Cardiopulmonary function training method and device, computer readable storage medium, computer program product and terminal equipment

Abstract

The invention provides a cardiopulmonary function training method and device, a computer readable storage medium, a computer program product and terminal equipment. The cardiopulmonary function training method comprises the steps that a target motion mode is determined according to physiological information of a user and first motion information, and the target motion mode is matched with a corresponding target heart rate interval; acquiring second motion information, wherein the second motion information comprises physiological feature data of the user and acceleration data of equipment used by the user; determining a suggested stride frequency according to the second motion information, wherein the heart rate of the user under the suggested stride frequency is close to the target heart rate interval or is within the target heart rate interval. The invention provides a scheme for flexibly carrying out stride frequency training and improving the cardiopulmonary function training effect.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a cardiopulmonary function training method and apparatus, a computer-readable storage medium, a computer program product, and a terminal device. Background Art

[0002] Currently, users have the need to conduct cadence training to standardize walking, running and other states and improve training effects; or to meet users' rehabilitation needs and improve cardiopulmonary function.

[0003] In the prior art, a user can perform cadence training by wearing a cadence reminder, in which a fixed beat is set, and the cadence reminder transmits the beat to the user, guiding the user to adjust the real-time cadence according to the beat.

[0004] However, the effect of using a fixed beat method to perform cadence training in the prior art is not good, and the effect of improving cardiopulmonary function is not good. Summary of the invention

[0005] The present application provides a solution for flexibly performing cadence training, improving the cadence training effect, and further improving the cardiopulmonary function training effect.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] In a first aspect, a cardiopulmonary function training method is provided, which includes: determining a target motion mode based on a user's physiological information and first motion information, wherein the target motion mode matches a corresponding target heart rate range; obtaining physiological characteristic data of the user and acceleration data of a device used by the user; determining a recommended step frequency based on the second motion information, wherein the user's heart rate at the recommended step frequency is close to the target heart rate range or is within the target heart rate range, wherein the heart rate being close to the target heart rate range indicates that an offset value between the heart rate and an upper limit value or a lower limit value of the target heart rate range is less than a preset threshold.

[0008] Optionally, the recommended step frequency includes at least one of the following: maintaining the current step frequency; reducing the current step frequency; and increasing the current step frequency.

[0009] Optionally, the cardiopulmonary function training method also includes: adjusting the beat of the guiding audio according to the recommended step frequency, the guiding audio is used to guide the user to exercise according to the beat of the guiding audio, wherein the number of beats of the guiding audio per unit time is positively correlated with the frequency of the recommended step frequency.

[0010] Optionally, determining the recommended step frequency based on the second motion information includes: performing feature extraction on the acceleration data to obtain distribution characteristics and gait characteristics of the acceleration data, the gait characteristics representing the movement posture of the user; determining the recommended step frequency based on the physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics.

[0011] Optionally, the gait feature includes at least one of the following: stride, pace and frequency, and the feature extraction of the acceleration data includes: detecting the peak value of the acceleration data; determining the pace according to the number of the peak values ​​per unit time; determining the pace and the stride according to the acceleration data and the positions of its peaks and troughs.

[0012] Optionally, determining the recommended step frequency based on the physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics includes: inputting the physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics into a trained classification model, and the classification model outputs the recommended step frequency.

[0013] Optionally, the cardiopulmonary function training method further includes: evaluating the user's gait according to the gait characteristics to obtain a gait evaluation score.

[0014] Optionally, the evaluating the user's gait based on the gait characteristics includes: calculating the difference between the step frequency in the gait characteristics and the beat of the guiding audio, and determining the step frequency asymmetry based on the relationship between the difference and at least one first threshold, the guiding audio being used to guide the user to move according to the beat of the guiding audio; calculating the relationship between the step speed in the gait characteristics and at least one second threshold to determine the step speed asymmetry; calculating the relationship between the stride in the gait characteristics and at least one third threshold to determine the stride asymmetry.

[0015] Optionally, the cardiopulmonary function training method further includes: evaluating the user's athletic ability according to a matching result between the gait evaluation score and the physiological characteristic data, wherein a higher gait evaluation score indicates that the user's gait is more consistent with a standard gait.

[0016] Optionally, the physiological characteristic data includes heart rate, and the evaluating the user's athletic ability based on the gait assessment score and the physiological characteristic data includes: when the gait assessment score is higher than a first score and the heart rate is higher than a first value, determining that the user's athletic ability is normal; when the gait assessment score is higher than the first score and the heart rate is lower than a second value, determining that the user's athletic ability is excellent, and the second value is lower than the first value; when the gait assessment score is lower than a second score and the heart rate is lower than the second value, determining that the user's athletic ability is weak, and the second score is lower than the first score.

[0017] Optionally, the target motion pattern is selected from a plurality of candidate motion patterns, the plurality of candidate motion patterns correspond to a plurality of motion intensities, and each candidate motion pattern matches a corresponding plurality of heart rate intervals.

