Exercise evaluation method and device, electronic equipment and readable storage medium

Abstract

The invention discloses an exercise evaluation method and device, and belongs to the field of data processing. The method comprises the following steps: when it is detected that the wearable device is worn on the wrist of a user and the working state of the wearable device is a motion data acquisition state, collecting motion data of the user through the wearable device; according to the exercise data, index data of preset exercise indexes are determined, and the preset exercise indexes comprise at least one of an attack index, a tolerance index, an outbreak index, a confrontation index, an exercise index, a strength index, an active index and a response index; and generating an exercise evaluation result according to the index data of the preset exercise index.

Description

Technical Field

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

[0002] As new consumer electronic products, smart watches and smart bracelets have gradually been recognized and popularized by the market in recent years. With the advancement of technology and consumers' pursuit of a healthy lifestyle, their functions have been continuously enriched and improved, especially in sports health monitoring, which has become an important tool in users' daily lives. Smart watches and smart bracelets have built-in multiple sensors, such as accelerometers, gyroscopes, heart rate sensors, blood oxygen sensors, and body temperature sensors, which can monitor the wearer's physical activity, exercise, heart rate, sleep, blood oxygen, body temperature and other sports health data in real time.

[0003] Although smart watches and smart bracelets have made significant progress in sports health monitoring, they are still insufficient in advanced sports data analysis. Currently, most smart watches and smart bracelets mainly provide basic data monitoring and simple data analysis functions, such as step count, calorie consumption, exercise time, etc. Although these data can reflect the user's exercise volume and basic health status, they cannot provide more in-depth sports data analysis.

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

[0005] The purpose of the embodiments of the present application is to provide a sports evaluation method, device, electronic device and readable storage medium, which can provide users with effective guidance on scientific exercise through wearable devices.

[0006] In a first aspect, an embodiment of the present application provides a motion evaluation method, the method comprising:

[0007] When it is detected that the wearable device is worn on the wrist of the user and the working state of the wearable device is a motion data collection state, the motion data of the user is collected by the wearable device;

[0008] Determine, according to the motion data, index data of a preset motion index, wherein the preset motion index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index;

[0009] A motion evaluation result is generated according to the indicator data of the preset motion indicator.

[0010] In a second aspect, an embodiment of the present application provides a motion evaluation device, the device comprising:

[0011] A collection module, configured 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 working state of the wearable device is a motion data collection state;

[0012] A determination module, configured to determine index data of a preset sports index according to the sports data, wherein the preset sports index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index;

[0013] The evaluation module is used to generate a motion evaluation result according to the index data of the preset motion index.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0016] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

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

[0018] In an embodiment of the present application, when it is detected that the wearable device is worn on the user's wrist and the working state of the wearable device is a motion data collection state, the user's motion data is collected through the wearable device. By real-time monitoring of the user's motion state, the accuracy and timeliness of the motion data can be ensured, and basic data can be provided for subsequent motion index analysis; based on the motion data, the index data of the preset motion index is determined, and the preset motion index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index. By quantifying the user's sports performance, the user is helped to understand his or her ability in different preset sports indicators, and data support is provided for generating sports evaluation results; based on the index data of the preset sports indicators, a sports evaluation result is generated. By providing a comprehensive sports evaluation result that can reflect the user's sports performance, the user can understand his or her own strengths and weaknesses, and provide effective guidance for the user in scientific exercise to improve the user's exercise effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of a motion evaluation method provided in an embodiment of the present application;

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

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

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

[0023] Figure 5 It is one of the hardware structure diagrams of the electronic device according to the embodiment of the present application;

[0024] Figure 6 This is the second schematic diagram of the hardware structure of the electronic device according to the embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application belong to the scope of protection of this application.

[0026] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "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.

[0027] In response to the problems arising in the related art, the embodiments of the present application provide a sports evaluation method, device, electronic device and readable storage medium, which can solve the problem in the related art that smart watches and smart bracelets cannot provide users with effective guidance in scientific exercise.

[0028] The motion evaluation method provided in the embodiment of the present application is described in detail below through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[0029] Figure 1 A flowchart of a motion evaluation method provided in an embodiment of the present application.

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

[0031] Step 110, when it is detected that the wearable device is worn on the wrist of the user and the working state of the wearable device is a motion data collection state, collect the user's motion data through the wearable device;

[0032] Wearable devices: electronic devices that can be worn on the user's body, usually including smart watches, 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] Sports data: Various data collected by wearable devices or other sensors during the user's exercise, such as heart rate, number of steps, speed, distance, calorie consumption, etc. It is used to analyze the user's exercise status and effect and help users understand their exercise performance.

[0034] Preset sports indicators: Indicators pre-set in sports data analysis to evaluate the user's sports performance, such as offensive indicators, endurance indicators, burst indicators, etc. These preset sports indicators can quantify the user's sports performance and help users understand their sports abilities in different aspects.

[0035] When the wearable device is worn on the user's wrist and is in the state of motion data collection, the wearable device will monitor the user's motion data in real time through the built-in sensors. Sensors such as accelerometers, heart rate sensors, etc. The motion data will be recorded and stored in the memory of the wearable device, or transmitted to the user's mobile phone or other devices through a wireless connection.

[0036] By real-time monitoring of the user's exercise status, the accuracy and timeliness of exercise data can be ensured, providing basic data for subsequent exercise indicator analysis.

