Health information management method for rehabilitation training

Abstract

The invention relates to the technical field of rehabilitation training, in particular to a health information management method for rehabilitation training. The method comprises the following steps: determining deformation segments with non-standard actions according to form change conditions of different motion segments divided on motion performance data; analyzing the association between the change of the physical sign data of the body and the morphological change of the motion segment for the deformation segment, analyzing and quantifying different factors generating deformation through the association condition, and determining a load segment; according to the deformation movement condition of the load section preorder and the physiological index data recovery trend, cumulative intensity is obtained; according to the non-load condition and the cumulative intensity of the load section, the rehabilitation training of the patient is adjusted from different classification condition results. The motion state of the patient is classified and analyzed by analyzing the association between the motion deformation condition and the body physiological index change in the rehabilitation training of the patient, so that the adjustment condition of the rehabilitation training is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation training, and in particular to a health information management method for rehabilitation training. Background Art

[0002] Rehabilitation training refers to physical activities that are conducive to the recovery or improvement of function after an injury. Except for severe injuries that require rest treatment, general injuries do not require complete cessation of physical exercises. Appropriate and scientific physical exercises play a positive role in the rapid healing of injuries and the promotion of functional recovery. When patients undergo rehabilitation training, doctors need to obtain the patient's health information to specify a rehabilitation training plan. The health information can be used to determine the patient's recovery status and the effectiveness of rehabilitation training. When designing a patient's rehabilitation training plan based on the acquired health information, artificial intelligence can be used to assist doctors in monitoring patient training. When doctors conduct designated rehabilitation training, they generally guide the training based on their own experience and the patient's information. However, artificial intelligence can be used to conduct more scientific training and reduce the difficulty of doctors in guiding rehabilitation training.

[0003] When guiding patients through rehabilitation training plans, patients may have non-standard movements, which may result in the inability to complete the designated training plan. However, the non-standard movements may not only be caused by poor compliance, but may also be caused by fatigue or heavy load. If the adjustments to the different training plans required for various situations where the movements are not up to standard are not considered, the patients may recover slowly in the future. Summary of the invention

[0004] In order to solve the technical problem that the prior art does not consider the adjustment of different training plans required when multiple movements do not meet the standards, which may lead to slow subsequent recovery of patients, the purpose of the present invention is to provide a health information management method for rehabilitation training, and the technical solution adopted is as follows: The present invention provides a health information management method for rehabilitation training, the method comprising: Acquire different physiological index data and sports performance data in time series during the patient's rehabilitation training; The patient's motion performance data is segmented to obtain different motion segments; the morphological matching between the data in each motion segment and the standard motion template is analyzed to obtain the morphological fluctuation index of each motion segment; the deformation segment is selected based on the size of the morphological fluctuation index of the motion segment; According to the numerical distribution and change rate of each physiological indicator data at each moment, the fluctuation characteristic index of each physiological indicator data at each moment is obtained; in each deformation segment, the correlation between the morphological fluctuation index and the fluctuation characteristic index of each physiological indicator data is analyzed to obtain the correlation index between each deformation segment and each physiological indicator data; Based on the size of the correlation index between each deformation segment and all physiological index data, the load segment is determined; according to the time difference between the load segment and the previous deformation segment and the number of previous movement segments, as well as the correlation index between the load segment and each physiological index data combined with the time series change trend, the cumulative intensity of the load segment is obtained; The patient's rehabilitation training is adjusted based on the cumulative intensity of the loaded segments and the number of unloaded segments in the deformation segmentation.

[0005] Furthermore, the method for obtaining the morphological fluctuation index includes: For any motion segment, the motion performance data on the motion segment is matched with the standard motion template using the DTW algorithm to obtain several matching pairs; In each matching pair, the data value difference between the mean value of the motion performance data on the motion segment and the mean value of the standard motion template is used as the deviation degree of each matching pair; The sum of the deviations of all matching pairs on the motion segment is normalized to obtain the morphological fluctuation index of the motion segment.

