Method for evaluating spinal stability of a seated human body in a vibrating environment, and related device

Abstract

This invention provides a method and apparatus for evaluating the stability of the human spine in a seated position under vibration conditions. The method includes: acquiring current human spinal posture data; performing curve calculation on the human spinal posture data to obtain a corresponding score; and generating a corresponding human spine report based on the score. By analyzing and calculating the curves corresponding to the human spinal posture data, a corresponding human spine report can be generated by combining the correlation indicators between the trunk and pelvis. The seated human spine is then evaluated based on this report. This method enables the calculation and assessment of the stability of the human spine in a seated position under vibration conditions, thereby improving the efficiency and accuracy of detecting human spinal movement patterns and data.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection, and in particular to a method, device, electronic device and storage medium for evaluating the stability of the human spine in a seated position under vibration. Background Technology

[0002] In modern office environments, prolonged sitting and poor posture have become major contributing factors to spinal problems. Current technologies primarily monitor posture through physical devices (such as pressure sensors and accelerometers) or software (such as visual recognition systems). While these technologies can provide immediate feedback on posture and some corrective suggestions, most do not deeply analyze the relationship between posture data and long-term spinal health, and often rely on expensive hardware or complex operation, making them difficult to widely adopt.

[0003] Because it is currently impossible to obtain current human spinal posture data, perform curve calculations on this data to obtain a score related to posture quality, and generate a corresponding human spine report based on the score, the existing human spine evaluation methods have the problem of not being able to obtain current human spinal posture data, perform curve calculations on this data to obtain a score related to posture quality, and generate a corresponding human spine report based on the score. Summary of the Invention

[0004] This invention provides a method for evaluating the stability of the human spine in a sitting posture under vibration conditions, in order to solve the problem that existing human spine evaluation methods cannot obtain current human spine sitting posture data, perform curve calculations on these data to obtain a score related to sitting posture quality, and generate a corresponding human spine report based on the score.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating the stability of the human spine in a seated position under vibration conditions, the method comprising the following steps:

[0006] Obtain current human spinal posture data;

[0007] Curve calculations were performed on the human spine sitting posture data to obtain the corresponding scores;

[0008] Based on the score, a corresponding human spine report is generated.

[0009] Optionally, the current human spinal posture data includes first thoracic vertebrae posture data and pelvic posture data, and obtaining the current human spinal posture data includes:

[0010] The sitting posture data of the first thoracic vertebra was sampled and detected using a triaxial vibration test bench to obtain the first vibration response curve data, the first phase curve data, the first amplitude curve data, and the first slope curve data.

[0011] The pelvic posture data were sampled and detected using a triaxial vibration test bench to obtain the corresponding second vibration response curve data, second phase curve data, second amplitude curve data, and second slope curve data.

[0012] Optionally, the step of performing curve calculation on the human spinal posture data to obtain the corresponding score includes:

[0013] The channel score is obtained by calculating the first vibration response curve data and the second vibration response curve data using a preset correlation algorithm.

[0014] A phase score is obtained by calculating the first phase curve data and the second phase curve data using a preset error algorithm.

[0015] The amplitude scores are obtained by calculating the first amplitude curve data and the second amplitude curve data using a preset error algorithm.

[0016] The slope score is obtained by calculating the first slope curve data and the second slope curve data using a preset error algorithm.

[0017] Optionally, the step of calculating the channel score by using a preset correlation algorithm on the first vibration response curve data and the second vibration response curve data includes:

[0018] By using a preset correlation algorithm, the maximum absolute amplitude between the first vibration response curve data and the second vibration response curve data is calculated to obtain channel error data;

[0019] Based on the channel error data, the channel range between the first vibration response curve and the second vibration response curve is determined;

[0020] Based on the channel range, the channel scores corresponding to the first vibration response curve data and the second vibration response curve data are determined.

[0021] Optionally, the step of calculating the phase score by using a preset error algorithm on the first phase curve data and the second phase curve data includes:

[0022] Determine the maximum time displacement limit value of the first vibration response curve data;

[0023] Based on the maximum time displacement limit value, determine the maximum cross-correlation coefficient;

[0024] Phase scores are obtained by calculating the first and second phase curve data using regression analysis and the maximum cross-correlation coefficient.

[0025] Optionally, the step of calculating the amplitude score by using a preset error algorithm on the first amplitude curve data and the second amplitude curve data includes:

[0026] The first vibration response curve and the second vibration response curve are transformed into a time series using a dynamic time warping algorithm to determine the similarity between them.

[0027] Based on the similarity, the amplitude scores corresponding to the first amplitude curve data and the second amplitude curve data are determined.

