CN115670444A - Motion monitoring method and device, wearable device and storage medium - Google Patents

Abstract

The present application relates to a motion monitoring method, device, wearable device and storage medium to obtain the user's body surface parameters; if the body surface parameters are determined to be greater than the preset value, the user's sweat data, exercise data and heart rate data are obtained; according to the sweat data , exercise data, and heart rate data determine that the user's current exercise amount exceeds the standard, and then generate exercise warning information. By acquiring the user's body surface parameters, and only acquiring the user's sweat data, exercise data and heart rate data when the body surface parameters are greater than the preset value, the purpose of reducing power consumption is achieved. At the same time, it can analyze and calculate according to the data in the user's exercise process. When the user's current exercise volume exceeds the standard based on sweat data, exercise data and heart rate data, an operation warning message will be generated to warn the user of the current exercise physical state to prevent excessive exercise. Abnormalities in vital signs.

Description

Motion monitoring method, device, wearable device and storage medium

technical field

The present application relates to the technical field of electrical data processing, in particular to a motion monitoring method, device, wearable device and storage medium.

Background technique

Under the modern fast-paced life, people are under great pressure from work and study, and are limited by the space and time of exercise, which leads to various health problems. With the advancement of technology and the continuous improvement of living standards, people pay more and more attention to their physical and mental health, and wear wearable devices during daily exercise to monitor some basic physiological parameters.

However, wearable devices currently on the market, after parameter detection in daily life and exercise, are generally only used to display static data for the user, reflecting the current basic state of the user, and cannot be detected according to the parameters during exercise. Users provide reasonable exercise suggestions.

Contents of the invention

Based on this, it is necessary to provide a motion monitoring method, device, wearable device and storage medium for the above technical problems, which can analyze and calculate according to the motion data, warn the user of the current state of physical fitness, and prevent excessive motion after reaching the limit. Abnormalities in vital signs.

In a first aspect, the present application provides a motion monitoring method applied to a wearable device, the method comprising:

Obtain the user's body surface parameters;

If it is determined that the body surface parameter is greater than the preset value, the sweat data, exercise data and heart rate data of the user are obtained;

According to the sweat data, the exercise data and the heart rate data, it is determined that the user's current physical activity exceeds the standard, and then an exercise warning message is generated.

In the second aspect, the present application also provides a motion monitoring device, which is applied to wearable devices, and the device includes:

A body surface parameter acquisition module, configured to acquire the user's body surface parameters;

A data acquisition module, configured to determine that the body surface parameter is greater than a preset value, then acquire the user's sweat data, exercise data and heart rate data;

The exercise early warning module is used to determine that the user's current physical activity exceeds the standard according to the sweat data, the exercise data and the heart rate data, and then generate exercise early warning information.

In the third aspect, the present application also provides a wearable device, including a processor and a motion detection module connected to the processor, a heart rate detection module, a sweat detection module, a temperature and humidity data collection module, and an interaction module. The device is used to monitor the user's movement according to the above method.

In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

The motion monitoring method, device, wearable device, and storage medium described above obtain the user's body surface parameters, and when the body surface parameters are greater than a preset value, obtain the user's sweat data, exercise data, and heart rate data, and based on the sweat data, When the exercise data and heart rate data determine that the user's current exercise volume exceeds the standard, the running warning information is generated to give the user an early warning of the amount of exercise. The exercise monitoring method of the present application can analyze and calculate the data in the user's exercise process to warn the user of the current exercise physical state and prevent abnormal vital signs caused by excessive exercise reaching the limit.

Description of drawings

Fig. 1 is the application environment diagram of motion monitoring method in an embodiment;

Fig. 2 is a schematic flow chart of a motion monitoring method in an embodiment;

Fig. 3 is a schematic flow chart of motion status index acquisition in an embodiment;

Fig. 4 is a schematic flow chart of motion state index acquisition in another embodiment;

Fig. 5 is a structural block diagram of a motion monitoring device in an embodiment;

Fig. 6 is a schematic diagram of a system block diagram of a wearable device in an embodiment;

Fig. 7 is a schematic flowchart of a wearable device in an embodiment.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

The exercise monitoring method provided in the embodiment of the present application can be applied to various wearable devices, such as smart watches, smart bracelets, head-mounted devices, and the like. As shown in Figure 1, the applied wearable device may include a processor 101 and a data detection and collection module connected to the processor 101, the data detection and collection module includes a motion detection module 102, a heart rate detection module 103, a sweat detection module 104 and the temperature and humidity data collection module 105, in addition, the wearable device may also include an interaction module 106. Among them, the processor 101 communicates with the data detection and acquisition module to obtain the user's body surface parameters; if the body surface parameters are determined to be greater than the preset value, then obtain the user's sweat data, exercise data and heart rate data; according to the sweat data, exercise data and If the heart rate data determines that the user's current physical activity exceeds the standard, motion warning information is generated; the motion early warning information is used to warn the wearable device wearer of the amount of exercise through the interaction module 106, so as to realize the motion monitoring of the wearable device wearer.

In one embodiment, as shown in FIG. 2 , an operation monitoring method is provided. The method is applied to the processor 101 in FIG. 1 as an example for illustration, including the following S200 to S600, wherein:

S200: Obtain body surface parameters of the user.

