CN112370058A - Method for identifying and monitoring emotion of user based on mobile terminal - Google Patents
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Abstract
The invention provides a method for identifying and monitoring the emotion of a user based on a mobile terminal, which is different from the traditional single prediction based on expression, behavior and language, and collects various data generated by the user such as communication condition, sleep condition, APP use condition and the like through a mobile phone and a bracelet which are carried by the user daily to construct a data set highly covering the life of the user so as to finish the accurate perception of the emotion of the user. According to the method, the collection process is simplified in the data collection process, the user friendliness is improved, the emotional characteristics of the user are described in a multi-dimensional view, the accuracy of the identification result is improved, meanwhile, in the data processing stage, the key characteristics are extracted for modeling, the complexity of a subsequent machine learning algorithm is reduced, and the algorithm operation time is shortened.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method for identifying and monitoring emotion of a user based on a mobile terminal.
Background
Currently, user emotion recognition is mainly divided into two modules: user emotion recognition based on the traditional way (face-based user emotion recognition, text-based user emotion recognition, voice-based user emotion recognition, and physiological feature-based user emotion recognition) and user emotion recognition based on the new way (gesture behavior-based user emotion recognition).
The traditional emotion technology identification method has good applicability to capturing instantaneous emotion of a user in scientific research, but in real life, the emotion of the user may change every moment, and the user may tend to obtain average emotion in a certain period of time compared with the analysis of the instantaneous emotion. The user emotion recognition based on the traditional method only describes the user emotion from a single angle, so that the accuracy of emotion recognition is reduced. Meanwhile, complex equipment is sometimes required to be worn for data collection based on user emotion recognition of the traditional method, for example, the user emotion analysis is carried out based on brain waves of the user, and the user is required to wear a responsible brain wave collecting device for collecting physiological characteristics. In addition, in subsequent training networks, emotion technology recognition based on the traditional method is to train the network by using an open-source data set, and the data may have the problems of low naturalness, poor soundness and the like, so that the emotion recognition accuracy is directly influenced.
Disclosure of Invention
Aiming at the defects, the invention provides a method for identifying and monitoring the emotion of a user based on a mobile terminal, which collects diversified daily data generated by equipment of the mobile terminal, such as a smart phone and a smart bracelet, extracts characteristic values and creates a model, so that the emotion of the user can be described as accurately as possible from multiple aspects and angles under the condition of not influencing the daily life and work of the user, and meanwhile, an analysis result is provided for other programs in need to be utilized.
The technical scheme of the invention is as follows: a method for identifying and monitoring emotion of a user based on a mobile terminal comprises the following steps:
step 1: collecting data of a user to generate a data set; the data heart rate data and the emotion data; the data set comprises a heart rate data set and an emotion data set;
step 2: the data preliminary processing comprises supplementing the condition that a part of data in a data set has characteristic value loss, integrating the data and extracting corresponding characteristic values;
and step 3: and performing emotion recognition, and selecting a model for recognition.
Further, the method for identifying and monitoring the emotion of the user based on the mobile terminal comprises the steps that heart rate data are divided into daytime heart rate data and nighttime heart rate data, and the daytime heart rate data are obtained through a heart rate sensor; the night heart rate data is obtained through a three-axis acceleration sensor and a heart rate sensor.
Further, the emotion data comprises an active class, a general class and a passive class.
Further, the method for identifying and monitoring the emotion of the user based on the mobile terminal is characterized in that the characteristic value is supplemented by an automatic strategy according to other known values and by means of an average value.
Furthermore, the method for identifying and monitoring the emotion of the user based on the mobile terminal is characterized in that after the corresponding characteristic values are extracted, part of data are integrated and the corresponding characteristic values are extracted after the data collected by the APP are classified and subjected to graphic visualization observation.
Further, a method for identifying and monitoring the emotion of a user based on a mobile terminal is provided, wherein the emotion characteristics relate to APP indexes, physiological indexes, sleep indexes and communication indexes;
the emotional characteristics are described as follows:
wherein
Is an APP index,
Is used as a physiological index,
Is used as an index of sleep,
Is the communication index.
Further, the mobile terminal-based emotion recognition and monitoring method for the user comprises an SVM (support vector machine) model and a neural network model.
