tangent direction to the arc length of the functional takeover response trajectory on the affected side at adjacent sampling points, the first curvature change characterizes the degree of functional stability on the affected side. Similarly, by calculating the ratio of the change in tangent direction to the arc length of the decreasing trajectory of the healthy side's synergistic regulation at adjacent sampling points, the second curvature change characterizes the degree of fluctuation in the synergistic regulation on the healthy side. When the first curvature change first exceeds the second curvature change, it indicates that the stability of the affected side's function has exceeded the fluctuation of the healthy side's synergistic regulation, and this moment is determined as the starting point of stable movement on the affected side.

[0057] S105: Extract the distribution of extreme points of the functional takeover response trajectory of the affected side within the target time period between the critical point of functional takeover on the affected side and the starting point of motor stability on the affected side. Determine the time interval between adjacent extreme points as the motor rhythm cycle of the affected side, and calculate the trend of time length change between each motor rhythm cycle of the affected side.

[0058] In this embodiment, the trend of time length variation refers to the variation characteristics of the affected side's motor rhythm cycle in the time series, used to characterize the evolution law of the affected side's motor rhythm. The affected side's motor rhythm cycle refers to the time interval between adjacent extreme points in the affected side's functional takeover response trajectory. This cycle reflects the complete process of a single movement during the affected side's movement, embodying the regularity and rhythmic characteristics of the affected side's movement.

[0059] Specifically, the time length difference between adjacent affected side motor rhythm cycles is first calculated to obtain a cycle change difference sequence. Then, the time point with the largest difference in the cycle change difference sequence is identified as the mutation point, and the cycle change difference sequence is divided into a pre-mutation segment and a post-mutation segment. By calculating the average cycle length within the two segments and comparing the degree of difference between the pre-mutation segment and the post-mutation segment, it is finally determined whether the time length change trend exhibits a step-like mutation or a stable gradual change.

[0060] Based on the above embodiments, as an optional embodiment, S105: the step of calculating the trend of time length changes between the motor rhythm cycles of each affected side may specifically include the following steps: S301: Calculate the time length difference between every two adjacent motor rhythm cycles on the affected side to form a cycle change difference sequence.

[0061] Specifically, the time series of two adjacent motility rhythm cycles on the affected side is obtained sequentially. The time difference is obtained by subtracting the time length of the previous cycle from the time length of the subsequent cycle. All the calculated differences are arranged in chronological order to form a sequence of periodic variation differences.

[0062] S302: Take the time point with the largest time length difference in the periodic variation difference sequence as the mutation point, and divide the periodic variation difference sequence into the pre-mutation segment and the post-mutation segment based on the mutation point.

[0063] Specifically, the process iterates through all differences in the cyclic variation difference sequence, identifying the largest difference and its corresponding time point. This largest difference represents the most dramatic change between two adjacent affected side motor rhythm cycles, and the time point at which this occurs is marked as the abrupt change point. Using the abrupt change point as the dividing line, the entire cyclic variation difference sequence is divided into two subsequences: the pre-abrupt change segment and the post-abrupt change segment. This sequence segmentation method based on the largest difference can capture key turning points in the process of affected side motor rhythm changes, providing a time reference point for subsequent analysis of the characteristics of changes at different stages.

[0064] S303: Calculate the first average cycle length of each affected side's motor rhythm cycle in the pre-mutation segment and the second average cycle length of each affected side's motor rhythm cycle in the post-mutation segment. When the difference between the second average cycle length and the first average cycle length exceeds a preset difference threshold, the trend of time length change is determined as a step change trend; otherwise, it is determined as a stable change trend.

[0065] Specifically, the average cycle length is calculated for both the pre-mutation and post-mutation segments. In the pre-mutation segment, the sum of the durations of all affected-side motor rhythm cycles within that segment is divided by the number of affected-side motor rhythm cycles in the pre-mutation segment to obtain the first average cycle length. Similarly, in the post-mutation segment, the sum of the durations of all affected-side motor rhythm cycles within that segment is divided by the number of affected-side motor rhythm cycles in the post-mutation segment to obtain the second average cycle length. The difference between the second and first average cycle lengths is then obtained. The absolute value of this difference is compared to a preset difference threshold. When the absolute value exceeds the preset threshold, it indicates a significant jump in the affected-side motor rhythm cycle before and after the mutation; therefore, the time length change trend is determined to be a step change trend. When the absolute value does not exceed the preset threshold, it indicates a relatively gradual change in the affected-side motor rhythm cycle before and after the mutation; therefore, the time length change trend is determined to be a stable change trend.

