Exercise risk assessment method and device and electronic equipment

Abstract

The invention discloses an exercise risk assessment method and device and electronic equipment, and belongs to the technical field of data processing. The method comprises the following steps: acquiring exercise data when a user uses exercise equipment to exercise; according to the motion data, motion risk indexes are analyzed and calculated, and the motion risk indexes comprise at least one of the following indexes: the elasticity index of the motion equipment and the motion posture index of the user in motion; and evaluating the exercise risk of the user according to the exercise risk index.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and specifically relates to a sports risk assessment method, device and electronic equipment. Background Art

[0002] Some sports, such as running, especially long-distance running, carry a certain risk of wear-related injuries. To mitigate this issue, existing technologies typically design a certain level of elasticity into the soles of sports equipment, such as running shoes, to protect the human body. Furthermore, generally good running form and cadence can increase the overall elasticity of the equipment, thereby reducing or mitigating this risk. However, with extended use, sports equipment gradually wears out, weakening its cushioning effect and failing to provide adequate protection. Poor running form can also further exacerbate the loss of overall elasticity, exposing users to a certain risk of sports injuries.

[0003] Currently, there's a lack of monitoring and alerts for poor running form. Wear and loss of elasticity are often determined solely by the user's foot feel, which is inaccurate and unreliable. By the time a user detects a change and identifies injury risk, the optimal time to replace their equipment has often passed. Therefore, there's an urgent need for a more accurate and reliable technology to assess user risk. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a motion risk assessment method, device and electronic device, which can solve the problem that the existing technology lacks a relatively accurate and reliable technology for assessing user motion risks.

[0005] In a first aspect, an embodiment of the present application provides a method for assessing sports risk, the method comprising:

[0006] Obtaining sports data of users when exercising with sports equipment;

[0007] Analyzing and calculating a sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: an elasticity index of the sports equipment, and a sports posture index of the user during exercise;

[0008] The user's exercise risk is assessed according to the exercise risk indicator.

[0009] In a second aspect, an embodiment of the present application provides a sports risk assessment device, comprising:

[0010] The first acquisition module is used to acquire the exercise data of the user when exercising with the sports equipment;

[0011] a first calculation module, configured to analyze and calculate a sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: an elasticity index of the sports equipment and a sports posture index of the user during exercise;

[0012] An evaluation module is used to evaluate the user's exercise risk based on the exercise risk indicator.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0015] In a fifth aspect, an embodiment of the present application provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the method described in the first aspect.

[0016] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the method described in the first aspect.

[0017] In an embodiment of the present application, motion data of a user while exercising with sports equipment is obtained; based on the motion data, a motion risk index is analyzed and calculated, wherein the motion risk index includes at least one of the following: an elasticity index of the sports equipment, an index of the user's motion posture during exercise; and based on the motion risk index, the user's motion risk is assessed. In this way, by analyzing and calculating the motion risk index based on the user's motion data, such as the elasticity index of the sports equipment, an index of the user's motion posture during exercise, etc., when analyzing and calculating the elasticity index, the elasticity state of the sports equipment can be accurately assessed based on the elasticity index of the sports equipment, so that when it is assessed that the equipment has insufficient elasticity or has a large elasticity loss, the user can be promptly reminded to replace it, thereby avoiding the risk of sports injuries caused by poor sports equipment; when analyzing and calculating the motion posture index, the user's motion posture can be accurately assessed based on the motion posture index, so that when it is assessed that the user has an unhealthy motion posture, a timely warning can be issued, thereby avoiding the risk of sports injuries caused by unhealthy motion posture. As can be seen, the embodiment of the present application provides an accurate and reliable motion risk index assessment solution that can accurately and reliably assess motion risk based on the user's motion data, thereby effectively ensuring the safety of the user during exercise. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of the sports risk assessment method provided in an embodiment of the present application;

[0019] Figure 2 is a structural diagram of a sports risk assessment device provided in an embodiment of the present application;

[0020] Figure 3 is a structural diagram of an electronic device provided in an embodiment of the present application;

[0021] Figure 4 This is a hardware structure diagram of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0023] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0024] An embodiment of the present application provides a method for using an inertial measurement unit (IMU) of a wearable device to monitor and measure the elasticity and impact characteristics of the ground contact during sports such as running, thereby tracking and estimating the wear of the running shoe soles and potential damage caused by poor running posture, thereby providing an early warning of potential injury risks during running.

[0025] The following describes in detail the sports risk assessment method provided by the embodiment of the present application through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0026] See Figure 1 , Figure 1 This is a flow chart of the sports risk assessment method provided in the embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0027] Step 101: Obtain exercise data of a user when using sports equipment for exercise.

[0028] The above-mentioned sports equipment may refer to equipment worn by the user during exercise, and may specifically refer to equipment that is most needed for the sport and can protect the user's body during exercise, and usually the loss of the sports equipment, such as loss of elasticity, poses a certain risk of injury to the user's body. The sports equipment for different types of sports are correspondingly different, that is, the specific name of the sports equipment depends on the specific type of sport. For example, when running, the corresponding sports equipment may refer to running shoes, and when playing basketball, football, etc., the corresponding sports equipment may be basketball shoes, football shoes, etc. For the convenience of explanation and understanding, the embodiments of this application mainly take running and running shoes as an example, and evaluate sports risks by analyzing the elasticity index of running shoes.

[0029] The above-mentioned motion data may include one or more of acceleration data, angular velocity data, speed data, displacement data, etc. By analyzing these motion data, relevant indicators of the user during exercise can be calculated, such as ground contact reaction force, vertical amplitude, running length, impact peak, etc. These indicators can be used to further calculate the user's sports risk indicators.

