A health monitoring method, device and electronic device applied to smart watch

Abstract

The present application provides a health monitoring method, device and electronic device applied to a smart watch, and relates to the technical field of monitoring technology. In the method, the physiological data of the user is obtained, and the physiological data is the physiological data of the user during exercise; the physiological data is processed to obtain physiological signals, and the physiological signals include electrocardiogram signals, pulse wave signals and blood oxygen saturation signals; the user's exercise state and physiological index values ​​are determined according to the electrocardiogram signals, pulse wave signals and blood oxygen saturation signals; if the user's exercise state is confirmed to be a vigorous exercise state, the preset physiological index range corresponding to the vigorous exercise state is obtained; the relationship between the physiological index value and the preset physiological index range is judged to generate a judgment result; the judgment result is identified to determine the user's health status. Implementing the technical solution provided by the present application is convenient for monitoring the health status of students.

Description

Technical Field

[0001] The present application relates to the technical field of monitoring technology, and specifically to a health monitoring method, device and electronic device applied to a smart watch. Background Art

[0002] With the continuous development of modern society, people's lifestyles and living environments have undergone tremendous changes. These changes have led to the emergence of many problems, one of which is the health of students. The health of students is not only related to their personal study and life, but also concerns schools and families.

[0003] Nowadays, many schools have realized the importance of student health and started to pay attention to the health management and education of students during school. However, in some cases, such as when students are doing sports in physical education class, it is difficult for teachers to accurately understand the health status of students. Because traditional health examination methods usually require students to report actively and rely too much on students' initiative, the above methods are not conducive to monitoring students' health status.

[0004] Therefore, there is an urgent need for a health monitoring method, device and electronic device applied to a smart watch. Summary of the invention

[0005] The present application provides a health monitoring method, device and electronic device applied to a smart watch, which is convenient for monitoring the health status of students.

[0006] In a first aspect of the present application, a health monitoring method applied to a smart watch is provided, the method comprising: obtaining physiological data of a user, the physiological data being the physiological data of the user during exercise; performing data processing on the physiological data to obtain physiological signals, the physiological signals comprising an electrocardiogram signal, a pulse wave signal and a blood oxygen saturation signal; determining the user's exercise state and physiological index values ​​according to the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal; if it is confirmed that the user's exercise state is an intense exercise state, obtaining a preset physiological index range corresponding to the intense exercise state; judging the relationship between the physiological index value and the preset physiological index range, and generating a judgment result; identifying the judgment result to determine the health status of the user.

[0007] By adopting the above technical solution, by acquiring the user's physiological data and then processing the physiological data, a physiological signal is obtained, which is convenient for obtaining an electrocardiogram signal, a pulse wave signal and a blood oxygen saturation signal. Next, according to the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal, the user's motion state and physiological index value can be determined. When it is determined that the user's motion state is a violent motion state, the preset physiological index range corresponding to the violent motion state will be obtained. Finally, by judging the relationship between the physiological index value and the preset physiological index range, and by identifying and judging the generated judgment result, the user's health status can be determined. Thus, by automatically acquiring various physiological data of the user and monitoring the user's motion state and physiological index value in real time, the user's health status can be understood, thereby facilitating monitoring the user's health status.

[0008] Optionally, the acquiring of the user's physiological data specifically includes: acquiring a physiological data packet of the user; preprocessing the physiological data packet to obtain the user's physiological data, wherein the preprocessing includes removing noise, filtering invalid data, smoothing fluctuating data, and normalizing data.

[0009] By adopting the above technical solution, the preprocessing steps include removing noise, filtering invalid data and smoothing fluctuating data, which helps to improve the overall quality of the acquired physiological data and reduce data errors caused by environmental interference, equipment misreading or other factors. By removing noise and filtering invalid data, the noise and outliers in the data can be reduced and the reliability of the data can be improved. This is crucial for subsequent data analysis and decision making. Smoothing fluctuating data and performing data normalization can make the data more stable and reliable, and improve the availability and credibility of the data. This is very important for applications that need to perform health status assessment or motion status identification based on these data. These preprocessing steps can be automatically completed by programs or algorithms, which simplifies the data processing process and improves data processing efficiency.

[0010] Optionally, the data processing of the physiological data to obtain the physiological signal specifically includes: extracting electrocardiogram data features of the physiological data to obtain the electrocardiogram signal; extracting pulse wave data features of the physiological data to obtain the pulse wave signal; extracting blood oxygen saturation data features of the physiological data to obtain the blood oxygen saturation signal; obtaining the physiological signal based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal.

