CN115670444A - Motion monitoring method and device, wearable device and storage medium - Google Patents
Motion monitoring method and device, wearable device and storage medium Download PDFInfo
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Abstract
The application relates to a motion monitoring method, a motion monitoring device, wearable equipment and a storage medium, wherein body surface parameters of a user are obtained; if the body surface parameter is determined to be larger than the preset value, sweat data, movement data and heart rate data of the user are obtained; and determining that the current exercise amount of the user exceeds the standard according to the sweat data, the exercise data and the heart rate data, and generating exercise early warning information. The sweat data, the exercise data and the heart rate data of the user are acquired only when the body surface parameters are larger than the preset value, so that the purpose of reducing power consumption is achieved. Meanwhile, analysis and calculation can be carried out according to data in the user movement process, when the fact that the current movement amount of the user exceeds the standard is determined according to sweat data, movement data and heart rate data, operation early warning information is generated to early warn the physical performance state of the current movement of the user, and abnormal vital signs caused by excessive movement reaching the limit are prevented.
Description
Technical Field
The present application relates to the field of electrical data processing technologies, and in particular, to a motion monitoring method and apparatus, a wearable device, and a storage medium.
Background
In modern fast-paced life, people have great working and learning pressure and are limited by movement space and time, so that various health problems occur. Along with the progress of science and technology and the continuous improvement of standard of living, people more and more pay more attention to self physical and mental health, also can wear wearable equipment when daily motion for monitor some basic physiological parameters.
However, wearable devices on the market at present are generally only used for static data display for users after parameter detection in daily life and exercise processes, present basic states of users are reflected, and reasonable exercise suggestions cannot be provided for users according to parameter detection in the exercise processes.
Disclosure of Invention
Therefore, it is necessary to provide a motion monitoring method, a motion monitoring device, a wearable device, and a storage medium for solving the above technical problems, which can perform analysis and calculation according to motion data, pre-warn a current physical performance state of a user, and prevent abnormal vital signs caused by excessive motion reaching a limit.
In a first aspect, the present application provides a motion monitoring method applied to a wearable device, the method including:
acquiring body surface parameters of a user;
if the body surface parameter is determined to be larger than a preset value, sweat data, movement data and heart rate data of the user are obtained;
and determining that the current amount of exercise of the user exceeds the standard according to the sweat data, the exercise data and the heart rate data, and generating exercise early warning information.
In a second aspect, the present application further provides a motion monitoring device applied to a wearable device, the device including:
the body surface parameter acquisition module is used for acquiring body surface parameters of a user;
the data acquisition module is used for acquiring sweat data, motion data and heart rate data of the user when the body surface parameter is determined to be larger than a preset value;
and the motion early warning module is used for determining that the current motion amount of the user exceeds the standard according to the sweat data, the motion data and the heart rate data, and generating motion early warning information.
In a third aspect, the present application further provides a wearable device, including a processor, and a motion detection module, a heart rate detection module, a sweat detection module, a temperature and humidity data acquisition module, and an interaction module connected to the processor, where the processor is configured to implement motion monitoring of a user according to the above method.
In a fourth aspect, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
According to the motion monitoring method, the motion monitoring device, the wearable device and the storage medium, the body surface parameters of the user are obtained, sweat data, motion data and heart rate data of the user are obtained when the body surface parameters are larger than the preset value, and operation early warning information is generated to perform motion amount early warning on the user when the current motion amount of the user is determined to be over the standard according to the sweat data, the motion data and the heart rate data. According to the exercise monitoring method, analysis and calculation can be carried out according to data in the exercise process of the user, the physical performance state of the current exercise of the user is early warned, and abnormal vital signs caused by excessive exercise reaching the limit are prevented.
Drawings
FIG. 1 is a diagram of an exemplary embodiment of a motion monitoring method;
FIG. 2 is a schematic flow chart diagram of a motion monitoring method in one embodiment;
FIG. 3 is a schematic diagram of a process for obtaining a motion state indicator according to an embodiment;
FIG. 4 is a schematic diagram of a process for obtaining a motion state indicator according to another embodiment;
FIG. 5 is a block diagram of an embodiment of a motion monitoring device;
FIG. 6 is a system block diagram schematic of a wearable device in one embodiment;
fig. 7 is a schematic flow diagram of a wearable device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The motion monitoring method provided by the embodiment of the application can be applied to various wearable devices, such as a smart watch, a smart bracelet, a head-mounted device and the like. As shown in fig. 1, the wearable device applied may include a processor 101 and a data detection and collection module connected to the processor 101, where the data detection and collection module includes a motion detection module 102, a heart rate detection module 103, a sweat detection module 104, and a temperature and humidity data collection module 105, and in addition, the wearable device may further include an interaction module 106. The processor 101 is communicated with the data detection and acquisition module to acquire the body surface parameters of the user; if the body surface parameter is determined to be larger than the preset value, sweat data, movement data and heart rate data of the user are obtained; determining that the current amount of exercise of the user exceeds the standard according to the sweat data, the exercise data and the heart rate data, and generating exercise early warning information; the motion early warning information is used for carrying out motion amount early warning on the wearable device wearer through the interaction module 106, and motion monitoring on the wearable device wearer is achieved.
In one embodiment, as shown in fig. 2, an operation monitoring method is provided, which is described by way of example as applied to the processor 101 in fig. 1, and includes the following steps S200 to S600, where:
s200: and acquiring the body surface parameters of the user.
