Motion evaluation method, apparatus, electronic device, and readable storage medium

Abstract

This application discloses a method and apparatus for sports evaluation, belonging to the field of data processing. The method includes: collecting the user's sports data through the wearable device when it is detected that a wearable device is worn on the user's wrist and the wearable device is in sports data collection mode; determining the index data of preset sports indicators based on the sports data, the preset sports indicators including at least one of the following: offensive index, endurance index, explosive index, confrontation index, training index, strength index, activity index, and reaction index; and generating a sports evaluation result based on the index data of the preset sports indicators.

Description

Technical Field

[0001] This application belongs to the field of data processing, and specifically relates to a motion evaluation method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] Smartwatches and smart bracelets, as new types of consumer electronics products, have gradually gained market recognition and popularity in recent years. With technological advancements and consumers' pursuit of healthy lifestyles, their functions have been continuously enriched and improved, especially in sports and health monitoring, where they have become important tools in users' daily lives. Smartwatches and smart bracelets incorporate various sensors, such as accelerometers, gyroscopes, heart rate sensors, blood oxygen sensors, and body temperature sensors, enabling real-time monitoring of the wearer's physical activity, exercise, heart rate, sleep, blood oxygen, body temperature, and other sports and health data.

[0003] While smartwatches and smart bands have made significant progress in sports and health monitoring, they still fall short in advanced sports data analysis. Currently, most smartwatches and smart bands primarily offer basic data monitoring and simple data analysis functions, such as step count, calorie consumption, and exercise time. Although this data can reflect the user's exercise volume and basic health status, it cannot provide more in-depth sports data analysis.

[0004] Therefore, wearable devices such as smartwatches and smart bracelets cannot provide users with effective guidance on scientific exercise. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, electronic device, and readable storage medium for evaluating exercise, which can provide effective guidance to users in scientific exercise through wearable devices.

[0006] In a first aspect, embodiments of this application provide a motion evaluation method, the method comprising:

[0007] When it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data acquisition mode, the user's motion data is collected through the wearable device.

[0008] Based on the exercise data, the indicator data of the preset exercise indicators are determined. The preset exercise indicators include at least one of the following: offensive indicators, endurance indicators, explosiveness indicators, confrontation indicators, training indicators, strength indicators, activity indicators, and reaction indicators.

[0009] Based on the index data of the preset exercise indicators, an exercise evaluation result is generated.

[0010] Secondly, embodiments of this application provide a motion evaluation device, the device comprising:

[0011] The data acquisition module is used to acquire the user's motion data through the wearable device when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data acquisition mode.

[0012] The determination module is used to determine the index data of the preset sports index based on the sports data. The preset sports index includes at least one of the following: offensive index, endurance index, explosive index, confrontation index, training index, strength index, activity index, and reaction index.

[0013] The evaluation module is used to generate exercise evaluation results based on the index data of the preset exercise indicators.

[0014] Thirdly, embodiments of this application provide an electronic device including a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0015] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0016] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0017] In a sixth aspect, embodiments of this application provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the method described in the first aspect.

[0018] In the embodiments of this application, when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data collection mode, the user's motion data is collected through the wearable device. By monitoring the user's motion status in real time, the accuracy and timeliness of the motion data can be ensured, providing basic data for subsequent motion index analysis. Based on the motion data, preset motion index data are determined. The preset motion indexes include at least one of the following: offensive index, endurance index, explosive index, confrontation index, training index, strength index, activity index, and reaction index. By quantifying the user's motion performance, the user can understand their abilities in different preset motion indexes, providing data support for generating motion evaluation results. Based on the preset motion index data, motion evaluation results are generated. By providing comprehensive motion evaluation results that reflect the user's motion performance, the user can understand their strengths and weaknesses, providing effective guidance for scientific training to improve the user's exercise results. Attached Figure Description

[0019] Figure 1 This is a flowchart of a motion evaluation method provided in an embodiment of this application;

[0020] Figure 2 This is a schematic diagram of a motion evaluation result provided in an embodiment of this application;

[0021] Figure 3 This is a schematic diagram of a motion evaluation result provided in an embodiment of this application;

[0022] Figure 4 This is a structural diagram of a motion evaluation device provided in an embodiment of this application;

[0023] Figure 5 This is one of the hardware structure diagrams of the electronic device according to an embodiment of this application;

[0024] Figure 6 This is the second schematic diagram of the hardware structure of the electronic device according to an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0026] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0027] In response to the problems in related technologies, embodiments of this application provide a method, device, electronic device, and readable storage medium for evaluating exercise, which can solve the problem that smartwatches and smart bracelets cannot provide effective guidance for users in scientific exercise.

[0028] The motion evaluation method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0029] Figure 1 This is a flowchart of a motion evaluation method provided in an embodiment of this application.

[0030] like Figure 1 As shown, the motion evaluation method may include steps 110-130. This method is applied to a motion evaluation device, as detailed below:

[0031] Step 110: When it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data collection mode, the user's motion data is collected through the wearable device.

[0032] Wearable devices: Electronic devices that can be worn on a user's body, typically including smartwatches, smart bracelets, etc. They can monitor and record the user's exercise data, such as heart rate, steps, running speed, distance, calories burned, etc.

[0033] Exercise data: Various data collected by wearable devices or other sensors during a user's exercise, such as heart rate, steps, speed, distance, and calorie consumption. This data is used to analyze the user's exercise status and effectiveness, helping them understand their athletic performance.

[0034] Preset athletic metrics: These are metrics pre-defined in sports data analysis to evaluate a user's athletic performance, such as offensive metrics, endurance metrics, and explosiveness metrics. These preset metrics quantify a user's athletic performance, helping them understand their abilities in different aspects of their sport.

[0035] When a wearable device is worn on a user's wrist and is in motion data acquisition mode, it monitors the user's motion data in real time using built-in sensors, such as accelerometers and heart rate sensors. The motion data is recorded and stored in the wearable device's memory or transmitted wirelessly to the user's phone or other devices.

[0036] By monitoring users' exercise status in real time, we can ensure the accuracy and timeliness of exercise data, providing a foundation for subsequent exercise indicator analysis.