[0011] By adopting the above technical solution, specific and targeted signals or data can be obtained by extracting features from ECG data, pulse wave data, and blood oxygen saturation data. This ensures that the acquired signals or data have directly relevant physiological meanings and can be used for further assessment of health status or exercise status. Specific feature extraction methods can more accurately extract useful information from raw physiological data, which is very important for accurate judgment of health status or exercise status. By processing data from three aspects, namely, ECG, pulse wave, and blood oxygen saturation, the obtained physiological signals are more comprehensive and systematic, and can provide richer physiological information. Processing and feature extraction of multiple physiological data can increase the reliability of the obtained physiological signals. The diversity of multiple sources of physiological data and feature extraction methods can reduce the errors that may be generated by a single data source or method. Feature extraction is a standardized data processing process that ensures that physiological data from different sources are comparable, making the assessment of health status or exercise status more standardized.

[0012] Optionally, determining the user's motion state and physiological indicator value based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal specifically includes: determining the user's heart rate based on the electrocardiogram signal; marking the relationship between the user's heart rate and a preset heart rate range as a first relationship; determining the user's pulse frequency based on the pulse wave signal; marking the relationship between the user's pulse frequency and a preset pulse frequency range as a second relationship; determining the user's blood oxygen saturation based on the blood oxygen saturation signal; marking the relationship between the user's blood oxygen saturation and a preset blood oxygen saturation range as a third relationship; determining the user's motion state based on the first relationship, the second relationship and the third relationship, the motion state including a strenuous motion state and a mild motion state.

[0013] By adopting the above technical solution, the user's motion state and physiological index values, such as heart rate, pulse frequency and blood oxygen saturation, can be determined by processing ECG signals, pulse wave signals and blood oxygen saturation signals. These physiological indicators can reflect the user's health status and motion state. The above process takes into account not only ECG signals, but also pulse wave signals and blood oxygen saturation signals, which makes the evaluation of the user's motion state and physiological condition more multi-dimensional and comprehensive. By comparing the user's physiological indicators with the preset range, the user's motion state can be distinguished as a strenuous motion state and a mild motion state. This helps to more accurately evaluate and guide the user's exercise type and amount of exercise. Long-term monitoring and analysis of the user's physiological indicators can help understand the user's health status, which is of great significance for preventive medicine and health management.

[0014] Optionally, determining the user's motion state and physiological indicator value based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal specifically also includes: assigning weights to the heart rate, the pulse frequency and the blood oxygen saturation according to a preset weight table, and calculating the physiological indicator value.

[0015] By adopting the above technical solution, weights are assigned to heart rate, pulse frequency and blood oxygen saturation through a preset weight table, which can make different physiological indicators have different importance when calculating physiological indicator values. This weight allocation method avoids the influence of subjective judgment on the calculation of physiological indicators, making the calculation results more objective and reliable. This method integrates multiple physiological indicators together, comprehensively considers the user's exercise status and physiological condition, and makes the evaluation results more comprehensive and comprehensive.

[0016] Optionally, the preset physiological indicator range includes a healthy physiological indicator range, a sub-healthy physiological indicator range and a disease physiological indicator range, and the identification of the judgment result to determine the health status of the user specifically includes: if the judgment result indicates that the physiological indicator value is within the healthy physiological indicator range, then the user's health status is determined to be healthy; if the judgment result indicates that the physiological indicator value is within the sub-healthy physiological indicator range, then the user's health status is determined to be sub-healthy; if the judgment result indicates that the physiological indicator value is within the disease physiological indicator range, then the user's health status is determined to be disease; and a prompt message is sent to the terminal device, and the prompt message is used to display the user's health status.

[0017] By adopting the above technical solution, by presetting the physiological indicator range into a healthy physiological indicator range, a sub-healthy physiological indicator range, and a disease physiological indicator range, and judging the user's health status accordingly, the user's health status can be understood more clearly and intuitively. This judgment method is simple and easy to implement, and has high operability, so that the results can be judged according to different health conditions, and targeted health guidance or suggestions can be provided to the user. This judgment method is based on a preset physiological indicator range and has a certain degree of reliability and accuracy. By real-time monitoring and analysis of the user's physiological indicators, the user's potential health problems can be discovered in time to avoid the occurrence or deterioration of the disease.

[0018] Optionally, target physiological data of a target user is obtained, where the target physiological data is physiological data of the target user during exercise, and the target user and the user are of the same type of user; based on the target physiological data, the preset physiological indicator range is calculated.

[0019] By adopting the above technical solution, by obtaining the physiological data of the target user, a preset physiological indicator range can be calculated for the target user, which is more customized and personalized, and can more accurately reflect the physiological condition of the target user. If users of the same type have similar physiological indicator ranges, then their health status or exercise status can be understood by comparing the physiological indicator values ​​of different users. By long-term monitoring and recording of the target user's physiological data, the changing trend of their physiological indicators can be observed to help predict changes in their health status or exercise status, so as to understand the general health status or exercise status of this type of users, thereby providing them with more targeted health guidance or suggestions.

