WO2022236726A1 - 运动能力的评估方法、装置、设备和存储介质 - Google Patents

运动能力的评估方法、装置、设备和存储介质 Download PDF

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Publication number
WO2022236726A1
WO2022236726A1 PCT/CN2021/093238 CN2021093238W WO2022236726A1 WO 2022236726 A1 WO2022236726 A1 WO 2022236726A1 CN 2021093238 W CN2021093238 W CN 2021093238W WO 2022236726 A1 WO2022236726 A1 WO 2022236726A1
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Prior art keywords
exercise
user
pace
standard
model equation
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PCT/CN2021/093238
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English (en)
French (fr)
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刘新
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广东高驰运动科技有限公司
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Priority to EP21941301.0A priority Critical patent/EP4335520A1/en
Priority to CN202180097284.9A priority patent/CN117202971A/zh
Priority to PCT/CN2021/093238 priority patent/WO2022236726A1/zh
Publication of WO2022236726A1 publication Critical patent/WO2022236726A1/zh
Priority to US18/506,517 priority patent/US20240075344A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • A63B2024/0068—Comparison to target or threshold, previous performance or not real time comparison to other individuals
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0075—Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
    • A63B2024/0078—Exercise efforts programmed as a function of time
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/30—Speed
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2220/00—Measuring of physical parameters relating to sporting activity
    • A63B2220/62—Time or time measurement used for time reference, time stamp, master time or clock signal
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2230/00—Measuring physiological parameters of the user
    • A63B2230/04—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations
    • A63B2230/06—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations heartbeat rate only
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B2230/00—Measuring physiological parameters of the user
    • A63B2230/20—Measuring physiological parameters of the user blood composition characteristics

Definitions

  • the present application relates to the technical field of sports data monitoring, for example, to a method, device, device and storage medium for evaluating sports ability.
  • running pace Since running is not restricted by factors such as venues and equipment, more and more users exercise through running. If the user's running pace is too fast, the user may not be able to run the entire distance, and if the user's running pace is too slow, it will affect the user's running performance. Therefore, it is necessary to reasonably set the exercise time according to the user's physical condition
  • the running pace can be used to realize the reasonable motion planning of the user while running.
  • the evaluator usually conducts a qualitative empirical analysis of the runner's overall running ability based on the runner's historical running conditions. For some amateur runners, the above-mentioned sports physical strength assessment method is difficult to achieve.
  • the present application provides an exercise ability evaluation method, device, equipment and storage medium, so as to realize accurate evaluation of the user's exercise ability and facilitate reasonable planning of the user's exercise.
  • Provides an assessment of exercise capacity including:
  • the exercise ability of the user is evaluated.
  • an assessment device for exercise capacity comprising:
  • a model calling module configured to call the target model equation pre-built for the user in response to the user's exercise ability evaluation instruction, wherein the target model equation reflects the relationship between the user's pace and exercise time;
  • the exercise ability evaluation module is configured to evaluate the user's exercise ability based on the user pace corresponding to the adapted exercise time point in the target model equation under at least one exercise dimension.
  • an electronic device comprising:
  • processors one or more processors
  • a storage device configured to store one or more programs
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors are made to implement the above-mentioned method for evaluating exercise ability.
  • FIG. 1 is a flow chart of a method for evaluating exercise capacity provided in Embodiment 1 of the present application;
  • FIG. 2 is a flow chart of a method for evaluating exercise capacity provided in Embodiment 2 of the present application;
  • FIG. 3 is a schematic structural diagram of an evaluation device for exercise ability provided in Embodiment 3 of the present application.
  • FIG. 4 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application.
  • FIG. 1 is a flow chart of a method for evaluating exercise capacity provided by Embodiment 1 of the present application. This embodiment is applicable in the case of evaluating the exercise ability of any user.
  • the method for assessing athletic ability provided in this embodiment can be performed by the apparatus for evaluating athletic ability provided in the embodiment of this application.
  • the apparatus can be implemented in the form of software and/or hardware, and integrated into the electronic system that executes the method. in the device.
  • this method comprises the steps:
  • the user's exercise ability is mainly divided into six exercise dimensions of explosive power, speed, speed endurance, aerobic, endurance and super endurance
  • these six exercise dimensions can be represented by the user's pace at different exercise time points , and the speed of the user's pace can also reflect the strength of the user's exercise ability.
  • the user's exercise in different exercise dimensions can be analyzed by analyzing the user's pace at different exercise time points. ability. Therefore, an objective model equation reflecting the relationship between the user's pace and exercise time can be constructed for the user to analyze the user's pace at different exercise time points to judge the user's exercise ability.
  • the target model equation pre-built for the user will be called first, and the target model equation can reflect the relationship between the user's pace and exercise time, so as to facilitate subsequent analysis of the user's different exercise times Click on the user's pace to judge the user's exercise ability.
  • the target model equation in this embodiment can be constructed in the following manner: based on the user's standard pace under the standard race schedule, determine the user's maximum oxygen uptake pace; use the maximum oxygen uptake pace and the user's actual Real-time pace during exercise, constructing a target model equation that reflects the relationship between user pace and exercise time.
  • the maximum exercise intensity that the user can bear is mainly analyzed.
  • the maximum oxygen uptake is the oxygen content that the human body can take in during strenuous exercise, it can effectively reflect the aerobic exercise capacity of the human body, and because the faster the pace of the user's exercise, it will also indicate the stronger the exercise ability, so
  • the maximum oxygen uptake pace can be used to analyze the exercise capacity of the user.
  • the maximum oxygen uptake pace is the pace that the user can achieve during strenuous exercise, it can be judged that the user supports running at the maximum exercise intensity by analyzing the user's exercise situation during the actual exercise.
  • the pace used for the full distance is to calculate the user's standard pace under the standard schedule, and then use the standard pace and the user's actual oxygen uptake under the standard schedule to determine the user's maximum oxygen uptake allocation. speed.
  • the standard race schedule in this embodiment can be divided into a half-marathon race schedule and a full-marathon race schedule.
  • the calculation process of the maximum oxygen uptake pace may include: using the user's standard pace under the standard schedule to determine the lactic acid threshold pace corresponding to the standard pace; Threshold Pace, which determines the user's VO2max pace.
  • the corresponding standard pace, lactate threshold pace and maximum oxygen uptake pace can be obtained.
  • the first correlation equation reflecting the relationship between the standard pace and the lactate threshold pace and the second correlation equation reflecting the relationship between the lactate threshold pace and the maximum oxygen uptake pace under the standard schedule can be fitted. Therefore, by inputting the standard pace of the user under the standard race schedule into the first correlation equation, the lactic acid threshold pace corresponding to the standard pace can be obtained, and then inputting the lactate threshold pace into the second correlation equation can be The maximum oxygen uptake pace corresponding to the lactate threshold pace is obtained, that is, the maximum oxygen uptake pace of the user in this embodiment.
  • the abscissa of the target model equation may represent changes in exercise time, and the ordinate may represent changes in user pace.
  • the user's maximum oxygen uptake pace can be referred to to to initially analyze the user's pace at different exercise time points, and then use The real-time pace of the user at different exercise time points in the actual exercise is used to judge whether the initially analyzed pace of the user at different exercise time points conforms to the actual exercise and make corresponding adjustments, so as to obtain a large number of different exercise time points.
  • the data points of the user's actual exercise can be used to fit the corresponding target model equation to accurately reflect the relationship between the user's pace and exercise time.