[0066] S106: Based on the trend of time length change, determine the functional autonomy maintenance score of the affected side within the target time period, and calculate the muscle function rhythm index of the affected upper limb based on the functional autonomy maintenance score.

[0067] In the embodiments of this application, the muscle function rhythm index is a comprehensive evaluation index used to characterize the recovery level of motor function of the affected upper limb, and is used to quantify the voluntary control ability of the affected muscle function and the stability of the motor rhythm.

[0068] Specifically, firstly, the synergistic contribution of the healthy side and the voluntary achievement of the affected side are obtained based on the movement trajectory feature points of the healthy and affected sides. Depending on the type of time-length variation trend, different calculation methods are used to obtain functional scores: when a stable trend is observed, the ratio of the synergistic contribution of the healthy side to the voluntary achievement of the affected side is calculated to obtain a progressive score; when a step-like trend is observed, the ratio after difference adjustment is calculated to obtain an adaptive score. Then, the score is multiplied by the number of rhythmic cycles on the affected side to obtain a cycle-adjusted score, which is then divided by the overall variation amplitude to obtain a functional voluntary maintenance score. Based on this score, the corresponding rhythm gain coefficient is found, and the difference between the voluntary achievement of the affected side and the synergistic contribution of the healthy side is adjusted for gain. Finally, the ratio of the target time period to the number of cycles is calculated to obtain the rhythm frequency, which is multiplied by the gain-adjusted functional difference to obtain the muscle function rhythm index reflecting the recovery level of motor function on the affected side.

[0069] Based on the above embodiments, as an optional embodiment, S106: the step of determining the functional self-maintenance score of the affected side within the target time period based on the trend of time length changes may specifically include the following steps: S401: The value of the healthy side's synergistic adjustment decreasing trajectory at the critical point of the affected side's functional takeover is taken as the healthy side's synergistic contribution, and the value of the affected side's functional takeover response trajectory at the starting point of the affected side's motor stability is taken as the affected side's autonomous achievement.

[0070] Specifically, firstly, the critical point of functional takeover on the affected side is located on the decreasing trajectory of the healthy side's synergistic regulation. This critical point marks the moment when the affected side begins to gradually take over motor control. The trajectory value corresponding to the critical point is extracted as the synergistic contribution of the healthy side. Simultaneously, the starting point of motor stability on the affected side is determined on the functional takeover response trajectory. This starting point indicates the moment when the motor pattern on the affected side begins to stabilize. The trajectory value corresponding to the starting point of motor stability on the affected side is extracted as the autonomous achievement quantity of the affected side.

[0071] S402: When the trend of change in time length is a stable trend, the ratio of the amount of spontaneous achievement on the affected side to the amount of collaborative contribution on the healthy side is used as the progressive score.

[0072] Specifically, when the temporal variation trend of the affected side's motor rhythm is determined to be a stable trend, it indicates that the change in the motor cycle on the affected side is relatively gradual, without significant jumps. In this case, the previously obtained voluntary achievement amount on the affected side is divided by the synergistic contribution amount on the healthy side to obtain a progressive score. This ratio-based scoring method fully considers the relative relationship between the voluntary control ability of the affected side and the synergistic effect of the healthy side, and can reflect the progressive recovery characteristics of the affected side's function in a stable state. When the ratio is larger, it indicates that the voluntary control ability of the affected side is stronger relative to the synergistic effect of the healthy side, indicating a better degree of functional recovery on the affected side.

[0073] S403: When the trend of change in time length is a step change trend, the ratio of the amount of autonomous achievement on the affected side minus the amount of collaborative contribution on the healthy side to the amount of collaborative contribution on the healthy side is used as the adaptation score.

[0074] Specifically, when the temporal variation trend of the affected side's motor rhythm is determined to be a step change, it indicates that a jump has occurred in the motor cycle on the affected side. In this case, the difference between the voluntary achievement amount on the affected side and the synergistic contribution amount on the healthy side is first calculated. Then, this difference is divided by the synergistic contribution amount on the healthy side to obtain the adaptation score. This functional increment-based scoring method considers the adaptive ability of the affected side under abrupt motor changes. When the voluntary achievement amount on the affected side is higher than the synergistic contribution amount on the healthy side, the adaptation score will increase accordingly, indicating that the affected side has a stronger ability to adjust motor patterns.