[0020] In a second aspect of the present application, a health monitoring device applied to a smart watch is provided, the health monitoring device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire physiological data of a user, the physiological data being the physiological data of the user during exercise; the processing module is used to process the physiological data to obtain physiological signals, the physiological signals comprising electrocardiogram signals, pulse wave signals and blood oxygen saturation signals; the processing module is also used to determine the user's exercise state and physiological index values ​​according to the electrocardiogram signals, the pulse wave signals and the blood oxygen saturation signals; the processing module is also used to, if it is confirmed that the user's exercise state is an intense exercise state, acquire a preset physiological index range corresponding to the intense exercise state; the processing module is also used to determine the relationship between the physiological index value and the preset physiological index range to generate a judgment result; the processing module is also used to identify the judgment result to determine the health status of the user.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. By acquiring the user's physiological data and then processing the physiological data, physiological signals are obtained, which facilitates the acquisition of electrocardiogram signals, pulse wave signals and blood oxygen saturation signals. Next, the user's exercise state and physiological index values ​​can be determined based on the electrocardiogram signals, pulse wave signals and blood oxygen saturation signals. When it is determined that the user's exercise state is a strenuous exercise state, the preset physiological index range corresponding to the strenuous exercise state will be obtained. Finally, the user's health status can be determined by judging the relationship between the physiological index value and the preset physiological index range, and by identifying and judging the generated judgment result. Therefore, by automatically acquiring various physiological data of the user and monitoring the user's exercise state and physiological index values ​​in real time, the user's health status can be understood, thereby facilitating the monitoring of the user's health status;

[0025] 2. Through the feature extraction of ECG data, pulse wave data and blood oxygen saturation data, specific and targeted signals or data can be obtained. This ensures that the acquired signals or data have directly relevant physiological meanings and can be used for further evaluation of health status or exercise status. Specific feature extraction methods can more accurately extract useful information from raw physiological data, which is very important for accurate judgment of health status or exercise status. By processing the data of ECG, pulse wave and blood oxygen saturation, the obtained physiological signals are more comprehensive and systematic, and can provide richer physiological information;

[0026] 3. By processing the ECG signal, pulse wave signal and blood oxygen saturation signal, the user's exercise state and physiological index values, such as heart rate, pulse frequency and blood oxygen saturation, can be determined. These physiological indicators can reflect the user's health status and exercise state. The above process takes into account not only the ECG signal, but also the pulse wave signal and blood oxygen saturation signal, which makes the evaluation of the user's exercise state and physiological condition more multi-dimensional and comprehensive. By comparing the user's physiological indicators with the preset range, the user's exercise state can be distinguished as a strenuous exercise state and a light exercise state. This helps to more accurately evaluate and guide the user's exercise type and amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a health monitoring method applied to a smart watch provided in an embodiment of the present application.

[0028] Figure 2 A module schematic diagram of a health monitoring device applied to a smart watch provided in an embodiment of the present application.

[0029] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0030] Explanation of the reference numerals: 21, acquisition module; 22, processing module; 31, processor; 32, communication bus; 33, user interface; 34, network interface; 35, memory. DETAILED DESCRIPTION

[0031] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0032] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0033] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0034] With the rapid development of modern society and the advancement of science and technology, people's lifestyles, living environments and learning environments have undergone tremendous changes. These changes have brought many benefits to a certain extent, but they have also brought some problems that cannot be ignored. Among them, an issue that has attracted increasing attention is the health of students. The health of students is not only directly related to their learning and quality of life, but also affects the sensitive nerves of schools and families. At present, many schools have realized the importance of student health, and therefore have begun to pay close attention to the health management and education of students during their school years.

[0035] However, in some cases, such as when students are doing sports in physical education class, it is difficult for teachers to accurately understand the health status of each student. This is mainly because traditional health screening methods usually require students to actively report or undergo expensive medical tests. First, this method of relying on students to actively report may cause some health problems to be overlooked, because some students may choose not to report their health problems for various reasons, such as fear, shyness or other personal reasons. Secondly, conducting expensive medical tests will increase the financial pressure on the school, and it will not be popularized to every student in a timely manner due to time and resource constraints. Therefore, the above methods are not conducive to monitoring the health status of students.

[0036] In order to solve the above technical problems, the present application provides a health monitoring method applied to a smart watch, referring to Figure 1 , Figure 1 A flow chart of a health monitoring method applied to a smart watch provided in an embodiment of the present application. The health monitoring method is applied to a smart watch, including steps S110 to S160, which are as follows:

[0037] S110: Acquire physiological data of the user, where the physiological data is the physiological data of the user during exercise.

[0038] Specifically, when students are doing physical education, the smart watches they wear will obtain their physiological data in real time. The students include but are not limited to university, middle school, and elementary school students, as well as professional athletes. The types of exercise include but are not limited to long-distance running, short-distance running, and aerobics. Physiological data is a general term for various physiological data of students, including electrocardiogram data, pulse wave data, and blood oxygen saturation data.