[0006] Furthermore, the method for obtaining the deformation segment includes: When the normalized value of the morphological fluctuation index is greater than the preset deformation threshold, the corresponding motion segment is used as the deformation segment.

[0007] Furthermore, the method for obtaining the fluctuation characteristic index includes: For any physiological index data, curve fitting is performed on the physiological index data in time series to obtain an index curve; Obtain the slope of the indicator curve at each moment; take the difference in slope between each moment and the previous moment in the time series as the degree of change difference; The product of the value of the physiological indicator data at each moment and the change difference is used as the fluctuation characteristic index of the physiological indicator data at each moment.

[0008] Furthermore, the method for obtaining the correlation index includes: For any deformation segment, each physiological index data is used as analysis data in turn; the mean value of the fluctuation characteristic index of the analysis data at all moments on the deformation segment is normalized to obtain the physiological fluctuation degree of the analysis data on the deformation segment; The difference between the physiological fluctuation degree and the morphological fluctuation index of the analysis data on the deformation segment is negatively correlated to obtain the correlation index between the deformation segment and the analysis data.

[0009] Furthermore, the method for determining the load segment includes: For each deformation segment, when there is a correlation index between the deformation segment and the physiological index data that is greater than a preset correlation threshold, the deformation segment is used as a load segment.

[0010] Furthermore, the method for obtaining the cumulative intensity includes: For any load segment, the total number of all exercise segments before the load segment is counted as the cumulative number of times for the load segment; Negative correlation mapping is performed on the time interval between the load segment and the previous load segment to obtain the deformation accumulation degree of the load segment; Combining the correlation index between the load segment and each physiological index data and the change trend of the physiological index data, the cumulative degree of the physical signs of the load segment is obtained; The cumulative intensity of the load segment is obtained by combining the cumulative number of times, cumulative deformation and cumulative signs of the load segment.

[0011] Furthermore, the method for obtaining the cumulative degree of physical signs includes: For any physiological index data, after curve fitting is performed on the physiological index data on the load segment, a time series decomposition algorithm is used to obtain the trend item of the physiological index data; the mean of all trend values ​​in the trend item is negatively correlated to obtain the trend flatness of the physiological index data; The product of the correlation index between the physiological index data and the load segment and the trend flatness is used as a possible trend index between the physiological index data and the load segment; The cumulative value of the trend possible index of the load segment and all physiological index data is normalized to obtain the cumulative degree of the physical signs of the load segment.

[0012] Furthermore, adjusting the patient's rehabilitation training based on the cumulative intensity of the load segment and the number of non-load segments in the deformation segment includes: The proportion of the number of non-load segments in the deformation segmentation is used as a compliance judgment index; when the compliance judgment index is greater than the preset compliance adjustment threshold, the current patient rehabilitation training is recorded as action adjustment; When the cumulative intensity of all load segments is not greater than the preset fatigue threshold, the current patient rehabilitation training is recorded as intensity adjustment; otherwise, the current patient rehabilitation training is recorded as frequency adjustment.

[0013] Furthermore, the method for acquiring the motion segments includes: dividing the motion performance data in time series into different motion segments by trajectory segmentation.

[0014] The present invention has the following beneficial effects: Through the morphological changes of different motion segments divided in the sports performance data, the deformation segments with non-standard movements are determined. For the non-standard deformation segments, the correlation between the changes in the physiological index data of the patient's body during rehabilitation training and the morphological changes of the motion segments is analyzed. Considering the difference between the stress response caused by fatigue and pain load and poor compliance when the movements are non-standard, the different situations after the deformation of the movement are quantified through the correlation analysis, and the load segment is determined to classify different influencing factors. And further from the deformation movement situation before the load segment and the recovery trend with the physiological index data, the accumulated situation of the patient's body movement intensity is reflected, and fatigue and pain are further classified. Finally, from the non-load situation and the cumulative intensity of the load segment, the results of different classification situations or the rehabilitation training of the patient are adjusted to improve the accuracy of the proposal of the auxiliary rehabilitation training plan. The present invention combines the analysis of the correlation between the deformation situation of the movement in the patient's rehabilitation training and the changes in the body's physiological indicators, and classifies and analyzes the patient's movement state, so that the adjustment of the rehabilitation training is more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0016] Figure 1 A flow chart of a health information management method for rehabilitation training provided by one embodiment of the present invention; Figure 2 A schematic diagram of different motion segments provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the health information management method for rehabilitation training proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, 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 belongs.

