WO2018214530A1 - Procédé et système d'évaluation d'état compétitif d'athlètes - Google Patents

Procédé et système d'évaluation d'état compétitif d'athlètes Download PDF

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WO2018214530A1
WO2018214530A1 PCT/CN2018/072338 CN2018072338W WO2018214530A1 WO 2018214530 A1 WO2018214530 A1 WO 2018214530A1 CN 2018072338 W CN2018072338 W CN 2018072338W WO 2018214530 A1 WO2018214530 A1 WO 2018214530A1
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athlete
state
fatigue
training
index
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PCT/CN2018/072338
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English (en)
Chinese (zh)
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包磊
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深圳市未来健身科技有限公司
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items

Definitions

  • the invention belongs to the technical field of wearable devices, and in particular relates to a method and system for evaluating an athlete's competitive state.
  • the athlete's competitive state includes his physical state and mental state.
  • the commonly used measures are mainly to judge whether the athlete's muscles are fatigued and whether there is excessive psychological stress.
  • the competitive state is an important indicator for judging whether an athlete can participate in training, competition, and even good grades. If an athlete participates in competitions and training in a bad state of competition, it is not only difficult to achieve good results, but may even be the athlete's body. And psychologically, it has a great impact, causing great losses to athletes and training teams.
  • the professional coach is often judged and evaluated based on his own experience on the competitive state of the athlete before training and competition. When the athlete is judged to be in a poor state, the athlete is prohibited from playing.
  • drawbacks 1. Because the professional level and coaching experience of different coaches are not the same, the accuracy of the coach's judgment on the athletes' competitive state is also different. limit. 2. It is sometimes difficult to identify and judge through the human eye. For example, sometimes the athlete's muscles are slightly damaged and cannot be observed by the human eye. Even the athletes themselves cannot perceive it. At this time, if the athletes are trained to play, they may Will increase the degree of muscle damage, leading to severe muscle damage.
  • the embodiment of the present invention provides a method and system for evaluating an athlete's competitive state, so as to solve the problem that the competitive state of the athlete cannot be accurately and effectively evaluated in the prior art.
  • a first aspect of the embodiments of the present invention provides a method for evaluating an athletic state of an athlete, including:
  • the output allows the training prompt
  • the training prohibition is output.
  • a second aspect of the embodiments of the present invention provides an athlete competitive state evaluation system, including:
  • An acquisition module configured to collect physiological data of the athlete, the physiological data including the myoelectric data and the electrocardiogram data;
  • a state evaluation module configured to calculate, according to the myoelectric data and the electrocardiogram data, a competitive state parameter of the athlete, and determine, according to the competitive state parameter, whether the athletic state of the athlete satisfies a training requirement, the competitive State parameters include fatigue index and HRV heart rate variability;
  • the prompting module is configured to output an allowable training prompt when the evaluation result is that the competitive state of the athlete satisfies the training requirement;
  • the prompting module is configured to output a prohibition training prompt when the evaluation result is that the competitive state of the athlete does not satisfy the training requirement.
  • the embodiment of the present invention has the beneficial effects that after the wearable device collects the physiological data of the athlete, the athletic state parameter of the athlete is calculated according to the collected physiological data, and the athlete is evaluated based on the competitive state parameter.
  • the competitive state makes the evaluation of the athlete's competitive state automatic, and is not affected by the subjective experience of the coach, which is more accurate and reliable.
  • the athlete's competitive state is obtained, the athlete is automatically judged according to the competitive state to meet the requirements of the training competition, and the corresponding prompt is output, so that the athlete can intuitively know whether his current competitive state is suitable for the training game.
  • Embodiment 1 is a flowchart showing an implementation of an athlete's competitive state evaluation method according to Embodiment 1 of the present invention
  • FIG. 2 is a flowchart of implementing an athlete's competitive state evaluation method according to Embodiment 2 of the present invention
  • Embodiment 3 is a flowchart of implementing an athlete's competitive state evaluation method according to Embodiment 3 of the present invention.
  • Embodiment 5 is a flowchart of implementing an athlete's competitive state evaluation method according to Embodiment 5 of the present invention.
  • FIG. 6 is a structural block diagram of an athlete competitive state evaluation system according to Embodiment 6 of the present invention.