[0075] S404: Calculate the number of affected-side motor rhythm cycles within the target time period, and multiply the progressive or adaptive score by the number of affected-side motor rhythm cycles to obtain the cycle adjustment score.

[0076] Specifically, firstly, the total number of complete motor rhythm cycles on the affected side within the target time period is counted. Then, based on the previously determined trend of change in time length, the corresponding scoring type is selected: a progressive scoring method is used when the trend is stable, and an adaptive scoring method is used when the trend is step-like. The progressive or adaptive score is multiplied by the number of motor rhythm cycles on the affected side to obtain the cycle adjustment score. This scoring adjustment method considers the continuity characteristics of movement on the affected side; the more cycles, the stronger the ability of the affected side to maintain stable movement, and the higher the corresponding cycle adjustment score will be. Through this scoring correction based on the number of motor cycles, the functional recovery level of the affected side can be more comprehensively assessed.

[0077] S405: Calculate the sum of the absolute values ​​of all differences in the periodic variation difference sequence as the overall variation amplitude, and divide the periodic adjustment score by the overall variation amplitude to obtain the functional self-maintenance score.

[0078] Specifically, firstly, the absolute value of each difference in the periodic variation difference sequence is taken, and all absolute values ​​are summed to obtain the overall variation amplitude. The overall variation amplitude reflects the degree of fluctuation in the motor rhythm of the affected side; a larger amplitude value indicates higher motor instability. Then, the previously obtained periodic adjustment score is divided by the overall variation amplitude to obtain the functional self-maintenance score.

[0079] Based on the above embodiments, as an optional embodiment, S106: the step of calculating the muscle function rhythm index of the affected upper limb according to the functional autonomy maintenance score may specifically include the following steps: S501: Based on the functional self-maintenance score, find the corresponding rhythm gain coefficient from the preset rhythm gain coefficient table.

[0080] Specifically, the process begins by searching a pre-defined rhythm gain coefficient table based on the obtained functional autonomy maintenance score. This table pre-sets rhythm gain coefficients corresponding to different score intervals. The search process uses interval matching to locate the functional autonomy maintenance score within a specific score interval, and then extracts the corresponding rhythm gain coefficient for that interval.

[0081] S502: Calculate the difference between the amount of autonomous achievement on the affected side and the amount of collaborative contribution on the healthy side as the original functional difference.

[0082] Specifically, the original functional difference is obtained by subtracting the contribution of the healthy side from the previously obtained autonomous achievement on the affected side. This calculation method based on numerical difference can intuitively reflect the relative level of functional recovery on the affected side compared to the synergistic effect on the healthy side, providing basic data support for subsequent functional assessment.

[0083] S503: Multiply the original function difference by the rhythm gain coefficient to obtain the function difference after gain adjustment.

[0084] Specifically, the previously calculated original functional difference is multiplied by the found rhythm gain coefficient to obtain the gain-adjusted functional difference. This dynamic gain adjustment based on rhythm characteristics can more accurately reflect the actual functional recovery level of the affected side.

[0085] S504: Calculate the ratio of the target time period to the number of rhythmic cycles on the affected side as the rhythm frequency.

[0086] Specifically, the rhythm frequency is obtained by dividing the total duration of the target time period by the number of rhythmic cycles of the affected side within that time period. This time-density-based calculation method can quantitatively describe the rhythmic characteristics of the affected side's movements, providing an important reference indicator for the final functional assessment.

[0087] S505: Multiply the gain-adjusted functional difference by the rhythm frequency to obtain the muscle function rhythm index.

[0088] Specifically, the gain-adjusted functional difference is multiplied by the rhythm frequency to obtain the muscle function rhythm index. Through this multi-dimensional comprehensive calculation method, the final muscle function rhythm index can more comprehensively and accurately assess the functional recovery status of the affected side.

[0089] Based on the above embodiments, as an optional embodiment, S106: after calculating the muscle function rhythm index of the affected upper limb according to the functional autonomy maintenance score, the process further includes updating the trend of time length changes, specifically including the following steps: S601: Analyze the extreme points extracted within the target time period based on the muscle function rhythm index to obtain the importance of each extreme point.