[0019] A specific scheme of a health information management method for rehabilitation training provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a health information management method for rehabilitation training provided by an embodiment of the present invention, the method mainly includes the following steps: S1: Obtain different physiological index data and sports performance data in time series during the patient's rehabilitation training.

[0021] During rehabilitation training, patients wear devices containing sensors to collect data representing the degree of movement during exercise as sports performance data. For example, patients who need to undergo knee flexion and extension rehabilitation training can wear smart knee pads with integrated accelerometers and gyroscopes to further collect the angle of knee flexion and extension, reflecting the range of movement quality as time-series sports performance data. For other exercises such as shoulder abduction in shoulder rehabilitation, the trajectory deviation of the arm from the vertical position to the horizontal position can be collected through an integrated smart bracelet.

[0022] The fundamental purpose of rehabilitation training is to stimulate muscle groups and cardiopulmonary function through specific movements to improve the patient's physical health. When patients are trained through specific movements, their physical index data will change according to the exercise situation, and the changes in physical indicators can reflect the effect of exercise to a certain extent.

[0023] Therefore, during the rehabilitation training process, it is also necessary to collect the patient's physiological index data in real time through sensor equipment, and the physiological index data at least includes heart rate, heart rate variability and electromyography signal, etc. In an embodiment of the present invention, the heart rate (HR) can be collected through a smart watch and an electrocardiogram (ECG) monitor, and the data format is usually the number of beats per minute (bpm). For example, the heart rate data format is: [72,75,80,70], which represents the heart rate at consecutive time points.

[0024] Heart rate variability (HRV) can be obtained by using electrocardiogram (ECG) equipment to obtain RR interval data. HRV is expressed by indicators such as the standard deviation of heartbeat intervals (SDNN) and RMSSD. For example, the SDNN value is 32ms and the RMSSD is 22ms. Heart rate variability indicates the changes in heartbeat intervals and is used to evaluate the function of the autonomic nervous system and the recovery of the body.

[0025] Electromyography (EMG) uses EMG sensors to collect data. The patches of EMG sensors are tightly installed on the muscle groups that need rehabilitation training. For example, when doing knee rehabilitation, the electrodes can be attached to the quadriceps on the front of the thigh or the biceps femoris on the back of the thigh. If the shoulder is involved in the training, the electrodes can be attached to the deltoid or rotator cuff muscles. The data is usually expressed as a voltage signal (mV). By analyzing the amplitude and frequency of the electromyogram, the activity and fatigue state of the muscle can be determined. For example, the data format of a segment of EMG signal is: [0.3, 0.5, 0.7, 1.2, 1.5], which represents the electrical signal strength of the muscle at different time points.

[0026] The reason why patients do not get good results from exercise during training is most likely due to non-standard movements. There are many situations in which patients' movements are not standard. For example, when patients have poor compliance, training will cause movements to be compensated by other muscles. In addition, when patients are overly tired or have a high pain load, their movements will also cause deformation. Therefore, the transmission, integration and analysis of physiological index data and sports performance data collected in real time can be used to assist in the formulation of rehabilitation training suggestions.

[0027] S2: Segment the patient's motion performance data to obtain different motion segments; analyze the morphological matching between each motion segment and the standard motion template to obtain the morphological fluctuation index of each motion segment; and select the deformation segments based on the size of the morphological fluctuation index of the motion segment.