[0030] In this step, there are many ways to obtain the motion data. For example, the inertial measurement unit (IMU) on the wearable device worn by the user during exercise, such as a smart watch, smart glasses, professional running sensor, etc., can be used to collect sensor data such as acceleration and angular velocity of the user when the user contacts the ground during exercise to obtain the user's motion data; for example, the user's motion video can be collected through a high-definition camera and a motion capture system, and the user's motion posture and other data in the video can be analyzed to obtain the user's motion data. Of course, there can be other data acquisition methods, and the embodiments of the present application do not impose any restrictions on this.

[0031] The acquired motion data usually contains various information during the motion process, such as various information during running. By processing and analyzing these motion data, features related to the elastic properties of sports equipment and / or the user's running posture features can be extracted.

[0032] Step 102: Analyze and calculate a motion risk index based on the motion data, wherein the motion risk index includes at least one of the following: an elasticity index of the sports equipment, and a motion posture index of the user during exercise.

[0033] In an embodiment of the present application, a suitable sports risk index for evaluating sports risks can be determined. The sports risk index is a relevant indicator that can relatively well evaluate sports risks. Specifically, considering that the elasticity index of sports equipment has a greater impact on athletes, bad sports posture not only easily causes physical injury to athletes, but also is the source of elasticity loss of sports equipment. Therefore, the embodiment of the present application can adopt at least one of the elasticity index and sports posture index of sports equipment as an indicator for evaluating sports risks.

[0034] In this step, the motion data obtained in step 101 can be analyzed and calculated accordingly according to the calculation method of the motion risk index to obtain the motion risk index. For example, the user's motion frequency, stride, impact peak, etc. can be calculated by analyzing the acceleration data during motion. For another example, by defining a calculation formula for the elasticity index, various parameters in the calculation formula for the elasticity index, such as ground contact reaction force, vertical amplitude, etc., are calculated based on acceleration data, angular velocity data, etc., and then substituted into the calculation formula to calculate the current elasticity index of the sports equipment.

[0035] It should be noted that in order to improve the quality of the data and ensure the accuracy of the calculation results, the acquired motion data can be preprocessed before calculating the motion risk index, such as data cleaning, filtering, extreme value processing, etc., that is, after step 101 and before step 102, the method also includes: preprocessing the motion data; step 102 includes: analyzing and calculating the motion risk index based on the preprocessed motion data.

[0036] For example, considering that the motion data collected by the IMU may contain some noise or outliers, these noises may affect the reliability and accuracy of the data. To improve the quality of the data, the data can be filtered. Common filtering methods include digital filters, such as low-pass filtering and median filtering.

[0037] Low-pass filtering can remove high-frequency noise, and its transfer function can be expressed as:

[0038] H(f)=1 / (1+(f / f c ) 2 )

[0039] Where, f represents the frequency, f c is the cutoff frequency. By low-pass filtering, low-frequency signals can be retained and high-frequency noise can be removed.

[0040] Median filtering can reduce the impact of impulse noise. Its basic principle is to take the median value of each point in the data sequence as the filtering result of the point. For example, for the data sequence x1, x2, ..., x n, take the median filter with a window size of 3, and the filtered data y i It can be expressed as:

[0041] y i =median(x i-1 ,x i ,x i+1 )

[0042] Other filtering methods such as Kalman filters and particle filters can be used for more complex filtering tasks. These filtering methods can be selected and applied according to the characteristics and requirements of the data to improve the accuracy and reliability of the data.

[0043] It should be noted that before performing the above filtering process on the collected motion data, the motion data may be first converted into frequency domain data, and then the above filtering process may be performed.

[0044] Optionally, the motion data includes acceleration data of the user during motion;

[0045] In the case where the sports risk indicator includes an elasticity indicator of the sports equipment, step 102 includes:

[0046] calculating a ground contact reaction force of the user during exercise based on the acceleration data and the user's body data;

[0047] Calculating the vertical amplitude of the user during movement based on the acceleration data;

[0048] An elasticity index of the sports equipment is calculated based on the ground contact reaction force and the vertical amplitude.

[0049] In some embodiments, the elasticity index used to characterize the elastic characteristics of the current sports equipment can be calculated based on the acceleration data and body data of the user during movement. Specifically, the analysis and calculation can be performed in combination with the corresponding elasticity index calculation formula, wherein the movement data at least includes the acceleration data of the user during movement. It can be understood that the acceleration data can be a sequence data, that is, it records the continuous acceleration data of the user for a period of time during dynamic movement; the body data can include at least one of height and weight. To ensure the accuracy of the evaluation, the user's height and weight can be combined for calculation.

[0050] Specifically, the acquired acceleration data can be converted from the time domain to the frequency domain. For example, assuming the acquired acceleration data is a(t) and the angular velocity data is ω(t), the Fourier transform can be used to obtain the acceleration data A(f) and angular velocity data Ω(f) in the frequency domain, where f represents the frequency. Then, elasticity-related features such as the elasticity index K can be extracted from these frequency domain data. Specifically, they can be calculated using the following formula:

[0051] K=Y C F

[0052] Among them, F is the ground contact reaction force, Y C is the vertical amplitude during movement, such as Y in running C Characterizes the amplitude of the user's up and down movement. The ground contact reaction force F can be calculated using acceleration data and the user's body data. Specifically, the corresponding spring coefficient can be calculated based on the user's height and weight or determined by table lookup. The velocity data is then determined based on the acceleration data, and then the angle of the velocity vector relative to the vertical direction is determined. Finally, the ground contact reaction force F is calculated by combining the spring coefficient, acceleration data, and angle. All three are directly proportional to the ground contact reaction force F.