[0028] Optionally, the step of calculating the slope score by using a preset error algorithm on the first slope curve data and the second slope curve data includes:

[0029] The first vibration response curve and the second vibration response curve are divided into multiple time intervals to obtain multiple slope interval curve data.

[0030] Differentiate the curve data of the multiple slope intervals to obtain the slope values ​​corresponding to the multiple intervals;

[0031] The slope values ​​corresponding to the multiple intervals are averaged to determine the slope scores corresponding to the first slope curve data and the second slope curve data.

[0032] Secondly, embodiments of the present invention also provide a device for evaluating the stability of a seated human spine under vibration, the device comprising:

[0033] The acquisition module is used to acquire current human spinal posture data.

[0034] The calculation module is used to perform curve calculations on the human spine sitting posture data to obtain the corresponding score;

[0035] The generation module is used to generate a corresponding human spine report based on the score.

[0036] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the method for evaluating the stability of the human spine in a vibration environment provided by embodiments of the present invention.

[0037] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the method for evaluating the stability of the human spine in a seated position under vibration provided in the embodiments of the present invention.

[0038] In this embodiment of the invention, current human spinal posture data is acquired; curve calculations are performed on the human spinal posture data to obtain a corresponding score; and a corresponding human spinal report is generated based on the score. By analyzing and calculating the curves corresponding to the human spinal posture data, a corresponding human spinal report can be generated by combining the correlation indicators between the trunk and pelvis. The report evaluates the human spine in a sitting posture. By acquiring current human spinal posture data and performing curve calculations on this data, a score related to posture quality is obtained, providing users with more scientific and targeted health advice. Furthermore, the generation of a corresponding human spinal report based on the obtained score allows for the calculation and evaluation of the stability of the human spine in a sitting posture under vibration conditions, thereby improving the efficiency and accuracy of detecting human spinal movement patterns and data. Attached Figure Description

[0039] To more clearly illustrate the technical solutions 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.

[0040] Figure 1 This is a flowchart of a method for evaluating the stability of the human spine in a sitting position under vibration, provided by an embodiment of the present invention.

[0041] Figure 2 This is a schematic diagram of the structure of a sitting human spinal stability evaluation device under vibration environment provided in an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0045] like Figure 1 As shown, Figure 1 This is a flowchart of a method for evaluating the stability of a seated human spine under vibration conditions, provided by an embodiment of the present invention. The method includes the following steps:

[0046] S101. Obtain current human spinal posture data.

[0047] In this embodiment of the invention, the above-mentioned method for evaluating the stability of the human spine in a sitting position under vibration can be applied to a device for evaluating the stability of the human spine in a sitting position under vibration. The device for evaluating the stability of the human spine in a sitting position under vibration has functions such as processing, transmitting and receiving, and storing human spine data in a sitting position. It can be built based on a server or server cluster. The server or server cluster can be an electronic device with the ability to process human spine data in a sitting position.

[0048] The aforementioned current human spinal posture data may include, but is not limited to, sitting posture data of the first thoracic vertebra and sitting posture data of the pelvis. Specifically, it may be dynamic response data of the human spine under specific vibration environments obtained through a triaxial vibration test bench. The aforementioned dynamic response data can cover various physical responses of the human spine under vibration conditions, such as vibration amplitude and phase changes, providing parameter conditions for assessing the stability of the human spine.

[0049] S102. Perform curve calculation on the human spine sitting posture data to obtain the corresponding score.

[0050] The curve calculation described above is a process of converting the aforementioned human spinal posture data into quantifiable indicators. Generally speaking, it can be viewed as a process of extracting information from data. Specifically, the curve calculation may include, but is not limited to, calculating the maximum absolute amplitude between two signal curves through correlation fitting, analyzing the error of specific curves (such as phase curves, amplitude curves, and slope curves), and thereby calculating channel scores, phase scores, amplitude scores, and slope scores.

[0051] In one possible embodiment, curve calculations are performed on the collected human spinal posture data, and the vibration responses corresponding to the first thoracic vertebra and pelvis are compared. Correlation algorithms and error algorithms are used for analysis and corresponding scores are calculated.

[0052] S103. Based on the score, generate the corresponding human spine report.

[0053] In one possible embodiment, the system automatically generates a comprehensive human spine report based on the scores obtained through curve calculation (channel score, phase score, amplitude score, and slope score). This report provides a detailed analysis of the subject's spinal stability under specific vibration conditions, including but not limited to the specific numerical values ​​of each score, explanations of the impact of these scores on spinal stability, and specific recommendations and preventative measures based on the score results. For example, if the phase score is low, the report may recommend possible posture adjustments or further medical evaluation. Through this detailed report, medical professionals can obtain key health indicators, thereby more effectively diagnosing, monitoring, and developing targeted treatment plans, particularly applicable to the fields of spinal health management and rehabilitation, enhancing the effectiveness of individual health management and preventative measures.

