A human health monitoring method and system based on bioelectric signal acquisition technology

Abstract

The present application belongs to the technical field of bioelectric monitoring, and provides a human health monitoring method and system based on bioelectric signal collection technology, in multiple historical test periods, the electromyographic signals of athletes during training in each historical test period are extracted respectively, and continuous integrity analysis is performed, the easy-to-lose stage is screened out, the easy-to-lose sequence is constructed, each easy-to-lose stage in the easy-to-lose sequence is analyzed respectively, the special easy-to-lose period in the easy-to-lose stage is screened out, and the special loss type of the special easy-to-lose period is identified, not only whether the electromyographic signal intensity exceeds the preset maximum value of the collection device is determined, but also the physiological state of the muscles of the athletes in different movement stages and the interaction between the physiological state and the movement state are further understood, which provides a basis for more accurately interpreting bioelectric signals, and helps to evaluate the muscle fatigue degree and potential injury risk of athletes in the training process.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of bioelectricity monitoring, and particularly relates to a human health monitoring method and system based on bioelectric signal acquisition technology. BACKGROUND

[0002] With the continuous development of bioelectric signal acquisition technology, electromyography, as an important bioelectric signal capable of reflecting the muscle activity state, can directly reflect the muscle contraction strength, fatigue degree and functional state of the neuromuscular system. Through the acquisition and analysis of electromyography during the training process of athletes, important basis can be provided for coaches to formulate scientific and reasonable training plans and evaluate the physical condition of athletes.

[0003] In the prior art, there is often a lack of effective analysis of the continuity of electromyography. During the training process of athletes, electromyography often loses due to rapid changes in the state of motion, limitations of acquisition equipment and other factors. Moreover, the prior art does not further screen out special loss periods and identify special loss types. In the training process, the electromyography of different stages may have different causes and characteristics, and these special cases cannot be distinguished, so it is not possible to accurately determine whether the signal loss is due to unreasonable device threshold setting or device acquisition accuracy, thereby it is not possible to adjust and optimize the acquisition equipment in a targeted manner, which limits the accuracy and reliability of electromyography acquisition.

[0004] Therefore, the present application provides a human health monitoring method and system based on bioelectric signal acquisition technology. SUMMARY

[0005] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0006] The technical scheme adopted by the present application to solve its technical problems is:

[0007] A human health monitoring method based on bioelectric signal acquisition technology, comprising the following steps:

[0008] In a plurality of historical test periods, electromyography of athletes during training in each historical test period is extracted respectively, and continuity analysis is performed to construct a loss-prone sequence;

[0009] Each loss-prone stage in the loss-prone sequence is analyzed respectively, a special loss-prone period in the loss-prone stage is screened out, and a special loss type of the special loss-prone period is identified;

[0010] Based on the identified special loss type, the stability of the special easy-to-lose period is analyzed, and it is evaluated whether the easy-to-lose type in the special easy-to-lose period is an out-of-limit loss type or a non-out-of-limit loss type;

[0011] If the easy-to-lose type is an out-of-limit loss type, the acquisition intensity adjustment amount is obtained, and the acquisition signal intensity of the electromyographic signal acquisition device is adjusted and optimized. If the easy-to-lose type is a non-out-of-limit loss type, the adjacent acquisition interval length is obtained, and the acquisition frequency adjustment amount is obtained. The acquisition frequency of the electromyographic signal acquisition device is adjusted and optimized.

[0012] As a preferred scheme, the process of constructing the easy-to-lose sequence is as follows:

[0013] The historical test period is divided into a historical test start phase, a historical test uniform speed phase, and a historical test sprint phase. The electromyographic intensity values collected in the historical test start phase are sorted in time sequence and integrated into a start electromyographic intensity set.

[0014] In the start electromyographic intensity set, adjacent sorted electromyographic intensity values are obtained, corresponding signal acquisition nodes are extracted, and the time length between the corresponding signal acquisition nodes is obtained as the adjacent sorting length. If the adjacent sorting length is not equal to the adjacent acquisition length, it is displayed as a collection loss signal, and the proportion of the number of collection loss signals in the total number of displayed signals is calculated to obtain the collection loss number ratio.

[0015] The collection loss number ratios corresponding to the historical test start phase, the historical test uniform speed phase, and the historical test sprint phase are extracted. The historical test phase corresponding to the maximum collection loss number ratio is taken as the easy-to-lose phase. The easy-to-lose phases corresponding to each historical test period are extracted and compared for coincidence. They are sorted in descending order to construct an easy-to-lose sequence.

[0016] As a preferred scheme, the screening process of the special easy-to-lose period is as follows:

[0017] An easy-to-lose phase is randomly selected as a target analysis phase. In the target analysis phase, an easy-to-lose period is randomly selected as a target analysis period.

[0018] The signal acquisition nodes corresponding to the electromyographic intensity values not collected in the target analysis period and the signal acquisition nodes at the end of the target analysis period are extracted.

[0019] If any two signal acquisition nodes in the target analysis period are located in the same historical test phase, and the other signal acquisition node exists in the target analysis period of other historical test phases, it is marked as a special easy-to-lose period.

