A motion quality evaluation method and system based on edge computing

Abstract

The present invention relates to the technical field of rehabilitation exercise quality evaluation, and specifically to an edge computing-based exercise quality evaluation method and system. The method of the present invention obtains the rehabilitation training regularity and rehabilitation training intensity of the rehabilitation personnel by analyzing the distribution characteristics of the training date and the distribution characteristics of the heart rate data in the historical training records; then, according to the rehabilitation training regularity and the rehabilitation training intensity, the rehabilitation training recovery stage level of the rehabilitation personnel is determined; then, the rehabilitation personnel's training data is recorded and analyzed at the edge end, and the rehabilitation personnel's exercise quality is evaluated based on the rehabilitation training recovery stage level. Through this method, the rehabilitation personnel can perform appropriate exercise quality evaluation at a reasonable rehabilitation training recovery stage level, avoiding the uneven evaluation quality problem caused by the same evaluation standard used for all rehabilitation personnel, and better providing more accurate and detailed exercise guidance for each rehabilitation personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation exercise quality evaluation, and in particular to an exercise quality evaluation method and system based on edge computing. Background Art

[0002] With the development of edge computing technology and the advancement of sports science, sports quality evaluation based on edge computing is gradually becoming a hot topic in research and application. Traditional sports quality evaluation methods usually rely on centralized data collection and processing, which limits their performance in real-time and flexibility. In sports quality evaluation based on edge computing, athletes' sports data, such as posture, speed, power output, etc., can be collected and analyzed in real time without waiting for the data to be uploaded to the cloud to obtain feedback.

[0003] Modern sports rehabilitation training has been widely used in all levels of society and among all groups of people. It is no longer limited to professional athletes. Most people can use sports rehabilitation training to help restore human functions or relieve pain due to sports injuries or certain surgeries. However, the traditional method of evaluating sports quality generally evaluates and analyzes all people without distinction. However, due to the different rehabilitation needs and different stages of rehabilitation training required by different groups of people, there will be obvious differences in rehabilitation training performance. Therefore, the general evaluation criteria may not be compatible with different groups of people. For example, professional athletes usually face higher intensity training and competitive pressure, so they need more complex rehabilitation training after injury or surgery, while for people with general sports injuries or minor surgeries, they may only need basic and simple training. In this way, using a unified evaluation standard for different groups of people may make the evaluation results of different groups of people inaccurate, and may also make rehabilitation personnel feel frustrated due to excessively high evaluation standards. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a motion quality evaluation method and system based on edge computing.

[0005] According to the first aspect of the embodiment of the present application, a motion quality evaluation method based on edge computing is provided, and the technical solution adopted is as follows:

[0006] Obtain the historical training records of all rehabilitation personnel involved in rehabilitation exercises over a period of time;

[0007] Analyzing the distribution characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel;

[0008] Analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel;

[0009] Determining the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity;

[0010] The training data of the rehabilitation personnel are recorded and analyzed at the edge, and the exercise quality of the rehabilitation personnel is evaluated based on the recovery stage level of the rehabilitation training.

[0011] In some embodiments of the present invention, the time interval characteristics of the training dates in the historical training records are analyzed to obtain the rehabilitation training regularity of the rehabilitation personnel.

[0012] In some embodiments of the present invention, the maximum interval time, the minimum interval time and the average interval time between adjacent training dates in the historical training records are analyzed to obtain the rehabilitation training regularity of the rehabilitation personnel.

[0013] In some embodiments of the present invention, the period of time is divided into at least two time periods, the maximum interval time, the minimum interval time and the average interval time of adjacent training dates in the historical training records within the time period are analyzed, and all the time periods are traversed to obtain the rehabilitation training regularity of the rehabilitation personnel.

[0014] In some embodiments of the present invention, analyzing the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel includes:

[0015] Analyze the numerical size characteristics and difference characteristics of the heart rate data in the historical training records during a single rehabilitation training to obtain the single rehabilitation training intensity of the rehabilitation personnel;

[0016] Based on the single rehabilitation training intensity of the rehabilitation person, the overall rehabilitation training intensity of the rehabilitation person and the consistency between all the single rehabilitation training intensities are analyzed to obtain the rehabilitation training intensity of the rehabilitation person.

