Standing-up walking test method and system based on inertial sensor and electromyography technology

Abstract

The application discloses a standing-up and walking test method and system based on inertial sensors and electromyography technology, relates to the technical field of gait assessment and rehabilitation detection, and comprises the following steps: data acquisition is completed by using inertial sensors and electromyography technology; through a first algorithm, differences between left and right support phases and swing phases are comprehensively evaluated to determine whether the gait is symmetrical and stable; and through a second algorithm, muscle coordination of a patient is evaluated. The method realizes synchronous data fusion of a behavior layer and a neural control layer, lays a data foundation for subsequent construction of a precise and multi-dimensional gait and electromyography evaluation model, realizes joint evaluation of two core indexes of gait symmetry and stability, improves detection capability of neurological gait disorders, provides reliable criteria for individualized rehabilitation intervention, realizes modeling and difference measurement of timing control characteristics, and improves recognition capability of neuromuscular control disorders.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gait assessment and rehabilitation detection, in particular to a standing and walking test method and system based on inertial sensors and electromyography technology. BACKGROUND

[0002] With the aggravation of population aging and the rising incidence of nervous system diseases (such as stroke, Parkinson's disease, etc.), gait analysis technology is increasingly widely used in clinical rehabilitation assessment. Inertial sensors (IMU) gradually replace traditional optical motion capture systems due to their low cost, convenient installation and strong real-time performance, and become an important tool for gait analysis. At the same time, electromyography (EMG) technology, as an important means of evaluating neuromuscular function, is widely used in the diagnosis and rehabilitation of motor dysfunction. In recent years, research on combining multiple sensors to improve gait analysis accuracy has been continuously advancing, and the fusion of inertial sensors and electromyography signals has become a focus of attention.

[0003] However, the existing technology still has many deficiencies in fusing inertial sensors and electromyography signals for gait assessment. Existing researches mostly stay at the data collection level, lack of in-depth quantitative evaluation mechanism for gait symmetry and stability, and cannot provide concrete indicators for clinical practice. Traditional gait assessment methods often analyze the movement information of left and right limbs independently, lack effective algorithms to compare the differences between support and swing phases, and are difficult to comprehensively judge gait symmetry. The analysis method of muscle coordination still mostly uses qualitative description, lacks objective quantitative standard based on algorithm, and is difficult to accurately reflect the neuromuscular function status. Therefore, the existing technology cannot achieve efficient evaluation of gait stability, symmetry and muscle coordination. In contrast, the present application introduces a double algorithm mechanism in the technical solution, which not only realizes accurate identification of gait phase differences, but also further quantifies the muscle coordination of patients through electromyography analysis algorithm, and is expected to provide more comprehensive indicators with clinical reference value for rehabilitation assessment. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing gait analysis technology has poor evaluation accuracy, low symmetry judgment ability, insufficient muscle coordination analysis, and how to realize comprehensive evaluation of the whole standing and walking process by fusing inertial sensors and electromyography technology.

[0006] To solve the above technical problems, the present application provides the following technical solution: a standing and walking test method based on inertial sensors and electromyography technology, comprising using inertial sensors and electromyography technology to complete data acquisition; through a first algorithm, the differences between left and right support phases and swing phases are comprehensively evaluated to judge whether the gait is symmetrical and stable; through a second algorithm, the muscle coordination of the patient is evaluated.

[0007] As a preferred embodiment of the stand-and-walk test method based on inertial sensors and electromyography technology described in the present invention, the data acquisition includes collecting inertial sensor data and electromyography signals from the subject's lower limbs, recording acceleration data through the inertial sensor, and recording surface electromyography signals of the target muscle group through the electromyography sensor. The acceleration data and the surface electromyography signals of the target muscle group are synchronously collected on a unified time axis.

[0008] As a preferred embodiment of the stand-and-go test method based on inertial sensors and electromyography technology described in the present invention, the first algorithm includes constructing a gait symmetry scoring function based on inertial sensor acceleration data; based on the acceleration difference and acceleration rate difference of the left and right lower limbs at each moment, and introducing a time weighting function to reflect gait stage differences and mutation sensitivity, the weighted results are averaged to obtain a gait symmetry score.

