CN112489815A - Depression emotion monitoring method and device and readable storage medium - Google Patents
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Abstract
The invention discloses a method and a device for monitoring depression emotion and a readable storage medium, wherein the method comprises the following steps: monitoring behavioral data of a user; calculating target data characteristics from the behavior data according to a preset rule; and predicting based on the target data characteristics through a preset prediction model to obtain a prediction result. Selecting target data characteristics from the data characteristics according to a preset rule; and predicting based on the target data characteristics through a preset prediction model, so that the influence of individual difference is reduced, and the monitoring and early warning effects on the mood change of the depression patient are achieved.
Description
Technical Field
The invention relates to the field of user behavior management, in particular to a method and a device for monitoring depression and emotion and a readable storage medium.
Background
Depression is a common mental disorder, estimated in 3.5 million patients worldwide. Depression, unlike general mood swings and transient emotional responses to daily life challenges, can be highly affected, leading to suicide at the most severe, with up to 100 million suicide deaths per year estimated. The current diagnosis method of depression is mainly performed in a way that doctors make an inquiry and patients remember past emotional states, and sometimes the memory of the patients is deviated and blurred. The course of treatment for depression is often long and physicians often need to adjust their treatment regimen by knowing the patient's long-term mental state.
Currently, related research is mainly divided into two directions, namely, a single model is used due to great individual difference of mobile phone use habits, and a personal customized model is developed aiming at individual patients.
The emotional states of all patients are difficult to predict by using a single model, a large amount of data is needed for supporting, the experimental grouping conditions of the depression patients are complex, the number of patients in a data set of most of current researches is limited, and the influence of individual difference is difficult to remove.
Disclosure of Invention
The embodiment of the invention provides a method and a device for monitoring depression mood and a readable storage medium, which are used for reducing the influence of individual difference and improving the depression detection effect.
In a first aspect, the embodiments of the present invention provide a method for monitoring mood of depression, including:
monitoring behavioral data of a user;
calculating target data characteristics from the behavior data according to a preset rule;
and predicting based on the target data characteristics through a preset prediction model to obtain a prediction result.
Optionally, the monitoring of the user behavior data includes:
monitoring the use behavior data of a user using the terminal and acquiring the scale data fed back by the user;
wherein the gauge data comprises: PHQ-9 self-rating scale fed back by user.
Optionally, calculating the target data feature from the behavior data according to a preset rule, including:
performing data processing on the behavior data, and eliminating incomplete data and dirty data;
and merging the prior designated times and the current scale data, classifying the merged scale data according to a preset classification standard, and setting a corresponding data label for the classified scale data.
Optionally, calculating the target data feature from the behavior data according to a preset rule, further including:
and determining the terminal use information, the sleep and movement characteristics of the user in each preset time period according to the behavior data.
Optionally, determining sleep and movement characteristics of the user in each preset time period includes:
cosine fitting is carried out on the sleep heart rate, and then corresponding sleep curve characteristics are extracted;
determining a feature value of the sleep curve feature;
the characteristic value includes at least one of: mean, median and standard deviation.
Optionally, calculating the target data feature from the behavior data according to a preset rule, further including:
selecting corresponding target data characteristics from the use behavior data and the scale data by using a preset characteristic selection model;
wherein the preset feature selection model at least comprises one of the following: tree-based feature selection, L1 paradigm feature selection.
Optionally, before predicting based on the target data feature by using a preset prediction model, the method further includes:
and training different machine learning models through the historical target data characteristics and the corresponding labels to determine a prediction model with the best prediction capability under the condition of using different target data characteristics.
Optionally, after the target data feature is predicted by using a preset prediction model, the method further includes: and feeding back the prediction result to a user.
In a second aspect, an embodiment of the present invention provides a depression mood monitoring device, including:
the data acquisition module is used for monitoring behavior data of a user;
the data processing module is used for calculating target data characteristics from the behavior data according to a preset rule;
and the prediction module is used for predicting based on the target data characteristics through a preset prediction model so as to obtain a prediction result.
In a third aspect, the present invention is embodied in a computer readable storage medium storing one or more computer programs, which are executable by one or more processors to implement the steps of the aforementioned depression mood monitoring method.