  • the exercise time point indicated by each exercise dimension Input it into the established target model equation to get the user's pace in each exercise dimension, and then analyze the speed of the user's pace in each exercise dimension to evaluate the user's overall exercise ability and realize the user's Accurate assessment of exercise capacity.
  • evaluating the user's exercise ability based on the user's pace corresponding to the adapted exercise time point in the target model equation under at least one exercise dimension may include: Adapted exercise time point, determine the user's pace in this exercise dimension in the target model equation; Athletic ability score under the exercise dimension; based on the user's exercise ability score under at least one exercise dimension, the comprehensive exercise ability of the user is evaluated.
  • the exercise time point for explosive force adaptation is the 10th second in the target model equation
  • the exercise time point for speed adaptation is the 60th second in the target model equation
  • the speed endurance is the 5th minute in the target model equation
  • the exercise time point for aerobic adaptation is the 60th minute in the target model equation
  • the exercise time point for endurance adaptation is the 120th minute in the target model equation
  • the exercise time point for super-endurance adaptation is the 600th minute in the target model equation.
  • this embodiment will pre-set a lookup table for the relationship between the user's pace and the athletic ability score under each exercise dimension, so as to use the user's pace under each exercise dimension According to the preset exercise ability score lookup table under the exercise dimension, the exercise ability score corresponding to the user's pace in the exercise dimension is determined.
  • the user's exercise ability score in that exercise dimension can be determined. Then, the average value of the user's multiple athletic ability scores is used as the user's comprehensive athletic ability.
  • the user's athletic ability can be divided into five levels: leisure (0-40), general (41-60), good (60-70), excellent (70-80) and elite (80-100), according to the user's
  • the comprehensive ability score can evaluate the user's athletic ability level. According to the user's exercise ability level, a reasonable exercise plan can be formulated for the user to ensure the health management of the user's exercise.
  • the target model equation pre-built for the user in response to the evaluation instruction of the user's exercise ability, the target model equation pre-built for the user is first invoked, and the target model equation can reflect the relationship between the user's pace and exercise time, and then consider the user's
  • the pace at different exercise time points can reflect the user's exercise ability in different dimensions, and the user's exercise ability can be evaluated by using the user's pace corresponding to the adapted exercise time point in the target model equation for each exercise dimension , so as to realize the automatic evaluation of the user's exercise ability, without the evaluator's subjective qualitative experience analysis, and improve the accuracy of the evaluation of the exercise ability, so as to formulate a reasonable exercise plan according to the user's exercise ability and ensure the health management of the user's exercise.
  • FIG. 2 is a flow chart of a method for evaluating exercise capacity provided by Embodiment 2 of the present application.
  • the embodiments of the present application are described on the basis of the foregoing embodiments.
  • this embodiment mainly describes the calculation process of the user's standard pace under the standard schedule and the construction process of the target model equation.
  • the method of this embodiment may include:
  • the user's age, gender, weight and other physiological characteristics, as well as the user's maximum heart rate and resting heart rate under the standard schedule, and the real-time exercise heart rate and real-time exercise speed during the next section of the standard schedule can be used to calculate The average exercise heart rate and average exercise speed, and analyze the maximum oxygen uptake supported by the user under the standard schedule.
  • the formula for calculating the maximum oxygen uptake in this embodiment can be: Among them, A is a constant from 40 to 50, P 1 is a constant from 7 to 8, S is a gender constant, 1 is male, and 0 is female; P 2 is a constant from 0.1 to 0.2, G is the user's weight; P 3 is 4 A constant of ⁇ 5, V is the average exercise speed, P 4 is a constant of 3 ⁇ 4, B is a constant of 1 ⁇ 2; C is a constant of 15 ⁇ 20, hr avg is the average exercise heart rate, hr 0 is the user is awake, The resting heart rate in a quiet state, hr max is the user's maximum heart rate; a is the user's age.
  • a reserve heart rate ratio query table corresponding to the standard schedule can be made.
  • the reserve heart rate ratios corresponding to users with different physiological characteristics and maximum oxygen uptake relative to the half-marathon race are recorded in the user reserve heart rate ratio query table.
  • the reserve heart rate ratio query table corresponding to the above half-marathon race schedule can be produced.
  • the reserve heart rate ratio query table under the standard race schedule can be queried according to the user's physiological characteristic data and the maximum oxygen uptake, and the user's target reserve heart rate ratio relative to the standard race schedule can be obtained.
  • the formula for calculating heart rate reserve ratio is Among them, hr i is the real-time heart rate of the user during exercise, hr max is the maximum heart rate of the user, and hr 0 is the resting heart rate of the user in an awake and quiet state. Substituting the user's target reserve heart rate ratio relative to the standard race schedule, as well as the user's maximum heart rate and resting heart rate into the above calculation formula, the user's standard exercise heart rate under the standard race schedule can be calculated.
  • the user's maximum heart rate and resting heart rate can be detected by a portable heart rate detection device, and can also be manually set by the user if the user knows the value of his maximum heart rate and resting heart rate.
  • this embodiment will divide the collected multi-type data according to the distance of movement, respectively divided into 0-10km, 10-20km, 20-30km, 30-40km, and above 40km, there are five segments, and a corresponding segment model equation can be established under each segment, so that different standards can be obtained according to multiple segments Race schedule, to calculate the standard pace of different standard races according to the corresponding movement speed.
  • this embodiment can obtain the real-time heart rate, pace, temperature, humidity, altitude, slope, distance, body temperature, exercise time and other data of the user during the historical exercise process, and select the heart rate and pace data that conform to the model construction ,Requirements are as follows:
  • the duration of the user's single continuous exercise reaches the preset duration (such as 10 minutes); 2) Acquire and judge whether the historical heart rate and historical exercise speed in each continuous unit period are in accordance with a certain movement cycle and in chronological order.
  • the heart rate fluctuation within a unit period is within the first preset range (not exceeding 12bpm);
  • the speed fluctuation within a unit period is within the second preset range (not exceeding 5m/s);
  • the real-time reserve heart rate ratio within a unit period is within the third preset range (50%-90%).
  • the continuous exercise duration is divided into a plurality of unit periods, and there is period overlap between adjacent unit periods, and the adjacent unit periods respectively correspond to
  • the time difference between the starting time points is an exercise cycle, and it is judged whether the historical heart rate and historical pace in each unit period meet the above conditions.
  • the historical heart rate and historical exercise speed within each target unit period that meet the above conditions can be continuously obtained, and the average value of the historical heart rate and historical exercise speed within each target unit period can be calculated as the corresponding associated data point.
  • heart rate and pace data points that meet the above conditions can be classified according to exercise time, temperature, humidity, altitude, distance segment, body temperature, and slope.
  • Exercise time can be divided into morning (5:00-7:00), morning (7:00-11:30), noon (11:30-14:30), afternoon (14:30-17:00), evening (17:00-19:00), evening (19:00-22:00) and late night (22:00-5:00)
  • the temperature can be divided into below -10 degrees, -10-0 degrees, 0-10 degrees degrees, 10-20 degrees, 20-25 degrees, 25-30 degrees and above 30 degrees
  • the humidity can be divided into below 30%, 30%-60%, 60%-80%, 80%-90% and above 90%
  • the altitude can be divided into below 500m, 500-1000m, 1000-1500m, 1500-2500m, 2500-3500m, 3500-4000m, 4000-5000m and above 5000m
  • the distance segment can be divided into 0-10km, 10-20km, 20- 30km, 30-40km and above 40km
  • the body temperature can be divided into below 36 degrees, 36-37 degrees,
  • the data sets corresponding to these five segments can be used to fit the segmental model equation for reflecting the relationship between the user's heart rate and motion speed under the segment by using the data set under each segment.
