Wearable high-risk infant muscle tension monitoring bracelet based on flexible sensor

Abstract

The present application relates to the technical field of muscle tension monitoring, in particular to a wearable high-risk infant muscle tension monitoring bracelet based on a flexible sensor. The flexible pressure sensor and the three-axis gyroscope synchronously collect pressure time series data and three-axis angular velocity time series data under passive movement, and the angular velocity is fused to obtain comprehensive angle time series data. For slow movement, the correlation characteristics, respective fluctuations and numerical characteristics of pressure and comprehensive angle are analyzed, and the tissue muscle tension performance value is quantified. For rapid movement (easy to trigger stretch reflex), the pressure and angle data of slow and rapid movement are integrated, and a spasm performance value model is established through pressure difference analysis. Finally, the tissue muscle tension performance value under slow movement and the spasm performance value under rapid movement are fused to generate a comprehensive dynamic monitoring index, realizing real-time tracking and grading evaluation of high-risk infant muscle tension, and effectively improving the monitoring accuracy.

Description

Technical Field

[0001] This invention relates to the field of muscle tone monitoring technology, specifically to a wearable muscle tone monitoring bracelet for high-risk infants based on a flexible sensor. Background Technology

[0002] Muscle tone refers to the tension of muscles at rest, and it plays an important role in maintaining body posture and coordinating movement. High-risk infants are prone to abnormal muscle tone due to premature birth, hypoxia, and other reasons. If it is not detected and intervened in time, it may affect their motor function development and even lead to serious consequences such as cerebral palsy. Therefore, real-time and continuous monitoring of muscle tone in high-risk infants is of great significance.

[0003] Current technologies for monitoring muscle tone in high-risk infants typically employ static assessment methods, relying on the physician's subjective palpation, such as observing the infant's muscle manifestations for subjective judgment. However, since abnormal muscle tone is usually accompanied by abnormal movement patterns and changes in pressure distribution, and the mechanisms of muscle tone manifestation differ between different movement patterns and under fast and slow movements, static observation methods cannot analyze the dynamic changes in muscle tone during movement. Insufficient dynamic monitoring leads to a decrease in the reliability of muscle tone assessment. Summary of the Invention

[0004] To address the issue that different movement patterns and the varying mechanisms of muscle tone expression under fast and slow movements mean that static observation methods cannot analyze the dynamic changes in muscle tone during exercise, resulting in insufficient dynamic monitoring and reduced reliability of muscle tone assessments, this invention aims to provide a wearable muscle tone monitoring bracelet for high-risk infants based on a flexible sensor. The specific technical solution adopted is as follows:

[0005] A wearable muscle tone monitoring bracelet for high-risk infants based on a flexible sensor is disclosed. The bracelet incorporates a flexible pressure sensor and a three-axis gyroscope. The flexible pressure sensor acquires pressure time-series data, and the three-axis gyroscope acquires angular velocity time-series data. The bracelet also includes a built-in control module capable of monitoring muscle tone based on the pressure and angular velocity time-series data. The method for monitoring muscle tone includes:

[0006] For each passive movement of high-risk infants, pressure time-series data and angular velocity time-series data in three directions were acquired, and comprehensive angle time-series data were obtained based on the angular velocity time-series data in three directions; among them, passive movement was divided into slow movement and fast movement;

[0007] By combining the relationship between the characteristics of pressure value changes and the characteristics of changes in the overall perspective under slow exercise, the numerical characteristics of the overall perspective, and the fluctuation characteristics of pressure value changes in the pressure time series data, the tissue muscle tone performance values ​​of high-risk infants under slow exercise are determined.

[0008] After integrating the pressure time-series data from two types of passive movement and the comprehensive angle time-series data, the differences between pressure values ​​are analyzed to obtain a pressure deviation sequence. Based on the numerical characteristics of the data values ​​in the pressure deviation sequence, the spasticity performance values ​​of high-risk infants under rapid movement are determined.

[0009] By integrating the tissue muscle tone values ​​of high-risk infants under slow movement and the spasticity values ​​under fast movement, muscle tone in high-risk infants can be monitored.

[0010] Furthermore, the method for acquiring the comprehensive angle time series data includes:

[0011] Under each passive motion, in the angular velocity time series data in each direction, the first moment is used as the lower limit and each subsequent moment is used as the upper limit. The angular velocity time series data is integrated to obtain the angle value at each subsequent moment.

[0012] Under each passive motion, the angle values ​​in the three directions at each moment are spatially vector-fused to obtain the comprehensive angle at each moment, thereby determining the comprehensive angle time series data under each passive motion.

[0013] Furthermore, the method for obtaining the tissue muscle tone performance value includes:

[0014] Under slow motion, the relationship between the characteristics of pressure value change and the characteristics of comprehensive angle change is analyzed to determine the static stiffness of muscle tissue.

[0015] Under slow motion, the degree of joint mobility restriction is determined based on the extreme numerical characteristics in the comprehensive angular time series data;

[0016] Under slow motion, analyze the characteristics of pressure value changes and fluctuations in pressure time series data to determine the complexity of pressure changes;

[0017] Based on the analytic hierarchy process (AHP), specific weights were determined for the static stiffness, joint mobility restriction, and pressure change complexity of high-risk infants. Based on these specific weights, the static stiffness, joint mobility restriction, and pressure change complexity of high-risk infants under slow movement were weighted and fused. The weighted result was then normalized and used as the tissue muscle tone performance value of high-risk infants under slow movement.