According to the embodiment of the invention, target data characteristics are selected from the behavior data according to a preset rule; and predicting based on the target data characteristics through a preset prediction model, so that the influence of individual difference is reduced, and the monitoring and early warning effects on the mood change of the depression patient are achieved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a basic flow diagram of a first embodiment of the present invention;
FIG. 2 is a general flow chart of the first embodiment of the present invention;
FIG. 3 is a flowchart illustrating model training according to a first embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example one
A first embodiment of the present invention provides a method for monitoring mood of depression, as shown in fig. 1, comprising the following specific steps:
s101, monitoring behavior data of a user;
s102, calculating target data characteristics from the behavior data according to a preset rule;
s103, forecasting is carried out on the basis of the target data characteristics through a preset forecasting model so as to obtain a forecasting result.
Monitoring user's action data can be through wearing intelligent wearing equipment such as bracelet, intelligent wrist-watch in this embodiment, and the cooperation cell-phone carries out data acquisition. Use cell-phone and bracelet as example, in this example, can install corresponding APP in the cell-phone, through APP monitoring user's terminal service data, can also pair through cell-phone and bracelet, through bracelet monitoring user's physiological characteristic data, for example the rhythm of the heart, sleep data, step number etc.. The data are combined to form corresponding behavior data of the user.
And then calculating target data characteristics in the user behavior data, selecting the target data characteristics with significance, and predicting through a preset prediction model according to the selected target data characteristics to obtain a prediction result of the corresponding user.
According to the embodiment of the invention, target data characteristics are selected from the behavior data according to a preset rule; and predicting based on the target data characteristics through a preset prediction model, so that the influence of individual differences is reduced, and the monitoring and early warning effects on the mood changes of the depression patients are achieved.
Optionally, the monitoring of the user behavior data includes:
monitoring the use behavior data of a user using the terminal and acquiring the scale data fed back by the user;
wherein the gauge data comprises: PHQ-9 self-rating scale fed back by user.
In some embodiments, the usage behavior data of the user using the terminal may be monitored, where the usage behavior data of the user using the terminal may include a mobile phone call: the number of calls, outgoing calls and refusing calls, the call duration, the number of people in the call and the like, and the service duration of the mobile phone is as follows: the method comprises the steps of total screen lightening time, screen lightening times, screen lightening time each time and the like of a mobile phone screen. The scale data may be a patient's regular active fill-in depression self-rating scale PHQ-9. The scale may be set to backtrack past week emotional states, with the patient fixed filling the scale at weeks 2, 4, 8, and 12 after enrollment, with the remaining time being at the discretion of the patient. Finally, the behavior data of the user can be determined according to the usage behavior data and the scale data.
Calculating target data characteristics from the behavior data according to preset rules, wherein the target data characteristics comprise:
performing data processing on the behavior data, and eliminating incomplete data and dirty data;
and merging the prior designated times and the current scale data, classifying the merged scale data according to a preset classification standard, and setting a corresponding data label for the classified scale data.
In some embodiments, the behavior data is subjected to data processing, including cleaning and preprocessing the data according to data acquisition conditions, and incomplete data and dirty data are removed. The preprocessing can be to calculate the entropy of the number of people in the call according to the total screen-lighting time, the screen-lighting times, the screen-lighting time of each time and the like of the mobile phone screen. The method comprises the following steps of determining the total screen lightening time, the screen lightening times and the screen lightening time of each time of the mobile phone screen, and accordingly determining the using times, the screen lightening time and the average screen lightening time of the mobile phone in different periods. The different time periods in this embodiment may be, for example, three periods early (6:00-12:00), medium (12:00-18:00), and late (18:00-24: 00).
As shown in fig. 2, in this embodiment, the data cleaning process may respectively perform data cleaning on the usage behavior data of the user usage terminal collected by the mobile phone and the physiological data collected by the bracelet, and remove incomplete data and dirty data. The using behavior data comprise the total screen-lighting duration, the screen-lighting times, the screen-lighting duration of each time and the like of a mobile phone screen, and the physiological data can comprise the heart rate, the sleep data, the step number and the like collected by the bracelet. Clean user behavior data can be obtained after data cleansing.
For the self-rating table PHQ-9 filled by the user, the previously specified times and the data of the current table may be merged in this embodiment, for example, the self-rating table PHQ-9 filled by the current user and the previously twice self-rating table PHQ-9 are merged into one data record, and then the data record is labeled according to the preset classification standard. Specific classification criteria are shown in table 1 below:
TABLE 1
And setting corresponding data labels after classifying according to the merged scale data.
Optionally, calculating the target data feature from the behavior data according to a preset rule, further including:
and determining the terminal use information, the sleep and movement characteristics of the user in each preset time period according to the behavior data.