[0037] Step 120, determining index data of a preset sports index according to the sports data, the preset sports index including at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index;

[0038] Indicator data: A specific value calculated based on the exercise data, used to indicate the user's performance on a preset exercise indicator. Indicator data is used to generate exercise evaluation results to help users understand their performance on different exercise indicators.

[0039] Based on the collected exercise data, the 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 the user's sports performance, it helps users understand their abilities in different sports indicators and provides data support for generating sports evaluation results.

[0041] Step 130, generating a motion evaluation result according to the index data of the preset motion index.

[0042] Sports evaluation results: Evaluation of the user's sports performance generated based on the indicator data, usually including text description and numerical score. Help users understand their sports performance and guide users to conduct more effective training.

[0043] Based on the calculated index data, the preset evaluation criteria and algorithms are used to generate evaluation results of the user's sports performance. The evaluation results usually include text descriptions and numerical scores to help users intuitively understand their sports performance.

[0044] By providing a comprehensive assessment of the user's sports performance, it helps users understand their strengths and weaknesses, and guides users to conduct more targeted training to improve exercise results.

[0045] Therefore, by monitoring the user's motion data in real time and generating detailed indicator data and motion evaluation results based on the motion data, it not only helps users understand their own sports performance, but also guides users to conduct more effective training, thereby improving exercise results.

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

[0047] Obtaining force data of each overhand ball from the motion data; wherein the force data is force data collected by the wearable device when a racket hitting action is detected;

[0048] According to the strength data of each upper hand ball, the indicator data of the offensive index is determined.

[0049] Overhand: In volleyball, overhand refers to a serve technique in which a player hits the ball from above with his palm or fist. Overhand usually has a higher arc and stronger power, and is a common serve in volleyball games.

[0050] Strength data: The strength data of the user when performing an overhand shot collected through wearable devices or other sensors. It is used to analyze the user's strength performance when hitting the ball and help evaluate the user's offensive ability.

[0051] Racket hitting action: The rapid acceleration and rotation of the wrist and arm when the user hits the ball with an overhand shot. By detecting this action, it is possible to accurately identify whether the user is hitting the ball and collect the corresponding force data.

[0052] Offensive index: An index pre-set in sports data analysis to evaluate the user's offensive ability, such as batting strength, batting speed, etc. Offensive indexes can quantify the user's offensive performance and help users understand their offensive capabilities.

[0053] When the 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 hitting the ball in real time. The force data includes information such as acceleration and speed changes when the racket hits the ball.

[0054] By monitoring the force data, the force of each overhand shot can be accurately captured, providing basic data for subsequent offensive index analysis. Based on the collected force data of each overhand shot, the preset algorithm and model are used to calculate the indicator data of the offensive index. For example, the user's offensive ability can be quantified by calculating the average force, maximum force, force variation range and other parameters of each shot.

[0055] Specifically, according to the strength data of each overhand ball, the index data of the offensive index is determined, which can be calculated by the following formula:

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

[0057] Among them: k1, k2, ..., km are the attack coefficients. The greater the hitting force, the greater the coefficient.

[0058] For example, a wearable device can detect the maximum range of hitting force [0, N] Newtons, and divide the interval range of 0-N into several small intervals:

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

[0060] Interval 2, [N1-N2), the corresponding attack coefficient is k2;

[0061] Interval three, [N2-N3), the corresponding attack coefficient is k3;

[0062] Interval 4, [N3-N4), the corresponding attack coefficient is k4;

[0063] Interval 5, [N4-N), the corresponding attack coefficient is k5;

[0064] First, in the strength data of each overhand ball that has been screened, the corresponding offensive coefficient is determined according to the hitting strength of each ball. Then, the offensive coefficients of multiple overhand balls obtained are added together to obtain the sum of the offensive coefficients; then, the sum of the offensive coefficients is divided by the total number of swings to determine the index data of the offensive index. The larger the index data of the offensive index, the higher the degree of offense during the movement. Among them, an overhand ball refers to a swing from top to bottom with an upward arc trajectory, such as a high ball or a flat shot. An underhand ball refers to a swing from bottom to top with a downward arc trajectory, such as a lob. The hitting strength is the strength detected by the watch sensor at the moment the racket hits the ball, using Newtons in physics as the unit.

[0065] As a result, the user's strength data when performing an overhand shot can be monitored in real time, and the offensive index data can be calculated based on this data. The user's offensive performance can be quantified to help the user understand their offensive capabilities. Data support is provided for generating sports evaluation results, especially for the evaluation of offensive indicators. This not only helps users understand their offensive performance, but also guides users to conduct more targeted training, thereby improving their offensive capabilities.

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

[0067] Obtain the user's swing times and exercise heart rate intensity data from the exercise data;

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

[0069] Based on the number of swings and exercise adequacy data, the indicator data of the endurance index is determined.

[0070] Number of swings: The number of times a user swings a racket when playing sports, such as table tennis, badminton, or tennis. It is used to assess the user's exercise volume and intensity, and to help analyze the user's endurance performance.

[0071] Exercise heart rate intensity data: Heart rate data collected by wearable devices or other sensors during exercise, usually including heart rate changes, heart rate intervals, etc. It is used to assess the user's exercise intensity and cardiopulmonary function, and help analyze the user's exercise effect.

[0072] Exercise adequacy data: A value indicating the user's exercise adequacy calculated based on the exercise heart rate intensity data. It is used to evaluate the user's exercise effect and help determine the user's endurance index.

