WO2020207317A1 - 用户健康评估方法、装置、存储介质及电子设备 - Google Patents

用户健康评估方法、装置、存储介质及电子设备 Download PDF

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Publication number
WO2020207317A1
WO2020207317A1 PCT/CN2020/082889 CN2020082889W WO2020207317A1 WO 2020207317 A1 WO2020207317 A1 WO 2020207317A1 CN 2020082889 W CN2020082889 W CN 2020082889W WO 2020207317 A1 WO2020207317 A1 WO 2020207317A1
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Prior art keywords
user
health
index
dimensional
electronic device
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PCT/CN2020/082889
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English (en)
French (fr)
Inventor
何明
陈仲铭
黄粟
刘耀勇
陈岩
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Oppo广东移动通信有限公司
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Publication of WO2020207317A1 publication Critical patent/WO2020207317A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • This application belongs to the technical field of electronic equipment, and in particular relates to a user health assessment method, device, storage medium and electronic equipment.
  • This application provides a user health evaluation method, device, storage medium, and electronic equipment, which can improve the accuracy of the electronic equipment in evaluating the user's health.
  • an embodiment of the present application provides a user health assessment method, including:
  • an embodiment of the present application provides a user health assessment device, including: a generation module, a construction module, a classification module, and an evaluation module;
  • the generating module is used to obtain the multi-dimensional feature information of the electronic device, and generate the user's multi-dimensional health index according to the multi-dimensional feature information;
  • the building module is used to build a user health evaluation level, and the user health evaluation level includes multiple health levels;
  • the classification module is configured to classify the multi-dimensional health index through a preset algorithm to obtain a corresponding health level
  • the evaluation module is configured to generate health evaluation information according to the health level and the multi-dimensional health index of the user, and display it on the screen of the electronic device.
  • an embodiment of the present application provides a storage medium on which a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the aforementioned user health assessment method.
  • an embodiment of the present application provides an electronic device, including a processor and a memory, the memory stores a plurality of instructions, and the processor loads the instructions in the memory to perform the following steps:
  • FIG. 1 is a schematic diagram of an application scenario of a user health assessment method provided by an embodiment of the application.
  • FIG. 2 is a schematic flowchart of a user health assessment method provided by an embodiment of the application.
  • FIG. 3 is a schematic diagram of another process of a user health assessment method provided by an embodiment of the application.
  • Fig. 4 is a schematic structural diagram of a user health assessment device provided by an embodiment of the application.
  • FIG. 5 is a schematic diagram of another structure of a user health assessment device provided by an embodiment of the application.
  • FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the application.
  • FIG. 7 is a schematic diagram of another structure of an electronic device provided by an embodiment of the application.
  • This technical solution builds a multi-dimensional user health index system by fully utilizing the user’s personal information, including sleep index, game index, application index, and visual health index, etc., which can more accurately assess the user’s health Status, and then provide users with targeted health assessments, and can give specific health assessment reports, which is conducive to users to adjust their behavior and status in a targeted manner, such as improving sleep habits and application usage habits.
  • FIG. 1 is a schematic diagram of an application scenario of a user health assessment method provided by an embodiment of the application.
  • the user health assessment method is applied to electronic equipment.
  • the electronic device is provided with a panoramic sensing architecture.
  • the panoramic perception architecture is the integration of hardware and software used to implement the user health assessment method in an electronic device.
  • the panoramic perception architecture includes an information perception layer, a data processing layer, a feature extraction layer, a scenario modeling layer, and an intelligent service layer.
  • the information perception layer is used to obtain the information of the electronic device itself or the information in the external environment.
  • the information perception layer may include multiple sensors.
  • the information sensing layer includes multiple sensors such as a distance sensor, a magnetic field sensor, a light sensor, an acceleration sensor, a fingerprint sensor, a Hall sensor, a position sensor, a gyroscope, an inertial sensor, a posture sensor, a barometer, and a heart rate sensor.
  • the distance sensor can be used to detect the distance between the electronic device and an external object.
  • Magnetic field sensors can be used to detect the magnetic field information of the environment in which electronic devices are located.
  • the light sensor can be used to detect the light information of the environment in which the electronic device is located.
  • the acceleration sensor can be used to detect the acceleration data of the electronic device.
  • the fingerprint sensor can be used to collect the user's fingerprint information.
  • Hall sensor is a kind of magnetic field sensor made according to Hall effect, which can be used to realize automatic control of electronic equipment.
  • the location sensor can be used to detect the current geographic location of the electronic device. Gyroscopes can be used to detect the angular velocity of electronic devices in various directions. Inertial sensors can be used to detect movement data of electronic devices.
  • the attitude sensor can be used to sense the attitude information of the electronic device.
  • the barometer can be used to detect the air pressure of the environment where the electronic device is located.
  • the heart rate sensor can be used to detect the user's heart rate information.
  • the data processing layer is used to process the data obtained by the information perception layer.
  • the data processing layer can perform data cleaning, data integration, data transformation, and data reduction on the data acquired by the information perception layer.
  • data cleaning refers to cleaning up a large amount of data obtained by the information perception layer to eliminate invalid data and duplicate data.
  • Data integration refers to the integration of multiple single-dimensional data acquired by the information perception layer into a higher or more abstract dimension to comprehensively process multiple single-dimensional data.
  • Data transformation refers to the data type conversion or format conversion of the data acquired by the information perception layer, so that the transformed data meets the processing requirements.
  • Data reduction means to minimize the amount of data while maintaining the original appearance of the data as much as possible.
  • the feature extraction layer is used to perform feature extraction on data processed by the data processing layer to extract features included in the data.
  • the extracted features can reflect the state of the electronic device itself or the state of the user or the environmental state of the environment in which the electronic device is located.
  • the feature extraction layer can extract features or process the extracted features through methods such as filtering, packaging, and integration.
  • the filtering method refers to filtering the extracted features to delete redundant feature data.
  • the packaging method is used to screen the extracted features.
  • the integration method refers to the integration of multiple feature extraction methods to construct a more efficient and accurate feature extraction method for feature extraction.
  • the scenario modeling layer is used to construct a model based on the features extracted by the feature extraction layer, and the obtained model can be used to represent the state of the electronic device or the state of the user or the environment.
  • the scenario modeling layer can construct key value models, pattern identification models, graph models, entity connection models, object-oriented models, etc. based on the features extracted by the feature extraction layer.
  • the intelligent service layer is used to provide users with intelligent services based on the model constructed by the scenario modeling layer.
  • the intelligent service layer can provide users with basic application services, can perform system intelligent optimization for electronic devices, and can also provide users with personalized intelligent services.
  • the panoramic perception architecture can also include multiple algorithms, each of which can be used to analyze and process data, and the multiple algorithms can form an algorithm library.
  • the algorithm library may include Markov algorithm, implicit Dirichlet distribution algorithm, Bayesian classification algorithm, support vector machine, K-means clustering algorithm, K nearest neighbor classification algorithm, conditional random field, residual Algorithms such as difference network, long short-term memory network, convolutional neural network, recurrent neural network, etc.
  • the embodiment of the application provides a user health assessment method
  • the execution subject of the user health assessment method may be the user health assessment device provided in the embodiment of the application, or an electronic device integrated with the user health assessment device, wherein the user health assessment
  • the device can be implemented in hardware or software.
  • the embodiments of the present application will be described from the perspective of a user health assessment device, which may be specifically integrated in an electronic device.
  • the user health assessment method includes: acquiring multi-dimensional feature information of an electronic device, and generating a user's multi-dimensional health index according to the multi-dimensional feature information;
  • the multi-dimensional health index includes a sleep index, acquiring multi-dimensional feature information of an electronic device, and generating a user's multi-dimensional health index according to the multi-dimensional feature information includes:
  • the sleep finger of the user is generated according to the sleep duration.
  • generating the sleep index of the user according to the sleep duration includes:
  • the sleep index of the user is generated according to the light sleep duration and the deep sleep duration.
  • the multi-dimensional health index includes mood index and disease index, acquiring multi-dimensional feature information of an electronic device, and generating a user's multi-dimensional health index according to the multi-dimensional feature information includes:
  • the emotional index of the user is generated according to the facial expression information
  • the disease index of the user is generated according to the complexion information.
