CN114098658A - Health state monitoring method and device - Google Patents
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Abstract
The application provides a method and a device for monitoring a health state. A health state monitoring device comprising: a health management module to: acquiring health management requirements and basic information of a user, wherein the health management requirements are used for indicating a health state required by the user, and the basic information is personal information related to the health state of the user; determining an exercise plan of the user according to the health management requirement and the basic information, and feeding the exercise plan back to the user so that the user executes the exercise plan; the health monitoring module is used for monitoring the exercise data, the daily activity data and the health state data of the user; the health management module is further configured to: and determining the health state of the user according to the exercise data, the daily activity data and the health state data. The monitoring device is used for realizing effective monitoring of the health state and improving the accuracy of health guidance suggestions.
Description
Technical Field
The application relates to the technical field of health monitoring, in particular to a method and a device for monitoring a health state.
Background
And the health management system is used for managing the health state of the user. In the health management process, various monitoring data (such as the amount of exercise) of a user are generally acquired, the health state of the user is determined by using the monitoring data, and corresponding guidance suggestions are given based on the determined health state.
In the prior art, only monitoring data is used for some simple evaluations, such as energy expenditure according to the amount of exercise, to guide the diet at the corresponding energy expenditure. In this way, the health status cannot be effectively monitored, and the accuracy of the guidance advice given is also low.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method and an apparatus for monitoring a health status, so as to achieve effective monitoring of the health status and improve accuracy of a health guidance suggestion.
In a first aspect, an embodiment of the present application provides a health status monitoring device, including: a health management module to: acquiring health management requirements and basic information of a user, wherein the health management requirements are used for indicating a health state required by the user, and the basic information is personal information related to the health state of the user; determining an exercise plan of the user according to the health management requirement and the basic information, and feeding the exercise plan back to the user so that the user executes the exercise plan; the health monitoring module is used for monitoring the exercise data, the daily activity data and the health state data of the user; the health management module is further configured to: and determining the health state of the user according to the exercise data, the daily activity data and the health state data.
In the embodiment of the application, compared with the prior art, the health management requirement and the basic information of the user are obtained, wherein the health management requirement can indicate the health state required by the user, and the basic information is related to the health state of the user; based on the two items of information with strong correlation with the health state, an exercise plan matched with the health state of the user can be determined, the exercise plan is equivalent to a health guidance suggestion, and the accuracy is high. After the exercise plan is fed back, the exercise data, the daily activity data and the health state data of the user are monitored so as to determine the health state of the user, and further effective and accurate monitoring of the health state is achieved.
As a possible implementation manner, the health management module is specifically configured to: and inputting the health management requirements and the basic information into a pre-trained motion plan generating model to obtain a motion plan output by the pre-trained motion plan generating model.
In the embodiment of the application, the accurate and effective determination of the exercise plan is realized through the pre-trained exercise plan generation model and the information with strong correlation with the health state.
As a possible implementation manner, the basic information includes: user age, user gender, user weight, user height, region of the user, continuous heart rate data, and continuous sleep data.
In the embodiment of the application, the health state of the user can be effectively reflected through the basic information.
As a possible implementation, the movement plan includes: sports items and time corresponding to the sports items; and daily activity items and the time corresponding to the daily activity items; the health management module is specifically configured to: reminding the user to develop the corresponding sports item according to the time corresponding to the sports item, and reminding the user to develop the corresponding daily activity item according to the time corresponding to the daily activity item.
In the embodiment of the application, the sport plan comprises a sport item and a time corresponding to the sport item; and the daily activity items and the time corresponding to the daily activity items, and based on the information, the user can be reminded to develop the corresponding exercise plan at the corresponding time.
As a possible implementation, the health status data includes: heart rate, heart rate variability, respiration rate, blood oxygen, sleep data, body temperature, body composition, blood pressure, blood glucose, mental stress, mood.
In the embodiment of the application, the health state can be effectively and accurately determined through the health state data.