According to the invention, the daily data of the user is collected through the smart phone and the smart bracelet, the characteristic data is found out for network training, so that the mapping relation between the external expression data and the internal emotion of the user is obtained, and the internal emotion of the user is identified. Daily data of a user are collected based on the mobile device, and the problems of low data naturalness, poor soundness and the like in traditional emotion recognition can be solved. Firstly, the high popularity of the smart phone in China can ensure the quantity of collectable data; secondly, different people have different conditions for using the mobile phone, so that the specificity of data and the soundness of experimental results can be ensured; finally, the mobile phone is portable and easy to carry, and different from traditional emotion recognition, the novel emotion recognition based on the smart phone can conveniently acquire data generated by a user in daily life to recognize the emotion of the user without the need of wearing an additional data collection device by the user.
The invention relates to a method for identifying and monitoring the emotion of a user based on a mobile terminal, which is different from the traditional method that the emotion of the user is simply predicted based on expression, behavior and language, and collects various data generated by the user such as communication condition, sleep condition, APP use condition and the like through a mobile phone and a bracelet which are carried by the user daily to construct a data set highly covering the life of the user, thereby finishing accurate perception of the emotion of the user. According to the method, the collection process is simplified in the data collection process, the user friendliness is improved, the emotional characteristics of the user are described in a multi-dimensional view, the accuracy of the identification result is improved, meanwhile, in the data processing stage, the key characteristics are extracted for modeling, the complexity of a subsequent machine learning algorithm is reduced, and the algorithm operation time is shortened.
Drawings
FIG. 1 is a flow chart of emotion recognition and monitoring based on a mobile end user.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings: as shown in fig. 1, a method for identifying and monitoring emotion of a user based on a mobile terminal includes the following steps.
Step 1: collecting data of a user to generate a data set; the data heart rate data and the emotion data; the data sets include a heart rate data set and an emotion data set.
In order to obtain a more prepared result, dividing the heart rate data into daytime heart rate data and nighttime heart rate data, wherein the daytime heart rate data is obtained through a heart rate sensor; the night heart rate data is obtained through a three-axis acceleration sensor and a heart rate sensor. The emotion data comprises an active class, a general class and a passive class.
Specifically, a mobile phone APP is designed to collect data generated by a user every day. Recording the service time of alpha application programs through the APP through the UsageStats to obtain a set
The function of the pedometer is realized through the newly added STEP DETECTOR and STEP COUNTER sensors of the Android, the number s of the STEPs of the user on the day and the movement distance d are recorded, and a set is obtained
The total communication time duration ct and the times cf of the user are collected through the PhoneStateListener to obtain data
Secondly, design a bracelet APP, through the rhythm of the heart sensor that carries on the bracelet, record daytime rhythm of the heart data
Three-axis acceleration sensor and heart rate sensor carried by bracelet set time node to record night heart rate data
And triaxial acceleration data
Collecting emotion data of a user every day, and dividing the emotion into three categories: 1 → positive, 0 → general, -1 → negative, resulting in the user's daily emotion data set
According to the data generated by the mobile terminal every day and the emotional change of the user every day, a data set can be obtained:
step 2: the data preliminary processing comprises supplementing the condition that a part of data in a data set has characteristic value loss, integrating the data and extracting corresponding characteristic values; supplementing the characteristic value by using an automatic strategy according to other known values and utilizing an average value; the corresponding characteristic value is extracted by integrating part of data after the data classification collected by APP is subjected to graphic visualization observation; the emotional characteristics relate to APP indexes, physiological indexes, sleep indexes and communication indexes.
The specific method comprises the following steps: the collected data is managed first, and is mainly divided into three parts: firstly, aiming at the condition that a part of data in a data set has characteristic value loss, an automatic strategy is adopted to supplement the characteristic value by using an average value according to other known values; and secondly, integrating data, and integrating part of data to extract corresponding characteristic values after carrying out graphic visual observation through data classification collected by APP.
APP index
The APP index comprises the type of APP, the using time of the APP and the using times of the APP. Considering the kinds of APP on the market and the influence on the emotion of the user, APP is currently classified into three main categories: work, entertainment, social. The total working time of the user on the day can be obtained according to different APP types
Total time of day's recreation
Total social duration of the day
Considering the difference of the influence degree of the use time of different APP types on the user emotion, carrying out weighting processing on the total APP durations of different types to obtain APP indexes:
physiological index
Firstly, obtaining the movement distance s and the step number d of a user every day through a mobile phone APP to obtain a set
Secondly design a bracelet APP, through the rhythm of the heart sensor that carries on the bracelet, set up the time node, once rhythm of the heart data and triaxial acceleration are collected to every 1h, obtain the maximum value set of rhythm of the heart:
and (3) obtaining the variance of the daytime heart rate data to obtain a daytime heart rate abnormal value:
further, the physiological indexes can be obtained:
sleep index
Setting a time node, collecting heart rate data and triaxial acceleration every 30min at night to obtain a heart rate maximum set:
further obtaining a sleep index:
communication index
Communication data
Indicates the duration of communication, and
the communication times are indicated, and the average communication time length can be obtained
Combining the indexes, the emotional characteristics are described as follows:
the above symbols have the following meanings:
h_mε-the ith user maximum daytime heart rate at the jth time interval on day j;
And step 3: and performing emotion recognition, and selecting a model for recognition. The model comprises an SVM model and a neural network model.