[0020] As a preferred scheme, the special loss type identification process is as follows:

[0021] The myoelectric intensity value in the easy-to-lose phase with a special easy-to-lose period is input into a two-dimensional coordinate system according to the time sequence corresponding to the signal acquisition node, an easy-to-lose curve is constructed, and the myoelectric signal intensity threshold is marked on the Y-axis as a straight line parallel to the X-axis, serving as a myoelectric intensity threshold line;

[0022] The signal acquisition nodes corresponding to the start and end of the special easy-to-lose period are extracted respectively, and the coordinate points of the signal acquisition node at the start of the special easy-to-lose period and the signal acquisition node at the end of the special easy-to-lose period on the easy-to-lose curve are obtained as the special easy-to-lose start coordinate and the special easy-to-lose end coordinate;

[0023] If at least one of the special easy-to-lose start coordinate and the special easy-to-lose end coordinate is located on the myoelectric intensity threshold line, it is identified as an out-of-limit loss type, and the special easy-to-lose period corresponding to the out-of-limit loss type is identified as an out-of-limit loss period;

[0024] If neither the special easy-to-lose start coordinate nor the special easy-to-lose end coordinate is located on the myoelectric intensity threshold line, it is identified as a non-out-of-limit loss type, and the special easy-to-lose period corresponding to the non-out-of-limit loss type is identified as a non-out-of-limit loss period.

[0025] As a preferred solution, the stability of the special easy-to-lose period is analyzed, and the process is as follows:

[0026] Extract the special easy-to-lose period in each historical test period, and obtain the special loss type corresponding to each special easy-to-lose period;

[0027] Extract all historical test periods with the out-of-limit loss type as the out-of-limit loss period, and calculate the proportion of the number of out-of-limit loss periods in the total number of historical test periods to obtain the out-of-limit loss number ratio;

[0028] In the out-of-limit loss period, the adjacent sorting duration corresponding to each out-of-limit loss period is extracted, and the difference between the adjacent acquisition duration is calculated and summed to obtain the out-of-limit loss duration;

[0029] Calculate the standard deviation of the out-of-limit loss duration corresponding to each out-of-limit loss period to obtain the out-of-limit loss duration standard deviation;

[0030] Calculate the ratio of the out-of-limit loss number ratio and the out-of-limit loss duration standard deviation to output the easy-to-lose type value.

[0031] As a preferred solution, the easy-to-lose type in the special easy-to-lose period is evaluated as an out-of-limit loss type or a non-out-of-limit loss type, and the process is as follows:

[0032] If the easy-to-lose type value is greater than the easy-to-lose type threshold value, the non-overrun loss type is displayed as the easy-to-lose type.

[0033] If the easy-to-lose type value is less than or equal to the easy-to-lose threshold value, the overrun loss type is displayed as the easy-to-lose type.

[0034] As a preferred scheme, the acquisition method of the collection intensity adjustment amount is:

[0035] If the easy-to-lose type is an overrun loss type, the adjacent ordering duration corresponding to each overrun loss period in each historical test period is subtracted from the adjacent collection duration to obtain a unit overrun amount.

[0036] All unit overrun amounts are subjected to mean value calculation to output a collection intensity adjustment amount.

[0037] As a preferred scheme, the acquisition process of the adjacent collection interval duration is as follows:

[0038] If the easy-to-lose type is a non-overrun loss type, the standard deviation of the duration corresponding to each non-overrun loss period in each historical test period is calculated to output a non-overrun loss duration standard deviation.

[0039] If the non-overrun loss duration standard deviation is greater than the non-overrun loss duration standard deviation threshold value, it indicates that the duration corresponding to the non-overrun loss period has a large deviation, and a non-overrun loss duration fluctuation signal is displayed. The duration corresponding to each non-overrun loss period is compared in size, and the duration corresponding to the maximum non-overrun loss period and the duration corresponding to the minimum non-overrun loss period are selected. After mean value calculation, the adjacent collection duration is subtracted to obtain an adjacent collection interval duration.

[0040] If the non-overrun loss duration standard deviation is less than or equal to the non-overrun loss duration standard deviation threshold value, it indicates that the duration corresponding to the non-overrun loss period has a small deviation, and a non-overrun loss duration stability signal is displayed. After mean value calculation of the duration corresponding to each non-overrun loss period, the adjacent collection duration is subtracted to obtain an adjacent collection interval duration.

[0041] As a preferred scheme, the acquisition process of the collection frequency adjustment amount is as follows:

[0042] The reciprocal of the adjacent collection interval duration is taken as the collection frequency adjustment amount.

[0043] Secondly, a human health monitoring coefficient based on bioelectric signal acquisition technology includes the following modules:

[0044] The sequence construction module: in multiple historical test periods, the electromyographic signals of the athletes during training in each historical test period are extracted respectively, and the continuity analysis is performed to construct an easy-to-lose sequence.

[0045] The type analysis module: analyze each loss-prone stage in the loss-prone sequence respectively, screen out a special loss-prone period in the loss-prone stage, and identify the special loss type of the special loss-prone period;

[0046] The loss-prone identification module: based on the identified special loss type, stability analysis is performed on the special loss-prone period to evaluate whether the loss-prone type in the special loss-prone period is an out-of-limit loss type or a non-out-of-limit loss type;

[0047] The adjustment and optimization module: if the loss-prone type is an out-of-limit loss type, an acquisition intensity adjustment amount is obtained to adjust and optimize the acquisition signal intensity of the electromyographic signal acquisition device, and if the loss-prone type is a non-out-of-limit loss type, an adjacent acquisition interval length is obtained to obtain an acquisition frequency adjustment amount to adjust and optimize the acquisition frequency of the electromyographic signal acquisition device.