  • the piecewise model equations under each section could be: It is used to calculate the corresponding exercise speed by using the user's standard exercise heart rate under the standard schedule, and then use the exercise speed to calculate the corresponding standard pace.
  • the segmented model equation can also be: Among them, time is the exercise time point, atmos is the temperature, humi is the humidity, hei is the altitude, slope is the slope, temp is the body temperature, A, B, C, D, E, F, G and H are all constants. By increasing the influencing parameters, the accuracy of multiple segmented model equations is guaranteed.
  • the segmental model equations for reflecting the relationship between the user's heart rate and movement speed can be established under the five segments of 0-10km, 10-20km, 20-30km, 30-40km, and above 40km.
  • the segmental model equations related to the standard schedule can be screened out, and then the user's standard exercise heart rate under the standard schedule is input into the segmental model equations related to the standard schedule to calculate The corresponding movement speed, and then calculate the corresponding standard pace according to the movement speed in the segmental model equation.
  • the segmented model equations under 0-10km and 10-20km are related to the half-marathon race, and input the standard exercise heart rate of the user under the half-marathon race into the 0-10km and 10km respectively.
  • the segmental model equation under -20km the first movement speed v 1 under 0-10km and the second movement speed v 2 under 10-20km can be obtained respectively, and the half-marathon pace can be calculated as
  • the user's standard pace under the standard schedule can be calculated through multiple segmented model equations and standard exercise heart rate under the standard schedule, so that the standard pace under the standard schedule can be used later to determine the corresponding lactic acid threshold pace, and based on the lactate threshold pace, the user's VO2max pace is determined.
  • this embodiment can use the user's maximum oxygen uptake pace relative to multiple percentages at different exercise time points.
  • To calculate the user's initial pace at different exercise time points so that multiple exercise time points and the user's initial pace at the multiple exercise time points can be combined to obtain a large number of data points, and then Using multiple data points, an initial model equation can be fitted that reflects the relationship between user pace and exercise time.
  • the user's initial pace at different exercise time points may be: the user pace corresponding to the 10s exercise time point is 120% of the maximum oxygen uptake pace, and the user pace corresponding to the 1min exercise time point is 115% of the maximum oxygen uptake pace, the user pace corresponding to the 3min exercise time point is 110% of the maximum oxygen uptake pace, and the user pace corresponding to the 5min exercise time point is 105% of the maximum oxygen uptake pace, 10min
  • the user pace corresponding to the exercise time point is 100% of the maximum oxygen uptake pace
  • the user pace corresponding to the 30min exercise time point is 95% of the maximum oxygen uptake pace
  • the user pace corresponding to the 90min exercise time point is 85% of the maximum oxygen uptake pace
  • the user pace corresponding to the 180min exercise time point is 80% of the maximum oxygen uptake pace
  • the user pace corresponding to the 240min exercise time point is 75% of the maximum oxygen uptake pace
  • 1200min The user pace corresponding to the exercise time point is 60% of the maximum oxygen uptake pace, and
  • the real-time pace of the user at different exercise time points in the actual exercise can be obtained, and by analyzing the real-time pace at different exercise time points Compared with the pace at this time point in the initial model equation, the preliminary model equation is adjusted accordingly, so that the pace change related to the exercise time in the initial model equation can be continuously realistic to the actual movement of the user, thus obtaining Corresponding objective model equation.
  • the user's real-time pace at each exercise time point in the actual exercise will be determined, which is different from the predicted speed at the exercise time point in the initial model equation.
  • the difference between estimated paces is estimated, and based on this difference, the initial model equations are adjusted.
  • the movement time point near the movement time point in the initial model equation is adjusted up If the real-time pace of the user at a certain movement time point in the actual movement is lower than the estimated pace at the movement time point in the initial model equation, the movement time point in the initial model equation is lowered
  • the user's pace in the vicinity makes the user's pace at multiple motion time points in the adjusted initial model equation realistic to the user's actual motion.
  • the technical solution provided by this embodiment firstly determines the user’s maximum oxygen uptake pace according to the user’s standard pace under the standard race schedule, and then uses the maximum oxygen uptake pace and the user’s real-time pace in actual exercise, Construct the target model equation that reflects the relationship between the user's pace and exercise time. Considering that the user's pace at different exercise time points can reflect the user's exercise ability in different dimensions, the adaptive model for each exercise dimension can be used. The user's pace corresponding to the exercise time point in the target model equation is used to evaluate the user's exercise ability, thereby realizing the automatic evaluation of the user's exercise ability, without the evaluator's subjective qualitative experience analysis, and improving the accuracy of the exercise ability assessment. The user's exercise ability formulates a reasonable exercise plan to ensure the healthy management of the user's exercise.
  • Fig. 3 is a schematic structural diagram of an evaluation device for exercise capacity provided in Embodiment 3 of the present application. As shown in Fig. 3, the device may include:
  • the model calling module 310 is configured to respond to the evaluation instruction of the user's athletic ability, calling the target model equation pre-built for the user, and the target model equation reflects the relationship between the user's pace and exercise time; the athletic ability evaluation module 320 , set to evaluate the user's exercise ability based on the user pace corresponding to the adapted exercise time point in the target model equation under at least one exercise dimension.
  • the target model equation in response to the evaluation instruction of the user's exercise ability, firstly call the target model equation pre-built for the user, the target model equation can reflect the relationship between the user's pace and exercise time, and then take into account
  • the user's pace at different exercise time points can reflect the user's exercise ability in different dimensions
  • the user's pace corresponding to the adapted exercise time point in each exercise dimension in the target model equation can be used to evaluate the user's exercise Ability, so as to realize the automatic evaluation of the user's exercise ability, without the need for subjective qualitative experience analysis by the evaluator, and improve the accuracy of the assessment of exercise ability, so as to formulate a reasonable exercise plan according to the user's exercise ability and ensure the health management of the user's exercise.
  • the evaluation device of the above-mentioned exercise capacity can also include: a oxygen uptake pace determination module and a model equation building block; the above-mentioned target model equation can be constructed by using the above-mentioned oxygen uptake pace determination module and a model equation building block:
  • the oxygen uptake pace determination module is set to determine the user's maximum oxygen uptake pace based on the user's standard pace under the standard schedule; the model equation construction module is set to use the maximum uptake pace Oxygen pace and the real-time pace of the user in the actual exercise, construct a target model equation reflecting the relationship between the user's pace and exercise time.
  • the above oxygen uptake pace determination module can be set as:
  • the above-mentioned evaluation device for exercise ability may also include:
  • the heart rate ratio determination module is set to use the user's maximum oxygen uptake under the standard schedule to determine the target reserve heart rate ratio corresponding to the maximum oxygen uptake; the standard heart rate calculation module is set to be based on the target reserve heart rate ratio and the user's maximum heart rate and resting heart rate to calculate the standard exercise heart rate of the user under the standard schedule; the standard pace calculation module is set to use the standard exercise heart rate and a number of pre-established relationships between the user's heart rate and pace that reflect the standard schedule The segmented model equation is used to calculate the standard pace of the user under the standard race schedule.