[0009] As a preferred embodiment of the stand-and-walk test method based on inertial sensors and electromyography technology described in the present invention, the time weighting function includes constructing a first time weighting function, which is constructed according to the distance between the current time point and the gait phase transition time point in the form of an exponential decay function to adjust the evaluation weight of the transition area between the stance phase and the swing phase.

[0010] As a preferred embodiment of the stand-and-walk test method based on inertial sensors and electromyography technology described in the present invention, the time weighted function also includes constructing a second time weighted function, which is constructed according to the distance between the current time point and the gait mutation time point in the form of an S-type logic function to adjust the evaluation weight of sudden gait instability events.

[0011] As a preferred embodiment of the stand-and-go test method based on inertial sensors and electromyography technology described in the present invention, the determination of whether the gait is symmetrical and stable includes characterizing the structural consistency and phase fluctuations of the subject during the gait cycle based on the value of the gait symmetry scoring function; if the gait symmetry scoring function value is within a first threshold range, the gait is judged to be well symmetrical; if the gait symmetry scoring function value is within a second threshold range, the gait is judged to be asymmetric and accompanied by instability.

[0012] As a preferred solution of the stand-and-walk test method based on inertial sensors and electromyography technology described in the present invention, the second algorithm includes modeling the collected electromyography signals, extracting the amplitude and phase characteristics of the synergistic muscles and antagonistic muscles respectively, introducing Fourier modulation terms to construct a timing modulation expression, calculating the absolute value of the difference and multiplying it by a timing-sensitive weighting function, and finally normalizing the difference to generate a muscle coordination score.

[0013] As a preferred embodiment of the stand-and-walk test method based on inertial sensors and electromyographic technology described in the present invention, the generation of the muscle coordination score includes constructing a timing-sensitive weighted function, the center point of which is the muscle peak activation time point, reflecting the difference sensitivity of the muscle during the high activity period; and constructing a muscle modulation signal based on the cosine term of the synergistic muscle and the sine term of the antagonistic muscle.

[0014] As a preferred embodiment of the stand-and-walk test method based on inertial sensors and electromyographic technology described in the present invention, the evaluation of the patient's muscle coordination includes comprehensively reflecting the collaborative working ability of muscle groups and the regularity of electromyographic activity based on the muscle coordination score; if the muscle coordination score value is in the third threshold interval, it is judged that the muscle coordination is good; if the muscle coordination score value is in the fourth threshold interval, it is judged that the muscle co-activation interference is strong and the coordination is poor.

[0015] Another object of the present invention is to provide a stand-and-walk test system based on inertial sensors and electromyography technology, which can comprehensively evaluate the differences between the left and right support phases and the swing phases through a first algorithm to determine whether the gait is symmetrical and stable, thereby solving the problems of low accuracy and slow response in symmetry and stability recognition in current gait assessment technologies.

[0016] As a preferred embodiment of the stand-and-walk test system based on inertial sensors and electromyography technology described in the present invention, it includes: a data acquisition module, a gait assessment module, and a muscle assessment module; the data acquisition module is used to complete data acquisition using inertial sensors and electromyography technology; the gait assessment module is used to comprehensively evaluate the differences between the left and right stance phases and the swing phases through a first algorithm to determine whether the gait is symmetrical and stable; and the muscle assessment module is used to evaluate the patient's muscle coordination through a second algorithm.

[0017] A computer device includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps of a stand-and-go test method based on inertial sensors and electromyography technology.

[0018] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a stand-and-go test method based on inertial sensors and electromyography technology.

[0019] Beneficial effects of the present invention: The stand-and-walk test method based on inertial sensors and electromyography technology provided by the present invention uses inertial sensors and electromyography technology to complete data acquisition, realizes synchronous data fusion of the behavioral layer and the neural control layer, and lays a data foundation for the subsequent construction of an accurate, multi-dimensional gait and electromyography evaluation model. Through the first algorithm, the difference between the left and right support phases and the swing phases is comprehensively evaluated to determine whether the gait is symmetrical and stable, thereby realizing a joint evaluation of the two core indicators of gait symmetry and stability, improving the detection ability of neurological gait disorders, and providing a reliable criterion for individualized rehabilitation intervention. Through the second algorithm, the patient's muscle coordination is evaluated, realizing the modeling and difference measurement of timing regulation characteristics, and improving the recognition ability of neuromuscular control disorders. The present invention has achieved better results in multimodal data acquisition and fusion, gait symmetry and stability evaluation, and muscle coordination quantitative analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is an overall flow chart of the stand-and-walk test method based on inertial sensors and electromyography technology provided in the first embodiment of the present invention.