[0048] The beneficial effects of the present application are as follows:

[0049] In the present application, the electromyographic signals of the athletes during training in each historical test period are extracted respectively, and the continuous integrity analysis is performed to screen out the loss-prone stage and construct the loss-prone sequence. Each loss-prone stage in the loss-prone sequence is analyzed respectively to screen out the special loss-prone period in the loss-prone stage and identify the special loss type of the special loss-prone period. Not only can it be determined whether the electromyographic signal intensity exceeds the preset maximum value of the acquisition device to analyze and diagnose whether the electromyographic intensity threshold in the electromyographic signal acquisition device needs to be reset or the acquisition accuracy of the electromyographic signal acquisition device itself needs to be optimized and adjusted, but also the physiological state of the athlete's muscles in different movement stages and the interaction between the physiological state and the movement state can be understood in depth, which provides a basis for more accurate interpretation of bioelectric signals and helps to evaluate the muscle fatigue degree and potential injury risk of the athletes during training.

[0050] The application is based on the identified special loss type, and stability analysis is performed on the special easy-to-lose period. If the easy-to-lose type is an out-of-limit loss type, the acquisition strength adjustment amount is obtained, and the acquisition signal strength of the electromyographic signal acquisition device is adjusted and optimized. If the easy-to-lose type is a non-out-of-limit loss type, the acquisition frequency adjustment amount is obtained, and the acquisition frequency of the electromyographic signal acquisition device is adjusted and optimized. The acquisition frequency adjustment amount is obtained to adjust and optimize the acquisition frequency of the electromyographic signal acquisition device, so as to avoid signal loss or inaccuracy due to inappropriate acquisition frequency, thereby ensuring the accuracy and integrity of the monitoring of the muscle state of the athlete. The acquisition strength adjustment amount is obtained to adjust the threshold of the signal strength acquired by the electromyographic signal acquisition device, so that the device can adapt to the case that the muscle exertion strength of the athlete increases sharply in a specific exercise stage, which helps to timely find the problems such as muscle fatigue accumulation, excessive force or improper exercise skill of the athlete, and avoid muscle strains caused by muscle fatigue accumulation, excessive force or improper exercise skill of the athlete. BRIEF DESCRIPTION OF DRAWINGS

[0051] The application will be further described below in combination with the drawings.

[0052] Figure 1 is a step flow chart of a human health monitoring method based on bioelectric signal acquisition technology according to the application;

[0053] Figure 2 is a judgment flow chart in a human health monitoring method based on bioelectric signal acquisition technology according to the application;

[0054] Figure 3 is a schematic diagram of a human health monitoring system based on bioelectric signal acquisition technology according to the application. DETAILED DESCRIPTION

[0055] In order to make the technical means, creative features, purposes and effects achieved by the application easy to understand, the application will be further described below in combination with specific embodiments.

[0056] Embodiment 1:

[0057] During the training of short-distance running, the muscle exertion strength of the athlete exists from a low-load warm-up state to a maximum power output state (from the start to the sprint stage during running training), and the amplitude of the electromyographic signal collected by the bioelectric signal acquisition technology will fluctuate several times or even dozens of times in a short time, which is easy to exceed the dynamic range of the acquisition device, thereby causing the collected electromyographic signal to appear over-saturation, and finally causing the acquisition parameter to be lost, thereby interfering with the muscle fatigue evaluation of the athlete during training, and unable to avoid muscle damage of the athlete during training in time;

[0058] Therefore, please refer to Figure 1 Figure 2 As shown in the method for monitoring human health based on bioelectric signal acquisition technology, the method comprises the following steps:

[0059] Step one: In a plurality of historical test periods, the electromyographic signals of the athletes during training in each historical test period are extracted respectively, and the continuous integrity analysis is performed to screen out the easy loss stage and construct the easy loss sequence.

[0060] It should be noted that the historical test period is the process of collecting real-time electromyographic signals of the athletes during running training, which can be understood as the time period of the entire running training process of the athletes;

[0061] In some embodiments, the historical test period is divided into a historical test starting stage, a historical test constant speed stage, and a historical test sprint stage.

[0062] In the historical test starting stage, the collected electromyographic intensity values are sorted according to the time sequence and integrated into a starting electromyographic intensity set.

[0063] Similarly, the collected electromyographic intensity values in the historical test constant speed stage and the historical test sprint stage are sorted according to the time sequence to obtain a constant speed electromyographic intensity set and a sprint electromyographic intensity set.

[0064] Specifically, the electromyographic signal intensity is obtained in the following manner:

[0065] For example, the historical test starting stage is equally divided into a plurality of signal acquisition nodes, the interval time between adjacent signal acquisition nodes is equal, the electromyographic signal intensity at each signal acquisition node is obtained as the electromyographic intensity value.

[0066] It should be noted that the electromyographic intensity values collected in the historical test constant speed stage and the historical test sprint stage are consistent with the electromyographic intensity values collected in the historical test starting stage.