[0017] In some embodiments of the present invention, the numerical size characteristics and difference characteristics of the heart rate data in the historical training records during a single rehabilitation training are analyzed to obtain the single rehabilitation training intensity of the rehabilitation personnel, including:

[0018] Based on a single rehabilitation training of the rehabilitation personnel, traverse the heart rate data of the time series in the historical training records, filter out the maximum heart rate value, and obtain the numerical size characteristics of the heart rate data in the historical training records during the single rehabilitation training;

[0019] Determining an analysis time range based on the heart rate maximum value;

[0020] Analyze the numerical difference between the maximum heart rate value and all heart rate data within the analysis time range, and obtain the difference characteristics of the heart rate data in the historical training record during a single rehabilitation training;

[0021] The single rehabilitation training intensity of the rehabilitation person is obtained according to the numerical value feature and the difference feature.

[0022] In some embodiments of the present invention, based on the single rehabilitation training intensity of the rehabilitation personnel, analyzing the overall rehabilitation training intensity of the rehabilitation personnel and the consistency between all the single rehabilitation training intensities to obtain the rehabilitation training intensity of the rehabilitation personnel includes:

[0023] Based on the single rehabilitation training intensity of the rehabilitation person, calculating the average value of all the single rehabilitation training intensities to obtain the overall rehabilitation training intensity of the rehabilitation person;

[0024] Based on the single rehabilitation training intensity of the rehabilitation personnel, calculating the standard deviation of all the single rehabilitation training intensities, and obtaining the consistency between all the single rehabilitation training intensities of the rehabilitation personnel;

[0025] The rehabilitation training intensity of the rehabilitation person is obtained according to the overall rehabilitation training intensity and the consistency.

[0026] In some embodiments of the present invention, determining the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity includes:

[0027] According to the rehabilitation training regularity and the rehabilitation training intensity, obtaining a rehabilitation training recovery stage level value of the rehabilitation personnel;

[0028] Based on the rehabilitation training recovery stage level values ​​of all the rehabilitation personnel, the rehabilitation training recovery stage level is divided by the K-means clustering method to determine the rehabilitation training recovery stage level of the rehabilitation personnel.

[0029] According to a second aspect of an embodiment of the present application, a motion quality evaluation system based on edge computing is provided, including: a memory and a processor, wherein:

[0030] The memory is used to store program codes;

[0031] The processor is used to read the program code stored in the memory and execute the method as described in the first aspect of the embodiment of the present application.

[0032] In some embodiments of the present invention, the processor comprises:

[0033] A historical training record acquisition module is used to acquire the historical training records of all rehabilitation personnel participating in rehabilitation exercises within a period of time;

[0034] A rehabilitation training regularity analysis module is used to analyze the distribution characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel;

[0035] A rehabilitation training intensity analysis module, used to analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel;

[0036] A rehabilitation training recovery stage level determination module, used to determine the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity;

[0037] The movement quality evaluation module is used to record and analyze the training data of the rehabilitation personnel at the edge end, and evaluate the movement quality of the rehabilitation personnel based on the recovery stage level of the rehabilitation training.

[0038] Compared with the prior art, the motion quality evaluation method and system based on edge computing provided by the present invention have the following beneficial effects:

[0039] The present invention determines the rehabilitation training regularity of the rehabilitation personnel by analyzing the distribution characteristics of the training dates in the historical training records of the rehabilitation personnel, and determines the rehabilitation training intensity by analyzing the distribution characteristics of the heart rate data in the historical training records of the rehabilitation personnel, and determines the rehabilitation training recovery stage level of the rehabilitation personnel based on the rehabilitation training regularity and rehabilitation training intensity of the rehabilitation personnel, so that when evaluating the exercise quality of the rehabilitation personnel, it can be more clear which rehabilitation training recovery stage level they should be in, which is convenient for analysis. And according to the edge end where the rehabilitation personnel are located, their current training data is obtained, which improves efficiency while ensuring the security and privacy of the data; then based on the rehabilitation training recovery stage level, the rehabilitation personnel are evaluated for exercise quality, so that the rehabilitation personnel can be evaluated for appropriate exercise quality at a reasonable rehabilitation training recovery stage level, avoiding the uneven evaluation quality problem caused by the same evaluation standard used for all rehabilitation personnel, and better providing more accurate and detailed exercise guidance for each rehabilitation personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] Figure 1 A basic flow chart of a motion quality evaluation method based on edge computing provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a basic flow chart of a method for analyzing the intensity of a single rehabilitation training session of a rehabilitation person provided by an embodiment of the present invention;

[0043] Figure 3 A basic flow chart of a method for analyzing the intensity of rehabilitation training of a rehabilitation person provided by one embodiment of the present invention;