[0053] Vertical amplitude Y C It can be calculated based on the acceleration data. For example, the velocity data can be obtained by integrating the acceleration data. By integrating the velocity data, specifically integrating the vertical component of the velocity data, the vertical displacement can be obtained. Then, the peak and valley values ​​of the vertical displacement are determined, and the difference between the peak and valley values ​​is calculated to obtain the vertical amplitude.

[0054] In this way, through this embodiment, the elasticity index used to characterize the elasticity characteristics of the sports equipment can be accurately calculated based on the acceleration data and the user's body data, thereby facilitating the accurate evaluation of the real-time elasticity characteristics of the sports equipment.

[0055] Optionally, calculating the ground contact reaction force of the user during exercise based on the acceleration data and the height and weight of the user includes:

[0056] Integrating the horizontal component of the acceleration data to obtain a horizontal velocity of the user during movement;

[0057] Integrating the vertical component of the acceleration data to obtain the vertical velocity of the user during movement;

[0058] Calculating an angle between a velocity vector of the user in motion and a vertical direction according to the horizontal velocity and the vertical velocity;

[0059] Determining a corresponding elastic coefficient according to the height and weight of the user;

[0060] The ground contact reaction force is calculated according to the spring coefficient, the acceleration data, and the included angle.

[0061] In some embodiments, the ground contact reaction force F may be calculated based on an elastic coefficient related to the user's height and weight, the acceleration data, and the angle between the velocity vector and the vertical direction. For example, the ground contact reaction force F may be calculated with reference to the following formula:

[0062] F=ηmax(A(f))·cosθ

[0063] Where η is a coefficient that can be calculated based on the user's physical data such as height and weight or determined by looking up a table. A(f) represents the acceleration data in the frequency domain, and θ is the angle between the velocity vector and the vertical direction during motion.

[0064] The velocity vector can be obtained by integrating the acceleration data; specifically, the horizontal component of the acceleration data, that is, the horizontal acceleration data a x By integrating, we can get the corresponding horizontal velocity v x , by the vertical component of the acceleration data, that is, the vertical acceleration data a z By performing integral calculation, we can get the corresponding vertical velocity v z , and finally the horizontal velocity v can be calculated x With vertical velocity v z The angle θ between the velocity vector and the vertical direction (Z axis) can be obtained by the angle between the velocity vector and the vertical direction (Z axis). For example, θ=arctan(v z / v x ).

[0065] Through this implementation, it is possible to ensure that the ground contact reaction force of the user during exercise such as running can be calculated more accurately, thereby ensuring the accuracy of the elasticity index calculated based on the ground contact reaction force.

[0066] Optionally, the motion data includes acceleration data of the user during motion;

[0067] In the case where the motion risk index includes the motion posture index, analyzing and calculating the motion risk index based on the motion data includes:

[0068] Calculating motion posture data of the user during exercise based on the acceleration data, the motion posture data including at least one of the following: cadence, stride, impact peak value, and vertical amplitude;

[0069] The motion posture index is calculated according to the motion posture data.

[0070] In some embodiments, the motion posture data of the user during motion, such as one or more indicator data of cadence, stride, impact peak, vertical amplitude, etc., can be calculated based on the acquired acceleration data. For example, the cadence can be determined based on the periodic change characteristics of the acceleration data sequence over a continuous period of time, the stride can be obtained by calculating the integral of the acceleration data in space, or by calculating the distance between extreme points in the acceleration data sequence, etc. The impact peak can be determined based on the maximum value in the acceleration data, and the vertical amplitude is obtained based on the integral of the acceleration data.

[0071] Then, the various motion posture data obtained by comprehensive calculation can be used to generate corresponding motion posture indicators. Specifically, appropriate calculation formulas can be designed to ensure that the motion posture indicators are positively correlated with the various motion posture data.

[0072] In this way, through this implementation, the user's motion posture data such as cadence, stride, impact peak, vertical amplitude, etc. can be analyzed based on the user's motion acceleration data, and then these data can be combined to calculate the motion posture index used to characterize the user's motion posture or the amplitude of posture change. Such a calculation process can ensure the accuracy and reliability of the final calculation results.

[0073] Optionally, calculating the motion posture data of the user during motion based on the acceleration data includes:

[0074] Analyzing periodic variation characteristics of the acceleration data to determine a gait cycle of the user during exercise;

[0075] determining a step frequency of the user during exercise according to the gait cycle;

[0076] Integrating the acceleration data according to the start time and end time of the gait cycle to obtain the stride of the user during exercise;

[0077] An impact peak value of the user during exercise is determined according to a maximum acceleration value in the acceleration data.

[0078] In some embodiments, the acceleration data can be analyzed to calculate the movement posture related indicators such as the step frequency, stride length, and impact peak. s The motion period T can be calculated s The reciprocal of is obtained, that is, it is calculated by the following formula:

[0079] f s =1 / T s

[0080] Among them, the motion period T sIt can be determined by analyzing the periodic variation pattern in the acceleration data sequence. For example, if the acceleration data from time t1 to time t2 and the acceleration data from time t2 to time t3 show similar variation patterns, then the difference between time t2 and time t1 is considered to be a gait cycle T. s .

[0081] The stride length can be obtained by calculating the integral of the acceleration data in space, or by calculating the distance between the extreme points in the acceleration sequence. For example, assuming that the acceleration data is a(t), the stride length S can be calculated by the following formula:

[0082]

[0083] Wherein, t1 and t2 are the start time and end time of the gait cycle respectively.

[0084] The impact peak value can be determined by detecting the peak point in the acceleration data and calculating the magnitude of the peak value. For example, for the acceleration data a(t), the impact peak value P can be expressed as:

[0085] P = max(a(t))

[0086] In this way, through this implementation, the user's cadence, stride, impact peak and other indicators that can reflect the user's movement posture or movement amplitude in running and other sports can be calculated, which helps to accurately analyze the user's movement posture indicators and measure the impact of movement posture on the elasticity loss of equipment.