[0037] Step 120: Based on the exercise data, determine the indicator data of the preset exercise indicators. The preset exercise indicators include at least one of the following: offensive indicators, endurance indicators, explosiveness indicators, confrontation indicators, training indicators, strength indicators, activity indicators, and reaction indicators.

[0038] Metric data: Specific numerical values ​​calculated from exercise data, used to represent a user's performance on a preset exercise metric. Metric data is used to generate exercise evaluation results, helping users understand their performance on different exercise metrics.

[0039] Based on the collected exercise data, preset algorithms and models are used to calculate the user's performance on various preset exercise indicators. For example, by analyzing the user's heart rate changes and exercise intensity, the user's endurance index can be calculated; by analyzing the user's cadence and stride length, the user's burst index can be calculated.

[0040] By quantifying users' athletic performance, we can help them understand their abilities in different athletic metrics and provide data support for generating athletic evaluation results.

[0041] Step 130: Generate exercise evaluation results based on the preset exercise index data.

[0042] Exercise evaluation results: Evaluations of a user's exercise performance generated based on indicator data, typically including textual descriptions and numerical scores. These help users understand their exercise performance and guide them towards more effective training.

[0043] Based on the calculated indicator data, and using preset evaluation criteria and algorithms, an evaluation result for the user's athletic performance is generated. The evaluation result typically includes a textual description and a numerical score, helping users intuitively understand their athletic performance.

[0044] By providing a comprehensive assessment of users' athletic performance, we help users understand their strengths and weaknesses, guide them in more targeted training, and improve their athletic results.

[0045] Therefore, by monitoring users' exercise data in real time and generating detailed indicator data and exercise evaluation results based on the exercise data, it can not only help users understand their exercise performance, but also guide users to conduct more effective training, thereby improving exercise results.

[0046] In one possible embodiment, step 120 may specifically include the following steps:

[0047] The force data for each ball is obtained from the motion data; the force data is the force data collected by the wearable device when the racket hitting action is detected.

[0048] Based on the power data of each attacking ball, determine the target data for the attack.

[0049] Overhand serve: In volleyball, an overhand serve refers to a serve in which a player hits the ball downwards from above using their palm or fist. Overhand serves typically have a high trajectory and strong power, and are a common serve in volleyball matches.

[0050] Force data: Data on the amount of force a user exerts during an overhand shot, collected through wearable devices or other sensors. Used to analyze the user's power performance during the shot and help assess the user's offensive capabilities.

[0051] Racquet hitting motion: The rapid acceleration and rotation of the wrist and arm when a user makes an overhand shot. By detecting this motion, it is possible to accurately identify whether the user is hitting the ball, thereby collecting corresponding force data.

[0052] Offensive metrics: These are pre-defined indicators used in sports data analysis to assess a user's offensive capabilities, such as hitting power and speed. Offensive metrics quantify a user's offensive performance, helping them understand their offensive abilities.

[0053] When a user performs an overhand shot, the wearable device uses built-in sensors such as accelerometers and gyroscopes to monitor the force data of the racket impact in real time. The force data includes information such as the racket's acceleration and velocity changes during impact.

[0054] By monitoring force data, the system can accurately capture the power of each shot, providing foundational data for subsequent offensive metric analysis. Based on the collected force data for each shot, it uses pre-defined algorithms and models to calculate offensive metric data. For example, it can quantify a user's offensive ability by calculating parameters such as average force, maximum force, and range of force variation for each shot.

[0055] Specifically, based on the power data of each attacking ball, the attacking indicator data is determined, which can be calculated using the following formula:

[0056] Offensive index = (overhand ball 1 * k1 + overhand ball 2 * k2 + ... + overhand ball n * km) / (total number of swings);

[0057] Where: k1, k2, ..., km are attack coefficients, and the greater the hitting power, the larger the coefficient.

[0058] For example, wearable devices can detect the maximum hitting force within the range of [0, N] Newtons, dividing the 0-N range into several smaller intervals:

[0059] In interval 1, the attack coefficient corresponding to [0-N1) is k1;

[0060] Interval 2, [N1-N2), has an attack coefficient of k2.

[0061] Interval 3, [N2-N3), has an attack coefficient of k3;

[0062] Interval four, [N3-N4), corresponds to an attack coefficient of k4;

[0063] Interval 5, [N4-N), corresponds to an attack coefficient of k5;

[0064] First, based on the power data of each selected topspin shot, an attack coefficient is determined. Then, the attack coefficients of multiple topspin shots are added together to obtain a sum. Next, the sum of attack coefficients is divided by the total number of swings to determine the attack index data. The higher the attack index data, the higher the degree of attack during the movement. A topspin shot refers to a swing motion from top to bottom with an upward arc, such as a high clear or a flat drive. A lowspin shot refers to a swing motion from bottom to top with a downward arc, such as a lob. The hitting power is the force detected by the watch sensor at the moment of impact, measured in Newtons (the units of time in physics).

[0065] This allows for real-time monitoring of the force data during a user's overhand shot, and the calculation of offensive metrics based on this data. It quantifies the user's offensive performance, helping them understand their offensive capabilities. This provides data support for generating sports evaluation results, particularly for offensive metrics. This not only helps users understand their offensive performance but also guides them in more targeted training, thereby improving their offensive abilities.

[0066] In one possible embodiment, step 120 may specifically include the following steps:

[0067] Obtain data on the number of swings and heart rate intensity of the user during exercise;

[0068] Determine the exercise sufficiency data based on exercise heart rate intensity data;

[0069] Based on the number of swings and the amount of training, the endurance index data are determined.

[0070] Number of swings: The number of times a user swings their racket during a sport, such as table tennis, badminton, or tennis. This is used to assess the user's exercise volume and intensity, and helps analyze their endurance performance.

[0071] Exercise heart rate intensity data: Heart rate data collected by wearable devices or other sensors during exercise, typically including information such as heart rate variability and heart rate zones. It is used to assess the user's exercise intensity and cardiopulmonary function, helping to analyze the user's training effectiveness.

[0072] Exercise sufficiency data: A numerical value calculated based on exercise heart rate intensity data, representing the degree to which a user has exercised. Used to assess the user's exercise effectiveness and help determine the user's endurance metrics.