[0073] Endurance index: An index pre-set in sports data analysis to evaluate the user's endurance ability, such as continuous exercise time, heart rate recovery speed, etc. The endurance index can quantify the user's endurance performance and help the user understand his or her endurance ability.

[0074] Wearable devices use built-in accelerometers and heart rate sensors to monitor the number of swings and heart rate changes of users in real time. The number of swings is calculated by detecting the rapid acceleration and rotation of the wrist, and the heart rate intensity data is collected in real time through the heart rate sensor.

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

[0076] Based on the collected exercise heart rate intensity data, the preset algorithms and models are used to calculate the exercise adequacy data. For example, the user's exercise adequacy can be evaluated by analyzing the user's heart rate changes and heart rate intervals and calculating the user's exercise time in different heart rate intervals.

[0077] By quantifying the user's exercise effects, it helps users understand their exercise performance in different heart rate ranges and provides data support for determining endurance indicators.

[0078] Based on the collected swing count and exercise sufficiency data, the preset algorithm and model are used to calculate the index data of the endurance index. For example, the user's endurance performance can be evaluated by calculating the relationship between the user's swing count and exercise sufficiency.

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

[0080] Specifically, the index data of the endurance index=number of swings / adequacy of exercise.

[0081] When two people play against each other and the number of swings of the racket is almost the same, then the degree of exercise is high. It can be roughly understood as people with high fatigue have low endurance, and vice versa.

[0082] The degree of exercise sufficiency is accumulated according to the intensity of the exercise heart rate. Wearable devices can detect the heart rate during exercise. The higher the heart rate, the greater the intensity of exercise; the longer the heart rate is in the high heart rate range, the higher the degree of exercise sufficiency.

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

[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] Wherein, n is a positive integer, the exercise intensity of interval 1 is low, and the exercise intensity of interval 5 is high. In addition, each heart rate interval can be determined by, for example, a reserve heart rate method, a lactate threshold heart rate method, etc.

[0090] For example, a user exercises for 60 minutes, during which he or she is in heart rate intensity zone 1 for a total of t1 minutes, in intensity zone 2 for a total of t2 minutes, in intensity zone 3 for a total of t3 minutes, in intensity zone 4 for a total of t4 minutes, and in intensity zone 5 for a total of 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 real-time monitoring of the user's swing times and exercise heart rate intensity data, and calculating the exercise adequacy data and endurance index data based on these data, this not only helps users understand their endurance performance, but also guides users to conduct more targeted training to improve endurance capabilities.

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

[0094] Obtaining swing speed data and swing force data of each swing action from the motion data;

[0095] According to the value of the swing speed data of each swing action, N swing speed data of the swing action are selected from the plurality of swing speed data; N is a positive integer;

[0096] According to the value of the swing force data of each swing action, M swing force data of the swing action are selected from the plurality of swing force data; M is a positive integer;

[0097] According to the swing speed data of the N swing actions and the swing force data of the M swing actions, the index data of the burst index is determined.

[0098] Swing speed data: Speed ​​data of the user's swing action collected through wearable devices or other sensors, usually including acceleration, speed change and other information during the swing. It is used to evaluate the user's swing speed and help analyze the user's explosive performance.

[0099] Swing force data: The force data of the user when swinging the racket collected through wearable devices or other sensors, usually including information such as acceleration and speed changes during the swing. It is used to evaluate the user's swing force and help analyze the user's explosive performance.

[0100] Explosive index: An index pre-set in sports data analysis to evaluate the user's explosive power, such as swing speed, swing strength, etc. The explosive index can quantify the user's explosive power performance and help the user understand his or her ability in explosive power.

[0101] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the speed and force of users' swings in real time. These data include information such as acceleration and speed changes during swings.

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

[0103] According to the collected swing speed data of each swing action, the data are sorted according to the value, and the swing speed data of the N fastest swing actions are selected. N is a positive integer, which represents the number of selected swing speed data.

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

[0105] According to the collected swing force data of each swing action, the swing force data are sorted according to the value, and the swing force data of the M swing actions with the largest force are screened out. M is a positive integer, which represents the number of the screened swing force data.

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

[0107] Based on the screened N swing speed data and M swing force data, the preset algorithm and model are used to calculate the index data of the explosive index. For example, the user's explosive performance can be quantified by calculating the average value, maximum value, range of variation and other parameters of these swing speeds and forces.

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

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

[0110] Explosive index = k1*average speed + k2*average force;

[0111] Among them, k1 and k2 are weighting coefficients.

[0112] Therefore, it is possible to monitor the speed and strength data of the user's swing in real time, and filter out the fastest and strongest swing data based on these data, so as to calculate the index data of the explosive index. This not only helps users understand their performance in terms of explosive power, but also guides users to conduct more targeted training to improve their explosive power.

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

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

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

[0116] Determine the indicator data for the confrontation indicator based on the number of swings and the duration of active exercise.

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

[0118] Cadence data: Cadence data of users during exercise collected through wearable devices or other sensors, usually including information such as the number of steps per minute. It is used to evaluate the user's movement speed and frequency and help analyze the user's ability to fight.

[0119] Arm motion data: The wearable device collects the user's arm motion data during exercise, usually including information such as arm acceleration and speed change. It is used to evaluate the frequency and amplitude of the user's arm motion and help analyze the user's resistance ability.