[0018] Furthermore, the method for obtaining the static stiffness includes:

[0019] In the pressure time series data, the first difference sequence of all pressure values ​​is obtained as the pressure value change sequence. In the comprehensive angle time series data, the first difference sequence of all angle values ​​is obtained as the angle change sequence.

[0020] The normalized Pearson correlation coefficients of the pressure value change sequence and angle change sequence are used as the static stiffness of the muscle tissue.

[0021] Furthermore, the method for obtaining the degree of limitation of joint movement includes:

[0022] In the time series data of the comprehensive angle corresponding to slow motion, the normalized value of the absolute value of the difference between the maximum comprehensive angle value and the preset maximum joint range of motion is used as the joint range of motion limitation.

[0023] Furthermore, the method for obtaining the complexity of the pressure change includes:

[0024] In the pressure time series data of slow motion, the difference between the pressure value at each moment and the preset theoretical pressure value is calculated as the relative pressure change value at each moment;

[0025] The normalized value of the variance of the relative pressure changes at all times is used as the pressure change complexity.

[0026] Furthermore, the method for obtaining the pressure deviation sequence includes:

[0027] Under each passive motion, a two-dimensional coordinate system is constructed with the comprehensive angle as the horizontal axis and the pressure as the vertical axis, so as to map the comprehensive angle and pressure value at each moment to the two-dimensional coordinate system. All data points in the two-dimensional coordinate system are fitted to obtain the motion pressure sequence corresponding to each passive motion.

[0028] In the motion pressure sequence corresponding to the two passive motions, the difference between the pressure values ​​of fast motion and slow motion under the same comprehensive angle is calculated as the pressure deviation value.

[0029] The pressure deviation values ​​under all combined angles are arranged in ascending order of the combined angles to obtain the pressure deviation sequence.

[0030] Furthermore, the method for obtaining the spasticity performance value includes:

[0031] In the pressure deviation sequence, the pressure deviation sequence is segmented based on the average numerical characteristics of all pressure deviation values ​​to obtain all subsequences;

[0032] The integral value of each subsequence is used as the spastic energy value of each subsequence. The maximum value in each subsequence is normalized and used as the spastic intensity. The spastic energy values ​​are weighted and fused using the spastic intensity of the subsequences in the pressure deviation sequence. The weighted result is then normalized and used as the spastic performance value of high-risk infants under rapid movement.

[0033] Furthermore, the method for obtaining the subsequence includes:

[0034] In the pressure deviation sequence, the average of all pressure deviation values ​​is used as the baseline value, and all pressure deviation values ​​exceeding the baseline value are used as marker points.

[0035] Among all the marked points, consecutive marked points are treated as a subsequence to obtain all the subsequences.

[0036] Furthermore, the method of monitoring muscle tone in high-risk infants by integrating tissue muscle tone values ​​during slow movement and spasticity values ​​during rapid movement includes:

[0037] Reference weights for tissue muscle tone values ​​under slow movement and spasticity values ​​under fast movement in high-risk infants were obtained based on the analytic hierarchy process.

[0038] The tissue muscle tone and spasticity values ​​of high-risk infants were weighted using reference weights, and the weighted results were normalized and used as the muscle tone characteristic values ​​of high-risk infants.

[0039] Within the age range of high-risk infants, if the muscle tone characteristic value of the high-risk infant is greater than or equal to a preset level four judgment threshold, it is considered that the muscle tone is severely increased; if the muscle tone characteristic value is less than the preset level four judgment threshold but greater than or equal to a preset level three judgment threshold, it is considered that the muscle tone is significantly increased; if the muscle tone characteristic value is less than the preset level three judgment threshold but greater than or equal to a preset level two judgment threshold, it is considered that the muscle tone is normal; if the muscle tone characteristic value is less than the preset level two judgment threshold but greater than or equal to a preset level one judgment threshold, it is considered that the muscle tone is significantly decreased; and if the muscle tone characteristic value is less than the preset level one judgment threshold, it is considered that the muscle tone is severely decreased.

[0040] The present invention has the following beneficial effects:

[0041] This invention utilizes multi-dimensional data fusion and dynamic analysis techniques, employing flexible pressure sensors and a three-axis gyroscope to acquire time-series pressure and angular velocity data of high-risk infants under various passive movements, enabling real-time tracking of dynamic changes in muscle tone. First, the angular velocity time-series data in three directions are fused to obtain comprehensive angular time-series data. Since the mechanisms of muscle tone differ under different movement speeds, a relationship exists between pressure and angle changes during slow movement. Therefore, under slow movement, the correlation between pressure values ​​and comprehensive angle, as well as the fluctuation and numerical characteristics of both pressure values ​​and comprehensive angle, are fused to quantify muscle tone under slow movement, yielding tissue muscle tone performance values. Furthermore, since stretch reflexes occur during rapid movement, and the main difference between rapid and slow movements is spasticity, the pressure time-series data and comprehensive angular time-series data from both passive movements are integrated to analyze the differences in pressure values. Based on these differences, numerical analysis is performed to establish a calculation model for spasticity performance values ​​in high-risk infants during rapid movement. Finally, the muscle tone performance under the two passive movements was integrated, that is, the tissue muscle tone performance value under slow movement and the spasticity performance value under fast movement were combined to obtain a comprehensive dynamic monitoring index, thereby monitoring the muscle tone of high-risk infants and effectively improving the accuracy of monitoring results. Attached Figure Description

[0042] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the structure of a control module provided in one embodiment of the present invention;

[0044] Figure 2 This is a flowchart illustrating a method for monitoring muscle tone according to an embodiment of the present invention.