Because the user just begins the motion, the condition that the amount of exercise exceeds standard can not appear to great probability, sweat data can just produce after motion lasts a period of time simultaneously. Therefore, the body surface parameters of the user can be acquired for judging the sweating state of the user, and after sweating is detected according to the body surface parameters of the user, acquisition of exercise process data and over-standard determination of the amount of exercise are started, so that the purpose of reducing power consumption is achieved. Specifically, the body surface parameter for determining the sweating state of the user may be one or more of a body surface temperature, a body surface humidity, and a body surface sweating amount of the user. For example, in the embodiment, the body surface parameters include a body surface temperature and a body surface humidity of the user.
S400: and if the body surface parameter is determined to be larger than the preset value, sweat data, movement data and heart rate data of the user are obtained.
Specifically, after the body surface parameters of the user are obtained, whether to perform the acquisition of the subsequent motion process data can be judged according to the preset values corresponding to the body surface parameters. The default values that the body surface parameters correspond can be set according to the types of the body surface parameters and the specific parameter requirements, for example, under the condition that the body surface parameters of the embodiment comprise body surface temperature and body surface humidity, the default values also comprise preset temperature and preset humidity. Correspondingly, the values of the preset temperature and the preset humidity can be selected in an empirical range, and can also be updated according to data generated by a user in the exercise process, so that the setting of the preset value is more in line with the exercise condition of the user. In one embodiment, the preset temperature is in a range of 36-37.2 ℃ and the preset humidity is in a range of 60-70%.
It can be understood that when the body surface parameters include a plurality of judgment bases, the judgment mode is not unique, sweat data, motion data and heart rate data of the user can be acquired if one body surface parameter is larger than a corresponding preset value, or sweat data, motion data and heart rate data of the user can be acquired if all body surface parameters are larger than corresponding preset values. In one embodiment, determining that the body surface parameter is greater than the predetermined value, obtaining sweat data, motion data, and heart rate data of the user comprises: and if the body surface temperature is higher than the preset temperature and the body surface humidity is higher than the preset humidity, sweat data, motion data and heart rate data of the user are acquired. In this embodiment, when confirming that the body surface temperature is greater than preset temperature, confirm that body surface humidity is greater than preset humidity, then carry out the action of acquireing user's sweat data, motion data and heart rate data. Namely, when the body surface temperature data detected by the temperature and humidity sensor reaches 36-37.2 ℃ and the body surface humidity data reaches 60% -70%, the acquisition triggering condition corresponding to the motion process data is judged to be met, and the motion detection module, the heart rate detection module and the sweat detection module can be started to detect to obtain the motion process data.
In one embodiment, the acquiring of the motion data of the user in S400 includes: and acquiring the motion data corresponding to the motion mode selection instruction of the user. Specifically, the content of the exercise data may be determined according to different exercise modes selected by the user, for example, in a running exercise mode, the exercise data may be data of running steps, strides, step frequencies, running distances, time and the like; if the bicycle is in the riding mode, the motion data can be data such as pedaling frequency, speed per hour, riding distance, time and the like; if the user is in the outdoor mountain climbing mode, the motion data can also comprise data such as elevation altitude and the like, and can be determined according to different motion modes selected by a motion mode selection instruction of the user.
S600: and determining that the current exercise amount of the user exceeds the standard according to the sweat data, the exercise data and the heart rate data, and generating exercise early warning information.
Specifically, the motion process data such as sweat data, motion data and heart rate data are the relevant data that the user gathered through wearing wearable equipment in the motion process, and are used for representing the motion condition and the body data of the user. Correspondingly, the sweat data are used for representing the sweat amount of the user after sweating in the exercise process, the content conditions of parameters such as electrolytes in the sweat and the like, the exercise data are used for representing the exercise consumption data of the exercise condition in the exercise process, and the heart rate data are used for representing the physiological data of the conditions such as the heart rate, the respiration and the like of the user in the exercise process. In addition, the athletic performance data may also include user identification data that characterizes information about the user's height, weight, and gender.
After obtaining the exercise process data such as sweat data, exercise data, heart rate data, and the like, the various data may be subjected to feature analysis to obtain various exercise state indexes, such as exercise intensity, fatigue, perspiration degree, dehydration degree, and the like. The motion state indexes are acquired in a non-unique manner, and may be acquired through analysis according to a specific calculation model, or through analysis according to a preset neural network recognition model obtained through training of historical data, or through matching and comparison according to a preset threshold.
And comparing the actual value corresponding to the motion state index with a preset condition to determine whether the current motion amount of the user exceeds the standard or not. The preset condition may be determined according to an expert inference system or a feature extraction algorithm, for example, the expert inference system may determine that the preset condition is obtained by setting by referring to common knowledge rules and parameters obtained from a historical knowledge base and a database, and represents the exercise state index representation values of the user in different physical states. The feature extraction algorithm may be a time domain analysis, a frequency domain analysis, or a wavelet analysis, etc. It can be understood that each motion state index corresponds to different preset conditions respectively, and can be determined according to different modes.
