Method, apparatus, and computer-readable medium for determining injury risk of a user during exercise

Abstract

The present invention discloses a method, apparatus, and computer-readable medium for determining the injury risk of a user during exercise. The user has performed an exercise training for a first period. The first period is divided into a plurality of time segments. A training load is determined in each of the time segments. A first portion of the training load is above a critical value of an exercise intensity, and the critical value of the exercise intensity is adjusted according to the physical condition of the user. An algorithm is executed to determine an indication pattern based on the training load, and the indication pattern presents a training condition of the exercise training of the user during the first period. A criterion for the injury risk is determined based on the first portion of the training load and the algorithm. The injury risk of the user is determined based on a comparison between the indication pattern and the criterion for the injury risk.

Description

Technical Field

[0001] The present invention relates to a method for monitoring a user's exercise, and more particularly to a method for determining the injury risk of a user during exercise. Background Art

[0002] Overtraining can increase the risk of injury during exercise; therefore, in order to avoid injury to users, it is very important to monitor the training load. Recently, the Acute Chronic Workload Ratio (ACWR) has been a parameter used to estimate the injury risk of exercise. This parameter is the ratio of the short-term accumulated training load to the long-term accumulated training load.

[0003] However, this parameter does not consider the physical fitness of the user (such as the fitness performance level; the best parameter for the fitness performance level is the maximum oxygen uptake (VO 2max 2)). Take this case as an example. The literature points out that the greater the positive value of subtracting 1.5 from the acute chronic workload ratio, the greater the risk of injury. Suppose a situation: User A and User B have the same age "that is, the age-based heart rate intervals of User A and User B are the same. The age-based heart rate interval is determined as follows: First, calculate the maximum heart rate according to the formula, for example, 220 minus the user's age (unit: beats per minute (BPM)); Second, each heart rate interval in the personalized heart rate interval (multiple heart rate intervals) is determined according to the proportional range of the maximum heart rate, and this proportional range is determined based on the common knowledge in the sports field." However, the fitness performance level of User A is higher than that of User B. When User A and User B have performed the same exercise training for 28 days (that is, the daily training impulse (TRIMP) of User A and User B is the same every day), the acute chronic workload ratio of User A is the same as that of User B. At this time, if the acute chronic workload ratios of both User A and User B are 1.6, User B with a lower fitness performance level may feel the risk of injury, but User A with a higher fitness performance level may feel no risk of injury; further, User A with a higher fitness performance level still wants to increase the exercise intensity and continue to exercise to further improve his fitness performance level; however, physiologically, the implication of the acute chronic workload ratio may cause User A with a higher fitness performance level to lose the motivation to train.

[0004] Accordingly, the present invention proposes a method for determining the injury risk of a user during exercise to overcome the above-mentioned drawbacks. Summary of the Invention

[0005] In the present invention, the injury risk of a user during a first period of exercise training is determined based on a comparison between an indication pattern presenting a training condition and a criterion for injury risk. The criterion for injury risk is determined based on a training load of high-exercise-intensity adjusted according to the physical condition of the user (i.e., a first portion of the training load above a critical value of exercise intensity).

[0006] In the present invention, the critical value of exercise intensity adjusted according to the physical condition of the user is a technical feature considering the physical condition of the user (such as the physical performance level). In other words, through this technical feature, the criterion for injury risk is determined based on a training load of high-exercise-intensity adjusted according to the physical condition of the user (i.e., a first portion of the training load above the critical value of exercise intensity), and different criteria for injury risk are applicable to users with different physical conditions. Once the physical condition of the user is determined, through this technical feature, the criterion for injury risk can be accurately determined for the user, and the injury risk of the user during the first period of exercise training performed can be accurately determined.

[0007] In a preferred embodiment of the present invention, the training load is determined based on a plurality of exercise intensity intervals, and the plurality of exercise intensity intervals are adjusted according to the physical condition of the user (such as the physical performance level). In addition to the criterion for injury risk (determined in the previous paragraph) considering the physical condition of the user (such as the physical performance level), since the training load is determined based on a plurality of exercise intensity intervals and the plurality of exercise intensity intervals are in turn adjusted according to the physical condition of the user (such as the physical performance level), the indication pattern determined based on at least one first parameter related to the training load also takes into account the physical condition of the user (such as the physical performance level). Once the physical condition of the user is determined, the injury risk of the user during the first period of exercise training performed can be accurately determined based on a comparison between the indication pattern presenting the training condition and the criterion for injury risk.