[0073] Endurance metrics: These are pre-defined indicators used in sports data analysis to assess a user's endurance capabilities, such as duration of continuous exercise and heart rate recovery speed. Endurance metrics quantify a user's endurance performance, helping them understand their endurance abilities.

[0074] Wearable devices use built-in accelerometers and heart rate sensors to monitor a user's swing count and heart rate changes in real time. The number of swings is calculated by detecting rapid acceleration and rotation of the wrist, while heart rate data is collected in real time by the heart rate sensor.

[0075] By monitoring the number of swings and heart rate data, the user's exercise volume and intensity can be accurately captured, providing basic data for subsequent endurance index analysis.

[0076] Based on the collected exercise heart rate intensity data, preset algorithms and models are used to calculate exercise sufficiency data. For example, by analyzing the user's heart rate changes and heart rate zones, the exercise time within different heart rate zones can be calculated, thereby assessing the user's exercise sufficiency.

[0077] By quantifying users' training effects, we can help them understand their exercise performance in different heart rate zones, providing data support for determining endurance metrics.

[0078] Based on the collected data on the number of swings and the adequacy of training, pre-defined algorithms and models are used to calculate endurance metrics. For example, a user's endurance performance can be assessed by calculating the relationship between the number of swings and the adequacy of training.

[0079] By quantifying users' endurance performance, it helps users understand their endurance capabilities. It provides data support for generating sports evaluation results, especially for endurance indicators.

[0080] Specifically, the endurance metric is calculated as: number of swings / training duration.

[0081] When two people are sparring and swing their rackets about the same number of times, the level of training is high. This can be roughly understood as the person with high fatigue has low endurance, and vice versa.

[0082] Exercise sufficiency is based on cumulative activity levels and heart rate intensity. Wearable devices can detect heart rate during exercise; a higher heart rate indicates greater exercise intensity, and a longer duration of high heart rate indicates greater exercise sufficiency.

[0083] To better describe exercise intensity, the user's maximum heart rate can be divided into five zones:

[0084] Interval 1: [Maximum heart rate n0%~n1%];

[0085] Interval 2: [Maximum heart rate n1%~n2%];

[0086] Interval 3: [Maximum heart rate n2%~n3%];

[0087] Interval 4: [Maximum heart rate n3%~n4%];

[0088] Interval 5: [Maximum heart rate n4%~n5%].

[0089] Where n is a positive integer, interval one has low exercise intensity, and interval five has high intensity. Alternatively, methods such as the heart rate reserve method and the lactate threshold heart rate method can be used to determine each heart rate interval.

[0090] For example, if a user exercises for 60 minutes, the cumulative time spent in intensity zone 1 is t1 minutes, intensity zone 2 is t2 minutes, intensity zone 3 is t3 minutes, intensity zone 4 is t4 minutes, and intensity zone 5 is t5 minutes.

[0091] Exercise sufficiency = t1*k1 + t2*k2 + t3*k3 + t4*k4 + t5*k5. Where k1…k5 are exercise intensity coefficients.

[0092] Therefore, by monitoring users' swing count and exercise heart rate intensity data in real time, and calculating exercise sufficiency and endurance metrics based on this data, users can not only understand their endurance performance but also be guided to conduct more targeted training, thereby improving their endurance capabilities.

[0093] In one possible embodiment, step 120 may specifically include the following steps:

[0094] Extract swing speed and swing force data for each swing motion from the motion data;

[0095] Based on the magnitude of the swing speed data for each swing motion, select N swing speed data points from multiple swing speed data points; N is a positive integer.

[0096] Based on the magnitude of the swing force data for each swing motion, select M swing force data for each swing motion from multiple swing force data; M is a positive integer;

[0097] Based on the swing speed data of N swing actions and the swing force data of M swing actions, determine the explosive power index data.

[0098] Swing speed data: Speed ​​data collected by wearable devices or other sensors during a user's swing motion, typically including acceleration and speed changes during the swing. Used to assess a user's swing speed and help analyze their explosive power performance.

[0099] Swing force data: Data collected by wearable devices or other sensors on the force exerted by the user during a racket swing, typically including information such as acceleration and velocity changes during the swing. Used to assess the user's swing force and help analyze their explosive power performance.

[0100] Explosive power metrics: These are pre-defined indicators used in sports data analysis to assess a user's explosive power, such as swing speed and swing force. Explosive power metrics quantify a user's explosive power performance, helping them understand their explosive power capabilities.

[0101] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the speed and force of a user's racket swing in real time. This data includes information such as acceleration and speed changes during the swing.

[0102] By monitoring swing speed and force data, the speed and power of each swing can be accurately captured, providing basic data for subsequent explosive index analysis.

[0103] Based on the collected swing speed data for each swing motion, the data are sorted according to their numerical values, and the N fastest swing speed data are selected. N is a positive integer representing the number of swing speed data selected.

[0104] By filtering out the swing speed data of the fastest swing motion, the user's explosive power performance can be assessed more accurately.

[0105] Based on the collected swing force data for each swing motion, the data are sorted according to their numerical values, and the M swing force data with the highest force are selected. M is a positive integer representing the number of swing force data points selected.

[0106] By filtering out the swing force data of the most powerful swing motion, the user's explosive power performance can be assessed more accurately.

[0107] Based on N selected swing speed data points and M swing force data points, a preset algorithm and model are used to calculate explosive power metrics. For example, the user's explosive power performance can be quantified by calculating parameters such as the average, maximum, and range of variation of these swing speeds and forces.

[0108] Specifically, the burst index data is calculated based on the highest N swing speed data and the highest M swing force data.

[0109] Determine the mean speed of N swing speed data points, and determine the mean force of M swing force data points;

[0110] Explosiveness Index = k1 * Average Speed ​​+ k2 * Average Power;

[0111] Where k1 and k2 are weighting coefficients.

[0112] This allows for real-time monitoring of the user's speed and force during racket swings, and the selection of the fastest and most powerful swings to calculate explosive power metrics. This not only helps users understand their explosive power performance but also guides them in more targeted training to improve their explosive power.

[0113] In one possible embodiment, step 120 may specifically include the following steps:

[0114] Extract swing count, stride frequency, and arm movement data from the sports data;

[0115] Determine the duration of active exercise based on cadence data and arm movement data;

[0116] The key performance indicators (KPIs) are determined based on the number of swings and the duration of active play.