[0044] Figure 4 A schematic diagram of the basic composition of a motion quality evaluation system based on edge computing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] 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 specific implementation method, structure, features and effects of a motion quality evaluation method and system based on edge computing proposed by the present invention 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.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0047] When evaluating the exercise quality of rehabilitation personnel, all rehabilitation personnel use a unified evaluation standard and fail to fully consider the different rehabilitation needs of different groups of people and the different stages of rehabilitation training. The method of this embodiment divides rehabilitation personnel into different rehabilitation training recovery stage levels by considering their rehabilitation training regularity and rehabilitation training intensity, so as to facilitate a more reasonable exercise quality evaluation for rehabilitation personnel at different rehabilitation training recovery stage levels.

[0048] The following is a detailed description of a motion quality evaluation method based on edge computing provided by the present invention in conjunction with the accompanying drawings.

[0049] See also Figure 1 , which shows the basic process of a motion quality evaluation method based on edge computing provided by an embodiment of the present invention.

[0050] like Figure 1 As shown, an embodiment of the present invention provides a motion quality evaluation method based on edge computing, which specifically includes:

[0051] S100: Obtain historical training records of all rehabilitation personnel participating in rehabilitation exercises within a period of time.

[0052] The historical training records of all rehabilitation personnel participating in rehabilitation exercises in the past two months are obtained, wherein the historical training records include training date, training duration, heart rate data, etc. The rehabilitation personnel include professional athletes and ordinary people.

[0053] At this point, the historical training records of the rehabilitation personnel were obtained.

[0054] When rehabilitation personnel conduct daily rehabilitation training, due to the different conditions and severity of the conditions of different rehabilitation personnel, their corresponding rehabilitation exercise performances are different. For different rehabilitation personnel, the focus of the rehabilitation exercise quality evaluation is different, which is closely related to the rehabilitation training needs of the rehabilitation personnel. For example, for professional athletes, they hope to restore their own sports performance through rehabilitation exercises, so they need more professional and scientific training programs to improve their sports performance and competition results; while for ordinary people, they are more for restoring their health or maintaining better physical conditions, so they need correct movement guidance and skill explanations (not demanding absolute standards for movement postures, etc.). Whether it is an ordinary person or a professional athlete, a correct and reasonable exercise quality evaluation can help improve the recovery effect of rehabilitation exercises. Therefore, the rehabilitation training recovery stage level of different groups of people is distinguished by the training habits and training intensity of rehabilitation personnel, so as to facilitate the subsequent rehabilitation personnel to conduct correct and reasonable exercise quality evaluation in a reasonable rehabilitation training recovery stage level.

[0055] The historical training records can reflect the rehabilitation training habits and the intensity of rehabilitation training recently carried out by the rehabilitation personnel. For example, for professional athletes and ordinary people, professional athletes usually carry out more scientific, regular and hard rehabilitation training to restore or improve sports performance; while ordinary people choose the time of rehabilitation training due to pressure from work, life and other aspects, which is generally determined in combination with their own free time, and the time is relatively irregular, and the intensity of rehabilitation training is usually relatively low. Based on this, the embodiment of the present application analyzes the regularity and intensity of rehabilitation training, and the specific steps include S200 and S300.

[0056] S200: Analyze the distribution characteristics of training dates in historical training records to obtain the rehabilitation training regularity of rehabilitation personnel.

[0057] An analysis is conducted on the regularity of rehabilitation training for each rehabilitation personnel. Usually, the rehabilitation training plan of professional athletes is scientifically and regularly formulated, and the training and rest arrangements are reasonable and balanced. Therefore, the distribution characteristics of the training dates in the historical training records are analyzed to obtain the regularity of rehabilitation training for the rehabilitation personnel. Further, the interval characteristics of the training dates in the historical training records are analyzed to obtain the regularity of rehabilitation training for the rehabilitation personnel. Furthermore, the maximum interval time, the minimum interval time and the average interval time of adjacent training dates in the historical training records are analyzed to obtain the regularity of rehabilitation training for the rehabilitation personnel. Considering that the reasonable combination of training and rest days will be adjusted over time, the two months are divided into multiple time periods during the analysis. The training plans within the same period are relatively consistent, so the analysis of training in different periods is carried out in this way. Specifically, a period of time is divided into at least two time periods, and the maximum interval time, the minimum interval time and the average interval time of adjacent training dates in the historical training records within the time period are analyzed. All time periods are traversed to obtain the regularity of rehabilitation training for the rehabilitation personnel. The specific operation method is:

[0058] In step S100, the historical training records of each rehabilitation personnel in the past two months are obtained, and the training date of each rehabilitation personnel is processed, and the dates are marked with 1, 2, 3... until the last day. At the same time, based on the actual training of the rehabilitation personnel in two months, the records are extracted and the numbers corresponding to the days when the rehabilitation personnel trained in two months are obtained, so as to obtain the numbering sequence of the rehabilitation personnel's training dates.