[0028] Patients usually perform repetitive training on a single action during rehabilitation training. Therefore, a registration analysis is performed with the standard for each movement segment to first screen out the time periods where deformation is possible for subsequent analysis of the possibility of deformation. First, the time periods are divided. In an embodiment of the present invention, the time series motion performance data is divided into different motion segments by trajectory segmentation. It should be noted that trajectory segmentation is a technical means well known to those skilled in the art and will not be elaborated or limited here. Please refer to Figure 2 , which shows a schematic diagram of different motion segments provided by an embodiment of the present invention, wherein the horizontal axis is time and the vertical axis is motion characterization data, and shows the situation where data in multiple motion segments are mapped to the same coordinate system.

[0029] The morphological matching reflects the degree of fluctuation between the data in each motion segment and the standard template, that is, the degree of non-standardization of the action in each motion segment. In an embodiment of the present invention, the method for obtaining the morphological fluctuation index includes: for any motion segment, the motion performance data on the motion segment is matched with the standard motion template using the DTW algorithm to obtain a number of matching pairs. In the DTW algorithm, the matching pairs obtained represent the results of the optimal alignment between the two time series. The matching pairs are the most similar parts between the two time series data at the time point, which is convenient for data morphological matching analysis. It should be noted that the process of obtaining matching pairs by the DTW algorithm is a technical means well known to those skilled in the art and will not be elaborated here.

[0030] Furthermore, in each matching pair, the data value difference between the mean of the motion performance data on the motion segment and the mean of the standard motion template is used as the deviation degree of each matching pair. Taking into account that the matching pairs not only have a one-to-one relationship, but also a one-to-many or many-to-many relationship, a difference analysis is performed between the data mean in the matching motion segment and the data mean in the corresponding matching standard template, and the motion performance fluctuation analysis of the matching part is performed as a whole.

[0031] Finally, the sum of the deviations of all matching pairs on the motion segment is normalized to obtain the morphological fluctuation index of the motion segment. The greater the deviation between the data of the motion segment in the matching pair and the standard situation, the greater the fluctuation of the motion segment relative to the standard template, and the more likely it is a segment that produces deformation. It should be noted that normalization is a technical means well known to those skilled in the art, and the choice of normalization can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0032] Therefore, further preliminary screening can be performed by the size of the morphological fluctuation index. In the embodiment of the present invention, the method for obtaining the deformation segmentation includes: when the value of the morphological fluctuation index after normalization is greater than the preset deformation threshold, the corresponding motion segmentation is used as the deformation segment. In the embodiment of the present invention, the preset deformation threshold is set to 0.6, and the specific value can be adjusted by the implementer according to the specific implementation scenario, and is not limited here.

[0033] Complete the preliminary screening analysis of non-standard movements, and then further analyze the factors causing the non-standard movements in combination with physiological data.

[0034] S3: According to the numerical distribution and change rate of each physiological indicator data at each moment, the fluctuation characteristic index of each physiological indicator data at each moment is obtained; in each deformation segment, the correlation between the morphological fluctuation index and the fluctuation characteristic index of each physiological indicator data is analyzed to obtain the correlation index between each deformation segment and each physiological indicator data.

[0035] The fluctuations of different physiological indicators have different meanings. For example, during training, heart rate will increase as the intensity of exercise increases. However, patients usually experience pain during training. When severe pain occurs due to increased pain load, the body may trigger a stress response, which will manifest as a sharp fluctuation or rapid increase in heart rate. At the same time, when patients experience pain or discomfort due to excessive exercise load, the sympathetic nerves are active, causing the heart rate to accelerate and produce a sense of fatigue. Pain or fatigue may cause the heart rate to recover more slowly and take longer to recover.

[0036] For sports performance data, if the patient feels pain during a certain movement, the patient may automatically reduce the range of motion. For example, during knee flexion and extension training, a patient who feels knee pain may subconsciously reduce the flexion angle of the knee joint. At the same time, pain may cause the movement frequency to slow down and unnatural pauses to occur.