Since there is a high probability that the amount of exercise will not exceed the standard when the user first starts exercising, and the sweat data may not be generated until the exercise lasts for a period of time. Therefore, by obtaining the user's body surface parameters, it can be used to judge the user's sweating state. After sweating is detected according to the user's body surface parameters, the acquisition of exercise process data and the determination of excessive exercise can be started to reduce power consumption. the goal of. Specifically, the body surface parameter for judging the sweating state of the user may be one or more of the user's body surface temperature, body surface humidity, and body surface sweating amount. For example, in this embodiment, the body surface parameters include the user's body surface temperature and body surface humidity.

S400: If it is determined that the body surface parameter is greater than a preset value, acquire sweat data, exercise data, and heart rate data of the user.

Specifically, after the body surface parameters of the user are acquired, it can be determined whether to perform subsequent exercise process data collection according to the preset values corresponding to the body surface parameters. The preset value corresponding to the body surface parameter can be set according to the type of the body surface parameter and specific parameter requirements. For example, in the case where the body surface parameter in this embodiment includes body surface temperature and body surface humidity, the preset value also includes the preset temperature and Preset humidity. Correspondingly, the values of the preset temperature and the preset humidity can be selected within the range of experience, and can also be updated according to the data generated by the user during exercise, so that the setting of the preset values is more in line with the user's exercise conditions. In one embodiment, the preset temperature ranges from 36°C to 37.2°C, and the preset humidity ranges from 60% to 70%.

It can be understood that when the body surface parameters include multiple judgment criteria, the judgment method is not unique. It may be that one of the body surface parameters is greater than its corresponding preset value, and then the sweat data, exercise data and heart rate data of the user are obtained. The user's sweat data, exercise data and heart rate data may be acquired only when all body surface parameters are greater than the corresponding preset values. In one embodiment, if it is determined that the body surface parameter is greater than a preset value, then acquiring the user's sweat data, exercise data, and heart rate data includes: confirming that the body surface temperature is greater than the preset temperature and the body surface humidity is greater than the preset humidity, then acquiring the user's sweat data, exercise data and heart rate data. In this embodiment, when it is confirmed that the body surface temperature is greater than the preset temperature and the body surface humidity is greater than the preset humidity, the action of acquiring the user's sweat data, exercise data and heart rate data is executed. That is, when the body surface temperature data detected by the temperature and humidity sensor reaches 36-37.2 degrees Celsius and the body surface humidity data reaches 60%-70%, it is judged that the acquisition trigger condition corresponding to the exercise process data is met, and the motion detection module and the heart rate detection module can be started 1. The sweat detection module detects the movement process data.

In one embodiment, acquiring the user's exercise data in S400 includes: acquiring the exercise data corresponding to the user's exercise mode selection instruction. Specifically, the content of the exercise data can be determined according to the different exercise modes selected by the user. For example, in the running exercise mode, the exercise data can be the number of running steps, stride length, stride frequency, running distance, time and other data; if in the riding mode, Sports data can include data such as cadence, speed, riding distance, time, etc.; if it is in outdoor mountain climbing mode, sports data can also include data such as elevation, which can be selected according to the user's sports mode. Sure.

S600: According to the sweat data, exercise data and heart rate data, it is determined that the user's current exercise amount exceeds the standard, and then generate exercise warning information.

Specifically, exercise process data such as sweat data, exercise data, and heart rate data are relevant data collected by the user wearing a wearable device during exercise, and are used to characterize the user's exercise situation and body data. Correspondingly, the sweat data is used to represent the amount of sweat the user sweats during exercise and the content of parameters such as electrolytes in the sweat, the exercise data is used to represent the exercise consumption data of the exercise situation during the exercise, and the heart rate data is used to represent Physiological data of the user's heart rate and breathing during exercise. In addition, the exercise process data may also include user identity data representing information such as the user's height, weight, and gender.

After acquiring sweat data, exercise data, and heart rate data and other exercise process data, various exercise status indicators, such as exercise intensity, fatigue, sweating degree, and dehydration degree, can be obtained by analyzing the characteristics of the above-mentioned various data wait. Among them, the acquisition method of each exercise state index is not unique, it can be obtained by analyzing a specific calculation model, or by analyzing a preset neural network recognition model obtained from historical data training, or by matching and comparing preset thresholds. get.

Then compare the actual value corresponding to the exercise state index with the preset condition to determine whether the user's current exercise amount exceeds the standard. Among them, the preset conditions can be determined according to the expert reasoning system or the feature extraction algorithm. For example, the expert reasoning system can be determined by referring to the common knowledge rules and parameters obtained from the historical knowledge base and database. The performance value of the exercise state index under. The feature extraction algorithm can be time domain analysis, frequency domain analysis or wavelet analysis, etc. It can be understood that each exercise state index corresponds to different preset conditions and can be determined in different ways.

Further, after it is determined that the user's current exercise volume exceeds the standard, corresponding exercise warning information can be generated to warn the user to avoid physical damage caused by continuing to exercise. The exercise warning information can report the user's body surface temperature and sweating degree, and remind the user to stop exercising immediately, cool down, replenish electrolytes and water, and inform the user that if there is any physical health or feeling unwell, please call 120 for emergency help immediately.