[0018] Optionally, the physiological information includes at least one of the following: height, weight, age, gender, and medical history; the first motion information includes first physiological characteristic data and first acceleration data of a device used by the user; determining the target motion pattern based on the user's physiological information and the first motion information includes: acquiring the physiological information and the first motion information of the user within a first time period; determining the user's gait characteristics based on the first motion information, the gait characteristics including at least one of the following: step frequency, step speed asymmetry, and stride asymmetry; inputting the physiological information and the gait characteristics into a prediction model to obtain the target motion pattern.

[0019] In a second aspect, the present application also discloses a cardiopulmonary function training device, which includes: a motion mode acquisition module, used to determine a target motion mode based on the user's physiological information and first motion information, and the target motion mode matches the corresponding target heart rate range; a motion data acquisition module, used to acquire second motion information, and the second motion information includes the user's physiological characteristic data and the acceleration data of the device used by the user; an evaluation module, used to determine a recommended step frequency based on the second motion information, and the user's heart rate at the recommended step frequency is close to the target heart rate range or is within the target heart rate range, wherein the heart rate close to the target heart rate range indicates that the offset value between the heart rate and the upper limit value or lower limit value of the target heart rate range is less than a preset threshold.

[0020] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. The computer program is executed by a processor to execute a method provided in the first aspect.

[0021] In a fourth aspect, a cardiopulmonary function training device is provided, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer program to execute a method provided in the first aspect.

[0022] According to a fifth aspect, a computer program product is provided, on which a computer program is stored, and the computer program is executed by a processor to execute a method provided in the first aspect.

[0023] In a sixth aspect, an embodiment of the present application further provides a chip (or a data transmission device) on which a computer program is stored, and when the computer program is executed by the chip, the steps of the above method are implemented.

[0024] In the seventh aspect, an embodiment of the present application also provides a system chip, which is applied to a terminal, and the chip system includes at least one processor and an interface circuit, the interface circuit and the at least one processor are interconnected through lines, and the at least one processor is used to execute instructions to execute a method provided in the first aspect.

[0025] Compared with the prior art, the technical solution of this application has the following beneficial effects:

[0026] In the technical solution of the present application, the physiological characteristic data of the user can reflect the user's exercise intensity, and the acceleration data of the device used by the user can reflect the user's exercise posture. The combination of the two can reflect the user's exercise ability. Therefore, the technical solution of the present application can determine the recommended cadence for the user through the physiological characteristic data and acceleration data of the user, so that when the user exercises at the recommended cadence, the heart rate can approach and eventually reach the target heart rate range, thereby flexibly adjusting the user's cadence, achieving the effect of training the user's cardiopulmonary function, and improving the user experience.

[0027] Furthermore, the technical solution of the present application adjusts the rhythm of the guiding audio according to the recommended step frequency, and the guiding audio is used to guide the user to exercise according to the rhythm of the guiding audio, that is, the user's step frequency is flexibly adjusted according to the user's exercise condition, thereby achieving flexibility in the user's step frequency training.

[0028] Furthermore, the technical solution of the present application can also evaluate the user's gait based on the gait characteristics to obtain a gait evaluation score. The gait evaluation score can reflect the standardization of the user's movement posture during exercise, especially in the scenario of user rehabilitation training, which can greatly improve the training effect.

[0029] Furthermore, the technical solution of the present application can also evaluate the user's athletic ability based on the matching results of the gait assessment score and the physiological characteristic data. By evaluating the user's athletic ability, it can assist the user in choosing the appropriate training intensity according to his or her own athletic ability, avoid sports risks, and thus ensure the safety of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of a cardiopulmonary function training method provided in an embodiment of the present application;

[0031] Figure 2 is a flow chart of another cardiopulmonary function training method provided in an embodiment of the present application;

[0032] Figure 3 is a flow chart of another cardiopulmonary function training method provided in an embodiment of the present application;

[0033] Figure 4 is a structural diagram of a communication device provided in an embodiment of the present application;

[0034] Figure 5 It is a schematic diagram of the hardware structure of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] As described in the background art, the effect of using a fixed beat method to perform cadence training in the prior art is not good.

[0036] Especially in sports rehabilitation scenarios, users are usually elderly people or people with respiratory problems. They need to pay more attention to controlling exercise intensity during exercise to prevent danger during exercise.

[0037] In the technical solution of the present application, the physiological characteristic data of the user can reflect the user's exercise intensity, and the acceleration data of the device used by the user can reflect the user's exercise posture. The combination of the two can reflect the user's exercise ability. Therefore, the technical solution of the present application can determine the recommended cadence for the user through the physiological characteristic data and acceleration data of the user, so that when the user exercises at the recommended cadence, the heart rate can approach and eventually reach the target heart rate range, thereby flexibly adjusting the user's cadence, achieving the effect of training the user's cardiopulmonary function, and improving the user experience.

[0038] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0039] See also Figure 1 The method provided in this application specifically includes steps 101 to 103.

[0040] In step 101, a target motion pattern is determined according to the physiological information and first motion information of the user, and the target motion pattern matches a corresponding target heart rate interval.

[0041] In one embodiment, the user may select a target exercise mode before exercising, in the hope of exercising at a heart rate within a target heart rate range to achieve a desired training effect.

[0042] In another embodiment, the user's physiological information and first motion information may also be collected, and a training plan suitable for the user may be automatically recommended based on the user's physiological information and the first motion information, that is, a target motion pattern may be determined.