[0059] Considering that the reasonable combination of training and rest days will be adjusted over time, the two months are divided into K time periods in the analysis, and the value of K can be 4; the training plans in the same time period are relatively consistent, so the training analysis of different time periods is carried out on this basis. Specifically, in the training of each time period, the interval time of adjacent training dates is considered (here it is presented in the form of number difference). The shorter the average interval time of adjacent training dates, the more regular the training of the rehabilitation personnel. At the same time, the minimum interval time and the maximum interval time ratio of the training dates in the current time period are also considered, so as to avoid the illusion of regular training formed by the fact that the rehabilitation personnel have a few times of not training on time in the current time period, but the overall average interval time is short.

[0060] The calculation formula of rehabilitation training regularity of rehabilitation personnel is:

[0061]

[0062] In the formula, Indicates The regularity of rehabilitation training of each rehabilitation personnel; Indicates the number of time periods; Indicates The rehabilitation personnel The first The difference between the numbers of the training dates corresponding to the training on the day and the adjacent days; Indicates The rehabilitation personnel The total number of days in the time period; express Function, take the minimum value of the data; express Function, to get the maximum value of the data.

[0063] Indicates the ratio of the minimum interval time to the maximum interval time of the training dates in the current time period. The larger the value, the smaller the difference between the minimum interval time and the maximum interval time of the training dates in the current time period, indicating that the rehabilitation training of the rehabilitation personnel is more regular, that is, the rehabilitation training regularity of the rehabilitation personnel is greater; It indicates the average interval time between adjacent training dates in the current time period. The smaller the value is, the smaller the interval time between adjacent training dates in the current time period is, indicating that the rehabilitation training of the rehabilitation personnel is more regular, that is, the greater the regularity of the rehabilitation training of the rehabilitation personnel is.

[0064] Thus, the rehabilitation training regularity of each rehabilitation personnel can be obtained. The higher the rehabilitation training regularity, the more likely the current rehabilitation personnel is to be a professional athlete.

[0065] S300: Analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel.

[0066] The rehabilitation training regularity of the rehabilitation personnel is analyzed, and the rehabilitation training intensity of each rehabilitation personnel is analyzed. The training intensity can be analyzed based on the changes in heart rate data during the training process. Whether it is for professional athletes or ordinary people, considering the differences in their medical conditions or rehabilitation training needs, the difficulty requirements for completing rehabilitation training movements are different when they are in different recovery stages during rehabilitation training. It may be that a basic movement requires a lot of energy in the early stage of rehabilitation, but it is generally relatively easy in the later stage of rehabilitation. Therefore, the distribution characteristics of the heart rate data in the historical training records are analyzed to obtain the rehabilitation training intensity of the rehabilitation personnel. Further, the numerical size characteristics and difference characteristics of the heart rate data in the historical training records during a single rehabilitation training are first analyzed to obtain the single rehabilitation training intensity of the rehabilitation personnel; then based on the single rehabilitation training intensity of the rehabilitation personnel, the overall rehabilitation training intensity of the rehabilitation personnel and the consistency between all single rehabilitation training intensities are analyzed to obtain the rehabilitation training intensity of the rehabilitation personnel.

[0067] See also Figure 2 , which shows the basic process of a single rehabilitation training intensity analysis method for rehabilitation personnel provided by an embodiment of the present invention.

[0068] like Figure 2 As shown, the numerical size characteristics and difference characteristics of the heart rate data in the historical training records during a single rehabilitation training are analyzed to obtain the single rehabilitation training intensity of the rehabilitation personnel, including:

[0069] S311: Based on a single rehabilitation training of the rehabilitation personnel, traverse the heart rate data of the time series in the historical training records, filter out the maximum heart rate value, and obtain the numerical size characteristics of the heart rate data in the historical training records during the single rehabilitation training.