[0067] In the starting electromyographic intensity set, the adjacent sorted electromyographic intensity values are obtained, the corresponding signal acquisition nodes are extracted, and the time length between the corresponding signal acquisition nodes is obtained as the adjacent sorting time length.

[0068] If the adjacent sorting time length is equal to the adjacent acquisition time length, it means that the adjacent sorted electromyographic intensity values are continuous and complete in the acquisition time dimension, which is displayed as no signal loss in acquisition.

[0069] ​If the adjacent ordering duration is not equal to the adjacent collection duration, it indicates that the adjacent ordering electromyography intensity value is not continuous and complete in the collection time dimension, which is displayed as a collection loss signal, and the signal collection node corresponding to the analyzed adjacent ordering electromyography intensity value is extracted, and the time period between the corresponding signal collection nodes is taken as an easy loss period;

[0070] It should be noted that the adjacent collection duration is the interval duration between adjacent signal collection nodes, which is not only applicable to the judgment in the historical test starting stage, but also applicable to the judgment in the historical test uniform speed stage and the historical test sprint stage;

[0071] The number of collection loss signals is counted, the proportion of the number of collection loss signals in the total number of display signals is calculated, and the collection loss number ratio is obtained;

[0072] For example, the total number of display signals represents the sum of the displayed collection loss signals and the actual collection non-loss signals in the historical test starting stage, which is not only applicable to the historical test starting stage, but also applicable to the historical test uniform speed stage and the historical test sprint stage;

[0073] In the historical test period, the collection loss number ratio corresponding to the historical test starting stage, the historical test uniform speed stage and the historical test sprint stage is extracted respectively, and the maximum collection loss number ratio is compared, and the historical test stage corresponding to the maximum collection loss number ratio is taken as the easy loss stage;

[0074] The easy loss stage corresponding to each historical test period is extracted, and the number of coincidences is compared, and the easy loss sequence is sorted in descending order;

[0075] Among them, the purpose of constructing the easy loss sequence is:

[0076] Purpose one: since the muscle strength of the athlete is switched from low load preheating to maximum power output during running training, which exceeds the collection threshold of the collection device, resulting in parameter loss, therefore, by constructing the easy loss sequence, it is clear which monitoring stage is prone to parameter loss, so as to optimize and adjust the collection function of the collection device in the easy loss stage;

[0077] Purpose two: the construction of the easy loss sequence helps to reflect the changes of the muscle state of the athlete in each stage, as well as the fatigue degree of the muscle of the athlete in different training stages, which provides strong support for dynamically adjusting the training plan and evaluating the muscle fatigue degree;

[0078] Step two: analyze each easy loss stage in the easy loss sequence respectively, screen out the special easy loss period in the easy loss stage, and identify the special loss type of the special easy loss period;

[0079] It should be noted that the special easy-to-lose period can be: the last signal acquisition node does not collect the electromyogram signal intensity in the historical test starting phase, the second-to-last signal acquisition node collects the electromyogram signal intensity in the historical test starting phase, and the first signal acquisition node collects the electromyogram signal intensity in the historical test constant speed phase, or the last signal acquisition node does not collect the electromyogram signal intensity in the historical test constant speed phase, the second-to-last signal acquisition node collects the electromyogram signal intensity in the historical test constant speed phase, and the first signal acquisition node collects the electromyogram signal intensity in the historical test sprint phase;

[0080] It can also be: the last signal acquisition node collects the electromyogram signal intensity in the historical test starting phase, the first signal acquisition node does not collect the electromyogram signal intensity in the historical test constant speed phase, and the second signal acquisition node collects the electromyogram signal intensity in the historical test constant speed phase, or the last signal acquisition node collects the electromyogram signal intensity in the historical test constant speed phase, the first signal acquisition node does not collect the electromyogram signal intensity in the historical test sprint phase, and the second signal acquisition node collects the electromyogram signal intensity in the historical test sprint phase;

[0081] It should be further explained that, for which easy-to-lose phase the special easy-to-lose period belongs to, the rule for judgment is: in the special easy-to-lose period, the historical test phase (including: the historical test starting phase, the historical test constant speed phase and the historical test sprint phase) in which the signal acquisition point that does not collect the electromyogram signal intensity is located is regarded as the easy-to-lose phase;

[0082] In some embodiments, the screening process of the special easy-to-lose period is as follows:

[0083] Any selected easy-to-lose phase is regarded as a target analysis phase, and in the target analysis phase, any selected easy-to-lose period is regarded as a target analysis period;

[0084] The signal acquisition nodes corresponding to the electromyogram intensity values that are not collected in the target analysis period and the signal acquisition nodes at the end of the target analysis period are extracted;

[0085] The signal acquisition nodes at the end of the target analysis period are the signal acquisition nodes corresponding to the starting end and the terminating end of the target analysis period, respectively;

[0086] If there are any two signal acquisition nodes in the same historical test phase in the target analysis period, and the other signal acquisition node exists in the target analysis period of other historical test phases, it is marked as a special easy-to-lose period;

[0087] If there is no any two signal acquisition nodes in the same historical test stage in the target analysis period, and another signal acquisition node is not in the target analysis period of other historical test stage, it is marked as a special non-easy loss period;

[0088] The purpose of screening is:

[0089] Purpose one: because there are some easy loss periods at the junction of different training stages, screening them as special easy loss periods helps to accurately locate the problem of easy loss of acquisition parameters in the process of training stage transition;