[0043] Specifically, the user's physiological information is obtained, and the physiological information includes but is not limited to the user's height, weight, age, gender, and medical history (for example, whether there is cardiovascular disease or respiratory disease, etc.). The first physiological characteristic data of the user in a first time period (for example, one minute) and the first acceleration data of the device used by the user can also be obtained. The user's gait characteristics are determined based on the first physiological characteristic data and the first acceleration data, and the gait characteristics include at least one of the following: step frequency, step speed asymmetry, and stride asymmetry; the physiological information and gait characteristics are input into the prediction model to obtain the target motion pattern. Among them, more descriptions of the first physiological characteristic data, the first acceleration data, and the gait characteristics can refer to the description of the subsequent embodiments, and will not be repeated here.

[0044] For example, the prediction model can be constructed using the random forest algorithm.

[0045] For example, the target exercise mode output by the prediction model is the primary mode, which matches the corresponding warm-up / recovery heart rate interval, and the heart rate of the warm-up / recovery heart rate interval is 50% to 60% of the user's maximum heart rate. The user's maximum heart rate HR can be calculated by formula (1) max :

[0046] HR max =208-(0.7* age) (1)

[0047] Among them, age represents the user's age.

[0048] For another example, the target exercise mode output by the prediction model is an intermediate mode, and the intermediate mode matches the corresponding easy aerobic heart rate interval, and the heart rate of the easy aerobic heart rate interval is 60% to 70% of the maximum heart rate.

[0049] In this embodiment, the flexibility of training can be achieved by recommending a target motion pattern suitable for the user.

[0050] For example, when user 1 just starts training, an entry-level mode is recommended for user 1. As the user's cardiopulmonary function improves, the physiological information and first exercise information of user 1 can be collected again, and an intermediate mode can be recommended for the user to achieve switching of exercise modes.

[0051] In step 102, second motion information is acquired, where the second motion information includes physiological characteristic data of the user and acceleration data of a device used by the user.

[0052] In this embodiment, when the user is training according to the target motion pattern, the second motion information of the user can be collected, and the second motion information can reflect the physical condition and running state of the user during the training process.

[0053] Specifically, a user may carry a device to exercise, and the acceleration data of the device may reflect the user's exercise posture.

[0054] For example, if a user carries a mobile phone while exercising, the acceleration data in the mobile phone can be obtained.

[0055] The user's physiological characteristic data, such as heart rate, blood oxygen, etc., can be obtained from the device used by the user, and the device has the function of collecting physiological characteristic data. If the device used by the user does not have the function of collecting physiological characteristic data, the user's physiological characteristic data can be obtained from a third-party device.

[0056] For example, a user may wear a third-party wearable device, which may collect the user's physiological characteristics in real time or periodically, such as heart rate, blood oxygen, etc. The user's physiological characteristic data may be obtained from the third-party smart wearable device through a short-range communication protocol, such as a Bluetooth protocol.

[0057] In step 103, a recommended step frequency is determined according to the second motion information, and the user's heart rate at the recommended step frequency is close to the target heart rate range or is within the target heart rate range.

[0058] In a specific implementation, a recommended cadence is provided to the user so that the user's cadence is adjusted, thereby adjusting the user's heart rate. Compared with the user's heart rate before the adjustment, the user's heart rate after the adjustment is closer to the upper limit or lower limit of the target heart rate range, or the user's heart rate after the adjustment is within the target heart rate range.

[0059] For example, when the user's heart rate is much greater than the upper limit of the target heart rate interval before adjustment, the offset between the user's heart rate and the upper limit of the target heart rate interval after adjustment is less than a preset threshold.

[0060] For example, when the user's heart rate is much lower than the upper limit of the target heart rate interval before adjustment, the offset between the user's heart rate and the lower limit of the target heart rate interval after adjustment is smaller than a preset threshold.

[0061] It should be noted that the specific value of the preset threshold can be set according to the actual application scenario, and this application does not impose any restrictions on this.

[0062] In this embodiment, the user's exercise status and whether the user's heart rate reaches the target heart rate range can be determined through the physiological characteristic data and the acceleration data, so that the recommended cadence can be determined accordingly. Through the recommended cadence, the purpose of increasing the user's heart rate, maintaining the user's heart rate, and reducing the user's heart rate can be approached and ultimately achieved, thereby realizing the training of the user's cardiopulmonary function.

[0063] In a specific embodiment, the recommended cadence may include at least one of the following:

[0064] Maintain current cadence;

[0065] Reduce the current cadence;

[0066] Increase your current cadence.

[0067] The user's step frequency is proportional to the user's heart rate, that is, the higher the user's step frequency, the higher the user's heart rate. Therefore, the recommended step frequency is to keep the current step frequency, so that the user's heart rate remains unchanged; the recommended step frequency is to reduce the current step frequency, so that the user's heart rate decreases; the recommended step frequency is to increase the current step frequency, so that the user's heart rate increases.

[0068] In a specific embodiment, the physiological characteristic data and acceleration data can be input into a trained classification model, and the classification model outputs a recommended cadence. The classification model can be pre-trained using training data, and the training data can include the physiological characteristic data, acceleration data and their corresponding recommended cadence.

[0069] For example, the classification model can be constructed using the random forest algorithm.