[0054] In this embodiment of the invention, current human spinal posture data is acquired; curve calculations are performed on the human spinal posture data to obtain a corresponding score; and a corresponding human spinal report is generated based on the score. By analyzing and calculating the curves corresponding to the human spinal posture data, a corresponding human spinal report can be generated by combining the correlation indicators between the trunk and pelvis. The report evaluates the human spine in a sitting posture. By acquiring current human spinal posture data and performing curve calculations on this data, a score related to posture quality is obtained, providing users with more scientific and targeted health advice. Based on the obtained score, a corresponding human spinal report is generated. This allows for the calculation and evaluation of the stability of the human spine in a sitting posture under vibration conditions, thereby improving the efficiency and accuracy of detecting human spinal movement patterns and data.

[0055] Optionally, in step S101, the step of obtaining the current human spinal posture data further includes steps S1011-S1012, wherein:

[0056] S1011. Using a triaxial vibration test bench, the sitting posture data of the first thoracic vertebra is sampled and detected to obtain the first vibration response curve data, the first phase curve data, the first amplitude curve data, and the first slope curve data.

[0057] In this embodiment of the invention, the aforementioned first vibration response curve data can be the instantaneous response data of the first thoracic vertebra under vibration input. It can generally be displayed as a curve in an image, which shows the overall trend of vibration over time. This can help identify the sensitivity of the spine to vibration at a specific frequency.

[0058] The aforementioned first phase curve data may describe the phase difference between the vibration of the first thoracic vertebra and the input vibration signal. This phase difference data can be used to analyze the synchronicity or delay of the dynamic behavior of the spine.

[0059] The first amplitude curve data mentioned above can record the changes in vibration amplitude, providing information on the maximum and minimum values ​​of vibration amplitude of the first thoracic vertebra, and is a key indicator for judging spinal stability and bearing capacity.

[0060] The first slope curve data mentioned above reflects the rate of change of the vibration response curve. By analyzing the slope, we can understand the response speed and trend of the first thoracic vertebra under different vibration conditions.

[0061] S1012. Using a triaxial vibration test bench, sample and test the pelvic sitting posture data to obtain the corresponding second vibration response curve data, second phase curve data, second amplitude curve data, and second slope curve data.

[0062] The aforementioned second vibration response curve data can record the instantaneous dynamic response of the pelvis when subjected to vibration input. It can also generally be displayed as a curve in an image, which can be used to show how the vibration signal changes over time.

[0063] The aforementioned second phase curve data can be curve data describing the phase relationship between pelvic vibration and excitation vibration. This curve data can be used to evaluate the dynamic synchronization or phase delay of the pelvis.

[0064] The second amplitude curve data mentioned above can be used to determine the detailed record of vibration amplitude, such as the maximum and minimum amplitude of the pelvic response.

[0065] The data from the second slope curve mentioned above can be used to analyze the vibration response speed and its changing trend, showing the rapid change region of the pelvic vibration response.

[0066] In one possible embodiment, a triaxial vibration test bench is used to sample and detect the sitting posture data of the first thoracic vertebra. Specifically, by controlling the vibration input of the test bench and collecting data from a sensor fixed at the position of the first thoracic vertebra, the first vibration response curve data, the first phase curve data, the first amplitude curve data, and the first slope curve data are obtained.

[0067] In another possible embodiment, a triaxial vibration test bench is used to accurately sample and detect pelvic posture data. Specifically, vibration data of the test subject is recorded by a vibration acceleration sensor placed at the pelvic position, and second vibration response curve data, second phase curve data, second amplitude curve data, and second slope curve data are collected.

[0068] Optionally, in step S102, the step of calculating the curve of the human spine sitting posture data to obtain the corresponding score further includes steps S1021-S1024, wherein:

[0069] S1021. The channel score is obtained by calculating the first vibration response curve data and the second vibration response curve data through a preset correlation algorithm.

[0070] In this embodiment of the invention, the aforementioned preset correlation algorithm can obtain a channel score by utilizing the channel range deviation between automatically generated correlation calculation curves. Specifically, the calculation can be completed by comparing the correlation between two vibration response curves, such as calculating the maximum absolute amplitude difference between them, as well as the degree of matching between phase and frequency.

[0071] In one possible embodiment, by inputting the data from two curves into a preset correlation algorithm, the algorithm analyzes the similarity and difference between the two data streams and outputs a value that reflects the relationship between the two test points in terms of vibration response. This value is called the "channel score," which is an indicator that quantifies the relationship between the two data points. The higher the score, the more consistent the vibration response of the two test points.