[0022] Figure 2 This is an overall schematic diagram of a stand-and-walk test system based on inertial sensors and electromyography technology provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0023] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0024] Example 1, with reference to Figure 1 , as one embodiment of the present invention, provides a stand-and-go test method based on inertial sensors and electromyography technology, comprising:

[0025] S1: Data acquisition is completed using inertial sensors and electromyography technology.

[0026] Furthermore, data collection includes collecting inertial sensor data and electromyographic signals from the subject's lower limbs, recording acceleration data through the inertial sensor, and recording surface electromyographic signals of the target muscle group through the electromyographic sensor. The acceleration data and the surface electromyographic signals of the target muscle group are synchronously collected on a unified time axis.

[0027] It should be noted that by placing inertial sensors and surface electromyography electrodes on the subjects' lower limbs, synchronous acquisition of acceleration signals and electromyography signals during gait is achieved, and the data are uniformly mapped to the same time axis. By fusing the data of the two types of heterogeneous sensors, it is possible to obtain both motion dynamics characteristics (such as acceleration fluctuations) and neuromuscular control signals synchronously, thereby constructing a complete motion-control mapping model. The implementation of this cross-modal synchronous acquisition mechanism enables subsequent algorithms to not only analyze based on motion trajectories, but also conduct comprehensive evaluations in combination with muscle activation patterns, thereby improving the integrity of data semantics and the depth of analysis dimensions. Compared with traditional solutions that only use a single sensor system, this improves the interpretability and accuracy of gait assessment, avoids analytical misjudgments or information omissions, and realizes synchronous data fusion at the behavioral layer and the neural control layer, laying a data foundation for the subsequent construction of accurate, multi-dimensional gait and electromyography assessment models, thereby enhancing diagnostic value and clinical application prospects.

[0028] S2: Through the first algorithm, the differences between the left and right support phases and the swing phases are comprehensively evaluated to determine whether the gait is symmetrical and stable.

[0029] Furthermore, the first algorithm includes constructing a gait symmetry scoring function based on the inertial sensor acceleration data.

[0030] Based on the acceleration difference and acceleration rate difference of the left and right lower limbs at each moment, time-weighted functions were introduced to reflect the gait stage difference and mutation sensitivity, and the weighted results were averaged to obtain the gait symmetry score.

[0031] It should be noted that the time weighting function includes constructing a first time weighting function, which is constructed according to the distance between the current time point and the gait phase transition time point and is in the form of an exponential decay function to adjust the evaluation weight of the transition area between the stance phase and the swing phase.

[0032] It should also be noted that the time weighted function also includes constructing a second time weighted function, which is constructed according to the distance between the current time point and the gait mutation time point, in the form of an S-type logic function, to adjust the evaluation weight of sudden gait instability events.

[0033] It should also be noted that judging whether the gait is symmetrical and stable includes characterizing the structural consistency and phase fluctuations of the subject during the gait cycle based on the value of the gait symmetry scoring function.

[0034] If the gait symmetry score function value is in the first threshold interval, it is determined that the gait symmetry is good, and if the gait symmetry score function value is in the second threshold interval, it is determined that the gait is asymmetric and unstable.