[0070] It should be noted that the classification model can also be constructed using any other feasible classification algorithm, and this application does not impose any restrictions on this.

[0071] It should be pointed out that the serial numbers of the steps in this embodiment do not limit the execution order of the steps.

[0072] It is understandable that, in a specific implementation, the cardiopulmonary function training method can be implemented in the form of a software program, and the software program runs in a processor integrated inside a chip or a chip module. The method can also be implemented in the form of software combined with hardware, which is not limited in this application.

[0073] In a non-limiting embodiment, the beat of the guiding audio is adjusted according to the recommended step frequency, and the guiding audio is used to guide the user to exercise according to the beat of the guiding audio, wherein the number of beats of the guiding audio per unit time is positively correlated with the frequency of the recommended step frequency.

[0074] In this embodiment, the user can be guided to perform walking training at a specific step frequency by guiding audio, such as the rhythm of music. In order to flexibly adjust the user's step frequency, the beat of the guiding audio can be adjusted according to the recommended step frequency.

[0075] For example, the recommended step frequency is to reduce the current step frequency, so the number of beats of the guide audio per unit time can be reduced.

[0076] For example, the recommended step frequency is to increase the current step frequency, so the number of beats of the guiding audio per unit time can be increased.

[0077] For example, the recommended step frequency is to maintain the current step frequency, so the beat of the guide audio can be kept unchanged.

[0078] This embodiment can provide a personalized user experience, and can also interact with the user to provide cadence suggestions.

[0079] In a non-limiting embodiment, feature extraction may be performed on the acceleration data, and the recommended step frequency may be determined based on the extracted features.

[0080] In this embodiment, before extracting features from the acceleration data, the acceleration data may be preprocessed to filter out noise in the acceleration data to ensure the accuracy of the acceleration data.

[0081] Specifically, for the three-axis acceleration, the DC component in the acceleration data can be removed. Specifically, the following formula (2) can be used to remove the DC component:

[0082] acc amp(dc_remove) =acc amp -mean(acc amp ), (2)

[0083] Among them, acc amp Indicates the absolute value of the three-axis acceleration, mean(acc amp ) represents the average value of the three-axis acceleration, acc amp(dc_remove) It represents the acceleration value after removing the DC component from the three-axis acceleration. The absolute value of the three-axis acceleration can be expressed by formula (3):

[0084]

[0085] Among them, acc_x represents the acceleration in the x-axis direction, acc_t represents the acceleration in the y-axis direction, and acc_t represents the acceleration in the z-axis direction.

[0086] It should be noted that the specific representation of acceleration data, such as the setting of the x-axis, y-axis and z-axis, can refer to the existing technology, and this application does not impose any limitation on this.

[0087] Specifically, the acceleration data may also be filtered to filter high-frequency noise.

[0088] For example, the absolute value of the acceleration is filtered using a Butterworth bandpass filter, and the upper and lower limits of the cutoff frequency of the Butterworth bandpass filter are 1 Hz and 10 Hz, respectively.

[0089] Please refer to Figure 2 In step 201, feature extraction is performed on the acceleration data to obtain distribution features and gait features of the acceleration data.

[0090] Specifically, the acceleration data may be collected and processed at a first preset time interval, such as 5 seconds (s), to obtain a set of distribution features and gait features. Then, at a second time interval, such as one minute, multiple sets of distribution features and gait features may be statistically analyzed, and a recommended step frequency within the next second time interval may be determined based on the statistical features.

[0091] In a specific embodiment, the distribution characteristics of the acceleration data may describe the distribution of the acceleration data in time or frequency. The distribution characteristics of the acceleration data may include at least one of the following:

[0092] Amplitude characteristics;

[0093] Frequency domain features;

[0094] Time domain characteristics;

[0095] Periodic characteristics;

[0096] Timing characteristics;

[0097] The skewness of the distribution;

[0098] Kurtosis characteristic.

[0099] The amplitude characteristics may include the peak value, the interval between peaks and valleys, and the root mean square (RMS), which reflect the amplitude of the acceleration data.

[0100] Among them, frequency domain features can include main frequency and energy distribution features, which can be extracted by Fourier transforming the acceleration data and used to analyze the frequency components of the acceleration data. Specifically, the acceleration data is converted to the complex domain using fast Fourier transform to obtain the amplitude components of the acceleration data at different frequencies, and then the amplitude components are squared to obtain the power value of each frequency component. In this way, the main frequency, energy size, kurtosis and skewness of energy distribution, etc. can be analyzed.

[0101] Among them, the time domain features include mean, variance, standard deviation, slope and kurtosis, which can describe the statistical characteristics of acceleration data in time.

[0102] The periodic features may include period length and periodicity degree, which are extracted by methods such as period detection or autocorrelation function.

[0103] Among them, the time series characteristics include time series statistical characteristics, autocorrelation function and correlation coefficient in the time domain, which are used to describe the sequence characteristics of acceleration data in time.

[0104] In a specific embodiment, the gait characteristics may include at least one of the following: stride length, stride speed, and stride frequency.

[0105] The number of steps can be represented by the number of peak points in the acceleration data, and the number of steps per unit time can represent the step frequency. The midpoint between the current peak and the trough and the midpoint between the next peak and the trough represent the start and end of a step respectively, and the stride and speed of each step can be calculated by the speed formula and the acceleration distance formula.