[0090] Purpose two: special easy loss periods reflect the muscle state of athletes in the process of motion state conversion, which helps to understand the interaction between human physiological state and motion state, and provides basis for more accurate interpretation of bioelectric signals;

[0091] The special loss type identification process is as follows:

[0092] Taking X-axis as time and Y-axis as electromyography intensity value, the electromyography intensity value in the easy loss stage with special easy loss period is input into the two-dimensional coordinate system according to the time sequence of the signal acquisition node, and an easy loss curve is constructed;

[0093] Mark the electromyography signal intensity threshold on the Y-axis of the easy loss curve, and draw a straight line parallel to the X-axis as the electromyography intensity threshold line;

[0094] Respectively extract the signal acquisition nodes corresponding to the start and end of the special easy loss period;

[0095] Get the coordinate point of the signal acquisition node at the start of the special easy loss period on the easy loss curve as the special easy loss start coordinate;

[0096] Similarly, get the coordinate point of the signal acquisition node at the end of the special easy loss period on the easy loss curve as the special easy loss end coordinate;

[0097] If there is at least one special easy loss start coordinate and special easy loss end coordinate on the electromyography intensity threshold line, it means that the electromyography signal intensity exceeds the preset maximum value of the electromyography signal intensity acquisition device in the process of collecting electromyography signal intensity, and it is identified as an overrun loss type. Identify the special easy loss period corresponding to the overrun loss type as an overrun loss period;

[0098] If the special easy-to-lose starting coordinate and the special easy-to-lose ending coordinate are both not located on the electromyogram intensity threshold line, it is indicated that the electromyogram intensity does not exceed the preset maximum value of the electromyogram signal intensity collection device in the process of collecting the electromyogram signal intensity, and the non-over-limit loss type is recognized, and the special easy-to-lose period corresponding to the non-over-limit loss type is marked as an un-over-limit loss period;

[0099] The purpose of the special loss type recognition is:

[0100] Purpose one: By determining whether the electromyogram intensity exceeds the preset maximum value of the collection device, it is analyzed and diagnosed whether the electromyogram intensity threshold in the electromyogram signal collection device needs to be reset or the collection accuracy of the electromyogram signal collection device itself needs to be optimized and adjusted;

[0101] Purpose two: The physiological state of the muscles of the athlete in different movement stages and the interaction between the physiological state and the movement state can be deeply understood, which provides a basis for more accurately interpreting the bioelectric signal, and helps to evaluate the muscle fatigue degree and potential damage risk of the athlete in the training process;

[0102] The specific implementation scheme of the embodiment is: in a plurality of historical test periods, the electromyogram signals of the athlete during training in each historical test period are extracted respectively, and continuous integrity analysis is performed, the easy-to-lose stages are screened out, the easy-to-lose sequence is constructed, each easy-to-lose stage in the easy-to-lose sequence is analyzed respectively, the special easy-to-lose period in the easy-to-lose stage is screened out, and the special loss type of the special easy-to-lose period is recognized. Not only by determining whether the electromyogram intensity exceeds the preset maximum value of the collection device, it is analyzed and diagnosed whether the electromyogram intensity threshold in the electromyogram signal collection device needs to be reset or the collection accuracy of the electromyogram signal collection device itself needs to be optimized and adjusted, but also the physiological state of the muscles of the athlete in different movement stages and the interaction between the physiological state and the movement state can be deeply understood, which provides a basis for more accurately interpreting the bioelectric signal, and helps to evaluate the muscle fatigue degree and potential damage risk of the athlete in the training process.

[0103] Embodiment 2:

[0104] Please refer to Figure 1 - Figure 2 As shown in the figure, the human health monitoring method based on the bioelectric signal collection technology comprises the following steps:

[0105] Step three: based on the recognized special loss type, stability analysis is performed on the special easy-to-lose period, and it is evaluated whether the easy-to-lose type in the special easy-to-lose period is an over-limit loss type or a non-over-limit loss type;

[0106] In some embodiments, special loss-prone periods in each historical test period are extracted, and the special loss type corresponding to each special loss-prone period is obtained;

[0107] All historical test periods with the over-limit loss type are extracted as over-limit loss periods, and the proportion of the number of over-limit loss periods in the total number of historical test periods is calculated to obtain an over-limit loss quantity ratio;

[0108] In the over-limit loss period, the adjacent sorting duration corresponding to each over-limit loss period is extracted, and the adjacent collection duration is subtracted to obtain the over-limit loss duration;

[0109] The standard deviation of the over-limit loss duration corresponding to each over-limit loss period is calculated to obtain the over-limit loss duration standard deviation;

[0110] The over-limit loss quantity ratio and the over-limit loss duration standard deviation are calculated by ratio to obtain the loss-prone type value;

[0111] It can be understood that the meaning represented by the loss-prone type value is that, on the one hand, the over-limit loss quantity ratio reflects the frequency of the serious loss condition of the electromyographic signal intensity exceeding the preset maximum value of the collection device in the entire historical test process, and on the other hand, the over-limit loss duration standard deviation reflects the dispersion degree of the over-limit loss duration, thereby reflecting the urgent need for adjustment and optimization of the human electromyographic signal intensity collection device in the collection process;