As a possible implementation, the motion data includes: at least two items of motion intensity, motion type, motion duration and motion frequency; the daily activity data includes: at least two of intensity of daily activity, type of daily activity, duration of daily activity, frequency of daily activity.
In the embodiment of the application, the effective and accurate determination of the health state can be realized through the exercise data and the daily activity data.
As a possible implementation manner, the health management module is further configured to: and updating the exercise plan according to the exercise data, the daily activity data and the health state data, and feeding the updated exercise plan back to the user so as to enable the user to execute the updated exercise plan.
In the embodiment of the application, the exercise plan can be updated through the exercise data, the daily activity data and the health status data.
As a possible implementation, the health management module is further configured to: determining whether the exercise plan needs to be terminated according to the exercise data, the daily activity data and the health state data; and if the movement plan is determined to need to be terminated, generating termination prompt information and feeding back the termination prompt information to the user.
In the embodiment of the application, whether the exercise plan needs to be terminated is judged through the exercise data, the daily activity data and the health state data, and if the exercise plan needs to be terminated, corresponding prompt information is generated and fed back to the user, so that more effective health state monitoring is realized.
As a possible implementation, the health management module is further configured to: when the health state is determined to be an abnormal health state, generating prompt information according to the health state data; and feeding back the prompt information.
In the embodiment of the application, if the health state is determined to be an abnormal health state, prompt information is generated and fed back, and the effectiveness of health state monitoring is improved.
As a possible implementation manner, the health management module is specifically configured to: acquiring medical institution information and contact information input by the user in advance; and feeding the prompt information back to the user, feeding the prompt information back to the corresponding medical institution according to the medical institution information, and feeding the prompt information back to the corresponding contact according to the contact information.
In the embodiment of the application, the effectiveness of health state monitoring is improved by feeding back the prompt information to the user, the corresponding medical institution and the corresponding contact.
In a second aspect, an embodiment of the present application provides a method for monitoring a health state, including: acquiring health management requirements and basic information of a user, wherein the health management requirements are used for indicating a health state required by the user, and the basic information is personal information related to the health state of the user; determining an exercise plan of the user according to the health management requirement and the basic information, and feeding the exercise plan back to the user so that the user executes the exercise plan; monitoring exercise data, daily activity data, and health status data of the user; and determining the health state of the user according to the motion data, the activity data and the state data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic structural diagram of a health status monitoring device according to an embodiment of the present disclosure;
fig. 2 is a monitoring flow chart of a health status monitoring device provided in an embodiment of the present application;
FIG. 3 is a flow chart of a training process of an exercise program generation model provided in an embodiment of the present application;
fig. 4 is a flowchart illustrating training of a health status prediction model according to an embodiment of the present disclosure.
Icon: 100-a health status monitoring device; 110-health management module; 120-health monitoring module.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
The health status monitoring device provided by the embodiment of the application can be applied to application scenarios requiring health status monitoring, such as: monitoring the daily health state of a common user; health status monitoring of hospital patients. Based on the application scenarios, the monitoring user corresponding to the monitoring device may be a general user or a patient.
The monitoring device may be integrated in a health management system or in a health management apparatus. Wherein the health management system is typically a hardware representation of an application; and the health management device is a hardware device, such as: wearable devices, desktop devices, and the like. Therefore, the hardware operating environment of the monitoring device is not limited in the embodiments of the present application.
In addition, whether it is a health management system or a health management device, some acquisition modules of monitoring data should be included or a communication connection can be established with the acquisition modules of monitoring data. Taking the wearable device as an example, the wearable device may also generally collect some information related to the health status of the user, such as: heart rate, blood pressure, blood glucose, etc.
Based on the above introduction of the application scenario and the hardware environment, referring to fig. 1, a schematic structural diagram of a health status monitoring device 100 according to an embodiment of the present application is provided, where the health status monitoring device 100 includes: a health management module 110 and a health monitoring module 120.