  • generating health assessment information according to the health level and the multi-dimensional health index of the user and displaying it on the screen of an electronic device includes:
  • the method further includes:
  • prompt information is generated and displayed on the screen of the electronic device.
  • the preset algorithm is a K nearest neighbor classification algorithm.
  • FIG. 2 is a schematic flowchart of a user health assessment method provided by an embodiment of the application.
  • the user health assessment method provided by the embodiment of the application is applied to electronic equipment, and the specific process may be as follows:
  • Step 101 Obtain multi-dimensional feature information of the electronic device, and generate a user's multi-dimensional health index based on the multi-dimensional feature information.
  • the electronic device obtains multiple characteristic information related to the user's health through the information perception layer of the panoramic perception framework, such as the setting information of the electronic device, application usage information, sensor data, etc., according to the multiple characteristic information. Generate the corresponding multi-dimensional user health index to obtain the user's health index system.
  • the user’s sleep index can be generated based on the user’s alarm clock time in the panoramic perception module, where the longer the user’s sleep time, the higher the corresponding sleep index; the user’s eye index is generated based on the user’s screen usage time, where the longer the screen usage time is Longer, the higher the eye index; the game index is generated based on the use time of game applications.
  • determine the game application and then obtain the use time of the above game applications and calculate the total game time. The longer the game time, The higher the game index; the user activity index is generated based on the user’s step count data.
  • the user’s step count can be obtained according to the pedometer in the electronic device.
  • the user’s daily diet composition can be simply constructed through the recipes and the recorded diet data when the user uses the diet application program, that is, the user’s nutritional index is generated; based on the user’s photo, such as the user Self-portrait images, facial feature recognition, identify the user's current emotions and complexion, and now construct the user's emotional index and disease index.
  • a user's health index system can be formed according to the obtained sleep index, eye index, game index, activity index, nutrition index, mood index, and disease index.
  • Step 102 Construct a user health evaluation level, where the user health evaluation level includes multiple health levels.
  • the above-mentioned health level may be 5, for example, it may include 5 levels of excellent, good, medium, poor, and extremely poor.
  • the above-mentioned health levels can also be classified according to numbers, such as the first health level, the second health level, the third health level, the fourth health level, and the fifth health level. The higher the level, the higher the The healthier the user is, that is, the fifth health level represents the healthiest user, and the first health level represents the worst health.
  • Step 103 Classify the multi-dimensional health index through a preset algorithm to obtain a corresponding health level.
  • the health index system constructed in step 101 is classified into the corresponding health level by a preset classification algorithm, that is, the electronic device predicts the user's current health level according to the user's health index.
  • the foregoing predetermined algorithm may be a K-Nearest Neighbor (KNN) algorithm.
  • KNN K-Nearest Neighbor
  • KNN is classified by measuring the distance between different feature values. The idea is: If most of the k most similar (ie, the nearest neighbors in the feature space) samples of a sample in the feature space belong to a certain category, the sample also belongs to this category, where K is usually not greater than An integer of 20.
  • the selected neighbors are all objects that have been correctly classified. This method only determines the category of the sample to be classified according to the category of the nearest one or several samples in the decision-making of classification.
  • Step 104 Generate health assessment information according to the health level and the user's multi-dimensional health index, and display it on the screen of the electronic device.
  • a health assessment report may be generated on a daily basis. For example, the user’s multi-dimensional health index within a day is obtained, and then the health level of the day is determined, and the health evaluation information is generated according to the health level of the day and the multi-dimensional health index. The user pushes and displays the health assessment information, and the user can intuitively see the health assessment level of the day and the specific health index.
  • the electronic device may also preset the standard value of the above-mentioned multi-dimensional health index, and when the health level is lower than a preset level, compare the above-mentioned multi-dimensional health index with the preset standard value, and then according to the comparison result Generate targeted prompt information to improve users’ living habits, so that electronic devices become health assistants in people’s lives, assist users in health management, guide users to form healthy living habits, and help improve users comprehensively and effectively Health status.
  • the multi-dimensional health index of the user generated every preset time can also be stored to establish a database, and the user’s habits are determined according to the database. For example, if the user’s daily activity index is higher during a period of time, it means Internal users often exercise, or the user’s daily game index is high during a period of time, which means that the user likes to play games during that period of time. According to the user habits, the prompt information can be further improved to ensure that the prompt information push is more scientific, reasonable and healthy.
  • the user’s basic physical condition information before generating the user’s mostly health index, and then generate the user’s multidimensional health index based on the above physical condition information and the multidimensional feature information of the electronic device.
  • the basic physical condition information of the user may include: age, height, weight, blood type, hobbies and so on.
  • the activity index is generated based on the number of steps counted by the pedometer and the height and weight information of the user.
  • the user health assessment method can obtain the multi-dimensional feature information of the electronic device, and generate the user's multi-dimensional health index according to the multi-dimensional feature information, and construct the user health evaluation level.
  • the user health evaluation level includes multiple health levels. , Classify the multi-dimensional health index through a preset algorithm to obtain the corresponding health level, generate health assessment information according to the health level and the user’s multi-dimensional health index, and display it on the screen of the electronic device.
  • the embodiment of the application can generate the user's multi-dimensional health information according to the multi-dimensional feature information of the electronic device, thereby determining the user's health level, and evaluating the user's health status. By using the user's multi-faceted panoramic information, the user's health evaluation is improved. accuracy.
  • FIG. 3 is a schematic diagram of another process of a user health assessment method provided by an embodiment of the application, and the user health assessment method includes:
  • Step 201 Obtain multi-dimensional feature information of the electronic device, and generate a multi-dimensional health index of the user according to the multi-dimensional feature information.
  • the electronic device obtains multiple characteristic information related to the user's health through the information perception layer of the panoramic perception framework, such as the setting information of the electronic device, application usage information, sensor data, etc., according to the multiple characteristic information. Generate the corresponding multi-dimensional user health index to obtain the user's health index system.
  • the above-mentioned user's health index may include sleep index, eye use index, game index, activity index, nutrition index, mood index, disease index, etc.
  • a user's health index system can be formed according to the obtained sleep index, eye index, game index, activity index, nutrition index, mood index, and disease index.
  • the sleep index may be monitored by monitoring the user's sleep state and recording sleep duration according to the sleep state, and then generating the user's sleep index according to the sleep duration, for example, the longer the sleep duration, the higher the sleep index.
  • the aforementioned sleep state may include a sleep state and a non-sleep state.
  • the user's motion information may be detected by an electronic device, and then the sleep state and the non-sleep state can be distinguished according to the motion information.
  • the sleep state can be further divided into a light sleep state and a deep sleep state, and then the user's sleep index is generated according to the light sleep duration and the deep sleep duration, so as to further improve the accuracy.
  • the user's action information can be obtained through a smart wearable device (such as a smart bracelet) associated with the electronic device.
  • a body motion sensor is installed on a smart wearable device.
  • the body motion sensor can be set on the side of the casing that contacts the wrist.
  • the body motion sensor can also be called a body motion recorder, which can be used according to the motion amplitude and frequency of the wrist. To measure the user’s sleep quality.
  • the body motion sensor can detect the tiny movement of the wrist, and then determine whether the user is awake, or in a light sleep state or a deep sleep state.
  • sleep monitoring may be to monitor a person's wrist movements through a body motion sensor, and perform cumulative calculations through a preset calculation method, such as recording the total value every 2 minutes, and at the same time, determine the sleep state by combining the motion information. For example, the muscles of people in deep sleep will relax, and the limbs will not produce large movements, or even do not move, while people in light sleep will produce certain slight movements.
  • the motion state of the wrist is monitored by the body motion sensor to determine the user's current sleep state. That is, generating the sleep index of the user according to the sleep duration includes:
  • the sleep index of the user is generated according to the light sleep duration and the deep sleep duration.
  • the calculation method of the aforementioned mood index and disease index may include:
  • the emotional index of the user is generated according to the facial expression information
  • the disease index of the user is generated according to the complexion information.
  • the user image may be an image obtained by the user through a self-portrait of the front camera.
  • the image may include the face area to be recognized, and may also include the background or other objects.