[0072] S1022. The first phase curve data and the second phase curve data are calculated using a preset error algorithm to obtain the phase score;

[0073] S1023. The amplitude scores are obtained by calculating the first amplitude curve data and the second amplitude curve data using a preset error algorithm.

[0074] S1024. Calculate the slope score by using a preset error algorithm on the first slope curve data and the second slope curve data.

[0075] The aforementioned preset error algorithm can analyze the characteristics of a specific curve to obtain scores for phase, amplitude, and slope, and finally calculate a comprehensive correlation score.

[0076] In this embodiment, the phase curves, amplitude curves, and slope curves of the first thoracic vertebra (T1) and the pelvis are analyzed using a preset error algorithm to calculate phase scores, amplitude scores, and slope scores. Specifically, the algorithm evaluates the time alignment of the phase curve data to obtain the phase score, compares the similarity of vibration intensity corresponding to the amplitude curve data of the two to calculate the amplitude score, and analyzes the slope curve data of the two to compare the consistency of the rate of change to calculate the slope score.

[0077] Optionally, in step S1021, the step of calculating the channel score by using a preset correlation algorithm on the first vibration response curve data and the second vibration response curve data further includes steps S10211-S10213, wherein:

[0078] S10211. Using a preset correlation algorithm, the maximum absolute amplitude between the first vibration response curve data and the second vibration response curve data is calculated to obtain channel error data.

[0079] S10212. Based on the channel error data, determine the channel range between the first vibration response curve and the second vibration response curve;

[0080] S10213. Based on the channel range, determine the channel scores corresponding to the first vibration response curve data and the second vibration response curve data.

[0081] In this embodiment of the invention, the aforementioned channel error data may be the value of the maximum absolute amplitude difference between two vibration response curves, used to measure the maximum difference between the two data points in vibration response.

[0082] The aforementioned channel range can be determined based on channel error data, representing the maximum and minimum difference intervals of the vibration response curves of the first thoracic vertebra and pelvis within the dynamic range.

[0083] The channel score mentioned above can be a score calculated based on the channel range, used to quantify the consistency between the first thoracic vertebra and the pelvis under given conditions. Generally speaking, the higher the channel score, the more consistent the responses between the two, indicating that the spine behaves more stably under these conditions.

[0084] In one possible embodiment, first vibration response curve data and second vibration response curve data with the same time starting point can be selected within the same sampling interval. The maximum absolute amplitude of the first vibration response curve data and the second vibration response curve data can be calculated using the aforementioned preset correlation algorithm to obtain the range of the inner and outer channels. Finally, the channel score is calculated using the range of the inner and outer channels. Specifically, it can be calculated using the following formula:

[0085]

[0086] Define parameters and

[0087] Absolute half-width inside the passage:

[0088] Absolute half-width outside the passage:

[0089] Finally, the total score for the channel is calculated using the curve:

[0090]

[0091] in, The absolute maximum amplitude T of the reference signal is represented. These represent the relative half-widths of the inner and outer corridors, respectively. The absolute half width of the inner and outer corridors. Let t be the channel score at time t. , Here are the T1 and pelvic signal curves at time t. To calculate the exponential coefficients for the scores of the inner and outer corridors, Let N be the starting time, and N be the total number of samples at the starting time. Let Z be the channel score at time t, and Z be the final total channel score. The channel scores corresponding to the first vibration response curve data and the second vibration response curve data can be calculated using the above formula.

[0092] In this embodiment, by calculating channel error data, channel range, and channel score, the dynamic response of different parts of the spine under the same vibration conditions can be accurately analyzed and compared, thereby providing scientific quantitative indicators for spinal stability assessment.

[0093] Optionally, in step S1022, the step of calculating the phase score by using a preset error algorithm on the first phase curve data and the second phase curve data further includes steps S10221-S10223, wherein:

[0094] S10221. Determine the maximum time displacement limit value of the first vibration response curve data;

[0095] S10222. Determine the maximum cross-correlation coefficient based on the maximum time displacement limit value;

[0096] S10223. Using regression analysis and the maximum cross-correlation coefficient, the phase scores are calculated on the first phase curve data and the second phase curve data.

[0097] In this embodiment of the invention, the maximum time displacement limit is the maximum time offset observed when comparing two phase curves, which can be used to define the maximum acceptable difference in signal synchronization.

[0098] The maximum cross-correlation coefficient mentioned above is a statistic that measures the similarity between two time series and is used to quantify the phase consistency of two vibration response curves.

[0099] The regression analysis method described above can be used to establish a statistical process for the relationship between one or more independent variables and the dependent variable. Here, it is applied to determine the optimal phase consistency and calculate the phase score accordingly.