Referring to fig. 2, a schematic diagram of a monitoring process of a monitoring device provided in an embodiment of the present application includes:
step 210: the health management module 110 obtains the health management needs and basic information of the user. The health management requirement is used for indicating the health state required by the user, and the basic information is personal information related to the health state of the user.
Step 220: the health management module 110 determines an exercise plan of the user according to the health management needs and the basic information, and feeds back the exercise plan to the user to enable the user to execute the exercise plan.
Step 230: the health monitoring module 120 monitors exercise data, daily activity data, health status data of the user.
Step 240: the health management module 110 determines the health status of the user based on the athletic data, daily activity data, and health status data.
As can be seen from the monitoring process, in the embodiment of the present application, the health management requirement and the basic information of the user are obtained, wherein the health management requirement may indicate the health status required by the user, and the basic information is related to the health status of the user; based on the two items of information with strong correlation with the health state, an exercise plan matched with the health state of the user can be determined, the exercise plan is equivalent to a health guidance suggestion, and the accuracy is high. After the exercise plan is fed back, the exercise data, the daily activity data and the health state data of the user are monitored so as to determine the health state of the user, and further effective and accurate monitoring of the health state is achieved.
Embodiments of the monitoring device and corresponding monitoring process are described in detail below.
In step 210, the health management needs obtained by the health management module 110 may be health management needs input by the user. The health management requirement may be a health management requirement directly input by the user; or may be a health management requirement selected by the user from a plurality of health management requirements provided.
The health management requirement is used for indicating the health status required by the user, and may be: the health care product can be used for losing weight, keeping stature, improving endurance, improving physical ability, improving flexibility, improving health condition, assisting in treating certain chronic diseases and the like, and is not limited in the embodiment of the application.
The basic information acquired by the health management module 110 may be the basic information input by the user, or may be locally stored and acquired basic information in advance. The basic information is related to the health state of the user and can reflect some basic conditions of the user.
As an optional implementation, the basic information includes: user age, user gender, user weight, user height, region of the user, continuous heart rate data, and continuous sleep data.
The continuous heart rate data may be understood as heart rate data of the user in a period of time, and the continuous sleep data may be understood as sleep data of the user in a period of time, for example: sleep several hours a day, etc.
Based on the health management needs and the underlying information, the health management module 110 determines an exercise plan for the user and feeds back the exercise plan to the user in step 220 for the user to execute the exercise plan.
As an alternative embodiment, the health management module 110 inputs the health management requirement and the basic information into the pre-trained exercise plan generating model to obtain the exercise plan output by the pre-trained exercise plan generating model.
An embodiment of the pre-trained motion plan generation model is described next.
Referring to fig. 3, a training flow chart of a motion planning generation model provided in the embodiment of the present application includes: respectively acquiring a first training sample set and a second training sample set; then setting labels for the two training sample sets respectively; after the labels are set, model training is carried out on the basis of the two training sample sets with the labels set respectively, and a trained motion plan generation model is obtained.
Each training sample set may correspond to one exercise plan generating model, and health management requirements corresponding to different exercise plan generating models are different, which may be specifically described in the following example.
The training of the exercise program generation model depends on the crowd big data, so the acquisition of the training data is needed firstly. As an alternative embodiment: the intelligent wearable device is used for acquiring crowd big data (desensitization data, hiding personal names, occupation, special social/physiological identifications and the like), including basic information such as height, age, sex, weight and the like, and monitoring data in nearly 6 months (conditions allow, and longer time period can be acquired). The monitoring data includes: exercise data, daily activity data, and physiological index data.
The physiological index data belongs to a health status data, such as: heart rate, heart rate variability, respiration rate, blood oxygen, etc.