[0106] In step 202, a recommended step frequency is determined based on the physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics.

[0107] The embodiment of the present application can extract effective information from the acceleration data by performing feature extraction on the acceleration data, which helps to adjust the user's cadence in a targeted manner and improve the flexibility of cadence training.

[0108] In a specific implementation, the physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics may be input into a trained classification model, and the classification model outputs a recommended step frequency.

[0109] Furthermore, before inputting the distribution features and gait features into the classification model, the distribution features and gait features may be screened. Specifically, the features may be screened using the correlation between the features, and the amount of data input into the model may be reduced by removing the features with high correlation.

[0110] For example, the correlation between the features is calculated using a correlation algorithm. When the correlation between two features is higher than a preset threshold, one of the two features may be retained and the other may be deleted.

[0111] The embodiments of the present application can remove redundancy, reduce the amount of data, and improve the calculation speed while ensuring the training effect.

[0112] In another non-limiting embodiment of the present application, the user's gait and athletic ability can also be evaluated based on the physiological characteristic data and acceleration data, so as to more comprehensively ensure the effect of cadence training.

[0113] Please refer to Figure 3 , Figure 3 The present invention shows a process of a cardiopulmonary function training method.

[0114] In step 301, a target motion pattern is determined according to the user's physiological information and first motion information, and the target motion pattern matches a corresponding target heart rate interval.

[0115] In step 302, the second motion information is acquired, and feature extraction is performed on acceleration data in the second motion information to obtain distribution features and gait features of the acceleration data.

[0116] In step 303, the user's gait is evaluated according to the gait characteristics to obtain a gait evaluation score, wherein the gait evaluation score can reflect the standard degree of the user's gait, and a higher gait evaluation score indicates that the user's gait is more in line with the standard gait.

[0117] In a specific embodiment, the gait assessment score can be measured from multiple dimensions, including cadence asymmetry, stride asymmetry, and stride length asymmetry. The difference between the user's cadence and the beat of the guidance audio can be used to represent cadence asymmetry, stride speed asymmetry represents the difference in the user's stride speed at different times, and stride length asymmetry represents the difference in the user's stride length at different times.

[0118] Specifically, the difference between the step frequency in the gait feature and the beat of the guide audio is calculated, and the step frequency asymmetry is determined according to the relationship between the difference and at least one first threshold.

[0119] Specifically, the relationship between the walking speed and at least one second threshold in the gait characteristics is calculated to determine the walking speed asymmetry.

[0120] Specifically, the relationship between the stride length and at least one third threshold in the gait characteristics is calculated to determine the stride length asymmetry.

[0121] It should be noted that the specific values ​​of the first threshold, the second threshold and the third threshold can be set according to the actual application scenario, and this application does not impose any restrictions on this.

[0122] In step 304, the user's athletic ability is determined based on the gait assessment score and the heart rate.

[0123] Wherein, when the gait assessment score is higher than the first score and the heart rate is higher than the first value, it is determined that the user's athletic ability is normal;

[0124] When the gait assessment score is higher than the first score and the heart rate is lower than the second value, it is determined that the user has excellent athletic ability and the second value is lower than the first value;

[0125] When the gait evaluation score is lower than the second score and the heart rate is lower than the second value, it is determined that the user's athletic ability is weak and the second score is lower than the first score.

[0126] In this embodiment, the relationship between the heart rate and the first value can represent the user's heart rate maintenance status and can be used to judge the user's athletic ability. The relationship between the heart rate and the first value refers to the relationship between the absolute value of the heart rate and the first value, or the relationship between the rate of change of the heart rate per unit time and the first value.

[0127] For example, the rate of change of the heart rate per minute is compared with the first value.

[0128] For another example, the rate of change of the heart rate in the last 30 seconds of each minute is compared with the first value.

[0129] Similarly, the relationship between the heart rate and the second value can refer to the relationship between the heart rate and the first value, which will not be repeated here.

[0130] Those skilled in the art will appreciate that steps 301 to 302 may be considered as similar to the above Figure 1 The steps 101 to 102 in the embodiment shown are corresponding execution steps, and the two complement each other in terms of specific implementation principles and logic. Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0131] In a non-limiting embodiment of the present application, the target motion mode is selected from a plurality of candidate motion modes, the plurality of candidate motion modes correspond to a plurality of motion intensities, and each candidate motion mode matches a plurality of corresponding heart rate intervals, wherein different heart rate intervals may also correspond to different motion intensities.

[0132] In a specific embodiment, the multiple heart rate intervals include at least one of the following:

[0133] Warm-up heart rate range, specifically 50% to 60% of maximum heart rate;

[0134] Easy aerobic heart rate range, specifically 60% to 70% of the maximum heart rate;

[0135] Endurance aerobic heart rate range, specifically 70% to 80% of the maximum heart rate;

[0136] Maximum oxygen uptake heart rate range, specifically 80% to 90% of the maximum heart rate;

[0137] The anaerobic heart rate range may specifically be 90% to 100% of the maximum heart rate.