The above exercise monitoring method obtains the user's body surface parameters, and obtains the user's sweat data, exercise data and heart rate data when the body surface parameters are greater than the preset value, and determines the user's current exercise amount based on the sweat data, exercise data and heart rate data. When the limit is exceeded, the running warning information is generated to give the user an early warning of the amount of exercise. The exercise monitoring method of the present application can analyze and calculate the data in the user's exercise process to warn the user of the current exercise physical state and prevent abnormal vital signs caused by excessive exercise reaching the limit.

In one embodiment, as shown in FIG. 3 and FIG. 4 , determining that the user's current physical activity exceeds the standard according to the sweat data, exercise data and heart rate data in S600 includes the following S610 to S650.

S610: Determine whether the user's exercise intensity satisfies a first preset condition according to the exercise data.

Specifically, exercise data represents the calorie consumption of the user during exercise, which can be used for analysis to obtain exercise intensity. Motion data can be detected by the acceleration sensor in the wearable device. During the user's motion, the X, Y, and Z axes in the acceleration sensor detect changes in the three-axis data of the motion state due to gravity sensing. Further, according to this change rule Get exercise data.

In one embodiment, determining the user's exercise intensity based on the exercise data in S610 includes: using a preset calorie calculation model to calculate and obtain real-time calorie consumption data according to the exercise data, and matching the real-time calorie consumption data to obtain exercise intensity.

Wherein, the preset calorie calculation model can be determined according to different exercise modes selected by the user. Taking the running exercise mode selected by the user as an example, the preset calorie calculation model can be determined according to the following formula: running calories (kcal)=weight (kg)×distance (km)×1.036. It can be understood that the calorie calculation model corresponding to some exercise modes may also need to use user identity data.

Specifically, when the user starts running, basic motion data such as the number of running steps, stride length, stride frequency, running distance, and time are obtained, as well as user identity data reflecting the user's height and weight information. Further, feature extraction is performed on various feature types in the exercise data and user identity data to obtain the running distance and user weight for calculating real-time calorie consumption data. Input the extracted data into the preset calorie calculation model to extract real-time calorie consumption data.

In one embodiment, the exercise intensity meeting the first preset condition in S610 includes: the exercise intensity matched according to the real-time calorie consumption data is greater than the exercise intensity exceeding a threshold.

Specifically, after the real-time calorie consumption data is obtained, it is combined with the corresponding relationship between the calorie consumption data and the exercise intensity in the historical database, and the exercise intensity corresponding to the current real-time calorie consumption data is predicted by the Kalman filter algorithm. Then, match the exercise intensity with the exercise intensity exceeding threshold to determine whether the user's exercise intensity satisfies the first preset condition.

The setting method of the exercise intensity exceeding the standard threshold may be determined according to the variation range of the exercise intensity obtained through actual calculation. For example, if the exercise intensity ranges from 0.5 to 2.0 in historical experience, and when the exercise intensity is less than 0.8, it represents a low exercise intensity; when the exercise intensity is greater than 0.8 but less than 1.2, it represents a moderate exercise intensity ; When the exercise intensity is greater than 1.2 but less than 1.5, it represents a higher intensity of exercise; when the exercise intensity is greater than 1.5, it represents the highest intensity of exercise. Correspondingly, the exercise intensity exceeding threshold can be set to 1.5 to realize whether the exercise intensity satisfies the first preset condition. That is, when the exercise intensity matched according to the real-time calorie consumption data is greater than 1.5, it is determined that the user's exercise intensity satisfies the first preset condition.

S620: Determine whether the user's fatigue level satisfies a second preset condition according to the heart rate data.

Specifically, the heart rate data characterizes the user's physiological performance during exercise, which can be used for analysis to obtain fatigue. The heart rate data can be obtained through the detection of the heart rate sensor in the wearable device. During the user's exercise, the heart rate sensor detects the change of blood flow in the skin, and extracts the optical signal representing the heart rate through time domain analysis or frequency domain analysis to obtain a certain period of time. The number of peaks of the internal PPG (Photoplethysmography, photoplethysmography) signal, and then calculate the heart rate value. Further, after the heart rate value is detected, the heart rate data may also include the HRV (Heart Rate Variability, heart rate variability) value obtained by performing continuous heartbeat interval tests based on the heart rate value, that is, the change of the difference between heartbeat cycles.

In one embodiment, determining that the user's fatigue level satisfies the second preset condition based on the heart rate data includes: performing feature extraction on the heart rate data to obtain heart rate interval data and HRV data; when the heart rate interval data is in the preset heart rate exceeding interval and HRV When the data is less than the HRV exceeding threshold, it is determined that the fatigue degree of the user satisfies the second preset condition.

Specifically, after the heart rate data is acquired, the heart rate value is extracted and compared with the heart rate interval table to obtain the heart rate interval data. Among them, the heart rate interval table can be determined according to the user's maximum heart rate value (220-age). For example, a 30-year-old healthy adult has a maximum heart rate of 220-30=190. 126 is between 60%-70% of the maximum heart rate (190×(60%-70%)=114-133), then the heart rate interval data is 60%-70%. Correspondingly, the heart rate interval within the preset heart rate exceeding range is used to indicate that the user's heart rate has reached the limit state. If the user's heart rate value has reached the preset heart rate exceeding range, continue to run until the heart rate continues to rise, which may increase sports injuries. risk. The preset heart rate exceeding range can be selected according to the experience range, for example, it is set to 80%-90%, and it can also be updated according to the data generated by the user during exercise, so that the setting of the preset heart rate exceeding range is more suitable for the user Actual exercise status.