And then, setting labels for the obtained data based on different health management requirements. Specifically, aiming at different health management requirements of losing weight, keeping stature, improving endurance/physical ability/flexibility, improving health condition, assisting in treating certain chronic diseases and the like, different evaluation standards are adopted to analyze basic information and physiological indexes in a data set to obtain labels of improvement, maintenance, no effect, deterioration and the like. For example, for the health management requirement of "weight loss," improvement "is defined as the change in weight of the user in accordance with the healthy tissue's interpretation for weight loss (0.5-1 kg weight loss per week, 1-3 kg weight loss per month)," maintenance "is defined as the lack of significant change in weight of the user," no effect "is defined as the user's weight fluctuating more or slightly, and" worsening "is defined as the user's weight increasing more significantly.
In the embodiment of the present application, the motion plan generation model may be a deep learning model, a random forest model, or the like, which is not limited herein.
Based on the data sample set, the training process of the motion plan generation model comprises the following steps: the method comprises the steps of taking the age, sex, weight, height, area, continuous heart rate and continuous sleep data (namely basic data) of a user as input, taking the intensity, frequency, type, duration and other variables of exercise and daily activities as output, respectively training by using a weight improvement (healthy weight loss) data sample set and a weight maintenance data sample set, and obtaining a trained weight improvement (healthy weight loss) model and a trained weight maintenance model by using a training method (such as a training method of a deep learning model) corresponding to a prediction model, wherein the two trained models can be used for generating personalized exercise and daily activity guidance, namely for generating an exercise plan.
After model training is performed on all sample sets respectively corresponding to the health management requirements, the obtained exercise plan generation model can directly output different exercise plans based on different health management requirements.
Further, based on the trained exercise plan generation model, the health management needs and basic information are input to the trained exercise plan generation model, and the model can output an exercise plan.
In the embodiment of the present application, based on the training data of the exercise plan model, a health state prediction model for predicting the health state of the user after executing the exercise plan output by the exercise plan generation model may be further trained. For example: assuming the user's health management needs are weight loss, the user's weight is predicted by the health status prediction model after the exercise program is executed.
The predicted health state output by the health state prediction model can be used for updating the exercise plan subsequently.
Referring to fig. 4, a training flow chart of the health status prediction model provided in the embodiment of the present application is shown, where the training flow includes: acquiring a training sample set; setting corresponding labels for the training sample set; and inputting the training sample set provided with the corresponding label into the initial health state prediction model, and training to obtain the trained health state prediction model.
The health state prediction model may be a deep learning model, a random forest model, or the like, which is not limited herein.
Taking the health management requirement as an example of weight loss, the processing process of the training sample set comprises the following steps: carrying out statistics and correlation analysis on the weight of the user for 3 months continuously (removing interference factors except the intensity, frequency, type and duration of exercise and daily activities), and finding out exercise and daily activities, behavior habits and the like which are specifically connected with the weight loss improvement, maintenance, no effect and deterioration of the user, wherein the intensity, frequency, type and duration of the exercise and daily activities and the like are included; data sample sets of "weight improvement (healthy weight loss)", "weight maintenance", "no effect" and "deterioration" were established, respectively.
Furthermore, a weight change prediction model (i.e., a health state prediction model) relating to the target of weight loss, weight control, body shape maintenance, etc. among the personalized targets can be obtained by training with the input of the user's age, sex, weight, height, area, and variables such as continuous heart rate, sleep data, exercise and intensity, frequency, type and duration of daily activities, and time to fall asleep and get up, and with the output of labels such as "weight improvement (healthy weight loss)", "weight maintenance", "no action", and "deterioration" in the sample set.
Further, the health management module 110, after determining the exercise plan, feeds back the exercise plan to the user for the user to execute the exercise plan.
The exercise plan generated by the exercise plan generation model contains exercise information and daily activity information at the same time, and as an optional implementation mode, the exercise plan comprises an exercise item and time corresponding to the exercise item; and the daily activity items and the time corresponding to the daily activity items. Therefore, for the health management module 110, the user is prompted to perform the corresponding sports item according to the time corresponding to the sports item, and the user is prompted to perform the corresponding daily activity item according to the time corresponding to the daily activity item.
In the embodiment of the application, the sport and the daily activities belong to two different concepts, and the sport is stronger and more demanding compared with the daily activities. For example, running, cycling, etc. pertain to sports; sleeping, eating and the like belong to daily activities.