Further, after the current motion amount of the user exceeds the standard, corresponding motion early warning information can be generated and used for early warning the user, and body damage caused by continuous motion is avoided. The sport early warning information can be used for reporting the body surface temperature and the sweating degree of the user, reminding the user to stop sports immediately, reducing the temperature, supplementing electrolyte and moisture, and informing that if the body is healthy or uncomfortable, the user asks to immediately dial 120 for emergency help.
According to the motion monitoring method, the body surface parameters of the user are obtained, the sweat data, the motion data and the heart rate data of the user are obtained when the body surface parameters are larger than the preset value, and the operation early warning information is generated to early warn the amount of motion of the user when the current amount of motion of the user is determined to be over standard according to the sweat data, the motion data and the heart rate data. According to the motion monitoring method, analysis and calculation can be carried out according to data in the motion process of the user, the physical performance state of the current motion of the user is early warned, and abnormal vital signs caused by excessive motion reaching the limit are prevented.
In one embodiment, as shown in fig. 3 and 4, the step of determining that the current amount of exercise of the user is over-standard according to the sweat data, the exercise data and the heart rate data in step S600 includes the following steps S610 to S650.
S610: and determining whether the exercise intensity of the user meets a first preset condition according to the exercise data.
Specifically, the exercise data represents the calorie consumption of the user during exercise, and can be used for analyzing the exercise intensity. The motion data can be obtained by detecting an acceleration sensor in the wearable device, and in the motion process of a user, three axes X, Y and Z in the acceleration sensor detect the three-axis data change of the motion state due to gravity sensing, so that the motion data can be further obtained according to the change rule.
In one embodiment, the determining the exercise intensity of the user according to the exercise data of S610 includes: and calculating by adopting a preset calorie calculation model according to the exercise data to obtain real-time calorie consumption data, and matching according to 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 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) = body weight (kg) × distance (kilometers) × 1.036. It will be appreciated that the calorie calculation model corresponding to some exercise patterns may also require user identity data.
Specifically, after the user starts running, basic exercise data such as running step number, stride, step frequency, running distance and time and the like are obtained, and user identity data reflecting height and weight information of the user are obtained. Further, the exercise data and a plurality of characteristic types in the user identity data are subjected to characteristic extraction, and running distance and user weight related to real-time calorie consumption data are obtained and calculated. And inputting the extracted data into a preset calorie calculation model, so as to extract and obtain real-time calorie consumption data.
In one embodiment, the exercise intensity of S610 satisfying the first preset condition includes: the exercise intensity obtained according to the real-time calorie consumption data matching is larger than the exercise intensity standard exceeding threshold value.
Specifically, after the real-time calorie consumption data is obtained, the real-time calorie consumption data is combined with the calorie consumption data in the historical database and the corresponding relation of the exercise intensity, and the exercise intensity corresponding to the current real-time calorie consumption data is predicted through a Kalman filter algorithm. Then, the exercise intensity is matched with an exercise intensity standard exceeding threshold value, and whether the exercise intensity of the user meets a first preset condition or not is determined.
The setting mode of the motion intensity overproof threshold can be determined according to the change range of the motion intensity obtained by actual calculation. For example, if the exercise intensity varies from 0.5 to 2.0 in the historical experience, and when the exercise intensity is less than 0.8, the exercise intensity is characterized to be lower; when the exercise intensity is greater than 0.8 but less than 1.2, the exercise intensity is characterized to be at a moderate level; when the exercise intensity is greater than 1.2 but less than 1.5, characterizing the exercise intensity at a higher intensity; when the exercise intensity is greater than 1.5, the exercise intensity is characterized as the highest intensity. Correspondingly, the threshold value of the exceeding exercise intensity can be set to 1.5 to realize the judgment of whether the exercise intensity meets the first preset condition. That is, when the exercise intensity obtained according to the real-time calorie consumption data matching is greater than 1.5, it is determined that the exercise intensity of the user satisfies the first preset condition.
S620: and determining whether the fatigue degree of the user meets a second preset condition or not according to the heart rate data.
Specifically, the heart rate data represents the physiological performance of the user during exercise, and can be used for analyzing fatigue. The heart rate data can be obtained by detecting a heart rate sensor in wearable equipment, in the motion process of a user, the heart rate sensor detects the change of skin blood flow, an optical signal representing the heart rate is extracted through time domain analysis or frequency domain analysis, the number of wave peaks of a PPG (Photoplethysmography) signal in a certain time is obtained, and then a heart rate value is obtained through calculation. Further, after the Heart Rate value is detected, the Heart Rate data may further include an HRV (Heart Rate variability) value obtained by performing consecutive Heart beat interval tests based on the Heart Rate value, that is, a variation of a difference of successive Heart beat periods.
In one embodiment, determining that the degree of fatigue of the user satisfies a second preset condition based on the heart rate data comprises: performing feature extraction on the heart rate data to obtain heart rate interval data and HRV data; and when the heart rate interval data is in a preset heart rate exceeding interval and the HRV data is smaller than the HRV exceeding threshold value, determining that the fatigue degree of the user meets a second preset condition.