[0039] In the embodiment of the present application, the ECG data reflects the electrophysiological activity of the heart. The smart watch obtains the ECG signal through the sensor, and then calculates the heart rate, heart rhythm, ECG waveform and other data, which can reflect the functional state of the heart, such as whether there are arrhythmias, myocardial ischemia and other problems. The pulse wave data reflects the blood flow waveform of the radial artery. The smart watch obtains the pulse wave signal of the radial artery through the sensor, and can calculate the pulse frequency, pulse waveform and other data, which can reflect the heart's pumping function, vascular elasticity and blood circulation status. The blood oxygen saturation data reflects the percentage of oxygenated hemoglobin in the blood, that is, the concentration of blood oxygen in the blood. The smart watch obtains the blood signal at the wrist through the sensor, and detects the light absorption spectrum after infrared and red light irradiation, so as to calculate the blood oxygen saturation. The normal blood oxygen saturation ranges from 95% to 100%. If the blood oxygen saturation is too low, it means that there are respiratory problems or blood circulation problems.

[0040] In a possible implementation, obtaining the physiological data of the user specifically includes: obtaining a physiological data packet of the user; preprocessing the physiological data packet to obtain the physiological data of the user, wherein the preprocessing includes removing noise, filtering invalid data, smoothing fluctuating data, and normalizing data.

[0041] Specifically, the smartwatch obtains students' physiological data packets through built-in sensors. These data packets contain students' ECG data, pulse wave data, blood oxygen saturation data, etc. Preprocessing refers to a series of processing of the original physiological data, including noise removal, filtering invalid data, smoothing fluctuating data, and data normalization. Among them, noise removal refers to removing useless or erroneous data generated by environmental interference, equipment problems, etc. when processing physiological data. For example, when obtaining ECG data, there may be some noise data generated by external electromagnetic interference, which needs to be removed. Filtering invalid data refers to eliminating data that cannot truly reflect the students' physiological conditions. For example, if the blood oxygen saturation data at a certain time point suddenly fluctuates abnormally, then this data may be invalid and should be filtered out. Smoothing fluctuating data refers to making the changes of physiological data smoother and reducing data fluctuations and mutations through certain algorithms. For example, mutations and noise in ECG signals can be smoothed by methods such as sliding average filtering. Data normalization refers to converting physiological data into a common numerical range for comparison and analysis. For example, all physiological data can be normalized to a range of 0-1, or heart rate can be converted to a percentage.

[0042] S120, processing the physiological data to obtain physiological signals, where the physiological signals include electrocardiogram signals, pulse wave signals, and blood oxygen saturation signals.

[0043] Specifically, after the smart watch obtains physiological data, it will process the physiological data to obtain physiological signals. Among them, physiological signals correspond to physiological data, and also include electrocardiogram signals, pulse wave signals, and blood oxygen saturation signals. The purpose of data processing is to convert physiological data into signals and extract features to facilitate subsequent health status analysis operations.

[0044] In a possible implementation, data processing is performed on physiological data to obtain physiological signals, specifically including: extracting ECG data features from the physiological data to obtain ECG signals; extracting pulse wave data features from the physiological data to obtain pulse wave signals; extracting blood oxygen saturation data features from the physiological data to obtain blood oxygen saturation signals; and obtaining physiological signals based on the ECG signals, pulse wave signals, and blood oxygen saturation signals.

[0045] Specifically, the above process is a specific way for a smart watch provided by an embodiment of the present application to convert physiological data into physiological signals. Among them, ECG data feature extraction is to extract features that can reflect the electrical activity of the heart from the ECG signal. For example, the smart watch can extract key features in ECG signals such as heart rate and heart rhythm through time domain analysis, frequency domain analysis and time-frequency analysis. Pulse wave data feature extraction is to extract features that can reflect the state of blood circulation from the pulse wave signal. For example, important features in the pulse wave signal such as pulse frequency and pulse waveform can be extracted. Blood oxygen saturation data feature extraction is to extract features that can reflect the ratio of oxygenated hemoglobin and reduced hemoglobin in the blood from the blood oxygen saturation signal. For example, blood oxygen saturation feature extraction methods include photoelectric volumetric pulse wave pulsation and pulse wave transit time measurement. Photoelectric volumetric pulse wave pulsation is to measure the pulse wave by measuring the change of reflected light, and then calculate the blood oxygen saturation; the pulse wave transit time measurement method is to measure the time it takes for the pulse wave to be transmitted from the heart to the wrist, and then calculate the blood oxygen saturation. These physiological signals include heart rate, heart rhythm, etc. in electrocardiogram signals, pulse frequency, pulse waveform, etc. in pulse wave signals, and blood oxygen saturation value, etc. in blood oxygen saturation signals.

[0046] S130: Determine the user's exercise state and physiological index values ​​according to the electrocardiogram signal, the pulse wave signal, and the blood oxygen saturation signal.