For example, the user is reminded at 10 pm: "suggest you are going to sleep within half an hour, finish 20-30 minutes fast walk between 8 o 'clock and 9 o' clock tomorrow, heart rate is controlled between about 90-110; noon can be about 30 minutes of afternoon nap; badminton or tennis is carried out for 40 minutes between 4 and 5 pm, the strength is moderate, and the heart rate is controlled to be about 120-.
For example, in the above example, in the exercise plan, it is already given that the user is suitable for falling asleep at about 10 points, and then, corresponding exercise plan information can be fed back to the user at about 10 points.
In the embodiment of the application, the sport plan comprises a sport item and a time corresponding to the sport item; and the daily activity items and the time corresponding to the daily activity items, and based on the information, the user can be reminded to develop the corresponding exercise plan at the corresponding time.
It will be appreciated that after the health management module 110 feeds back the exercise program to the user, the user will execute the corresponding exercise program. At this point, the fitness monitoring module 120 monitors the user's athletic data, daily activity data, and health status data in step 230.
The health status data may include: the physiological index described in the foregoing embodiment, and the physical state of the user; the physical state of the user can be determined by performing an operation on the physiological index data.
As an alternative embodiment, the health status data comprises: heart rate, heart rate variability, respiration rate, blood oxygen, sleep data, body temperature, body composition, blood pressure, blood glucose, mental stress, mood.
In these health status data, some data can be directly monitored by health monitoring module 120, some data need be determined by health monitoring module 120 after operating other monitoring data, and other monitoring data can come from intelligent wearable devices such as intelligent wrist-watch, heart rate area. The data operation can be realized by an algorithm model.
For example: the sleep data is obtained by performing algorithm modeling calculation on data acquired by the inertial sensor and data acquired by the blood pressure acquisition device. The body composition is obtained by performing algorithmic modeling calculation on the data acquired by the body impedance acquisition module. The mental stress is obtained by carrying out algorithm modeling calculation on the data collected by the blood pressure collecting device and the data collected by the skin impedance collecting module. The emotion is obtained by performing algorithm modeling calculation on data collected by the blood pressure collecting device and data collected by the skin impedance collecting module. Alternatively, the above data may be determined by other embodiments, and are not limited in the examples of the present application.
In the embodiment of the application, the health state can be effectively and accurately determined through the health state data.
For the motion data, it may include: at least two items of motion intensity, motion type, motion duration and motion frequency. For daily activity data, it may include: at least two of intensity of daily activity, type of daily activity, duration of daily activity, frequency of daily activity.
In addition to the above data, some other data may be included in the motion data, such as: sedentary, oxygen uptake, maximum oxygen uptake, aerobic/anaerobic exercise effect, exercise load, energy consumption, etc., which are not limited in the embodiments of the present application.
In the embodiment of the application, the effective and accurate determination of the health state can be realized through the exercise data and the daily activity data.
For the health status prediction model, the health status can be predicted according to various basic information of the user and various data monitored by the health monitoring module 120.
For example, based on the age, sex, weight, height, area, and intensity, frequency, type and duration of continuous exercise and daily activities, heart rate and sleep data of the user, it is possible to predict the possible weight change of the user over the next period of time, and to prompt the user to try to follow the personalized exercise and daily activity guidance, modify the personalized target, or terminate the target when the user sets the personalized weight loss/control goal, but does not strictly follow the personalized exercise and daily activity guidance, or even goes against the weight loss/control goal according to the weight prediction model.