Specifically, after the heart rate data is obtained, the heart rate value is extracted and compared with the heart rate interval table to obtain heart rate interval data. Wherein the heart rate interval table may be determined according to the maximum heart rate value (220-age) of the user, for example, a healthy adult of 30 years old with a maximum heart rate of 220-30=190, the heart rate is detected to be 126 when the user performs a running exercise, and the heart rate interval data is 60% -70% because 126 is between 60% -70% (190 × (60% -70%) = 114-133) of the maximum heart rate. Correspondingly, the preset heart rate exceeding interval is used for representing the heart rate interval when the heart rate of the user reaches the limit state, if the heart rate value of the user reaches the preset heart rate exceeding interval, the user still continues to operate until the heart rate continuously rises, and the risk of sports injury is possibly increased. The preset heart rate exceeding interval can be selected according to an experience range, for example, the preset heart rate exceeding interval is set to be 80% -90%, and the preset heart rate exceeding interval can also be updated according to data generated by a user in the exercise process, so that the setting of the preset heart rate exceeding interval is more consistent with the actual exercise condition of the user.
Secondly, after the heart rate data are obtained, continuous HRV values are extracted and subjected to time domain statistical analysis, and parameters such as SDNN, RMSSD, SDSD, SDNN/RMSSD, pNN50 and the like are obtained and used as HRV data for analyzing and obtaining the fatigue degree of the user. The following is an example of the SDNN parameter as HRV data, and the following table is a table of the parameter versus the degree of stress:
in a normal state of the body, the value of the SDNN parameter is basically 100ms, and in the exercise training process, the value of the SDNN parameter is gradually reduced along with the acceleration of the heartbeat. And when the SDNN parameter value is less than 50ms, the psychological pressure value of the user is basically over standard, the physical burden or the mental pressure is large, and the probability of myocardial infarction is also large. Correspondingly, the HRV standard exceeding threshold value can be set to be 50ms according to the empirical data, and whether the obtained HRV data is smaller than the HRV standard exceeding threshold value is judged according to whether the SDNN parameter value is smaller than 50 ms. Of course, the HRV out-of-limit threshold may also be updated according to data generated by the user during the exercise process, so that the setting of the HRV out-of-limit threshold more conforms to the actual exercise condition of the user.
Further, in this embodiment, when the obtained heart rate interval data of the user is 80% -90% and the SDNN parameter value is less than 50ms, it is determined that the fatigue degree of the user meets the second preset condition, and the user can be correspondingly reminded to actively release pressure and stop exercising in time, so as to prevent abnormal vital signs caused by excessive exercising reaching a limit.
S630: determining from the sweat data whether the degree of dehydration of the user meets a third preset condition.
In particular, sweat data is used to characterize a user's sweating during exercise, including sweat amount data and sweat parameter data. The sweat parameter data represent the physiological parameter condition of the user discharged through sweat in the exercise process, and can be used for analyzing and obtaining the dehydration degree. Sweat parameter data may be detected by a sweat sensor in the wearable device, e.g., K + ion concentration, na + ion concentration, cl-ion concentration, pH (pH), lactate index, sweat glucose level, etc., detected by the electrodes.
In one embodiment, determining a degree of dehydration for a user from sweat data comprises: performing characteristic extraction on the sweat data to obtain sweat parameter data related to the dehydration degree; analyzing sweat parameter data related to the dehydration degree by adopting a preset dehydration degree recognition model to obtain the dehydration degree; and the preset dehydration degree recognition model is obtained by training according to the historical sweat parameter database.
It can be understood that, among the plurality of sweat parameter data detected by the sweat sensor, sweat parameter data related to the dehydration degree, such as PH value and electrolyte, may be correspondingly extracted for obtaining the dehydration degree.
Specifically, the preset dehydration degree recognition model is a recognition model with a good dehydration degree recognition effect, which is obtained after a database is formed and input into the neural network model for training, parameter adjustment and verification after a large amount of sweat parameters are acquired through a sweat sensor. Furthermore, after sweat parameter data related to the dehydration degree is actually collected on line, the sweat parameter data is input into a preset dehydration degree recognition model, the dehydration degree can be output through the model, and recognition of the dehydration degree obtained according to analysis of the sweat parameter data is achieved. For example, under normal physiological data, the Na + ion concentration is about 125mmol/L, and the pH value is between 4.5 and 7.5. If the sweat parameter data beyond the normal range is input into a preset dehydration degree identification model, the dehydration degree, namely the percentage of the dehydrated body weight, can be analyzed. It will be appreciated that a higher percentage of dehydration over body weight indicates a higher degree of dehydration for the user.
In one embodiment, the dehydration degree satisfying the third preset condition includes: and analyzing by adopting a preset dehydration degree recognition model to obtain the dehydration degree which is greater than a dehydration degree standard exceeding threshold value.
Correspondingly, the threshold value of the excessive dehydration degree can be selected according to the empirical range, for example, when the human body is dehydrated by more than 2% of the body weight, dry mouth, urine volume reduction and other symptoms can occur normally; when the dehydration is more than 6% of the body weight, symptoms such as dizziness, panic, irritability and the like can appear; when dehydration exceeds 7% to 15% of body weight, toxic shock and loss of consciousness symptoms may result. The excessive sweating threshold may be set to dehydrate more than 6% of the body weight, and when the dehydration degree is more than 6%, it is determined that the dehydration degree of the user satisfies the third preset condition. And the dehydration degree exceeding threshold value can be updated according to data generated by the user in the movement process, so that the setting of the dehydration degree exceeding threshold value is more consistent with the actual movement condition of the user.