[0090] Specifically, based on the calculated muscle function rhythm index, the extreme points extracted within the target time period are analyzed. The analysis process mainly considers the following aspects: First, the motion state characteristics of each extreme point are matched with the boundary characteristics of the standard affected side's motor rhythm cycle. This includes checking whether the signal change trend before and after the extreme point conforms to the typical pattern during the transition of the motor cycle, assessing whether the motion amplitude at this point reaches the threshold level required for the cycle transition, and verifying the rationality of the transition point in conjunction with the muscle function rhythm index. The matching degree is converted into the first importance score. Second, the time interval between the current extreme point and the extreme points before and after it is calculated. These time intervals are compared with the range of normal motor rhythm cycles obtained from historical statistics to determine the rationality of the time intervals. At the same time, it is examined whether the time series composed of multiple consecutive extreme points exhibits regular periodic characteristics. The rationality and regularity are converted into the second importance score. Third, the waveform characteristics of the extreme point are constructed, and the amplitude relationship between the extreme point and several sampling points before and after it is analyzed. Abnormal fluctuations caused by data noise or transient muscle tremors are identified. At the same time, the significance level of the extreme point in the overall waveform is assessed, and local fluctuation points that do not have periodic boundary characteristics are excluded. The effectiveness of the waveform characteristics is converted into the third importance score. Finally, the importance scores of the three aspects are weighted and summed to obtain the importance of each extreme point.

[0091] S602: Mark extreme points whose importance exceeds a preset importance threshold as critical extreme points.

[0092] Specifically, the importance values ​​of all extreme points are compared with a preset importance threshold. When the importance value of an extreme point exceeds the preset importance threshold, it is marked as a critical extreme point. This effectively filters out invalid extreme points caused by factors such as muscle tremors and signal noise, retaining only those key moment points that truly represent the transition of the movement cycle.

[0093] S603: Calculate the numerical gradient between adjacent critical extreme points.

[0094] Specifically, the numerical values ​​and corresponding time points of each pair of adjacent critical extreme points are obtained sequentially, and the gradient of numerical change between them is calculated. The calculation first calculates the numerical difference between the two critical extreme points, then obtains the time interval between them, and divides the numerical difference by the time interval to obtain the gradient of change for that interval.

[0095] S604: Generate the distribution results of extreme points based on the distribution of each numerical gradient.

[0096] Specifically, the distribution characteristics of the calculated gradient values ​​are analyzed. The analysis process first involves statistically analyzing the numerical range and frequency of each gradient value to establish a histogram of gradient value distribution; then, the central tendency and dispersion of the gradient values ​​are analyzed, including calculating the mean, variance, and other statistical measures of the gradient values; next, abnormal intervals of gradient values ​​are identified, and the extreme points corresponding to gradient values ​​that significantly deviate from the normal distribution range are marked to generate extreme point distribution results.

[0097] S605: Update the trend of time length changes between the motor rhythm cycles of each affected side based on the extreme point distribution results.

[0098] Specifically, the process begins by counting the number of data points within each rhythmic cycle to identify sparse data points in the cycles. Then, consecutive sparse cycles are merged and integrated into cycle units. Next, discontinuous sparse cycles are processed and merged based on the data distribution characteristics of their adjacent cycles. After that, the time length variation trend between the merged cycles is recalculated. Finally, based on the updated variation trend, a more accurate basis is provided for calculating the autonomous maintenance score of the function.

[0099] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating an update of the time length variation trend provided in an embodiment of this application. Based on the above embodiment, as an optional embodiment, S605: the step of updating the time length variation trend between the motor rhythm cycles of each affected side based on the extreme point distribution results may specifically include the following steps: S701: Based on the extreme point distribution results, count the number of data points contained in each affected side's motor rhythm cycle, and mark the affected side's motor rhythm cycle with fewer than the preset minimum data volume threshold as a sparse cycle.

[0100] Specifically, based on the obtained extreme point distribution results, data points within each affected side's motor rhythm cycle are statistically analyzed. The statistical process first determines the start and end positions of each affected side's motor rhythm cycle, then calculates the total number of data points within that cycle. The number of data points is compared to a pre-set minimum data volume threshold. If the number of data points within a particular affected side's motor rhythm cycle is lower than the minimum threshold, it indicates an anomaly or data loss in the data acquisition for that cycle, and it is marked as a sparse cycle. If no sparse cycle exists, there is no need to update the time length variation trend between each affected side's motor rhythm cycle.

[0101] S702: For consecutive first sparse periods, merge consecutive first sparse periods.

[0102] Specifically, the process involves identifying and processing all consecutive first sparse periods. The process begins by scanning the labeled sparse periods to find those that are temporally closely connected. For each group of consecutive first sparse periods, they are treated as a whole and merged, retaining the start and end times of these periods to form a new periodic unit.