  • the wearable device may be a wearable smart fitness garment, or may be a collection of one or more collection modules that are wearable and attachable.
  • the wearable device when the wearable device is a wearable smart fitness garment, it may be a garment or pants made of a flexible fabric, and a plurality of collection modules are embedded on the side of the flexible fabric close to the human skin. Each collection module is fixed at different points of the smart fitness garment so that after the user wears the smart fitness garment, each collection module can be attached to each muscle of the user's body.
  • at least one control module is also embedded, and each of the acquisition modules is separately connected to the control module. In the prior art, generally only one control module is used to implement control of the acquisition module.
  • a wire and a circuit board may be disposed in the wearable device, wherein the circuit board is used to fix various communication buses and the acquisition module.
  • the circuit board and its various solder joints are wrapped with a waterproof glue.
  • the wearable device can be washed by fixing a waterproof trace on the laundry.
  • each acquisition module may include only an acquisition electrode having a somatosensory sensor function, or an integrated circuit having an acquisition function.
  • the above collection electrodes include, but are not limited to, fabric electrodes, rubber electrodes, gel electrodes, and the like.
  • each acquisition module is an integrated circuit having an acquisition function and a wireless transmission function, and the integrated circuit includes the above-mentioned acquisition electrode having a somatosensory sensor function.
  • the EMG signal collected by the acquisition module is transmitted to the remote control module through the wireless network, and the control module is located in the remote terminal device or the remote control box used in conjunction with the acquisition module.
  • FIG. 1 is a flowchart showing an implementation process of an athlete's competitive state evaluation method according to Embodiment 1 of the present invention, which is described in detail as follows:
  • S101 Control an acquisition module in the wearable device to collect physiological data of the athlete, where the physiological data includes electromyogram data and electrocardiogram data.
  • the ECG data refers to the data that the heart is excited by the pacemaker, the atria, and the ventricle in each cardiac cycle, accompanied by changes in bioelectricity.
  • the electrocardiographic data acquisition of the athlete is performed by using an electrode measurement method by embedding the method of the flexible thin film electrode in the wearable device.
  • the muscles of different training programs are different, for example, football training mainly uses leg muscles, while basketball training requires the use of whole body muscles. Therefore, muscle parts that may have muscle fatigue may also be used for different training programs.
  • the myoelectric data required to be collected is also different.
  • the required electromyography data can be specifically set by the user according to the actual training item. For example, when the training item is football, the myoelectric data of the user's leg muscles can be set to the electromyography data used for the collection.
  • the wearable device After the user activates the wearable device, the user needs to select and set the collection object of the myoelectric data. After receiving the electromyography data and the setting completion command set by the user, the wearable device activates the corresponding acquisition module to start collecting and recording the user's myoelectric data. If the user does not set the EMG data acquisition target within the preset time (for example, five minutes) after the user activates the wearable device, the user's last setting is extended by default. If the wearable device is activated for the first time, or the last setting data is lost, all acquisition modules are activated by default for EMG data acquisition.
  • the preset time for example, five minutes
  • the technician can pre-distort the muscles of the human body and provide a human-computer interaction interface for the user to select and set the EMG data collection object.
  • the muscle group can be set as the target of myoelectric data collection.
  • the human muscles are simply divided into leg muscles, chest muscles, back muscles, abdominal muscles, shoulder muscles, and hand muscles. When the user is playing football, the leg muscles can be directly selected. Set as the EMG data acquisition object.
  • the technician in order to facilitate the use of the user, can pre-set a plurality of different training modes, such as a soccer training mode, a basketball training mode, a soldier ball training mode, etc., and for each different The training mode sets the corresponding muscle group, and the corresponding muscle group is the electromyography data acquisition object corresponding to the training mode.
  • the user only needs to select the corresponding training mode after activating the wearable device. .
  • S102 Calculate the athletic state parameter of the athlete according to the physiological data, and evaluate whether the competitive state of the athlete satisfies the training requirement according to the competitive state parameter, and the competitive state parameter includes a fatigue index and a HRV heart rate variability.
  • HRV heart rate variability refers to the small fluctuation between successive heartbeat cycles, which is caused by the modulation of the rhythm of the autonomic nervous system on the sinus of the heart, so that the heartbeat interval fluctuates within a range of tens of milliseconds.