S640: and determining whether the sweating degree of the user meets 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 in the exercise process, and can be used for analyzing the sweat degree. The sweat amount data can be represented by the actual sweat amount detected by a sweat sensor in the wearable device, or by the sweat amount per unit time. In the application, the sweat amount in unit time is taken as sweat amount data, and the user analyzes the sweat amount data to obtain the sweat degree.
In one embodiment, determining that the user's level of perspiration satisfies a fourth preset condition based on the sweat data comprises: extracting the characteristics of the sweat data to obtain the sweat amount data of the user; and when the sweating amount data is larger than the excessive sweating amount threshold, determining that the sweating degree of the user meets a fourth preset condition.
Specifically, after the sweat amount data of the user is extracted from the sweat data, the sweat amount data can be compared with the excessive sweat amount threshold value, and whether the sweat degree of the user meets a fourth preset condition or not is determined. The excessive sweating amount threshold is used for representing that the sweating degree of a user is high, and actions such as cooling are needed to reduce the sweating amount.
The excessive sweating threshold may be empirically selected, such as a range of normal physiological sweating from 0.15 μ l/min to 3 μ l/min, which may indicate that the sweating does not meet normal physiological conditions. Correspondingly, the excessive sweating threshold value can be set to be 3 mu l/min, and when the sweating data is larger than 3 mu l/min, the sweating degree of the user is determined to meet the fourth preset condition. And the excessive sweating threshold value can be updated according to data generated by the user in the exercise process, so that the setting of the excessive sweating threshold value is more in line with the actual exercise condition of the user.
S650: and determining that the current exercise amount of the user exceeds the standard based on the exercise intensity meeting a first preset condition, the fatigue degree meeting a second preset condition, the dehydration degree meeting a third preset condition and the sweating degree meeting a fourth preset condition. It can be understood that, in the embodiment, whether the current amount of motion of the user exceeds the standard is determined by adopting a joint judgment manner, that is, the current amount of motion of the user exceeds the standard is determined only after all the indexes of motion states of the user meet corresponding preset conditions. Certainly, on the premise of not departing from the above judgment concept, it is determined that the current amount of exercise of the user exceeds the standard according to any deformation mode in which one or more motion state indexes meet the preset condition, and the method belongs to the protection scope of the present application.
In addition, in other embodiments, before determining that the current amount of exercise of the user exceeds the standard according to the sweat data, the motion data and the heart rate data and generating the motion early warning information, after determining the current amount of exercise of the user according to the sweat data, the motion data and the heart rate data, multi-level motion reminding can be performed according to the current amount of exercise of the user. For example, when the current exercise amount of the user is determined to be in the medium intensity according to the sweat data, the exercise data and the heart rate data, first exercise reminding information is generated, the body surface temperature and the sweating degree of the user are reported, 200-300 milliliters of water is reminded to drink every 20 minutes, the user appropriately takes a rest for 10 minutes, and physical recovery is performed. And generating second exercise reminding information when the current exercise amount of the user is determined to be higher intensity according to the sweat data, the exercise data and the heart rate data, reporting the body surface temperature and the sweating degree, reminding the body surface to be cooled and supplementing the exercise beverage and the moisture of the electrolyte immediately, and resting for 20-30 minutes to recover the physical fitness.
The method for determining the current exercise amount of the user to be in the medium exercise intensity and the method for determining the current exercise amount of the user to be in the high exercise intensity can be set by referring to the method for determining the current exercise amount of the user to be out of limits, and the corresponding preset conditions are adjusted according to the experience value range corresponding to the exercise state indexes. It is understood that the current amount of user motion is less than the current amount of user motion at a higher intensity when the current amount of user motion is at a medium intensity, and less than the current amount of user motion at a higher intensity.
In one embodiment, after obtaining sweat data for the user at S400, the method further comprises: performing characteristic extraction on sweat data to obtain sweat parameter data related to uric acid degree; when the state of hyperuricemia is judged according to sweat parameter data related to the degree of uric acid, hyperuricemia reminding information is generated.
Specifically, the sweat data detected by the sweat sensor includes various sweat parameter data, and the sweat parameter data related to the uric acid degree, such as uric acid and tyrosine, in the sweat data can be correspondingly extracted to judge whether the user is in a high uric acid state at present.
Further, whether the user is in a high uric acid state or not can be judged according to the comparison between sweat parameter data related to the uric acid degree and the high uric acid threshold. The data show that the normal limit of uric acid in blood of human body is 420 μ Mol (adult male) and 360 μ Mol (adult female), while the normal limit of uric acid in sweat is 40 μ Mol, and the correlation between the two reaches 0.864 according to statistics. Therefore, whether the user is in the high uric acid state can be judged through the uric acid value in the sweat, and the high uric acid threshold is set to be 40 μ Mol, namely when the uric acid value in the sweat parameter data is less than 40 μ Mol, the user is judged to be in the high uric acid state. Of course, the hyperuricemia threshold may also be set to the tyrosine normal limit, and the hyperuricemia state is determined by the fact that the tyrosine in the sweat parameter data is less than the tyrosine normal limit. Or jointly judging according to the uric acid and tyrosine parameters in the sweat parameter data, and judging to be in a high uric acid state when the tyrosine is less than the normal limit of the tyrosine and the uric acid value is less than the normal limit of the uric acid value of the sweat.