[0008] Through an algorithm configured in a computer of the present invention, the computer of the present invention performs the action steps described in the claims or the following description to accurately determine the injury risk of a user during a first period of exercise training performed.

[0009] In one embodiment, the present invention discloses a method for determining an injury risk of a user who has performed a sports training for a first period. The method includes the steps of: dividing the first period into a plurality of time segments; determining, by a processing unit, a training load in each of the time segments, wherein a first portion of the training load is above a critical value of a sports intensity, and the critical value of the sports intensity is adjusted according to a fitness condition of the user; executing, by the processing unit, an algorithm to determine an indication pattern based on at least a first parameter related to the training load, wherein the indication pattern presents a training condition of the sports training of the user in the first period; determining, by the processing unit, a criterion for the injury risk based on at least a second parameter related to the first portion of the training load and the algorithm for determining the indication pattern; and determining, by the processing unit, the injury risk of the user who has performed the sports training for the first period based on a comparison between the indication pattern presenting the training condition and the criterion for the injury risk.

[0010] In one embodiment, the present invention discloses a method for determining an injury risk of a user who has performed a sports training for a first period. The method includes the steps of: dividing the first period into a plurality of time segments; determining, by a processing unit, a training load in each of the time segments, wherein the training load is determined based on a plurality of sports intensity intervals, each of the sports intensity intervals having a first portion of the training load, and the training load is a sum of the plurality of first portions of the plurality of sports intensity intervals, wherein a second portion of the training load is above a critical value of a sports intensity, and the plurality of sports intensity intervals and the critical value of the sports intensity are adjusted according to a fitness condition of the user; executing, by the processing unit, an algorithm to determine an indication pattern based on at least a first parameter related to the training load, wherein the indication pattern presents a training condition of the sports training of the user in the first period; determining, by the processing unit, a criterion for the injury risk based on at least a second parameter related to the first portion of the training load and the algorithm for determining the indication pattern; and determining, by the processing unit, the injury risk of the user who has performed the sports training for the first period based on a comparison between the indication pattern presenting the training condition and the criterion for the injury risk.

[0011] After referring to the embodiments and detailed techniques of the present invention described in the following paragraphs and the accompanying drawings, those with ordinary knowledge in the technical field can understand the technical features and implementation aspects of the present invention. Description of the Drawings

[0012] The foregoing aspects of the present invention and the attendant advantages will be more fully understood by reference to the following detailed description and the accompanying drawings, in which:

[0013] Figure 1 Illustrate a schematic block diagram of an exemplary device in the present invention;

[0014] Figure 2 Illustrate a method for determining the injury risk of a user during a first period of exercise training;

[0015] Figure 3A Illustrate the daily training load (TL) in an embodiment of the present invention;

[0016] Figure 3B Illustrate the training load (TL) of higher exercise intensity per day in an embodiment of the present invention;

[0017] Figure 3C Illustrate the training load (TL) of lower exercise intensity per day in an embodiment of the present invention;

[0018] Figure 4A Illustrate the relationship between the user's physical performance level and the threshold of exercise intensity for adjustment;

[0019] Figure 4B Illustrate that the physical performance level of the user can be graded so that each physical performance level has a corresponding threshold of exercise intensity;

[0020] Figure 5A Illustrate a two-dimensional heart rate interval;

[0021] Figure 5B Illustrate the derivation of a one-dimensional heart rate interval from the two-dimensional heart rate interval in Figure 5A ; and

[0022] Figure 6 Illustrate that the criteria for injury risk vary with exercise time.

[0023] Description of reference numerals: 101 - input unit; 102 - processing unit; 103 - memory unit; 104 - output unit; 200 - method; 201 - step; 202 - step; 203 - step; 204 - step; 205 - step; 300 - training load distribution; 310 - distribution of training load of higher exercise intensity; 320 - distribution of training load of lower exercise intensity; 500 - two-dimensional heart rate interval (multiple two-dimensional heart rate interval segments); 510 - one-dimensional heart rate interval (multiple one-dimensional heart rate interval segments); 600 - distribution of criteria for injury risk. Detailed Description

[0024] A detailed description of the present invention will be described subsequently. The preferred embodiments described herein are for illustrative and descriptive purposes and are not intended to limit the scope of the present invention.