[0035] It should also be pointed out that one preferred scheme for constructing the gait symmetry score function specifically includes using an improved algorithm to calculate the gait symmetry index, which takes into account the differences between the left and right support phases and swing phases, and introduces a dynamic weighting coefficient to adjust the influence of different gait phases. On the basis of the traditional method, the gait acceleration change rate is considered, so as to more accurately evaluate the stability and symmetry of the gait, and construct the gait symmetry score function, which is expressed as:

[0036] ;

[0037] ;

[0038] ;

[0039] wherein, is the gait symmetry score, represents the total number of data points of gait symmetry analysis, represents the gait stability weighting coefficient, which dynamically adjusts the influence of gait stability at different time points, represents the gait change weighting coefficient, which enhances the weight in the gait transition stage (for example, from the support phase to the swing phase), represents the th gait analysis time point, is the acceleration change rate of the left gait, is the acceleration change rate of the right gait, which captures the dynamic changes of the gait, and are the acceleration data of the left and right gaits at the th time point, and are the acceleration data of the left and right gaits at the th time point.

[0040] The gait stability weighting coefficient is calculated and expressed as:

[0041] ;

[0042] wherein, is the gait stability adjustment coefficient, is the gait phase transition time point, such as the transition stage from the support phase to the swing phase.

[0043] The gait change weighting coefficient is calculated and expressed as:

[0044] ;

[0045] in, is the gait variation adjustment coefficient, It is the time point of sudden changes in gait internal fluctuations, including moments of sudden changes or movement instability (such as sudden acceleration / deceleration).

[0046] It should be noted that the gait stability adjustment factor The initial value of can be determined based on the gait test results of normal people through statistical analysis of the acceleration change rate near the transition time points of multiple gait phases. It is generally recommended to set it within the range of [0.5, 2.0]. The larger the value, the more concentrated the weight is in the transition area, which is suitable for identifying mutation-sensitive gaits; gait change adjustment coefficient The acceleration slope change intensity and response time window of the sudden event are generally set in [1.0,5.0]. The larger the value, the faster the system responds to sudden changes; the present invention adopts the default setting =1.2, =3.0, which meets the characteristics of most elderly people and people with mild to moderate functional disabilities.

[0047] It should also be noted that a preferred solution for determining whether the gait is symmetrical and stable specifically includes: When the gait is symmetrical, the difference between the left and right stance phases is small, the gait is stable, and the symmetry is good, indicating that the subjects show good balance and coordination during standing and walking. In this case, the dynamic changes of gait have little effect on symmetry; when When the subject's gait has obvious left-right asymmetry, it may manifest as unstable gait or excessive muscle load on one side; the calculation of gait symmetry index takes into account the differences in the left and right support phases and swing phases, and combines the changes in gait stages. During exercise, the subject's gait may change due to different physical conditions, fatigue levels and other factors. Especially in complex movements such as standing and walking, gait changes and instability require extra attention. By introducing the acceleration change rate, the optimized algorithm can more accurately capture the asymmetry and instability of gait, providing more detailed information for evaluating athletic ability.

[0048] It should be noted that if the gait symmetry score is in the range of [0.7, 0.9), it indicates a certain degree of mild asymmetry. Such subjects need to be judged based on their medical history to determine whether they are in the compensatory stage or functional recovery stage. If the score is in the range of [0.5, 0.7), it can be preliminarily judged as moderate gait instability, and further clinical intervention should be recommended. A score below 0.5 indicates a significant unstable gait state.

[0049] Regarding muscle coordination scores, if the score is in the range of [0.1, 0.3), it indicates that the degree of muscle coactivation is high but not completely disordered, and further analysis is required in combination with the synergistic / antagonistic muscle strength ratio; if the score is <0.1, it is considered that there may be a neurological control disorder, and clinical auxiliary indicators should be combined for analysis.

[0050] It should also be noted that by constructing a gait symmetry scoring function, a dynamic evaluation is performed based on the difference and change rate difference in the time domain between the acceleration signals of the left and right lower limbs. An exponential decay function is introduced to weight the transition area between the stance phase and the swing phase, thereby improving the sensitivity to the accuracy of phase transitions. The sudden gait abnormality moments are weighted with an S-type logic function to enhance the algorithm's ability to identify unstable phenomena. It can accurately capture the coordination consistency and fluctuation trend of the left and right limbs in different phases during the gait cycle, and can not only distinguish between symmetrical and asymmetrical gaits, but also assess whether they are accompanied by structural instability. The introduction of a weight function processing method avoids the analysis bias of a specific time period, making the scoring function more universal and clinically interpretable, and realizing the joint evaluation of the two core indicators of gait symmetry and stability. It improves the detection ability of neurological gait disorders and provides a reliable criterion for individualized rehabilitation intervention. It is superior to traditional gait assessment methods that rely solely on mean or frequency domain analysis.