Secondly, after the heart rate data is obtained, the continuous HRV values are extracted for time-domain statistical analysis, and SDNN, RMSSD, SDSD, SDNN/RMSSD, pNN50 and other parameters are obtained as HRV data, which are used to analyze and obtain the user's fatigue degree. The following is an explanation using SDNN parameters as HRV data as an example. The following table shows the correspondence between the parameters and the degree of pressure:

Under normal physical conditions, the SDNN parameter value is basically at 100ms. During exercise training, as the heartbeat speeds up, the SDNN parameter value will gradually decrease. When the SDNN parameter value is less than 50ms, it basically indicates that the user's psychological pressure exceeds the standard, the physical burden or mental pressure is heavy, and the probability of myocardial infarction is also relatively high. Correspondingly, the HRV exceeding threshold can be set to 50 ms based on the empirical data, and whether the obtained HRV data is less than the HRV exceeding threshold can be judged according to whether the SDNN parameter value is less than 50 ms. Of course, the HRV exceeding threshold can also be updated according to the data generated by the user during exercise, so that the setting of the HRV exceeding threshold is more in line with the user's actual exercise conditions.

Further, in this embodiment, when the obtained user's heart rate interval data is between 80% and 90% and the SDNN parameter value is less than 50ms, it is determined that the user's fatigue level meets the second preset condition, and the user can be reminded to actively release pressure accordingly. Stop exercising in time to prevent abnormal vital signs caused by excessive exercise reaching the limit.

S630: Determine whether the user's dehydration degree satisfies a third preset condition according to the sweat data.

Specifically, the sweat data is used to characterize the sweating situation of the user during exercise, including sweat volume data and sweat parameter data. Among them, the sweat parameter data represents the physiological parameters discharged by the user through sweat during exercise, and can be used for analysis to obtain the degree of dehydration. Sweat parameter data can be detected by sweat sensors in wearable devices, for example, K+ ion concentration, Na+ ion concentration, Cl- ion concentration, pH value (acid-base value), lactic acid index, sweat glucose level detected by electrodes wait.

In one embodiment, determining the degree of dehydration of the user according to the sweat data includes: performing feature extraction on the sweat data to obtain sweat parameter data related to the degree of dehydration; using the preset degree of dehydration for the sweat parameter data related to the degree of dehydration The degree of dehydration is obtained through the analysis of the recognition model; wherein, the preset recognition model of the degree of dehydration is trained according to the historical sweat parameter database.

It can be understood that among the numerous sweat parameter data detected by the sweat sensor, the sweat parameter data related to the degree of dehydration such as pH value and electrolyte can be correspondingly extracted to obtain the degree of dehydration.

Specifically, the preset dehydration degree recognition model is a recognition with a good dehydration degree recognition effect obtained after a large number of sweat parameters collected by the sweat sensor are formed into a database and input into the neural network model for training, parameter adjustment and verification. Model. Further, after actually collecting the sweat parameter data related to the dehydration degree online, input it into the preset dehydration degree recognition model, and the dehydration degree can be output through the model, that is, the recognition of the dehydration degree is realized based on the analysis of the sweat parameter data . For example, under normal physiological data, the Na+ ion concentration is around 125mmol/L, and the pH value is between 4.5-7.5. If the sweat parameter data beyond the above normal range is input into the preset dehydration degree identification model, the degree of dehydration can be analyzed, that is, the percentage of dehydration exceeding body weight. It will be appreciated that a higher percentage of dehydrated excess body weight indicates a more dehydrated user.

In one embodiment, the dehydration degree meeting the third preset condition includes: using a preset dehydration degree identification model to analyze and obtain a dehydration degree greater than a dehydration degree exceeding threshold.

Correspondingly, the dehydration threshold can be selected according to the empirical range. For example, when the body is dehydrated more than 2% of body weight, symptoms such as dry mouth and decreased urine output will appear; when dehydration exceeds 6% of body weight, symptoms may occur. Symptoms such as dizziness, panic, and irritability; when dehydration exceeds 7% to 15% of body weight, symptoms of toxic shock and loss of consciousness may occur. Correspondingly, the excessive sweating threshold can be set as dehydration exceeding 6% of body weight, and when the dehydration degree is greater than 6%, it is determined that the user's dehydration degree satisfies the third preset condition. Similarly, the dehydration degree exceeding threshold can also be updated according to the data generated by the user during exercise, so that its setting is more in line with the actual exercise status of the user.

S640: Determine whether the sweating degree of the user satisfies a fourth preset condition according to the sweat data.

Specifically, the sweat amount data in the sweat data represents the sweat amount of the user during exercise, which can be used for analysis to obtain the degree of sweating. The sweat volume data can be expressed by the actual sweat volume detected by the sweat sensor in the wearable device, or by the sweat volume per unit time. In this application, the amount of sweat per unit time is used as the sweat amount data, and the user analyzes the sweating degree.

In one embodiment, determining that the user's sweating degree satisfies the fourth preset condition based on the sweat data includes: performing feature extraction on the sweat data to obtain the user's sweat volume data; when the sweat volume data is greater than the sweat volume exceeding threshold , it is determined that the sweating degree of the user satisfies the fourth preset condition.