[0025] Name Definition

[0026] Fitness condition

[0027] The fitness condition can be defined by the fitness performance level. The fitness performance level of one user may be different from that of another user; if two users want to have the same training effect, the user with a higher fitness performance level requires more intense exercise guidance and a higher exercise intensity than the user with a lower fitness performance level. The fitness performance level may include health-related fitness or sports / skill-related fitness enhanced through participation in physical activities or exercise training. For example, the parameters of the fitness performance level can be the maximum oxygen uptake (VO 2max ) or the maximum metabolic equivalent (MET max ) (the maximum oxygen uptake capacity compared to the resting oxygen consumption: equal to the maximum oxygen uptake (VO 2max ) / 3.5), and the maximum oxygen uptake (VO 2max ) is preferred. Generally, the unit of the maximum oxygen uptake (VO 2max ) can be presented in an absolute manner, such as oxygen uptake (ml / min), or in a relative manner, such as weight-based oxygen uptake (ml / kg / min).

[0028] Exercise intensity

[0029] The exercise intensity can refer to the energy consumed during exercise. The exercise intensity can be defined as the difficulty level at which the body must work to perform a task / exercise. The exercise intensity can be measured in the form of internal workload. The exercise intensity parameters related to the internal workload can be related to heart rate, oxygen uptake, pulse, breathing rate, or the rating of perceived exertion (RPE). The exercise intensity can be measured in the form of external workload. The exercise intensity parameters related to the external workload can be related to speed, power consumption, force, motion intensity, motion cadence, or other dynamic data generated by the external workload that causes energy consumption. Heart rate is often used as a parameter of exercise intensity.

[0030] The method of the present invention is applicable to a variety of devices, such as a motion measurement system, a wrist-mounted device, a mobile device, a server, or a combination of at least one of a motion measurement system, a wrist-mounted device, a mobile device, and a server. Figure 1 A schematic block diagram illustrating an exemplary device 100 in the present invention. The device 100 may include an input unit 101, a processing unit 102, a memory unit 103, and an output unit 104. The input unit 101 may include a first sensor that can measure the exercise intensity related to physiological data, cardiovascular data, and internal workload from the user's body. The exercise intensity can be measured by applying skin contact from the chest, wrist, or any other body part. Preferably, the exercise intensity is the heart rate and the sensor is a heart rate sensor. The input unit 101 may include a second sensor (such as a motion sensor) that can measure the exercise intensity related to external workload. The second sensor may include at least one of an accelerometer, a magnetometer, and a gyroscope. The input unit 101 may further include a positioning sensor (such as the Global Positioning System (GPS)). In the present invention, exercise-related parameters (such as Training Load (TL)) are calculated based on the exercise intensity measured by the sensors. The processing unit 102 may be any processing device suitable for executing software instructions, such as a central processing unit (CPU). The memory unit 103 may include a random access memory (RAM) and a read-only memory (ROM), but the memory unit 103 is not limited to this example. The memory unit 103 may include any suitable non-transitory computer-readable medium, such as a read-only memory (ROM), a compact disc read-only memory (CD-ROM), a digital versatile disc read-only memory (DVD-ROM), and so on. Furthermore, the non-transitory computer-readable medium is a tangible medium. The non-transitory computer-readable medium contains computer program code. When the computer program code is executed by the device 100, the computer program code causes the device 100 to perform the desired operations (such as the operations shown in the claims). The output unit 104 may be a display for displaying exercise guidance, exercise programs, or exercise indices. The display mode can be presented in the form of text, sound, or video.

[0031] Figure 2 A method 200 for determining the injury risk of a user during a first period of an exercise training is described. In Figure 2The program in [it] starts at step 201: dividing the first period into multiple time segments (by processing unit 102). Each time segment may have the same time length or different time lengths. Preferably, each time segment may have the same time length. For ease of description, the first period is 28 days; however, the present invention is not limited to this case.