[0120] Active exercise duration: The duration of the user's active state during exercise, calculated based on the step frequency data and arm movement data. It is used to evaluate the user's exercise activity level and help determine the user's resistance ability.

[0121] Confrontation index: An index pre-set in sports data analysis to evaluate the user's confrontation ability, such as the number of swings, active sports duration, etc. The confrontation index can quantify the user's confrontation ability and help the user understand his or her performance in confrontation.

[0122] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the number of swings, cadence, and arm motion data of users in real time. The number of swings is calculated by detecting the rapid acceleration and rotation of the wrist, the cadence data is calculated by detecting the number of steps taken by the user, and the arm motion data is calculated by detecting the acceleration and speed changes of the arm.

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

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

[0125] Based on the collected number of swings and active sports duration, the preset algorithms and models are used to calculate the indicator data of the confrontation index. For example, the user's confrontation ability can be evaluated by calculating the relationship between the number of swings and active sports duration. The user's confrontation ability is quantified to help the user understand his or her performance in confrontation. This provides data support for generating sports evaluation results, especially for the evaluation of confrontation indicators.

[0126] Specifically, the index data of the confrontation index=total number of swings / active time.

[0127] Therefore, it is possible to monitor the user's swing times, stride frequency data and arm movement data in real time, and calculate the indicator data of active exercise duration and confrontation index based on these data. This not only helps users understand their performance in confrontation, but also guides users to conduct more targeted training, thereby improving their confrontation ability.

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

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

[0130] Determine the index data of the exercise index based on the exercise heart rate intensity data.

[0131] Exercise heart rate intensity data: Heart rate data collected by wearable devices or other sensors during exercise, usually including heart rate changes, heart rate intervals, etc. It is used to assess the user's exercise intensity and cardiopulmonary function, and help analyze the user's exercise effect.

[0132] Exercise indicators: Indicators pre-set in exercise data analysis to evaluate the user's exercise effect, such as heart rate range, exercise time, etc. Exercise indicators can quantify the user's exercise effect and help users understand their performance in exercise.

[0133] Wearable devices use built-in heart rate sensors to monitor the user's heart rate changes during exercise in real time. These data include heart rate changes, heart rate intervals and other information. By monitoring exercise heart rate intensity data, the user's heart rate changes during exercise can be accurately captured, providing basic data for subsequent exercise indicator analysis.

[0134] Based on the collected exercise heart rate intensity data, the preset algorithms and models are used to calculate the indicator data of the exercise indicators. For example, the user's heart rate changes and heart rate intervals can be analyzed to calculate the user's exercise time in different heart rate intervals, thereby evaluating the user's exercise effect. Quantify the user's exercise effect and help the user understand their exercise performance in different heart rate intervals. Provide data support for generating exercise evaluation results, especially for the evaluation of exercise indicators.

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

[0136] Among them, k1…k5 are the exercise intensity coefficients;

[0137] t1, t2, t3, t4 and t5 are the cumulative duration of the five heart rate intervals;

[0138] The indicator data of the exercise index accumulates activities according to the intensity of the exercise heart rate. Wearable devices can detect the heart rate during exercise. The higher the heart rate, the greater the intensity of the exercise; the longer the heart rate is in the high heart rate range, the more sufficient the exercise is.

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

[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 of interval 1 is low, and the exercise intensity of interval 5 is high. In addition, each heart rate interval can be determined by, for example, a reserve heart rate method, a lactate threshold heart rate method, etc.

[0146] For example, a user exercises for 60 minutes, during which he or she is in heart rate intensity zone 1 for a total of t1 minutes, in intensity zone 2 for a total of t2 minutes, in intensity zone 3 for a total of t3 minutes, in intensity zone 4 for a total of t4 minutes, and in intensity zone 5 for a total of t5 minutes.

[0147] As a result, the user's exercise heart rate intensity data can be monitored in real time, and the index data of the exercise index can be calculated based on this data. This not only helps users understand their performance in exercise, but also guides users to conduct more targeted training, thereby improving the exercise effect.

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

[0149] Obtaining swing force data of each swing action from the motion data;

[0150] According to the value of the swing force data of each swing action, K swing force data of the swing action are selected from the plurality of swing force data; K is a positive integer;

[0151] According to the swing force data of K swing actions, the index data of the strength index is determined.

[0152] Swing force data: The force data of the user when swinging the racket collected through wearable devices or other sensors, usually including information such as acceleration and speed changes during the swing. It is used to evaluate the user's swing force and help analyze the user's explosive power and strength performance.

[0153] Strength index: An index pre-set in sports data analysis to evaluate the user's strength performance, such as swing strength, maximum strength, etc. The strength index can quantify the user's explosive power and strength performance, helping the user understand his or her strength capabilities.

[0154] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the force data of users during swinging movements in real time. These data include information such as acceleration and speed changes during swinging. By monitoring the force data of the swing, the force of each swing can be accurately captured, providing basic data for subsequent force index analysis.

[0155] The collected swing force data of each swing action will be sorted according to the value, and the swing force data of the K swing actions with the largest force will be selected. K is a positive integer, indicating the number of selected swing force data.

[0156] By filtering out the swing force data of the most powerful swing action, the user's explosive power and strength performance can be evaluated more accurately.

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

[0158] By quantifying the user's explosive power and strength performance, it helps users understand their strength capabilities. It provides data support for generating sports evaluation results, especially for the evaluation of strength indicators.