[0103] S703: For the discontinuous second sparse cycle, based on the data density distribution characteristics of the adjacent affected side motor rhythm cycle, the second sparse cycle is merged with the adjacent affected side motor rhythm cycle to obtain each merged affected side motor rhythm cycle.

[0104] Specifically, each discontinuous second sparse period is processed. The process first calculates the average time interval and its fluctuation range of data points in adjacent periods before and after the sparse period, obtaining the statistical characteristics of the data amplitude and the trend characteristics of data changes. The absolute value of the difference between the average time interval of the sparse period and its adjacent periods is divided by the average time interval of the adjacent periods to obtain the difference coefficient of the time interval. Then, the overlap degree of the amplitude distribution is obtained by comparing the percentage overlap of data points in the amplitude interval between the sparse period and the adjacent periods. Finally, the overlap degree and the difference coefficient are weighted and summed to obtain the comprehensive similarity between the sparse period and its adjacent periods. Adjacent periods with a comprehensive similarity greater than a preset similarity threshold are selected as merging objects. After determining the merging objects, the boundary of the second sparse period is extended to the corresponding boundary position of the selected affected side's motor rhythm period to obtain the merged affected side's motor rhythm period. By performing the above merging process on all discontinuous second sparse periods, each merged affected side's motor rhythm period is obtained.

[0105] S704: Calculate the new trend of time length changes between the combined affected side motor rhythm cycles.

[0106] Specifically, following the same calculation method as S105, the new trend of time length changes between the motor rhythm cycles of the affected side after merging is calculated, which will not be elaborated here.

[0107] S705: Based on the new trend of time length changes, determine the functional autonomy maintenance score of the affected side within the target time period.

[0108] Specifically, the new functional autonomy maintenance score, determined based on the new time length change trend, is calculated using the same method as in S106, and will not be elaborated here.

[0109] refer to Figure 4 This application also provides a schematic diagram of a muscle function rhythm analysis system based on unaffected side synergy, which specifically includes: The data acquisition module is used to acquire electromyographic signals of the patient's unaffected upper limb during the execution of a preset functional movement, as well as joint angle signals and motion trajectory signals generated by the affected upper limb after the unaffected upper limb performs the functional movement. The trajectory extraction module is used to extract the healthy side's coordinated adjustment decreasing trajectory based on the electromyographic signals, and to extract the affected side's functional takeover response trajectory based on the joint angle signals and motion trajectory signals. The critical point calculation module is used to identify the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's coordinated adjustment and the functional takeover response trajectory on the affected side. The starting point calculation module is used to calculate the first and second curvatures of the healthy side's coordinated adjustment decreasing trajectory and the affected side's functional takeover response trajectory respectively during a preset time period before and after the affected side's functional takeover critical point, and to take the time point when the first curvature is greater than the second curvature as the starting point of the affected side's motion stability. The rhythm cycle calculation module is used to extract the extreme point distribution of the functional takeover response trajectory of the affected side within the target time period between the critical point of functional takeover on the affected side and the starting point of motor stability on the affected side, determine the time interval between adjacent extreme points as the rhythm cycle of motor movement on the affected side, and calculate the time length variation trend between each rhythm cycle of motor movement on the affected side. The rhythm index calculation module is used to determine the functional self-maintenance score of the affected side within the target time period based on the trend of the time length change, and to calculate the muscle function rhythm index of the affected upper limb based on the functional self-maintenance score.

[0110] Optionally, the critical point calculation module is specifically used for: Calculate the numerical difference between the healthy side's collaborative adjustment decreasing trajectory and the affected side's functional takeover response trajectory at the same time point, and take the time point when the numerical difference is less than a preset trajectory difference threshold as the approach time point; Calculate the decreasing rate of the healthy side's coordinated adjustment trajectory and the increasing rate of the affected side's functional takeover response trajectory at the near time point; Within a preset observation period after the difference between the rising speed and the falling speed is greater than a preset difference, the cumulative value of the affected side functional takeover response trajectory exceeds the value of the healthy side collaborative adjustment decreasing trajectory. The moment when the accumulated value first reaches a preset multiple of the preset trajectory difference threshold is determined as the critical point of functional takeover on the affected side.