[0087] Optionally, calculating the motion posture index according to the motion posture data includes:

[0088] Determine weight coefficients corresponding to the cadence, the stride, and the impact peak respectively;

[0089] The cadence, the stride, and the peak impact value are weightedly calculated according to the weight coefficients corresponding to the cadence, the stride, and the peak impact value, to obtain the motion posture index.

[0090] In other words, in some embodiments, the influence of running posture data such as cadence, stride length, and peak impact on the overall running posture can be used to determine corresponding weight coefficients, and the overall running posture index can be calculated using a weighted approach. For example, running posture-related parameters such as cadence, stride length, and peak impact can be weighted to determine the running posture change index, and the main source of the actual elasticity loss of the running shoe can be further identified based on the running posture change index.

[0091] For example, assuming the step frequency is f s, the stride is S, the impact peak is P, and the motion posture index can be calculated by the following formula, which can also be called the running posture change index P in running pose :

[0092] P pose =αf s +βS+γP

[0093] Among them, α, β, and γ are the step frequency f s The weight coefficients of stride S and impact peak P can be adjusted according to actual needs. pose , can analyze whether the user's running posture is appropriate, whether there is a bad running posture, etc., such as calculating P pose When the value is greater than a certain value, the user is reminded that the current running posture changes too much and there may be a risk of injury. Alternatively, the user can pose Further determine the main source of actual elasticity loss of running shoes, such as P pose If it is too large, it means that the current running speed is too fast and the running posture changes greatly, which causes a greater impact on the wear of running shoes. pose Normal, it means that the current running posture change is within the normal range, and the wear of the running shoes is small, so it can be based on P pose The analysis provides users with more accurate suggestions, such as buying new running shoes or adjusting running posture, to avoid running risks.

[0094] It should be noted that in some embodiments, the vertical amplitude Y C As one of the factors affecting the running posture change index, it is also calculated pose It can also be called vertical amplitude Y C Participate in weighted calculation, such as the calculation formula: P pose =αf s +βS+γP+λY C , λ is the vertical amplitude Y C The weight coefficient of .

[0095] Through this implementation, it is possible to ensure that motion posture data such as motion frequency, stride length and impact peak are reasonably utilized to measure motion posture indicators, ensuring that the measured motion posture indicators of the user during exercise are accurate and reliable, and the weight coefficients during calculation can be flexibly adjusted according to actual needs to achieve fine-tuning of the calculation results and ensure the accuracy and rationality of the calculation results.

[0096] Through the above feature extraction method, feature data reflecting the wear of sports equipment and bad sports posture can be obtained, providing a basis for subsequent evaluation and early warning.

[0097] Step 103: Evaluate the user's exercise risk based on the exercise risk indicator.

[0098] In this step, based on the sports risk index calculated in step 102, it can be well evaluated whether the user has sports risks. Specifically, when the sports risk index includes the elasticity index of the sports equipment, the current elasticity of the sports equipment can be determined based on the elasticity index, and the degree of change of the current elasticity index relative to the initial elasticity index of the sports equipment can also be calculated to analyze the elasticity loss of the sports equipment, so as to know whether the current elasticity of the sports equipment is insufficient or whether the elasticity loss is too much and cannot ensure that the user is not injured during exercise. If so, it can be assessed that there is currently a sports risk, and the lower the elasticity or the greater the loss (the corresponding lower the elasticity index), the higher the risk.

[0099] When the sports risk index includes the sports posture, it can be determined whether the user's current sports posture is correct and whether there is a bad sports posture based on the sports posture. If the sports posture is normal, it can be assessed that there is no sports risk. If there is a bad sports posture, it indicates that the current sports posture is likely to cause the user to be injured during exercise, or is likely to cause greater wear and tear on sports equipment. Therefore, it can be assessed that there is currently a sports risk, and the more serious the bad sports posture (such as the higher the sports posture index), the higher the risk.

[0100] Optionally, the sports risk index includes an elasticity index of the sports equipment;

[0101] Before step 103, the method further includes:

[0102] Acquiring initial exercise data of the user when using the sports equipment for the first time to exercise;

[0103] calculating an initial elasticity index of the sports equipment according to the initial sports data;

[0104] The step of evaluating the user's exercise risk according to the exercise risk indicator includes:

[0105] determining the elasticity loss degree of the sports equipment according to the elasticity index and the initial elasticity index;

[0106] The user's exercise risk is assessed according to the elasticity loss degree.

[0107] In some embodiments, the initial elasticity index of the sports equipment can be evaluated based on the user's initial exercise data, that is, the exercise data collected when the sports equipment is used for exercise for the first time, as a reference basis for evaluating the elasticity loss of the sports equipment in subsequent exercises, wherein the initial elasticity index can be understood as the elasticity index of the brand new sports equipment when it is used for the first time.

[0108] Specifically, an initial elastic index calculation model can be established based on the motion data collected during the user's first exercise, especially the impact characteristic data of the motion elastic domain. For example, machine learning methods such as support vector machines (SVM) or neural networks with features as input, as well as linear or nonlinear fitting or classification algorithms, can be used. These algorithms can basically obtain theoretically similar results.

[0109] For example, similar to the above-mentioned method of calculating the elasticity index, the vertical amplitude Y can be calculated based on the initial motion data. C The included angle θ between the velocity vector and the vertical direction, after calculating the coefficient η according to the user's height and weight, is substituted into the calculation formula F = ηmax (A (f)) cosθ and K = Y C F is calculated to obtain the initial elasticity index of the sports equipment.

[0110] The calculated initial elasticity index can provide a benchmark for subsequent monitoring and evaluation of the sports equipment. Specifically, when assessing sports risk, the initial elasticity index can be compared with the real-time data, i.e., the elasticity index calculated in step 102, to calculate the actual elasticity loss of the sports equipment.