  • the HRV signal contains a large amount of information about cardiovascular regulation.
  • the acquisition and analysis of this information can quantitatively assess the tension and balance of cardiac sympathetic and parasympathetic activities.
  • HRV heart rate variability Through the calculation and processing of HRV heart rate variability, users can Mental states such as stress index and anxiety level are characterized.
  • Muscle fatigue can be divided into fatigue and non-sensation fatigue.
  • non-sensing fatigue the human body can not perceive or perceive weak, and often does not attract people's attention. If it is fatigued for a long time, it will cause damage to the muscles of the human body.
  • the fatigue index is selected as the mathematical statistical index of the physiological state of the athlete.
  • the fatigue index of the athlete can be calculated by the linear analysis technique of the electromyogram signal, the frequency analysis technique of the myoelectric signal, the fatigue estimation method of the complex covariance function, and the calculation method of the fatigue index is not the main invention point of the present invention. Therefore, there is no limitation in the present specification.
  • the calculation of HRV heart rate variability is generally done by time domain analysis, frequency domain analysis and nonlinear analysis.
  • the nonlinear analysis is still in the research and exploration stage.
  • the time domain analysis has the characteristics of simple calculation and intuitiveness, but its sensitivity and specificity.
  • the low degree of sexuality can not accurately analyze the psychological state, and the time domain analysis is widely used in clinical and medical experiments because of its mature theory, simple algorithm and clear meaning of various indicators.
  • the present invention in order to improve the validity of the calculated HRV heart rate variability, it is preferable to use a combination of time domain analysis and frequency domain analysis to first process the ECG data, and obtain heart rate data and pulse data, etc.
  • the heart rate data and the pulse data are processed to obtain the desired HRV heart rate variability, and the athlete's psychological state is evaluated.
  • the time domain analysis it is necessary to calculate the peak-to-peak spacing of the pulse data in the ECG data, and then obtain the corresponding RR interval according to the peak-peak interval. Finally, the time-domain statistics of the RR interval are used to obtain the heart rate variability.
  • Time domain parameters include a standard deviation (SDNN) including all heartbeat intervals, a root mean square (RMSSD) of the difference between adjacent RR intervals, and a difference between adjacent heartbeat intervals of more than 50 milliseconds.
  • SDNN standard deviation
  • RMSSD root mean square
  • the number of the heartbeats is equal to the total number of heart beats (PNNS0), etc., according to the correlation between the SDNN beat interval criteria and the psychological tension of the test subject, the standard deviation of the SDNN beat interval is preferably used.
  • the standard deviation of the SDNN beat interval is preferably used.
  • the SDNN is positively related to the degree of human body tension, that is, the more the human body is more nervous, the larger the SDNN is. Therefore, in the embodiment of the present invention, when the athlete's HRV heart rate variability is judged, the SDNN is used as the tension index, and a tension threshold is set. When the SDNN is greater than the threshold, the athlete is determined to be in a state of tension.
  • the instantaneous heart rate curve of the heart rate data is obtained from the ECG data, and then the fast Fourier transform (FFT) is used to obtain the spectrum map, and the frequency domain statistical analysis is performed to obtain the heart rate variability frequency domain parameter.
  • the obtained frequency domain variability frequency domain parameters include frequency domain indicators such as extremely low frequency (VLF), low frequency (LF), high frequency (HF), total energy (TP), and low frequency high frequency ratio (LF/HF), wherein
  • VLF/HF low-frequency high-frequency ratio represents the degree of activity between the sympathetic nervous system and the parasympathetic nervous system, that is, the degree of balance of the entire autonomic nervous system. Using this ratio, the sympathetic nerve activity can be evaluated to obtain the degree of anxiety of the subject.
  • LF is generally about 1.5 times that of HF. At this time, it can be regarded as the current equilibrium state of the autonomic nervous system, LF/HF low-frequency high-frequency ratio.