In some embodiments, as shown in fig. 2, after obtaining the cleaned usage behavior data and physiological data, performing feature calculation according to a preset rule, in this embodiment, the mobile phone APP is classified into 8 types according to functions:
-communication: WeChat, QQ, etc
-social interaction: micro-blogs, twigs, small red books, etc
-shopping: taobao, Jingdong, Shuduo, etc
-entertainment: tremble, beep li, you cool, etc
-music: network music, QQ music, dried shrimp music, etc
-take out: beautifying the ball, hungry and doing so
-the other: hundred degrees and have a way
And then determining the terminal use information of the user in each preset time period according to the behavior data.
For example, the average usage length, standard deviation, entropy, and usage length and usage number of APP in each period (0:00-3:00am,3:00-6:00am,6:00-9:00am,9:00-12:00pm,12:00-3:00pm,3:00-6:00pm,6:00-9:00pm,9:00-12:00am) are calculated for each type of APP.
Movement and sleep: and calculating the sleep time length, the standard deviation of the change of the sleep time length, the deep sleep light sleep time length and the standard deviation, wherein the deep sleep light sleep time length accounts for the proportion of the total sleep time length. Calculating the average step number, the standard deviation of the step number, and the step number of the following time periods (0:00-3:00am,3:00-6:00am,6:00-9:00am,9:00-12:00pm,12:00-3:00pm,3:00-6:00pm,6:00-9:00pm,9:00-12:00 am).
Optionally, determining sleep and movement characteristics of the user in each preset time period includes:
cosine fitting is carried out on the sleep heart rate, and then corresponding sleep curve characteristics are extracted;
determining a feature value of the sleep curve feature;
the characteristic value includes at least one of: mean, median and standard deviation.
In another embodiment, for example, after the sleep heart rate is determined by the bracelet, data processing is further performed, cosine fitting is performed on the sleep heart rate, then curve features are extracted, and the average value, median and standard deviation are calculated. The target data feature in this embodiment includes a feature calculated after cosine fitting of the sleep heart rate, thereby identifying a variation before and after the sleep heart rate.
In some alternative embodiments, as shown in fig. 2, the method of the present invention performs feature calculations based on the usage behavior data and the physiological data, respectively, to thereby obtain target data features. Because different people use mobile phones or sleep conditions are different, but the trend that corresponding behavior data change along with depression conditions possibly has a certain rule, the method of the embodiment predicts by calculating the change conditions of various behavior data as characteristic values so as to reduce individual differences, can avoid one-sidedness of single data, and improves monitoring and early warning effects on mood changes of depression patients.
Optionally, calculating the target data feature from the behavior data according to a preset rule, further including:
selecting corresponding target data characteristics from the use behavior data and the scale data by using a preset characteristic selection model;
wherein the preset feature selection model at least comprises one of the following: tree-based feature selection, L1 paradigm feature selection.
In another embodiment, the target data features with significance can be selected by using a feature selection model such as tree-based feature selection, L1 paradigm feature selection, and the like, and the target data features selected by different models can be different.
Optionally, as shown in fig. 3, before the predicting based on the target data feature by using the preset prediction model, the method further includes:
and training different machine learning models through the historical target data characteristics and the corresponding labels to determine a prediction model with the best prediction capability under the condition of using different target data characteristics.
Optionally, after the target data feature is predicted by using a preset prediction model, the method further includes: and feeding back the prediction result to a user.
In an optional implementation mode, the prediction result can be fed back to the patient, family members of the patient and a doctor through the APP, and early warning information is pushed if the emotion changes remarkably.
In summary, the embodiment of the present invention establishes a prediction model for the emotion variation, so that the influence of individual differences of mobile phone usage behaviors is reduced as much as possible when a single model is used. The method can make up the condition that the patient lacks of emotion data records during the interval of the follow-up examination, so that the doctor can better know the emotion change of the patient. Meanwhile, the early warning can be given to the patient, the family members of the patient and the doctor when the patient has large emotional fluctuation.
Example two
A depression emotion monitoring apparatus according to a second embodiment of the present invention includes:
the data acquisition module is used for monitoring behavior data of a user;
the data processing module is used for calculating target data characteristics from the behavior data according to a preset rule;
and the prediction module is used for predicting based on the target data characteristics through a preset prediction model so as to obtain a prediction result.
Embodiments of the present invention also provide a computer-readable storage medium storing one or more computer programs, which are executable by one or more processors to implement the steps of the depression mood monitoring method of the first embodiment.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method of mood monitoring for depression, comprising:
monitoring behavioral data of a user;
calculating target data characteristics from the behavior data according to a preset rule;
and predicting based on the target data characteristics through a preset prediction model to obtain a prediction result.