[0117] Number of swings: The number of times a user swings their racket during exercise. Used to assess the user's exercise volume and intensity, and to help analyze the user's competitive ability.

[0118] Cadence data: This refers to the user's cadence during exercise, collected through wearable devices or other sensors. It typically includes information such as steps per minute. It is used to assess the user's movement speed and frequency, helping to analyze the user's combat capabilities.

[0119] Arm motion data: Data collected by wearable devices on the user's arm movements during exercise, typically including information such as arm acceleration and velocity changes. This data is used to assess the frequency and amplitude of the user's arm movements, helping to analyze the user's resistance capabilities.

[0120] Activity duration: Calculated based on cadence and arm movement data, representing the duration of a user's active state during exercise. Used to assess a user's activity level and help determine their competitive ability.

[0121] Competitive metrics: These are pre-defined indicators used in sports data analysis to assess a user's competitive ability, such as the number of swings and the duration of active play. Competitive metrics quantify a user's competitive ability, helping them understand their performance in competitive situations.

[0122] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor a user's swing count, stride frequency, and arm movement data in real time. The swing count is calculated by detecting rapid acceleration and rotation of the wrist, stride frequency is calculated by detecting the number of steps the user takes, and arm movement data is calculated by detecting changes in arm acceleration and speed.

[0123] By monitoring the number of swings, stride frequency, and arm movement data, the user's exercise volume and intensity can be accurately captured, providing basic data for subsequent analysis of competitive indicators.

[0124] Based on the collected cadence and arm movement data, preset algorithms and models are used to calculate the duration of active exercise. For example, by analyzing a user's cadence and arm movement frequency, the user's level of activity in different time periods can be calculated, thereby determining the duration of active exercise. By quantifying the user's level of activity, it helps the user understand their activity level during exercise, providing data support for determining resistance indicators.

[0125] Based on the collected number of swings and duration of active play, pre-defined algorithms and models are used to calculate competitive performance metrics. For example, the user's competitive ability can be assessed by calculating the relationship between the number of swings and the duration of active play. Quantifying a user's competitive ability helps them understand their performance in competitive situations. This provides data support for generating sports evaluation results, especially for evaluating competitive performance metrics.

[0126] Specifically, the counter-indicator data = total number of swings / active time.

[0127] Therefore, by monitoring users' swing count, stride frequency, and arm movement data in real time, and calculating active exercise duration and competitive performance metrics based on this data, users can not only understand their performance in competitive situations but also be guided to conduct more targeted training, thereby improving their competitive ability.

[0128] In one possible embodiment, step 120 may specifically include the following steps:

[0129] Obtain exercise heart rate intensity data from exercise data;

[0130] The training indicators are determined based on exercise heart rate intensity data.

[0131] Exercise heart rate intensity data: Heart rate data collected by wearable devices or other sensors during exercise, typically including information such as heart rate variability and heart rate zones. It is used to assess the user's exercise intensity and cardiopulmonary function, helping to analyze the user's training effectiveness.

[0132] Exercise metrics: These are pre-defined indicators used in exercise data analysis to evaluate a user's exercise effectiveness, such as heart rate zones and exercise time. Exercise metrics quantify a user's exercise results, helping them understand their performance in exercise.

[0133] Wearable devices use built-in heart rate sensors to monitor a user's heart rate changes in real time during exercise. This data includes heart rate variability and heart rate zones. By monitoring exercise heart rate intensity data, the device can accurately capture changes in a user's heart rate during exercise, providing a foundation for subsequent training metric analysis.

[0134] Based on the collected exercise heart rate intensity data, preset algorithms and models are used to calculate exercise metrics. For example, by analyzing the user's heart rate changes and heart rate zones, the exercise time within different heart rate zones can be calculated, thereby assessing the user's exercise effectiveness. Quantifying the user's exercise effect helps them understand their exercise performance in different heart rate zones. This provides data support for generating exercise evaluation results, especially for evaluating exercise metrics.

[0135] Specifically, the indicator data for the exercise index = t1*k1 + t2*k2 + t3*k3 + t4*k4 + t5*k5;

[0136] Where k1…k5 are the exercise intensity coefficients;

[0137] t1, t2, t3, t4, and t5 are the cumulative durations of heart rate in five different zones.

[0138] Exercise metrics are based on cumulative activity levels, including heart rate intensity. Wearable devices can detect heart rate during exercise; a higher heart rate indicates greater exercise intensity, and a longer duration of high heart rate indicates a more thorough workout.

[0139] To better describe exercise intensity, the user's maximum heart rate can be divided into five zones:

[0140] Interval 1: [Maximum heart rate n0%~n1%];

[0141] Interval 2: [Maximum heart rate n1%~n2%];

[0142] Interval 3: [Maximum heart rate n2%~n3%];

[0143] Interval 4: [Maximum heart rate n3%~n4%];

[0144] Interval 5: [Maximum heart rate n4%~n5%].

[0145] The exercise intensity in zone one is low, while the intensity in zone five is high. Alternatively, methods such as the heart rate reserve method and the lactate threshold heart rate method can be used to determine each heart rate zone.

[0146] For example, if a user exercises for 60 minutes, the cumulative time spent in intensity zone 1 is t1 minutes, intensity zone 2 is t2 minutes, intensity zone 3 is t3 minutes, intensity zone 4 is t4 minutes, and intensity zone 5 is t5 minutes.

[0147] This allows for real-time monitoring of the user's exercise heart rate intensity data and the calculation of exercise metrics based on this data. This not only helps users understand their exercise performance but also guides them in more targeted training, thereby improving exercise effectiveness.

[0148] In one possible embodiment, step 120 may specifically include the following steps:

[0149] Extract the swing force data for each swing motion from the motion data;

[0150] Based on the magnitude of the swing force data for each swing motion, select K swing force data for each swing motion from multiple swing force data; K is a positive integer;

[0151] Based on the swing force data of K swing motions, determine the power index data.

[0152] Swing force data: Data collected by wearable devices or other sensors on the force exerted by the user during a racket swing, typically including information such as acceleration and velocity changes during the swing. This data is used to assess the user's swing force and helps analyze their explosiveness and power performance.