[0112] The loss-prone type value is compared with the loss-prone type threshold value, and the process is as follows:

[0113] If the loss-prone type value is greater than the loss-prone type threshold value, it means that the over-limit loss phenomenon not only occurs less frequently, but also the over-limit loss duration is less stable, indicating that the non-over-limit loss type is the loss-prone type;

[0114] If the loss-prone type value is less than or equal to the loss-prone threshold value, it means that the over-limit loss phenomenon not only occurs more frequently, but also the over-limit loss duration is more stable, indicating that the over-limit loss type is the loss-prone type;

[0115] The purpose of evaluating the loss-prone type is that, from the perspective of device optimization, if the loss-prone type is identified as the over-limit loss type, it means that the electromyographic signal intensity exceeds the preset maximum value of the collection device, and the threshold value of the electromyographic signal intensity collected by the device needs to be adjusted urgently, and if the loss-prone type is identified as the non-over-limit loss type, it means that the electromyographic signal collection device itself has a problem in collection accuracy, and the accuracy of the electromyographic signal collected by the device needs to be adjusted and optimized urgently;

[0116] Secondly, from the perspective of the muscle state of the athlete, if the easy-to-lose type is identified as the out-of-limit loss type, it indicates that the muscle strength of the athlete increases sharply in a specific exercise stage, which exceeds the normal range, which may be related to muscle fatigue accumulation, excessive force or improper exercise skills, etc., so that the athlete appears muscle fatigue during the training process, and even there is a potential risk of muscle injury. If the easy-to-lose type is identified as the non-out-of-limit loss type, it indicates that there are other physiological changes in the muscle during the exercise state conversion process, such as muscle coordination change, neural control adjustment, etc. Therefore, it is helpful to adjust the training plan in time to avoid muscle damage caused by overtraining of the athlete, and to protect the physical health and training effect of the athlete.

[0117] Step four: if the easy-to-lose type is the out-of-limit loss type, the acquisition strength adjustment amount is obtained to adjust and optimize the acquisition signal strength of the electromyographic signal acquisition device, and if the easy-to-lose type is the non-out-of-limit loss type, the adjacent acquisition interval duration is obtained to obtain the acquisition frequency adjustment amount to adjust and optimize the acquisition frequency of the electromyographic signal acquisition device.

[0118] In some embodiments, if the easy-to-lose type is the out-of-limit loss type, the adjacent ordering duration corresponding to each out-of-limit loss period in each historical test period is subtracted from the adjacent acquisition duration to obtain a unit out-of-limit amount.

[0119] The mean value of all unit out-of-limit amounts is calculated to output the acquisition strength adjustment amount.

[0120] If the easy-to-lose type is the non-out-of-limit loss type, the standard deviation of the duration corresponding to each non-out-of-limit loss period in each historical test period is calculated to output the non-out-of-limit loss duration standard deviation.

[0121] If the non-out-of-limit loss duration standard deviation is greater than the non-out-of-limit loss duration standard deviation threshold, it indicates that the duration deviation corresponding to the non-out-of-limit loss period is large, which is displayed as a non-out-of-limit loss duration fluctuation signal.

[0122] If the non-out-of-limit loss duration standard deviation is less than or equal to the non-out-of-limit loss duration standard deviation threshold, it indicates that the duration deviation corresponding to the non-out-of-limit loss period is small, which is displayed as a non-out-of-limit loss duration stable signal.

[0123] Based on the non-out-of-limit loss duration fluctuation signal, the durations corresponding to each non-out-of-limit loss period are compared in size, the duration corresponding to the maximum non-out-of-limit loss period and the duration corresponding to the minimum non-out-of-limit loss period are selected, and the sum and mean value calculation is performed, and then the adjacent acquisition duration is subtracted to obtain the adjacent acquisition interval duration.

[0124] Based on the non-overrun loss duration stable signal, the duration corresponding to each non-overrun loss period is summed and averaged, and then the adjacent collection duration is obtained by difference.

[0125] The reciprocal of the adjacent collection interval duration is taken as the collection frequency adjustment amount.

[0126] The specific scheme of the embodiment is: based on the identified special loss type, the stability of the special easy loss period is analyzed, if the easy loss type is an overrun loss type, the collection intensity adjustment amount is obtained, the collection signal intensity of the electromyographic signal collection device is adjusted and optimized, if the easy loss type is a non-overrun loss type, the collection frequency adjustment amount is obtained, the collection frequency of the electromyographic signal collection device is adjusted and optimized, the collection frequency adjustment amount is obtained to adjust and optimize the collection frequency of the electromyographic signal collection device, to avoid signal loss or inaccuracy due to inappropriate collection frequency, so as to ensure the accuracy and integrity of the monitoring of the muscle state of the athlete, the collection intensity adjustment amount is obtained to adjust the threshold of the signal intensity collected by the electromyographic signal collection device, so that the device can adapt to the case that the muscle exertion intensity of the athlete increases sharply in a specific exercise stage, which helps to timely find the problems such as muscle fatigue accumulation, excessive force or improper exercise skill of the athlete, and avoid muscle strain caused by muscle fatigue accumulation, excessive force or improper exercise skill of the athlete.