  • the continuous exercise duration is divided into multiple unit periods, and there is period overlap between adjacent unit periods, and the corresponding start times of adjacent unit periods
  • the time difference between the points is an exercise cycle, and it is judged whether the historical heart rate and historical pace in each unit period meet the following conditions: 1) the heart rate fluctuation in the unit period is within the first preset range; 2) the unit The speed fluctuation within the time period is within the second preset range; 3) The real-time reserve heart rate ratio change within the unit period is within the third preset range; calculate the historical heart rate and historical heart rate ratio within each target unit period that meets the above conditions
  • the average speed is used as an associated data point reflecting the relationship between the user's heart rate and pace, and according to the preset multiple segmented exercise distances, the associated data points corresponding to multiple target unit periods are classified; each segment is used The associated data points corresponding to multiple target unit time periods within the movement distance are used to fit the segmental model equation under the segmental movement distance.
  • the above model equation building blocks can include:
  • the initial model construction unit is set to use the maximum oxygen uptake pace to construct an initial model equation reflecting the relationship between the user's pace and exercise time; the model adjustment unit is set to be based on the real-time pace of the user in actual exercise , adjust the initial model equation to obtain the target model equation.
  • the above model adjustment unit can be set as:
  • the above-mentioned athletic ability evaluation module 320 can be set as:
  • the exercise ability assessment device provided in this embodiment can be applied to the exercise ability assessment method provided in any of the above embodiments, and has corresponding functions and effects.
  • FIG. 4 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application.
  • the electronic equipment includes a processor 40, a storage device 41 and a communication device 42; the number of processors 40 in the electronic equipment can be one or more, and one processor 40 is taken as an example in Figure 4; the electronic equipment
  • the processor 40, the storage device 41, and the communication device 42 can be connected through a bus or in other ways. In FIG. 4, the connection through a bus is taken as an example.
  • the storage device 41 can be configured to store software programs, computer-executable programs and modules, such as the modules corresponding to the method for evaluating athletic ability in the embodiments of the present application (for example, in the apparatus for evaluating athletic ability
  • the model calling module 310 and the athletic ability assessment module 320) The processor 40 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the storage device 41 , that is, to realize the above-mentioned evaluation method of exercise ability.
  • the storage device 41 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required by a function; the data storage area may store data created according to the use of the terminal, and the like.
  • the storage device 41 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices.
  • the storage device 41 may include memories that are located remotely relative to the multi-function controller 40, and these remote memories may be connected to the electronic device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the communication device 42 may be configured to realize network connection or mobile data connection between devices.
  • An electronic device provided in this embodiment can be configured to execute the method for evaluating exercise ability provided in any of the above embodiments, and has corresponding functions and effects.
  • Embodiment 5 of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for evaluating exercise ability in any of the above-mentioned embodiments can be realized.
  • the method includes:
  • the target model equation In response to the evaluation instruction of the user's exercise ability, call the target model equation pre-built for the user, the target model equation reflects the relationship between the user's pace and exercise time; based on the exercise time point adapted under at least one exercise dimension At a corresponding user pace in the target model equation, the user's athletic performance is assessed.
  • a storage medium containing computer-executable instructions provided in an embodiment of the present application the computer-executable instructions are not limited to the above-mentioned method operations, and can also perform any of the methods for evaluating athletic ability provided in any embodiment of the present application. related operations.
  • the present application can be implemented by software and necessary general hardware, or by hardware.
  • the technical solution of the present application can be embodied in the form of software products in essence, and the computer software products can be stored in computer-readable storage media, such as computer floppy disks, read-only memory (Read-Only Memory, ROM), random access Memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disc, etc., including a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the described embodiment of the present application.
  • the multiple units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be realized; in addition, multiple functions
  • the names of the units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application.

Abstract

本文公开了一种运动能力的评估方法、装置、设备和存储介质。该运动能力的评估方法包括：响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，其中，所述目标模型方程反映用户配速和运动时间之间的关系；基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。

Description

运动能力的评估方法、装置、设备和存储介质 技术领域
本申请涉及运动数据监测技术领域，例如涉及一种运动能力的评估方法、装置、设备和存储介质。
背景技术
由于跑步运动不受场地、器材等因素的制约，越来越多的用户通过跑步运动来进行锻炼。如果用户的跑步配速过快，那么用户可能坚持不到跑完全程，而如果用户的跑步配速过慢，则会影响到用户的跑步成绩，因此需要根据用户的体力情况，合理制定运动时的跑步配速，以实现用户在跑步时的合理运动规划。
对于运动体力的评估，通常由评估者依据跑步运动员的历史跑步情况对跑步运动员的整体跑步能力进行定性经验分析，对于一些业余跑步爱好者来说，上述运动体力的评估方式难以实现。
发明内容
本申请提供了一种运动能力的评估方法、装置、设备和存储介质，实现用户运动能力的准确评估，便于用户运动的合理规划。
提供了一种运动能力的评估方法，包括：
响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，其中，所述目标模型方程反映用户配速和运动时间之间的关系；
基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。
还提供了一种运动能力的评估装置，包括：
模型调用模块，设置为响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，其中，所述目标模型方程反映用户配速和运动时间之间的关系；
运动能力评估模块，设置为基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。
还提供了一种电子设备，包括：
一个或多个处理器；
存储装置，设置为存储一个或多个程序；
当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现上述的运动能力的评估方法。
还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现上述的运动能力的评估方法。
附图说明
图1为本申请实施例一提供的一种运动能力的评估方法的流程图；
图2为本申请实施例二提供的一种运动能力的评估方法的流程图；
图3为本申请实施例三提供的一种运动能力的评估装置的结构示意图；
图4为本申请实施例四提供的一种电子设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请作进行说明。
实施例一
图1为本申请实施例一提供的一种运动能力的评估方法的流程图。本实施例可适用于对任一用户的运动能力进行评估的情况中。本实施例提供的一种运动能力的评估方法可以由本申请实施例提供的一种运动能力的评估装置来执行，该装置可以通过软件和/或硬件的方式实现，并集成在执行本方法的电子设备中。
参考图1，该方法包括如下步骤：
S110，响应于用户运动能力的评估指令，调用为用户预构建的目标模型方程，该目标模型方程反映用户配速和运动时间之间的关系。
可选的，由于用户的运动能力主要分为爆发力、速度、速度耐力、有氧、耐力和超耐力这六个运动维度，这六个运动维度可以通过不同运动时间点下的用户配速来表示，且用户配速的快慢也能够反映用户运动能力的强弱，在此基础上，本实施例中可以通过分析在不同运动时间点下的用户配速，来分析用户在不同运动维度下的运动能力。因此，可以为用户构建出反映用户配速和运动时间之间的关系的目标模型方程，来分析该用户在不同运动时间点下的用户配速，以此判断该用户的运动能力。
在检测到用户运动能力的评估指令时，首先会调用为该用户预先构建的目标模型方程，该目标模型方程能够反映用户配速和运动时间之间的关系，以便 后续分析该用户在不同运动时间点下的用户配速，以此判断该用户的运动能力。
示例性的，本实施例中的目标模型方程可以采用如下方式进行构建：基于用户在标准赛程下的标准配速，确定用户的最大摄氧量配速；利用最大摄氧量配速和用户实际运动中的实时配速，构建反映用户配速和运动时间之间的关系的目标模型方程。
可选的，在对用户的运动能力进行分析时，主要会分析用户所能够承受的最大运动强度。考虑到最大摄氧量为人体进行剧烈运动时所能摄入的氧气含量，能够有效反映人体的有氧运动能力，而且由于用户运动时的配速越快，也会表明运动能力越强，因此本实施例可以采用最大摄氧量配速来分析用户的运动能力。
在本实施例中，由于最大摄氧量配速为用户在剧烈运动时所能达到的配速，因此可以通过分析用户在实际运动过程中的运动情况，来判断用户在最大运动强度下支持跑完全程时所采用的配速，也就是计算用户在标准赛程下的标准配速，然后利用该标准配速与用户在标准赛程下的实际摄氧量情况，确定出用户的最大摄氧量配速。其中，本实施例中的标准赛程可以分为半马赛程和全马赛程等。
作为本实施例中的一种可选方案，对于最大摄氧量配速的计算过程，可以包括：利用用户在标准赛程下的标准配速，确定标准配速对应的乳酸阈值配速；基于乳酸阈值配速，确定用户的最大摄氧量配速。
以标准赛程为半马赛程为例，如下表1所示，预先采集用户在标准赛程的历史运动过程中的多项数据，形成标准配速、乳酸阈值配速和最大摄氧量配速之间的匹配数据表：
表1半马赛程下标准配速、乳酸阈值配速和最大摄氧量配速之间的匹配数据表
标准配速 乳酸阈值配速 最大摄氧量配速
2:45 2:44 2:44/120％
3:25 3:24 3:24/120％
4:05 4:02 4:02/120％
4:47 4:40 4:40/118％
5:30 5:16 5:16/117％
6:13 6:03 6:03/115％
6:52 6:27 6:27/114％
7:25 7:15 7:15/113％
8:03 7:40 7:40/112％
8:55 8:20 8:20/111％
9:29 9:10 9:10/110％
在不同的标准赛程下，可以得到对应的标准配速、乳酸阈值配速和最大摄氧量配速，按照不同标准赛程下相应的标准配速、乳酸阈值配速和最大摄氧量配速，可以拟合出标准赛程下反映标准配速和乳酸阈值配速关系的第一关联方程和反映乳酸阈值配速和最大摄氧量配速关系的第二关联方程。因此，将用户在标准赛程下的标准配速输入到第一关联方程中，可以得到该标准配速对应的乳酸阈值配速，然后将该乳酸阈值配速输入到第二关联方程中，便可以得到该乳酸阈值配速对应的最大摄氧量配速，也就是本实施例中用户的最大摄氧量配速。