  • the LF/HF low frequency high frequency ratio is preferably used as the mathematical statistics index of the anxiety degree of the athlete. Since LF/HF is positively correlated with the degree of anxiety of the human body, that is, the more anxiety the human body has, the more LF/HF is, so the embodiment of the present invention uses LF/HF as an anxiety index when judging anxiety of the HRV heart rate variability of the athlete. An anxiety threshold is set. When LF/HF is greater than the threshold, the athlete is considered to be in an anxious state.
  • the required fatigue index and HRV heart rate variability are calculated, and the athlete's competitive state is evaluated according to the fatigue index and the HRV heart rate variability, and whether the athlete has muscle fatigue, excessive tension or Excessive anxiety and other abnormal state of competition.
  • the athlete's three indicators are all in normal, the athlete is considered to be in a good competitive state and meets the training requirement, and can perform a normal training competition.
  • One or more of the above three indicators When the indicator is in an abnormal range, the athlete's competitive state is considered to have certain problems, and the training requirements are not met, and normal training and competition cannot be performed.
  • the output allows the training prompt.
  • it is estimated that the athlete's competitive state is good in S102 it is determined that the athlete meets the training requirement, and at this time, a corresponding allowed training prompt is generated, and the athlete is allowed to perform a training prompt through the prompting module, and the training item that the athlete can perform the evaluation test is notified. training.
  • the prompting module in the wearable device may be combined to allow or prohibit the training prompt (such as using a voice prompting module to output a voice prompt, or using a vibration prompting module to perform a vibration prompt), or outputting the prompt signal to the Other devices are prompted.
  • the collected physiological data further includes brain electrical data, and further includes:
  • the emotional state of the athlete is judged based on the EEG data.
  • the psychological state judgment of the athlete is only based on the HRV heart rate variability, and sometimes an inaccurate situation may occur.
  • the brain electrical data of the athlete is also collected, and the emotional state of the athlete is identified according to the brain electrical data, and finally, according to the HRV heart rate variability and the recognition by the brain electrical energy
  • the emotional state that comes out comes as an indicator of the athlete's mental state judgment.
  • a method for recognizing an emotional state by using electroencephalogram data includes, but is not limited to, a support vector machine identification method based on recursive feature screening. Since the use of electroencephalogram data to identify an emotional state is not the main invention of the present invention, the present specification Without further elaboration, interested readers can refer to relevant materials.
  • the present invention it is only necessary to determine whether the mental state of the athlete affects the normal training game. Therefore, in the embodiment of the present invention, in order to reduce the workload of the control module, it is preferable to use the brain wave for emotion recognition. At that time, it is only necessary to easily recognize the athlete's pleasure, without the need for accurate emotional positioning recognition.
  • the athlete's athletic status is evaluated. Different from the method of assessing the degree of stress and anxiety in the HRV heart rate variability in S102, in the embodiment of the present invention, in judging whether there is a problem in the athlete's competitive state, the athlete's pleasure needs to be considered. Degree, that is, the need to simultaneously detect fatigue index, tension, anxiety and pleasure. In the embodiment of the present invention, when one or more of the above four indicators are in an abnormal range, the athlete's competitive state is considered to have certain problems, the training requirement is not satisfied, and the normal training game cannot be performed.
  • the respiratory frequency data and/or the body temperature data of the athlete may also be referred to to enhance the accuracy of the evaluation of the athletic status of the athlete.
  • the respiratory frequency data can be directly extracted from the collected ECG data.
  • the body temperature data is required to activate the corresponding body temperature collection module in the wearable device for collection.
  • the physiological data collected in S101 further includes the body temperature data of the athlete.
  • the method includes:
  • an athlete's current activity state and the activity state includes a rest state and a motion state.
  • the athlete may be in a state of rest, or may be in a state of exercise, and the stability of the physiological parameter of the athlete may be different under different activities.
  • two different fatigue calculation methods are selected for the characteristics of the athlete's two different activity states. deal with.
  • the current activity state of the athlete may be manually input by other users of the athlete, and the current activity state of the athlete may be identified by automatically recognizing the activity state of the athlete through the electromyography data.
  • the static fatigue algorithm is selected to calculate the fatigue index of the athlete.
  • the athlete's physiological index parameters are relatively stable. Therefore, it is not necessary to consider the stability of the physiological index parameters excessively when calculating the fatigue index.