[0127] Referring to Figure 2 The human health monitoring method based on the bioelectric signal collection technology comprises the following modules:

[0128] The sequence construction module: in a plurality of historical test periods, the electromyographic signals of the athletes during training in each historical test period are extracted respectively, and the continuity analysis is performed to construct an easy loss sequence.

[0129] The type analysis module: each easy loss stage in the easy loss sequence is analyzed respectively, the special easy loss period in the easy loss stage is screened out, and the special loss type of the special easy loss period is identified.

[0130] The easy loss identification module: based on the identified special loss type, the stability of the special easy loss period is analyzed, and it is evaluated whether the easy loss type in the special easy loss period is an overrun loss type or a non-overrun loss type.

[0131] The adjustment and optimization module: if the easy loss type is an overrun loss type, the collection intensity adjustment amount is obtained, the collection signal intensity of the electromyographic signal collection device is adjusted and optimized, if the easy loss type is a non-overrun loss type, the adjacent collection interval duration is obtained, the collection frequency adjustment amount is obtained, and the collection frequency of the electromyographic signal collection device is adjusted and optimized.

[0132] The foregoing presents and describes the basic principles, main features and advantages of the present application. It should be understood by those skilled in the art that the present application is not limited to the above-described embodiments, and the above-described embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A human health monitoring method based on bioelectric signal acquisition technology, characterized in that: The method comprises the following steps: In a plurality of historical test periods, the electromyographic signals of the athletes during training in each historical test period are extracted respectively, and continuous integrity analysis is performed to construct an easy loss sequence; The process of constructing the easy loss sequence is as follows: The historical test period is divided into a historical test starting phase, a historical test constant speed phase and a historical test sprint phase, the electromyographic intensity values collected in the historical test starting phase are sorted according to time sequence, and integrated into a starting electromyographic intensity set; In the starting electromyographic intensity set, the adjacent sorted electromyographic intensity values are randomly obtained, the corresponding signal collection nodes are extracted respectively, and the time length between the corresponding signal collection nodes is obtained as the adjacent sorting duration, if the adjacent sorting duration is not equal to the adjacent collection duration, the collection loss signal is displayed, the proportion of the number of collection loss signals in the total number of displayed signals is counted, and the collection loss number ratio is obtained; The collection loss number ratios corresponding to the historical test starting phase, the historical test constant speed phase and the historical test sprint phase are extracted, the historical test phase corresponding to the maximum collection loss number ratio is taken as the easy loss phase, the easy loss phases corresponding to each historical test period are extracted, the number of coincidences is compared, and the easy loss sequences are constructed in descending order of the number; Each easy loss phase in the easy loss sequence is analyzed respectively, the special easy loss period in the easy loss phase is screened out, and the special loss type of the special easy loss period is identified; Based on the identified special loss type, the stability of the special easy loss period is analyzed, and it is evaluated whether the easy loss type in the special easy loss period is an overrun loss type or a non-overrun loss type; The process of stability analysis of the special easy loss period is as follows: The special easy loss period in each historical test period is extracted, and the special loss type corresponding to each special easy loss period is obtained; All historical test periods with the overrun loss type are extracted as the overrun loss periods, and the proportion of the number of overrun loss periods in the total number of historical test periods is counted to obtain the overrun loss number ratio; In the overrun loss period, the adjacent sorting duration corresponding to each overrun loss period is extracted, and the adjacent collection duration is subtracted to obtain the overrun loss duration; The standard deviation of the overrun loss duration corresponding to each overrun loss period is calculated to obtain the overrun loss duration standard deviation; The overrun loss number ratio and the overrun loss duration standard deviation are calculated by ratio to output the easy loss type value; If the easy loss type is the overrun loss type, the collection intensity adjustment amount is obtained, the collection signal intensity of the electromyographic signal collection device is adjusted and optimized, if the easy loss type is the non-overrun loss type, the adjacent collection interval duration is obtained, the collection frequency adjustment amount is obtained, and the collection frequency of the electromyographic signal collection device is adjusted and optimized.

2. The human health monitoring method based on bioelectric signal acquisition technology according to claim 1, characterized in that: The screening process of the special easy loss period is as follows: An easy loss phase is randomly selected as a target analysis phase, and an easy loss period is randomly selected in the target analysis phase as a target analysis period; extracting the signal acquisition nodes corresponding to the myoelectric intensity values not collected in the target analysis period and the signal acquisition nodes at the end of the target analysis period; if there are any two signal acquisition nodes in the same historical test stage and another signal acquisition node in the target analysis period of other historical test stages in the target analysis period, it is marked as a special easy loss period.

3. The human health monitoring method based on bioelectric signal acquisition technology according to claim 1, characterized in that: The special loss type identification process is as follows: input the myoelectric intensity values in the easy loss stage with special easy loss period into the two-dimensional coordinate system according to the time sequence of the signal acquisition nodes, construct the easy loss curve, and mark the myoelectric signal intensity threshold on the Y axis as a straight line parallel to the X axis, as the myoelectric intensity threshold line; extract the signal acquisition nodes corresponding to the start and end of the special easy loss period, and obtain the coordinates of the signal acquisition nodes at the start and end of the special easy loss period on the easy loss curve as the special easy loss start coordinates and the special easy loss end coordinates; if at least one of the special easy loss start coordinates and the special easy loss end coordinates is located on the myoelectric intensity threshold line, it is identified as an overrun loss type, and the special easy loss period corresponding to the overrun loss type is identified as an overrun loss period; if the special easy loss start coordinates and the special easy loss end coordinates are not located on the myoelectric intensity threshold line, it is identified as a non-overrun loss type, and the special easy loss period corresponding to the non-overrun loss type is identified as a non-overrun loss period.