[0100] In one possible embodiment, the phase curve data is processed by determining the maximum time displacement limit value of the first vibration response curve data, calculating the maximum cross-correlation coefficient based on the limit value, and obtaining the phase score by using regression analysis and the obtained maximum cross-correlation coefficient.

[0101] Specifically, the maximum permissible percentage of time displacement in the first vibration response curve data is determined. The maximum time displacement limit is reached by adjusting the left and right displacements of the first vibration response curve data, and the maximum cross-correlation coefficient is calculated. The phase error is calculated, and the displacement intercept time history curves of the first thoracic vertebra and pelvis curves are obtained. Finally, the phase fraction is obtained through regression analysis. The maximum time displacement limit can be calculated using the following formula:

[0102] Based on the translation curve derived from the maximum displacement, the correlation coefficients (i.e., cross-correlation coefficients) of the left and right displacements are calculated respectively:

[0103] Shifting left, the cross-correlation coefficient between T (T1 curve) and P (pelvic curve):

[0104]

[0105] Right shift, cross-correlation coefficient between T and P:

[0106]

[0107] Maximum cross-correlation number: ;

[0108] Phase error: If the optimal phase fraction is "1", then the maximum cross-correlation of the initial curve can be achieved without shifting the curve. If time shift... Equal to or greater than the maximum permissible time shift limit If the phase fraction is 0, then the phase fraction is "0".

[0109] Phase score:

[0110]

[0111] in, This represents the maximum translation displacement, where m represents the time step of the translation. This represents the left and right cross-correlation values ​​of the T-curve, where n is the number of curve samples. For time displacement and truncation nodes, The time interval between adjacent data points. It is the mean of the T and P curves. For the start time, End time, This represents the maximum cross-correlation value when moving left or right. The phase error after translation, This represents the calculated phase score. The exponential factor for calculating the phase score is N, where N is the total number of samples between the start and end times. The phase score can be obtained using the above formula.

[0112] Optionally, in step S1023, the step of calculating the amplitude scores by using a preset error algorithm on the first amplitude curve data and the second amplitude curve data further includes steps S10231-S10232, wherein:

[0113] S10231. Using a dynamic time warping algorithm, perform time series transformation on the first vibration response curve and the second vibration response curve to determine the similarity between the first vibration response curve and the second vibration response curve.

[0114] S10232. Based on similarity, determine the amplitude scores corresponding to the first amplitude curve data and the second amplitude curve data.

[0115] In this embodiment of the invention, the above-mentioned dynamic time warping algorithm can be any time series analysis method. By dynamically adjusting the lengths of two time series, the optimal alignment is found, thereby calculating the minimum distance between the two to measure the similarity between the time series.

[0116] The aforementioned time series transformation can refer to adjusting the speed or time axis of a time series using the DTW algorithm, so that two time series are aligned on the time axis, thereby allowing comparison of their similarities or differences.

[0117] The aforementioned similarity can be the degree of matching between the two vibration response curves in terms of time and morphology after analysis using the DTW algorithm.

[0118] The amplitude score mentioned above can be used to calculate the similarity using the DTW algorithm. This score measures the degree of matching between the two curves in terms of amplitude.

[0119] In one possible embodiment, the vibration response curves of the first thoracic vertebra (T1) and the pelvis are transformed into a time series using the Dynamic Time Warping (DTW) algorithm to determine the similarity between the two. The DTW algorithm is used to adjust the scaling of the time series to obtain the best matching time point of the two curves and calculate the minimum distance between them. Based on this similarity, the amplitude scores corresponding to the first amplitude curve data and the second amplitude curve data are further calculated.

[0120] Specifically, we can first establish a rebalancing path index matrix, and use the matrix to record the optimal rebalancing path index:

[0121] A new curve is obtained by using the translational truncation curves of T1 and the pelvis corresponding to the first and second columns of the w matrix, respectively. Calculate the amplitude error:

[0122] The final amplitude score is calculated as follows:

[0123]

[0124]

[0125] Where w is the optimal resetting path matrix, satisfying three constraints:

[0126]

[0127] For the minimum regular path, This means that the new amplitude curve is obtained by calculating the first and second columns of the w matrix. For amplitude error, To calculate the exponential factor of the amplitude error, For the maximum allowable amplitude error, This is the final amplitude score, which can be calculated using the above formula.

[0128] Optionally, in step S1024, the step of calculating the slope score by using a preset error algorithm on the first slope curve data and the second slope curve data further includes steps S10241-S10243, wherein:

[0129] S10241. Divide the first vibration response curve and the second vibration response curve into multiple time intervals to obtain multiple slope interval curve data.