[0153] Strength metrics: These are pre-defined indicators used in sports data analysis to assess a user's strength performance, such as swing power and maximum force. Strength metrics quantify a user's explosive power and strength performance, helping them understand their strength capabilities.

[0154] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the force data of users' racket swings in real time. This data includes information such as acceleration and speed changes during the swing. By monitoring the force data of the swing, the magnitude of the force of each swing can be accurately captured, providing a basis for subsequent force index analysis.

[0155] The system will sort the collected swing force data for each swing action according to their numerical values, and then select the K swing force data with the highest force. K is a positive integer representing the number of swing force data points selected.

[0156] By filtering out the swing force data of the most powerful swing motion, the user's explosiveness and power performance can be assessed more accurately.

[0157] Based on the selected K swing force data, a preset algorithm and model are used to calculate power index data. By calculating parameters such as the average, maximum, and range of variation of these swing forces, the user's explosive power and strength performance can be quantified.

[0158] By quantifying users' explosive power and strength performance, this helps users understand their strength capabilities. It provides data support for generating sports evaluation results, particularly for strength indicators.

[0159] Specifically, the strength indicator data = (N1 + N2 + ... + N) K ) / K;

[0160] Where N1, N2, ..., N K This refers to the swing force data. K represents the K largest swing forces among all the swing force data.

[0161] This allows for real-time monitoring of the force data during a user's racket swing, and the selection of the most powerful swing data to calculate strength metrics. This not only helps users understand their strength performance but also guides them in more targeted training, thereby improving their explosiveness and overall strength.

[0162] In one possible embodiment, step 120 may specifically include the following steps:

[0163] Obtain total exercise duration, cadence data, and arm movement data from the exercise data;

[0164] Determine the duration of active exercise based on cadence data and arm movement data;

[0165] The activity index data are determined based on the duration of active exercise and the total duration of exercise.

[0166] Total exercise duration: The total time a user spends exercising, such as playing volleyball. This is used to assess the user's exercise volume and intensity, helping to analyze the user's activity level.

[0167] Cadence data: This refers to the user's cadence during exercise, collected through wearable devices or other sensors. It typically includes information such as steps per minute. It is used to assess the user's movement speed and frequency, helping to analyze the user's activity level.

[0168] Arm motion data: This refers to the data collected by wearable devices or other sensors during a user's arm movements. It typically includes information such as arm acceleration and velocity changes. This data is used to assess the frequency and amplitude of the user's arm movements, helping to analyze the user's activity level.

[0169] Activity Duration: Calculated based on cadence and arm movement data, this represents the duration of a user's active state during exercise. It is used to assess a user's activity level and help determine their activity metrics.

[0170] Activity metrics: These are pre-defined indicators used in sports data analysis to assess user activity levels, such as active duration of exercise and total exercise time. These metrics quantify user activity levels and help users understand their performance in terms of activity.

[0171] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the user's total exercise time, cadence, and arm movement data in real time. Total exercise time is calculated using a timing function, cadence data is calculated by detecting the user's steps, and arm movement data is calculated by detecting changes in arm acceleration and speed.

[0172] By monitoring total exercise duration, cadence, and arm movement data, users' exercise volume and intensity can be accurately captured, providing basic data for subsequent activity metric analysis.

[0173] Based on the collected cadence and arm movement data, preset algorithms and models are used to calculate the duration of active exercise. For example, by analyzing a user's cadence and arm movement frequency, the user's level of activity in different time periods can be calculated, thereby determining the duration of active exercise.

[0174] By quantifying users' activity levels, this helps them understand their activity level during exercise, providing data support for determining activity metrics.

[0175] Based on the collected active exercise time and total exercise time, preset algorithms and models are used to calculate activity metrics. For example, the user's activity level can be assessed by calculating the ratio of active exercise time to total exercise time.

[0176] By quantifying user activity levels, it helps users understand their performance in terms of activity. This provides data support for generating activity evaluation results, particularly for evaluating activity metrics.

[0177] Specifically, it refers to the percentage of active time in the total exercise time. If the number of steps exceeds a certain value or the watch swings more than n times within one minute, that minute is considered an active minute. The cumulative active minutes are the active time of this exercise.

[0178] Active time refers to the duration of time spent participating in the game; resting or watching from the sidelines is not considered active time. Active time can be identified from stride frequency and arm movements. Smartwatches can detect stride frequency; if the user is sitting to rest or standing to watch, their stride frequency will be relatively low and will not be counted as active time. However, if the user is walking and the watch is displaying a racket swing, then it will be counted as active time.

[0179] This allows for real-time monitoring of a user's total exercise duration, cadence, and arm movement data, and the calculation of activity duration and activity metrics based on this data. This not only helps users understand their activity levels but also guides them in more targeted training, thereby increasing their activity levels.

[0180] In one possible embodiment, step 120 may specifically include the following steps:

[0181] Extract the swing time and hit time associated with each hitting action from the motion data;

[0182] The reaction time for each hitting action is determined based on the swing time and hitting time associated with each hitting action.

[0183] Based on the reaction time of each hitting action, determine the indicator data for the reaction index.

[0184] Hitting motion: The action of hitting a ball performed by the user during exercise. Used to assess the user's reaction speed and coordination, helping to analyze the user's reaction performance.

[0185] Swing time: The time from the start of the swing to the moment of impact when a user performs a hitting motion. It is used to assess the user's swing speed and coordination, and helps analyze the user's reaction performance.

[0186] Time of impact: The time from the moment of impact to the ball leaving the racket during a user's hitting motion. Used to assess the user's hitting speed and coordination, and to help analyze the user's reaction performance.

[0187] Reaction time: The total time from the start of the swing to the ball leaving the racket during a hitting motion. Used to assess a user's reaction speed and coordination, helping to determine their reaction metrics.

[0188] Reaction metrics: These are pre-defined indicators used in sports data analysis to assess a user's reaction speed and coordination, such as ball-hitting reaction time and reaction speed. These metrics quantify a user's reaction performance, helping them understand their reaction capabilities.

[0189] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the user's swing time and impact time in real time during a hitting motion. This data includes the time from the start of the swing to the moment of impact and the time from the moment of impact to the ball leaving the racket.