2. The method for mood monitoring in depression according to claim 1, wherein monitoring behavioral data of the user comprises:
monitoring the use behavior data of a user using the terminal and acquiring the scale data fed back by the user;
wherein the gauge data comprises: PHQ-9 self-rating scale fed back by user.
3. The method for mood monitoring in depression according to claim 2, wherein calculating target data characteristics from the behavioral data according to preset rules comprises:
performing data processing on the behavior data, and eliminating incomplete data and dirty data;
and merging the prior designated times and the current scale data, classifying the merged scale data according to a preset classification standard, and setting a corresponding data label for the classified scale data.
4. The method for mood monitoring in depression according to claim 2, wherein calculating target data characteristics from the behavioral data according to preset rules further comprises:
and determining the terminal use information, the sleep and movement characteristics of the user in each preset time period according to the behavior data.
5. The method for mood monitoring in depression according to claim 4, wherein determining sleep and movement characteristics of the user over respective predetermined periods of time comprises:
cosine fitting is carried out on the sleep heart rate, and then corresponding sleep curve characteristics are extracted;
determining a feature value of the sleep curve feature;
the characteristic value includes at least one of: mean, median and standard deviation.
6. The method for mood monitoring in depression according to any one of claims 2-5, wherein calculating target data characteristics from the behavioral data according to preset rules further comprises:
selecting corresponding target data characteristics from the use behavior data and the scale data by using a preset characteristic selection model;
wherein the preset feature selection model at least comprises one of the following: tree-based feature selection, L1 paradigm feature selection.
7. The method for mood monitoring in depression according to claim 3, wherein before predicting based on the target data characteristics by a preset prediction model, further comprising:
and training different machine learning models through the historical target data characteristics and the corresponding labels to determine a prediction model with the best prediction capability under the condition of using different target data characteristics.
8. The method for mood monitoring in depression according to any one of claims 1 to 5, wherein after prediction based on the target data characteristic by a preset prediction model, the method further comprises: and feeding back the prediction result to a user.
9. A depressive mood monitoring device, comprising:
the data acquisition module is used for monitoring behavior data of a user;
the data processing module is used for calculating target data characteristics from the behavior data according to a preset rule;
and the prediction module is used for predicting based on the target data characteristics through a preset prediction model so as to obtain a prediction result.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more computer programs executable by one or more processors to implement the steps of the depression mood monitoring method according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113111258A (en) * | 2021-03-31 | 2021-07-13 | 青岛海尔科技有限公司 | Menu pushing method and device |
| CN115859773A (en) * | 2022-11-07 | 2023-03-28 | 中电科大数据研究院有限公司 | Early warning method and device based on group emotion prediction model and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104584017A (en) * | 2012-08-16 | 2015-04-29 | 金格输入输出有限公司 | Method for modeling behavior and health changes |
| US20150370993A1 (en) * | 2012-08-16 | 2015-12-24 | Ginger.io, Inc. | Method for modeling behavior and depression state |
| US20170249437A1 (en) * | 2016-02-25 | 2017-08-31 | Samsung Electronics Co., Ltd. | Sensor assisted depression detection |
| US20190117143A1 (en) * | 2017-10-23 | 2019-04-25 | Massachusetts Institute Of Technology | Methods and Apparatus for Assessing Depression |
-
2020
- 2020-11-17 CN CN202011282054.3A patent/CN112489815A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104584017A (en) * | 2012-08-16 | 2015-04-29 | 金格输入输出有限公司 | Method for modeling behavior and health changes |
| US20150370993A1 (en) * | 2012-08-16 | 2015-12-24 | Ginger.io, Inc. | Method for modeling behavior and depression state |
| US20170249437A1 (en) * | 2016-02-25 | 2017-08-31 | Samsung Electronics Co., Ltd. | Sensor assisted depression detection |
| US20190117143A1 (en) * | 2017-10-23 | 2019-04-25 | Massachusetts Institute Of Technology | Methods and Apparatus for Assessing Depression |
Non-Patent Citations (1)
| Title |
|---|
| 熊笑颜等: "基于行为数据的心理抑郁症状检测研究", 《现代电子技术》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113111258A (en) * | 2021-03-31 | 2021-07-13 | 青岛海尔科技有限公司 | Menu pushing method and device |
| CN115859773A (en) * | 2022-11-07 | 2023-03-28 | 中电科大数据研究院有限公司 | Early warning method and device based on group emotion prediction model and storage medium |
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