本实施例中，目标模型方程的横坐标可以表示运动时间的变化，纵坐标可以表示用户配速的变化。
由于用户在实际运动过程中，用户的实时配速会随着运动时间增长而相应降低，因此可以参考用户的最大摄氧量配速来初步分析用户在不同运动时间点下的配速，然后采用用户在实际运动中的不同运动时间点下的实时配速，来判断为用户在不同运动时间点下所初步分析的配速是否符合实际运动并进行相应调整，从而得到大量不同运动时间点下符合用户实际运动的数据点，通过这些数据点能够拟合出对应的目标模型方程，以准确反映用户配速与运动时间之间的关系。
S120，基于至少一个运动维度下适配的运动时间点在目标模型方程中对应的用户配速，评估用户的运动能力。
可选的，由于运动维度中的爆发力、速度、速度耐力、有氧、耐力和超耐力分别通过用户在不同运动时间点下的配速来表示，因此将每一运动维度所指示的运动时间点输入到所建立的目标模型方程中，即可得到用户在每一运动维度下的用户配速，然后分析每一运动维度下的用户配速的快慢，以此评估用户的整体运动能力，实现用户运动能力的准确评估。
作为本实施例中的一种可选方案，基于至少一个运动维度下适配的运动时间点在目标模型方程中对应的用户配速，评估用户的运动能力，可以包括：基 于每个运动维度下适配的运动时间点，在目标模型方程中确定该运动维度下的用户配速；基于每个运动维度下的用户配速和该运动维度下预设定的运动能力得分查询表，确定用户在该运动维度下的运动能力得分；基于用户在至少一个运动维度下的运动能力得分，评估用户的综合运动能力。
确定每一运动维度下适配的运动时间点，如爆发力适配的运动时间点为目标模型方程中的第10秒，速度适配的运动时间点为目标模型方程中的第60秒，速度耐力适配的运动时间点为目标模型方程中的第5分钟，有氧适配的运动时间点为目标模型方程中的第60分钟，耐力适配的运动时间点为目标模型方程中的第120分钟，超耐力适配的运动时间点为目标模型方程中的第600分钟。将每一运动维度下适配的运动时间点分别输入到已构建的目标模型方程中，即可确定用户在每一运动维度下的用户配速。
为了分析每一运动维度下的运动能力得分，本实施例会预先在每一运动维度下设定一个用户配速与运动能力得分之间关系的查询表，以便采用每一运动维度下的用户配速和该运动维度下预设定的运动能力得分查询表，确定出用户在该运动维度下用户配速对应的运动能力得分。
示例性的，每一运动维度下的运动能力得分查询表如表2-表7所示。
表2爆发力得分
用户配速(分/km) 爆发力得分
1:35 100
1:50 80
2:00 70
2:10 60
2:30 40
3:20 0
表3速度得分
用户配速(分/km) 速度得分
1:50 100
2:30 80
2:42 70
3:00 60
3:20 40
5:00 0
表4有氧得分
用户配速(分/km) 有氧得分
2:45 100
3:00 80
3:30 70
4:30 60
6:00 40
9:00 0
表5速度耐力得分
用户配速(分/km) 速度耐力得分
2:00 100
2:40 80
3:20 70
3:50 60
4:30 40
7:00 0
表6耐力得分
用户配速(分/km) 耐力得分
2:50 100
3:20 80
4:00 70
4:30 60
5:30 40
8:00 0
表7超耐力得分
用户配速(分/km) 超耐力得分
3:40 100
4:30 80
5:30 70
7:00 60
8:00 40
20:00 0
由上表中，可以根据每一运动维度下的用户配速，确定出用户在该运动维度下的运动能力得分。然后，将用户在多个运动能力得分的平均值，作为用户的综合运动能力。用户的运动能力可以划分为休闲(0-40)、一般(41-60)、良好(60-70)、优秀(70-80)和菁英(80-100)这五个等级，根据用户的综合能力得分即可评估出用户的运动能力等级。根据用户的运动能力等级，即可为用户制定出合理的运动规划，保证用户运动的健康管理。
本实施例提供的技术方案，响应于用户运动能力的评估指令，首先调用为该用户预构建的目标模型方程，该目标模型方程能够反映用户配速和运动时间之间的关系，然后考虑到用户在不同运动时间点下的配速能够反映用户在不同维度下的运动能力，可以采用每一运动维度下适配的运动时间点在目标模型方程中对应的用户配速，来评估用户的运动能力，从而实现用户运动能力的自动评估，无需评估者进行主观定性经验分析，提高了运动能力的评估准确性，以便按照用户的运动能力制定合理的运动规划，确保用户运动的健康管理。
实施例二
图2为本申请实施例二提供的一种运动能力的评估方法的流程图。本申请实施例是在上述实施例的基础上进行说明。可选的，本实施例中主要对用户在标准赛程下的标准配速的计算过程和目标模型方程的构建过程进行说明。
参见图2，本实施例的方法可以包括：
S210，利用用户在标准赛程下的最大摄氧量，确定最大摄氧量对应的目标储备心率比。
本实施例可以通过用户年龄、性别、体重等生理特征，以及用户在标准赛程下的最大心率和静息心率，以及利用在标准赛程下一段运动过程中的实时运动心率和实时运动速度所计算出的平均运动心率和平均运动速度，分析出用户在标准赛程下所支持的最大摄氧量，本实施例中最大摄氧量的计算公式可以为：
Figure PCTCN2021093238-appb-000001
其中，A为40～50的常数，P 1为7～8的常数，S为性别常数，男性为1，女性为0；P 2为0.1～0.2的常数，G为用户体重；P 3为4～5的常数，V为平均运动速度，P 4为3～4的常数，B为1～2的常数；C为15～20的常数，hr avg为平均运动心率，hr 0为用户在清醒、安静状态下的静息心率，hr max为用户的最大心率；a为用户年龄。
根据用户在标准赛程下的实际运动，能够制作与标准赛程相对应的储备心率比查询表。以半马赛程为例，如下表8和表9所示，用户储备心率比查询表中记录有相对于半马赛程具有不同生理特征和最大摄氧量的用户所对应的储备心率比。
表8男性在半马赛程下的储备心率比
Figure PCTCN2021093238-appb-000002
Figure PCTCN2021093238-appb-000003
表9女性在半马赛程下的储备心率比
Figure PCTCN2021093238-appb-000004
针对不同的标准赛程，均可以制作出上述半马赛程对应的储备心率比查询表。可以根据用户的生理特征数据和最大摄氧量查询在标准赛程下的储备心率比查询表，获得用户相对于标准赛程的目标储备心率比。
S220，基于目标储备心率比以及用户的最大心率和静息心率，计算用户在标准赛程下的标准运动心率。
储备心率比的计算公式为
Figure PCTCN2021093238-appb-000005
其中，hr i为用户在运动过程中的实时心率，hr max为用户的最大心率，hr 0为用户在清醒、安静状态下的静息心率。将用户相对于标准赛程的目标储备心率比，以及用户的最大心率和静息心率代入到上述计算公式中，即可计算出用户在标准赛程下的标准运动心率。
本实施例中用户的最大心率和静息心率可通过便携式心率检测设备检测得到，在用户知道自己的最大心率和静息心率数值的情况下，也可由用户手动设置。另外，最大心率还可通过用户年龄计算获得，即hr max＝208-0.7a，a为用户年龄。
S230，利用标准运动心率以及预建立的反映标准赛程下用户心率和运动速度之间的关系的多个分段模型方程，计算用户在标准赛程下的标准配速。
为了采用标准运动心率计算出用户在标准赛程下的标准配速，则需要选取大量有效历史心率和历史运动速度，来建立反映标准赛程下用户心率和运动速度关系的模型方程。然而，由于标准赛程分为半马赛程和全马赛程等不同赛程，为了便于计算每一标准赛程下的标准配速，本实施例会对所采集的多类数据按照运动距离进行划分，分别划分为0-10km、10-20km、20-30km、30-40km、40km以上这五个分段，并在每一分段下能够建立对应的分段模型方程，便于根据多个分段得到不同的标准赛程，以根据对应的运动速度计算不同标准赛程的标准配速。
示例性的，本实施例可以获取用户在历史运动过程中的实时心率、配速、温度、湿度、海拔、坡度、距离、体温、运动时间等数据，并选取符合模型搭建的心率、配速数据，要求如下：
1)用户单次连续运动时长达到预设时长(如10分钟)；2)以一定的移动周期，按照时间顺序，依次获取并判断每个连续的单位时段内的历史心率和历史运动速度是否符合下述条件：a、单位时段内的心率波动在第一预设范围(未超过12bpm)内；b、单位时段内的速度波动在第二预设范围(未超过5m/s)内；c、单位时段内的实时储备心率比在第三预设范围(50％～90％)内。也即在用户的连续运动时长达到预设时长的情况下，将所述连续运动时长划分为多个单位时段，相邻的单位时段之间存在时段重叠，且相邻的单位时段分别对应的起始时间点之间的时间差为一个运动周期，判断每个单位时段内的历史心率和历史配速是否符合上述条件。可以不断获取到符合上述条件的每个目标单位时段内的历史心率和历史运动速度，并计算出每个目标单位时段内历史心率和历史运动速度的平均值，作为对应的关联数据点。例如，以2分钟为一个单位时段，以1秒的移动周期，不断记录每个2分钟内的心率和运动速度，计算出每个2分钟内的心率均值和运动速度均值，也就是每隔1秒统计一个2分钟的单位时 段，从而不断获取第1-120秒、第2-121秒、第3-122秒、……等多个连续的单位时段内的历史心率和历史运动速度等多个数据。