The above are methods for generating and predicting personalized exercise and daily activity guidance for weight loss/control goals; the personalized exercise and daily activity guidance generation and prediction method for other targets is the same, for example, if the target is improving/maintaining the cardiopulmonary function, the related health and physiological state data in the population big data are heart rate data (rest heart rate, maximum heart rate, Heart Rate Variability (HRV) and the like) under various scenes, and are divided into data sets of 'improving', 'maintaining', 'no action', 'worsening' and the like according to whether the heart rate index is improved or not (for example, the HRV in a sleep state is manually analyzed); an "improved"/"maintained" model of cardiopulmonary function, and a short-term predictive model of cardiopulmonary function, can then be established, respectively, with the user's age, gender, weight, height, region, intensity, frequency, type, and duration of continuous exercise and daily activity, continuous heart rate and sleep data, and labels "improved", "maintained", "not effected", "deteriorated", etc.
Based on the various items of data monitored by the health monitoring module 120, in step 240, the health management module 110 determines the health status of the user according to the exercise data, the daily activity data, and the health status data.
As an alternative embodiment, the health management module 110 may evaluate (predict) the health status data of the user based on the exercise data and the daily activity data, and determine an evaluation result indicating an expected health status. After obtaining the evaluation result, comparing the evaluation result with the actually monitored health state data, and if the evaluation result is close to the actually monitored health state data, directly judging whether the health state of the user is abnormal according to the health state data; and if the evaluation result is far from the actually monitored health state data, directly judging the health state of the user as abnormal.
When the health state of the user is judged to be abnormal according to the health state data, the judgment can be realized by detecting whether various physiological index parameters exceed standards.
When the health state of the user is predicted according to the exercise data and the daily activity data, as an optional implementation mode: the variation of the health state data corresponding to various sports items or daily activities is preset, and then according to the actually executed sports items or daily activities, the corresponding variation is added or subtracted on the basis of the health state data before the exercise plan is executed, so that the health state data after the sports data and the daily activity data is determined.
In addition to the embodiments described above, in determining the health state, the athletic data and daily activity data are compared to an athletic program; and comparing the health status data with expected health status data (i.e., predicted health status data output by the aforementioned health status prediction model); if the exercise data and the daily activity data do not conform to the exercise plan and/or the health status data do not conform to the expected health status data, determining that the health status of the user is abnormal.
It is to be understood that the finally determined health status may include a normal status and an abnormal status; if the state is a normal state, the health condition of the user has no problem; if the state is abnormal, the health state of the user is represented to be problematic.
In the embodiment of the application, after the health state is determined, corresponding reminding can be performed based on the health state. Therefore, as an alternative embodiment, the health management module 110 generates the prompt message according to the health status data when determining that the health status is an abnormal health status; and feeding back prompt information.
As an optional implementation manner, the prompt message includes: severity level of abnormal health condition. In connection with the different embodiments of determining health status described above, the abnormality level may be determined from a difference between expected health status data and actual health status data, or from a difference between athletic data and daily activity data and an athletic program, and the like. For example: and presetting the abnormal grades corresponding to different difference values respectively, and further determining the abnormal grade corresponding to the current difference value as the current abnormal grade.
In addition to the exception level, corresponding handling measures may be included. The corresponding treatment measures may also be determined from the difference between the health status data and the actual health status data, or from the difference between the athletic data and the daily activity data and the athletic schedule, etc. For example: and presetting processing measures corresponding to different difference values respectively, and further determining the processing measure corresponding to the current difference value as the current processing measure.
In the embodiment of the present application, the prompt information may also adopt other embodiments, such as: including more information and is not limited in the embodiments of the present application.
Further, as an optional implementation manner, the feeding back the prompt information includes: acquiring medical institution information and contact information input by a user in advance; and feeding the prompt information back to the user, feeding the prompt information back to the corresponding medical institution according to the medical institution information, and feeding the prompt information back to the corresponding contact according to the contact information.
In such an embodiment, the user would enter the healthcare facility information and contact information in advance, such as: the medical institution information includes: contact and name of the medical institution; the contact information includes: contact names and contact addresses, etc.
Based on the medical institution information and the contact information, the health management module 110 feeds back the prompt information to the corresponding medical institution, for example: and sending the prompt information to a person in charge corresponding to the medical institution. And feeding back the prompt information to the corresponding contact, for example: the prompt information is sent to the contact person and the like in a form of a short message, or other feedback modes are adopted, which is not limited in the embodiment of the application.