[0045] Figure 3 This is a flowchart illustrating a method for obtaining tissue muscle tone performance values ​​according to an embodiment of the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a wearable high-risk infant muscle tone monitoring bracelet based on a flexible sensor proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details a specific solution for a wearable high-risk infant muscle tone monitoring bracelet based on a flexible sensor provided by the present invention.

[0049] Current methods for monitoring muscle tone mainly include clinical assessment, which involves doctors observing muscle performance during clinical practice to make subjective judgments. However, when measuring muscle tone manually, subjective feelings and judgment standards vary among different individuals, making precise quantitative analysis and comparison difficult. Furthermore, accurate manual measurement of muscle tone requires extensive clinical experience and professional knowledge, which can hinder accurate judgments by inexperienced medical staff. This limits the reliability and consistency of measurement results. In this embodiment of the invention, electronic monitoring is used to monitor the muscle tone of high-risk infants, providing objective data support to aid in accurate judgment.

[0050] The wristband is made of flexible material and incorporates flexible pressure sensors for better conformity to the limbs, avoiding interference from rigid devices. The flexible pressure sensors capture changes in contact pressure on the limbs, quantifying muscle contraction strength and directly reflecting biomechanical responses related to muscle tone. A three-axis gyroscope on the wristband acquires angular velocity time-series data to reconstruct limb movement trajectories, providing kinematic evidence for analyzing the movement pattern-muscle tone correlation. Simultaneously, the wristband also includes a built-in control module that processes the pressure and angular velocity time-series data to achieve muscle tone monitoring. This module includes at least a memory and a processor. Please refer to [link to relevant documentation]. Figure 1The diagram illustrates the structure of a control module in one embodiment of the present invention, including a processor 100, a memory 101, a bus 102, and a communication interface 103. The processor 100, the communication interface 103, and the memory 101 are connected via the bus 102. The memory 101 may include high-speed random access memory, and the bus 102 may be an ISA bus, PCI bus, or EISA bus, etc. The processor 100 may be an integrated circuit chip with signal processing capabilities. The memory 101 stores at least one instruction, at least one program, code set, or instruction set. The steps of a method for implementing muscle tone monitoring when the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor include:

[0051] Please see Figure 2 The diagram illustrates a flowchart of a method for monitoring muscle tone according to an embodiment of the present invention, which includes the following steps:

[0052] Step S1: Under each passive movement of high-risk infants, acquire pressure time-series data and angular velocity time-series data in three directions, and acquire comprehensive angle time-series data based on the angular velocity time-series data in three directions; among them, passive movement is divided into slow movement and fast movement.

[0053] The stretch reflex plays a crucial role in muscle tone monitoring. It is a fundamental neural reflex mechanism where, when a muscle is rapidly stretched, receptors within the muscle spindle detect this change and transmit signals to the spinal cord via afferent nerves. Subsequently, motor neurons in the spinal cord are activated, causing the stretched muscle to contract in response to the stretch. This reflexive contraction helps maintain muscle tone and postural stability, preventing muscle overstretching injuries.

[0054] Abnormal muscle tone is often associated with dysregulation of the stretch reflex. For example, spastic hypertonia is caused by an overactive stretch reflex, manifested as increased resistance to passive stretching. By monitoring the muscle response to passive stretching, the activity level of the stretch reflex can be indirectly assessed, thus determining whether muscle tone is normal. Therefore, the stretch reflex is a key physiological basis for understanding changes in muscle tone. When joint movement speed is <100° / s, the primary afferent fibers of the muscle spindles are not activated at their activation threshold and do not induce the stretch reflex. When joint speed is >200° / s, the muscle spindle fibers are strongly activated, triggering spinal motor neurons, causing reflexive muscle contraction, and inducing the stretch reflex. Therefore, the muscle resistance and stretch reflex performance of the subject can be assessed through the aforementioned rapid and slow movement processes, thereby determining the overall muscle tone status of high-risk infants.