[0051] S3: Assess the patient's muscle coordination through the second algorithm.

[0052] Furthermore, the second algorithm includes modeling the collected electromyographic signals, extracting the amplitude and phase characteristics of the synergistic and antagonistic muscles respectively, introducing Fourier modulation terms to construct a timing modulation expression, calculating the absolute value of the difference and multiplying it by a timing-sensitive weighting function, and finally normalizing the difference to generate a muscle coordination score.

[0053] It should be noted that generating a muscle coordination score includes constructing a timing-sensitive weighted function, with the center point being the peak muscle activation time point, reflecting the difference sensitivity during the muscle's high activity period.

[0054] The muscle modulation signal is constructed based on the cosine term of the synergistic muscle and the sine term of the antagonistic muscle.

[0055] It should also be noted that the assessment of a patient's muscle coordination includes a comprehensive reflection of the ability of muscle groups to work together and the regularity of myoelectric activity based on the muscle coordination score.

[0056] If the muscle coordination score value is within the third threshold range, it is judged that the muscle coordination is good. If the muscle coordination score value is within the fourth threshold range, it is judged that the muscle co-activation interference is strong and the coordination is poor.

[0057] It should also be noted that a preferred solution for generating muscle coordination scores specifically includes using an improved algorithm to calculate a muscle coordination index, which evaluates the coordination between muscle groups, especially the co-activation between positive muscles and antagonistic muscles. In order to more accurately evaluate coordination, Fourier transform frequency analysis is introduced to combine the amplitude and periodic changes of muscle activation to evaluate the collaborative work of muscles and calculate the muscle coordination index. , expressed as:

[0058] ;

[0059] in, It indicates the muscle coordination index, indicating the quality of muscle coordination. The closer it is to 0, the worse the coordination is, and the closer it is to 1, the better the coordination is. represents the total number of data points in the muscle coordination analysis, represents the time-dependent weighting coefficient, which indicates the sensitivity of muscle coordination at different time points. Indicates the Muscle coordination analysis time points, and represent the electromyographic signals of the synergistic and antagonistic muscles respectively, represents the frequency coefficient, which indicates the periodic changes in the muscle signal.

[0060] Calculate time-dependent weighting coefficients , expressed as:

[0061] ;

[0062] in, Indicates the sensitivity of muscle coordination to time during movement. It is the peak time of muscle activity, usually the stage when the muscle is most active or under higher load.

[0063] Furthermore, the time sensitivity coefficient is set based on the distribution of peak muscle activation intensity times during different gait phases, typically in the range of [0.05, 0.3] seconds. For muscle groups with concentrated peak activation times, a value of 0.1 can achieve good weighted concentration. For situations with longer activation periods or strong signal interference, the time sensitivity coefficient can be appropriately increased to expand the weighted response area and improve robustness. This paper adopts an empirical default value of 0.15.

[0064] Indicates in At this moment, the electromyographic signal of the synergistic muscle reflects the activation strength of the muscle. The synergistic muscle is responsible for the main driving force of the movement, usually the muscle group that promotes the movement, and the corresponding electromyographic signal , expressed as:

[0065] ;

[0066] in, For the The amplitude of the electromyographic signal of the coordinated muscles at all times, is the phase of the muscle potential.

[0067] Indicates in At this moment, the electromyographic signal of the antagonistic muscle reflects the activity level of the antagonistic muscle during exercise. The function of the antagonistic muscle is to control and slow down the movement. , expressed as:

[0068] ;

[0069] in, For the The amplitude of the electromyographic signal of the antagonistic muscle at all times, is the phase of the muscle potential.