[0111] For example, the elasticity index calculated in step 102 is compared with the initial elasticity index, and mathematical methods such as regression analysis and correlation analysis can be used to evaluate the relationship between the actual data and the initial data. For example, a linear regression model can be used to calculate the actual elasticity loss degree; assuming that the initial elasticity index is K0 and the elasticity index in the real-time data is K t , then the actual elastic loss ΔK can be calculated by the following formula:

[0112] ΔK=K0-K t

[0113] By comparing and analyzing the initial and actual elasticity indicators, the actual degree of elasticity loss in sports equipment, such as running shoes, can be inferred. This may involve mathematical methods such as pattern recognition, machine learning algorithms, or optimization algorithms. These methods can accurately assess the wear of the sole and the impact of poor movement posture on elasticity, providing a scientific basis for subsequent early warning. For example, a greater elasticity loss ΔK indicates a greater elasticity loss in the running shoe, and correspondingly, a higher risk of exercise. When the threshold is exceeded, a timely warning can be issued, prompting the user to replace the running shoes.

[0114] Through this implementation, the initial elasticity index of the sports equipment is calculated based on the initial sports data, so that when assessing risks, the potential sports risks can be assessed more accurately and reliably based on the degree of deviation of the current actual elasticity index of the sports equipment relative to the initial elasticity index, providing a scientific means of protection for the user's sports safety.

[0115] It should be noted that, in some embodiments, the initial elasticity index of the sports equipment may also be predetermined and can be directly obtained and used. For example, the sports equipment may be given a factory elasticity index when it leaves the factory.

[0116] Optionally, step 103 includes at least one of the following:

[0117] When the motion posture index exceeds a first threshold, determining that the user has a potential motion risk, and outputting a first reminder message for reminding the user to adjust the motion posture;

[0118] When the elasticity index is lower than the second threshold, or when it is determined based on the elasticity index and the initial elasticity index of the sports equipment that the degree of elasticity loss of the sports equipment exceeds a third threshold, it is determined that the user is at a potential sports risk, and a second reminder message is output to remind the user to replace the sports equipment.

[0119] That is, in the embodiment of the present application, the change state of the sports posture and the wear of the sports equipment can be judged based on the sports posture index, elasticity index or elasticity loss degree. When the set threshold is reached, it triggers a reminder for the user to avoid sports risks by replacing new sports equipment or paying attention to sports posture.

[0120] Specifically, in some embodiments, the motion posture index can be used to evaluate whether the changes in the user's motion posture during exercise affect the user's motion safety. For example, a first threshold can be set, and whether the motion posture index exceeds the first threshold can be used to determine whether the user's motion posture is professional or whether it exhibits a bad motion posture. The first threshold can be defined according to the correct running posture of the actual sport type. When the first threshold is exceeded, it can be considered that the user's motion posture is bad and there is a risk of injury to the user. Therefore, it can be determined that there is a potential motion risk at present, and a corresponding reminder can be output, such as outputting a first reminder message, to remind the user to pay attention to adjusting the motion posture, such as adjusting the running posture to avoid the risk of sprains while running.

[0121] In other embodiments, the elasticity index can be used to evaluate whether the sports equipment is currently severely worn and lacks elasticity, thereby failing to ensure the user's sports safety. For example, a second threshold can be set, and whether the sports equipment lacks elasticity can be determined based on whether the elasticity index exceeds the second threshold. The second threshold can be defined based on the elasticity index that the sports equipment should at least have to play a protective role. When the second threshold is exceeded, it can be considered that the sports equipment lacks elasticity, such as if the sole is severely worn, and cannot well protect the user's sports safety, that is, there is a risk of user injury. Therefore, it can be determined that there is a potential sports risk at present, and a corresponding reminder can be output, such as a second reminder message, to remind the user to replace the sports equipment in time.

[0122] In some other embodiments, a potential sports risk assessment can be performed based on the actual elasticity loss calculated in the aforementioned embodiments. Specifically, an elasticity loss threshold ΔKthreshold can be set. When the actual elasticity loss ΔK reaches or exceeds this threshold, it is determined that the current sports equipment is severely worn and lacks elasticity, and the user needs to purchase new sports equipment. For example, mathematical methods such as decision trees, threshold judgments, or setting probability distributions can be used to make this determination. For example, assuming a set threshold of ΔKthreshold, when ΔK ≥ ΔKthreshold, a reminder message is triggered, reminding the user to replace their sports equipment or pay attention to sports safety.

[0123] In this way, through this implementation, it is possible to accurately assess sports risks and determine the source of risks based on sports posture, elasticity indicators, degree of elasticity loss, etc., and issue timely warnings based on the assessment results to remind users to pay attention to sports safety.

[0124] It should be noted that in actual evaluation, two main sources of elasticity loss can be considered. Taking running as an example, these include the wear of running shoes and changes in gait, and these two factors are often linked. Here, it is necessary to use running posture-related parameters such as cadence, stride length, vertical amplitude, etc. to identify running posture changes in order to further distinguish the main sources of actual elasticity loss. For example, when the running posture change index P is calculated, pose When it is high, it is considered that the elasticity loss of running shoes caused by the change of user's running posture accounts for a high proportion; otherwise, it can be considered that the elasticity loss is mainly caused by the normal wear of the running shoes themselves. pose , can further determine the main source of actual elasticity loss, thereby providing users with more accurate suggestions, such as buying new running shoes or adjusting running posture to avoid running risks.

[0125] It should also be noted that the embodiment of the present application can not only be used for running posture safety reminders, but can actually also be applied to various ground sports, such as Pilates, indoor high-intensity interval training (HIIT), and other similar sports that require maintaining flexibility to maintain safety.