[0055] During monitoring, the wristband is worn on the wrist or ankle of the high-risk child, ensuring a tight seal against the skin without gaps. This helps the sensors accurately detect the joint's angle of motion and force. Before formal monitoring, the wristband needs to undergo three no-load movements (i.e., movements without holding the child's joint, only the device itself moving). This is to automatically zero the sensors, eliminate initial errors, and ensure the accuracy of subsequent measurements. Then, through the device's display interface or the accompanying app, select the joint to be tested (e.g., the right ankle). After selection, the system will play a demonstration video showing the standard movements for that joint: completing "slow flexion" (slow bending) and "rapid extension" (rapid straightening) within a specified time, helping caregivers understand the operational requirements. The guardian should hold the infant's corresponding test area (e.g., right ankle) and guide them to complete two passive movements (the guardian moves the joint, and the high-risk infant does not actively exert force): Slow movement: Move the joint slowly at a speed of 5° / s (error not exceeding ±0.5° / s), the movement should be smooth; Fast movement: Move the joint rapidly at a speed of 200° / s (error not exceeding ±30° / s), the movement should be significantly faster. Perform each movement once, for a total of two operations, to obtain the pressure timing data and angular velocity timing data in three directions for each passive movement in the high-risk infant. The timing data collection for each passive movement should be performed simultaneously at a consistent frequency, which can be set to 50Hz. The specific frequency can be adjusted according to the implementation scenario and is not limited here.

[0056] After obtaining the angular velocity time series data in three directions, comprehensive angular time series data can be obtained by integrating the angular velocity time series data and fusing the angle values ​​in the three directions under each passive motion, thereby constructing a quantitative representation of the multidimensional motion trajectory and providing basic spatial data for dynamic analysis of muscle tension.

[0057] Preferably, in one embodiment of the present invention, the method for obtaining comprehensive angle time series data includes:

[0058] Angular velocity describes the rate of rotation around a certain axis per unit time, while angle is the cumulative result of rotation. Therefore, under each passive movement, the angular velocity time series data in each direction is integrated with the first moment as the lower limit and each subsequent moment as the upper limit to obtain the angle value at each subsequent moment. At the same time, since human movement is a complex movement in three-dimensional space, angle data in a single direction can only reflect local movement characteristics. Therefore, under each passive movement, the angle values ​​in the three directions at each moment are fused into spatial vectors to obtain the comprehensive angle at each moment, thereby determining the comprehensive angle time series data under each passive movement. The comprehensive angle time series data can reflect the overall range of limb movement and provide data support for subsequent analysis of muscle tension and movement patterns.

[0059] In the embodiments of this invention, the collection and acquisition of various data are all authorized by the relevant users, and the process does not violate relevant laws and regulations, nor does it violate public order and good morals.

[0060] Step S2: Under slow movement, the relationship between the changes in pressure value and the changes in the overall angle, the numerical characteristics of the overall angle, and the fluctuation characteristics of pressure value in the pressure time series data are integrated to determine the tissue muscle tone performance value of high-risk infants under slow movement.

[0061] When performing slow movements, the resistance measured comes entirely from the passive biomechanical characteristics of muscles, tendons, and joint capsules. Therefore, the resistance characteristics of muscles can be judged by the changes in resistance and the overall angle changes during slow movements, thereby assessing the muscle tone of high-risk infants. During slow movements, the change in pressure with the stretching angle indicates the static stiffness of muscle tissue, i.e., the ability of muscle tissue to resist deformation. The stronger this ability, the greater the muscle tone of the subject. Furthermore, during slow movements, the overall range of motion reflects the degree of joint limitation. The greater the limitation of joint movement, the more likely there is shortening of muscles / tendons / joint capsules, resulting in greater muscle tone. Because during normal muscle stretching, pressure increases with the increase of the stretching angle, and the overall change shows strong uniformity, a decrease in the uniformity of pressure data indicates a greater difference in the change of muscle surface tension at different stretching angles. This increases the likelihood of muscle stiffness and intermittent contraction tremors, resulting in greater muscle tone. Therefore, in this embodiment of the invention, under slow movement, the relationship between the change characteristics of pressure value and the change characteristics of comprehensive angle in the pressure time series data can be analyzed, and the tissue muscle tone performance value of high-risk infants under slow movement can be determined by combining the numerical characteristics of comprehensive angle and the fluctuation characteristics of pressure value.

[0062] Preferably, in one embodiment of the present invention, the method for obtaining tissue muscle tone performance values ​​includes:

[0063] Please see Figure 3 The diagram illustrates a method flowchart for obtaining tissue muscle tone performance values ​​according to an embodiment of the present invention, the method comprising the following steps:

[0064] Step S201: Under slow motion, analyze the relationship between the characteristics of pressure value change and the characteristics of comprehensive angle change to determine the static stiffness of muscle tissue.

[0065] Static stiffness reflects the resistance of muscles during passive stretching and is a core characteristic of muscle tension. The change of pressure with angle can be used to reflect the static stiffness of muscle tissue. Therefore, in the pressure time series data, the first difference sequence of all pressure values ​​is obtained as the pressure value change sequence, and in the comprehensive angle time series data, the first difference sequence of all angle values ​​is obtained as the angle change sequence. The values ​​in the pressure value change sequence and the angle change sequence reflect the changes in pressure value and comprehensive angle, respectively, which helps to focus more on the correlation characteristics of the change trend and can directly reflect the limitation of muscle resistance on joint movement.