  • the average power fatigue MPF and/or the median frequency MF of the myoelectric data are used to characterize the degree of muscle fatigue (ie, the fatigue index) in the resting state, that is, the static fatigue algorithm is mainly used to calculate the muscle.
  • the average power fatigue of the electrical data is MPF and/or the median frequency MF.
  • f is the frequency of the myoelectric data and P(f) is its power density spectrum
  • P(f) can be calculated using the classical power spectrum technique based on Fourier analysis.
  • the fatigue index may be characterized by MPF and/or MF, and as long as the MPF and/or MF reach a certain threshold, it is determined that the athlete has muscle fatigue.
  • the dynamic fatigue algorithm preferably uses the median frequency IMDF and the average frequency IMNF in the cohen-like time-frequency distribution technique to characterize the degree of muscle fatigue.
  • f is the frequency of the myoelectric data
  • S(t, f) is the time-frequency spectrum, which is calculated by the cohen-like time-frequency distribution technique.
  • the method includes:
  • the athlete's fatigue index and muscle coordination index are calculated according to the electromyography data, and the HRV heart rate variability is calculated according to the electrocardiogram data.
  • the competitive state For athletes, in order to get a good result safely, the competitive state must be kept optimal before the game, but especially for training programs that require high muscle coordination skills such as weightlifting.
  • the arm can't coordinate the force, it will not only affect the athlete's performance, but also bring great danger to the athlete.)
  • the muscle coordination ability refers to the ability to control the time of force, the size of the force and the speed of the force for different groups of muscles.
  • the time of force, the size of the force and the speed of force are all required by the EMG data and corresponding
  • the electromyography time data analysis shows that, therefore, in the embodiment of the present invention, while collecting the myoelectric data, the corresponding time stamp of each myoelectric data is also recorded, so as to provide the subsequent time, force and force. Analysis of speed.
  • the athletes are allowed to train the competition when the physiological state and the mental state of the athlete are both satisfied. Therefore, in the embodiment of the present invention, Athletes' fatigue index, muscle coordination index, tension index and anxiety index are required to be allowed to train the game when they are within the preset threshold.
  • Abnormality may affect the athletes, so that the athletes can't train the game normally. Therefore, in this embodiment, as long as any index abnormality occurs, the athlete's competitive state is considered to be a problem. In this case, in order to ensure the safety of the athletes. It will be judged that the athlete's competitive state is not suitable for the training competition, that is, the training requirements are not met.
  • the fatigue index exceeds the preset fatigue threshold, the HRV heart rate variability is in an abnormal state, and the muscle coordination index is lower than the preset coordination threshold, the user does not appear to read the training item input by the user and the preset
  • the EMG data sample and based on the training program, the EMG data sample and the fatigue index, the athlete is subjected to the first fatigue prediction, and the first fatigue prediction is used to determine whether the athlete can safely complete the training program.
  • the muscle electrical data in order to realize the prediction of muscle fatigue, it is necessary to calculate the muscle electrical data to obtain the required fatigue index at each moment, and then perform a curve fitting on the fatigue index calculated in S302 to obtain a muscle fit along with it.
  • the dynamic trend of the movement combined with the time required for the specific training program, to determine whether the athlete can safely complete the training program.
  • any algorithm that fits the fatigue index change trend graph or the change trend function formula can be used for curve fitting, for example, a common least squares method can be used for curve fitting.
  • the second embodiment of the present invention can be optimized according to the second embodiment of the present invention, that is, the fatigue index in S302 is calculated by using the static fatigue algorithm in the second embodiment of the present invention.
  • the athlete's competitive state evaluation result is determined to meet the training requirement. If the result of the first fatigue prediction is that the athlete cannot safely complete the training item, it is determined that the athlete's competitive state evaluation result does not satisfy the training requirement.
  • the competitive state is evaluated based on the predicted result.
  • the method includes:
  • S101 collects the muscle electrical data and ECG data of the athletes under exercise. Because of the difficulty in collecting the EMG data and ECG data under the motion state, the interference data components in the collected data. More. Therefore, in the embodiment of the present invention, before calculating the fatigue index of the athlete based on the myoelectric data and calculating the variability of the HRV heart rate based on the electrocardiographic data, it is preferable to perform interference data on the collected myoelectric data and the electrocardiogram data.