Specifically, after the sweat volume data of the user is extracted from the sweat data, the sweat volume data can be compared with the sweat volume exceeding threshold to determine whether the user's sweat volume satisfies the fourth preset condition. The sweating volume exceeding the threshold is used to indicate that the user is already sweating too much, and actions such as cooling down are required to reduce the sweating volume.

The threshold of excessive sweating can be selected according to the empirical range. For example, the normal physiological sweating range is 0.15 μl/min to 3 μl/min. If it exceeds this range, it may indicate that the sweating volume does not meet the normal physiological conditions. Correspondingly, the sweat volume exceeding threshold can be set to 3 μl/min, and when the sweat volume data is greater than 3 μl/min, it is determined that the user's sweating degree satisfies the fourth preset condition. Similarly, the threshold for excessive sweating can also be updated according to the data generated by the user during exercise, so that its setting is more in line with the actual exercise status of the user.

S650: Based on the exercise intensity meeting the first preset condition, the fatigue degree meeting the second preset condition, the dehydration degree meeting the third preset condition, and the sweating degree meeting the fourth preset condition, determine that the user's current physical activity exceeds the standard. It can be understood that this embodiment adopts a joint judgment method to determine whether the user's current physical activity exceeds the standard, that is, it is determined that the user's current physical activity exceeds the standard only after all the user's exercise state indicators meet the corresponding preset conditions. Of course, on the premise of not departing from the above-mentioned judgment concept, judging and determining that the user's current exercise amount exceeds the standard according to any deformation mode in which one or more exercise state indicators meet the preset conditions should fall within the scope of protection of this application.

In addition, in other embodiments, before determining that the user's current exercise amount exceeds the standard based on sweat data, exercise data, and heart rate data, and generating exercise warning information, after determining the user's current exercise amount based on sweat data, exercise data, and heart rate data, according to the user's Multi-level exercise reminder for the current amount of exercise. For example, when it is determined that the user is currently exercising at a moderate intensity based on sweat data, exercise data, and heart rate data, the first exercise reminder message can be generated to report the user's body surface temperature and sweating level, and remind to drink 200-300 ml of water every 20 minutes , take a proper rest for 10 minutes, and do physical recovery. It can also generate a second exercise reminder message when it is determined that the user's current exercise is at a relatively high intensity based on sweat data, exercise data, and heart rate data, reporting body surface temperature and sweating level, and reminding to immediately carry out exercise to cool down the body surface and replenish electrolytes Drink and water, rest for 20-30 minutes to recover.

Among them, the method of determining that the user’s current physical activity is at a moderate exercise intensity, and the method of determining that the user’s current physical activity is at a relatively high exercise intensity can be set by referring to the above-mentioned method of determining that the user’s current physical activity exceeds the standard, and the corresponding preset conditions are set according to the above exercise state indicators The experience value range can be adjusted. It can be understood that when the user's current exercise amount is at a medium intensity, it is smaller than when the user's current exercise amount is at a high intensity, and at the same time, when the user's current exercise amount is at a high intensity, it is smaller than the user's current exercise amount that exceeds the standard.

In one embodiment, after acquiring the sweat data of the user in S400, the method further includes: performing feature extraction on the sweat data to obtain sweat parameter data related to uric acid level; When the uric acid is in the state, a high uric acid reminder message will be generated.

Specifically, the sweat data detected by the sweat sensor includes a variety of sweat parameter data, and sweat parameter data related to uric acid levels, such as uric acid and tyrosine, can be correspondingly extracted to determine whether the user is currently in a state of high uric acid.

Further, it can be judged whether the user is in a high uric acid state according to the sweat parameter data related to the uric acid level compared with the high uric acid threshold. According to the data, the normal limit of human blood uric acid is 420 μMol (adult male) and 360 μMol (adult female), while the normal limit of sweat uric acid is 40 μMol. According to statistics, the correlation between the two reaches 0.864. Therefore, it can be judged whether the user is in a high uric acid state by the uric acid value in the sweat, and the high uric acid threshold is set to 40 μMol, that is, when the uric acid value in the sweat parameter data is less than 40 μMol, it is judged to be in a high uric acid state. Of course, the high uric acid threshold can also be set as the normal limit of tyrosine, and it can be judged that it is in a state of high uric acid because the tyrosine in the sweat parameter data is less than the normal limit of tyrosine. It can also be judged jointly based on the uric acid and tyrosine parameters in the sweat parameter data. When the tyrosine is lower than the normal limit of tyrosine, and the uric acid value is lower than the normal limit of sweat uric acid, it is judged to be in a hyperuric acid state.

The high uric acid reminder information is used to give a high uric acid early warning to the user when it is detected that the user is in a high uric acid state based on sweat. High uric acid reminder information can include uric acid data and corresponding health guidance data, etc. The way of early warning can be to display or broadcast the uric acid data and health guidance data through the interactive module of the wearable device to remind users to keep exercising and eat a low-purine diet Habit. In addition, the user's uric acid data and corresponding health guidance data can also be stored for follow-up continuous tracking reports and to check the user's uric acid improvement status.

In this embodiment, the user's uric acid level can be identified through the sweat parameter data related to the uric acid level, and can be tracked for a long time, which can play a good guiding role in improving human health indicators.