[0066] Finally, the Pearson correlation coefficients for the pressure value change series and the angle change series were calculated. The Pearson correlation coefficient quantifies the degree of linear correlation between the two series. A high positive correlation between pressure change and the combined angle change indicates greater muscle resistance; that is, the closer the Pearson correlation coefficient is to 1, the greater the static stiffness of the muscle tissue. Therefore, the Pearson correlation coefficient was normalized to obtain the static stiffness of the muscle tissue. Greater static stiffness indicates a stronger ability of the muscle tissue to resist deformation, thus indicating greater muscle tone in high-risk infants. Since the Pearson correlation coefficient here can be either positive or negative, the normalization method used was... function.

[0067] Step S202: Under slow motion, determine the degree of joint movement restriction based on the extreme numerical characteristics in the comprehensive angular time series data.

[0068] Abnormal muscle tone often leads to abnormal joint range of motion. When muscle tone is too high, the joint may not be able to reach its normal maximum angle under passive traction. Under slow movement, the maximum combined angle of joint movement is usually the limit of passive joint movement and best reflects the actual range of motion. Therefore, in the time-series data of the combined angle corresponding to slow movement, the normalized value of the absolute difference between the maximum combined angle value and the preset maximum joint range of motion angle is used as the joint movement limitation. The preset maximum joint range of motion angle is a normal reference value (such as the maximum passive extension range of a child's joint). The larger the absolute value of this difference, the greater the deviation between the joint range of motion angle of the high-risk child and the normal reference value, and therefore the greater the limitation of joint movement and the higher the muscle tone. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0069] It should be noted that in this embodiment of the present invention, the maximum preset joint movement angle is 90°. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0070] Step S203: Under slow motion, analyze the characteristics of pressure value changes and fluctuations in the pressure time series data to determine the complexity of pressure changes.

[0071] Normal muscle tone requires stable regulatory ability, such as uniform pressure changes during passive traction. High-risk infants, due to immature neurological development, may exhibit abnormal pressure fluctuations. Therefore, in the pressure time series data of slow movement, the difference between the pressure value at each moment and the preset theoretical pressure value is calculated as the relative pressure change value at each moment. The preset theoretical pressure value is the expected pressure curve of passive joint traction during slow movement, which can be modeled and obtained based on data from healthy children. The relative pressure change value reflects the "regulatory deviation". The smaller the value, the better the muscle control of the high-risk infant.

[0072] Further analysis can be conducted on the uniformity of muscle control in high-risk infants. Since variance reflects the dispersion of a set of data, the variance of the relative pressure changes at all times was calculated. A larger variance indicates greater fluctuation, lower uniformity, and greater variation in muscle tension, resulting in more pronounced muscle tone. Therefore, the normalized value of this difference is used as the pressure change complexity. A greater pressure change complexity indicates lower uniformity of muscle pressure changes in high-risk infants during normal muscle stretching, a higher likelihood of muscle stiffness and intermittent contraction tremors, and greater muscle tone. Normalization is a well-known technique in the field, and the normalization function can be linear or standard normalization, etc. Specific normalization methods are not limited here.

[0073] Step S204: Under slow movement, the static stiffness of muscle tissue, the complexity of pressure changes, and the limitation of joint movement are integrated to determine the tissue muscle tone performance value of high-risk infants under slow movement.

[0074] Based on the aforementioned steps, we can obtain three indicators that characterize the degree of muscle tone in high-risk infants under slow movement. These three independent dimensions are used to assess the muscle tone of high-risk infants. In this sub-step, we can integrate these three independent dimensions.

[0075] First, based on the analytic hierarchy process (AHP), specific weights are determined for the static stiffness, joint mobility limitation, and pressure variation complexity of high-risk infants. Then, based on these weights, a weighted fusion of these three factors under slow movement is performed. Specifically, static stiffness is multiplied by its specific weight, joint mobility limitation by its specific weight, and pressure variation complexity by its specific weight. Finally, the sum of these three products is normalized and used as the tissue muscle tone performance value for high-risk infants under slow movement. A higher tissue muscle tone performance value indicates greater severity of muscle rigidity in high-risk infants. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0076] It should be noted that the Analytic Hierarchy Process (AHP) can determine specific weights by constructing a judgment matrix (such as expert scoring or data-driven calibration), and the process is a well-known technique, so the specific process will not be described in detail here. At the same time, in other embodiments of the present invention, specific weights can also be set according to the implementation scenario, and the method of obtaining them is not limited here.

[0077] Step S3: After integrating the pressure time series data under the two passive movements and the comprehensive angle time series data, analyze the differences between the pressure values ​​to obtain the pressure deviation sequence; in the pressure deviation sequence, based on the numerical characteristics of the data values, determine the spasticity performance value of high-risk infants under rapid movement.

[0078] When performing rapid movement, the stretch reflex is generated because the movement speed exceeds the neural activation threshold. At this time, the resistance includes passive tissue resistance and neural reflex resistance. The main difference between rapid and slow movement is spasticity (neurogenic). Spasticity is manifested in the data as the folding knife phenomenon, that is, when stretching rapidly, strong resistance is encountered first (stretch reflex burst). When muscle tension continues to increase, the Golgi organs (receptors located in the tendons) are activated, inhibiting motor neurons through inhibitory interneurons, resulting in sudden muscle relaxation and sudden disappearance of resistance. Therefore, in the embodiments of the present invention, the change in resistance can be quantified by analyzing the difference in pressure values ​​under two types of movement to reflect the performance of the stretch reflex. The stronger the performance of the stretch reflex, the more obvious the spasticity and the greater the muscle tension.