本实施例中对于符合上述条件的心率、配速数据点，可以根据运动时辰、温度、湿度、海拔、距离段、体温、坡度进行分类。运动时间可以分为早上(5:00-7:00)、上午(7:00-11:30)、中午(11:30-14:30)、下午(14:30-17:00)、傍晚(17:00-19:00)、晚上(19:00-22:00)和深夜(22:00-5:00)，温度可以分为-10度以下、-10-0度、0-10度、10-20度、20-25度、25-30度和30度以上，湿度可以分为30％以下、30％-60％、60％-80％、80％-90％和90％以上，海拔可以分为500m以下、500-1000m、1000-1500m、1500-2500m、2500-3500m、3500-4000m、4000-5000m和5000m以上，距离段可以分为0-10km、10-20km、20-30km、30-40km和40km以上，体温可以分为36度以下、36-37度、37-38度、38-39度和39度以上，坡度可以分为-45度以下、-45--30度、-30--20度、-20--10度、-10-0度、0-5度、5-10度、10-20度、20-30度、30-40度、40-50度和50度以上。
按照多个运动距离，对多个目标单位时段内由历史心率和历史运动速度组成的关联数据点进行距离段分类，组成0-10km、10-20km、20-30km、30-40km、40km以上等这五个分段下对应的数据集合，从而利用每一分段下的数据集合，能够拟合出该分段下用于反映用户心率和运动速度关系的分段模型方程。例如，每一分段下的分段模型方程可以为：
Figure PCTCN2021093238-appb-000006
用于利用用户在标准赛程下的标准运动心率计算对应的运动速度，然后采用该运动速度计算出对应的标准配速。或，为了保证标准配速的准确性，分段模型方程还可以为：
Figure PCTCN2021093238-appb-000007
其中，time为运动时间点，atmos为气温，humi为湿度，hei为海拔，slope为坡度，temp为体温，A，B，C，D，E，F，G和H均为常数。通过增加影响参数，来保证多个分段模型方程的准确性。
因此，通过上述历史数据分析，可以建立出0-10km、10-20km、20-30km、30-40km、40km以上等这五个分段下用于反映用户心率和运动速度关系的分段模型方程，从这五个分段模型方程可以筛选出与标准赛程相关的分段模型方程，然后，将用户在标准赛程下的标准运动心率输入到与标准赛程相关的分段模型方程中，以计算出相应的运动速度，进而按照分段模型方程中的运动速度计算出对应的标准配速。
以半马赛程作为标准赛程为例，可以确定0-10km和10-20km下的分段模型方程与半马赛程相关，将用户在半马赛程下的标准运动心率分别输入到0-10km和10-20km下的分段模型方程中，可以分别得到0-10km下的第一运动速度v 1和10-20km下的第二运动速度v 2，可以计算出半马配速为
Figure PCTCN2021093238-appb-000008
参照上述步骤，通过标准赛程下的多个分段模型方程和标准运动心率，即可计算出用户在标准赛程下的标准配速，以便后续采用标准赛程下的标准配速，来确定对应的乳酸阈值配速，并基于该乳酸阈值配速，确定用户的最大摄氧量配速。
S240，基于用户在标准赛程下的标准配速，确定用户的最大摄氧量配速。
S250，利用最大摄氧量配速，构建反映用户配速和运动时间之间的关系的初始模型方程。
可选的，由于在用户的实际运动中，用户配速会随着运动时间增长而逐渐降低，因此本实施例可以采用用户的最大摄氧量配速相对于不同运动时间点下的多个百分占比，来初步计算出用户在不同运动时间点下的用户初始配速，从而将多个运动时间点和该多个运动时间点下的用户初始配速组合，可以得到大量数据点，然后利用多个数据点，可以拟合出反映用户配速和运动时间之间的关系的初始模型方程。
示例性的，用户在不同运动时间点下的用户初始配速可以为：10s的运动时间点对应的用户配速为120％最大摄氧量配速，1min的运动时间点对应的用户配速为115％最大摄氧量配速，3min的运动时间点对应的用户配速为110％最大摄氧量配速，5min的运动时间点对应的用户配速为105％最大摄氧量配速，10min的运动时间点对应的用户配速为100％最大摄氧量配速，30min的运动时间点对应的用户配速为95％最大摄氧量配速，90min的运动时间点对应的用户配速为85％最大摄氧量配速，180min的运动时间点对应的用户配速为80％最大摄氧量配速，240min的运动时间点对应的用户配速为75％最大摄氧量配速，1200min的运动时间点对应的用户配速为60％最大摄氧量配速，而大于1200min的运动时间点对应的用户配速为60％最大摄氧量配速。
S260，基于用户在实际运动中的实时配速，调整初始模型方程，得到目标模型方程。
可选的，为了保证目标模型方程的准确性，在构建出初始模型方程后，可以获取用户在实际运动中在不同运动时间点下的实时配速，通过分析不同运动时间点下的实时配速与初始模型方程中该运动时间点下的配速进行比对，来对应的调整初步模型方程，以使初始模型方程中与运动时间相关的配速变化能够不断逼真于用户的实际运动，从而得到对应的目标模型方程。
作为本实施例中的一种可选方案，调整初始模型方程时，会确定用户在实际运动中的每一运动时间点下的实时配速，与初始模型方程中的该运动时间点下的预估配速之间的差异，并基于该差异，调整初始模型方程。也就是说，如 果用户在实际运动中的一运动时间点下的实时配速高于初始模型方程中的该运动时间点下的预估配速，则上调初始模型方程中的该运动时间点附近的用户配速，如果用户在实际运动中的一运动时间点下的实时配速低于初始模型方程中的该运动时间点下的预估配速，则下调初始模型方程中的该运动时间点附近的用户配速，使得调整后的初始模型方程中的多个运动时间点下的用户配速能够逼真于用户的实际运动。
S270，基于至少一个运动维度下适配的运动时间点在目标模型方程中对应的用户配速，评估用户的运动能力。
本实施例提供的技术方案，首先根据用户在标准赛程下的标准配速，确定出用户的最大摄氧量配速，然后利用该最大摄氧量配速和用户实际运动中的实时配速，构建反映用户配速和运动时间之间的关系的目标模型方程，考虑到用户在不同运动时间点下的配速能够反映用户在不同维度下的运动能力，可以采用每一运动维度下适配的运动时间点在目标模型方程中对应的用户配速，来评估用户的运动能力，从而实现用户运动能力的自动评估，无需评估者进行主观定性经验分析，提高了运动能力的评估准确性，以便按照用户的运动能力制定合理的运动规划，确保用户运动的健康管理。
实施例三
图3为本申请实施例三提供的一种运动能力的评估装置的结构示意图，如图3所示，该装置可以包括：
模型调用模块310，设置为响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，所述目标模型方程反映用户配速和运动时间之间的关系；运动能力评估模块320，设置为基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。
本实施例提供的技术方案，响应于用户运动能力的评估指令，首先调用为该用户预构建的目标模型方程，，该目标模型方程能够反映用户配速和运动时间之间的关系，然后考虑到用户在不同运动时间点下的配速能够反映用户在不同维度下的运动能力，可以采用每一运动维度下适配的运动时间点在目标模型方程中对应的用户配速，来评估用户的运动能力，从而实现用户运动能力的自动评估，无需评估者进行主观定性经验分析，提高了运动能力的评估准确性，以便按照用户的运动能力制定合理的运动规划，确保用户运动的健康管理。
上述运动能力的评估装置还可以包括：摄氧量配速确定模块和模型方程构建模块；上述目标模型方程可以采用上述摄氧量配速确定模块和模型方程构建 模块构建：
所述摄氧量配速确定模块，设置为基于用户在标准赛程下的标准配速，确定所述用户的最大摄氧量配速；所述模型方程构建模块，设置为于利用所述最大摄氧量配速和用户在实际运动中的实时配速，构建反映用户配速和运动时间之间的关系的目标模型方程。
上述摄氧量配速确定模块，可以设置为：
利用用户在标准赛程下的标准配速，确定标准配速对应的乳酸阈值配速；基于所述乳酸阈值配速，确定所述用户的最大摄氧量配速。
上述运动能力的评估装置，还可以包括：
心率比确定模块，设置为利用用户在标准赛程下的最大摄氧量，确定最大摄氧量对应的目标储备心率比；标准心率计算模块，设置为基于所述目标储备心率比以及用户的最大心率和静息心率，计算用户在标准赛程下的标准运动心率；标准配速计算模块，设置为利用所述标准运动心率以及预建立的反映标准赛程下用户心率和配速之间的关系的多个分段模型方程，计算所述用户在标准赛程下的标准配速。
上述多个分段模型方程可以采用如下步骤建立：
在用户的连续运动时长达到预设时长的情况下，将所述连续运动时长划分为多个单位时段，相邻的单位时段之间存在时段重叠，且相邻的单位时段分别对应的起始时间点之间的时间差为一个运动周期，判断每个单位时段内的历史心率和历史配速是否符合下述条件：1)该单位时段内的心率波动在第一预设范围内；2)该单位时段内的速度波动在第二预设范围内；3)该单位时段内的实时储备心率比变化在第三预设范围内；计算每一符合上述条件的目标单位时段内的历史心率和历史配速的平均值，作为反映用户心率和配速关系的关联数据点，并按照预设定的多个分段运动距离，对多个目标单位时段对应的关联数据点进行分类；利用每一分段运动距离内多个目标单位时段对应的关联数据点，拟合出该分段运动距离下的分段模型方程。
上述模型方程构建模块，可以包括：
初始模型构建单元，设置为利用所述最大摄氧量配速，构建反映用户配速和运动时间之间的关系的初始模型方程；模型调整单元，设置为基于用户在实际运动中的实时配速，调整所述初始模型方程，得到目标模型方程。
上述模型调整单元，可以设置为：
确定用户在实际运动中的每一运动时间点下的实时配速，与所述初始模型 方程中该运动时间点下的预估配速之间的差异，并基于所述差异，调整所述初始模型方程。
上述运动能力评估模块320，可以设置为：
基于每个运动维度下适配的运动时间点，在所述目标模型方程中确定该运动维度下的用户配速；基于每个运动维度下的用户配速和该运动维度下预设定的运动能力得分查询表，确定所述用户在该运动维度下的运动能力得分；基于所述用户在至少一个运动维度下的运动能力得分，评估所述用户的综合运动能力。