  • face detection By performing face detection on the image to be recognized, the face area in the image to be recognized can be determined, and then the face area is cut out from the image to be recognized.
  • the intercepted face regions are of different shapes and different sizes. Therefore, the intercepted face regions can be further normalized, and the intercepted faces of different sizes
  • the image of the face region is normalized to a size suitable for the pre-trained convolutional neural network model, and then processed to obtain expression information and facial color information.
  • Step 202 Construct a user health evaluation level, where the user health evaluation level includes multiple health levels.
  • the above-mentioned health level may be 5, for example, it may include 5 levels of excellent, good, medium, poor, and extremely poor.
  • the above-mentioned health levels can also be classified according to numbers, such as the first health level, the second health level, the third health level, the fourth health level, and the fifth health level. The higher the level, the higher the The healthier the user is, that is, the fifth health level represents the healthiest user, and the first health level represents the worst health.
  • Step 203 Classify the multi-dimensional health index by the K nearest neighbor classification algorithm to obtain the corresponding health level.
  • Step 204 Generate a dynamic avatar according to the health level and display it on the screen of the electronic device.
  • a dynamic virtual image may be generated according to the health level and displayed on the screen of the electronic device.
  • the form of the corresponding avatar can be determined according to the health level, and displayed in the dynamic wallpaper of the electronic device, such as fish, turtles, cartoon characters, etc. on the dynamic wallpaper.
  • Step 205 Receive the user's touch operation on the dynamic avatar, and display the user's multi-dimensional health index according to the touch operation.
  • the user’s multi-dimensional health index can be further displayed so that the user can see more specific data, such as the user’s low health level, mainly because the game index is too high. Sleep index is too low.
  • Step 206 Select an abnormal health index that does not meet the preset index interval among the multidimensional health indexes, and generate prompt information according to the abnormal health index and display it on the screen of the electronic device.
  • a preset interval may be set for the above-mentioned multi-dimensional health index, as a standard for measuring whether the health index meets the standard, the above-mentioned multi-dimensional health index is compared with its corresponding preset interval, and the selection does not meet the preset interval.
  • the abnormal health index in the index range is then generated and displayed on the screen of the electronic device according to the abnormal health index. For example, the game index is too high and the sleep index is too low, that is, the user uses the game for too long and sleeps too little. Furthermore, users can take corresponding improvement strategies to improve their own health based on the assessed health level and abnormal health index.
  • the user health assessment method provided by the embodiments of the present application can obtain the multi-dimensional feature information of the electronic device, and generate the user's multi-dimensional health index according to the multi-dimensional feature information, and construct the user health evaluation level.
  • the user health evaluation level includes multiple health levels.
  • the user’s multi-dimensional health index selects the abnormal health index that does not meet the preset index range among the multi-dimensional health indexes, and generates prompt information according to the abnormal health index and displays it on the screen of the electronic device.
  • the embodiment of the application can generate the user's multi-dimensional health information according to the multi-dimensional feature information of the electronic device, thereby determining the user's health level, and evaluating the user's health status. By using the user's multi-faceted panoramic information, the user's health evaluation is improved. accuracy.
  • FIG. 4 is a schematic structural diagram of a user health assessment device provided by an embodiment of the application.
  • the user health assessment device 30 includes a generation module 301, a construction module 302, a classification module 303, and an evaluation module 304;
  • the generating module 301 is configured to obtain multi-dimensional feature information of the electronic device, and generate a user's multi-dimensional health index according to the multi-dimensional feature information.
  • the electronic device obtains multiple characteristic information related to the user's health through the information perception layer of the panoramic perception framework, such as the setting information of the electronic device, application program usage information, sensor data, etc., and the generating module 301 is based on the foregoing multiple information.
  • This feature information generates a corresponding multi-dimensional user health index to obtain the user’s health index system.
  • the user’s sleep index can be generated based on the user’s alarm clock time in the panoramic perception module, where the longer the user’s sleep time, the higher the corresponding sleep index; the user’s eye index is generated based on the user’s screen usage time, where the longer the screen usage time is Longer, the higher the eye index; the game index is generated based on the use time of game applications.
  • determine the game application and then obtain the use time of the above game applications and calculate the total game time. The longer the game time, The higher the game index; the user activity index is generated based on the user’s step count data.
  • the user’s step count can be obtained according to the pedometer in the electronic device.
  • the user’s daily diet composition can be simply constructed through the recipes and the recorded diet data when the user uses the diet application program, that is, the user’s nutritional index is generated; based on the user’s photo, such as the user Self-portrait images, facial feature recognition, identify the user's current emotions and complexion, and now construct the user's emotional index and disease index.
  • a user's health index system can be formed according to the obtained sleep index, eye index, game index, activity index, nutrition index, mood index, and disease index.
  • the construction module 302 is configured to construct a user health evaluation level, and the user health evaluation level includes multiple health levels.
  • the above-mentioned health level may be 5, for example, it may include 5 levels of excellent, good, medium, poor, and extremely poor.
  • the above-mentioned health levels can also be classified according to numbers, such as the first health level, the second health level, the third health level, the fourth health level, and the fifth health level. The higher the level, the higher the The healthier the user is, that is, the fifth health level represents the healthiest user, and the first health level represents the worst health.
  • the classification module 303 is configured to classify the multi-dimensional health index through a preset algorithm to obtain a corresponding health level.
  • the classification module 303 classifies the health index system constructed in step 101 to the corresponding health level through a preset classification algorithm, that is, the electronic device predicts the user's current health level according to the user's health index.
  • the foregoing preset algorithm may be a K nearest neighbor classification algorithm.
  • the evaluation module 304 is configured to generate health evaluation information according to the health level and the multi-dimensional health index of the user, and display it on the screen of the electronic device.
  • the evaluation module 304 may generate a health evaluation report on a daily basis, for example, obtain the user's multi-dimensional health index in a day, then determine the health level of the day and generate a health evaluation based on the day’s health level and the multi-dimensional health index Information, push and display the health assessment information to the user, and the user can intuitively see the health assessment level of the day and the specific health index.
  • the electronic device may also preset the standard value of the above-mentioned multi-dimensional health index, and when the health level is lower than a preset level, compare the above-mentioned multi-dimensional health index with the preset standard value, and then according to the comparison result Generate targeted prompt information.
  • FIG. 5 is a schematic structural diagram of a user health assessment device provided by an embodiment of the application, wherein the multi-dimensional health index includes a sleep index, and the generating module 301 may include a monitoring sub Module 3011 and generating sub-module 3012;
  • the monitoring submodule 3011 is used to monitor the sleep state of the user and record the sleep duration according to the sleep state;
  • the generating submodule 3012 is configured to generate the sleep index of the user according to the sleep duration
  • the generating module 301 may also include an obtaining sub-module 3013;
  • the acquisition submodule 3013 is configured to acquire the user's action information, and divide the sleep duration into light sleep duration and deep sleep duration according to the action information;
  • the generating submodule 3012 is specifically configured to generate the sleep index of the user according to the light sleep duration and the deep sleep duration.
  • the evaluation module 304 includes a display sub-module 3041 and a feedback sub-module 3042;
  • the display sub-module 3041 is configured to generate a dynamic avatar according to the health level and display it on the screen of the electronic device;
  • the feedback sub-module 3042 is configured to receive a user's touch operation on the dynamic avatar, and display the user's multidimensional health index according to the touch operation.
  • evaluation module 304 may also include a prompt sub-module 3043;
  • the prompt sub-module 3043 is configured to select abnormal health indexes that do not meet a preset index interval among the multi-dimensional health indexes, and generate prompt information according to the abnormal health indexes and display them on the screen of the electronic device.
  • the user health assessment device of the embodiment of the present application can obtain multi-dimensional feature information of the electronic device, and generate a user's multi-dimensional health index based on the multi-dimensional feature information to construct a user health evaluation level.
  • the user health evaluation level includes multiple health levels.
  • the multi-dimensional health index is classified by a preset algorithm to obtain the corresponding health level, and the health evaluation information is generated according to the health level and the user's multi-dimensional health index, and displayed on the screen of the electronic device.