4. The human health monitoring method based on bioelectric signal acquisition technology according to claim 1, characterized in that: The process of evaluating whether the easy loss type in the special easy loss period is an overrun loss type or a non-overrun loss type is as follows: if the easy loss type value is greater than the easy loss type threshold, the non-overrun loss type is displayed as the easy loss type; if the easy loss type value is less than or equal to the easy loss threshold, the overrun loss type is displayed as the easy loss type.

5. The human health monitoring method based on bioelectric signal acquisition technology according to claim 1, characterized in that: The acquisition of the intensity adjustment amount is as follows: if the easy loss type is an overrun loss type, the adjacent ordering duration corresponding to each overrun loss period in each historical test cycle is subtracted from the adjacent acquisition duration to obtain a unit overrun amount; all unit overrun amounts are calculated by mean value to output the acquisition intensity adjustment amount.

6. The human health monitoring method based on bioelectric signal acquisition technology according to claim 1, characterized in that: The process of obtaining the adjacent acquisition interval duration is as follows: if the easy loss type is a non-overrun loss type, the duration of each non-overrun loss period in each historical test cycle is calculated by standard deviation to output the non-overrun loss duration standard deviation; if the non-overrun loss duration standard deviation is greater than the non-overrun loss duration standard deviation threshold, it is displayed as a non-overrun loss duration fluctuation signal, the duration of each non-overrun loss period is compared in size, the duration corresponding to the maximum non-overrun loss period and the duration corresponding to the minimum non-overrun loss period are selected, and the sum is calculated by mean value and subtracted from the adjacent acquisition duration to obtain the adjacent acquisition interval duration; if the non-overrun loss duration standard deviation is less than or equal to the non-overrun loss duration standard deviation threshold, it is displayed as a non-overrun loss duration stable signal, the duration of each non-overrun loss period is calculated by sum and mean value, and the adjacent acquisition duration is subtracted to obtain the adjacent acquisition interval duration.

7. The human health monitoring method based on bioelectric signal acquisition technology according to claim 1, characterized in that: The acquisition frequency adjustment amount is obtained in the following manner: The reciprocal of the adjacent acquisition interval length is taken as the acquisition frequency adjustment amount.

8. A human health monitoring system based on bioelectric signal acquisition technology, characterized in that: The following modules are included: The sequence construction module: in multiple historical test periods, the electromyographic signals of athletes during training in each historical test period are extracted and analyzed for continuity to construct an easy-loss sequence. The process of constructing the easy-loss sequence is as follows: The historical test period is divided into a historical test start phase, a historical test constant-speed phase, and a historical test sprint phase. The electromyographic intensity values obtained during the historical test start phase are sorted in time sequence and integrated into a start electromyographic intensity set. In the start electromyographic intensity set, adjacent sorted electromyographic intensity values are randomly extracted, and the corresponding signal acquisition nodes are extracted. The time length between the corresponding signal acquisition nodes is obtained as the adjacent sorting length. If the adjacent sorting length is not equal to the adjacent acquisition length, it is displayed as a collection loss signal. The proportion of the number of collection loss signals to the total number of displayed signals is calculated to obtain the collection loss quantity ratio. The collection loss quantity ratios of the historical test start phase, the historical test constant-speed phase, and the historical test sprint phase are extracted. The historical test phase corresponding to the maximum collection loss quantity ratio is taken as the easy-loss phase. The easy-loss phases of each historical test period are extracted and compared for coincidence. They are sorted in descending order to construct the easy-loss sequence. The type analysis module: each easy-loss phase in the easy-loss sequence is analyzed to filter out special easy-loss periods in the easy-loss phase and identify the special loss type of the special easy-loss period. The easy-loss identification module: based on the identified special loss type, the stability of the special easy-loss period is analyzed to evaluate whether the easy-loss type in the special easy-loss period is an overrun loss type or a non-overrun loss type. The process of stability analysis of the special easy-loss period is as follows: The special easy-loss period in each historical test period is extracted, and the special loss type corresponding to each special easy-loss period is obtained. All historical test periods with an overrun loss type are extracted as overrun loss periods. The proportion of the number of overrun loss periods to the total number of historical test periods is calculated to obtain the overrun loss quantity ratio. In the overrun loss period, the adjacent sorting length corresponding to each overrun loss period is extracted and subtracted from the adjacent acquisition length. The sum is calculated to obtain the overrun loss length. The standard deviation of the overrun loss length corresponding to each overrun loss period is calculated to obtain the overrun loss length standard deviation. The overrun loss quantity ratio and the overrun loss length standard deviation are calculated to obtain the easy-loss type value. The adjustment and optimization module: if the easy-loss type is an overrun loss type, the collection intensity adjustment amount is obtained to adjust and optimize the collection signal intensity of the electromyographic signal collection device. If the easy-loss type is a non-overrun loss type, the adjacent acquisition interval length is obtained to obtain the acquisition frequency adjustment amount, and the acquisition frequency of the electromyographic signal collection device is adjusted and optimized.

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