[0079] Since the time required for fast and slow movements is not the same, directly comparing the deviations between time-series data may result in large errors. Therefore, in this embodiment of the invention, comprehensive angle data that can reflect the overall movement progress can be used as a benchmark to match and align the data under fast and slow movements. Based on this, the difference in pressure values ​​between the two movements can be analyzed to obtain a pressure deviation sequence, providing data support for subsequent quantification of the degree of spasticity in high-risk infants under fast movements.

[0080] Preferably, in one embodiment of the present invention, the method for obtaining the pressure deviation sequence includes:

[0081] Since the comprehensive angle and pressure time-series data for each passive motion are collected synchronously at the same frequency, they can be considered to have two attribute values ​​at each moment: pressure and comprehensive angle. Therefore, for each passive motion, the comprehensive angle and pressure time-series data are integrated: a two-dimensional coordinate system is constructed with the comprehensive angle as the horizontal axis and pressure as the vertical axis, thereby mapping the comprehensive angle and pressure values ​​at each moment to this two-dimensional coordinate system. All data points in this two-dimensional coordinate system are then fitted (the least squares method can be used, which is a well-known technique and will not be elaborated upon), thus obtaining the motion pressure sequence corresponding to each passive motion. When the data volume of the motion pressure sequences of the two passive motions is mismatched, interpolation can be used to process them.

[0082] Since fast movements, compared to slow movements, include data on spasms caused by stretch reflexes of nerve tissue, the difference between the pressure values ​​of fast and slow movements at the same comprehensive angle is calculated in the movement pressure sequences corresponding to the two passive movements. This difference is used as the pressure deviation value, which can then represent the spasm characteristics during the movement.

[0083] Finally, the pressure deviation values ​​under all combined angles are arranged in ascending order of the combined angles to obtain the pressure deviation sequence.

[0084] Thus, a pressure deviation sequence for measuring spasticity characteristics can be obtained. Because when spasticity is caused by a stretch reflex of nerve tissue during rapid movement, the pressure data of the two movement processes will have a large difference, resulting in a peak in the pressure deviation sequence. Therefore, based on the numerical characteristics of the data values ​​in the pressure deviation sequence, the spasticity performance value of high-risk infants during rapid movement can be determined.

[0085] Preferably, in one embodiment of the present invention, the method for obtaining the spasticity performance value of high-risk infants under rapid movement includes:

[0086] Since spasms usually occur intermittently, the pressure deviation sequence is segmented based on the average value of all pressure deviation values ​​to obtain all subsequences: in the pressure deviation sequence, the mean of all pressure deviation values ​​is used as the baseline value, and all pressure deviation values ​​exceeding the baseline value are used as marker points. The marker points are the data points where spasms may occur. Therefore, among all the marker points, consecutive marker points are considered as a subsequence, thus obtaining all the subsequences. At this time, each subsequence can represent a continuous spasm feature; a single isolated marker point is not considered as a subsequence.

[0087] The integral value can simultaneously capture the duration and intensity of spasms. Therefore, the integral value of each subsequence is used as the spasm energy value of each subsequence. The larger the spasm energy value, the longer the duration may be, the greater the pressure deviation value, and the more severe the overall impact. Since the maximum value is the most prominent feature in a spasm event, the normalized value of the maximum value in each subsequence is used as the spasm intensity. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0088] Finally, the spasticity energy value is weighted and fused using the spasticity intensity of subsequences in the pressure deviation sequence. This involves multiplying the spasticity intensity of each subsequence by its spasticity energy value, and then normalizing the sum of these products across all subsequences. This normalized sum is used as the spasticity performance value for high-risk infants during rapid movement. Based on the aforementioned analysis, a higher spasticity performance value indicates a greater likelihood of muscle rigidity or spasticity in high-risk infants during rapid movement. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0089] Step S4: Combine the tissue muscle tone values ​​of high-risk infants under slow movement and the spasticity values ​​under fast movement to monitor the muscle tone of high-risk infants.

[0090] In the aforementioned process, the dynamic movement of muscles in high-risk infants was analyzed to obtain the muscle tone characteristics and spasticity characteristics of high-risk infants under slow and fast movements. Here, the indicators of the two movement states can be combined to monitor the muscle tone of high-risk infants more comprehensively.

[0091] Preferably, in one embodiment of the present invention, the monitoring of muscle tone in high-risk infants is achieved by integrating tissue muscle tone values ​​during slow movement and spasticity values ​​during rapid movement, including:

[0092] The reference weights for tissue muscle tone values ​​under slow movement and spasticity values ​​under fast movement in high-risk infants were obtained using the analytic hierarchy process.