Model selection
In order to select a better model, a plurality of classification methods capable of identifying and monitoring the emotion of the user are used for comparison.
Based on SVM model
SVM is a very common and efficient classifier that establishes a maximally spaced hyperplane in space by mapping vectors into a higher latitude space.
The invention adopts default Graph of TensorFlow to create ops, and uses placeholder () as an input container of data, and indicates the corresponding node of TensorFlow operation diagram through sess. And continuously training through the collected training data to obtain a usable classification model.
The main steps of the experiment are as follows: analyzing a data set, performing cross validation, preprocessing data, drawing a data flow chart, defining a Gaussian kernel function, creating a dual loss function, creating a classification kernel function, creating a classification function, setting an optimizer, initializing, training and evaluating a model.
Neural network model
The neural network model is also called artificial neural network, is a derivative for simulating the working principle of human neuron cerebral neurons by using a mathematical method, and is also divided into an input layer, a hidden layer and an output layer. The input layer inputs the characteristic data, defines the number of layers of the hidden layer and the number of neurons of each layer, and the output layer is used for outputting results.
The neural network model constructed by the project is trained by using a data set generated in the daily life of the user and collected by the mobile terminal, and an available emotion recognition model is trained through continuous optimization to recognize and monitor the daily emotion of the user. Here mainly the TensorFlow two higher order categories are used: APIEstimator and Dataset to construct a neural network model. The collected data were divided into two categories, one for training as a sample and one for evaluation, while the training process was visualized using a TensorBoard.
The experiment is divided into the following steps: importing and analyzing a data set, describing data, defining a model type, defining a training input function, training a model, defining a test model, and evaluating the model.
Claims (7)
1. A method for identifying and monitoring emotion of a user based on a mobile terminal is characterized by comprising the following steps: the method comprises the following steps:
step 1: collecting data of a user to generate a data set; the data heart rate data and the emotion data; the data set comprises a heart rate data set and an emotion data set;
step 2: the data preliminary processing comprises supplementing the condition that a part of data in a data set has characteristic value loss, integrating the data and extracting corresponding characteristic values;
and step 3: and performing emotion recognition, and selecting a model for recognition.
2. The method for recognizing and monitoring the emotion of a user based on a mobile terminal as claimed in claim 1, wherein: the heart rate data are divided into daytime heart rate data and nighttime heart rate data, and the daytime heart rate data are obtained through a heart rate sensor; the night heart rate data is obtained through a three-axis acceleration sensor and a heart rate sensor.
3. The method for recognizing and monitoring the emotion of a user based on a mobile terminal as claimed in claim 1, wherein: the emotion data comprises an active class, a general class and a passive class.
4. The method for recognizing and monitoring the emotion of a user based on a mobile terminal as claimed in claim 1, wherein: and supplementing the characteristic value by using an automatic strategy according to other known values and utilizing an average value.
5. The method for recognizing and monitoring the emotion of a user based on a mobile terminal as claimed in claim 1, wherein: and the corresponding characteristic value is extracted by integrating part of data after the data classification collected by the APP is subjected to graphic visual observation.
6. The method for recognizing and monitoring the emotion of a user based on a mobile terminal as claimed in claim 5, wherein: the emotional characteristics relate to APP indexes, physiological indexes, sleep indexes and communication indexes;
the emotional characteristics are described as follows:
wherein
Is an APP index,
Is used as a physiological index,
Is used as an index of sleep,
Is the communication index.
7. The method for recognizing and monitoring the emotion of a user based on a mobile terminal as claimed in claim 1, wherein: the model comprises an SVM model and a neural network model.
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| CN116631628A (en) * | 2023-07-21 | 2023-08-22 | 北京中科心研科技有限公司 | Method and device for identifying dysthymia and wearable equipment |
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