[0111] Optionally, the rhythm cycle calculation module is specifically used for: Calculate the time difference between every two adjacent motor rhythm cycles on the affected side to form a sequence of cycle variation differences; The time point with the largest time length difference in the periodic variation difference sequence is taken as the mutation point, and the periodic variation difference sequence is divided into a pre-mutation segment and a post-mutation segment based on the mutation point. The first average cycle length of each of the affected side's motor rhythm cycles in the pre-mutation segment and the second average cycle length of each of the affected side's motor rhythm cycles in the post-mutation segment are calculated respectively. When the difference between the second average cycle length and the first average cycle length exceeds a preset difference threshold, the trend of change in time length is determined to be a step change trend; otherwise, it is determined to be a stable change trend.

[0112] Optionally, the rhythm index calculation module is specifically used for: The value of the healthy side's synergistic adjustment decreasing trajectory at the critical point of the affected side's functional takeover is taken as the healthy side's synergistic contribution, and the value of the affected side's functional takeover response trajectory at the starting point of the affected side's motor stability is taken as the affected side's autonomous achievement. When the trend of the time length change is a stable trend, the ratio of the amount of autonomous achievement on the affected side to the amount of collaborative contribution on the healthy side is used as the progressive score; When the time length changes in a step-like trend, the ratio of the amount of autonomous achievement on the affected side minus the amount of collaborative contribution on the healthy side to the amount of collaborative contribution on the healthy side is used as the adaptation score. Calculate the number of affected-side motor rhythm cycles within the target time period, and multiply the progressive score or the adaptive score by the number of affected-side motor rhythm cycles to obtain the cycle adjustment score; The sum of the absolute values ​​of all differences in the periodic variation difference sequence is calculated as the overall variation amplitude. The periodic adjustment score is divided by the overall variation amplitude to obtain the functional autonomous maintenance score.

[0113] Optionally, the rhythm index calculation module is further specifically used for: The corresponding rhythm gain coefficient is retrieved from the preset rhythm gain coefficient table based on the autonomous maintenance score of the function. The difference between the amount of autonomous achievement on the affected side and the amount of collaborative contribution on the healthy side is calculated as the original functional difference. Multiplying the original functional difference by the rhythm gain coefficient yields the functional difference after gain adjustment. The ratio of the target time period to the number of cycles of the motor rhythm on the affected side is calculated as the rhythm frequency; The muscle function rhythm index is obtained by multiplying the functional difference after gain adjustment by the rhythm frequency.

[0114] Optionally, the rhythm cycle calculation module is further specifically used for: The importance of each extreme point is determined by analyzing the extreme points extracted within the target time period based on the muscle function rhythm index. The extreme points whose importance exceeds a preset importance threshold are marked as critical extreme points; Calculate the numerical gradient between adjacent critical extreme points; Based on the distribution of the numerical change gradients, the extreme point distribution results are generated; The trend of time length variation between the motor rhythm cycles of each affected side is updated based on the extreme point distribution results.

[0115] The optional rhythm cycle calculation module is further specifically used for: Based on the extreme point distribution results, the number of data points contained in each of the affected side's motor rhythm cycles is counted, and the affected side's motor rhythm cycles with fewer than the preset minimum data volume threshold are marked as sparse cycles. For consecutive first sparse periods, the consecutive first sparse periods are merged; For the discontinuous second sparse period, based on the data density distribution characteristics of the adjacent affected side motor rhythm period of the second sparse period, the second sparse period is merged with the adjacent affected side motor rhythm period to obtain each merged affected side motor rhythm period. Calculate the new trend of time length changes among the combined affected-side motor rhythm cycles; The determination of the functional autonomy maintenance score of the affected side within the target time period based on the trend of the time length change includes: Based on the new trend in time length changes, the functional autonomy maintenance score of the affected side during the target time period is determined.

[0116] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0117] This embodiment also discloses an electronic device, as shown in the reference. Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 013 may include: at least one processor 901, at least one communication bus 902, a user interface 903, a network interface 904, and at least one memory 905.

[0118] The communication bus 902 is used to enable communication between these components.

[0119] The user interface 903 may include a display screen and a camera. Optionally, the user interface 903 may also include a standard wired interface and a wireless interface.

[0120] The network interface 904 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0121] The processor 901 may include one or more processing cores. The processor 901 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 905, and by calling data stored in the memory 905. Optionally, the processor 901 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array. The processor 901 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 901 and may be implemented as a separate chip.

[0122] The memory 905 may include random access memory (RAM) or read-only memory. Optionally, the memory 905 may include a non-transitory computer-readable storage medium. The memory 905 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 905 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 905 may also be at least one storage device located remotely from the aforementioned processor 901. (See reference...) Figure 5 The memory 905, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for analyzing muscle function rhythms based on healthy-side coordination.