[0070] First, we analyze the single rehabilitation training of the rehabilitation personnel, and traverse the heart rate data of the time series in the historical training records, where the heart rate data is in units of minutes. We select multiple heart rate maxima from the heart rate data of the time series, and if one of the heart rate data is smaller than the heart rate data before and after it in the time series, then this heart rate data is the heart rate maximum. Thus, we obtain the numerical size characteristics of the heart rate data in the historical training records during a single rehabilitation training. The larger the value of the heart rate maximum, the greater the intensity of the single rehabilitation training of the corresponding rehabilitation personnel may be.

[0071] S312: Determine the analysis time range based on the maximum heart rate value.

[0072] In the time series, a preset time range is taken around the time point corresponding to the maximum heart rate value to determine the analysis time range.

[0073] S313: Analyze the numerical difference between the maximum heart rate value and all heart rate data within the analysis time range, and obtain the difference characteristics of the heart rate data in the historical training records during a single rehabilitation training.

[0074] The value of the maximum heart rate can reflect the intensity of a single rehabilitation training of the rehabilitation personnel to a certain extent, but it may also be due to the influence of the external environment or the breathing light of the self, causing the heart rate data to suddenly increase at certain moments, but in fact the overall heart rate data is not very high; however, if the difference between the maximum heart rate value and the heart rate data within the analysis time range in which it is located is small, the illusion of a large intensity of a single rehabilitation training caused by a sudden increase in the heart rate data can be avoided. Therefore, this embodiment further analyzes the numerical difference between the maximum heart rate value and all the heart rate data within the analysis time range in which it is located, and obtains the difference characteristics of the heart rate data in the historical training records during a single rehabilitation training. The larger the maximum heart rate value and the smaller the difference in the heart rate data within the analysis time range in which it is located, the greater the intensity of a single rehabilitation training of the rehabilitation personnel.

[0075] S314: Obtaining the single rehabilitation training intensity of the rehabilitation personnel according to the numerical size feature and the difference feature.

[0076] According to the numerical size characteristics and difference characteristics, the single rehabilitation training intensity of the rehabilitation personnel is obtained. The calculation formula for the single rehabilitation training intensity is:

[0077]

[0078] In the formula, Indicates Rehabilitation staff in The intensity of a single rehabilitation training session during the first rehabilitation training; Indicates Rehabilitation staff in The number of heart rate maxima screened out during each rehabilitation training; Indicates Rehabilitation staff in The first rehabilitation training The value of the maximum heart rate; Indicates that based on Rehabilitation staff in The first rehabilitation training The number of other time points within the analysis time range determined by the heart rate maximum; Indicates that based on Rehabilitation staff in The first rehabilitation training The heart rate maximum value is determined within the analysis time range. The value of the heart rate data corresponding to a time point.

[0079] Indicates the value of the maximum heart rate. The larger the value, the greater the intensity of the rehabilitation training of the rehabilitation personnel. It represents the sum of the numerical differences between the maximum heart rate value and all the heart rate data within the analysis time range. The smaller the value is, the smaller the difference between the maximum heart rate value and the heart rate data within the analysis time range is, which means that the intensity of a single rehabilitation training for the rehabilitation personnel is greater. Therefore, the larger the maximum heart rate value is and the smaller the difference between the heart rate data within the analysis time range is, the greater the intensity of a single rehabilitation training for the corresponding rehabilitation personnel is.

[0080] In this way, the rehabilitation training intensity of each rehabilitation person during each rehabilitation training can be obtained.

[0081] Considering that rehabilitation personnel usually maintain a relatively constant intensity of training during staged rehabilitation training, the corresponding single rehabilitation training intensity of each training should be relatively close, based on which the rehabilitation training intensity of each rehabilitation personnel is analyzed from a long-term perspective. Therefore, based on the single rehabilitation training intensity of the rehabilitation personnel, the overall rehabilitation training intensity of the rehabilitation personnel and the consistency between all single rehabilitation training intensities are analyzed to obtain the rehabilitation training intensity of the rehabilitation personnel.

[0082] See also Figure 3 , which shows the basic process of a rehabilitation training intensity analysis method for rehabilitation personnel provided by an embodiment of the present invention.

[0083] like Figure 3 As shown, based on the single rehabilitation training intensity of the rehabilitation personnel, the overall rehabilitation training intensity of the rehabilitation personnel and the consistency between all single rehabilitation training intensities are analyzed to obtain the rehabilitation training intensity of the rehabilitation personnel, including:

[0084] S321: Based on the single rehabilitation training intensity of the rehabilitation personnel, the average value of all single rehabilitation training intensities is calculated to obtain the overall rehabilitation training intensity of the rehabilitation personnel.