[0138] Among them, the order of exercise intensity from small to large is: warm-up heart rate zone, easy aerobic heart rate zone, easy aerobic heart rate zone, maximum oxygen uptake heart rate zone, and anaerobic heart rate zone. The maximum heart rate in the heart rate zone can be determined according to the user's physiological information.

[0139] For example, the maximum heart rate may be determined based on the age of the user.

[0140] For example, for an average user, the maximum heart rate HR is calculated using the following formula (4): max :

[0141] HR max =208-(0.7×age) (4)

[0142] Among them, age represents the user's age.

[0143] Specifically, for unhealthy users, the maximum heart rate can be calculated in a different way from the maximum heart rate of ordinary users. Generally speaking, the maximum heart rate of unhealthy users is lower than that of ordinary users to ensure the safety of unhealthy users' exercise.

[0144] For example, for unhealthy users, the maximum heart rate HR is calculated using the following formula (5): max :

[0145] HR max =200-age (5)

[0146] Furthermore, different candidate exercise modes can be obtained by combining the above-mentioned different heart rate intervals to meet the needs of different users for different training intensities.

[0147] For example, the candidate exercise modes include an entry-level mode, and the heart rate interval combination of the entry-level mode is as follows: 3×n minutes of warm-up heart rate interval, 5×n minutes of easy aerobic interval, 7×n minutes of endurance aerobic heart rate interval, and 5×n minutes of easy aerobic interval. Wherein, n is a time coefficient, ranging from (1-2), which can be dynamically adjusted according to user usage.

[0148] For example, the candidate exercise modes include the intermediate mode, and the heart rate interval combination of the intermediate mode is as follows: 3×n minutes of warm-up heart rate interval, 3×n minutes of easy aerobic interval, 3×n minutes of endurance aerobic heart rate interval, 10×n minutes of maximum oxygen uptake heart rate interval, 10×n minutes of endurance aerobic heart rate interval, 3×n minutes of easy aerobic interval. Among them, n is the time coefficient, ranging from (1-1.5), which can be dynamically adjusted according to user usage.

[0149] For example, the candidate exercise modes include an advanced mode, and the heart rate zone combination of the advanced mode is as follows: after a 5-minute warm-up heart rate zone, a 3-minute maximum oxygen uptake heart rate zone and a 3-minute easy aerobic zone are combined as a group, and 2-3 groups are performed.

[0150] Among them, the order of exercise intensity from small to large is: beginner mode, intermediate mode and advanced mode.

[0151] It should be noted that the combination of the heart rate intervals in the candidate motion patterns may also be any other feasible manner, and the present application does not impose any limitation on this.

[0152] In a non-limiting embodiment of the present application, the user's physiological characteristic data may also include blood oxygen, and the user may be prompted with exercise risks based on the blood oxygen value, thereby ensuring the user's exercise safety.

[0153] Specifically, when the blood oxygen content is in the first interval, it means that the blood oxygen content is low and there may be exercise risks, so the first prompt information can be output to remind the user to pay attention to exercise risks.

[0154] For example, the first interval may be 90% to 94%. That is, when the user's blood oxygen value is 93%, which is within the range of 90% to 94%, the first prompt information may be output.

[0155] Specifically, when the blood oxygen content is lower than the first threshold, it means that the current blood oxygen content is too low, so the second prompt information can be output to prompt the user to stop exercising immediately.

[0156] For example, the first threshold may be 90%, that is, the user's blood oxygen value is 89%, which is lower than the first threshold of 90%. At this time, the second prompt information may be output.

[0157] In a specific application scenario, the device used by the user is loaded with an application for cardiopulmonary function training. When the user uses the application for the first time, he or she can enter physiological information, such as height, weight, age, gender, medical history, etc. The application can prompt the user to carry the device and walk for 1 minute in the same way as daily walking. By collecting the user's walking data within one minute, including heart rate, acceleration data, etc., the application recommends a suitable target exercise mode for the user in combination with the user's physiological information.

[0158] For example, if the target exercise mode recommended to the user is the beginner mode, the application will guide the user's training process according to the beginner mode. During the user's training process, the user's physiological characteristic data and the acceleration data of the device used by the user are collected in real time, and suggestions for adjusting the cadence are given. Then, the user's cardiopulmonary function training process is guided by adjusting the rhythm of the guiding audio, and the user's cardiopulmonary function training is completed on the basis of ensuring the user's exercise safety.

[0159] Please refer to Figure 4 , Figure 4 A cardiopulmonary function training device 40 is shown, and the cardiopulmonary function training device 40 may include:

[0160] A motion mode acquisition module 401 is used to determine a target motion mode according to the user's physiological information and the first motion information, wherein the target motion mode matches a corresponding target heart rate interval;

[0161] The motion data acquisition module 402 is used to acquire second motion information, where the second motion information includes physiological characteristic data of the user and acceleration data of a device used by the user;

[0162] The evaluation module 403 is used to determine a recommended cadence according to the physiological characteristic data and the acceleration data, and the user's heart rate at the recommended cadence is close to or within the target heart rate range.

[0163] Furthermore, the cardiopulmonary function training device 40 may also include: an adjustment module, used to adjust the beat of the guiding audio according to the recommended step frequency, the guiding audio is used to guide the user to exercise according to the beat of the guiding audio, wherein the number of beats of the guiding audio per unit time is positively correlated with the frequency of the recommended step frequency.