[0032] Step 202: determining a training load (TL: Training Load) in each of the time segments (by processing unit 102). For ease of description, the time length of each time segment in the present invention is 1 day; however, the present invention is not limited to this case. In the present invention, the training load is calculated every day. Figure 3A Illustrate that the training load (TL) per day in an embodiment of the present invention is 300. In the following description, the standard for injury risk is calculated based on the training load of higher exercise intensity; the training load of higher exercise intensity (see the black filled part) is defined as the part of the training load above the critical value of exercise intensity. Figure 3B Illustrate that the training load (TL) of higher exercise intensity per day in an embodiment of the present invention is 310. In Figure 3B it, the training load of higher exercise intensity is above the critical value of exercise intensity. Figure 3C Illustrate that the training load (TL) of lower exercise intensity per day in an embodiment of the present invention is 320. In Figure 3A it, the training load per day is Figure 3B the sum of the training load of higher exercise intensity per day in Figure 3C it and the training load of lower exercise intensity per day in

[0033] The critical value of exercise intensity is adjusted according to the fitness condition of the user (such as fitness performance level; the parameter of fitness performance level is preferably maximum oxygen uptake (VO 2max 2)). In other words, the critical value of exercise intensity is adjusted to have different values based on different fitness performance levels. Figure 4A The following table illustrates the relationship between the fitness performance level of the user and the critical value of exercise intensity for adjustment. In Figure 4A it, the parameter of fitness performance level is maximum oxygen uptake (VO 2max 2) and the parameter of the critical value of exercise intensity is heart rate. This relationship can be obtained through statistical analysis of big data. If the maximum oxygen uptake (VO 2max 2) of the user is not in the table, corresponding to the maximum oxygen uptake (VO 2max) The critical value of the exercise intensity can be determined by interpolation or extrapolation. Figure 4B It is described that the physical performance levels of the user can be classified so that each physical performance level has a corresponding critical value of exercise intensity. In Figure 4B , the physical performance levels are classified so that higher physical performance levels have larger levels, and the parameter of the critical value of exercise intensity is heart rate. The physical condition of the user can be determined with reference to U.S. Patent Application No. 16 / 843,853. For example, the parameter presenting the physical condition can be the gradient of a cumulative physiological index. For example, the parameter is the decreasing rate of a fitness index or a stamina index. The parameter presenting the physical condition can be the gradient of a physiological index. For example, the parameter is the increasing rate of physiological data (such as heart rate). A combination of multiple parameters can also present the physical condition. The combination of multiple parameters can be presented in the form of a composite index (such as a weighted average, e*P + f*Q). When a change in the physical condition of the user is detected by monitoring physiological data, cardiovascular data, or the internal workload measured by an exercise measurement system (or sensor), the critical value of the exercise intensity also changes. Generally, when the physical condition of the user improves, the critical value of the exercise intensity is adjusted upward.

[0034] The training load can be determined based on multiple exercise intensity intervals. Each exercise intensity interval has a partial training load (such as the product of exercise intensity and exercise time), where the training load is the sum of the multiple partial training loads of the multiple exercise intensity intervals. At least one exercise intensity interval among the multiple exercise intensity intervals is adjusted according to the physical condition of the user or based on different physical performance levels. All of the exercise intensity intervals among the multiple exercise intensity intervals are adjusted according to the physical condition of the user or based on different physical performance levels. In one embodiment, the training load is determined based on the multiple exercise intensity intervals in U.S. Patent Application No. 16 / 733,180 incorporated herein by reference. Apparently, U.S. Patent Application No. 16 / 733,180 discloses that multiple exercise intensity intervals are adjusted according to the physical condition of the user (such as the physical performance level) or based on different physical performance levels (see the two-dimensional heart rate interval 500 in Figure 5A and in Figure 5BThe one-dimensional heart rate range in (510); details can be found in the description corresponding to U.S. Patent Application No. 16 / 733,180. The critical value of the exercise intensity is the lower bound of an exercise intensity interval segment that has the highest exercise intensity range among multiple exercise intensity interval segments (if the parameter of the exercise intensity is heart rate, the upper bound of an exercise intensity interval segment that has the highest exercise intensity range among multiple exercise intensity interval segments can be the maximum heart rate).

[0035] In one embodiment, the training load is presented in the form of a training impulse (TRIMP: Training Impulse). However, the present invention is not limited to this case.

[0036] Step 203: Execute an algorithm to determine an indication pattern based on at least one first parameter W related to the training load i where the indication pattern presents a training status of the user's exercise training during the first period (by the processing unit 102). To understand the training status of the user's exercise training during the first period (such as training time or training distribution), the indication pattern can be determined by executing an algorithm. In one embodiment, at least one first parameter W i One of the first parameters W i can further be related to the accumulated training load. By using the accumulated training load as the input of parameter W i parameter W i can be presented in a relative manner or an absolute manner. The algorithm can take parameter W presented in an absolute manner i , such as the accumulated training load in the past few days or 28 days. The algorithm can take parameter W presented in a relative manner i , such as the ratio of the short-term accumulated training load to the long-term accumulated training load (such as the acute-chronic training load ratio); the long term can be the first period (such as 28 days) and the short term can be the second period (such as 7 days).