  • the embodiment of the application can generate the user's multi-dimensional health information according to the multi-dimensional feature information of the electronic device, thereby determining the user's health level, and evaluating the user's health status. By using the user's multi-faceted panoramic information, the user's health evaluation is improved. accuracy.
  • the user health assessment device and the user health assessment method in the above embodiments belong to the same concept. Any method provided in the user health assessment method embodiment can be run on the user health assessment device, and its specific implementation process For details, refer to the embodiment of the user health assessment method, which will not be repeated here.
  • module used herein can be regarded as a software object executed on the operating system.
  • the different components, modules, engines, and services described in this article can be regarded as implementation objects on the computing system.
  • the devices and methods described herein can be implemented in the form of software, or of course, can also be implemented on hardware, and they are all within the protection scope of the present application.
  • the embodiment of the present application also provides a storage medium on which a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the above-mentioned user health assessment method.
  • the embodiment of the present application also provides an electronic device, such as a tablet computer, a mobile phone, and so on.
  • the processor in the electronic device will load the instructions corresponding to the process of one or more application programs into the memory according to the following steps, and the processor will run the application programs stored in the memory to realize various functions:
  • the multi-dimensional health index includes a sleep index
  • the processor is configured to perform the following steps:
  • the sleep index of the user is generated according to the sleep duration.
  • the processor when generating the sleep index of the user according to the sleep duration, the processor is configured to perform the following steps:
  • the sleep index of the user is generated according to the light sleep duration and the deep sleep duration.
  • the multi-dimensional health index includes mood index and disease index, when acquiring multi-dimensional feature information of an electronic device, and generating a user's multi-dimensional health index according to the multi-dimensional feature information, the processor is configured to perform the following steps:
  • the emotional index of the user is generated according to the facial expression information
  • the disease index of the user is generated according to the complexion information.
  • the processor when the health assessment information is generated according to the health level and the multi-dimensional health index of the user and displayed on the screen of the electronic device, the processor is configured to perform the following steps:
  • the processor is further configured to perform the following steps:
  • prompt information is generated and displayed on the screen of the electronic device.
  • the preset algorithm is a K nearest neighbor classification algorithm.
  • the electronic device 400 includes a processor 401 and a memory 402.
  • the processor 401 is electrically connected to the memory 402.
  • the processor 400 is the control center of the electronic device 400. It uses various interfaces and lines to connect the various parts of the entire electronic device. It executes the electronic device by running or loading the computer program stored in the memory 402 and calling the data stored in the memory 402. Various functions of the device 400 and processing data, so as to monitor the electronic device 400 as a whole.
  • the memory 402 can be used to store software programs and modules.
  • the processor 401 executes various functional applications and data processing by running the computer programs and modules stored in the memory 402.
  • the memory 402 may mainly include a storage program area and a storage data area.
  • the storage program area may store an operating system, a computer program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; Data created by the use of electronic equipment, etc.
  • the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.
  • the processor 401 in the electronic device 400 will load the instructions corresponding to the process of one or more computer programs into the memory 402 according to the following steps, and the processor 401 will run and store the instructions in the memory 402 In order to realize various functions in the computer program, as follows:
  • the electronic device 400 may further include a display 403, a radio frequency circuit 404, an audio circuit 405, and a power supply 406.
  • the display 403, the radio frequency circuit 404, the audio circuit 405, and the power supply 406 are electrically connected to the processor 401, respectively.