[0085] Intensity of single rehabilitation training based on rehabilitation personnel , calculate the average intensity of all single rehabilitation training , the average intensity of a single rehabilitation training The overall rehabilitation training intensity of rehabilitation personnel.

[0086] S322: Based on the single rehabilitation training intensity of the rehabilitation personnel, the standard deviation of all single rehabilitation training intensities is calculated to obtain the consistency between all single rehabilitation training intensities of the rehabilitation personnel.

[0087] Intensity of single rehabilitation training based on rehabilitation personnel , calculate the standard deviation of all single rehabilitation training intensities ,in Indicates The number of rehabilitation training sessions for each rehabilitation worker and the standard deviation of the intensity of a single rehabilitation training session To provide consistency between the intensity of all single rehabilitation training sessions for rehabilitation personnel.

[0088] S323: Obtaining the rehabilitation training intensity of the rehabilitation personnel according to the overall rehabilitation training intensity and consistency.

[0089] According to the overall rehabilitation training intensity and consistency , and the rehabilitation training intensity of the rehabilitation personnel is obtained. The calculation formula for rehabilitation training intensity is:

[0090]

[0091] In the formula, Indicates The intensity of rehabilitation training for each rehabilitation worker; Indicates The average intensity of a single rehabilitation training session for each rehabilitation person during all rehabilitation training sessions; Indicates The standard deviation of the intensity of a single rehabilitation training session for all rehabilitation training sessions of each rehabilitation staff; Indicates The number of rehabilitation training sessions for each rehabilitation personnel.

[0092] By analyzing the average training intensity of all rehabilitation training of rehabilitation personnel (i.e. ) and the consistency of the intensity of daily rehabilitation training (i.e. ) to reflect the actual rehabilitation training intensity of rehabilitation personnel.

[0093] Thus, the rehabilitation training intensity of each rehabilitation person can be determined.

[0094] S400: Determine the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and rehabilitation training intensity.

[0095] Based on the rehabilitation training regularity and rehabilitation training intensity of each rehabilitation personnel determined by the above process, the differences between people with different sports abilities can be analyzed. Generally, the rehabilitation training regularity and rehabilitation training intensity of professional athletes are stronger, while those of ordinary people are relatively lower. Therefore, the level of rehabilitation training recovery stage of each rehabilitation personnel can be determined. Therefore, this embodiment determines the level of rehabilitation training recovery stage of the rehabilitation personnel according to the rehabilitation training regularity and rehabilitation training intensity. Further, it includes:

[0096] First, according to the rehabilitation training regularity and rehabilitation training intensity, the rehabilitation training recovery stage level value of the rehabilitation personnel is obtained. The calculation formula of the rehabilitation training recovery stage level value is:

[0097]

[0098] In the formula, Indicates The level of rehabilitation training recovery stage of each rehabilitation personnel; Indicates The regularity of rehabilitation training of each rehabilitation personnel; Indicates The intensity of rehabilitation training for each rehabilitation personnel.

[0099] Rehabilitation training regularity The larger the value is, the greater the rehabilitation training recovery stage level is; the greater the rehabilitation training intensity is, the greater the rehabilitation training recovery stage level is. Therefore, we can determine the rehabilitation training recovery stage level value of each rehabilitation personnel.

[0100] Then, based on the rehabilitation training recovery stage level values ​​of all rehabilitation personnel, the rehabilitation training recovery stage level is divided by the K-means clustering method to determine the rehabilitation training recovery stage level of the rehabilitation personnel. Specifically, based on the rehabilitation training recovery stage level values ​​of each rehabilitation personnel, the rehabilitation training recovery stage level is determined by the K-means clustering method. Here, the number of rehabilitation training recovery stage levels is preset to K, and the value of K can be 2, 3, 4, 5, 6, 7, 8, etc. The actual value of K can be determined according to the rehabilitation training recovery stage level value. The span value is determined by The value of span K can be 2 or 3. The value of K with a larger span can be 7 or 8, so that K rehabilitation training recovery stage levels can be determined. At the same time, the high and low rehabilitation training recovery stage levels are distinguished according to the average rehabilitation training recovery stage level value of the rehabilitation personnel in each rehabilitation training recovery stage level. The one with the largest average rehabilitation training recovery stage level value corresponds to the first rehabilitation training recovery stage level, and so on, until the Kth rehabilitation training recovery stage level.