[0037] In a further embodiment, the indication pattern can be further determined based on at least one parameter V related to the recovery status (such as recovery time or recovery distribution) i The recovery status can include a series of time segments, and each time segment does not have a training load therein (i.e., the training load is 0). For example, the algorithm can take parameter W i and parameter V i in combination, such as a weighted composite index of parameter W i and parameter V i (such as a*W i +b*V i, each of the coefficients a and b can be a fixed value or vary according to the observation of physiological phenomena); a parameter V related to the recovery status i can be the parameter V 1 : the number of consecutive days without a training load before the current training load or the parameter V 2 : the number of days without a training load during a reference period before the current training load. For example, see Figure 3A , if the current training load occurs on the 8th day, the number of consecutive days without a training load is 1 (i.e., the 7th day); if the current training load occurs on the 13th day and the number of days in the reference period before the current training load is 10 (i.e., from the 3rd day to the 12th day), the number of days without a training load is 4 (i.e., the 3rd day, the 7th day, the 10th day, the 12th day).

[0038] Step 204: Determine a criterion for the injury risk based on at least one second parameter X related to the first part of the training load i and the algorithm that determines the indication pattern (by the processing unit 102). The criterion for the injury risk can be determined dynamically. The criterion for the injury risk can be determined relative to a predetermined criterion related to the algorithm. The predetermined criterion can be fixed. For example, the literature points out that: the greater the positive value obtained by subtracting 1.5 from the acute-to-chronic training load ratio, the greater the injury risk; when the algorithm only takes the acute-to-chronic training load ratio to present the training status of the user's sports training during the first period in step 203, the predetermined criterion is 1.5. In another example, the predetermined criterion can be varied. In addition, the predetermined criterion can be defined by the user.

[0039] The parameter X related to the first part of the training load 1 can be presented in an absolute manner, such as the first part of the current training load. The parameter X related to the first part of the training load 2 can be presented in a relative manner, such as the ratio of the first part of the current training load to the current overall training load. The parameter X related to the first part of the training load 3 can be the number of days with the first part of the training load during a reference period before the first part of the current training load. For example, see Figure 3B , if the first part of the current training load occurs on the 18th day and the number of days in the reference period before the first part of the current training load is 17 (i.e., from the 1st day to the 17th day), the number of days with the first part of the training load is 3 (i.e., the 4th day, the 11th day, the 14th day).

[0040] In one embodiment, when the algorithm only adopts the acute chronic workload ratio (ACWR) to present the training status of the user's sports training during the first period in step 203, the determination of the standard of injury risk can use: (1) the standard of injury risk = function f(X 1 ) = c1*X 1 ; or (2) the standard of injury risk = function f(X 2 ) = c2*X 2 ; or (3) the standard of injury risk (the combination of X 2 and X 3 ) = function f(X 2 , X 3 ) = c3*X 2 + c4*X 3 . Each of the coefficients c1, c2, c3, c4 can be a fixed value or vary according to the observation of physiological phenomena. Taking case (I) as an example: the standard of injury risk (the combination of X 2 and X 3 ) = function f(X 2 , X 3 ) = c3*X 2 + c4*X 3 ; each of the coefficients c3, c4 is a positive number. The larger the parameter X 2 , the smaller the standard of injury risk is adjusted, and the injury risk increases. The larger the parameter X 3 , the smaller the standard of injury risk is adjusted, and the injury risk increases. The larger the parameter X 2 and the parameter X 3 are simultaneously, the smaller the standard of injury risk is adjusted, and the injury risk increases. Figure 6 It shows that the standard of injury risk 600 varies with the exercise time in this example.

[0041] In a further embodiment, the standard of injury risk can be further determined based on at least one parameter Y i related to the recovery status. The recovery status can include a series of time periods, and each time period does not have a first part of the training load therein (that is, the first part of the training load is 0). The parameter Y i related to the recovery status can be the parameter Y 1 : the number of consecutive days without the first part of the training load before the first part of the current training load or the parameter Y 2 : the number of days without the first part of the training load during the reference period before the first part of the current training load. For example, see Figure 3B, if the first part of the current training load occurs on the 11th day, the number of consecutive days without the first part of the training load is 6 (i.e., the 5th to the 10th day); if the first part of the current training load occurs on the 14th day and the number of days in the reference period before the first part of the current training load is 10 (i.e., the 4th to the 13th day), the number of days without the first part of the training load is 8 (i.e., the 5th to the 10th day, the 12th to the 13th day).