  • the display 403 may be used to display information input by the user or information provided to the user, and various graphical user interfaces. These graphical user interfaces may be composed of graphics, text, icons, videos, and any combination thereof.
  • the display 403 may include a display panel.
  • the display panel may be configured in the form of a liquid crystal display (LCD), or an organic light-emitting diode (OLED).
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • the radio frequency circuit 404 may be used to transmit and receive radio frequency signals to establish wireless communication with network equipment or other electronic equipment through wireless communication, and to transmit and receive signals with the network equipment or other electronic equipment.
  • the audio circuit 405 may be used to provide an audio interface between the user and the electronic device through a speaker or a microphone.
  • the power supply 406 can be used to power various components of the electronic device 400.
  • the power supply 406 may be logically connected to the processor 401 through a power management system, so that functions such as charging, discharging, and power consumption management can be managed through the power management system.
  • the electronic device 400 may also include a camera, a Bluetooth module, etc., which will not be repeated here.
  • the storage medium may be a magnetic disk, an optical disc, a read only memory (Read Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.
  • the relevant hardware can be controlled by a computer program.
  • the computer program can be stored in a computer readable storage medium, such as stored in the memory of an electronic device, and executed by at least one processor in the electronic device.
  • the execution process can include such as the user health assessment method.
  • the storage medium can be magnetic disk, optical disk, read-only memory, random access memory, etc.
  • the user health assessment device of the embodiment of the present application its functional modules may be integrated into one processing chip, or each module may exist alone physically, or two or more modules may be integrated into one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk.

Abstract

一种用户健康评估方法，包括：获取电子设备的多维特征信息，根据多维特征信息生成用户的多维健康指数（101），通过预设算法对多维健康指数进行分类，以得到对应的健康等级（103），根据健康等级以及用户的多维健康指数生成健康评估信息（104）。

Description

用户健康评估方法、装置、存储介质及电子设备
本申请要求于2019年04月09日提交中国专利局、申请号为201910282445.6，发明名称为“用户健康评估方法、装置、存储介质及电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于电子设备技术领域，尤其涉及一种用户健康评估方法、装置、存储介质及电子设备。
背景技术
随着电子技术的发展和健康意识的提升，越来越多的人喜欢利用具有运动健康监测功能的可穿戴设备(例如，运动手环、智能手表、眼镜、服饰、鞋等)对自身的运动情况和健康状况进行监测。现有方式中已存在利用数据模型来预估用户的健康状况的方法，但是健康模型是基于较为完整的用户健康数据得到，因此健康预测时，用户提供的数据完整度将会影响到预估结果的准确。
发明内容
本申请提供一种用户健康评估方法、装置、存储介质及电子设备，可以提升电子设备对用户健康评估的准确性。
第一方面，本申请实施例提供一种用户健康评估方法，包括：
获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
第二方面，本申请实施例提供一种用户健康评估装置，包括：生成模块、构建模块、分类模块以及评估模块；
所述生成模块，用于获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
所述构建模块，用于构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
所述分类模块，用于通过预设算法对所述多维健康指数进行分类，以得到 对应的健康等级；
所述评估模块，用于根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
第三方面，本申请实施例提供一种存储介质，其上存储有计算机程序，当所述计算机程序在计算机上运行时，使得所述计算机执行上述的用户健康评估方法。
第四方面，本申请实施例提供一种电子设备，包括处理器和存储器，所述存储器存储有多条指令，所述处理器加载所述存储器中的指令用于执行以下步骤：
获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍。显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的用户健康评估方法的应用场景示意图。
图2为本申请实施例提供的用户健康评估方法的一种流程示意图。
图3为本申请实施例提供的用户健康评估方法的另一流程示意图。
图4为本申请实施例提供的用户健康评估装置的一种结构示意图。
图5为本申请实施例提供的用户健康评估装置的另一结构示意图。
图6为本申请实施例提供的电子设备的结构示意图。
图7为本申请实施例提供的电子设备的另一结构示意图。
具体实施方式
请参照图式，其中相同的组件符号代表相同的组件，本申请的原理是以实施在一适当的运算环境中来举例说明。以下的说明是基于所例示的本申请具体实施例，其不应被视为限制本申请未在此详述的其它具体实施例。
在以下的说明中，本申请的具体实施例将参考由一部或多部计算机所执行 的步骤及符号来说明，除非另有述明。因此，这些步骤及操作将有数次提到由计算机执行，本文所指的计算机执行包括了由代表了以一结构化型式中的数据的电子信号的计算机处理单元的操作。此操作转换该数据或将其维持在该计算机的内存系统中的位置处，其可重新配置或另外以本领域测试人员所熟知的方式来改变该计算机的运作。该数据所维持的数据结构为该内存的实体位置，其具有由该数据格式所定义的特定特性。但是，本申请原理以上述文字来说明，其并不代表为一种限制，本领域测试人员将可了解到以下所述的多种步骤及操作亦可实施在硬件当中。
本申请中的术语“第一”、“第二”和“第三”等是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或模块的过程、方法、系统、产品或设备没有限定于已列出的步骤或模块，而是某些实施例还包括没有列出的步骤或模块，或某些实施例还包括对于这些过程、方法、产品或设备固有的其它步骤或模块。
现有技术当中，随着手机与用户的联系越来越紧密，可通过手机相关信息实现用户健康状态的实时评估。但现有的用户健康状态的评估所利用到的信息比较有限，无法准确地评估出用户的健康状态。
本技术方案通过全方位的利用用户的个人相关信息，构建了多维度的用户健康指数体系，主要包括睡眠指数、游戏指数、应用程序指数以及视觉健康指数等，能够更加准确地评估出用户的健康状态，进而为用户提供针对性的健康评估，并能给出具体的健康评估报告，有利于用户针对性的调整自己的行为和状态，如改善睡眠习惯以及应用程序使用习惯等。
参考图1，图1为本申请实施例提供的用户健康评估方法的应用场景示意图。所述用户健康评估方法应用于电子设备。所述电子设备中设置有全景感知架构。所述全景感知架构为电子设备中用于实现所述用户健康评估方法的硬件和软件的集成。
其中，全景感知架构包括信息感知层、数据处理层、特征抽取层、情景建模层以及智能服务层。
信息感知层用于获取电子设备自身的信息或者外部环境中的信息。所述信息感知层可以包括多个传感器。例如，所述信息感知层包括距离传感器、磁场传感器、光线传感器、加速度传感器、指纹传感器、霍尔传感器、位置传感器、陀螺仪、惯性传感器、姿态感应器、气压计、心率传感器等多个传感器。
其中，距离传感器可以用于检测电子设备与外部物体之间的距离。磁场传 感器可以用于检测电子设备所处环境的磁场信息。光线传感器可以用于检测电子设备所处环境的光线信息。加速度传感器可以用于检测电子设备的加速度数据。指纹传感器可以用于采集用户的指纹信息。霍尔传感器是根据霍尔效应制作的一种磁场传感器，可以用于实现电子设备的自动控制。位置传感器可以用于检测电子设备当前所处的地理位置。陀螺仪可以用于检测电子设备在各个方向上的角速度。惯性传感器可以用于检测电子设备的运动数据。姿态感应器可以用于感应电子设备的姿态信息。气压计可以用于检测电子设备所处环境的气压。心率传感器可以用于检测用户的心率信息。