[0101] S500: Record and analyze the training data of rehabilitation personnel at the edge, and evaluate the exercise quality of rehabilitation personnel based on the level of rehabilitation training recovery stage.

[0102] The training data of current rehabilitation personnel are recorded at the edge, and analyzed directly at the edge through edge computing methods, without the need to transmit it back to the cloud for processing. This improves efficiency while ensuring the security and privacy of the data.

[0103] Through the above steps, the rehabilitation training recovery stage level of different rehabilitation personnel is determined, so that when conducting sports quality evaluation and analysis on rehabilitation personnel, it can be more clear at which sports ability level they should be analyzed. Therefore, based on the rehabilitation training recovery stage level, the rehabilitation personnel's sports quality can be evaluated through the existing method, that is, the three-dimensional motion analysis system. At the same time, if there are rehabilitation personnel who are training for the first time, due to the lack of data on the rehabilitation training recovery stage level they are in, by default they will be placed in the Kth rehabilitation training recovery stage level for analysis, so as to avoid improper influence on the rehabilitation personnel when analyzing at too high a rehabilitation training recovery stage level.

[0104] Based on the same inventive concept as the above method, this embodiment also provides a motion quality evaluation system based on edge computing.

[0105] See also Figure 4 , which shows the basic composition of a motion quality evaluation system based on edge computing provided by an embodiment of the present invention.

[0106] like Figure 4 As shown, a motion quality evaluation system based on edge computing includes: a memory 10 and a processor 20, wherein:

[0107] A memory 10, used for storing program codes;

[0108] The processor 20 is used to read the program code stored in the memory 10, and execute to obtain the historical training records of all rehabilitation personnel participating in the rehabilitation exercise within a period of time; analyze the distribution characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel; analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel; determine the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity; record and analyze the training data of the rehabilitation personnel at the edge, and evaluate the exercise quality of the rehabilitation personnel based on the rehabilitation training recovery stage level.

[0109] Furthermore, the processor 20 includes: a historical training record acquisition module 21 , a rehabilitation training regularity analysis module 22 , a rehabilitation training intensity analysis module 23 , a rehabilitation training recovery stage level determination module 24 , and a movement quality evaluation module 25 .

[0110] The historical training record acquisition module 21 is used to acquire the historical training records of all rehabilitation personnel participating in the rehabilitation exercise within a period of time;

[0111] The rehabilitation training regularity analysis module 22 is used to analyze the distribution characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel;

[0112] The rehabilitation training intensity analysis module 23 is used to analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel;

[0113] A rehabilitation training recovery stage level determination module 24 is used to determine the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and rehabilitation training intensity;

[0114] The exercise quality evaluation module 25 is used to record and analyze the training data of rehabilitation personnel at the edge end, and evaluate the exercise quality of rehabilitation personnel based on the level of rehabilitation training recovery stage.

[0115] 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.