[0130] S10242. Differentiate the curve data for multiple slope intervals to obtain the slope values ​​corresponding to multiple intervals;

[0131] S10243. Calculate the average of the slope values ​​corresponding to multiple intervals to determine the slope score corresponding to the first slope curve data and the second slope curve data.

[0132] In this embodiment of the invention, the slope interval curve data can be the slope information of the curve obtained by differentiation in each time interval, which characterizes the rate of change of vibration response in that interval.

[0133] The slope value mentioned above can be the specific value of the slope interval curve within each time interval, reflecting the speed and direction of the curve change in that interval.

[0134] The slope score mentioned above can be a score derived from the averaging of slope values ​​or other statistical processing, used to quantify the stability performance of the spine throughout the vibration process.

[0135] In one possible embodiment, a time series transformation is performed using a dynamic time warping algorithm to divide the first vibration response curve and the second vibration response curve into multiple time intervals. The slope of each interval is calculated, and then the derivative of the slope of each interval is calculated and averaged. The average obtained is used as the slope score corresponding to the data of the first and second vibration response curves.

[0136] Specifically, the slope score mentioned above can be based on the displacement truncation time history curves of T1 and the pelvic curve, divided into multiple intervals with a fixed length of 10 data points (if the total number of data points in the entire signal is not a multiple of 10, the remaining data points are used as the last interval), and the average slope of each interval is calculated to obtain the slope curves of T1 and the pelvic curve. The slope error is obtained using the following formula:

[0137] The slope score is finally calculated:

[0138]

[0139] in, These represent the slope curves obtained by calculating the average slope of each interval. The slope error is calculated using the slope curve. To calculate the exponential factor of the slope fraction, To the maximum permissible slope error, Score is given based on the slope.

[0140] In another possible embodiment, a comprehensive correlation score is calculated by weighting the channel, phase, amplitude, and slope correlation scores with different weighting coefficients, and a corresponding human spine report is generated based on the comprehensive correlation score. Specifically, the range (channel) correlation between curves is calculated according to the correlation algorithm, and the phase, amplitude, and slope correlation between curves are compared by the error algorithm. Finally, the comprehensive correlation score R value (R=0-1) is calculated by weighting the channel, phase, amplitude, and slope correlation scores with weighting coefficients of 0.4, 0.2, 0.2, and 0.2. The larger the value, the higher the stability.

[0141]

[0142] in, These represent the channel, phase, amplitude, and slope fraction, respectively.

[0143] like Figure 2 As shown, this embodiment of the invention also provides a sitting human spinal stability evaluation device 200 under vibration environment, which includes:

[0144] Module 201 is used to acquire current human spinal posture data;

[0145] Calculation module 202 is used to perform curve calculation on the human spine sitting posture data to obtain the corresponding score;

[0146] The generation module 203 is used to generate a corresponding human spine report based on the score.

[0147] Optionally, the acquisition module 201 mentioned above includes:

[0148] The first sampling module is used to sample and detect the sitting posture data of the first thoracic vertebra through a triaxial vibration test bench, and obtain the first vibration response curve data, the first phase curve data, the first amplitude curve data, and the first slope curve data.

[0149] The second sampling module is used to sample and detect the pelvic posture data through a triaxial vibration test bench to obtain the corresponding second vibration response curve data, second phase curve data, second amplitude curve data, and second slope curve data.

[0150] Optionally, the above-mentioned calculation module 202 includes:

[0151] The first molecular module is used to calculate the channel score by using a preset correlation algorithm on the first vibration response curve data and the second vibration response curve data.

[0152] The second molecular module is used to calculate the phase score by using a preset error algorithm on the first phase curve data and the second phase curve data.

[0153] The third molecule module is used to calculate the amplitude score by using a preset error algorithm on the first amplitude curve data and the second amplitude curve data.

[0154] The fourth molecular module is used to calculate the slope score by using a preset error algorithm on the first slope curve data and the second slope curve data.

[0155] Optionally, the first molecular module mentioned above includes:

[0156] The first calculation unit is used to calculate the maximum absolute amplitude between the first vibration response curve data and the second vibration response curve data using a preset correlation algorithm to obtain channel error data.

[0157] The first determining unit is used to determine the channel range between the first vibration response curve and the second vibration response curve based on the channel error data.

[0158] The second determining unit is used to determine the channel score corresponding to the first vibration response curve data and the second vibration response curve data based on the channel range.

[0159] Optionally, the second molecular module mentioned above includes:

[0160] The third determining unit is used to determine the maximum time displacement limit value of the first vibration response curve data;

[0161] The fourth determining unit is used to determine the maximum cross-correlation coefficient based on the maximum time displacement limit value;

[0162] The second calculation unit is used to calculate the phase score from the first phase curve data and the second phase curve data using regression analysis and the maximum cross-correlation coefficient.