The hyperuricemia reminding information is used for carrying out hyperuricemia early warning on the user when detecting that the user is in a hyperuricemia state according to sweat. The hyperuricemia reminding information can comprise uric acid data, corresponding health guidance data and the like, and the early warning can be performed in a manner that the uric acid data and the health guidance data are displayed or broadcasted through an interaction module of the wearable device to remind a user of keeping the exercise habit and the low-purine dietary habit. In addition, the uric acid data of the user and the corresponding health guidance data and the like can be stored for follow-up continuous tracking report, and the uric acid improvement condition of the user can be checked.
In the embodiment, the uric acid degree of the user is identified through sweat parameter data related to the uric acid degree, the user can track the sweat parameter data for a long time, and a good guiding effect of improving the human health index can be achieved.
In one embodiment, before the obtaining of the body surface parameters of the user in S200, the method further includes: detecting whether a user is in a standard wearing state; and when the user is determined to be in the standard wearing state, entering a step of acquiring body surface parameters of the user. Specifically, the wearing detection module in the wearable device may determine whether the user is in a wearing state according to whether contact is detected. And further, whether the user is in the standard wearing state or not can be judged according to the comparison between the detected actual heart rate data and the standard heart rate data of the user in the standard wearing state. And when the user is determined to be in the standard wearing state, the step S200 is entered for obtaining the body surface parameters of the user. Wherein, wearing detection module of wearable equipment can be realized with capacitive sensor optionally.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a motion monitoring device for realizing the motion monitoring method. The solution to the problem provided by the device is similar to the solution described in the above method, so the specific limitations in one or more embodiments of the motion monitoring device provided below can be referred to the limitations of the motion monitoring method in the above, and are not described herein again.
In one embodiment, as shown in fig. 5, there is provided a motion monitoring device comprising: a body surface parameter obtaining module 510, a data obtaining module 520 and a motion early warning module 530, wherein:
a body surface parameter obtaining module 510, configured to obtain body surface parameters of a user;
the data acquisition module 520 is used for acquiring sweat data, exercise data and heart rate data of the user when the body surface parameters are determined to be larger than the preset values;
and the motion early warning module 530 is configured to determine that the current amount of motion of the user exceeds the standard according to the sweat data, the motion data, and the heart rate data, and generate motion early warning information.
In this embodiment, through obtaining user's body surface parameter to obtain user's sweat data, motion data and heart rate data when the body surface parameter is greater than the default, and when confirming that user's current amount of exercise exceeds the standard according to sweat data, motion data and heart rate data, generate operation early warning information and carry out the amount of exercise early warning to the user. According to the motion monitoring method, analysis and calculation can be carried out according to data in the motion process of the user, the physical performance state of the current motion of the user is early warned, and abnormal vital signs caused by excessive motion reaching the limit are prevented.
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 further configured to acquire sweat data, exercise data and heart rate data of the user if it is determined that the body surface temperature is greater than the preset temperature and the body surface humidity is greater than the preset humidity; the preset temperature is in a range of 36-37.2 ℃, and the preset humidity is in a range of 60-70%.
In one embodiment, the motion warning module 530 is further configured to determine whether the motion intensity of the user meets a first preset condition according to the motion data; determining whether the fatigue degree of the user meets a second preset condition or not according to the heart rate data; determining whether the dehydration degree of the user meets a third preset condition according to the sweat data; determining whether the sweating degree of the user meets a fourth preset condition or not according to the sweat data; and determining that the current exercise amount of the user exceeds the standard based on the exercise intensity meeting a first preset condition, the fatigue degree meeting a second preset condition, the dehydration degree meeting a third preset condition and the sweating degree meeting a fourth preset condition.
In one embodiment, the exercise early warning module 530 is further configured to calculate real-time calorie consumption data according to the exercise data by using a preset calorie calculation model, and match the real-time calorie consumption data to obtain exercise intensity.
In one embodiment, the exercise early warning module 530 is further configured to determine that the exercise intensity of the user meets a first preset condition when the exercise intensity obtained from the real-time calorie consumption data matching is greater than an exercise intensity standard exceeding threshold.
In one embodiment, the motion early warning module 530 is further configured to perform feature extraction on the heart rate data to obtain heart rate interval data and HRV data; and when the data of the heart rate interval is in a preset heart rate exceeding interval and the HRV data is smaller than the HRV exceeding threshold value, determining that the fatigue degree of the user meets a second preset condition.
In one embodiment, the motion early warning module 530 is further configured to perform feature extraction on the sweat data to obtain sweat parameter data related to the dehydration degree; analyzing sweat parameter data related to the dehydration degree by adopting a preset dehydration degree recognition model to obtain the dehydration degree; and the preset dehydration degree recognition model is obtained by training according to the historical sweat parameter database.
In an embodiment, the movement early warning module 530 is further configured to determine that the dehydration degree of the user meets a third preset condition when the dehydration degree obtained by analyzing the preset dehydration degree recognition model is greater than a dehydration degree exceeding threshold.
In one embodiment, the motion early warning module 530 is further configured to perform feature extraction on the sweat data to obtain sweat amount data of the user; and when the sweating amount data is larger than the excessive sweating amount threshold, determining that the sweating degree of the user meets a fourth preset condition.
In one embodiment, the device further comprises a uric acid early warning module for performing feature extraction on the sweat data to obtain sweat parameter data related to uric acid degree; and when the sweat parameter data related to the uric acid degree is judged to be in a high uric acid state, generating high uric acid reminding information.