[0070] It should also be noted that a preferred method for evaluating the patient's muscle coordination specifically includes: When the muscles are well coordinated, the activities of the synergistic and antagonistic muscles are more coordinated, the signal amplitude and frequency components are normal, and there is no excessive muscle co-activation. At this time, the muscle burden is lighter and the exercise efficiency is higher. When the muscle coordination is poor, it means that the muscle co-activation degree is high during the exercise, which may be caused by excessive tension or coordination disorder, which usually leads to reduced exercise efficiency and may cause muscle fatigue or injury. If the frequency of muscle signals is high during exercise (larger values), may indicate that muscles are activated more rapidly and that there is more co-activation between synergistic and antagonistic muscles. In this case, muscle coordination may be affected, leading to muscle fatigue and inefficient movement.

[0071] The calculation of muscle coordination indicators not only takes into account the activation intensity of the muscles, but also combines the frequency characteristics of the signal. Through Fourier transform, it can analyze the periodic changes in muscle signals. Especially in fast or high-intensity exercise, the coordinated activation pattern of muscles is crucial to the coordination of movements. It can accurately capture the collaborative work between different muscles and provide richer information for the assessment of athletic ability.

[0072] It should also be noted that by modeling the electromyographic signals, extracting the amplitude and phase characteristics of the synergistic and antagonistic muscles, and combining them with Fourier modulation terms to construct a temporal modulation expression, a temporal-sensitive weighting function is used while considering the muscle activation period to improve the response sensitivity to differences in high-activity periods. The resulting muscle coordination score not only quantifies the coordination pattern between muscle groups, but also reflects potential electromyographic abnormalities or co-activation interference. Considering the relative differences in muscle responses in the time dimension, a quantitative indicator that can be directly used for classification and discrimination is formed through normalization and weighted fusion. This method combines dynamics and discriminative power, can accurately identify abnormal coordination between muscle groups, and provide a basis for the identification of neural or compensatory movement patterns. It breaks through the limitation of traditional electromyographic analysis that only focuses on amplitude, realizes the modeling and difference measurement of temporal regulation characteristics, improves the ability to identify neuromuscular control disorders, and provides quantitative support for early rehabilitation assessment and intervention effect tracking.

[0073] Example 2 is an embodiment of the present invention, which provides a stand-and-walk test method based on inertial sensors and electromyography technology. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0074] First, the experiment selected 6 subjects (Subjects A to F), aged between 40 and 65 years old, of whom 3 had a history of mild movement disorders. The experimental site was a standard gait laboratory equipped with a six-axis inertial measurement unit (IMU) and a surface electromyography acquisition system (sEMG). The sampling frequency was unified at 1000Hz, and the data was synchronously transmitted to the central processing unit via a wireless module. First, the IMU was fixed at symmetrical positions on the subjects' lower limbs (hips, knees, and ankles), and surface electromyography electrodes were placed on the quadriceps femoris, hamstrings, and tibialis anterior muscles to ensure stable sensor fit and minimal interference. Before all data collection begins, a standard warm-up routine (including standing, rising, walking, turning, and returning to sit) is uniformly performed for baseline synchronization calibration. After data collection is completed, the system automatically calls the first algorithm to process the subject's left and right stance and swing phase acceleration data, calculating gait symmetry scores and gait stability scores. This score integrates acceleration difference and rate of change difference, and combines exponential decay and S-shaped functions to construct a time-weighted function to capture the dynamic characteristics of gait transition phases and sudden changes.

[0075] Gait stability score is a gait symmetry score function The symmetric difference part is removed from the equation, and only the acceleration rate difference is retained and dynamically weighted, which is specifically expressed as:

[0076] ;

[0077] in, Score gait stability. and are the acceleration change rates of the left and right legs, respectively, reflecting the degree of phase inconsistency and speed mutation during gait. Indicates the total number of time sampling points in the gait analysis time period. A time index variable representing the sampling instant.

[0078] The judgment criteria are based on a set threshold to distinguish between well-symmetrical and unstable states. At the same time, the second algorithm is used to analyze the electromyographic data. The system constructs muscle modulation signals, extracts the amplitude and phase characteristics of synergistic and antagonistic muscles, and combines Fourier modulation and timing-sensitive weighting functions to generate a muscle coordination score. This score not only quantifies the synergistic efficiency between muscle groups, but also reflects the subject's muscle control regularity. Refer to Table 1 to record and analyze some experimental data.