[0164] Furthermore, the evaluation module 403 may include:

[0165] A feature extraction unit, used to extract features from the acceleration data to obtain distribution features and gait features of the acceleration data, wherein the gait features represent the movement posture of the user;

[0166] The evaluation unit is used to determine the recommended step frequency according to the physiological characteristic data, the distribution characteristics of the acceleration data and the gait characteristics.

[0167] Furthermore, the cardiopulmonary function training device 40 may also include: a gait assessment module, which is used to assess the user's gait according to the gait characteristics to obtain a gait assessment score.

[0168] Furthermore, the gait assessment module may include:

[0169] a first calculation unit, configured to calculate a difference between a step frequency in a gait feature and a beat of a guiding audio, and determine a step frequency asymmetry according to a relationship between the difference and at least one first threshold, wherein the guiding audio is used to guide the user to move according to the beat of the guiding audio;

[0170] A second calculation unit, configured to calculate a relationship between a pace in a gait feature and at least one second threshold value to determine asymmetry of the pace;

[0171] The third calculation unit is used to calculate the relationship between the stride in the gait feature and at least one third threshold to determine the stride asymmetry.

[0172] Furthermore, the cardiopulmonary function training device 40 may also include: an athletic ability assessment module, which is used to assess the user's athletic ability based on the matching result of the gait assessment score and the physiological characteristic data. The higher the gait assessment score, the more the user's gait conforms to the standard gait.

[0173] In a specific implementation, the above-mentioned cardiopulmonary function training device 40 can correspond to a chip with data processing function in a terminal device, such as a system-on-a-chip (SOC), a baseband chip, etc.; or correspond to a chip module with data processing function in a terminal device; or correspond to a chip module with a chip with data processing function, or correspond to a terminal device.

[0174] For other related descriptions about the cardiopulmonary function training device 40 , reference may be made to the related descriptions in the aforementioned embodiments, which will not be repeated here.

[0175] Regarding the various modules / units included in the various devices and products described in the above embodiments, they can be software modules / units, or hardware modules / units, or they can be partially software modules / units and partially hardware modules / units. For example, for various devices and products applied to or integrated in a chip, the various modules / units included therein can all be implemented in the form of hardware such as circuits, or at least some of the modules / units can be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in a chip module, the various modules / units included therein can all be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component of the chip module (such as a chip, circuit module, etc.) or in different components, or at least some of the modules / units can be implemented in the form of software programs. It is implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0176] The present application also discloses a storage medium, which is a computer-readable storage medium and stores a computer program, which can be executed when the computer program is run. Figures 1 to 3 The steps of the method shown in . The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. The storage medium may also include a non-volatile memory (non-volatile) or a non-transitory memory, etc.

[0177] Please refer to Figure 5 The embodiment of the present application also provides a hardware structure diagram of a communication device. The device includes a processor 501, a memory 502 and a transceiver 503.

[0178] Processor 501 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application. Processor 501 may also include multiple CPUs, and processor 501 may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. The processor here may refer to one or more devices, circuits, or processing cores for processing data (such as computer program instructions).

[0179] The memory 502 may be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, and the embodiments of the present application do not impose any restrictions on this. The memory 502 may exist independently (in this case, the memory 502 may be located outside the device or inside the device), or it may be integrated with the processor 501. Among them, the memory 502 may contain a computer program code. The processor 501 is used to execute the computer program code stored in the memory 502, thereby realizing the method provided in the embodiments of the present application.

[0180] The processor 501, the memory 502 and the transceiver 503 are connected via a bus. The transceiver 503 is used to communicate with other devices or a communication network. Optionally, the transceiver 503 may include a transmitter and a receiver. The device used to implement the receiving function in the transceiver 503 can be regarded as a receiver, and the receiver is used to perform the receiving step in the embodiment of the present application. The device used to implement the sending function in the transceiver 503 can be regarded as a transmitter, and the transmitter is used to perform the sending step in the embodiment of the present application.

[0181] when Figure 5When the structural schematic diagram shown is used to illustrate the structure of the terminal device involved in the above embodiment, the processor 501 is used to control and manage the actions of the terminal device, for example, the processor 501 is used to support the terminal device to perform the actions performed by the terminal device in other processes described in the embodiments of the present application. The processor 501 can communicate with other network entities through the transceiver 503, for example, communicate with the above network devices. The memory 502 is used to store program codes and data of the terminal device.

[0182] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0183] The "plurality" appearing in the embodiments of the present application refers to two or more.

[0184] The first, second, etc. descriptions appearing in the embodiments of the present application are only used for illustration and distinction of the description objects. There is no order, nor do they indicate any special limitation on the number of devices in the embodiments of the present application, and cannot constitute any limitation on the embodiments of the present application.

[0185] The "connection" that appears in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0186] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless means.

[0187] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0188] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are merely schematic; for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0189] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0190] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0191] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the method described in each embodiment of the present application.