[0159] Specifically, the index data of the strength index = (N1+N2+…+N K ) / K;

[0160] Among them, N1, N2, ..., N K is the swing force data. K refers to the first K strongest swing force data among all the swing force data.

[0161] Therefore, it is possible to monitor the strength data of the user when swinging the racket in real time, and filter out the swing data with the greatest strength based on these data, so as to calculate the index data of the strength index. This not only helps users understand their performance in terms of strength, but also guides users to conduct more targeted training, so as to improve explosive power and strength capabilities.

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

[0163] Obtain total exercise duration, step frequency data and arm movement data from exercise data;

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

[0165] Determine the indicator data of the active indicator based on the active exercise time and the total exercise time.

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

[0167] Cadence data: Cadence data of users during exercise collected through wearable devices or other sensors, usually including information such as the number of steps per minute. It is used to evaluate the user's movement speed and frequency and help analyze the user's activity level.

[0168] Arm motion data: The arm motion data of the user during exercise collected by wearable devices or other sensors, usually including information such as arm acceleration and speed change. It is used to evaluate the frequency and amplitude of the user's arm motion and help analyze the user's activity level.

[0169] Active exercise duration: The duration of a user's active state during exercise, calculated based on the step frequency data and arm movement data. It is used to evaluate the user's exercise activity level and help determine the user's active indicators.

[0170] Activity indicators: Indicators pre-set in sports data analysis to evaluate user activity, such as active exercise duration, total exercise duration, etc. These indicators can quantify the user's activity level 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, step frequency, and arm movement data in real time. The total exercise time is calculated by the timing function, the step frequency data is calculated by detecting the user's steps, and the arm movement data is calculated by detecting the acceleration and speed changes of the arm.

[0172] By monitoring the total exercise duration, step frequency and arm movement data, the user's exercise volume and intensity can be accurately captured, providing basic data for subsequent active indicator analysis.

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

[0174] By quantifying the user's sports activity, it helps users understand their activity status during exercise and provides data support for determining activity indicators.

[0175] Based on the collected active exercise time and total exercise time, the preset algorithm and model are used to calculate the indicator data of the activity indicator. For example, the user's activity level can be evaluated by calculating the ratio of the user's active exercise time to the total exercise time.

[0176] By quantifying the user's activity level, it helps users understand their performance in terms of activity. It provides data support for generating sports evaluation results, especially for the evaluation of activity indicators.

[0177] Specifically, the proportion of active time in the total exercise time. If the number of steps in one minute is greater than a certain value or the watch swings more than n times, then the minute is considered active, and the accumulated active minutes are the active time of this exercise.

[0178] Active time refers to the time spent on exercise. Resting on the sidelines or watching the game does not count as active time. Active time can be identified from cadence and arm movements. Smart watches can detect cadence. If the user is sitting to rest or standing to watch the game, the cadence will be relatively low and not counted as active time. If the user is walking and the watch is swinging, it will be counted as active time.

[0179] Therefore, the user's total exercise time, step frequency data and arm movement data can be monitored in real time, and the indicator data of active exercise time and active index can be calculated based on these data. This not only helps users understand their performance in terms of activity, but also guides users to conduct more targeted training to improve their activity level.

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

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

[0182] Determine the batting reaction time of each batting action according to the swing time and the batting time associated with each batting action;

[0183] According to the batting reaction time of each batting action, indicator data of the reaction index is determined.

[0184] Batting action: The user bats the ball while exercising. This is used to evaluate the user's reaction speed and coordination ability, and to help analyze the user's reaction performance.

[0185] Swing time: The time from the start of the swing to the moment of hitting the ball. It is used to evaluate the user's swing speed and coordination ability, and help analyze the user's reaction performance.

[0186] Hitting time: The time from the moment the ball hits the ball to the moment the ball leaves the racket. It is used to evaluate the user's hitting speed and coordination ability, and help analyze the user's reaction performance.

[0187] Batting reaction time: The total time from the start of swinging the racket to the ball leaving the racket when the user is performing a batting action. It is used to evaluate the user's reaction speed and coordination ability, and help determine the user's reaction index.

[0188] Reaction index: Indicators pre-set in sports data analysis to evaluate the user's reaction speed and coordination ability, such as batting reaction time, reaction speed, etc. These indicators can quantify the user's reaction performance and help users understand their ability in reaction.

[0189] Wearable devices use built-in sensors such as accelerometers and gyroscopes to monitor the user's swing time and hitting time in real time. These data include the time from the start of the swing to the moment of hitting the ball and the time from the moment of hitting the ball to the time the ball leaves the racket.

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

[0191] According to the collected batting reaction time of each batting action, the indicator data of the reaction index is calculated using the preset algorithm and model. For example, the user's reaction performance can be quantified by calculating the average value, minimum value, variation range and other parameters of these batting reaction times.

[0192] By quantifying the user's reaction performance, it helps the user understand his or her ability in terms of reaction speed and coordination. It provides data support for generating sports evaluation results, especially for the evaluation of reaction indicators.

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

[0194] Among them, t1, t2, …, t L is the batting reaction time, and L refers to the minimum value of the first L reaction times among all batting reaction times.

[0195] As a result, the user's swing time and hitting time can be monitored in real time, and the hitting reaction time and reaction index data of each hitting action can be calculated based on these data. This not only helps users understand their performance in terms of reaction speed and coordination ability, but also guides users to conduct more targeted training to improve their reaction ability.