数据处理层用于对信息感知层获取到的数据进行处理。例如，数据处理层可以对信息感知层获取到的数据进行数据清理、数据集成、数据变换、数据归约等处理。
其中，数据清理是指对信息感知层获取到的大量数据进行清理，以剔除无效数据和重复数据。数据集成是指将信息感知层获取到的多个单维度数据集成到一个更高或者更抽象的维度，以对多个单维度的数据进行综合处理。数据变换是指对信息感知层获取到的数据进行数据类型的转换或者格式的转换等，以使变换后的数据满足处理的需求。数据归约是指在尽可能保持数据原貌的前提下，最大限度的精简数据量。
特征抽取层用于对数据处理层处理后的数据进行特征抽取，以提取所述数据中包括的特征。提取到的特征可以反映出电子设备自身的状态或者用户的状态或者电子设备所处环境的环境状态等。
其中，特征抽取层可以通过过滤法、包装法、集成法等方法来提取特征或者对提取到的特征进行处理。
过滤法是指对提取到的特征进行过滤，以删除冗余的特征数据。包装法用于对提取到的特征进行筛选。集成法是指将多种特征提取方法集成到一起，以构建一种更加高效、更加准确的特征提取方法，用于提取特征。
情景建模层用于根据特征抽取层提取到的特征来构建模型，所得到的模型可以用于表示电子设备的状态或者用户的状态或者环境状态等。例如，情景建模层可以根据特征抽取层提取到的特征来构建关键值模型、模式标识模型、图模型、实体联系模型、面向对象模型等。
智能服务层用于根据情景建模层所构建的模型为用户提供智能化的服务。例如，智能服务层可以为用户提供基础应用服务，可以为电子设备进行系统智能优化，还可以为用户提供个性化智能服务。
此外，全景感知架构中还可以包括多种算法，每一种算法都可以用于对数 据进行分析处理，所述多种算法可以构成算法库。例如，所述算法库中可以包括马尔科夫算法、隐含狄里克雷分布算法、贝叶斯分类算法、支持向量机、K均值聚类算法、K最近邻分类算法、条件随机场、残差网络、长短期记忆网络、卷积神经网络、循环神经网络等算法。
本申请实施例提供一种用户健康评估方法，该用户健康评估方法的执行主体可以是本申请实施例提供的用户健康评估装置，或者集成了该用户健康评估装置的电子设备，其中该用户健康评估装置可以采用硬件或者软件的方式实现。
本申请实施例将从用户健康评估装置的角度进行描述，该用户健康评估装置具体可以集成在电子设备中。该用户健康评估方法包括：获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
一实施例中，所述多维健康指数包括睡眠指数，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数，包括：
监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长；
根据所述睡眠时长生成所述用户的睡眠指。
一实施例中，根据所述睡眠时长生成所述用户的睡眠指数，包括：
获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
一实施例中，所述多维健康指数包括情绪指数和疾病指数，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数，包括：
获取用户图像；
根据卷积神经网络模型对所述用户图像进行处理，以得到用户的表情信息以及面色信息；
根据所述表情信息生成所述用户的情绪指数，根据所述面色信息生成所述用户的疾病指数。
一实施例中，根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕，包括：
根据所述健康等级生成动态虚拟形象并显示至所述电子设备屏幕；
接收用户针对所述动态虚拟形象的触摸操作，并根据所述触摸操作展示所 述用户的多维健康指数。
一实施例中，在根据所述触摸操作展示所述用户的多维健康指数之后，所述方法还包括：
选取所述多维健康指数当中不满足预设指数区间的异常健康指数；
根据所述异常健康指数生成提示信息并显示至所述电子设备屏幕。
一实施例中，所述预设算法为K最近邻分类算法。
请参阅图2，图2为本申请实施例提供的用户健康评估方法的流程示意图。本申请实施例提供的用户健康评估方法应用于电子设备，具体流程可以如下：
步骤101，获取电子设备的多维特征信息，并根据多维特征信息生成用户的多维健康指数。
在一实施例中，电子设备通过全景感知框架的信息感知层获取多个与用户健康相关的特征信息，比如电子设备的设置信息、应用程序使用信息、传感器数据等等，根据上述多个特征信息生成相应多维的用户健康指数，以得到用户的健康指数体系。
比如，可以基于全景感知模块中用户闹钟时间，生成用户睡眠指数，其中，用户睡眠时长越长，对应的睡眠指数越高；基于用户屏幕使用时间，生成用户用眼指数，其中，屏幕使用时长越长，用眼指数越高；基于游戏类应用程序使用时长，生成游戏指数，其中，首先确定游戏类应用程序，然后分别获取上述游戏应用程序的使用时长并计算总游戏时长，游戏时长越长，游戏指数越高；基于用户步数数据，生成用户活动指数，其中，可以根据电子设备当中的计步器获取用户的步数，步数越多，活动指数越高；基于用户饮食类应用程序数据，构建用户营养指数，其中，可以通过用户使用饮食类应用程序时查看的菜谱以及记录的饮食类数据，可以简单构建出用户每天的饮食构成，即生成用户的营养指数；基于用户照片，比如用户自拍的图像，进行脸部特征识别，识别出用户当前的情绪和面色，今儿构建出用户的情绪指数和疾病指数。进一步的，还可以根据上述获取的睡眠指数、用眼指数、游戏指数、活动指数、营养指数、情绪指数和疾病指数联合形成用户的健康指数体系。
步骤102，构建用户健康评估级别，用户健康评估级别包括多个健康等级。
在一实施例中，上述健康等级可以为5个，比如可以包括优、良、中、差、极差5个等级。在其他实施例中，上述健康等级还可以根据数字来进行分类，比如第一健康等级、第二健康等级、第三健康等级、第四健康等级以及第五健康等级，其中，等级越高则表示用户越健康，也即第五健康等级表示用户最为健康，而第一健康等级则表示健康程度最差。
步骤103，通过预设算法对多维健康指数进行分类，以得到对应的健康等级。
在一实施例中，通过预设分类算法将步骤101构建的健康指数体系分类到相应的健康等级上，即为电子设备根据用户的多为健康指数预测出用户当前的健康等级。在一实施例中，上述预设算法可以为K最近邻分类(k-Nearest Neighbor，KNN)算法。其中，KNN是通过测量不同特征值之间的距离进行分类。它的思路是：如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别，其中K通常是不大于20的整数。KNN算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。
步骤104，根据健康等级以及用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
在一实施例中，可以以一天为单位生成一次健康评估报告，比如获取用户在一天内的多维健康指数，然后确定当天的健康等级并根据当天的健康等级以及多维健康指数生成健康评估信息，向用户推送并展示该健康评估信息，用户可以直观地看到当天的健康评估等级以及具体的健康指数。
在一实施例中，电子设备还可以预设上述多维健康指数的标准值，当健康等级低于一预设等级时，将上述多维健康指数与预设的标准值进行对比，然后根据对比的结果生成具有针对性的提示信息，用以改善用户的生活习惯，从而使电子设备成为人们生活中的健康助理，协助用户进行健康管理，引导用户形成健康的生活习惯，以助于全面有效地改善用户的健康状况。
进一步的，还可以存储每隔预设时间所生成用户的多维健康指数以建立数据库，根据该数据库确定用户的习惯，比如在一段时间内用户每日的活动指数较高，则说明在该时间段内用户常常锻炼，或者在一段时间内用户每日的游戏指数较高，则说明该段时间用户喜欢玩游戏等等。根据该用户习惯可以进一步完善提示信息，确保提示信息的推送更加科学合理及健康。
在一实施例中，还可以在生成用户的多为健康指数之前，获取用户的基本身体状况信息，然后再根据上述身体状况信息以及电子设备的多维特征信息，生成用户的多维健康指数，可以进一步提升生成的健康指数的准确性。其中上述用户的基本身体状况信息可以包括：年龄、身高、体重、血型、兴趣爱好等等。比如，根据计步器统计的步数以及用户的身高体重信息生成活动指数。
由上可知，本申请实施例提供的用户健康评估方法可以获取电子设备的多 维特征信息，并根据多维特征信息生成用户的多维健康指数，构建用户健康评估级别，用户健康评估级别包括多个健康等级，通过预设算法对多维健康指数进行分类，以得到对应的健康等级，根据健康等级以及用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。本申请实施例可以根据电子设备的多维特征信息生成用户的多维健康信息，从而确定用户的健康等级，并对用户的健康状况进行评估，通过利用用户多方面的全景信息，提升了用户健康评估的准确性。
下面将在上述实施例描述的方法基础上，对本申请的清理方法做进一步介绍。参阅图3，图3为本申请实施例提供的用户健康评估方法的另一流程示意图，该用户健康评估方法包括：
步骤201，获取电子设备的多维特征信息，并根据多维特征信息生成用户的多维健康指数。
在一实施例中，电子设备通过全景感知框架的信息感知层获取多个与用户健康相关的特征信息，比如电子设备的设置信息、应用程序使用信息、传感器数据等等，根据上述多个特征信息生成相应多维的用户健康指数，以得到用户的健康指数体系。
比如，上述用户的健康指数可以包括睡眠指数、用眼指数、游戏指数、活动指数、营养指、情绪指数和疾病指数等。进一步的，还可以根据上述获取的睡眠指数、用眼指数、游戏指数、活动指数、营养指数、情绪指数和疾病指数联合形成用户的健康指数体系。
在一实施例中，上述睡眠指数可以通过监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长，然后根据睡眠时长生成所述用户的睡眠指数，比如睡眠时长越长睡眠指数越高。其中，上述睡眠状态可以包括睡眠状态和非睡眠状态，具体可以通过电子设备来检测用户的动作信息，然后根据动作信息区分睡眠状态和非睡眠状态。
进一步的，还可以进一步将睡眠状态分为浅度睡眠状态和深度睡眠状态，然后根据浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数，进一步提升准确性。其中，可以通过与电子设备关联的智能穿戴设备(如智能手环)获取用户的动作信息。比如在智能穿戴设备上设置体动传感器，该体动传感器可以设置于壳体与手腕接触的一侧，该体动传感器又可称为体动记录仪，可用于根据手腕的动作幅度和动作频率来衡量用户的睡眠质量。体动传感器可以监测到手腕的微小运动，进而判断该用户是处于清醒状态，还是处于浅度睡眠状态或深度睡眠状态。具体的，睡眠监测可以是通过体动传感器监测人的腕部动作， 通过预设计算方式进行累计计算，诸如，每2分钟记录一次合计值，与此同时结合动作信息判断睡眠状态。诸如，深度睡眠的人的肌肉会松弛，并且肢体不会产生较大的运动，甚至不会动，而浅度睡眠的人会产生一定的轻微运动。通过体动传感器监测手腕的运动状态，来确定用户当前的睡眠状态。也即根据所述睡眠时长生成所述用户的睡眠指数，包括：
获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
在一实施例中，上述情绪指数和疾病指数的计算方式可以包括：
获取用户图像；
根据卷积神经网络模型对所述用户图像进行处理，以得到用户的表情信息以及面色信息；
根据所述表情信息生成所述用户的情绪指数，根据所述面色信息生成所述用户的疾病指数。