[0190] By monitoring the swing time and the time of impact, the timing information of each hitting action can be accurately captured, providing basic data for subsequent reaction index analysis.

[0191] Based on the collected reaction time for each shot, preset algorithms and models are used to calculate reaction metrics. For example, the user's reaction performance can be quantified by calculating parameters such as the average, minimum, and range of these reaction times.

[0192] By quantifying user reaction performance, it helps users understand their abilities in reaction speed and coordination. This provides data support for generating motion evaluation results, particularly for the evaluation of reaction indicators.

[0193] Specifically, the index data of the reaction index = (t1 + t2 + ... + t L ) / L;

[0194] Where t1, t2, ..., t L It is the ball-hitting reaction time, and L refers to the smallest value among the first L reaction times out of all ball-hitting reaction times.

[0195] This allows for real-time monitoring of the user's swing time and impact time during each hitting motion, and the calculation of reaction time and other reaction metrics for each hitting action. This not only helps users understand their performance in reaction speed and coordination but also guides them in more targeted training, thereby improving their reaction capabilities.

[0196] In one possible embodiment, step 130 may specifically include the following steps:

[0197] Based on the data of the preset exercise indicators, determine the indicator level of the preset exercise indicators.

[0198] The exercise evaluation results are generated based on the preset exercise index levels.

[0199] Indicator Levels: Different levels are assigned based on the indicator data to represent a user's performance level on a preset exercise indicator. These levels are used to generate exercise evaluation results, helping users intuitively understand their performance across different exercise indicators.

[0200] Exercise evaluation results: An evaluation of the user's exercise performance generated based on indicator data and indicator levels, typically including textual descriptions and numerical scores. This helps users understand their exercise performance and guides them in more effective training.

[0201] Based on the collected data for each preset exercise metric, a pre-defined algorithm and model are used to determine the level of each metric. For example, different thresholds can be set to classify the metric data into different levels, such as excellent, good, average, and poor. By quantifying the user's exercise performance, this helps the user understand their performance level on different exercise metrics and provides data support for generating exercise evaluation results.

[0202] Based on the defined index levels for each preset exercise indicator, an evaluation result for the user's exercise performance is generated. The evaluation result typically includes a textual description and numerical score, helping users intuitively understand their exercise performance. By providing a comprehensive assessment of user exercise performance, it helps users understand their strengths and weaknesses, guiding them to conduct more targeted training and improve exercise effectiveness.

[0203] like Figure 1 As shown, motion evaluation results can be expressed graphically. For example, a radar chart can be used to display evaluations across various indicator dimensions, with 5 circles representing level 5, and the outer circles indicating higher evaluations. Figure 2 As shown, a bar chart can also be used to display the evaluation of the exercise, with the horizontal axis representing the evaluation level and the longer the line segment, the higher the evaluation.

[0204] Therefore, based on the preset exercise index data, the system can determine the level of each index and generate exercise evaluation results based on these levels. This not only helps users understand their performance on different exercise indices but also guides them to conduct more targeted training, thereby improving exercise results.

[0205] In the embodiments of this application, when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data collection mode, the user's motion data is collected through the wearable device. By monitoring the user's motion status in real time, the accuracy and timeliness of the motion data can be ensured, providing basic data for subsequent motion index analysis. Based on the motion data, preset motion index data are determined. The preset motion indexes include at least one of the following: offensive index, endurance index, explosive index, confrontation index, training index, strength index, activity index, and reaction index. By quantifying the user's motion performance, the user can understand their abilities in different preset motion indexes, providing data support for generating motion evaluation results. Based on the preset motion index data, motion evaluation results are generated. By providing comprehensive motion evaluation results that reflect the user's motion performance, the user can understand their strengths and weaknesses, guide the user to conduct more targeted training, and improve motion results.

[0206] The motion evaluation method provided in this application can be executed by a motion evaluation device. This application uses an example of a motion evaluation device executing the motion evaluation method to illustrate the motion evaluation device provided in this application.

[0207] Figure 4 This is a block diagram of a motion evaluation device provided in an embodiment of this application. The device 400 includes:

[0208] The acquisition module 410 is used to acquire the user's motion data through the wearable device when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data acquisition mode.

[0209] The determination module 420 is used to determine the indicator data of the preset sports indicators based on the sports data. The preset sports indicators include at least one of the following: offensive indicators, endurance indicators, explosiveness indicators, confrontation indicators, training indicators, strength indicators, activity indicators, and reaction indicators.

[0210] The evaluation module 430 is used to generate exercise evaluation results based on the index data of preset exercise indicators.

[0211] In one possible embodiment, the determining module 420 is specifically used for:

[0212] The force data for each overhand ball is obtained from the motion data; wherein, the force data is the force data collected by the wearable device when the racket hitting action is detected;

[0213] Based on the power data of each attacking ball, the indicator data of the offensive index are determined.

[0214] In one possible embodiment, the determining module 420 is specifically used for:

[0215] The user's number of swings and exercise heart rate intensity data are obtained from the aforementioned exercise data;

[0216] Based on the exercise heart rate intensity data, determine the exercise sufficiency data;

[0217] The endurance index data is determined based on the number of swings and the training sufficiency data.

[0218] In one possible embodiment, the determining module 420 is specifically used for:

[0219] The swing speed and swing force data for each swing motion are obtained from the motion data.

[0220] Based on the numerical value of the swing speed data for each swing action, select N swing speed data for each swing action from the multiple swing speed data; where N is a positive integer;

[0221] Based on the numerical value of the swing force data for each swing action, select M swing force data for each swing action from the multiple swing force data; where M is a positive integer;

[0222] Based on the swing speed data of the N swing actions and the swing force data of the M swing actions, the index data of the explosive index are determined.

[0223] In one possible embodiment, the determining module 420 is specifically used for:

[0224] The number of swings, stride frequency, and arm movement data are obtained from the motion data.

[0225] The duration of active exercise is determined based on the cadence data and the arm movement data.

[0226] The indicator data of the confrontation index are determined based on the number of swings and the duration of active movement.

[0227] In one possible embodiment, the determining module 420 is specifically used for:

[0228] Obtain exercise heart rate intensity data from the exercise data;

[0229] The exercise index data is determined based on the exercise heart rate intensity data.