本实施例提供的一种运动能力的评估装置可适用于上述任意实施例提供的运动能力的评估方法，具备相应的功能和效果。
实施例四
图4为本申请实施例四提供的一种电子设备的结构示意图。如图4所示，该电子设备包括处理器40、存储装置41和通信装置42；电子设备中处理器40的数量可以是一个或多个，图4中以一个处理器40为例；电子设备的处理器40、存储装置41和通信装置42可以通过总线或其他方式连接，图4中以通过总线连接为例。
存储装置41作为一种计算机可读存储介质，可设置为存储软件程序、计算机可执行程序以及模块，如本申请实施例中的运动能力的评估方法对应的模块(例如，运动能力的评估装置中的模型调用模块310和运动能力评估模块320)。处理器40通过运行存储在存储装置41中的软件程序、指令以及模块，从而执行电子设备的多种功能应用以及数据处理，即实现上述的运动能力的评估方法。
存储装置41可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据终端的使用所创建的数据等。此外，存储装置41可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储装置41可包括相对于多功能控制器40远程设置的存储器，这些远程存储器可以通过网络连接至电子设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
通信装置42可设置为实现设备间的网络连接或者移动数据连接。
本实施例提供的一种电子设备可设置为执行上述任意实施例提供的运动能力的评估方法，具备相应的功能和效果。
实施例五
本申请实施例五还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时可实现上述任意实施例中的运动能力的评估方法。该方法包括：
响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，所述目标模型方程反映用户配速和运动时间之间的关系；基于至少一个运动维度下适配的运动时间点在在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。
本申请实施例所提供的一种包含计算机可执行指令的存储介质，其计算机可执行指令不限于如上所述的方法操作，还可以执行本申请任意实施例所提供的运动能力的评估方法中的相关操作。
通过以上关于实施方式的描述，本申请可借助软件及必需的通用硬件来实现，也可以通过硬件实现。本申请的技术方案本质上可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如计算机的软盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、闪存(FLASH)、硬盘或光盘等，包括多个指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请实施例所述的方法。
上述运动能力的评估装置的实施例中，所包括的多个单元和模块只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，多个功能单元的名称也只是为了便于相互区分，并不用于限制本申请的保护范围。

Claims (11)

  1. 一种运动能力的评估方法，包括：
    响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，其中，所述目标模型方程反映用户配速和运动时间之间的关系；
    基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。
  2. 根据权利要求1所述的方法，其中，所述目标模型方程采用如下步骤构建：
    基于所述用户在标准赛程下的标准配速，确定所述用户的最大摄氧量配速；
    利用所述最大摄氧量配速和所述用户在实际运动中的实时配速，构建所述目标模型方程。
  3. 根据权利要求2所述的方法，其中，所述基于所述用户在标准赛程下的标准配速，确定所述用户的最大摄氧量配速，包括：
    利用所述用户在标准赛程下的标准配速，确定所述标准配速对应的乳酸阈值配速；
    基于所述乳酸阈值配速，确定所述用户的最大摄氧量配速。
  4. 根据权利要求2所述的方法，在所述基于用户在标准赛程下的标准配速，确定所述用户的最大摄氧量配速之前，还包括：
    利用所述用户在标准赛程下的最大摄氧量，确定所述最大摄氧量对应的目标储备心率比；
    基于所述目标储备心率比以及所述用户的最大心率和静息心率，计算所述用户在标准赛程下的标准运动心率；
    利用所述标准运动心率以及预建立的反映标准赛程下用户心率和运动速度之间的关系的多个分段模型方程，计算所述用户在标准赛程下的标准配速。
  5. 根据权利要求4所述的方法，其中，所述多个分段模型方程采用如下步骤建立：
    在用户的连续运动时长达到预设时长的情况下，将所述连续运动时长划分为多个单位时段，相邻的单位时段之间存在时段重叠，且相邻的单位时段分别对应的起始时间点之间的时间差为一个运动周期，判断每个单位时段内的历史心率和历史配速是否符合下述条件：所述每个单位时段内的心率波动在第一预设范围内；所述每个单位时段内的速度波动在第二预设范围内；所述每个单位时段内的实时储备心率比变化在第三预设范围内；
    计算每一符合上述条件的目标单位时段内的历史心率和历史运动速度的平均值，作为反映用户心率和运动速度关系的关联数据点，并按照预设定的多个分段运动距离，对多个目标单位时段对应的关联数据点进行分类；
    利用每一分段运动距离内多个目标单位时段对应的关联数据点，拟合出所述每一分段运动距离下的分段模型方程。
  6. 根据权利要求2所述的方法，其中，所述利用所述最大摄氧量配速和所述用户在实际运动中的实时配速，构建所述目标模型方程，包括：
    利用所述最大摄氧量配速，构建反映用户配速和运动时间之间的关系的初始模型方程；
    基于所述用户在实际运动中的实时配速，调整所述初始模型方程，得到所述目标模型方程。
  7. 根据权利要求6所述的方法，其中，所述基于所述用户在实际运动中的实时配速，调整所述初始模型方程，包括：
    确定所述用户在实际运动中的每一运动时间点下的实时配速，与所述初始模型方程中的所述每一运动时间点下的预估配速之间的差异，并基于所述差异，调整所述初始模型方程。
  8. 根据权利要求1-7任一项所述的方法，其中，所述基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力，包括：
    基于每个运动维度下适配的运动时间点，在所述目标模型方程中确定所述每个运动维度下的用户配速；
    基于每个运动维度下的用户配速和所述每个运动维度下预设定的运动能力得分查询表，确定所述用户在所述每个运动维度下的运动能力得分；
    基于所述用户在所述至少一个运动维度下的运动能力得分，评估所述用户的综合运动能力。
  9. 一种运动能力的评估装置，包括：
    模型调用模块，设置为响应于用户运动能力的评估指令，调用为所述用户预构建的目标模型方程，其中，所述目标模型方程反映用户配速和运动时间之间的关系；
    运动能力评估模块，设置为基于至少一个运动维度下适配的运动时间点在所述目标模型方程中对应的用户配速，评估所述用户的运动能力。
  10. 一种电子设备，包括：
    至少一个处理器；
    存储装置，设置为存储至少一个程序；
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-8中任一项所述的运动能力的评估方法。
  11. 一种计算机可读存储介质，存储有计算机程序，其中，所述程序被处理器执行时实现如权利要求1-8中任一项所述的运动能力的评估方法。
PCT/CN2021/093238 2021-05-12 2021-05-12 运动能力的评估方法、装置、设备和存储介质 WO2022236726A1 (zh)

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Citations (3)

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Publication number Priority date Publication date Assignee Title
CN107595273A (zh) * 2017-09-12 2018-01-19 广东远峰电子科技股份有限公司 一种心率估算方法和装置
CN111530037A (zh) * 2020-05-13 2020-08-14 广东高驰运动科技有限公司 用于评估跑步运动中身体机能反应状态的方法及设备
CN111544853A (zh) * 2020-05-13 2020-08-18 广东高驰运动科技有限公司 跑步运动中体力指标评估方法和设备

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Publication number Priority date Publication date Assignee Title
CN107595273A (zh) * 2017-09-12 2018-01-19 广东远峰电子科技股份有限公司 一种心率估算方法和装置
CN111530037A (zh) * 2020-05-13 2020-08-14 广东高驰运动科技有限公司 用于评估跑步运动中身体机能反应状态的方法及设备
CN111544853A (zh) * 2020-05-13 2020-08-18 广东高驰运动科技有限公司 跑步运动中体力指标评估方法和设备

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