其中，用户图像可以为用户通过前置摄像头自拍得到的图像，具体的，在获取到图像后，该图像可能包含有待识别的人脸区域，还可能包含背景或其他物体。通过对待识别图像进行人脸检测，可以确定待识别图像中的人脸区域，然后将人脸区域从待识别图像中截取出来。根据待识别图像的内容的不同，通常，截取出的人脸区域是不同形状、不同尺寸的，因此还可以进一步对截取出的人脸区域进行归一化处理，将截取出的不同尺寸的人脸区域的图像归一化为适用于预先训练得到的卷积神经网络模型的尺寸，然后进行处理以得到表情信息以及面色信息。
步骤202，构建用户健康评估级别，用户健康评估级别包括多个健康等级。
在一实施例中，上述健康等级可以为5个，比如可以包括优、良、中、差、极差5个等级。在其他实施例中，上述健康等级还可以根据数字来进行分类，比如第一健康等级、第二健康等级、第三健康等级、第四健康等级以及第五健康等级，其中，等级越高则表示用户越健康，也即第五健康等级表示用户最为健康，而第一健康等级则表示健康程度最差。
步骤203，通过K最近邻分类算法对多维健康指数进行分类，以得到对应的健康等级。
步骤204，根据健康等级生成动态虚拟形象并显示至电子设备屏幕。
在本申请实施例中，为提升展示用户健康等级的趣味性，可以根据健康等级生成动态虚拟形象并显示至电子设备屏幕。比如可以根据健康等级确定对应 虚拟形象的形态，并显示至电子设备的动态壁纸当中，比如动态壁纸上的鱼、海龟、卡通人物等。
步骤205，接收用户针对动态虚拟形象的触摸操作，并根据触摸操作展示用户的多维健康指数。
在一实施例中，当用户点击桌面上的动态虚拟形象后，还可以进一步展示用户的多维健康指数，以便用户看到更加具体的数据，比如用户健康等级低，主要原因是游戏指数太高，睡眠指数太低。
步骤206，选取多维健康指数当中不满足预设指数区间的异常健康指数，根据异常健康指数生成提示信息并显示至电子设备屏幕。
在一实施例中，可以对上述多维健康指数分别设置一个预设区间，作为衡量该健康指数是否达标的标准，将上述多维健康指数分别于其对应的预设区间进行对比，选取不满足预设指数区间的异常健康指数，然后根据该异常健康指数生成具有针对性的提示信息并显示至电子设备屏幕。比如，游戏指数太高，睡眠指数太低，即用户使用游戏时间太长，而睡眠太少。进一步，用户根据评估的健康级别和异常健康指数，能够针对性的采取相应的改善策略以提升自身的健康水平。
由上可知，本申请实施例提供的用户健康评估方法可以获取电子设备的多维特征信息，并根据多维特征信息生成用户的多维健康指数，构建用户健康评估级别，用户健康评估级别包括多个健康等级，通过K最近邻分类算法对多维健康指数进行分类，以得到对应的健康等级，根据健康等级生成动态虚拟形象并显示至电子设备屏幕，接收用户针对动态虚拟形象的触摸操作，并根据触摸操作展示用户的多维健康指数，选取多维健康指数当中不满足预设指数区间的异常健康指数，根据异常健康指数生成提示信息并显示至电子设备屏幕。本申请实施例可以根据电子设备的多维特征信息生成用户的多维健康信息，从而确定用户的健康等级，并对用户的健康状况进行评估，通过利用用户多方面的全景信息，提升了用户健康评估的准确性。
请参阅图4，图4为本申请实施例提供的用户健康评估装置的一种结构示意图。其中该用户健康评估装置30包括生成模块301、构建模块302、分类模块303以及评估模块304；
所述生成模块301，用于获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数。
在一实施例中，电子设备通过全景感知框架的信息感知层获取多个与用户健康相关的特征信息，比如电子设备的设置信息、应用程序使用信息、传感器 数据等等，生成模块301根据上述多个特征信息生成相应多维的用户健康指数，以得到用户的健康指数体系。
比如，可以基于全景感知模块中用户闹钟时间，生成用户睡眠指数，其中，用户睡眠时长越长，对应的睡眠指数越高；基于用户屏幕使用时间，生成用户用眼指数，其中，屏幕使用时长越长，用眼指数越高；基于游戏类应用程序使用时长，生成游戏指数，其中，首先确定游戏类应用程序，然后分别获取上述游戏应用程序的使用时长并计算总游戏时长，游戏时长越长，游戏指数越高；基于用户步数数据，生成用户活动指数，其中，可以根据电子设备当中的计步器获取用户的步数，步数越多，活动指数越高；基于用户饮食类应用程序数据，构建用户营养指数，其中，可以通过用户使用饮食类应用程序时查看的菜谱以及记录的饮食类数据，可以简单构建出用户每天的饮食构成，即生成用户的营养指数；基于用户照片，比如用户自拍的图像，进行脸部特征识别，识别出用户当前的情绪和面色，今儿构建出用户的情绪指数和疾病指数。进一步的，还可以根据上述获取的睡眠指数、用眼指数、游戏指数、活动指数、营养指数、情绪指数和疾病指数联合形成用户的健康指数体系。
所述构建模块302，用于构建用户健康评估级别，所述用户健康评估级别包括多个健康等级。
在一实施例中，上述健康等级可以为5个，比如可以包括优、良、中、差、极差5个等级。在其他实施例中，上述健康等级还可以根据数字来进行分类，比如第一健康等级、第二健康等级、第三健康等级、第四健康等级以及第五健康等级，其中，等级越高则表示用户越健康，也即第五健康等级表示用户最为健康，而第一健康等级则表示健康程度最差。
所述分类模块303，用于通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级。
在一实施例中，分类模块303通过预设分类算法将步骤101构建的健康指数体系分类到相应的健康等级上，即为电子设备根据用户的多为健康指数预测出用户当前的健康等级。在一实施例中，上述预设算法可以为K最近邻分类算法。
所述评估模块304，用于根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
在一实施例中，评估模块304可以以一天为单位生成一次健康评估报告，比如获取用户在一天内的多维健康指数，然后确定当天的健康等级并根据当天的健康等级以及多维健康指数生成健康评估信息，向用户推送并展示该健康评 估信息，用户可以直观地看到当天的健康评估等级以及具体的健康指数。
在一实施例中，电子设备还可以预设上述多维健康指数的标准值，当健康等级低于一预设等级时，将上述多维健康指数与预设的标准值进行对比，然后根据对比的结果生成具有针对性的提示信息。
在一实施例中，请参阅图5，图5为本申请实施例提供的用户健康评估装置的一种结构示意图，其中，所述多维健康指数包括睡眠指数，所述生成模块301可以包括监测子模块3011和生成子模块3012；
所述监测子模块3011，用于监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长；
所述生成子模块3012，用于根据所述睡眠时长生成所述用户的睡眠指数
进一步的，所述生成模块301还可以包括获取子模块3013；
所述获取子模块3013，用于获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
所述生成子模块3012，具体用于根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
在一实施例中，所述评估模块304包括显示子模块3041和反馈子模块3042；
所述显示子模块3041，用于根据所述健康等级生成动态虚拟形象并显示至所述电子设备屏幕；
所述反馈子模块3042，用于接收用户针对所述动态虚拟形象的触摸操作，并根据所述触摸操作展示所述用户的多维健康指数。
进一步的，所述评估模块304还可以包括提示子模块3043；
所述提示子模块3043，用于选取所述多维健康指数当中不满足预设指数区间的异常健康指数，根据所述异常健康指数生成提示信息并显示至所述电子设备屏幕。
由上述可知，本申请实施例的用户健康评估装置可以获取电子设备的多维特征信息，并根据多维特征信息生成用户的多维健康指数，构建用户健康评估级别，用户健康评估级别包括多个健康等级，通过预设算法对多维健康指数进行分类，以得到对应的健康等级，根据健康等级以及用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。本申请实施例可以根据电子设备的多维特征信息生成用户的多维健康信息，从而确定用户的健康等级，并对用户的健康状况进行评估，通过利用用户多方面的全景信息，提升了用户健康评估的准确性。
本申请实施例中，用户健康评估装置与上文实施例中的用户健康评估方法属于同一构思，在用户健康评估装置上可以运行用户健康评估方法实施例中提供的任一方法，其具体实现过程详见用户健康评估方法的实施例，此处不再赘述。
本文所使用的术语“模块”可看作为在该运算系统上执行的软件对象。本文所述的不同组件、模块、引擎及服务可看作为在该运算系统上的实施对象。而本文所述的装置及方法可以以软件的方式进行实施，当然也可在硬件上进行实施，均在本申请保护范围之内。
本申请实施例还提供一种存储介质，其上存储有计算机程序，当所述计算机程序在计算机上运行时，使得所述计算机执行上述的用户健康评估方法。
本申请实施例还提供一种电子设备，如平板电脑、手机等。电子设备中的处理器会按照如下的步骤，将一个或一个以上的应用程序的进程对应的指令加载到存储器中，并由处理器来运行存储在存储器中的应用程序，从而实现各种功能：
获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
在一实施例中，所述多维健康指数包括睡眠指数，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数时，所述处理器用于执行以下步骤：
监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长；
根据所述睡眠时长生成所述用户的睡眠指数。
在一实施例中，根据所述睡眠时长生成所述用户的睡眠指数时，所述处理器用于执行以下步骤：
获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
在一实施例中，所述多维健康指数包括情绪指数和疾病指数，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数时，所述处理器用于执行以下步骤：
获取用户图像；
根据卷积神经网络模型对所述用户图像进行处理，以得到用户的表情信息以及面色信息；
根据所述表情信息生成所述用户的情绪指数，根据所述面色信息生成所述用户的疾病指数。
在一实施例中，根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕时，所述处理器用于执行以下步骤：
根据所述健康等级生成动态虚拟形象并显示至所述电子设备屏幕；
接收用户针对所述动态虚拟形象的触摸操作，并根据所述触摸操作展示所述用户的多维健康指数。
在一实施例中，在根据所述触摸操作展示所述用户的多维健康指数之后，所述处理器还用于执行以下步骤：
选取所述多维健康指数当中不满足预设指数区间的异常健康指数；
根据所述异常健康指数生成提示信息并显示至所述电子设备屏幕。
在一实施例中，所述预设算法为K最近邻分类算法。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
请参阅图6，电子设备400包括处理器401以及存储器402。其中，处理器401与存储器402电性连接。
处理器400是电子设备400的控制中心，利用各种接口和线路连接整个电子设备的各个部分，通过运行或加载存储在存储器402内的计算机程序，以及调用存储在存储器402内的数据，执行电子设备400的各种功能并处理数据，从而对电子设备400进行整体监控。