In the embodiment of the application, the effectiveness of health state monitoring is improved by feeding back the prompt information to the user, the corresponding medical institution and the corresponding contact.
In the embodiment of the present application, after obtaining the exercise data, daily activity data, and health status data, the health management module 110 may update the exercise program in addition to determining the health status. Therefore, as an alternative embodiment, the health management module 110 updates the exercise plan according to the exercise data, the daily activity data and the health status data, and feeds back the updated exercise plan to the user so that the user executes the updated exercise plan.
With reference to the determined implementation of the movement plan in the foregoing embodiment, in this implementation, the process of updating the movement plan may include: the exercise data, daily activity data, and health status data are input into the exercise plan generating model, and the exercise plan generating model may output an updated exercise plan.
Further, the feedback mode of the updated exercise plan refers to the description of the foregoing embodiment, and the description is not repeated here.
In the embodiment of the application, the exercise plan can be updated through the exercise data, the daily activity data and the health status data.
In addition to updating the exercise program, the health management module 110 may also determine the execution of the exercise program, and thus, as an alternative embodiment, the health management module 110 determines whether the exercise program needs to be terminated based on the exercise data, the daily activity data, and the health status data; and if the movement plan is determined to need to be terminated, generating termination prompt information and feeding back the termination prompt information to the user.
In such an embodiment, determining whether the exercise program needs to be terminated may be done in conjunction with the state of health in the previous embodiment. As an alternative implementation, the health management module 110 determines the health status according to the foregoing implementation, determines an abnormal level of the health status if the health status is abnormal, and further determines whether termination is required according to the abnormal level.
Specifically, an abnormal level condition which needs to be terminated may be preset, and if the determined abnormal level meets the abnormal level condition which needs to be terminated, it is determined that the exercise plan needs to be terminated; determining that the movement plan does not need to be terminated if the determined abnormality level does not meet the abnormality level condition requiring termination.
Further, if the movement plan is determined to need to be terminated, generating corresponding prompt information and feeding the prompt information back to the user; for example: the voice prompt comprises the following contents: terminate the current movement plan, etc.
In the embodiment of the application, whether the exercise plan needs to be terminated is judged through the exercise data, the daily activity data and the health state data, and if the exercise plan needs to be terminated, corresponding prompt information is generated and fed back to the user, so that more effective health state monitoring is realized.
It will be appreciated that after the exercise plan is determined and fed back to the user, i.e., after step 220, the steps 230-240 and subsequent monitoring process continues until the user cancels the health management needs configured in step 210; or monitoring that the user has completed the health management needs; i.e. no health management needs anymore.
For example, the health management requirement may be a personalized goal of the user, and after the user cancels the personalized goal, the monitoring device may only monitor the regular data of the user, and does not need to generate an exercise plan and update the exercise plan.
In addition, in the embodiment of the present application, the monitoring device may further include a display module, and the display module may display various data processed by the health management module 110 and the health monitoring module 120, or various data obtained, so that the user can know the data in time. And the system can also be used as a man-machine interaction interface to enable a user to input various information and the like. The display module may also have other functions related to human-computer interaction, which are not described herein.
By adopting the monitoring device and the corresponding monitoring process provided by the embodiment of the application, the following advantages are achieved:
the monitoring of the physiological indexes and physical states (i.e. health state data), movement and daily activities of the user is automatically carried out, and besides basic information such as height, sex, age, weight and the like and personalized targets (i.e. health management requirements) of the user, all other information is obtained through monitoring and analysis under the condition that the user is completely insensitive.
And according to the personalized target of the user, combining the current physiological indexes and the physical state of the user to formulate and optimize a personalized exercise plan.
The exercise plan is dynamically adjusted in real time by monitoring and analyzing the physiological indexes and the physical state of the user, the exercise and daily activity conditions and the execution condition of the exercise plan, and the personalized target is achieved with the optimal exercise benefit (namely, a better exercise effect is achieved with less exercise).