[0163] Optionally, the third molecular module mentioned above includes:

[0164] The fifth determining unit is used to perform time series transformation on the first vibration response curve and the second vibration response curve using a dynamic time warping algorithm to determine the similarity between the first vibration response curve and the second vibration response curve.

[0165] The sixth determining unit is used to determine the amplitude scores corresponding to the first amplitude curve data and the second amplitude curve data based on the similarity.

[0166] Optionally, the fourth molecular module mentioned above includes:

[0167] The first acquisition unit is used to divide the first vibration response curve and the second vibration response curve into multiple time intervals to obtain multiple slope interval curve data.

[0168] The second acquisition unit is used to differentiate the multiple slope interval curve data to obtain the slope values ​​corresponding to the multiple intervals.

[0169] The seventh determining unit is used to calculate the average of the slope values ​​corresponding to the multiple intervals to determine the slope scores corresponding to the first slope curve data and the second slope curve data.

[0170] like Figure 3 As shown, this embodiment of the invention also provides an electronic device 300, including a processor, which can execute any of the above-mentioned methods for evaluating the stability of the human spine in a seated position under vibration conditions.

[0171] Specifically, it includes a processor 301 and a memory 302, as well as a computer program stored in the memory 302 and capable of running on the processor 301, which executes a method for evaluating the stability of the human spine in a seated position under vibration conditions, wherein:

[0172] The processor 301 executes the calculator program stored in memory 302, which is a method for evaluating the stability of the human spine in a seated position under vibration conditions, and performs the following steps:

[0173] Obtain current human spinal posture data;

[0174] Curve calculations were performed on the human spine sitting posture data to obtain the corresponding scores;

[0175] Based on the score, a corresponding human spine report is generated.

[0176] Optionally, the processor 301 executes the current human spinal posture data, including the first thoracic vertebrae posture data and the pelvic posture data. The process of obtaining the current human spinal posture data includes:

[0177] The sitting posture data of the first thoracic vertebra was sampled and detected using a triaxial vibration test bench to obtain the first vibration response curve data, the first phase curve data, the first amplitude curve data, and the first slope curve data.

[0178] The pelvic posture data were sampled and detected using a triaxial vibration test bench to obtain the corresponding second vibration response curve data, second phase curve data, second amplitude curve data, and second slope curve data.

[0179] Optionally, the processor 301 performs curve calculation on the human spine sitting posture data to obtain a corresponding score, including:

[0180] The channel score is obtained by calculating the first vibration response curve data and the second vibration response curve data using a preset correlation algorithm.

[0181] A phase score is obtained by calculating the first phase curve data and the second phase curve data using a preset error algorithm.

[0182] The amplitude scores are obtained by calculating the first amplitude curve data and the second amplitude curve data using a preset error algorithm.

[0183] The slope score is obtained by calculating the first slope curve data and the second slope curve data using a preset error algorithm.

[0184] Optionally, the processor 301 executes the calculation of the first vibration response curve data and the second vibration response curve data using a preset correlation algorithm to obtain a channel score, including:

[0185] By using a preset correlation algorithm, the maximum absolute amplitude between the first vibration response curve data and the second vibration response curve data is calculated to obtain channel error data;

[0186] Based on the channel error data, the channel range between the first vibration response curve and the second vibration response curve is determined;

[0187] Based on the channel range, the channel scores corresponding to the first vibration response curve data and the second vibration response curve data are determined.

[0188] Optionally, the processor 301 executes the calculation of the first phase curve data and the second phase curve data using a preset error algorithm to obtain a phase score, including:

[0189] Determine the maximum time displacement limit value of the first vibration response curve data;

[0190] Based on the maximum time displacement limit value, determine the maximum cross-correlation coefficient;

[0191] Phase scores are obtained by calculating the first and second phase curve data using regression analysis and the maximum cross-correlation coefficient.

[0192] Optionally, the processor 301 further performs the calculation of the first amplitude curve data and the second amplitude curve data using a preset error algorithm to obtain amplitude scores, including:

[0193] The first vibration response curve and the second vibration response curve are transformed into a time series using a dynamic time warping algorithm to determine the similarity between them.

[0194] Based on the similarity, the amplitude scores corresponding to the first amplitude curve data and the second amplitude curve data are determined.

[0195] The optional processor 301 further performs the calculation of the first slope curve data and the second slope curve data using a preset error algorithm to obtain a slope score, including:

[0196] The first vibration response curve and the second vibration response curve are divided into multiple time intervals to obtain multiple slope interval curve data.