[0042] In one embodiment, when the algorithm only uses the acute-chronic workload ratio (ACWR) to present the training status of the user's sports training in the first period in step 203, the injury risk criterion can be determined using the injury risk criterion (X 2 and Y 1 combination) = function f(X 2 ,Y 1 )=c5*X 2 +c6*Y 1 ; Each coefficient of coefficient c5 and c6 can be a fixed value or can be changed according to the observation of physiological phenomena. For example, each coefficient of coefficient c5 and c6 is a positive number. Parameter X 2 The larger the value, the smaller the standard adjustment of the risk of injury is, and the risk of injury increases. 1 The larger the value, the greater the standard adjustment of the risk of injury, and the lower the risk of injury. 2 and parameter Y 1 When both increase, the standard visual function f(X 2 ,Y 1 ) in the parameter X 2 and parameter Y 1 Depends on the competition.

[0043] Finally, step 205: determining the injury risk of the user who has performed the first period of the exercise training (by the processing unit 102) based on a comparison between the indication pattern presenting the training condition and the criterion of the injury risk. 2 and X 3 combination) = function f(X 2 ,X 3 )=c3*X 2 +c4*X 3 ; Each of the coefficients c3 and c4 is a positive number. Figure 6It is stated that for user A with a relatively high physical performance level, the injury risk criterion is between 2 and 2.5. Even though user A's acute-to-chronic training load ratio is 1.6, which is greater than the 1.5 defined in the literature, due to user A's high physical performance level, user A's acute-to-chronic training load ratio is still less than the injury risk criterion between 2 and 2.5. User A with a relatively high physical performance level can still increase the exercise intensity and continue to exercise until the acute-to-chronic training load ratio reaches at least 2. Therefore, user A with a relatively high physical performance level still maintains the motivation to train to further increase their physical performance level.

[0044] In the above description, the indication pattern and the injury risk criterion are presented digitally in the form of values or indices. Assuming that the indication pattern is less than the injury risk criterion, there is no injury risk or a low injury risk; on the contrary, assuming that the indication pattern is greater than the injury risk criterion, there is an injury risk or a high injury risk. The indication pattern and the injury risk criterion can be presented in any suitable form. For example, the indication pattern and the injury risk can be presented in the form of pattern 1 and pattern 2 respectively; thus, the comparison between the indication pattern presenting the training situation and the injury risk criterion can be determined based on the degree of deviation between pattern 1 and pattern 2.

[0045] Although the present invention has been disclosed above in the foregoing preferred embodiments, it is not intended to limit the present invention. Any person skilled in the relevant art can make some modifications and refinements without departing from the spirit and scope of the present invention. Although all these possible modifications and substitutions are not fully disclosed in the above description, the appended claims of this specification essentially cover all these aspects.

Claims

1. A method for determining the injury risk of a user during exercise, wherein the user has performed an exercise training for a first period, characterized in that, the method comprises the steps of: dividing the first period into a plurality of time segments by a processing unit; determining a training load in each of the time segments by the processing unit, wherein the training load is determined based on a plurality of exercise intensity intervals, and the plurality of exercise intensity intervals are adjusted according to the physical fitness condition of the user; executing an algorithm by the processing unit to determine an indication pattern based on at least one first parameter related to the training load, wherein the indication pattern presents a training condition of the exercise training of the user during the first period; determining a criterion for the injury risk by the processing unit based on at least one second parameter related to a first part of the training load and the algorithm for determining the indication pattern, wherein the first part of the training load is above an exercise intensity threshold value of an exercise intensity, and the exercise intensity threshold value is determined according to the physical fitness condition of the user, so that the first part of the training load above the exercise intensity threshold value is determined according to the physical fitness condition of the user, and the first part is the training load of high exercise intensity; and determining the injury risk of the user who has performed the exercise training for the first period by the processing unit based on a comparison between the indication pattern presenting the training condition and the criterion for the injury risk.

2. The method according to claim 1, characterized in that, each of the exercise intensity intervals has a second part of the training load, and the training load is the sum of the plurality of second parts of the plurality of exercise intensity intervals.