[0037] Therefore, each physiological indicator data needs to pay attention to the fluctuation change and the value size. The fluctuation characteristics of the physiological indicator data at each moment are comprehensively reflected by the instability of the value size and the degree of the previous change of the physiological indicator data at each moment. Preferably, in an embodiment of the present invention, the method for obtaining the fluctuation characteristic index of each physiological indicator data at each moment includes: First, for any physiological indicator data, curve fitting is performed on the physiological indicator data in the time series to obtain an indicator curve, which characterizes the overall change of the data, and the slope of each moment on the indicator curve can be further obtained. By taking the slope difference between each moment and the previous moment in the time series as the change difference, it is reflected whether the change state of the data in the time series has a large fluctuation, such as a rapid rise. In the embodiment of the present invention, the fitting can be performed by the spline interpolation method. Curve fitting is a technical means well known to those skilled in the art, and no further explanation or limitation is made here.

[0038] Finally, the product of the value of the physiological index data at each moment and the degree of change is taken as the fluctuation characteristic index of the physiological index data at each moment. The value also reflects the state of physical training. For example, if the heart rate rises and remains high, it can reflect that the training has produced an effect. However, if the heart rate rises too fast, it may be caused by excessive exercise or high pain load. Therefore, the value and the degree of change can reflect the influence of exercise on the physical state.

[0039] Furthermore, in each deformation segment, the correlation between the body's physiological indicator data and sports performance data can be analyzed and quantified. The correlation analysis can be used to observe whether the fluctuation of the physiological indicator data is related to the movement. For example, when the heart rate fluctuation increases too quickly, the patient's movement will generally fluctuate, reflecting that the patient's current exercise state is under a heavy load.

[0040] Preferably, in an embodiment of the present invention, the method for obtaining the correlation index includes: For any deformation segment, each physiological index data is used as analysis data in turn, and correlation analysis is performed on each physiological index data in turn on the deformation segment. The mean of the fluctuation characteristic index of the analysis data at all moments on the deformation segment is normalized to obtain the physiological fluctuation degree of the analysis data on the deformation segment. The fluctuation characteristic index corresponding to all moments in the time period is integrated in the form of the mean to obtain the physiological fluctuation degree, which characterizes the degree of high fluctuation change of the analysis data on the deformation segment.

[0041] Then, the difference between the physiological fluctuation degree and the morphological fluctuation index of the analysis data on the deformation segment is negatively correlated to obtain the correlation index between the deformation segment and the analysis data. When the physiological fluctuation degree is larger, it means that the high fluctuation change of the physiological index data represented by the analysis data on the deformation segment is more significant, and when the morphological fluctuation index is larger, it means that the deformation of the action in the time period is more significant. Therefore, by analyzing the consistency of significant situations, the degree of correlation is reflected. When the difference between the physiological fluctuation degree and the morphological fluctuation index is smaller, it means that the situation of significant change is higher, so the correlation index is larger.

[0042] In the embodiment of the present invention, the negative correlation mapping may be in the form of a negative exponential power. Negative correlation mapping is a technical means well known to those skilled in the art. An inverse proportional or negative linear function may also be used, which will not be elaborated or limited here.

[0043] S4: Determine the load segment based on the size of the correlation index between each deformation segment and all physiological index data; obtain the cumulative intensity of the load segment based on the time difference between the load segment and the previous deformation segment and the number of previous movement segments, as well as the correlation index between the load segment and each physiological index data combined with the temporal change trend.

[0044] When the correlation index is larger, it reflects that the degree of simultaneous change of the physiological state when deformation occurs is higher, and it is more likely that the patient's physical state during training is under load. Therefore, the load segment with the load state is first determined by the size of the correlation index. In an embodiment of the present invention, the method for obtaining the load segment includes: For each deformation segment, when there is a correlation index between the deformation segment and the physiological index data greater than the preset correlation threshold, that is, when there is a correlation index between any physiological index data and the deformation segment greater than the preset correlation threshold, the deformation segment is used as a load segment.