存储器402可用于存储软件程序以及模块，处理器401通过运行存储在存储器402的计算机程序以及模块，从而执行各种功能应用以及数据处理。存储器402可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的计算机程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据电子设备的使用所创建的数据等。此外，存储器402可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。相应地，存储器 402还可以包括存储器控制器，以提供处理器401对存储器402的访问。
在本申请实施例中，电子设备400中的处理器401会按照如下的步骤，将一个或一个以上的计算机程序的进程对应的指令加载到存储器402中，并由处理器401运行存储在存储器402中的计算机程序，从而实现各种功能，如下：
获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
请一并参阅图7，在一些实施方式中，电子设备400还可以包括：显示器403、射频电路404、音频电路405以及电源406。其中，其中，显示器403、射频电路404、音频电路405以及电源406分别与处理器401电性连接。
显示器403可以用于显示由用户输入的信息或提供给用户的信息以及各种图形用户接口，这些图形用户接口可以由图形、文本、图标、视频和其任意组合来构成。显示器403可以包括显示面板，在一些实施方式中，可以采用液晶显示器(Liquid Crystal Display，LCD)、或者有机发光二极管(Organic Light-Emitting Diode，OLED)等形式来配置显示面板。
射频电路404可以用于收发射频信号，以通过无线通信与网络设备或其他电子设备建立无线通讯，与网络设备或其他电子设备之间收发信号。
音频电路405可以用于通过扬声器、传声器提供用户与电子设备之间的音频接口。
电源406可以用于给电子设备400的各个部件供电。在一些实施例中，电源406可以通过电源管理系统与处理器401逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。
尽管图7中未示出，电子设备400还可以包括摄像头、蓝牙模块等，在此不再赘述。
在本申请实施例中，存储介质可以是磁碟、光盘、只读存储器(Read Only Memory，ROM)、或者随机存取记忆体(Random Access Memory，RAM)等。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
需要说明的是，对本申请实施例的用户健康评估方法而言，本领域普通测 试人员可以理解实现本申请实施例用户健康评估方法的全部或部分流程，是可以通过计算机程序来控制相关的硬件来完成，计算机程序可存储于一计算机可读取存储介质中，如存储在电子设备的存储器中，并被该电子设备内的至少一个处理器执行，在执行过程中可包括如用户健康评估方法的实施例的流程。其中，的存储介质可为磁碟、光盘、只读存储器、随机存取记忆体等。
对本申请实施例的用户健康评估装置而言，其各功能模块可以集成在一个处理芯片中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，也可以存储在一个计算机可读取存储介质中，存储介质譬如为只读存储器，磁盘或光盘等。
以上对本申请实施例所提供的一种用户健康评估方法、装置、存储介质及电子设备进行了详细介绍，本文中应用了具体个例对本申请的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本申请的方法及其核心思想；同时，对于本领域的技术人员，依据本申请的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本申请的限制。

Claims (20)

  1. 一种用户健康评估方法，其中，所述方法包括以下步骤：
    获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
    构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
    通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
    根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
  2. 根据权利要求1所述的用户健康评估方法，所述多维健康指数包括睡眠指数，其中，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数，包括：
    监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长；
    根据所述睡眠时长生成所述用户的睡眠指数。
  3. 根据权利要求2所述的用户健康评估方法，其中，根据所述睡眠时长生成所述用户的睡眠指数，包括：
    获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
    根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
  4. 根据权利要求1所述的用户健康评估方法，所述多维健康指数包括情绪指数和疾病指数，其中，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数，包括：
    获取用户图像；
    根据卷积神经网络模型对所述用户图像进行处理，以得到用户的表情信息以及面色信息；
    根据所述表情信息生成所述用户的情绪指数，根据所述面色信息生成所述用户的疾病指数。
  5. 根据权利要求1所述的用户健康评估方法，其中，根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕，包括：
    根据所述健康等级生成动态虚拟形象并显示至所述电子设备屏幕；
    接收用户针对所述动态虚拟形象的触摸操作，并根据所述触摸操作展示所述用户的多维健康指数。
  6. 根据权利要求5所述的用户健康评估方法，其中，在根据所述触摸操 作展示所述用户的多维健康指数之后，所述方法还包括：
    选取所述多维健康指数当中不满足预设指数区间的异常健康指数；
    根据所述异常健康指数生成提示信息并显示至所述电子设备屏幕。
  7. 根据权利要求1所述的用户健康评估方法，其中，所述预设算法为K最近邻分类算法。
  8. 一种用户健康评估装置，其中，所述装置包括：生成模块、构建模块、分类模块以及评估模块；
    所述生成模块，用于获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
    所述构建模块，用于构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
    所述分类模块，用于通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
    所述评估模块，用于根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
  9. 根据权利要求8所述的用户健康评估装置，所述多维健康指数包括睡眠指数，其中，所述生成模块包括：监测子模块和生成子模块；
    所述监测子模块，用于监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长；
    所述生成子模块，用于根据所述睡眠时长生成所述用户的睡眠指数。
  10. 根据权利要求9所述的用户健康评估装置，其中，所述生成模块还包括获取子模块；
    所述获取子模块，用于获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
    所述生成子模块，具体用于根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
  11. 根据权利要求8所述的用户健康评估装置，其中，所述评估模块包括显示子模块和反馈子模块；
    所述显示子模块，用于根据所述健康等级生成动态虚拟形象并显示至所述电子设备屏幕；
    所述反馈子模块，用于接收用户针对所述动态虚拟形象的触摸操作，并根据所述触摸操作展示所述用户的多维健康指数。
  12. 根据权利要求11所述的用户健康评估装置，其中，所述评估模块还 包括提示子模块；
    所述提示子模块，用于选取所述多维健康指数当中不满足预设指数区间的异常健康指数，根据所述异常健康指数生成提示信息并显示至所述电子设备屏幕。
  13. 一种存储介质，其上存储有计算机程序，其中，当所述计算机程序在计算机上运行时，使得所述计算机执行如权利要求1至7任一项所述的用户健康评估方法。
  14. 一种电子设备，包括处理器和存储器，所述存储器存储有多条指令，其中，所述处理器加载所述存储器中的指令用于执行以下步骤：
    获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数；
    构建用户健康评估级别，所述用户健康评估级别包括多个健康等级；
    通过预设算法对所述多维健康指数进行分类，以得到对应的健康等级；
    根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕。
  15. 根据权利要求14所述的电子设备，所述多维健康指数包括睡眠指数，其中，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数时，所述处理器用于执行以下步骤：
    监测用户的睡眠状态并根据所述睡眠状态记录睡眠时长；
    根据所述睡眠时长生成所述用户的睡眠指数。
  16. 根据权利要求15所述的电子设备，其中，根据所述睡眠时长生成所述用户的睡眠指数时，所述处理器用于执行以下步骤：
    获取用户的动作信息，并根据所述动作信息将所述睡眠时长划分为浅度睡眠时长和深度睡眠时长；
    根据所述浅度睡眠时长和深度睡眠时长生成所述用户的睡眠指数。
  17. 根据权利要求14所述的电子设备，所述多维健康指数包括情绪指数和疾病指数，其中，获取电子设备的多维特征信息，并根据所述多维特征信息生成用户的多维健康指数时，所述处理器用于执行以下步骤：
    获取用户图像；
    根据卷积神经网络模型对所述用户图像进行处理，以得到用户的表情信息以及面色信息；
    根据所述表情信息生成所述用户的情绪指数，根据所述面色信息生成所述用户的疾病指数。
  18. 根据权利要求14所述的电子设备，其中，根据所述健康等级以及所述用户的多维健康指数生成健康评估信息，并显示至电子设备屏幕时，所述处理器用于执行以下步骤：
    根据所述健康等级生成动态虚拟形象并显示至所述电子设备屏幕；
    接收用户针对所述动态虚拟形象的触摸操作，并根据所述触摸操作展示所述用户的多维健康指数。
  19. 根据权利要求18所述的电子设备，其中，在根据所述触摸操作展示所述用户的多维健康指数之后，所述处理器还用于执行以下步骤：
    选取所述多维健康指数当中不满足预设指数区间的异常健康指数；
    根据所述异常健康指数生成提示信息并显示至所述电子设备屏幕。
  20. 根据权利要求14所述的电子设备，其中，所述预设算法为K最近邻分类算法。
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