[0230] In one possible embodiment, the determining module 420 is specifically used for:

[0231] The swing force data for each swing motion is obtained from the motion data;

[0232] Based on the numerical value of the swing force data for each swing action, select K swing force data for each swing action from the multiple swing force data; where K is a positive integer;

[0233] Based on the swing force data of the K swing actions, the index data of the power index are determined.

[0234] In one possible embodiment, the determining module 420 is specifically used for:

[0235] The total exercise duration, cadence data, and arm movement data are obtained from the exercise data.

[0236] The duration of active exercise is determined based on the cadence data and the arm movement data.

[0237] The activity index data is determined based on the active exercise duration and the total exercise duration.

[0238] In one possible embodiment, the determining module 420 is specifically used for:

[0239] The swing time and hitting time associated with each hitting action are obtained from the motion data;

[0240] The reaction time for each of the aforementioned hitting actions is determined based on the swing time and hitting time associated with each hitting action.

[0241] The index data of the reaction index are determined based on the reaction time of each of the said hitting actions.

[0242] In one possible embodiment, the evaluation module 430 is specifically used for:

[0243] Based on the index data of the preset exercise index, determine the index level of the preset exercise index;

[0244] The exercise evaluation results are generated based on the index levels of the preset exercise indicators.

[0245] In the embodiments of this application, when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data collection mode, the user's motion data is collected through the wearable device. By monitoring the user's motion status in real time, the accuracy and timeliness of the motion data can be ensured, providing basic data for subsequent motion index analysis. Based on the motion data, preset motion index data are determined. The preset motion indexes include at least one of the following: offensive index, endurance index, explosive index, confrontation index, training index, strength index, activity index, and reaction index. By quantifying the user's motion performance, the user can understand their abilities in different preset motion indexes, providing data support for generating motion evaluation results. Based on the preset motion index data, motion evaluation results are generated. By providing comprehensive motion evaluation results that reflect the user's motion performance, the user can understand their strengths and weaknesses, guide the user to conduct more targeted training, and improve motion results.

[0246] The motion evaluation device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0247] The motion evaluation device in this application embodiment can be a device with a motion system. The motion system can be an Android motion system, an iOS motion system, or other possible motion systems; this application embodiment does not specifically limit it.

[0248] The motion evaluation device provided in this application embodiment can realize all the processes implemented in the above method embodiments, and will not be described again here to avoid repetition.

[0249] Optionally, such as Figure 5As shown, this application embodiment also provides an electronic device 510, including a processor 511, a memory 512, and a program or instructions stored in the memory 512 and executable on the processor 511. When the program or instructions are executed by the processor 511, they implement the various steps of any of the above-described motion evaluation method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0250] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0251] Figure 6 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0252] The electronic device 600 includes, but is not limited to, components such as: radio frequency unit 601, network module 602, audio output unit 603, input unit 604, sensor 605, display unit 606, user input unit 607, interface unit 608, memory 609, and processor 610.

[0253] Those skilled in the art will understand that the electronic device 600 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 610 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 6 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0254] The processor 610 is used to collect the user's motion data through the wearable device when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data collection mode.

[0255] The processor 610 is also used to determine the index data of preset sports indicators based on the sports data. The preset sports indicators include at least one of the following: offensive indicators, endurance indicators, explosive indicators, confrontation indicators, training indicators, strength indicators, activity indicators, and reaction indicators.

[0256] The processor 610 is further configured to generate a sports evaluation result based on the index data of preset sports indicators. Optionally, the processor 610 is further configured to obtain the force data of each ball strike from the sports data; wherein the force data is the force data collected by the wearable device when the racket hitting action is detected.

[0257] The processor 610 is also used to determine the index data of the offensive index based on the force data of each ball.

[0258] Optionally, the processor 610 is also configured to acquire data on the number of swings and heart rate intensity of the user from the motion data;

[0259] The processor 610 is also configured to determine exercise sufficiency data based on the exercise heart rate intensity data;

[0260] The processor 610 is also configured to determine the index data of the endurance index based on the number of swings and the training sufficiency data.

[0261] Optionally, the processor 610 is also configured to acquire swing speed data and swing force data for each swing motion from the motion data;

[0262] The processor 610 is further configured to filter the swing speed data of N swing actions from a plurality of swing speed data based on the numerical value of the swing speed data of each swing action; where N is a positive integer.

[0263] The processor 610 is further configured to filter M swing force data from a plurality of swing force data based on the numerical value of the swing force data for each swing action; where M is a positive integer.

[0264] The processor 610 is also configured to determine the index data of the burst index based on the swing speed data of the N swing actions and the swing force data of the M swing actions.

[0265] Optionally, the processor 610 is also configured to acquire the number of swings, cadence data, and arm movement data from the motion data;

[0266] The processor 610 is also configured to determine the duration of active exercise based on the cadence data and the arm movement data;

[0267] The processor 610 is also configured to determine the index data of the confrontation index based on the number of swings and the duration of active movement.

[0268] Optionally, the processor 610 is further configured to acquire exercise heart rate intensity data from the exercise data;

[0269] The processor 610 is also configured to determine the indicator data of the exercise index based on the exercise heart rate intensity data.

[0270] Optionally, the processor 610 is also configured to acquire swing force data for each swing motion from the motion data;

[0271] The processor 610 is further configured to filter K swing force data from a plurality of swing force data based on the numerical value of the swing force data for each swing action; wherein K is a positive integer;

[0272] The processor 610 is also configured to determine the index data of the power index based on the swing force data of the K swing actions.

[0273] Optionally, the processor 610 is also configured to obtain total exercise duration, cadence data, and arm movement data from the exercise data;

[0274] The processor 610 is also configured to determine the duration of active exercise based on the cadence data and the arm movement data;

[0275] The processor 610 is also configured to determine the indicator data of the activity index based on the active duration of the exercise and the total duration of the exercise.

[0276] Optionally, the processor 610 is also configured to obtain the swing time and hitting time associated with each hitting action from the motion data;

[0277] The processor 610 is also configured to determine the hitting reaction time of each of the said hitting actions based on the swing time and hitting time associated with each of the said hitting actions;

[0278] The processor 610 is also configured to determine index data of the reaction index based on the reaction time of each said hitting action.