[0123] exist Figure 5In the electronic device shown, the user interface 903 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 901 can be used to call an application program stored in the memory 905 that is based on healthy side coordination muscle function rhythm analysis. When executed by one or more processors 901, the electronic device 013 performs one or more of the methods described in the above embodiments.

[0124] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0125] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0126] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of 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 system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0127] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0130] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for analyzing the rhythm of muscle function based on unaffected side synergy, characterized in that, Applied to electronic devices, the method includes: Acquire electromyographic signals of the patient's unaffected upper limb during the execution of a preset functional movement, and joint angle signals and motion trajectory signals generated by the affected upper limb after the unaffected upper limb has performed the functional movement. Based on the electromyographic signals, the healthy side's coordinated regulation decreasing trajectory is extracted; based on the joint angle signals and motion trajectory signals, the affected side's functional takeover response trajectory is extracted. The critical point of functional takeover on the affected side is identified based on the numerical change trends of the decreasing trajectory of the healthy side's coordinated regulation and the response trajectory of the affected side's functional takeover. The first curvature and the second curvature of the healthy side's synergistic adjustment decreasing trajectory and the affected side's functional takeover response trajectory are calculated respectively during a preset time period before and after the critical point of the affected side's functional takeover. The time point when the first curvature is greater than the second curvature is taken as the starting point of the affected side's motion stability. Extract the distribution of extreme points of the functional takeover response trajectory of the affected side within the target time period between the critical point of functional takeover on the affected side and the starting point of motor stability on the affected side. Determine the time interval between adjacent extreme points as the motor rhythm cycle of the affected side, and calculate the trend of time length change between each motor rhythm cycle of the affected side. Based on the trend of the time length change, the functional self-maintenance score of the affected side within the target time period is determined, and the muscle function rhythm index of the affected upper limb is calculated based on the functional self-maintenance score.

2. The method for analyzing muscle function rhythms based on healthy-side synergy according to claim 1, characterized in that, The step of identifying the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's coordinated regulation and the response trajectory of the affected side's functional takeover includes: Calculate the numerical difference between the healthy side's collaborative adjustment decreasing trajectory and the affected side's functional takeover response trajectory at the same time point, and take the time point when the numerical difference is less than a preset trajectory difference threshold as the approach time point; Calculate the decreasing rate of the healthy side's coordinated adjustment trajectory and the increasing rate of the affected side's functional takeover response trajectory at the near time point; Within a preset observation period after the difference between the rising speed and the falling speed is greater than a preset difference, the cumulative value of the value of the affected side functional takeover response trajectory exceeds the value of the healthy side collaborative adjustment decreasing trajectory. The moment when the accumulated value first reaches a preset multiple of the preset trajectory difference threshold is determined as the critical point of functional takeover on the affected side.

3. The method for analyzing muscle function rhythms based on healthy side synergy according to claim 1, characterized in that, The calculation of the time length variation trend between each of the affected side's motor rhythm cycles includes: Calculate the time difference between every two adjacent motor rhythm cycles on the affected side to form a sequence of cycle variation differences; The time point with the largest time length difference in the periodic variation difference sequence is taken as the mutation point, and the periodic variation difference sequence is divided into a pre-mutation segment and a post-mutation segment based on the mutation point. The first average cycle length of each of the affected side's motor rhythm cycles in the pre-mutation segment and the second average cycle length of each of the affected side's motor rhythm cycles in the post-mutation segment are calculated respectively. When the difference between the second average cycle length and the first average cycle length exceeds a preset difference threshold, the trend of change in time length is determined to be a step change trend; otherwise, it is determined to be a stable change trend.

4. The method for analyzing muscle function rhythms based on healthy-side synergy according to claim 3, characterized in that, The determination of the functional autonomy maintenance score of the affected side within the target time period based on the trend of the time length change includes: The value of the healthy side's synergistic adjustment decreasing trajectory at the critical point of the affected side's functional takeover is taken as the healthy side's synergistic contribution, and the value of the affected side's functional takeover response trajectory at the starting point of the affected side's motor stability is taken as the affected side's autonomous achievement. When the trend of the time length change is a stable trend, the ratio of the amount of autonomous achievement on the affected side to the amount of collaborative contribution on the healthy side is used as the progressive score; When the time length changes in a step-like trend, the ratio of the amount of autonomous achievement on the affected side minus the amount of collaborative contribution on the healthy side to the amount of collaborative contribution on the healthy side is used as the adaptation score. Calculate the number of affected-side motor rhythm cycles within the target time period, and multiply the progressive score or the adaptive score by the number of affected-side motor rhythm cycles to obtain the cycle adjustment score; The sum of the absolute values ​​of all differences in the periodic variation difference sequence is calculated as the overall variation amplitude. The periodic adjustment score is divided by the overall variation amplitude to obtain the functional autonomous maintenance score.