[0047] Specifically, after obtaining the ECG signal, pulse wave signal and blood oxygen saturation signal, the smart watch will determine the student's exercise state and physiological index value based on the ECG signal, pulse wave signal and blood oxygen saturation signal. Among them, the exercise state can include intense exercise state and light exercise state, which can specifically include long-distance running, short-distance running, walking and sitting still. The physiological index value is a comprehensive value to measure the student's physical health status, which is used to reflect the health status. For example, the higher the physiological index value is within the preset health range, the healthier the student is.

[0048] In a possible implementation, the user's motion state and physiological indicator values ​​are determined based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal, specifically including: determining the user's heart rate based on the electrocardiogram signal; marking the relationship between the user's heart rate and a preset heart rate range as a first relationship; determining the user's pulse frequency based on the pulse wave signal; marking the relationship between the user's pulse frequency and a preset pulse frequency range as a second relationship; determining the user's blood oxygen saturation based on the blood oxygen saturation signal; marking the relationship between the user's blood oxygen saturation and a preset blood oxygen saturation range as a third relationship; determining the user's motion state based on the first relationship, the second relationship and the third relationship, the motion state including a strenuous motion state and a mild motion state.

[0049] Specifically, the following is how the smart watch obtains heart rate and pulse frequency. For example, there is a generator and a photosensitive receiver at the end of the smart watch that contacts the skin. When the generator irradiates a light beam of a certain wavelength onto the skin surface, the light beam will return to the photosensitive receiver by transmission or reflection. In this process, the light beam is absorbed and attenuated by the wrist skin muscle tissue and blood, and the light intensity detected by the detector is weakened. Among them, the absorption of light by skin, muscle tissue, etc. remains constant throughout the blood circulation, while the blood content in the skin will change regularly under the action of the heart pumping blood, causing the light intensity received by the light receiver to change pulsatingly. By collecting this change information and converting it into an electrical signal, the heart rate can be obtained through algorithm calculation. The method of obtaining blood oxygen saturation is based on photoelectric volumetric pulse wave recording and pulse wave transit time measurement, which will not be repeated here.

[0050] Among them, the preset heart rate range is a normal heart rate range determined according to the age, gender, physical condition, etc. of the student. By comparing the student's actual measured heart rate with the preset heart rate range, it can be concluded whether the student's heart rate is normal, thereby marking the first relationship. The preset pulse frequency range is also a normal pulse frequency range determined according to the student's age, gender, physical condition, etc. By comparing the student's actual measured pulse frequency with the preset pulse frequency range, it can be concluded whether the student's pulse frequency is normal, thereby marking the second relationship. The preset blood oxygen saturation range is also a normal blood oxygen saturation range determined according to the student's age, gender, physical condition, etc. By comparing the student's actual measured blood oxygen saturation with the preset blood oxygen saturation range, it can be concluded whether the student's blood oxygen saturation is normal, thereby marking the third relationship. According to the above three relationships, it can be judged whether the student is in a state of strenuous exercise or a state of mild exercise. For example, if the student's heart rate, pulse rate and blood oxygen saturation are all within the normal range of mild exercise, it means that the student is in a state of mild exercise; if the student's heart rate, pulse rate and blood oxygen saturation are all beyond the normal range of mild exercise, it means that the student is in a state of strenuous exercise.

[0051] In a possible implementation, the user's motion state and physiological indicator values ​​are determined based on electrocardiogram signals, pulse wave signals and blood oxygen saturation signals, which specifically includes: according to a preset weight table, weights are assigned to heart rate, pulse frequency and blood oxygen saturation to calculate the physiological indicator values.

[0052] Specifically, the preset weight table is derived from a large amount of experimental data and statistical analysis, which can reflect the importance of heart rate, pulse frequency and blood oxygen saturation to students' physical health and exercise status. According to the preset weight table, different physiological indicators will get different weight values. By multiplying the student's actual physiological indicator value with the weight value in the preset weight table, the contribution of each physiological indicator to the overall health and exercise status can be obtained, thereby calculating the physiological indicator value.

[0053] For example, if in the preset weight table, the weight of heart rate is 0.3, the weight of pulse frequency is 0.3, and the weight of blood oxygen saturation is 0.4, then the calculation formula of physiological index value is: physiological index value = heart rate * 0.3 + pulse frequency * 0.3 + blood oxygen saturation * 0.4. In this way, a single index that integrates multiple physiological indexes can be obtained, thereby better reflecting the health status and exercise status of students.

[0054] S140: If it is confirmed that the user's exercise state is a vigorous exercise state, a preset physiological indicator range corresponding to the vigorous exercise state is obtained.

[0055] Specifically, when the smart watch determines that the student's exercise state is a vigorous exercise state after analysis, it will obtain the preset physiological indicator range corresponding to the vigorous exercise state. The preset physiological indicator range can be determined by relevant international standards, or it can be determined based on the past historical exercise records of other students of the same age and with high similarity in physical function who match the student. Similarly, when the smart watch determines that the student's exercise state is a light exercise state, it will also obtain the preset physiological indicator range corresponding to the light exercise state.