The physiological indexes and the physical state, the movement and daily activity of the user are monitored, analyzed and predicted in real time, and when the system predicts that the personalized target of the user is unreasonable in the current physical state or the movement and daily activity are unreasonable, the movement plan is adjusted in time or even the plan is terminated.
The system can interact with a user in an interface display mode, display real-time physiological indexes and body states of the user, exercise and daily activity conditions, exercise plan execution conditions, historical states, exercise statistical data and the like, and timely send out reminding, improvement methods and medical guidance to the user, a contact person, a medical institution and the like when the physiological indexes and body states of the user, the exercise and daily activity conditions, the exercise plan execution conditions and the like are abnormal.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
In addition, units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Furthermore, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (11)
1. A health status monitoring device, comprising:
a health management module to: acquiring health management requirements and basic information of a user, wherein the health management requirements are used for indicating a health state required by the user, and the basic information is personal information related to the health state of the user; determining an exercise plan of the user according to the health management requirement and the basic information, and feeding the exercise plan back to the user so that the user executes the exercise plan;
the health monitoring module is used for monitoring the exercise data, the daily activity data and the health state data of the user;
the health management module is further configured to: and determining the health state of the user according to the motion data, the activity data and the state data.
2. The monitoring device of claim 1, wherein the health management module is specifically configured to:
and inputting the health management requirements and the basic information into a pre-trained motion plan generation model to obtain a motion plan output by the pre-trained prediction model.
3. The monitoring device of claim 2, wherein the base information comprises:
user age, user gender, user weight, user height, region of the user, continuous heart rate data, and continuous sleep data.
4. The monitoring device of claim 1, wherein the movement plan comprises: sports items and time corresponding to the sports items; and daily activity items and the time corresponding to the daily activity items;
the health management module is specifically configured to: reminding the user to develop the corresponding sports item according to the time corresponding to the sports item, and reminding the user to develop the corresponding daily activity item according to the time corresponding to the daily activity item.
5. The monitoring device of claim 1, wherein the health status data comprises: heart rate, heart rate variability, respiration rate, blood oxygen, sleep data, body temperature, body composition, blood pressure, blood glucose, mental stress, mood.
6. The monitoring device of claim 1, wherein the motion data comprises:
at least two items of motion intensity, motion type, motion duration and motion frequency;
the daily activity data includes:
at least two of intensity of daily activity, type of daily activity, duration of daily activity, frequency of daily activity.
7. The monitoring device of claim 1, wherein the health management module is further configured to: and updating the exercise plan according to the exercise data, the daily activity data and the health state data, and feeding the updated exercise plan back to the user so as to enable the user to execute the updated exercise plan.
8. The monitoring device of claim 1, wherein the health management module is further configured to: determining whether the exercise plan needs to be terminated according to the exercise data, the daily activity data and the health state data; and if the movement plan is determined to need to be terminated, generating termination prompt information and feeding back the termination prompt information to the user.
9. The monitoring device of claim 1, wherein the health management module is further configured to: when the health state is determined to be an abnormal health state, generating prompt information according to the health state data; and feeding back the prompt information.
10. The monitoring device of claim 9, wherein the health management module is specifically configured to: acquiring medical institution information and contact information input by the user in advance; and feeding the prompt information back to the user, feeding the prompt information back to the corresponding medical institution according to the medical institution information, and feeding the prompt information back to the corresponding contact according to the contact information.
11. A method of health status monitoring, comprising:
acquiring health management requirements and basic information of a user, wherein the health management requirements are used for indicating a health state required by the user, and the basic information is personal information related to the health state of the user;
determining an exercise plan of the user according to the health management requirement and the basic information, and feeding the exercise plan back to the user so that the user executes the exercise plan;
monitoring exercise data, daily activity data, and health status data of the user;
and determining the health state of the user according to the motion data, the activity data and the state data.
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