[0279] Optionally, the processor 610 is further configured to determine the index level of the preset motion index based on the index data of the preset motion index.

[0280] The processor 610 is also used to generate a motion evaluation result based on the index level of the preset motion index.

[0281] In the embodiments of this application, when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data collection mode, the user's motion data is collected through the wearable device. By monitoring the user's motion status in real time, the accuracy and timeliness of the motion data can be ensured, providing basic data for subsequent motion index analysis. Based on the motion data, preset motion index data are determined. The preset motion indexes include at least one of the following: offensive index, endurance index, explosive index, confrontation index, training index, strength index, activity index, and reaction index. By quantifying the user's motion performance, the user can understand their abilities in different preset motion indexes, providing data support for generating motion evaluation results. Based on the preset motion index data, motion evaluation results are generated. By providing comprehensive motion evaluation results that reflect the user's motion performance, the user can understand their strengths and weaknesses, guide the user to conduct more targeted training, and improve motion results.

[0282] It should be understood that, in this embodiment, the input unit 604 may include a graphics processing unit (GPU) 6041 and a microphone 6042. The GPU 6041 processes image data of still images or video images obtained by an image capture device (such as a camera) in video image capture mode or image capture mode. The display unit 606 may include a display panel 6061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 607 includes at least one of a touch panel 6071 and other input devices 6072. The touch panel 6071 is also called a touch screen. The touch panel 6071 may include a touch detection device and a touch controller. Other input devices 6072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here. The memory 609 can be used to store software programs and various data, including but not limited to applications and motion systems. Processor 610 may integrate an application processor and a modem processor. The application processor primarily handles the action system, user page, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into processor 610.

[0283] The memory 609 can be used to store software programs and various data. The memory 609 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 609 may include volatile memory or non-volatile memory, or it may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 609 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0284] Processor 610 may include one or more processing units; optionally, processor 610 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 610.

[0285] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described motion evaluation method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0286] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0287] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described motion evaluation method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0288] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0289] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the motion evaluation method embodiments described above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0290] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0291] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they 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 this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0292] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A motion evaluation method, characterized in that, The method includes: When it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data acquisition mode, the user's motion data is collected through the wearable device. Based on the aforementioned sports data, the indicator data of preset sports indicators are determined. The preset sports indicators include offensive indicators, reaction indicators, endurance indicators, explosiveness indicators, confrontation indicators, training indicators, strength indicators, and activity indicators. Based on the index data of the preset exercise indicators, an exercise evaluation result is generated; The step of determining the preset exercise index data based on the exercise data includes: The wearable device acquires the following data from the motion data: the force data for each overhand ball, the swing time and hitting time associated with each hitting action, the swing speed data and swing force data for each swing action, as well as the number of swings, exercise heart rate intensity data, cadence data, arm movement data, and total exercise duration. The force data is the force data collected by the wearable device when the racket hitting action is detected. Based on the power data of each attack ball, determine the indicator data of the offensive index; Based on the swing time and hitting time associated with each hitting action, determine the hitting reaction time for each hitting action; based on the hitting reaction time for each hitting action, determine the index data of the reaction index; Based on the exercise heart rate intensity data, the exercise sufficiency data is determined; based on the number of racket swings and the exercise sufficiency data, the endurance index data is determined. Based on the numerical value of the swing speed data for each swing action, N swing speed data for each swing action are selected from a plurality of swing speed data; based on the numerical value of the swing force data for each swing action, M swing force data for each swing action are selected from a plurality of swing force data; where N and M are positive integers; based on the swing speed data of the N swing actions and the swing force data of the M swing actions, the index data of the explosive index are determined; Based on the cadence data and the arm movement data, the duration of active exercise is determined; based on the number of swings and the duration of active exercise, the indicator data of the resistance index is determined. The exercise index data is determined based on the exercise heart rate intensity data; Based on the numerical value of the swing force data for each swing action, select K swing force data for each swing action from a plurality of swing force data; where K is a positive integer; determine the index data of the power index based on the swing force data of the K swing actions; The activity index data is determined based on the active exercise duration and the total exercise duration.

2. A motion evaluation device, characterized in that, The device includes: The data acquisition module is used to acquire the user's motion data through the wearable device when it is detected that the wearable device is worn on the user's wrist and the wearable device is in motion data acquisition mode. The determination module is used to determine the index data of preset sports indicators based on the sports data. The preset sports indicators include offensive indicators, reaction indicators, endurance indicators, explosiveness indicators, confrontation indicators, training indicators, strength indicators, and activity indicators. The evaluation module is used to generate exercise evaluation results based on the index data of the preset exercise indicators; Specifically, the determining module is used to: acquire from the motion data the force data of each overhand ball, the swing time and hitting time associated with each hitting action, the swing speed data and swing force data of each swing action, as well as the user's number of swings, exercise heart rate intensity data, cadence data, arm movement data, and total exercise duration; the force data is the force data collected by the wearable device when the racket hitting action is detected; determine the index data of the offensive indicator based on the force data of each overhand ball; determine the training sufficiency data based on the exercise heart rate intensity data; determine the index data of the endurance indicator based on the number of swings and the training sufficiency data; select N swing speed data from multiple swing speed data based on the value of the swing speed data of each swing action; and determine the swing force data of each swing action based on the value of the swing force data. Based on the numerical values ​​of the swing force data, M swing force data for each swing motion are selected from multiple swing force data; where N and M are positive integers; the explosive power index is determined based on the swing speed data of the N swing motions and the swing force data of the M swing motions; the exercise activity duration is determined based on the stride frequency data and the arm movement data; the resistance index is determined based on the number of swings and the exercise activity duration; the training index is determined based on the exercise heart rate intensity data; based on the numerical values ​​of the swing force data for each swing motion, K swing force data for each swing motion are selected from multiple swing force data; where K is a positive integer; the strength index is determined based on the swing force data of the K swing motions; the activity index is determined based on the exercise activity duration and the total exercise duration.

3. An electronic device, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the method as described in claim 1.

4. A readable storage medium, characterized in that, A program or instructions are stored on the readable storage medium, which, when executed by a processor, implement the steps of the method as described in claim 1.

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