[0056] S150: Determine the relationship between the physiological indicator value and the preset physiological indicator range, and generate a determination result.

[0057] S160: Identify the judgment result and determine the user's health condition.

[0058] Specifically, the smart watch will also determine the relationship between the physiological indicator value and the preset physiological indicator range, and generate a corresponding judgment result. Finally, by identifying the judgment result, the student's health status can be determined. Among them, the relationship between the physiological indicator value and the preset physiological indicator range is a relationship of belonging or not belonging. When the physiological indicator value belongs to a preset physiological indicator range, it is considered that the health status of the student corresponding to the physiological indicator value is the same as the physical health status of the population corresponding to the preset physiological indicator range. Therefore, by automatically obtaining various physiological data of students and monitoring the students' exercise status and physiological indicator values ​​in real time, the students' health status can be understood, thereby facilitating the monitoring of the students' health status.

[0059] In a possible implementation, the preset physiological indicator range includes a healthy physiological indicator range, a sub-healthy physiological indicator range and a disease physiological indicator range, and the judgment result is identified to determine the user's health status, specifically including: if the judgment result indicates that the physiological indicator value is within the healthy physiological indicator range, then the user's health status is determined to be healthy; if the judgment result indicates that the physiological indicator value is within the sub-healthy physiological indicator range, then the user's health status is determined to be sub-healthy; if the judgment result indicates that the physiological indicator value is within the disease physiological indicator range, then the user's health status is determined to be disease; and a prompt message is sent to the terminal device, and the prompt message is used to display the user's health status.

[0060] Specifically, the preset physiological index range includes a healthy physiological index range, a sub-healthy physiological index range, and a disease physiological index range. These ranges are determined based on a large amount of experimental data and medical knowledge, and represent the normal range of physiological indicators under healthy, sub-healthy and disease states, respectively. The above judgment result is used to indicate which physiological index range the student's physiological index value is within. According to the comparison of the judgment result with the preset physiological index range, the student's health status can be determined. For example, if the physiological index value is within the healthy physiological index range, then the student's health status can be determined to be healthy; if the physiological index value is within the sub-healthy physiological index range, then the student's health status can be determined to be sub-healthy; if the physiological index value is within the disease physiological index range, then the student's health status can be determined to be disease. This identification of health status can provide students with timely health feedback, help teachers better understand the physical condition of their students, and take corresponding health management measures. The user corresponding to the terminal device is the teacher, and the terminal device is a smart watch or mobile phone worn by the teacher. For example, if a student suddenly has a tachycardia or a decrease in blood oxygen saturation during exercise, the smart watch can send it to the teacher based on the determined sub-health status, so that the teacher can remind the student to take a proper rest or seek medical help.

[0061] In a possible implementation, target physiological data of a target user is obtained, where the target physiological data is physiological data of the target user during exercise, and the target user and the user are of the same type of user; based on the target physiological data, a preset physiological indicator range is calculated.

[0062] Specifically, the above process is a specific method for determining a preset physiological index range provided by an embodiment of the present application. By acquiring the physiological data of the target student, the smart watch can calculate a preset physiological index range for the target student, which is more customized and personalized, and can more accurately reflect the physiological condition of the target student. If students of the same type have similar physiological index ranges, their health status or exercise status can be understood by comparing the physiological index values ​​of different students. The same type of user can be understood in the embodiment of the present application as: multiple other students with a high degree of similarity to the student's age, physical function and other factors. By long-term monitoring and recording of the physiological data of the target student, the changing trend of its physiological indicators can be observed to help predict changes in its health status or exercise status, so as to understand the general health status or exercise status of this type of students, thereby providing them with more targeted health guidance or suggestions.

[0063] This application also provides a health monitoring device for a smart watch, referring to Figure 2 , Figure 2 A module schematic diagram of a health monitoring device applied to a smart watch provided in an embodiment of the present application. The health monitoring device is a smart watch, which includes an acquisition module 21 and a processing module 22, wherein the acquisition module 21 is used to acquire the user's physiological data, which is the user's physiological data during exercise; the processing module 22 is used to process the physiological data to obtain physiological signals, which include electrocardiogram signals, pulse wave signals, and blood oxygen saturation signals; the processing module 22 is also used to determine the user's exercise state and physiological index values ​​according to the electrocardiogram signals, pulse wave signals, and blood oxygen saturation signals; the processing module 22 is also used to obtain the preset physiological index range corresponding to the intense exercise state if it is confirmed that the user's exercise state is an intense exercise state; the processing module 22 is also used to determine the relationship between the physiological index value and the preset physiological index range, and generate a judgment result; the processing module 22 is also used to identify the judgment result and determine the user's health status.