[0126] In summary, the present embodiment uses an IMU mounted on a wearable device to extract the elasticity and impact characteristics of the ground during running. This information is then compared with historical elasticity characteristics, and by measuring elasticity loss, it estimates sole wear and changes in poor running form. When elasticity decreases to a certain level, the system initiates an alert, warning the user of injury risk.

[0127] The technical solution of the embodiments of this application can be used to monitor and estimate the wear of running shoe soles and changes in the user's gait in real time, notifying the user in advance to purchase new running shoes or adjust their running posture to protect their health. In addition, through scientific data measurement and analysis, users can more accurately understand their running safety status.

[0128] The sports risk assessment method in the embodiment of the present application obtains the sports data of the user when exercising with sports equipment; analyzes and calculates the sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: the elasticity index of the sports equipment and the sports posture index of the user during exercise; and assesses the sports risk of the user based on the sports risk index. In this way, by analyzing and calculating the sports risk index based on the user's sports data, such as the elasticity index of the sports equipment and the sports posture index of the user during exercise, when analyzing and calculating the elasticity index, the elasticity state of the sports equipment can be accurately assessed based on the elasticity index of the sports equipment, so that when it is assessed that the equipment has insufficient elasticity or has a large elasticity loss, the user can be promptly reminded to replace it, thereby avoiding the risk of sports injuries caused by poor sports equipment; when analyzing and calculating the sports posture index, the user's sports posture can be accurately assessed based on the sports posture index, so that when it is assessed that the user has an unhealthy sports posture, a timely warning can be issued, thereby avoiding the risk of sports injuries caused by unhealthy sports posture. It can be seen that the embodiment of the present application provides an accurate and reliable sports risk index assessment scheme that can accurately and reliably assess sports risks based on the user's sports data, thereby effectively ensuring the safety of the user during exercise.

[0129] The sports risk assessment method provided in the embodiment of the present application can be executed by a sports risk assessment device. In the embodiment of the present application, the sports risk assessment device provided in the embodiment of the present application is described by taking the sports risk assessment device executing the sports risk assessment method as an example.

[0130] See Figure 2 , Figure 2 This is a schematic diagram of the structure of the sports risk assessment device provided in the embodiment of the present application, as shown in FIG. Figure 2 As shown, the sports risk assessment device 200 includes:

[0131] The first acquisition module 201 is used to acquire the exercise data of the user when exercising with the sports equipment;

[0132] A first calculation module 202 is configured to analyze and calculate a sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: an elasticity index of the sports equipment and a sports posture index of the user during exercise;

[0133] The evaluation module 203 is configured to evaluate the user's exercise risk based on the exercise risk index.

[0134] Optionally, the motion data includes acceleration data of the user during motion;

[0135] In the case where the sports risk index includes the elasticity index of the sports equipment, the first calculation module 202 includes:

[0136] a first calculation unit, configured to calculate a ground contact reaction force of the user during exercise based on the acceleration data and the user's body data;

[0137] a second calculation unit, configured to calculate a vertical amplitude of the user during movement based on the acceleration data;

[0138] A third calculation unit is configured to calculate an elasticity index of the sports equipment according to the ground contact reaction force and the vertical amplitude.

[0139] Optionally, the first computing unit is configured to:

[0140] Integrating the horizontal component of the acceleration data to obtain a horizontal velocity of the user during movement;

[0141] Integrating the vertical component of the acceleration data to obtain the vertical velocity of the user during movement;

[0142] Calculating an angle between a velocity vector of the user in motion and a vertical direction according to the horizontal velocity and the vertical velocity;

[0143] Determining a corresponding elastic coefficient according to the height and weight of the user;

[0144] The ground contact reaction force is calculated according to the spring coefficient, the acceleration data, and the included angle.

[0145] Optionally, the motion data includes acceleration data of the user during motion;

[0146] In the case where the motion risk indicator includes the motion posture indicator, the first calculation module 202 includes:

[0147] a fourth calculation unit, configured to calculate motion posture data of the user during exercise based on the acceleration data, wherein the motion posture data includes at least one of the following: cadence, stride, impact peak value, and vertical amplitude;

[0148] A fifth calculation unit is configured to calculate the motion posture index based on the motion posture data.

[0149] Optionally, the fourth computing unit is configured to:

[0150] Analyzing periodic variation characteristics of the acceleration data to determine a gait cycle of the user during exercise;

[0151] determining a step frequency of the user during exercise according to the gait cycle;

[0152] Integrating the acceleration data according to the start time and end time of the gait cycle to obtain the stride of the user during exercise;

[0153] An impact peak value of the user during exercise is determined according to a maximum acceleration value in the acceleration data.

[0154] Optionally, the fifth computing unit is configured to:

[0155] Determine weight coefficients corresponding to the cadence, the stride, and the impact peak respectively;

[0156] The cadence, the stride, and the peak impact value are weightedly calculated according to the weight coefficients corresponding to the cadence, the stride, and the peak impact value, to obtain the motion posture index.

[0157] Optionally, the sports risk index includes an elasticity index of the sports equipment;

[0158] The sports risk assessment device 200 further includes:

[0159] A second acquisition module is used to acquire the initial exercise data of the user when the user uses the sports equipment for the first time to exercise;

[0160] a second calculation module, configured to calculate an initial elasticity index of the sports equipment based on the initial sports data;

[0161] The evaluation module 203 includes:

[0162] a determining unit, configured to determine a degree of elasticity loss of the sports equipment according to the elasticity index and the initial elasticity index;

[0163] An evaluation unit is configured to evaluate the user's exercise risk based on the elasticity loss degree.