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

[0197] Determining the indicator level of the preset sports indicator according to the indicator data of the preset sports indicator;

[0198] Generate a motion evaluation result based on the indicator level of the preset motion indicator.

[0199] Indicator level: Different levels divided according to indicator data, used to indicate the user's performance level on a preset sports indicator. Used to generate sports evaluation results to help users intuitively understand their performance on different sports indicators.

[0200] Sports evaluation results: Evaluation of the user's sports performance generated based on indicator data and indicator levels, usually including text descriptions and numerical scores. Help users understand their sports performance and guide users to conduct more effective training.

[0201] Based on the collected indicator data of each preset sports indicator, the preset algorithm and model are used to determine the level of each indicator. For example, different thresholds can be set to divide the indicator data into different levels, such as excellent, good, average, poor, etc. By quantifying the user's sports performance, it helps the user understand his or her performance level in different sports indicators. Provide data support for generating sports evaluation results.

[0202] Based on the indicator level of each preset sports indicator, an evaluation result of the user's sports performance is generated. The evaluation result usually includes a text description and a numerical score to help users intuitively understand their sports performance. By providing a comprehensive evaluation of the user's sports performance, it helps users understand their strengths and weaknesses. It guides users to conduct more targeted training and improve exercise results.

[0203] like Figure 1 As shown in the figure, the sports evaluation results can be expressed in a graphical way. For example, the evaluation of various indicator dimensions can be displayed in the form of a radar. The radar chart uses 5 circles to represent the 5th level, and the outer circles represent higher evaluations. Figure 2 As shown, a bar graph can also be used to display the sports evaluation, where the horizontal axis represents the level of evaluation, and the longer the line segment, the higher the evaluation.

[0204] Therefore, the level of each indicator can be determined according to the indicator data of the preset sports indicators, and the sports evaluation results can be generated according to these levels. This not only helps users understand their performance in different sports indicators, but also guides users to conduct more targeted training, thereby improving the effect of exercise.

[0205] In an embodiment of the present application, when it is detected that the wearable device is worn on the user's wrist and the working state of the wearable device is a motion data collection state, the user's motion data is collected through the wearable device. By real-time monitoring of the user's motion state, the accuracy and timeliness of the motion data can be ensured, and basic data can be provided for subsequent motion index analysis; based on the motion data, the index data of the preset motion index is determined, and the preset motion index includes at least one of the following: offensive index, endurance index, burst index, confrontation index, exercise index, strength index, active index and reaction index. By quantifying the user's sports performance, the user is helped to understand his ability in different preset sports indicators, and data support is provided for generating sports evaluation results; based on the index data of the preset sports indicators, the sports evaluation results are generated. By providing a comprehensive sports evaluation result that can reflect the user's sports performance, it can help the user understand his own strengths and weaknesses, guide the user to conduct more targeted training, and improve the exercise effect.

[0206] The motion evaluation method provided in the embodiment of the present application can be executed by a motion evaluation device. In the embodiment of the present application, the motion evaluation device provided in the embodiment of the present application is described by taking the motion evaluation method executed by the motion evaluation device as an example.

[0207] Figure 4 4 is a block diagram of a motion evaluation device provided in an embodiment of the present application, the device 400 includes:

[0208] The collection module 410 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 working state of the wearable device is a motion data collection state;

[0209] The determination module 420 is used to determine the index data of the preset sports index according to the sports data, where the preset sports index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index;

[0210] The evaluation module 430 is used to generate a motion evaluation result according to the indicator data of the preset motion indicator.

[0211] In a possible embodiment, the determination module 420 is specifically configured to:

[0212] Acquire force data of each overhand ball from the motion data; wherein the force data is force data collected by the wearable device when a racket hitting action is detected;

[0213] According to the strength data of each overhand ball, the index data of the offensive index is determined.

[0214] In a possible embodiment, the determination module 420 is specifically configured to:

[0215] Acquire the user's swing times and exercise heart rate intensity data from the exercise data;

[0216] Determining exercise adequacy data according to the exercise heart rate intensity data;

[0217] The index data of the endurance index is determined according to the number of swings and the exercise sufficiency data.

[0218] In a possible embodiment, the determination module 420 is specifically configured to:

[0219] Acquire the swing speed data and the swing force data of each swing action from the motion data;

[0220] According to the numerical value of the swing speed data of each swing action, N swing speed data of the swing action are selected from the plurality of swing speed data; wherein N is a positive integer;

[0221] According to the value of the swing force data of each swing action, M pieces of swing force data of the swing action are selected from the plurality of swing force data; wherein M is a positive integer;

[0222] The index data of the burst index is determined according to the swing speed data of the N swing actions and the swing force data of the M swing actions.

[0223] In a possible embodiment, the determination module 420 is specifically configured to:

[0224] Obtaining the number of swings, stride frequency data and arm motion data from the motion data;

[0225] Determining the active exercise duration according to the step frequency data and the arm movement data;

[0226] According to the number of swings and the active exercise duration, indicator data of the confrontation indicator is determined.

[0227] In a possible embodiment, the determination module 420 is specifically configured to:

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

[0229] Determine the index data of the exercise index according to the exercise heart rate intensity data.