In one embodiment, the device further comprises a wearing detection module for detecting whether the user is in a standard wearing state; when the user is determined to be in the standard wearing state, the body surface parameter obtaining module 510 is invoked to obtain the body surface parameters of the user.
In an embodiment, the data obtaining module 520 is further configured to obtain motion data corresponding to the motion mode selecting instruction of the user.
The various modules in the motion monitoring apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, as shown in fig. 6, a wearable device is provided, which includes a processor, and a motion detection module, a heart rate detection module, a sweat detection module, a temperature and humidity data collection module, and an interaction module connected to the processor, where the processor is configured to implement motion monitoring on a wearer of the wearable device according to the motion monitoring method described above.
Specifically, the sweat detection module is used for detecting physiological signs of human sweat, and mainly analyzes inorganic ions, organic molecules, amino acids, hormones, proteins, polypeptides and other secretions in the human sweat, such as K +, na +, cl-and pH values of the sweat. The electrolyte imbalance degree, lactic acid index, sweat glucose level and dehydration state of the human body can be analyzed through monitoring the sweat components. The temperature and humidity data acquisition module is a hygrothermograph and is used for detecting the skin temperature and the skin humidity of a user. The motion detection module is a Gsense (acceleration sensor) and is used for detecting the motion state and calorie consumption of the user. The processor is a Bluetooth MCU and is used for collecting detection data obtained by each sensor, performing data fusion processing, uploading the relevant detection data and generated motion reminding information to the control end or the cloud end, and facilitating the management of the health state of a user.
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 may be a key, a track ball or a touch pad arranged on the casing of the computer device, or may be an external keyboard, a touch pad or a mouse, etc. In this embodiment, the interaction module may further include a Speaker (Speaker) and a microphone (Mic), which may implement output of the exercise reminding information, play music and record a call, and implement functions such as voice assistant interaction.
In this embodiment, through obtaining user's body surface parameter to obtain user's sweat data, motion data and heart rate data when the body surface parameter is greater than the default, and when confirming that user's current amount of exercise exceeds the standard according to sweat data, motion data and heart rate data, generate operation early warning information and carry out the amount of exercise early warning to the user. According to the exercise monitoring method, analysis and calculation can be carried out according to data in the exercise process of the user, the physical performance state of the current exercise of the user is early warned, and abnormal vital signs caused by excessive exercise reaching the limit are prevented.
In another embodiment, the wearable device further comprises a wearing detection module connected with the processor, and the capacitive sensor is selected to detect whether the wearable device is in a standard wearing state.
The following explains a process of implementing motion monitoring by a wearable device by taking the process of fig. 7 as an example, and in this embodiment, explains a process of implementing motion monitoring by a wearable device as an example of smart glasses.
Step 1: the intelligent glasses are worn and started, the glasses are started to be worn and detected for timing, and meanwhile, a user can open a sweat detection mode at a mobile phone app end or can set the sweat detection mode to be default in a wearing state.
Step 2: when a user selects different motion modes, the Gsense records motion data such as step number, distance and the like under various motion modes (such as running, riding and outdoor climbing); the heart rate module starts the real-time supervision rhythm of the heart, data such as pressure, glasses humiture module regularly detects data such as skin body surface temperature and humidity simultaneously, when detecting human skin temperature and reach 36 ~ 37.2 degrees centigrade and humidity and surpass 65%, it is obvious that the explanation motion is generated heat and is perspired, MCU starts sweat sensor and collects and detects sweat physiological data, acquire the rhythm of the heart in real time simultaneously, pressure value data variation trend, by MCU fusion algorithm basic data analysis judgement such as Gsensor motion data, rhythm of the heart data, sweat electrolyte, ph value.
And 3, step 3: the processor calculates the exercise condition of the human body, such as exercise intensity, fatigue degree and sweat volatile electrolyte dehydration degree grade, and displays the basic data of human body exercise calorie consumption, human body electrolyte sodium ions, lactic acid index, heart rate, pressure and the like in a correlation manner to prompt or early warn the current exercise physical ability state of the user.
And 4, step 4: when the human body lasts high-load motion, the sweating amount and the sweating time are continuously increased, when the sweat sensor detects that the sweat lactic acid concentration and the ph value exceed the normal threshold value of the human body, voice alarm is actively made by combining the motion heart rate data, the human body is reminded to stop motion to make a relaxing rest, and physical energy consumption due to strenuous motion is avoided.
And 5: the intelligent glasses report the motion data and sweat detection data of the human body to the mobile phone app, so that the user can inquire the physical ability and related health indexes of the user, and the intensity and the duration of the motion are reasonably arranged.
Step 6: the sweat detection sensor can also detect uric acid and tyrosine in sweat, can guide the high-uric-acid crowd to reasonably control the intake of high-purine diet, and can detect the sweat for a long time, gradually improve the indexes of uric acid and tyrosine, and improve the indexes of human health to play a good guiding role.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of the method described above.
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 for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.
Claims (16)
1. A motion monitoring method applied to a wearable device is characterized by comprising the following steps:
acquiring body surface parameters of a user;
if the body surface parameter is determined to be larger than a preset value, sweat data, movement data and heart rate data of the user are obtained;
and determining that the current amount of exercise of the user exceeds the standard according to the sweat data, the exercise data and the heart rate data, and generating exercise early warning information.