[0093] Then, the tissue muscle tone and spasticity values ​​of high-risk infants are weighted using reference weights. Specifically, the tissue muscle tone value of high-risk infants under slow movement is multiplied by the corresponding reference weight, and the spasticity value of high-risk infants under fast movement is multiplied by the corresponding reference weight. Finally, the sum of these two products is normalized and used as the muscle tone characteristic value of high-risk infants. The larger the muscle tone characteristic value, the greater the severity of muscle rigidity in high-risk infants. A muscle tone characteristic value that is too low may also indicate muscle weakness. Normalization is a technique well known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0094] As children's muscles develop at different stages with age, their muscle tone varies at different ages. Therefore, in this embodiment of the invention, different judgment threshold standards are set based on the child's age group. Thus, it is necessary to evaluate the muscle tone characteristic value of high-risk infants using the corresponding judgment thresholds within the corresponding age group: when the muscle tone characteristic value of a high-risk infant is greater than or equal to a preset level four judgment threshold, it is considered that the muscle tone is severely increased; when the muscle tone characteristic value is less than the preset level four judgment threshold but greater than or equal to a preset level three judgment threshold, it is considered that the muscle tone is significantly increased; when the muscle tone characteristic value is less than the preset level three judgment threshold but greater than or equal to a preset level two judgment threshold, it is considered that the muscle tone is normal; when the muscle tone characteristic value is less than the preset level two judgment threshold but greater than or equal to a preset level one judgment threshold, it is considered that the muscle tone is significantly decreased; and when the muscle tone characteristic value is less than the preset level one judgment threshold, it is considered that the muscle tone is severely decreased.

[0095] The method for obtaining the judgment thresholds for each age group is as follows: For each age group, obtain the muscle tone characteristic values ​​of more than 100 normal children (obtainable through static assessment by a doctor), perform statistical feature extraction using the 3σ method (σ represents standard deviation), and obtain the median of the muscle tone characteristic values ​​of all normal children in each age group. The preset four-level judgment threshold is... The preset three-level judgment threshold is: The preset secondary judgment threshold is The preset first-level judgment threshold is: The specific threshold settings can be adjusted according to the implementation scenario, and are not limited here.

[0096] It should be noted that the Analytic Hierarchy Process (AHP) can determine reference weights by constructing a judgment matrix (such as expert scoring or data-driven calibration), and the process is a well-known technique, so the specific process will not be described in detail here. At the same time, in other embodiments of the present invention, the reference weights can also be set according to the implementation scenario, and the method of obtaining them is not limited here.

[0097] In summary, this invention utilizes multi-dimensional data fusion and dynamic analysis techniques, employing flexible pressure sensors and a three-axis gyroscope to acquire pressure and angular velocity time-series data for high-risk infants under various passive movements, enabling real-time tracking of dynamic changes in muscle tone. First, the angular velocity time-series data in three directions are fused to obtain comprehensive angular time-series data. Since the mechanisms of muscle tone differ under different movement speeds, a relationship exists between pressure and angle changes during slow movement. Therefore, under slow movement, the correlation between pressure values ​​and comprehensive angle, as well as the fluctuation and numerical characteristics of pressure values ​​and comprehensive angles, are fused to quantify muscle tone under slow movement, yielding tissue muscle tone performance values. Furthermore, since stretch reflexes occur during rapid movement, and the main difference between rapid and slow movements is spasticity, the pressure time-series data and comprehensive angular time-series data from both passive movements are integrated to analyze the differences in pressure values. Based on these differences, numerical analysis is performed to establish a calculation model for spasticity performance values ​​in high-risk infants under rapid movement. Finally, the muscle tone performance under the two passive movements was integrated, that is, the tissue muscle tone performance value under slow movement and the spasticity performance value under fast movement were combined to obtain a comprehensive dynamic monitoring index, thereby monitoring the muscle tone of high-risk infants and effectively improving the accuracy of monitoring results.

[0098] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A wearable high-risk infant muscle tension monitoring bracelet based on a flexible sensor, characterized in that, A flexible pressure sensor and a three-axis gyroscope are installed on a bracelet, the flexible pressure sensor is used to obtain pressure time series data, and the three-axis gyroscope is used to obtain angular velocity time series data; the bracelet also has a control module built-in, the control module can perform muscle tension monitoring based on the pressure time series data and the angular velocity time series data, wherein the muscle tension monitoring method comprises: Under each passive movement of the high-risk child, pressure time series data and angular velocity time series data in three directions are obtained, and comprehensive angle time series data is obtained based on the angular velocity time series data in three directions; wherein the passive movement is divided into slow movement and fast movement; Under slow movement, the relationship between the change characteristics of the pressure value and the change characteristics of the comprehensive angle, the numerical characteristics of the comprehensive angle, and the fluctuation characteristics of the pressure value in the pressure time series data are fused to determine the organizational muscle tension performance value of the high-risk child under slow movement; After integrating the pressure time series data and the comprehensive angle time series data under the two passive movements, the difference between the pressure values is analyzed to obtain a pressure deviation sequence; in the pressure deviation sequence, based on the numerical characteristics of the data values, the spasm performance value of the high-risk child under fast movement is determined; The muscle tension of the high-risk child is monitored by fusing the organizational muscle tension performance value of the high-risk child under slow movement and the spasm performance value of the high-risk child under fast movement; The method for obtaining the organizational muscle tension performance value comprises: Under slow movement, the relationship between the change characteristics of the pressure value and the change characteristics of the comprehensive angle is analyzed to determine the static stiffness of the muscle tissue; Under slow movement, based on the extreme numerical characteristics in the comprehensive angle time series data, the joint activity restriction degree is determined; Under slow movement, the fluctuation characteristics of the pressure value in the pressure time series data are analyzed to determine the pressure change complexity; The specific weights of the static stiffness, the joint activity restriction degree, and the pressure change complexity of the high-risk child are determined based on the analytic hierarchy process, the static stiffness, the joint activity restriction degree, and the pressure change complexity of the high-risk child under slow movement are weighted and fused based on the specific weights, and the normalized value of the obtained weighted result is taken as the organizational muscle tension performance value of the high-risk child under slow movement; The method for obtaining the spasm performance value comprises: In the pressure deviation sequence, the pressure deviation sequence is segmented based on the average numerical characteristics of all pressure deviation values, thereby obtaining all sub-sequences; The integral value of each sub-sequence is taken as the spasm energy value of each sub-sequence, the maximum value in each sub-sequence is normalized to obtain the spasm intensity, the spasm energy values are weighted and fused using the spasm intensity of the sub-sequences in the pressure deviation sequence, and the normalized value of the obtained weighted result is taken as the spasm performance value of the high-risk child under fast movement; The method for obtaining the sub-sequence comprises: In the pressure deviation sequence, the mean value of all pressure deviation values is taken as a reference value, and all pressure deviation values exceeding the reference value are taken as marker points; In all marker points, consecutive marker points are taken as a sub-sequence to obtain all sub-sequences.