  • the filtering process requires noise filtering of the collected data.
  • the third embodiment of the present invention is directed to the evaluation of the competitive state when the athlete is in the rest state
  • the fourth embodiment of the present invention is directed to the evaluation of the competitive state when the athlete is in the sports state.
  • the specific action required for the muscle coordination ability test cannot be made, and the muscle coordination ability test cannot be performed at this time. Therefore, in the fourth embodiment of the present invention, only the fatigue index is used as the physiological index when evaluating the competitive state. .
  • the muscle fatigue threshold and the damage threshold need to be set in advance by the technician according to the actual situation of the athlete.
  • the athlete When the fatigue index exceeds the preset damage threshold in S403, the athlete has already suffered from muscle damage. If training is performed, the athlete's personal safety may be seriously damaged. At this time, the athlete should be required to rest or treat.
  • the fatigue index does not reach the fatigue threshold, that is, the athlete does not experience muscle fatigue. At this time, the athlete's physical condition is normal, and the training can be continued.
  • the fatigue index reaches the preset fatigue threshold and does not exceed the preset damage threshold, read the training remaining time input by the user and the preset EMG data sample, and according to the HRV heart rate variability, the EMG data sample, and the remaining time of the training. And the fatigue index, the second fatigue prediction is performed for the athlete, and the second fatigue prediction is used to judge whether the athlete can safely complete the remaining training.
  • the training is performed. It has not yet been completed. Because of the sportsmanship, athletes generally do not give up training easily when there is no injury in the body. Therefore, it is necessary to further evaluate the athletes' state of the athletes, evaluate the athletes' mental state and predict muscle fatigue, and judge Whether the athlete can safely complete the remaining training.
  • the same fatigue prediction method as in the third embodiment of the present invention may be used, and other fatigue prediction methods may be used.
  • the final fatigue index is judged in the fourth embodiment of the present invention.
  • the indicator should be the damage threshold, not the fatigue threshold.
  • the embodiment of the present invention does not immediately generate a fatigue prediction result after normally determining whether the athlete can not have muscle injury for the remaining time of the training, and also refers to the real-time mental state parameter HRV heart rate variability of the athlete, that is, the reference athlete is also needed.
  • the tension index and anxiety index only when the athlete can not have muscle damage during the remaining time of training, and the tension index and anxiety index do not exceed the preset threshold, it is considered that the athlete can safely complete the remaining training and generate the corresponding second.
  • the fatigue prediction result at this time, can determine that the athlete's competitive state meets the training requirements.
  • the athlete's competitive state evaluation result is determined to meet the training requirement. If the result of the second fatigue prediction is that the athlete cannot safely complete the remaining training, it is determined that the athlete's competitive state evaluation result does not satisfy the training requirement.
  • the fourth embodiment of the present invention considers that the athlete cannot safely complete the remaining training, that is, S405.
  • the second prediction result that the athlete cannot safely complete the remaining training is generated. At this time, it is determined that the athlete's competitive state does not satisfy the training requirement.
  • the method includes:
  • S501 if the fatigue index exceeds the preset damage threshold, determining that the athlete's competitive state evaluation result does not satisfy the training requirement.
  • S303 is further refined from the perspective of whether the athlete is fatigued, so as to personally meet the needs of different users.
  • the athlete's fatigue index exceeds the preset injury threshold, the athlete has already suffered from muscle damage. If training is performed, the athlete's personal safety may be seriously damaged. Therefore, the athlete's competitive state does not meet the training requirements. At this time, the athlete should be required to rest or treatment.
  • the preset athlete restoring power data and the remaining rest time input by the user are read, and the recovery time and the fatigue index of the athlete are used to calculate the recovery time required by the athlete. According to the recovery time and remaining rest time, judge whether the athlete can complete the training project safely.
  • the restoring force refers to the speed at which the fatigue index decreases during the rest of the athletes, and the restoring force of each person is different. Therefore, in the embodiment of the present invention, the technician needs to pre-store the recovery of the athlete who needs the evaluation of the competitive state. Force data for subsequent processing.
  • the athlete While the athlete is in a state of rest, although there is muscle fatigue, considering that it may be able to return to the state of non-muscle fatigue for the rest of the time, the athlete cannot be generalized to continue training.