[0230] In a possible embodiment, the determination module 420 is specifically configured to:

[0231] Acquire the swing force data of each swing action from the motion data;

[0232] According to the numerical value of the swing force data of each swing action, K pieces of swing force data of the swing action are selected from the plurality of swing force data; wherein K is a positive integer;

[0233] The index data of the strength index is determined according to the swing force data of the K swing actions.

[0234] In a possible embodiment, the determination module 420 is specifically configured to:

[0235] Obtaining total exercise duration, step frequency data, and arm motion data from the exercise data;

[0236] Determining the active exercise duration according to the step frequency data and the arm movement data;

[0237] Determine the indicator data of the activity indicator according to the active exercise duration and the total exercise duration.

[0238] In a possible embodiment, the determination module 420 is specifically configured to:

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

[0240] Determine the hitting reaction time of each hitting action according to the swing time and hitting time associated with each hitting action;

[0241] According to the hitting reaction time of each hitting action, the index data of the reaction index is determined.

[0242] In a possible embodiment, the evaluation module 430 is specifically configured to:

[0243] Determining the index level of the preset sports index according to the index data of the preset sports index;

[0244] A motion evaluation result is generated according to the index level of the preset motion index.

[0245] In an embodiment of the present application, when it is detected that the wearable device is worn on the user's wrist and the working state of the wearable device is a motion data collection state, the user's motion data is collected through the wearable device. By real-time monitoring of the user's motion state, the accuracy and timeliness of the motion data can be ensured, and basic data can be provided for subsequent motion index analysis; based on the motion data, the index data of the preset motion index is determined, and the preset motion index includes at least one of the following: offensive index, endurance index, burst index, confrontation index, exercise index, strength index, active index and reaction index. By quantifying the user's sports performance, the user is helped to understand his ability in different preset sports indicators, and data support is provided for generating sports evaluation results; based on the index data of the preset sports indicators, the sports evaluation results are generated. By providing a comprehensive sports evaluation result that can reflect the user's sports performance, it can help the user understand his own strengths and weaknesses, guide the user to conduct more targeted training, and improve the exercise effect.

[0246] The motion evaluation 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 other than the terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtualreality, VR) device, 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 (personalcomputer, 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.

[0247] The motion evaluation device of the embodiment of the present application may be a device having an action system. The action system may be an Android action system, an iOS action system, or other possible action systems, which are not specifically limited in the embodiment of the present application.

[0248] The motion evaluation device provided in the embodiment of the present application can implement each process implemented in the above method embodiment, and will not be described again here to avoid repetition.

[0249] Alternatively, if Figure 5As shown, an embodiment of the present application also provides an electronic device 510, including a processor 511, a memory 512, and a program or instruction stored in the memory 512 and executable on the processor 511. When the program or instruction is executed by the processor 511, each step of any of the above-mentioned motion evaluation method embodiments is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

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

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

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

[0253] Those skilled in the art will appreciate that the electronic device 600 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 610 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Figure 6 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 described in detail here.

[0254] The processor 610 is configured 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 working state of the wearable device is a motion data collection state;

[0255] The processor 610 is further configured to determine index data of a preset sports index according to the sports data, where the preset sports index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index;

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

[0257] The processor 610 is further configured to determine the index data of the offensive index according to the strength data of each overhand ball.

[0258] Optionally, the processor 610 is further configured to obtain the user's swing times and exercise heart rate intensity data from the exercise data;

[0259] The processor 610 is further configured to determine exercise adequacy data according to the exercise heart rate intensity data;

[0260] The processor 610 is further configured to determine the index data of the endurance index according to the number of swings and the exercise sufficiency data.

[0261] Optionally, the processor 610 is further configured to obtain swing speed data and swing force data of each swing action from the motion data;

[0262] The processor 610 is further configured to filter N pieces of swing speed data of the swing action from the plurality of swing speed data according to the value of the swing speed data of each swing action; wherein N is a positive integer;

[0263] The processor 610 is further configured to filter M pieces of swing force data of the swing action from the plurality of swing force data according to the value of the swing force data of each of the swing actions; wherein M is a positive integer;

[0264] The processor 610 is further configured to determine the indicator data of the burst indicator according to 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 further configured to obtain the number of swings, step frequency data and arm motion data from the motion data;

[0266] The processor 610 is further configured to determine the active exercise duration according to the step frequency data and the arm motion data;

[0267] The processor 610 is further configured to determine the indicator data of the confrontation indicator according to the number of swings and the active exercise duration.

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

[0269] The processor 610 is further configured to determine the index data of the exercise index according to the exercise heart rate intensity data.

[0270] Optionally, the processor 610 is further configured to obtain swing force data of each swing action from the motion data;

[0271] The processor 610 is further configured to filter K pieces of swing force data of the swing action from the plurality of swing force data according to the value of the swing force data of each of the swing actions; wherein K is a positive integer;

[0272] The processor 610 is further configured to determine the index data of the strength index according to the swing force data of the K swing actions.

[0273] Optionally, the processor 610 is further configured to obtain total exercise duration, step frequency data, and arm motion data from the exercise data;

[0274] The processor 610 is further configured to determine the active exercise duration according to the step frequency data and the arm motion data;

[0275] The processor 610 is further configured to determine the indicator data of the activity indicator according to the active exercise duration and the total exercise duration.

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

[0277] The processor 610 is further configured to determine a hitting reaction time of each hitting action according to a racket swing time and a hitting time associated with each hitting action;

[0278] The processor 610 is further configured to determine the indicator data of the reaction indicator according to the hitting reaction time of each hitting action.