In one embodiment, before acquiring the user's body surface parameters in S200, the method further includes: detecting whether the user is in the standard wearing state; when it is determined that the user is in the standard wearing state, entering the step of acquiring the user's body surface parameters. Specifically, the wearing detection module in the wearable device can judge whether the user is in the wearing state according to whether the contact is detected. Furthermore, it can be judged whether the user is in the standard wearing state by comparing the detected actual heart rate data with the standard heart rate data of the user in the standard wearing state. When it is determined that the user is in the standard wearing state, the user's body surface parameters are acquired in S200. Among them, the wearing detection module of the wearable device can be realized by using a capacitive sensor.

It should be understood that although the steps in the flow charts involved in the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flow charts involved in the above-mentioned embodiments may include multiple steps or stages, and these steps or stages are not necessarily executed at the same time, but may be performed at different times For execution, the execution order of these steps or stages is not necessarily performed sequentially, but may be executed in turn or alternately with other steps or at least a part of steps or stages in other steps.

Based on the same inventive concept, an embodiment of the present application further provides a motion monitoring device for implementing the above-mentioned motion monitoring method. The solution to the problem provided by the device is similar to the implementation described in the above method, so the specific limitations in one or more embodiments of the motion monitoring device provided below can refer to the above definition of the motion monitoring method, I won't repeat them here.

In one embodiment, as shown in FIG. 5 , a motion monitoring device is provided, including: a body surface parameter acquisition module 510, a data acquisition module 520, and a motion early warning module 530, wherein:

A body surface parameter acquisition module 510, configured to acquire the user's body surface parameters;

The data acquisition module 520 is used to determine that the body surface parameter is greater than the preset value, then acquire the user's sweat data, exercise data and heart rate data;

The exercise warning module 530 is configured to determine that the user's current physical activity exceeds the standard according to the sweat data, exercise data and heart rate data, and then generate exercise warning information.

In this embodiment, the user's body surface parameters are obtained, and when the body surface parameters are greater than the preset value, the user's sweat data, exercise data and heart rate data are obtained, and the user's current condition is determined according to the sweat data, exercise data and heart rate data. When the amount of exercise exceeds the standard, an operation warning message is generated to give the user an early warning of the amount of exercise. The exercise monitoring method of the present application can analyze and calculate the data in the user's exercise process to warn the user of the current exercise physical state and prevent abnormal vital signs caused by excessive exercise reaching the limit.

In one embodiment, the body surface parameters include body surface temperature and body surface humidity, and the preset values include preset temperature and preset humidity; the data acquisition module 520 is also used to confirm that the body surface temperature is greater than the preset temperature and the body surface humidity If the humidity is greater than the preset humidity, the sweat data, exercise data and heart rate data of the user will be obtained; the value range of the preset temperature is 36°C-37.2°C, and the value range of the preset humidity is 60%-70%.

In one embodiment, the exercise warning module 530 is also used to determine whether the user's exercise intensity meets the first preset condition according to the exercise data; determine whether the user's fatigue level meets the second preset condition according to the heart rate data; Whether the degree of dehydration of the user meets the third preset condition; determine whether the degree of sweating of the user meets the fourth preset condition according to the sweat data; satisfy the first preset condition based on the exercise intensity, the degree of fatigue meets the second preset condition, and the degree of dehydration If the third preset condition is met and the degree of sweating meets the fourth preset condition, it is determined that the user's current physical activity exceeds the standard.

In one embodiment, the exercise warning module 530 is also used to calculate and obtain real-time calorie consumption data according to the exercise data using a preset calorie calculation model, and obtain exercise intensity according to the real-time calorie consumption data.

In one embodiment, the exercise warning module 530 is further configured to determine that the user's exercise intensity satisfies the first preset condition when the exercise intensity matched according to the real-time calorie consumption data is greater than the exercise intensity exceeding threshold.

In one embodiment, the exercise early warning module 530 is also used to extract features from the heart rate data to obtain heart rate interval data and HRV data; and when the heart rate interval data is in the preset heart rate exceeding interval and the HRV data is less than the HRV exceeding threshold The fatigue degree of the user satisfies the second preset condition.

In one embodiment, the exercise warning module 530 is also used to extract features from the sweat data to obtain sweat parameter data related to the degree of dehydration; analyze the sweat parameter data related to the degree of dehydration using a preset recognition model for the degree of dehydration The dehydration degree is obtained; wherein, the preset dehydration degree identification model is obtained according to the historical sweat parameter database training.

In one embodiment, the exercise warning module 530 is further configured to determine that the user's dehydration degree satisfies the third preset condition when the dehydration degree obtained through analysis using the preset dehydration degree identification model is greater than the dehydration degree exceeding threshold.

In one embodiment, the exercise warning module 530 is also used to extract features from the sweat data to obtain the user's sweat volume data; when the sweat volume data is greater than the sweat volume exceeding threshold, determine that the user's sweat level meets the Four preset conditions.

In one embodiment, the device also includes a uric acid early warning module, which is used to extract features from the sweat data to obtain sweat parameter data related to the uric acid level; when it is judged to be in a state of high uric acid based on the sweat parameter data related to the uric acid level, generate High uric acid reminder information.

In one embodiment, the device further includes a wearing detection module for detecting whether the user is in the standard wearing state; when it is determined that the user is in the standard wearing state, call the body surface parameter acquisition module 510 to obtain the user's body surface parameters.