[0045] The load segment reflects that the patient may have deformation caused by changes in physical state, which may be caused by fatigue or pain. On the contrary, when the deformation segment is a non-load segment, it reflects that the patient's compliance is poor, the action does not meet the standard, or the action does not meet the standard due to distraction, etc. In the embodiment of the present invention, the preset correlation threshold is set to 0.7, and the specific value can be adjusted by the implementer, and is not limited here.

[0046] For the load segment, there are different factors caused by pain or fatigue, and the corresponding adjustments required are different. For pain, the training intensity needs to be adjusted to avoid the possibility of secondary injury. For fatigue, the training frequency can be controlled and the patient's rest rhythm can be adjusted to ensure the quality of training.

[0047] Considering that deformation caused by fatigue is more likely to be caused by accumulated intensity, a comprehensive evaluation is performed through the cumulative number of times and deformation intervals, as well as the overall trend of physiological index data. The more cumulative times of exercise movements, it means that the current patient has done enough exercise, and the exercise deformation may be caused by fatigue. When the interval between two adjacent deformations is shorter, it means that the current patient can no longer complete the movement normally due to fatigue. At the same time, the trend of physiological indicators is also considered. The faster the trend changes, the more likely it is caused by pain. The changes in physiological indicators caused by exercise fatigue are a relatively stable cumulative increase process.

[0048] Preferably, in an embodiment of the present invention, the method for obtaining the cumulative intensity includes: For any load segment, the total number of all movement segments before the load segment is counted as the cumulative number of times for the load segment. The more movement segments there are before the load segment, the more times the action is trained, and fatigue is very likely to occur.

[0049] Furthermore, the time interval between the load segment and the previous load segment is negatively correlated to obtain the deformation accumulation of the load segment. The smaller the time interval, the more likely it is that the correct posture cannot be maintained due to fatigue. It can be understood that for the first load segment without the previous load segment, the frequency approximation can be reflected by the time interval with the next load segment. In other embodiments of the present invention, the cumulative strength analysis of the first load segment can also be excluded and not involved in the subsequent fatigue analysis.

[0050] Then, the sign accumulation degree of the load segment is obtained by combining the correlation index of the load segment and each physiological indicator data and the change trend of the physiological indicator data. From the perspective of physiological indicators, the smoother the trend of the physiological indicator data in the load segment, the more likely it is caused by fatigue. The reliability of the analysis is adjusted in combination with the correlation index. In an embodiment of the present invention, the method for obtaining the sign accumulation degree includes: First, for any physiological indicator data, after curve fitting of the physiological indicator data on the load segment, the time series decomposition algorithm is used to obtain the trend item of the physiological indicator data. The curve can be decomposed into trend item, seasonal item and residual by time series decomposition. The trend item reflects the overall change trend of the data. Therefore, the mean of all trend values ​​in the trend item is negatively correlated to obtain the trend flatness of the physiological indicator data. When the overall trend value is smaller, the trend is flatter and the drastic degree of the physical sign change is not high.

[0051] Then, the product of the correlation index between the physiological indicator data and the load segment and the trend smoothness is used as a possible trend indicator of the physiological indicator data and the load segment. Through the influence of the correlation index, for the physiological indicator data with a higher degree of correlation, more consideration is given to the trend smoothness.

[0052] Finally, the cumulative value of the trend possible index of the load segment and all physiological index data is normalized to obtain the cumulative degree of physical signs of the load segment. Combined with the trend analysis of all physiological index data, the flatter the overall trend level, the greater the possibility of fatigue accumulation.