[0064] In a possible implementation, the acquisition module 21 acquires the physiological data of the user, specifically including: the acquisition module 21 acquires the physiological data packet of the user; the processing module 22 preprocesses the physiological data packet to obtain the physiological data of the user, and the preprocessing includes removing noise, filtering invalid data, smoothing fluctuating data, and normalizing data.

[0065] In a possible implementation, the processing module 22 performs data processing on the physiological data to obtain a physiological signal, specifically including: the processing module 22 performs electrocardiogram data feature extraction on the physiological data to obtain an electrocardiogram signal; the processing module 22 performs pulse wave data feature extraction on the physiological data to obtain a pulse wave signal; the processing module 22 performs blood oxygen saturation data feature extraction on the physiological data to obtain a blood oxygen saturation signal; the processing module 22 obtains the physiological signal based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal.

[0066] In a possible implementation, the processing module 22 determines the user's motion state and physiological indicator value based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal, specifically including: the processing module 22 determines the user's heart rate based on the electrocardiogram signal; the processing module 22 marks the relationship between the user's heart rate and the preset heart rate range as a first relationship; the processing module 22 determines the user's pulse frequency based on the pulse wave signal; the processing module 22 marks the relationship between the user's pulse frequency and the preset pulse frequency range as a second relationship; the processing module 22 determines the user's blood oxygen saturation based on the blood oxygen saturation signal; the processing module 22 marks the relationship between the user's blood oxygen saturation and the preset blood oxygen saturation range as a third relationship; the processing module 22 determines the user's motion state based on the first relationship, the second relationship and the third relationship, and the motion state includes a strenuous motion state and a slight motion state.

[0067] In a possible implementation, the processing module 22 determines the user's motion state and physiological indicator values ​​based on the electrocardiogram signal, pulse wave signal and blood oxygen saturation signal, and specifically also includes: the processing module 22 assigns weights to the heart rate, pulse frequency and blood oxygen saturation according to a preset weight table, and calculates the physiological indicator values.

[0068] In a possible implementation, the preset physiological indicator range includes a healthy physiological indicator range, a sub-healthy physiological indicator range and a disease physiological indicator range. The processing module 22 identifies the judgment result and determines the user's health status, specifically including: if the judgment result indicates that the physiological indicator value is within the healthy physiological indicator range, the processing module 22 determines that the user's health status is healthy; if the judgment result indicates that the physiological indicator value is within the sub-healthy physiological indicator range, the processing module 22 determines that the user's health status is sub-healthy; if the judgment result indicates that the physiological indicator value is within the disease physiological indicator range, the processing module 22 determines that the user's health status is disease; the processing module 22 sends a prompt message to the terminal device, and the prompt message is used to display the user's health status.

[0069] In a possible implementation, the acquisition module 21 acquires the target physiological data of the target user, which is the physiological data of the target user during exercise, and the target user and the user are the same type of user; the processing module 22 calculates a preset physiological indicator range based on the target physiological data.

[0070] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0071] The present application also provides an electronic device, referring to Figure 3 , Figure 3 The electronic device may include: at least one processor 31 , at least one network interface 34 , a user interface 33 , a memory 35 , and at least one communication bus 32 .

[0072] The communication bus 32 is used to realize the connection and communication between these components.

[0073] The user interface 33 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 33 may also include a standard wired interface and a wireless interface.

[0074] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0075] Among them, the processor 31 may include one or more processing cores. The processor 31 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 35, and calling data stored in the memory 35. Optionally, the processor 31 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 31 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 31, but may be implemented separately through a chip.

[0076] The memory 35 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 35 includes a non-transitory computer-readable storage medium. The memory 35 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 35 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 35 may optionally be at least one storage device located away from the aforementioned processor 31. As Figure 3 As shown, the memory 35 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a health monitoring method applied to a smart watch.

[0077] exist Figure 3In the electronic device shown, the user interface 33 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 31 can be used to call an application stored in the memory 35 for a health monitoring method applied to a smart watch. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0078] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0079] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0085] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A health monitoring method applied to a smart watch, characterized in that: The method comprises: Acquiring physiological data of a user, wherein the physiological data is physiological data of the user during exercise; Processing the physiological data to obtain physiological signals, wherein the physiological signals include electrocardiogram signals, pulse wave signals, and blood oxygen saturation signals; Determining the user's exercise state and physiological index value according to the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal; If it is confirmed that the user's exercise state is a vigorous exercise state, obtaining a preset physiological indicator range corresponding to the vigorous exercise state; Determine the relationship between the physiological indicator value and the preset physiological indicator range, and generate a determination result; Identifying the judgment result to determine the health status of the user; The determining the user's exercise state and physiological index value according to the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal specifically includes: Determining the user's heart rate based on the electrocardiogram signal; Marking the relationship between the user's heart rate and a preset heart rate range as a first relationship; determining a pulse frequency of the user based on the pulse wave signal; marking the relationship between the user's pulse frequency and a preset pulse frequency range as a second relationship; Determining the blood oxygen saturation of the user based on the blood oxygen saturation signal; Marking the relationship between the user's blood oxygen saturation and a preset blood oxygen saturation range as a third relationship; Determine the user's motion state according to the first relationship, the second relationship, and the third relationship, where the motion state includes a violent motion state and a mild motion state; The determining the user's exercise state and physiological index value according to the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal specifically includes: According to a preset weight table, weights are assigned to the heart rate, the pulse frequency, and the blood oxygen saturation, and the physiological indicator value is calculated; The preset physiological index range includes a healthy physiological index range, a sub-healthy physiological index range and a disease physiological index range, and the identifying of the judgment result to determine the health status of the user specifically includes: If the judgment result indicates that the physiological index value is within the healthy physiological index range, determining that the health status of the user is healthy; If the judgment result indicates that the physiological index value is within the sub-health physiological index range, determining that the health condition of the user is sub-healthy; If the judgment result indicates that the physiological index value is within the range of the disease physiological index, determining that the health condition of the user is a disease; Sending a prompt message to a terminal device, wherein the prompt message is used to display the health status of the user.