2. The method of claim 1, wherein the body surface parameters comprise a body surface temperature and a body surface humidity, and the preset values comprise a preset temperature and a preset humidity; if the body surface parameter is determined to be larger than the preset value, sweat data, movement data and heart rate data of the user are acquired, and the method comprises the following steps:
and confirming that the body surface temperature is greater than the preset temperature and the body surface humidity is greater than the preset humidity, and acquiring sweat data, motion data and heart rate data of the user.
3. The method according to claim 2, wherein the preset temperature is in a range of 36 ℃ to 37.2 ℃ and the preset humidity is in a range of 60% to 70%.
4. The method of claim 1, where determining from the sweat data, the motion data, and the heart rate data that a user's current amount of motion is out of compliance comprises:
determining whether the exercise intensity of the user meets a first preset condition or not according to the exercise data;
determining whether the fatigue degree of the user meets a second preset condition or not according to the heart rate data;
determining whether the dehydration degree of the user meets a third preset condition according to the sweat data;
determining whether the sweating degree of the user meets a fourth preset condition or not according to the sweat data;
and determining that the current exercise amount of the user exceeds the standard 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.
5. The method of claim 4, wherein determining the intensity of the motion of the user based on the motion data comprises:
and calculating by adopting a preset calorie calculation model according to the exercise data to obtain real-time calorie consumption data, and matching according to the real-time calorie consumption data to obtain exercise intensity.
6. The method according to claim 5, wherein the motion intensity satisfying the first preset condition comprises: and the exercise intensity obtained according to the real-time calorie consumption data matching is greater than an exercise intensity standard exceeding threshold value.
7. The method of claim 4, wherein determining that the degree of fatigue of the user satisfies a second preset condition based on the heart rate data comprises:
performing feature extraction on the heart rate data to obtain heart rate interval data and HRV data;
and when the heart rate interval data is in a preset heart rate exceeding interval and the HRV data is smaller than an HRV exceeding threshold, determining that the fatigue degree of the user meets a second preset condition.
8. The method of claim 4 where determining a degree of dehydration for the user from the sweat data includes:
performing feature extraction on the sweat data to obtain sweat parameter data related to the dehydration degree;
analyzing the sweat parameter data related to the dehydration degree by adopting a preset dehydration degree recognition model to obtain the dehydration degree; and the preset dehydration degree recognition model is obtained by training according to a historical sweat parameter database.
9. The method according to claim 8, wherein the dehydration degree satisfying the third preset condition comprises: and the dehydration degree obtained by analyzing the preset dehydration degree recognition model is greater than the dehydration degree standard exceeding threshold value.
10. The method of claim 4 where determining, based on the sweat data, that the user's level of sweating satisfies a fourth preset condition comprises:
performing characteristic extraction on the sweat data to obtain sweat amount data of the user;
and when the sweating amount data is larger than the excessive sweating amount threshold, determining that the sweating degree of the user meets a fourth preset condition.
11. The method of any one of claims 1-10, where after the obtaining sweat data for the user, the method further comprises:
performing characteristic extraction on the sweat data to obtain sweat parameter data related to uric acid degree;
and generating hyperuricemia reminding information when the sweat parameter data related to the uric acid degree is judged to be in a hyperuricemia state.
12. The method of any of claims 1-10, wherein prior to the obtaining body surface parameters of the user, the method further comprises:
detecting whether a user is in a standard wearing state;
and when the user is determined to be in the standard wearing state, entering the step of acquiring the body surface parameters of the user.
13. The method of any one of claims 1-10, wherein obtaining athletic data for the user comprises:
and acquiring the motion data corresponding to the motion mode selection instruction of the user.
14. A motion monitoring device applied to wearable equipment is characterized by comprising:
the body surface parameter acquisition module is used for acquiring body surface parameters of a user;
the data acquisition module is used for acquiring sweat data, motion data and heart rate data of the user when the body surface parameter is determined to be larger than a preset value;
and the motion early warning module is used for determining that the current motion amount of the user exceeds the standard according to the sweat data, the motion data and the heart rate data, and generating motion early warning information.
15. A wearable device comprising a processor and a motion detection module, a heart rate detection module, a sweat detection module, a temperature and humidity data collection module, an interaction module connected to the processor, the processor configured to implement motion monitoring of a user according to the method of any one of claims 1-10.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116341686A (en) * | 2023-05-31 | 2023-06-27 | 煤炭科学技术研究院有限公司 | Body fluid pH calculation model training method, downhole fatigue early warning method and device |
| CN117854678A (en) * | 2024-03-07 | 2024-04-09 | 广东海洋大学 | Exercise health management method, system and medium based on wearable equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116341686A (en) * | 2023-05-31 | 2023-06-27 | 煤炭科学技术研究院有限公司 | Body fluid pH calculation model training method, downhole fatigue early warning method and device |
| CN116341686B (en) * | 2023-05-31 | 2024-01-23 | 煤炭科学技术研究院有限公司 | Body fluid pH calculation model training method, downhole fatigue early warning method and device |
| CN117854678A (en) * | 2024-03-07 | 2024-04-09 | 广东海洋大学 | Exercise health management method, system and medium based on wearable equipment |
| CN117854678B (en) * | 2024-03-07 | 2024-05-31 | 广东海洋大学 | Exercise health management method, system and medium based on wearable equipment |
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