[0164] Optionally, the evaluation module 203 is configured to perform at least one of the following:

[0165] When the motion posture index exceeds a first threshold, determining that the user has a potential motion risk, and outputting a first reminder message for reminding the user to adjust the motion posture;

[0166] When the elasticity index is lower than the second threshold, or when it is determined based on the elasticity index and the initial elasticity index of the sports equipment that the degree of elasticity loss of the sports equipment exceeds a third threshold, it is determined that the user is at a potential sports risk, and a second reminder message is output to remind the user to replace the sports equipment.

[0167] The sports risk assessment device 200 in the embodiment of the present application obtains the sports data of the user when exercising with sports equipment; analyzes and calculates the sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: the elasticity index of the sports equipment, the sports posture index of the user during exercise; and assesses the sports risk of the user based on the sports risk index. In this way, by analyzing and calculating the sports risk index based on the user's sports data, such as the elasticity index of the sports equipment, the sports posture index of the user during exercise, etc., when analyzing and calculating the elasticity index, the elasticity state of the sports equipment can be accurately assessed based on the elasticity index of the sports equipment, so that when it is assessed that the equipment has insufficient elasticity or has a large elasticity loss, the user can be promptly reminded to replace it, thereby avoiding the risk of sports injuries caused by poor sports equipment; when analyzing and calculating the sports posture index, the user's sports posture can be accurately assessed based on the sports posture index, so that when it is assessed that the user has an unhealthy sports posture, a timely warning can be issued, thereby avoiding the risk of sports injuries caused by unhealthy sports posture. It can be seen that the embodiment of the present application provides an accurate and reliable sports risk index assessment solution that can accurately and reliably assess sports risks based on the user's sports data, thereby effectively ensuring the safety of the user during exercise.

[0168] The motion risk assessment device in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices except the terminal. Exemplary, the electronic device can be a mobile phone, a tablet computer, a notebook computer, a palmtop computer, an on-board electronic device, a mobile internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) equipment, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., and the embodiment of the present application is not specifically limited.

[0169] The sports risk assessment device in the embodiment of the present application can be a device having an operating system. The operating system can be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0170] The sports risk assessment device provided in the embodiment of the present application can achieve Figure 1 The various processes implemented by the method embodiment can achieve the same technical effect, and to avoid repetition, they will not be described here.

[0171] Alternatively, as Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a processor 301 and a memory 302, wherein the memory 302 stores a program or instruction that can be run on the processor 301, and when the program or instruction is executed by the processor 301, the various steps of the above-mentioned motion risk assessment method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0172] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0173] Figure 4 A schematic diagram of the hardware structure of an electronic device implementing an embodiment of the present application.

[0174] The electronic device 400 includes but is not limited to components such as a radio frequency unit 401 , a network module 402 , an audio output unit 403 , an input unit 404 , a sensor 405 , a display unit 406 , a user input unit 407 , an interface unit 408 , a memory 409 , and a processor 410 .

[0175] Those skilled in the art will understand that the electronic device 400 may also include a power source (such as a battery) to power each component, and the power source may be logically connected to the processor 410 through a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system. Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be repeated here.

[0176] The processor 410 is configured to:

[0177] Obtaining sports data of users when exercising with sports equipment;

[0178] Analyzing and calculating a sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: an elasticity index of the sports equipment, and a sports posture index of the user during exercise;

[0179] The user's exercise risk is assessed according to the exercise risk indicator.

[0180] Optionally, the motion data includes acceleration data of the user during motion;

[0181] The processor 410 is further configured to:

[0182] When the sports risk indicator includes an elasticity indicator of the sports equipment, calculating a ground contact reaction force of the user during exercise based on the acceleration data and the user's body data;

[0183] Calculating the vertical amplitude of the user during movement based on the acceleration data;

[0184] An elasticity index of the sports equipment is calculated based on the ground contact reaction force and the vertical amplitude.

[0185] Optionally, the processor 410 is further configured to:

[0186] Integrating the horizontal component of the acceleration data to obtain a horizontal velocity of the user during movement;

[0187] Integrating the vertical component of the acceleration data to obtain the vertical velocity of the user during movement;

[0188] Calculating an angle between a velocity vector of the user in motion and a vertical direction according to the horizontal velocity and the vertical velocity;

[0189] Determining a corresponding elastic coefficient according to the height and weight of the user;

[0190] The ground contact reaction force is calculated according to the spring coefficient, the acceleration data, and the included angle.

[0191] Optionally, the motion data includes acceleration data of the user during motion;

[0192] The processor 410 is further configured to:

[0193] In a case where the motion risk indicator includes the motion posture indicator, motion posture data of the user during motion is calculated based on the acceleration data, the motion posture data including at least one of the following: cadence, stride, impact peak value, and vertical amplitude;

[0194] The motion posture index is calculated according to the motion posture data.

[0195] Optionally, the processor 410 is further configured to:

[0196] Analyzing periodic variation characteristics of the acceleration data to determine a gait cycle of the user during exercise;

[0197] determining a step frequency of the user during exercise according to the gait cycle;

[0198] Integrating the acceleration data according to the start time and end time of the gait cycle to obtain the stride of the user during exercise;

[0199] An impact peak value of the user during exercise is determined according to a maximum acceleration value in the acceleration data.

[0200] Optionally, the processor 410 is further configured to:

[0201] Determine weight coefficients corresponding to the cadence, the stride, and the impact peak respectively;

[0202] The cadence, the stride, and the peak impact value are weightedly calculated according to the weight coefficients corresponding to the cadence, the stride, and the peak impact value, to obtain the motion posture index.

[0203] Optionally, the sports risk index includes an elasticity index of the sports equipment;

[0204] The processor 410 is further configured to:

[0205] Acquiring initial exercise data of the user when using the sports equipment for the first time to exercise;

[0206] calculating an initial elasticity index of the sports equipment according to the initial sports data;

[0207] determining the elasticity loss degree of the sports equipment according to the elasticity index and the initial elasticity index;

[0208] The user's exercise risk is assessed according to the elasticity loss degree.