  • the restoring force data and remaining rest time of the athlete who reads the competitive state evaluation are obtained.
  • calculate the recovery time required by the athletes calculate the recovery time required by the athletes, and then compare the remaining rest time and recovery time to judge whether the athlete can complete the training project safely.
  • the athlete has sufficient The recovery time can safely complete the training program, and if the remaining rest time is not greater than the recovery time, the athlete is not enough to return to the non-muscle fatigue state during the rest of the rest time, and it is difficult to complete the training program safely.
  • the wearable device collects the muscle electrical data and the electrocardiogram data of the athlete
  • the fatigue index, the muscle coordination index, the tension index, and the anxiety index of the athlete are calculated according to the myoelectric data and the electrocardiogram data, and according to the The different activities of the athletes
  • the athletes may be divided into detailed scenarios, and then select different competitive state parameters from the fatigue index, muscle coordination index, tension index and anxiety index according to the actual situation, and the athletes' competitive state is carried out.
  • the embodiment of the present invention quantitatively calculates and evaluates the competitive state parameters, and is not affected by the subjective experience of the coach, and is more accurate and reliable.
  • the athlete's competitive state is obtained, the athlete is automatically judged according to the competitive state to meet the requirements of the training competition, and the corresponding prompt is output, so that the athlete can intuitively know whether his current competitive state is suitable for the training game.
  • FIG. 6 is a structural block diagram of an athlete's competitive state evaluation system according to an embodiment of the present invention. For the convenience of description, only parts related to the embodiment of the present invention are shown.
  • the athlete's competitive state evaluation system includes:
  • the collecting module 61 is configured to collect physiological data of the athlete, and the physiological data includes electromyogram data and electrocardiogram data.
  • the state evaluation module 62 is configured to calculate an athletic state parameter of the athlete according to the physiological data, and determine whether the competitive state of the athlete satisfies the training requirement according to the competitive state parameter, and the competitive state parameter includes a fatigue index and a HRV heart rate variability.
  • the prompting module 63 is configured to output an allowable training prompt when the evaluation result is that the athletic state of the athlete satisfies the training requirement.
  • the prohibition prompting module 64 is configured to output a prohibition training prompt when the evaluation result is that the athletic state of the athlete does not satisfy the training requirement.
  • the state evaluation module 62 includes:
  • the first state recognition sub-module is configured to identify the current activity state of the athlete, and the activity state includes a rest state and a motion state.
  • the static fatigue calculation sub-module is used to calculate the fatigue index of the athlete if the active state is the rest state.
  • the dynamic fatigue calculation sub-module if the active state is the motion state, selects the dynamic fatigue algorithm to calculate the athlete's fatigue index.
  • the athletic state parameter further includes a muscle coordination index
  • the state evaluation module 62 includes:
  • the second state recognition sub-module is configured to identify the current activity state of the athlete.
  • the first parameter calculation sub-module is configured to calculate an athlete's fatigue index and a muscle coordination index according to the electromyography data if the active state is a resting state, and calculate the HRV heart rate variability according to the electrocardiogram data.
  • the first state evaluation sub-module is configured to determine the competitive state if a fatigue index exceeds a preset fatigue threshold, the HRV heart rate variability is in an abnormal state, and the muscle coordination index is lower than a preset coordination threshold. The result of the assessment is that the training requirements are not met.
  • the first fatigue prediction sub-module is used to read the user input if the fatigue index exceeds the preset fatigue threshold, the HRV heart rate variability is in an abnormal state, and the muscle coordination index is lower than the preset coordination threshold.
  • the training program and the preset EMG data samples, and based on the training items, the EMG data samples and the fatigue index, the athletes are subjected to the first fatigue prediction, and the first fatigue prediction is used to determine whether the athlete can safely complete the training program.
  • the second state evaluation sub-module is configured to determine that the athlete's competitive state evaluation result satisfies the training requirement if the result of the first fatigue prediction is that the athlete can safely complete the training item. If the result of the first fatigue prediction is that the athlete cannot safely complete the training item, it is determined that the athlete's competitive state evaluation result does not satisfy the training requirement.
  • state evaluation module 62 further includes:
  • the third state recognition sub-module is configured to identify the current activity state of the athlete.