2. The wearable high-risk infant muscle tension monitoring bracelet based on a flexible sensor according to claim 1, wherein, The method for obtaining the comprehensive angle time series data comprises: In each passive movement, the angular velocity time series data in each direction is integrated from the first time point as the lower limit to each subsequent time point as the upper limit, so as to obtain the angle value at each subsequent time point; In each passive movement, the angle values in three directions at each time point are fused into a spatial vector to obtain the comprehensive angle at each time point, so as to determine the comprehensive angle time series data under each passive movement. 3.The wearable high-risk infant muscle tension monitoring bracelet based on a flexible sensor according to claim 1, wherein, The method for obtaining the static stiffness comprises: In the pressure time series data, a first-order difference sequence of all pressure values is obtained as a pressure value change sequence, and in the comprehensive angle time series data, a first-order difference sequence of all angle values is obtained as an angle change sequence; The normalized value of the Pearson correlation coefficient of the pressure value change sequence and the angle change sequence is taken as the static stiffness of the muscle tissue. 4.The wearable high-risk infant muscle tension monitoring bracelet based on a flexible sensor according to claim 1, wherein, The method for obtaining the joint activity limitation degree comprises: In the comprehensive angle time series data corresponding to the slow movement, the absolute value of the difference between the maximum comprehensive angle value and the preset maximum joint activity angle is normalized to obtain the joint activity limitation degree. 5.The wearable high-risk infant muscle tension monitoring bracelet based on flexible sensor according to claim 1, wherein, The method for obtaining the pressure change complexity comprises: In the pressure time series data of the slow movement, the difference between the pressure value at each time point and the preset theoretical pressure value is calculated as the pressure relative change value at each time point; The normalized value of the variance of the pressure relative change values at all time points is taken as the pressure change complexity. 6.The wearable high-risk infant muscle tension monitoring bracelet based on flexible sensor according to claim 1, wherein, The method for obtaining the pressure deviation sequence comprises: In each passive movement, a two-dimensional coordinate system is constructed with the comprehensive angle as the horizontal axis and the pressure as the vertical axis, so as to map the comprehensive angle and the pressure value at each time point into the two-dimensional coordinate system, and all data points in the two-dimensional coordinate system are fitted to obtain the movement pressure sequence corresponding to each passive movement; In the movement pressure sequences corresponding to two passive movements, the difference between the pressure values of the fast movement and the slow movement at the same comprehensive angle is calculated as the pressure deviation value; All pressure deviation values at different comprehensive angles are arranged in ascending order of comprehensive angle to obtain the pressure deviation sequence. 7.The wearable high-risk infant muscle tension monitoring bracelet based on flexible sensor according to claim 1, wherein, The method for monitoring the muscle tension of the high-risk child comprises: Based on the analytic hierarchy process, reference weights of the muscle tension performance value of the high-risk child under slow movement and the spasm performance value of the high-risk child under fast movement are obtained respectively; The muscle tension performance value and the spasm performance value of the high-risk child are weighted using the reference weights, and the normalized value of the obtained weighted result is taken as the muscle tension characteristic value of the high-risk child. When the muscle tension characteristic value of the high-risk child is greater than or equal to a preset four-level judgment threshold value, it is considered that the muscle tension is seriously increased; when the muscle tension characteristic value is less than the preset four-level judgment threshold value and greater than or equal to a preset three-level judgment threshold value, it is considered that the muscle tension is obviously increased; when the muscle tension characteristic value is less than the preset three-level judgment threshold value and greater than or equal to a preset two-level judgment threshold value, it is considered that the muscle tension is normal; when the muscle tension characteristic value is less than the preset two-level judgment threshold value and greater than or equal to a preset one-level judgment threshold value, it is considered that the muscle tension is obviously decreased; and when the muscle tension characteristic value is less than the preset one-level judgment threshold value, it is considered that the muscle tension is seriously decreased.

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