[0279] Optionally, the processor 610 is further configured to determine an indicator level of the preset motion indicator according to the indicator data of the preset motion indicator;

[0280] The processor 610 is further configured to generate a motion evaluation result according to the index level of the preset motion index.

[0281] In an embodiment of the present application, when it is detected that the wearable device is worn on the user's wrist and the working state of the wearable device is a motion data collection state, the user's motion data is collected through the wearable device. By real-time monitoring of the user's motion state, the accuracy and timeliness of the motion data can be ensured, and basic data can be provided for subsequent motion index analysis; based on the motion data, the index data of the preset motion index is determined, and the preset motion index includes at least one of the following: offensive index, endurance index, burst index, confrontation index, exercise index, strength index, active index and reaction index. By quantifying the user's sports performance, the user is helped to understand his ability in different preset sports indicators, and data support is provided for generating sports evaluation results; based on the index data of the preset sports indicators, the sports evaluation results are generated. By providing a comprehensive sports evaluation result that can reflect the user's sports performance, it can help the user understand his own strengths and weaknesses, guide the user to conduct more targeted training, and improve the exercise effect.

[0282] It should be understood that in the embodiment of the present application, the input unit 604 may include a graphics processor (Graphics Processing Unit, GPU) 6041 and a microphone 6042, and the graphics processor 6041 processes the image data of the static picture or video image obtained by the image capture device (such as a camera) in the video image capture mode or the image capture mode. The display unit 606 may include a display panel 6061, and the display panel 6061 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 two parts: a touch detection device and a touch controller. Other input devices 6072 may include, but are not limited to, a physical keyboard, a function key (such as a volume control button, a switch button, etc.), a trackball, a mouse, and an action rod, which will not be repeated here. The memory 609 can be used to store software programs and various data, including but not limited to applications and action systems. The processor 610 may integrate an application processor and a modem processor, wherein the application processor mainly processes the action system, user pages and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 610.

[0283] The memory 609 can be used to store software programs and various data. The memory 609 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, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 609 may include a volatile memory or a non-volatile memory, or the memory x09 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 609 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0284] The processor 610 may include one or more processing units; optionally, the processor 610 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 610.

[0285] 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 evaluation method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0286] 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.

[0287] 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 evaluation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0288] 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.

[0289] 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 evaluation method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0290] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out 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 reverse 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.

[0291] 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 a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, 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, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0292] 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 the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A motion evaluation method, characterized in that: The method comprises: When it is detected that the wearable device is worn on the wrist of the user and the working state of the wearable device is a motion data collection state, the motion data of the user is collected by the wearable device; Determine, according to the motion data, index data of a preset motion index, wherein the preset motion index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index; A motion evaluation result is generated according to the indicator data of the preset motion indicator.

2. The method according to claim 1, characterized in that The step of determining the index data of the preset sports index according to the sports data includes: Acquire force data of each overhand ball from the motion data; wherein the force data is force data collected by the wearable device when a racket hitting action is detected; According to the strength data of each overhand ball, the index data of the offensive index is determined.

3. The method according to claim 1, characterized in that The step of determining the index data of the preset sports index according to the sports data includes: Acquire the user's swing times and exercise heart rate intensity data from the exercise data; Determining exercise adequacy data according to the exercise heart rate intensity data; The index data of the endurance index is determined according to the number of swings and the exercise sufficiency data.

4. The method according to claim 1, characterized in that: The step of determining the index data of the preset sports index according to the sports data includes: Acquire the swing speed data and the swing force data of each swing action from the motion data; According to the numerical value of the swing speed data of each swing action, N swing speed data of the swing action are selected from the plurality of swing speed data; wherein N is a positive integer; According to the value of the swing force data of each swing action, M pieces of swing force data of the swing action are selected from the plurality of swing force data; wherein M is a positive integer; The index data of the burst index is determined according to the swing speed data of the N swing actions and the swing force data of the M swing actions.

5. The method according to claim 1, characterized in that The step of determining the index data of the preset sports index according to the sports data includes: Obtaining the number of swings, stride frequency data and arm motion data from the motion data; Determining the active exercise duration according to the step frequency data and the arm movement data; According to the number of swings and the active exercise duration, indicator data of the confrontation indicator is determined.

6. The method according to claim 1, characterized in that The step of determining the index data of the preset sports index according to the sports data includes: Acquiring exercise heart rate intensity data from the exercise data; Determine the index data of the exercise index according to the exercise heart rate intensity data.

7. The method according to claim 1, characterized in that The step of determining the index data of the preset sports index according to the sports data includes: Acquire the swing force data of each swing action from the motion data; According to the numerical value of the swing force data of each swing action, K pieces of swing force data of the swing action are selected from the plurality of swing force data; wherein K is a positive integer; The index data of the strength index is determined according to the swing force data of the K swing actions.

8. A motion evaluation device, characterized in that: The device comprises: A collection module, configured 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 working state of the wearable device is a motion data collection state; A determination module, configured to determine index data of a preset sports index according to the sports data, wherein the preset sports index includes at least one of the following: an offensive index, an endurance index, an explosive index, a confrontation index, a training index, a strength index, an active index, and a reaction index; The evaluation module is used to generate a motion evaluation result according to the index data of the preset motion index.

9. 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 method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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