[0209] Optionally, the processor 410 is further configured to perform at least one of the following:

[0210] When the motion posture index exceeds a first threshold, determining that the user has a potential motion risk, and outputting a first reminder message for reminding the user to adjust the motion posture;

[0211] When the elasticity index is lower than the second threshold, or when it is determined based on the elasticity index and the initial elasticity index of the sports equipment that the degree of elasticity loss of the sports equipment exceeds a third threshold, it is determined that the user is at a potential sports risk, and a second reminder message is output to remind the user to replace the sports equipment.

[0212] It should be understood that in an embodiment of the present application, the input unit 404 may include a graphics processing unit (GPU) 4041 and a microphone 4042, and the graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 406 may include a display panel 4061, and the display panel 4061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 407 includes a touch panel 4071 and at least one of other input devices 4072. The touch panel 4071 is also called a touch screen. The touch panel 4071 may include two parts: a touch detection device and a touch controller. Other input devices 4072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and an operating stick, which will not be repeated here.

[0213] The memory 409 can be used to store software programs and various data. The memory 409 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 409 may include a volatile memory or a non-volatile memory, or the memory 409 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct memory bus random access memory (DRRAM). The memory 109 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0214] Processor 410 may include one or more processing units. Optionally, processor 410 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 410.

[0215] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned motion risk assessment method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0216] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0217] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned motion risk assessment method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0218] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0219] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned motion risk assessment method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0220] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0221] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0222] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A method for sports risk assessment, characterized in that: include: Obtaining sports data of users when exercising with sports equipment; Analyzing and calculating a sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: an elasticity index of the sports equipment, and a sports posture index of the user during exercise; The user's exercise risk is assessed according to the exercise risk indicator.

2. The method according to claim 1, characterized in that The motion data includes acceleration data of the user during motion; In the case where the sports risk index includes an elasticity index of the sports equipment, analyzing and calculating the sports risk index based on the sports data includes: calculating a ground contact reaction force of the user during exercise based on the acceleration data and the user's body data; Calculating the vertical amplitude of the user during movement based on the acceleration data; An elasticity index of the sports equipment is calculated based on the ground contact reaction force and the vertical amplitude.

3. The method according to claim 2, characterized in that Calculating the ground contact reaction force of the user during exercise based on the acceleration data and the height and weight of the user includes: Integrating the horizontal component of the acceleration data to obtain a horizontal velocity of the user during movement; Integrating the vertical component of the acceleration data to obtain the vertical velocity of the user during movement; Calculating an angle between a velocity vector of the user in motion and a vertical direction according to the horizontal velocity and the vertical velocity; Determining a corresponding elastic coefficient according to the height and weight of the user; The ground contact reaction force is calculated according to the spring coefficient, the acceleration data, and the included angle.

4. The method according to claim 1, wherein The motion data includes acceleration data of the user during motion; In the case where the motion risk index includes the motion posture index, analyzing and calculating the motion risk index based on the motion data includes: Calculating motion posture data of the user during exercise based on the acceleration data, the motion posture data including at least one of the following: cadence, stride, impact peak value, and vertical amplitude; The motion posture index is calculated according to the motion posture data.

5. The method according to claim 4, characterized in that Calculating the motion posture data of the user during motion based on the acceleration data includes: Analyzing periodic variation characteristics of the acceleration data to determine a gait cycle of the user during exercise; determining a step frequency of the user during exercise according to the gait cycle; Integrating the acceleration data according to the start time and end time of the gait cycle to obtain the stride of the user during exercise; An impact peak value of the user during exercise is determined according to a maximum acceleration value in the acceleration data.

6. The method according to claim 5, characterized in that Calculating the motion posture index according to the motion posture data includes: Determine weight coefficients corresponding to the cadence, the stride, and the impact peak respectively; The cadence, the stride, and the peak impact value are weightedly calculated according to the weight coefficients corresponding to the cadence, the stride, and the peak impact value, to obtain the motion posture index.

7. The method according to any one of claims 1 to 6, characterized in that The sports risk index includes an elasticity index of the sports equipment; Before evaluating the user's exercise risk based on the exercise risk indicator, the method further includes: Acquiring initial exercise data of the user when using the sports equipment for the first time to exercise; calculating an initial elasticity index of the sports equipment according to the initial sports data; The step of evaluating the user's exercise risk according to the exercise risk indicator includes: determining the elasticity loss degree of the sports equipment according to the elasticity index and the initial elasticity index; The user's exercise risk is assessed according to the elasticity loss degree.

8. The method according to any one of claims 1 to 6, characterized in that The step of evaluating the user's exercise risk according to the exercise risk indicator includes at least one of the following: When the motion posture index exceeds a first threshold, determining that the user has a potential motion risk, and outputting a first reminder message for reminding the user to adjust the motion posture; When the elasticity index is lower than the second threshold, or when it is determined based on the elasticity index and the initial elasticity index of the sports equipment that the degree of elasticity loss of the sports equipment exceeds a third threshold, it is determined that the user is at a potential sports risk, and a second reminder message is output to remind the user to replace the sports equipment.

9. A sports risk assessment device, characterized in that: include: The first acquisition module is used to acquire the exercise data of the user when exercising with the sports equipment; a first calculation module, configured to analyze and calculate a sports risk index based on the sports data, wherein the sports risk index includes at least one of the following: an elasticity index of the sports equipment and a sports posture index of the user during exercise; An evaluation module is used to evaluate the user's exercise risk based on the exercise risk indicator.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the motion risk assessment method according to any one of claims 1 to 8 are implemented.

Images