3. The method according to claim 1, characterized in that, the exercise intensity threshold value of the exercise intensity is the lower bound of an exercise intensity interval having the highest exercise intensity range among the plurality of exercise intensity intervals.

4. The method according to claim 1, characterized in that, the training load is presented in the form of a training impulse.

5. The method according to claim 1, characterized in that, when the physical fitness condition of the user improves, the exercise intensity threshold value of the exercise intensity is adjusted upward.

6. The method according to claim 1, characterized in that, the criterion for the injury risk is determined relative to a predetermined criterion related to the algorithm.

7. The method according to claim 1, characterized in that, a third parameter of the exercise intensity is related to a heart rate, an oxygen uptake, a pulse, a breathing rate or a rating of perceived exertion.

8. The method according to claim 1, characterized in that, a third parameter of the exercise intensity is related to a speed, a power consumption, a force, a movement intensity or a movement rhythm.

9. The method according to claim 1, characterized in that, the training load is calculated based on the exercise intensity measured by a sensor.

10. The method according to claim 9, characterized in that, the exercise intensity is a heart rate and the sensor is a heart rate sensor.

11. The method according to claim 9, characterized in that, The exercise intensity is related to an external workload and the sensor is a motion sensor.

12. The method according to claim 1, wherein, one of the at least one first parameter is further related to the accumulated training workload.

13. The method according to claim 1, wherein, the indication pattern of the training status of the user's exercise training in the first period is further determined based on at least one third parameter related to a recovery status.

14. The method according to claim 13, wherein, the recovery status includes a series of time segments, and each of the time segments does not have the training workload therein.

15. The method according to claim 1, wherein, the criterion of the injury risk is further determined based on at least one third parameter related to a recovery status.

16. The method according to claim 15, wherein, the recovery status includes a series of time segments, and each of the time segments does not have the first part of the training workload therein.

17. A device for determining the injury risk of a user during exercise, wherein the user has performed an exercise training for a first period, wherein, the device includes: a processing unit; and a memory unit including a computer program code, wherein when the computer program code is executed by the device, the computer program code causes the device to execute a program, and the program includes: dividing, by the processing unit, the first period into a plurality of time segments; determining, by the processing unit, a training workload in each of the time segments, wherein the training workload is determined based on a plurality of exercise intensity intervals, and the plurality of exercise intensity intervals are adjusted according to the physical fitness condition of the user; executing, by the processing unit, an algorithm to determine an indication pattern based on at least one first parameter related to the training workload, wherein the indication pattern presents a training status of the user's exercise training in the first period; determining, by the processing unit, a criterion of the injury risk based on at least one second parameter related to a first part of the training workload and the algorithm for determining the indication pattern, wherein the first part of the training workload is above an exercise intensity threshold value, and the exercise intensity threshold value is determined according to the physical fitness condition of the user, so that the first part of the training workload above the exercise intensity threshold value is determined according to the physical fitness condition of the user, and the first part is the training workload of high exercise intensity; and determining, by the processing unit, the injury risk of the user who has performed the exercise training for the first period based on a comparison between the indication pattern presenting the training status and the criterion of the injury risk.

18. A computer-readable medium for determining the injury risk of a user during exercise, including at least one computer program code, when the at least one computer program code is executed by a device, the device executes a method for determining the injury risk of a user, wherein the user has performed an exercise training for a first period, wherein, the method includes the steps: Through a processing unit, the first period is divided into a plurality of time segments. Through the processing unit, a training load is determined in each of the time segments, where the training load is determined based on a plurality of exercise intensity intervals, and the plurality of exercise intensity intervals are adjusted according to the physical fitness condition of the user; Through the processing unit, an algorithm is executed to determine an indication pattern based on at least one first parameter related to the training load, where the indication pattern presents a training condition of the user's exercise training during the first period; Through the processing unit, a criterion for the injury risk is determined based on at least one second parameter related to a first part of the training load and the algorithm for determining the indication pattern, where the training load is determined based on an exercise intensity, where the first part of the training load is determined based on the exercise intensity above an exercise intensity threshold, and the exercise intensity threshold is determined according to the physical fitness condition of the user, so that the first part of the training load determined based on the exercise intensity above the exercise intensity threshold is determined according to the physical fitness condition of the user, and the first part is the training load of high exercise intensity; and Through the processing unit, the injury risk of the user who has performed the exercise training during the first period is determined based on a comparison between the indication pattern presenting the training condition and the criterion for the injury risk.

Images