2. The health monitoring method applied to a smart watch according to claim 1, characterized in that: The obtaining of the user's physiological data specifically includes: Acquiring a physiological data packet of the user; The physiological data packet is preprocessed to obtain the physiological data of the user, wherein the preprocessing includes removing noise, filtering invalid data, smoothing fluctuating data, and normalizing data.

3. The health monitoring method applied to a smart watch according to claim 1, characterized in that: The processing of the physiological data to obtain the physiological signal specifically includes: Extracting electrocardiogram (ECG) data features from the physiological data to obtain the ECG signal; Extracting pulse wave data features from the physiological data to obtain the pulse wave signal; Extracting blood oxygen saturation data features from the physiological data to obtain the blood oxygen saturation signal; The physiological signal is obtained according to the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal.

4. The health monitoring method for a smart watch according to claim 1, characterized in that: The method further comprises: Acquire target physiological data of a target user, where the target physiological data is physiological data of the target user during exercise, and the target user and the user are of the same type of user; Based on the target physiological data, the preset physiological indicator range is calculated.

5. A health monitoring device applied to a smart watch, characterized in that: The health monitoring device comprises an acquisition module (21) and a processing module (22), wherein: The acquisition module (21) is used to acquire physiological data of the user, wherein the physiological data is the physiological data of the user during exercise; The processing module (22) is used to process the physiological data to obtain physiological signals, wherein the physiological signals include electrocardiogram signals, pulse wave signals and blood oxygen saturation signals; The processing module (22) is further used to determine the user's exercise state and physiological index value based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal; The processing module (22) is further configured to obtain a preset physiological index range corresponding to the intense exercise state if it is confirmed that the user's exercise state is an intense exercise state; The processing module (22) is further used to determine the relationship between the physiological index value and the preset physiological index range, and generate a determination result; The processing module (22) is further used to identify the judgment result and determine the health status of the user; The processing module (22) is also used to determine the user's motion state and physiological index value based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal, specifically including: determining the user's heart rate based on the electrocardiogram signal; marking the relationship between the user's heart rate and a preset heart rate range as a first relationship; determining the user's pulse frequency based on the pulse wave signal; marking the relationship between the user's pulse frequency and a preset pulse frequency range as a second relationship; determining the user's blood oxygen saturation based on the blood oxygen saturation signal; marking the relationship between the user's blood oxygen saturation and a preset blood oxygen saturation range as a third relationship; determining the user's motion state based on the first relationship, the second relationship and the third relationship, the motion state including a violent motion state and a mild motion state; The processing module (22) is also used to determine the user's exercise state and physiological index value based on the electrocardiogram signal, the pulse wave signal and the blood oxygen saturation signal, and specifically includes: assigning weights to the heart rate, the pulse frequency and the blood oxygen saturation according to a preset weight table, and calculating the physiological index value; The processing module (22) is also used for the preset physiological indicator range including a healthy physiological indicator range, a sub-healthy physiological indicator range and a disease physiological indicator range, and the identification of the judgment result to determine the health status of the user specifically includes: if the judgment result indicates that the physiological indicator value is within the healthy physiological indicator range, then the user's health status is determined to be healthy; if the judgment result indicates that the physiological indicator value is within the sub-healthy physiological indicator range, then the user's health status is determined to be sub-healthy; if the judgment result indicates that the physiological indicator value is within the disease physiological indicator range, then the user's health status is determined to be disease; and sending a prompt message to the terminal device, wherein the prompt message is used to display the user's health status.

6. An electronic device, characterized in that: The electronic device comprises a processor (31), a memory (35), a user interface (33) and a network interface (34), wherein the memory (35) is used to store instructions, the user interface (33) and the network interface (34) are both used to communicate with other devices, and the processor (31) is used to execute the instructions stored in the memory (35) so that the electronic device executes the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 4 is performed.

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