[0116] 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 motion quality evaluation method based on edge computing, characterized in that: The method comprises: Obtain the historical training records of all rehabilitation personnel involved in rehabilitation exercises over a period of time; Analyzing the distribution characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel; Analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel; Determining the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity; Recording and analyzing the training data of the rehabilitation personnel at the edge, and evaluating the exercise quality of the rehabilitation personnel based on the recovery stage level of the rehabilitation training; The distribution characteristics of the heart rate data in the historical training records are analyzed to obtain the rehabilitation training intensity of the rehabilitation personnel: Analyze the numerical size characteristics and difference characteristics of the heart rate data in the historical training records during a single rehabilitation training to obtain the single rehabilitation training intensity of the rehabilitation personnel; based on the single rehabilitation training intensity of the rehabilitation personnel, analyze the overall rehabilitation training intensity of the rehabilitation personnel and the consistency between all the single rehabilitation training intensities to obtain the rehabilitation training intensity of the rehabilitation personnel; Analyze the time interval characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel; Analyze the maximum interval time, the minimum interval time and the average interval time of adjacent training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel; Divide the period of time into at least two time periods, analyze the maximum interval time, the minimum interval time and the average interval time of adjacent training dates in the historical training records within the time period, traverse all the time periods, and obtain the rehabilitation training regularity of the rehabilitation personnel; The calculation formula of rehabilitation training regularity of rehabilitation personnel is: In the formula, Indicates The regularity of rehabilitation training of each rehabilitation personnel; Indicates the number of time periods; Indicates The rehabilitation personnel The first The difference between the numbers of the training dates corresponding to the training on the day and the adjacent days; Indicates The rehabilitation personnel The total number of days in the time period; express Function, take the minimum value of the data; express Function, take the maximum value of the data; Analyzing the numerical size characteristics and difference characteristics of the heart rate data in the historical training records during a single rehabilitation training to obtain the single rehabilitation training intensity of the rehabilitation personnel includes: Based on a single rehabilitation training of the rehabilitation personnel, traverse the heart rate data of the time series in the historical training records, filter out the maximum heart rate value, and obtain the numerical size characteristics of the heart rate data in the historical training records during the single rehabilitation training; Determining an analysis time range based on the heart rate maximum value; Analyze the numerical difference between the maximum heart rate value and all heart rate data within the analysis time range, and obtain the difference characteristics of the heart rate data in the historical training record during a single rehabilitation training; Obtaining the single rehabilitation training intensity of the rehabilitation person according to the numerical value feature and the difference feature; The calculation formula for the intensity of a single rehabilitation training session is: In the formula, Indicates Rehabilitation staff in The intensity of a single rehabilitation training session during rehabilitation training; Indicates Rehabilitation staff in The number of heart rate maxima screened out during each rehabilitation training; Indicates Rehabilitation staff in The first rehabilitation training The value of the maximum heart rate; Indicates that based on Rehabilitation staff in The first rehabilitation training The number of other time points within the analysis time range determined by the heart rate maximum; Indicates that based on Rehabilitation staff in The first rehabilitation training The heart rate maximum value is determined within the analysis time range. The value of the heart rate data corresponding to each time point; Based on the single rehabilitation training intensity of the rehabilitation personnel, analyzing the overall rehabilitation training intensity of the rehabilitation personnel and the consistency between all the single rehabilitation training intensities to obtain the rehabilitation training intensity of the rehabilitation personnel, including: Based on the single rehabilitation training intensity of the rehabilitation person, calculating the average value of all the single rehabilitation training intensities to obtain the overall rehabilitation training intensity of the rehabilitation person; Based on the single rehabilitation training intensity of the rehabilitation personnel, calculating the standard deviation of all the single rehabilitation training intensities, and obtaining the consistency between all the single rehabilitation training intensities of the rehabilitation personnel; Obtaining the rehabilitation training intensity of the rehabilitation personnel according to the overall rehabilitation training intensity and the consistency; The calculation formula for rehabilitation training intensity is: In the formula, Indicates The intensity of rehabilitation training for each rehabilitation worker; Indicates The average intensity of a single rehabilitation training session for each rehabilitation person during all rehabilitation training sessions; Indicates The standard deviation of the intensity of a single rehabilitation training session for all rehabilitation training sessions of each rehabilitation staff; Indicates The number of rehabilitation training sessions for each rehabilitation staff member.

2. The motion quality evaluation method based on edge computing according to claim 1 is characterized in that: Determining the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity includes: According to the rehabilitation training regularity and the rehabilitation training intensity, obtaining a rehabilitation training recovery stage level value of the rehabilitation personnel; Based on the rehabilitation training recovery stage level values ​​of all the rehabilitation personnel, the rehabilitation training recovery stage level is divided by the K-means clustering method to determine the rehabilitation training recovery stage level of the rehabilitation personnel.

3. A motion quality evaluation system based on edge computing, characterized in that: The system comprises: a memory and a processor, wherein: The memory is used to store program codes; The processor is configured to read the program code stored in the memory and execute the method according to any one of claims 1 to 2.

4. The motion quality evaluation system based on edge computing according to claim 3 is characterized in that: The processor comprises: A historical training record acquisition module is used to acquire the historical training records of all rehabilitation personnel participating in rehabilitation exercises within a period of time; A rehabilitation training regularity analysis module is used to analyze the distribution characteristics of the training dates in the historical training records to obtain the rehabilitation training regularity of the rehabilitation personnel; A rehabilitation training intensity analysis module, used to analyze the distribution characteristics of the heart rate data in the historical training records to obtain the rehabilitation training intensity of the rehabilitation personnel; A rehabilitation training recovery stage level determination module, used to determine the rehabilitation training recovery stage level of the rehabilitation personnel according to the rehabilitation training regularity and the rehabilitation training intensity; The movement quality evaluation module is used to record and analyze the training data of the rehabilitation personnel at the edge end, and evaluate the movement quality of the rehabilitation personnel based on the recovery stage level of the rehabilitation training.

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