In one embodiment, the data acquisition module 520 is further configured to acquire exercise data corresponding to the user's exercise mode selection instruction.

Each module in the above-mentioned motion monitoring device can be fully or partially realized by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.

In one embodiment, as shown in Figure 6, a wearable device is provided, including a processor and a motion detection module connected to the processor, a heart rate detection module, a sweat detection module, a temperature and humidity data acquisition module, and an interaction module, The processor is configured to realize the motion monitoring of the wearable device wearer according to the above motion monitoring method.

Specifically, the sweat detection module is used to detect the physiological signs of human sweat, focusing on the analysis of secretions such as inorganic ions, organic molecules, amino acids, hormones, proteins, and polypeptides in human sweat, such as detecting K+, Na+, Cl- and pH values of sweat . Through the monitoring of sweat composition, the body's electrolyte imbalance, lactic acid index, sweat glucose level and dehydration status can be analyzed. The temperature and humidity data acquisition module is a temperature and humidity meter, which is used to detect the user's skin temperature and skin humidity. The motion detection module is Gsensor (acceleration sensor), which is used to detect the user's motion status and calorie consumption. The processor is a Bluetooth MCU, which is used to collect the detection data obtained by each sensor for data fusion processing, and upload the relevant detection data and the generated exercise reminder information to the control terminal or the cloud, which is convenient for users to manage their health status.

Further, the interaction module may include a display device and an input device, and the display device may be a display screen, a projection device or a virtual reality imaging device. The input device may be a touch layer covered on the display screen, or a button, a trackball or a touch pad provided on the casing of the computer equipment, or an external keyboard, touch pad or mouse. In this embodiment, the interaction module can also include a speaker and a microphone, which can realize the output of exercise reminder information, can also perform music playback and call recording, and can also realize functions such as voice assistant interaction.

In this embodiment, the user's body surface parameters are obtained, and when the body surface parameters are greater than the preset value, the user's sweat data, exercise data and heart rate data are obtained, and the user's current condition is determined according to the sweat data, exercise data and heart rate data. When the amount of exercise exceeds the standard, an operation warning message is generated to give the user an early warning of the amount of exercise. The exercise monitoring method of the present application can analyze and calculate the data in the user's exercise process to warn the user of the current exercise physical state and prevent abnormal vital signs caused by excessive exercise reaching the limit.

In another embodiment, the wearable device further includes a wearing detection module connected to the processor, and a capacitive sensor is selected to detect whether the wearable device is in a standard wearing state.

The following uses the flow of FIG. 7 as an example to explain the flow of the wearable device implementing motion monitoring. In this embodiment, the wearable device is smart glasses as an example for explanation.

Step 1: The smart glasses are worn and turned on, and the glasses start the wearing detection timing. At the same time, the user can turn on the sweat detection mode on the mobile phone app or the default sweat detection mode in the wearing state.

Step 2: When the user selects different exercise modes, Gsensor records the exercise data in various exercise modes (such as running, cycling, outdoor mountain climbing), such as the number of steps, distance, etc.; the heart rate module starts real-time monitoring of heart rate, pressure and other data, At the same time, the temperature and humidity module of the glasses regularly detects data such as skin surface temperature and humidity. When it detects that the human skin temperature reaches 36-37.2 degrees Celsius and the humidity exceeds 65%, it means that the sweating is obvious during exercise, and the MCU starts the sweat sensor to collect and detect sweat physiological data. At the same time, the trend of heart rate and pressure value data is obtained in real time, and the MCU fusion algorithm is used to analyze and judge basic data such as Gsensor motion data, heart rate data, sweat electrolytes, and ph value.

Step 3: The processor calculates the human body's exercise conditions, such as exercise intensity, fatigue, sweat volatile electrolyte dehydration level, and makes a correlation with basic data such as human body exercise calorie consumption, body electrolyte sodium ions, lactic acid index, heart rate, and pressure Present, prompt or warn the user's current physical fitness status.

Step 4: When the human body continues high-load exercise, the amount of sweat and the sweating time continue to increase. When the sweat sensor detects that the concentration of sweat lactic acid and the pH value exceed the normal threshold of the human body, it will actively make a voice alarm based on the exercise heart rate data to remind The human body stops exercising and takes a soothing rest to avoid the consumption of physical energy due to strenuous exercise.

Step 5: The smart glasses report each exercise data and sweat detection data of the human body to the mobile app, so that users can query exercise fitness and related health indicators, and reasonably arrange the intensity and duration of exercise.

Step 6: The sweat detection sensor can also detect uric acid and tyrosine in sweat, and will guide people with high uric acid to reasonably control the intake of high-purine diet, and conduct sweat detection for a long time, gradually improve the uric acid and tyrosine indicators, and improve the human body Health indicators play a good guiding role.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the above method are realized.

It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all It is information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the computer programs can be stored in a non-volatile computer-readable memory In the medium, when the computer program is executed, it may include the processes of the embodiments of the above-mentioned methods. Wherein, any reference to storage, database or other media used in the various embodiments provided in the present application may include at least one of non-volatile and volatile storage. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive variable memory (ReRAM), magnetic variable memory (Magnetoresistive Random Access Memory, MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (Phase Change Memory, PCM), graphene memory, etc. The volatile memory may include random access memory (Random Access Memory, RAM) or external cache memory. As an illustration and not a limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM). The databases involved in the various embodiments provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a blockchain-based distributed database, etc., but is not limited thereto. The processors involved in the various embodiments provided by this application can be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., and are not limited to this.