[0197] Differentiate the curve data of the multiple slope intervals to obtain the slope values ​​corresponding to the multiple intervals;

[0198] The slope values ​​corresponding to the multiple intervals are averaged to determine the slope scores corresponding to the first slope curve data and the second slope curve data.

[0199] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the method for evaluating the stability of the human spine in a seated position under vibration or the method for evaluating the stability of the human spine in a seated position under vibration provided in this invention, and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0200] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware, and can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0201] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for evaluating the stability of the human spine in a seated position under vibration conditions, characterized in that, include: Acquire current human spinal sitting posture data, which includes sitting posture data of the first thoracic vertebra and sitting posture data of the pelvis. Curve calculations were performed on the human spine sitting posture data to obtain the corresponding scores; Based on the score, a corresponding human spine report is generated; The acquisition of current human spinal posture data includes: The sitting posture data of the first thoracic vertebra was sampled and detected using a triaxial vibration test bench to obtain the first vibration response curve data, the first phase curve data, the first amplitude curve data, and the first slope curve data. The pelvic posture data were sampled and detected using a triaxial vibration test bench to obtain the corresponding second vibration response curve data, second phase curve data, second amplitude curve data, and second slope curve data. The process of calculating a curve from the human spine sitting posture data to obtain a corresponding score includes: The first vibration response curve data and the second vibration response curve data are calculated using a preset correlation algorithm. The preset correlation algorithm uses the channel range deviation between the generated correlation calculation curves to obtain the channel score. The channel range represents the maximum and minimum difference interval between the vibration response curves of the first thoracic vertebra and pelvis within the dynamic range. The first phase curve data and the second phase curve data are calculated using a preset error algorithm. The phase score is obtained by evaluating the time alignment of the phase curve data using the algorithm. The first amplitude curve data and the second amplitude curve data are calculated using a preset error algorithm, and the amplitude score is obtained by comparing the vibration intensity similarity corresponding to the amplitude curve data. The first slope curve data and the second slope curve data are calculated using a preset error algorithm. The slope curve data are analyzed, and the consistency of the rate of change is compared to obtain the slope score.

2. The method for evaluating the stability of the human spine in a seated position under vibration environment as described in claim 1, characterized in that, The process involves calculating channel scores from the first and second vibration response curve data using a preset correlation algorithm, including: By using a preset correlation algorithm, the maximum absolute amplitude between the first vibration response curve data and the second vibration response curve data is calculated to obtain channel error data; Based on the channel error data, the channel range between the first vibration response curve and the second vibration response curve is determined; Based on the channel range, the channel scores corresponding to the first vibration response curve data and the second vibration response curve data are determined.

3. The method for evaluating the stability of the human spine in a seated position under vibration environment as described in claim 1, characterized in that, The step of calculating the phase score by using a preset error algorithm on the first phase curve data and the second phase curve data includes: Determine the maximum time displacement limit value of the first vibration response curve data; Based on the maximum time displacement limit value, determine the maximum cross-correlation coefficient; Phase scores are obtained by calculating the first and second phase curve data using regression analysis and the maximum cross-correlation coefficient.

4. The method for evaluating the stability of the human spine in a seated position under vibration environment as described in claim 1, characterized in that, The step of calculating the amplitude score by using a preset error algorithm on the first amplitude curve data and the second amplitude curve data includes: The first vibration response curve and the second vibration response curve are transformed into a time series using a dynamic time warping algorithm to determine the similarity between them. Based on the similarity, the amplitude scores corresponding to the first amplitude curve data and the second amplitude curve data are determined.

5. The method for evaluating the stability of the human spine in a seated position under vibration environment as described in claim 1, characterized in that, The step of calculating the slope score by using a preset error algorithm on the first slope curve data and the second slope curve data includes: The first vibration response curve and the second vibration response curve are divided into multiple time intervals to obtain multiple slope interval curve data. Differentiate the curve data of the multiple slope intervals to obtain the slope values ​​corresponding to the multiple intervals; The slope values ​​corresponding to the multiple intervals are averaged to determine the slope scores corresponding to the first slope curve data and the second slope curve data.

6. A device for evaluating the stability of a seated human spine under vibration, used to implement the method for evaluating the stability of a seated human spine under vibration as described in claim 1, characterized in that, include: The acquisition module is used to acquire current human spinal posture data. The calculation module is used to perform curve calculations on the human spine sitting posture data to obtain the corresponding score; The generation module is used to generate a corresponding human spine report based on the score.

7. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps in the method for evaluating the stability of the human spine in a seated position under vibration as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps in the method for evaluating the stability of the human spine in a seated position under vibration conditions as described in any one of claims 1 to 5.

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