  • the second parameter calculation sub-module is configured to calculate an athlete's fatigue index according to the electromyographic data if the active state is a motion state, and calculate the HRV heart rate variability according to the electrocardiogram data.
  • the third state evaluation sub-module is configured to determine that the athlete's competitive state evaluation result does not satisfy the training requirement if the fatigue index exceeds the preset damage threshold, and the preset damage threshold is greater than the preset fatigue threshold.
  • the fourth state evaluation sub-module is configured to determine that the athlete's competitive state evaluation result satisfies the training requirement if the fatigue index does not reach the preset fatigue threshold.
  • the second fatigue prediction sub-module is configured to read the user-entered training remaining time and the preset EMG data sample if the fatigue index reaches the preset fatigue threshold and does not exceed the preset damage threshold, and according to the HRV heart rate variability, the muscle
  • the electrical data sample, the remaining time of the training and the fatigue index are used to predict the second fatigue of the athlete, and the second fatigue prediction is used to determine whether the athlete can safely complete the remaining training.
  • the fifth state evaluation sub-module is configured to determine that the athlete's competitive state evaluation result satisfies the training requirement if the result of the second fatigue prediction is that the athlete can safely complete the remaining training. If the result of the second fatigue prediction is that the athlete cannot safely complete the remaining training, it is determined that the athlete's competitive state evaluation result does not satisfy the training requirement.
  • the first state evaluation submodule includes:
  • the preset athlete resilience data and the remaining rest time input by the user are read, and the recovery time and the fatigue index of the athlete are used to calculate the recovery time required by the athlete, according to the recovery. Time and remaining rest time to determine whether the athlete can safely complete the training program.
  • the result of the competitive state evaluation is determined to satisfy the training requirement. If the result of the judgment is that the athlete cannot safely complete the training item, it is determined that the result of the competitive state evaluation does not satisfy the training requirement.
  • each functional unit and module in the above system may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit, and the integrated unit may be hardware.
  • Formal implementation can also be implemented in the form of software functional units.
  • the specific names of the respective functional units and modules are only for the purpose of facilitating mutual differentiation, and are not intended to limit the scope of protection of the present application.
  • the disclosed apparatus and method may be implemented in other manners.
  • the system embodiment described above is merely illustrative.
  • the division of the module or unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be used. Combinations can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • the medium includes a plurality of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the methods described in various embodiments of the embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

Abstract

L'invention concerne un procédé et un système d'évaluation de l'état compétitif d'athlètes. Le procédé consiste à : commander un module d'acquisition (61) dans un appareil portable pour acquérir des données physiologiques d'un athlète (S101) ; à calculer des paramètres d'état compétitif de l'athlète, et à évaluer si l'état compétitif de l'athlète satisfait des exigences d'entraînement (S102) ; si l'état compétitif de l'athlète satisfait les exigences d'entraînement, à émettre une invite d'autorisation d'entraînement (S103) ; et si l'état compétitif de l'athlète ne satisfait pas les exigences d'entraînement, à émettre une invite d'interdiction d'entraînement (S104). Après l'acquisition des données physiologiques de l'athlète, l'appareil portable calcule les paramètres d'état compétitif de l'athlète, et évalue l'état compétitif de l'athlète sur la base des paramètres d'état compétitif, de telle sorte qu'une évaluation plus précise et fiable de l'état compétitif pour des athlètes peut être obtenue. En déterminant automatiquement, après obtention d'un état compétitif d'un athlète, si l'athlète satisfait les exigences d'entraînement et de compétition en fonction de l'état compétitif, et en émettant une invite correspondante, l'athlète peut savoir si son propre état compétitif actuel est approprié pour l'entraînement et les compétitions.
PCT/CN2018/072338 2017-05-25 2018-01-12 Procédé et système d'évaluation d'état compétitif d'athlètes WO2018214530A1 (fr)

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CN109829664A (zh) * 2019-04-12 2019-05-31 深圳市一起去赛网络技术有限公司 一种青少年体育公共服务绩效量化评估的方法和系统
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CN110141204A (zh) * 2019-05-05 2019-08-20 新疆农业大学 一种选择速度型马匹的方法
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