The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be It is considered to be within the range described in this specification.

The above-mentioned embodiments only express several implementation modes of the present application, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be determined by the appended claims.

Claims (16)

1. A motion monitoring method applied to a wearable device, wherein the method comprises: Obtain the user's body surface parameters; If it is determined that the body surface parameter is greater than the preset value, the sweat data, exercise data and heart rate data of the user are obtained; According to the sweat data, the exercise data and the heart rate data, it is determined that the user's current physical activity exceeds the standard, and then an exercise warning message is generated. 2. The method according to claim 1, wherein the body surface parameters include body surface temperature and body surface humidity, and the preset values include preset temperature and preset humidity; the determination of the body surface If the parameter is greater than the preset value, the user's sweat data, exercise data and heart rate data will be obtained, including: After confirming that the body surface temperature is higher than the preset temperature and the body surface humidity is higher than the preset humidity, the user's sweat data, exercise data and heart rate data are acquired. 3 . The method according to claim 2 , wherein the preset temperature ranges from 36° C. to 37.2° C., and the preset humidity ranges from 60% to 70%. 4 . 4. The method according to claim 1, characterized in that, according to the sweat data, the exercise data and the heart rate data, it is determined that the user's current physical activity exceeds the standard, including: determining whether the user's exercise intensity satisfies a first preset condition according to the exercise data; determining whether the user's fatigue level satisfies a second preset condition according to the heart rate data; determining whether the degree of dehydration of the user satisfies a third preset condition according to the sweat data; determining whether the user's sweating degree satisfies a fourth preset condition according to the sweat data; Based on the exercise intensity satisfying the first preset condition, the fatigue degree satisfying the second preset condition, the dehydration degree satisfying the third preset condition, and the sweating degree satisfying the fourth If the preset condition is met, it is determined that the user's current physical activity exceeds the standard. 5. The method according to claim 4, wherein the determining the user's exercise intensity according to the exercise data comprises: According to the exercise data, the preset calorie calculation model is used to calculate the real-time calorie consumption data, and the exercise intensity is matched according to the real-time calorie consumption data. 6. The method according to claim 5, wherein the exercise intensity meeting the first preset condition comprises: the exercise intensity matched according to the real-time calorie consumption data is greater than an exercise intensity exceeding threshold. 7. The method according to claim 4, wherein determining that the user's fatigue level satisfies a second preset condition based on the heart rate data includes: performing feature extraction on the heart rate data to obtain heart rate interval data and HRV data; When the heart rate interval data is in the preset heart rate exceeding interval and the HRV data is less than the HRV exceeding threshold, it is determined that the fatigue degree of the user satisfies the second preset condition. 8. The method according to claim 4, wherein the determining the degree of dehydration of the user according to the sweat data comprises: performing feature extraction on the sweat data to obtain sweat parameter data related to the degree of dehydration; The dehydration degree is obtained by analyzing the sweat parameter data related to the dehydration degree by using a preset dehydration degree recognition model; wherein, the preset dehydration degree recognition model is trained according to a historical sweat parameter database. 9 . The method according to claim 8 , wherein the dehydration degree meeting the third preset condition comprises: the dehydration degree obtained through analysis using a preset dehydration degree identification model is greater than a dehydration degree exceeding threshold. 10. The method according to claim 4, wherein determining that the user's sweating degree satisfies a fourth preset condition based on the sweat data includes: Carrying out feature extraction on the sweat data to obtain the sweat volume data of the user; When the sweat volume data is greater than the sweat volume exceeding threshold, it is determined that the user's sweat level satisfies the fourth preset condition. 11. The method according to any one of claims 1-10, characterized in that, after said acquiring the user's sweat data, said method further comprises: performing feature extraction on the sweat data to obtain sweat parameter data related to uric acid level; When it is judged to be in a high uric acid state according to the sweat parameter data related to the uric acid level, a high uric acid reminder message is generated. 12. The method according to any one of claims 1-10, characterized in that, before the acquisition of the user's body surface parameters, the method further comprises: Detect whether the user is in the standard wearing state; When it is determined that the user is in the standard wearing state, enter the step of acquiring the user's body surface parameters. 13. The method according to any one of claims 1-10, wherein obtaining the user's motion data comprises: The exercise data corresponding to the user's exercise mode selection instruction is acquired. 14. A motion monitoring device applied to a wearable device, characterized in that the device comprises: A body surface parameter acquisition module, configured to acquire the user's body surface parameters; A data acquisition module, configured to determine that the body surface parameter is greater than a preset value, then acquire the user's sweat data, exercise data and heart rate data; The exercise early warning module is used to determine that the user's current physical activity exceeds the standard according to the sweat data, the exercise data and the heart rate data, and then generate exercise early warning information. 15. A wearable device, characterized in that it includes a processor and a motion detection module connected to the processor, a heart rate detection module, a sweat detection module, a temperature and humidity data acquisition module, and an interaction module, and the processor is used to The method according to any one of claims 1 to 10 realizes the motion monitoring of the user. 16. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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