[0079] Table 1 Part of the experimental data record

[0080]

[0081] The table shows that in terms of gait symmetry scores, subjects A, B, and F, who scored high (>0.85), all had low acceleration differences (<0.2 m / s²). Their gait stability scores all exceeded 8 points. This indicates that the first algorithm enhances its sensitivity to the dynamic transition between the stance and swing phases through a time-weighted function, accurately capturing the consistency of gait structure. Traditional methods, which often rely on mean comparison or visual analysis, are often unable to quantitatively identify stage-by-stage instability and may miss detections.

[0082] In particular, for subjects C and E, the difference in acceleration change was greater than 0.3 m / s² and the symmetry score was significantly low (<0.7), indicating obvious gait asymmetry. This was also reflected in the gait stability score (<7 points), indicating that the system of the present invention can effectively reveal gait fluctuations caused by mild functional impairments and has early detection and warning capabilities.

[0083] The second algorithm also showed significant advantages in muscle coordination. Subject F's muscle coordination score reached 0.91, with synergistic muscle activation reaching 0.96 mV, while the antagonistic muscle activation was only 0.18 mV, indicating high muscle coordination efficiency and minimal co-activation interference. In contrast, subjects E and C scored 0.49 and 0.52, respectively, with significantly increased antagonistic muscle activation, indicating muscle control coordination impairments and suggesting compensatory or tension responses during functional movements. This information cannot be effectively revealed through traditional EMG mean analysis. However, the proposed method, through Fourier modulation and temporal weighting mechanisms, can deeply reflect the dynamic changes in muscle control.

[0084] In summary, the data fully verifies the technical advantages of the present invention in terms of refinement of gait symmetry assessment, enhancement of dynamic recognition capabilities, and in-depth analysis of myoelectric collaborative modeling. It not only makes up for the blind spots of existing assessment methods in identifying timing characteristics and minor abnormalities, but also provides a quantitative basis for the formulation of rehabilitation programs and tracking of therapeutic effects. It has significant novelty, creativity and practical value.

[0085] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a stand-and-walk test system based on inertial sensors and electromyography technology, including a data acquisition module 100, a gait assessment module 200, and a muscle assessment module 300.

[0086] Among them, S4: the data acquisition module 100 is used to complete data acquisition using inertial sensors and electromyography technology.

[0087] It should also be noted that after the system is activated, the data acquisition module 100 first performs its task. Using inertial sensors and electromyographic sensors deployed on the subject's lower limbs, it synchronously collects acceleration signals and surface electromyographic signals from the target muscles. This module ensures that both types of data are precisely synchronized on the same timeline and performs preliminary filtering and normalization to generate high-quality, time-consistent raw data streams. The data acquisition module 100 serves as both the system's starting point and the data distribution center, transmitting the processed signals to the gait assessment module 200 and the muscle assessment module 300, respectively.

[0088] S5: The gait assessment module 200 is used to comprehensively assess the differences between the left and right stance phases and the swing phases through a first algorithm to determine whether the gait is symmetrical and stable.

[0089] It should also be noted that the gait assessment module 200 receives acceleration data, calls a preset first algorithm for processing, calculates the acceleration difference and the rate of change difference between the support phase and the swing phase of the left and right lower limbs at each moment, and combines two types of time-weighted functions to establish a gait symmetry scoring function. The scoring result can quantitatively determine whether the individual's gait has structural consistency and stage stability throughout the entire cycle. The gait assessment module 200 not only performs an in-depth analysis of the gait characteristics, but also feeds the scoring results back to the muscle assessment module 300 for cross-correlation analysis with the muscle coordination results.

[0090] S6: The muscle assessment module 300 is used to assess the patient's muscle coordination through a second algorithm.