[0192] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A cardiopulmonary function training method, characterized in that: include: Determining a target motion mode according to the user's physiological information and the first motion information, wherein the target motion mode matches a corresponding target heart rate interval; Acquire second motion information, where the second motion information includes physiological characteristic data of the user and acceleration data of a device used by the user; The recommended step frequency is determined based on the second motion information, and the user's heart rate at the recommended step frequency is close to the target heart rate range or is within the target heart rate range, wherein the heart rate close to the target heart rate range indicates that the offset value between the heart rate and the upper limit value or the lower limit value of the target heart rate range is less than a preset threshold.

2. The cardiopulmonary function training method according to claim 1, characterized in that: The recommended cadence includes at least one of the following: Maintain current cadence; reducing the current cadence; Increase the current cadence.

3. The cardiopulmonary function training method according to claim 1, characterized in that: Also includes: The beat of the guiding audio is adjusted according to the recommended step frequency, and the guiding audio is used to guide the user to exercise according to the beat of the guiding audio, wherein the number of beats of the guiding audio per unit time is positively correlated with the frequency of the recommended step frequency.

4. The cardiopulmonary function training method according to claim 1, characterized in that: The determining the recommended step frequency according to the second motion information includes: Performing feature extraction on the acceleration data to obtain distribution features and gait features of the acceleration data, wherein the gait features represent the movement posture of the user; The recommended step frequency is determined according to the physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics.

5. The cardiopulmonary function training method according to claim 4, characterized in that: The gait feature includes at least one of the following: stride, pace and frequency, and the feature extraction of the acceleration data includes: Detecting a peak value of the acceleration data; Determine the step frequency according to the number of the peak values ​​per unit time; The pace and the stride are determined according to the acceleration data and the positions of the peaks and troughs thereof.

6. The cardiopulmonary function training method according to claim 4, characterized in that: The determining the recommended step frequency according to the physiological characteristic data, the distribution characteristics of the acceleration data and the gait characteristics comprises: The physiological characteristic data, the distribution characteristics of the acceleration data, and the gait characteristics are input into a trained classification model, and the classification model outputs the recommended step frequency.

7. The cardiopulmonary function training method according to claim 4, characterized in that: Also includes: The user's gait is evaluated according to the gait characteristics to obtain a gait evaluation score.

8. The cardiopulmonary function training method according to claim 7, characterized in that: The evaluating the user's gait according to the gait feature comprises: Calculating a difference between a step frequency in the gait feature and a beat of a guiding audio, and determining a step frequency asymmetry according to a relationship between the difference and at least one first threshold, wherein the guiding audio is used to guide the user to move according to the beat of the guiding audio; The relationship between the walking speed in the gait feature and at least one second threshold is calculated to determine the walking speed asymmetry; and the relationship between the walking length in the gait feature and at least one third threshold is calculated to determine the walking length asymmetry.

9. The cardiopulmonary function training method according to claim 7, characterized in that: Also includes: The user's athletic ability is evaluated according to the matching result of the gait evaluation score and the physiological characteristic data, and the higher the gait evaluation score is, the more the user's gait conforms to the standard gait.

10. The cardiopulmonary function training method according to claim 9, characterized in that: The physiological characteristic data includes heart rate, and the evaluating the user's athletic ability according to the gait evaluation score and the physiological characteristic data includes: When the gait assessment score is higher than a first score and the heart rate is higher than a first value, determining that the user's athletic ability is normal; When the gait evaluation score is higher than the first score and the heart rate is lower than a second value, it is determined that the user has excellent athletic ability and the second value is lower than the first value; When the gait evaluation score is lower than a second score and the heart rate is lower than the second value, it is determined that the user's athletic ability is weak and the second score is smaller than the first score.

11. The cardiopulmonary function training method according to claim 1, characterized in that: The physiological information includes at least one of the following: height, weight, age, gender, and medical history; the first motion information includes first physiological characteristic data and first acceleration data of the device used by the user; Determining the target motion mode according to the user's physiological information and the first motion information includes: Acquire the physiological information and first motion information of the user within a first time period; Determine a gait feature of the user according to the first motion information, wherein the gait feature includes at least one of the following: step frequency, step speed asymmetry, and step length asymmetry; The physiological information and the gait characteristics are input into a prediction model to obtain the target motion pattern.

12. The cardiopulmonary function training method according to claim 1, characterized in that: The target motion pattern is selected from a plurality of candidate motion patterns, the plurality of candidate motion patterns correspond to a plurality of motion intensities, and each candidate motion pattern matches a corresponding plurality of heart rate intervals.

13. A cardiopulmonary function training device, characterized in that: include: A motion mode acquisition module, used to determine a target motion mode according to the user's physiological information and the first motion information, wherein the target motion mode matches a corresponding target heart rate interval; A motion data acquisition module, used to acquire second motion information, where the second motion information includes physiological characteristic data of the user and acceleration data of a device used by the user; An evaluation module is used to determine a recommended step frequency based on the second motion information, the user's heart rate at the recommended step frequency is close to the target heart rate range or is within the target heart rate range, wherein the heart rate being close to the target heart rate range indicates that the offset value between the heart rate and the upper limit or lower limit of the target heart rate range is less than a preset threshold.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cardiopulmonary function training method according to any one of claims 1 to 12 are executed.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the cardiopulmonary function training method described in any one of claims 1 to 12 are implemented.

16. A terminal device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor runs the computer program, the steps of the cardiopulmonary function training method according to any one of claims 1 to 12 are performed.

Images