[0053] Finally, the cumulative intensity of the load segment is obtained by combining the cumulative number of times, the cumulative deformation and the cumulative physical sign of the load segment. In the embodiment of the present invention, the product of the cumulative number of times, the cumulative deformation and the cumulative physical sign of the load segment is normalized to obtain the cumulative intensity of the load segment. The greater the cumulative intensity, the higher the possibility that the load segment is fatigued.

[0054] S5: Adjust the patient's rehabilitation training based on the cumulative intensity of the loaded segments and the number of unloaded segments in the deformation segmentation.

[0055] The number of non-load segments of the patient after deformation can be used to reflect the patient's current training compliance, so that adjustments can be made to the movements. The cumulative intensity of the load segments can be used to further divide the fatigue and pain load conditions, so that different adjustments can be made to the subsequent training plan, making the adjustment suggestions for rehabilitation training more reliable and accurate.

[0056] In the embodiment of the present invention, the proportion of the number of non-load segments in the deformation segmentation is used as a compliance judgment index. When there are a large number of non-load segments in the deformation, it is necessary to consider the non-standard situation of the patient to avoid affecting the rehabilitation effect. Therefore, when the compliance judgment index is greater than the preset compliance adjustment threshold, the current patient rehabilitation training is recorded as action adjustment, and it is necessary to pay attention to and adjust the action standard of the patient training in a timely manner. In the embodiment of the present invention, the compliance adjustment threshold is set to 0.7, and the specific value can be adjusted by the implementer.

[0057] Furthermore, when the cumulative intensity of all load segments is not greater than the preset fatigue threshold, it means that the existing load segment is likely to be caused by fatigue, and it is very likely that the patient's pain is the cause of the high load. The current patient rehabilitation training is recorded as intensity adjustment. At this time, technical attention needs to be paid to whether the patient may have secondary injuries, and the training intensity should be adjusted. Otherwise, the current patient rehabilitation training is recorded as frequency adjustment, indicating that the patient may have become fatigued and needs to pay attention to whether to rest to ensure the quality of training. In an embodiment of the present invention, the preset fatigue threshold is set to 0.65, and the specific value can be adjusted by the implementer.

[0058] In summary, the deformation segments with non-standard movements are determined by the morphological changes of different movement segments divided by the sports performance data. For the non-standard deformation segments, the correlation between the changes in the physiological index data of the patient's body during rehabilitation training and the morphological changes of the movement segments is analyzed. Considering the difference between the stress response caused by fatigue and pain load and poor compliance when the movements are non-standard, the different situations after the movement deformation are quantified through the correlation analysis, and the load segment is determined to classify different influencing factors. And further from the deformation movement situation before the load segment and the recovery trend with the physiological index data, the accumulated movement intensity of the patient's body is reflected, and fatigue and pain are further classified. Finally, from the non-load situation and the cumulative intensity of the load segment, the results of different classification situations or the rehabilitation training of the patient are adjusted to improve the accuracy of the proposal of the auxiliary rehabilitation training plan. The present invention combines the analysis of the correlation between the deformation situation of the movement in the patient's rehabilitation training and the changes in the physiological indicators of the body, and classifies and analyzes the patient's movement state, so that the adjustment of the rehabilitation training is more accurate and reliable.

[0059] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A health information management method for rehabilitation training, characterized in that: The method comprises: Acquire different physiological index data and sports performance data in time series during the patient's rehabilitation training; The patient's motion performance data is segmented to obtain different motion segments; the morphological matching between the data in each motion segment and the standard motion template is analyzed to obtain the morphological fluctuation index of each motion segment; the deformation segment is selected based on the size of the morphological fluctuation index of the motion segment; According to the numerical distribution and change rate of each physiological indicator data at each moment, the fluctuation characteristic index of each physiological indicator data at each moment is obtained; in each deformation segment, the correlation between the morphological fluctuation index and the fluctuation characteristic index of each physiological indicator data is analyzed to obtain the correlation index between each deformation segment and each physiological indicator data; Based on the size of the correlation index between each deformation segment and all physiological index data, the load segment is determined; according to the time difference between the load segment and the previous deformation segment and the number of previous movement segments, as well as the correlation index between the load segment and each physiological index data combined with the time series change trend, the cumulative intensity of the load segment is obtained; The patient's rehabilitation training is adjusted based on the cumulative intensity of the loaded segments and the number of unloaded segments in the deformation segmentation.

2. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for obtaining the morphological fluctuation index includes: For any motion segment, the motion performance data on the motion segment is matched with the standard motion template using the DTW algorithm to obtain several matching pairs; In each matching pair, the data value difference between the mean value of the motion performance data on the motion segment and the mean value of the standard motion template is used as the deviation degree of each matching pair; The sum of the deviations of all matching pairs on the motion segment is normalized to obtain the morphological fluctuation index of the motion segment.

3. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for obtaining the deformation segment comprises: When the normalized value of the morphological fluctuation index is greater than the preset deformation threshold, the corresponding motion segment is used as the deformation segment.

4. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for obtaining the fluctuation characteristic index includes: For any physiological index data, curve fitting is performed on the physiological index data in time series to obtain an index curve; Obtain the slope of the indicator curve at each moment; take the difference in slope between each moment and the previous moment in the time series as the degree of change difference; The product of the value of the physiological indicator data at each moment and the change difference is used as the fluctuation characteristic index of the physiological indicator data at each moment.

5. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for obtaining the associated index includes: For any deformation segment, each physiological index data is used as analysis data in turn; the mean value of the fluctuation characteristic index of the analysis data at all moments on the deformation segment is normalized to obtain the physiological fluctuation degree of the analysis data on the deformation segment; The difference between the physiological fluctuation degree and the morphological fluctuation index of the analysis data on the deformation segment is negatively correlated to obtain the correlation index between the deformation segment and the analysis data.

6. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for determining the load segment includes: For each deformation segment, when there is a correlation index between the deformation segment and the physiological index data that is greater than a preset correlation threshold, the deformation segment is used as a load segment.

7. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for obtaining the cumulative intensity includes: For any load segment, the total number of all exercise segments before the load segment is counted as the cumulative number of times for the load segment; Negative correlation mapping is performed on the time interval between the load segment and the previous load segment to obtain the deformation accumulation degree of the load segment; Combining the correlation index between the load segment and each physiological index data and the change trend of the physiological index data, the cumulative degree of the physical signs of the load segment is obtained; The cumulative intensity of the load segment is obtained by combining the cumulative number of times, cumulative deformation and cumulative signs of the load segment.

8. A health information management method for rehabilitation training according to claim 7, characterized in that: The method for obtaining the physical sign accumulation degree includes: For any physiological index data, after curve fitting is performed on the physiological index data on the load segment, a time series decomposition algorithm is used to obtain the trend item of the physiological index data; the mean of all trend values ​​in the trend item is negatively correlated to obtain the trend flatness of the physiological index data; The product of the correlation index between the physiological index data and the load segment and the trend flatness is used as a possible trend index between the physiological index data and the load segment; The cumulative value of the trend possible index of the load segment and all physiological index data is normalized to obtain the cumulative degree of the physical signs of the load segment.

9. A health information management method for rehabilitation training according to claim 1, characterized in that: The step of adjusting the patient's rehabilitation training based on the cumulative strength of the load segment and the number of the non-load segments in the deformation segmentation includes: The proportion of the number of non-load segments in the deformation segmentation is used as a compliance judgment index; when the compliance judgment index is greater than the preset compliance adjustment threshold, the current patient rehabilitation training is recorded as action adjustment; When the cumulative intensity of all load segments is not greater than the preset fatigue threshold, the current patient rehabilitation training is recorded as intensity adjustment; otherwise, the current patient rehabilitation training is recorded as frequency adjustment.

10. A health information management method for rehabilitation training according to claim 1, characterized in that: The method for acquiring the motion segments includes: dividing the motion performance data in time series into different motion segments by trajectory segmentation.

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