[0091] It should also be noted that the muscle evaluation module 300 receives electromyographic data, uses the second algorithm to extract the timing characteristics of the synergistic muscles and antagonistic muscles, constructs a modulation expression through Fourier modulation and timing-sensitive weighting function, and generates a muscle coordination score to reflect the synergistic efficiency and control consistency between muscle groups. In some application scenarios, the muscle evaluation module 300 can also call the rhythm information from the gait evaluation module (such as the start and end points of the gait cycle, mutation time period, etc.) as a reference input to further enhance the timeliness and pertinence of the coordination evaluation; finally, the system integrates the scoring results from the gait evaluation module 200 and the muscle evaluation module 300 to form a comprehensive evaluation report on gait stability, symmetry and muscle synergistic control ability. The three modules operate collaboratively through a mechanism of data dependence and logical feedback to ensure the accuracy, comprehensiveness and clinical reference value of the evaluation results.

Claims

1. A stand-and-walk test method based on inertial sensors and electromyography technology, characterized in that: include: Data collection is accomplished using inertial sensors and electromyography technology; The first algorithm comprehensively evaluates the differences between the left and right stance phases and the swing phase to determine whether the gait is symmetrical and stable. Through the second algorithm, the patient's muscle coordination is assessed; The data collection includes: Inertial sensor data and electromyographic signals are collected from the subject's lower limbs. Acceleration data is recorded using the inertial sensor, while surface electromyographic signals of the target muscle group are recorded using the electromyographic sensor. Acceleration data and surface electromyographic signals of the target muscle group are collected synchronously on a unified timeline. The first algorithm includes, Constructing a gait symmetry scoring function based on inertial sensor acceleration data; Based on the acceleration difference and acceleration rate difference of the left and right lower limbs at each moment, a time-weighted function is introduced to reflect the gait phase difference and mutation sensitivity. The weighted results are averaged to obtain the gait symmetry score. The time weighting function includes, constructing a first time weighting function, which is constructed according to the distance between the current time point and the gait phase transition time point and is in the form of an exponential decay function, so as to adjust the evaluation weight of the transition area between the stance phase and the swing phase; The time weighting function also includes, Constructing a second time weighting function based on the distance between the current time point and the gait mutation time point in the form of an S-type logistic function to adjust the evaluation weight of the sudden gait instability event; The determination of whether the gait is symmetrical and stable includes: The structural consistency and phase fluctuation of the subject's gait cycle are characterized based on the gait symmetry scoring function value; If the gait symmetry score function value is within the first threshold range, the gait is judged to be well symmetrical; if the gait symmetry score function value is within the second threshold range, the gait is judged to be asymmetric and unstable. The second algorithm includes, The collected electromyographic signals are modeled to extract the amplitude and phase characteristics of the synergistic and antagonistic muscles respectively. The Fourier modulation term is introduced to construct a temporal modulation expression. The absolute value of the difference is calculated and multiplied by a timing-sensitive weighting function. Finally, the difference is normalized to generate a muscle coordination score. Generating a muscle coordination score comprises, Construct a time-sensitive weighted function, with the center point being the peak muscle activation time point, reflecting the difference sensitivity during the muscle's high activity period; The muscle modulation signal is constructed based on the cosine term of the synergistic muscle and the sine term of the antagonistic muscle.

2. The stand-and-go test method based on inertial sensors and electromyography technology according to claim 1, wherein: The assessment of the patient's muscle coordination includes: The muscle coordination score comprehensively reflects the ability of muscle groups to work together and the regularity of myoelectric activity; If the muscle coordination score value is within the third threshold range, it is judged that the muscle coordination is good. If the muscle coordination score value is within the fourth threshold range, it is judged that the muscle co-activation interference is strong and the coordination is poor.

3. A stand-and-walk test system based on inertial sensors and electromyography technology, using the method according to any one of claims 1-2, characterized in that: It includes a data acquisition module (100), a gait assessment module (200), and a muscle assessment module (300); The data acquisition module (100) is used to complete data acquisition using inertial sensors and electromyography technology; The gait assessment module (200) is used to comprehensively assess the difference between the left and right stance phases and the swing phases through a first algorithm to determine whether the gait is symmetrical and stable; The muscle assessment module (300) is used to assess the patient's muscle coordination through a second algorithm.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the stand-and-walk test method based on inertial sensors and electromyography technology are implemented as described in any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the stand-and-walk test method based on inertial sensors and electromyography technology according to any one of claims 1 to 2 are implemented.

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