5. The method for analyzing muscle function rhythms based on healthy-side synergy according to claim 4, characterized in that, The calculation of the muscle function rhythm index of the affected upper limb based on the functional self-maintenance score includes: The corresponding rhythm gain coefficient is retrieved from the preset rhythm gain coefficient table based on the autonomous maintenance score of the function. The difference between the amount of autonomous achievement on the affected side and the amount of collaborative contribution on the healthy side is calculated as the original functional difference. Multiplying the original functional difference by the rhythm gain coefficient yields the functional difference after gain adjustment. The ratio of the target time period to the number of cycles of the motor rhythm on the affected side is calculated as the rhythm frequency; The muscle function rhythm index is obtained by multiplying the functional difference after gain adjustment by the rhythm frequency.

6. The method for analyzing muscle function rhythms based on healthy-side synergy according to claim 1, characterized in that, After calculating the muscle function rhythm index of the affected upper limb based on the functional autonomy maintenance score, the method further includes: The importance of each extreme point is determined by analyzing the extreme points extracted within the target time period based on the muscle function rhythm index. The extreme points whose importance exceeds a preset importance threshold are marked as critical extreme points; Calculate the numerical change gradient between adjacent critical extreme points; Based on the distribution of the numerical change gradients, the extreme point distribution results are generated; The trend of time length variation between the motor rhythm cycles of each affected side is updated based on the extreme point distribution results.

7. The method for analyzing muscle function rhythms based on healthy-side synergy according to claim 6, characterized in that, The updating of the time length variation trend between each of the affected side's motor rhythm cycles based on the extreme point distribution results includes: Based on the extreme point distribution results, the number of data points contained in each of the affected side's motor rhythm cycles is counted, and the affected side's motor rhythm cycles with fewer than the preset minimum data volume threshold are marked as sparse cycles. For consecutive first sparse periods, the consecutive first sparse periods are merged; For the discontinuous second sparse period, based on the data density distribution characteristics of the adjacent affected side motor rhythm period of the second sparse period, the second sparse period is merged with the adjacent affected side motor rhythm period to obtain each merged affected side motor rhythm period. Calculate the new trend of time length changes among the combined affected-side motor rhythm cycles; The determination of the functional autonomy maintenance score of the affected side within the target time period based on the trend of the time length change includes: Based on the new trend in time length changes, the functional autonomy maintenance score of the affected side during the target time period is determined.

8. A muscle function rhythm analysis system based on unaffected side coordination, characterized in that, The system is applied to electronic devices and includes: The data acquisition module is used to acquire electromyographic signals of the patient's unaffected upper limb during the execution of a preset functional movement, as well as joint angle signals and motion trajectory signals generated by the affected upper limb after the unaffected upper limb performs the functional movement. The trajectory extraction module is used to extract the healthy side's coordinated adjustment decreasing trajectory based on the electromyographic signals, and to extract the affected side's functional takeover response trajectory based on the joint angle signals and motion trajectory signals. The critical point calculation module is used to identify the critical point of functional takeover on the affected side based on the numerical change trend of the decreasing trajectory of the healthy side's coordinated adjustment and the functional takeover response trajectory on the affected side. The starting point calculation module is used to calculate the first and second curvatures of the healthy side's coordinated adjustment decreasing trajectory and the affected side's functional takeover response trajectory respectively during a preset time period before and after the affected side's functional takeover critical point, and to take the time point when the first curvature is greater than the second curvature as the starting point of the affected side's motion stability. The rhythm cycle calculation module is used to extract the extreme point distribution of the functional takeover response trajectory of the affected side within the target time period between the critical point of functional takeover on the affected side and the starting point of motor stability on the affected side, determine the time interval between adjacent extreme points as the rhythm cycle of motor movement on the affected side, and calculate the time length variation trend between each rhythm cycle of motor movement on the affected side. The rhythm index calculation module is used to determine the functional self-maintenance score of the affected side within the target time period based on the trend of the time length change, and to calculate the muscle function rhythm index of the affected upper limb based on the functional self-maintenance score.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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