Disclosure of Invention
The application aims to solve the technical problem of providing a method for identifying dysthymia, a device for identifying dysthymia and wearable equipment, so as to solve the problem that the accuracy of the identification result of the dysthymia is influenced due to the risk of confusion in the identification result of the dysthymia and other types of mental disorders.
To solve or improve the above technical problem to some extent, according to an aspect of the present application, there is provided a method of identifying dysthymia, the method comprising:
acquiring dysphoria related data of a target user, wherein the dysphoria related data comprises behavior rule data, daily mood data and sleep index data;
comparing each piece of the related data of the dysthymia with a preset dysthymia identification index, wherein the dysthymia identification index comprises index categories related to dysthymia and index scores used for representing the association degree between each index category and the dysthymia, and the index categories comprise behavior rule indexes, daily mood indexes and sleep indexes;
and determining that the type of mental disorder of the target user is dysthymia in response to the dysthymia-related data matching the dysthymia identification index.
In some embodiments, the comparing each of the dysthymia-related data with a preset dysthymia identification index comprises:
comparing the dysphoria-related data to the index score;
the determining that the target user is a dysthymia patient in response to the dysthymia-related data matching the dysthymia identification index comprises:
and determining that the type of mental disorder of the target user is dysthymia in response to the dysthymia-related data being the same as the index score or a difference between the dysthymia-related data and the index score being less than a predetermined threshold.
In some embodiments, the behavior rules data includes behavior stability data and/or behavior volatility data.
In some embodiments, the daily mood data includes at least one of:
a phonetic text emotion index;
the ratio of positive and negative part of speech;
the proportion of body language positive and negative.
In some embodiments, the sleep index data includes at least one of:
sleep duration;
number of wakeups;
deep sleep duration;
time to fall asleep;
the time of getting up;
sleep period.
In some embodiments, the obtaining the dysphoria-related data of the target user comprises:
acquiring and obtaining multi-mode original data of the target user based on the wearable equipment worn by the target user, and extracting the data of the multi-mode original data to obtain the related data of the target user, wherein the related data of the dysphoria is obtained.
According to another aspect of the application, there is provided an apparatus for identifying dysthymic disorders, the apparatus comprising:
the system comprises a dysthymia related data obtaining unit, a sleep index obtaining unit and a storage unit, wherein the dysthymia related data obtaining unit is used for obtaining dysthymia related data of a target user, and the dysthymia related data comprises behavior rule data, daily mood data and sleep index data;
a dysthymia recognition index comparison unit, configured to compare each dysthymia related data with a preset dysthymia recognition index, where the dysthymia recognition index includes index categories related to dysthymia and index scores for characterizing a degree of association between each index category and dysthymia, and the index categories include a behavior rule index, a daily mood index, and a sleep index;
and the dysthymia determining unit is used for determining that the type of the mental disorder of the target user is dysthymia in response to the dysthymia related data being matched with the dysthymia identification index.
According to another aspect of the application, a wearable device is provided, which may perform the method as described above.
Compared with the prior art, the application has the following advantages:
after obtaining the related data of the dysthymia of a target user, comparing the related data of the dysthymia with preset dysthymia identification indexes, wherein the dysthymia identification indexes comprise index categories related to the dysthymia and index scores used for representing the association degree between the index categories and the dysthymia, the related data of the dysthymia comprise behavior rule data, daily mood data and sleep index data, and the index categories comprise behavior rule indexes, daily mood indexes and sleep indexes; and when the dysthymia related data meets the dysthymia identification index, determining that the type of the mental disorder of the target user is dysthymia. According to the method, the dysthymia related data of different forms or dimensions such as the behavior rule data, the daily mood data and the sleep index data are taken as consideration factors when the dysthymia is identified, so that the effect of a single factor or the same category factor, namely the physical or physiological condition, is considered in the dysthymia identification process, the effect of multiple types of factors related to the dysthymia of different dimensions and different forms is considered, different association degrees between the various types of factors and different mental disorders are fully utilized, the dysthymia identification result can be clearly distinguished from other types of mental disorders, and the accuracy of the dysthymia identification result is effectively improved.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than those herein described, and those skilled in the art will readily appreciate that the present application may be similarly embodied without departing from the spirit or essential characteristics thereof, and therefore the present application is not limited to the specific embodiments disclosed below.
Aiming at a dysthymia identification scene, the application provides a method for identifying dysthymia in order to improve the accuracy of a dysthymia identification result. The application also provides a device and wearable equipment for identifying dysthymia, which correspond to the method. The following provides examples to describe the above method, apparatus, and wearable device in detail.
An embodiment of the present application provides a method for identifying dysthymic disorder, whose application body may be a computing device application for identifying dysthymic disorder of a user, which may be running in a wearable device or in a server for identifying dysthymic disorder. Fig. 1 is a flowchart of a method for identifying dysthymia according to an embodiment of the present application, and the method provided in this embodiment is described in detail below with reference to fig. 1. The embodiments referred to in the following description are intended to illustrate the method principles and not to limit the practical use.
As shown in fig. 1, the method for identifying dysthymia provided in this embodiment includes the following steps:
s101, obtaining the dysphoria related data of the target user.
The step is used for obtaining the related data of the dysthymia, wherein the related data of the dysthymia refers to various types of data which have relevance to the dysthymia, and each type of related data of the dysthymia can be used as a consideration factor for identifying dysthymia, and in the embodiment, the related data of the dysthymia comprises behavior rule data, daily mood data and sleep index data corresponding to different dimensions and different forms.
Wherein the behavior rules data include behavior stability (Interdaily Stability, IS) data and/or behavior volatility (Intradaily Variability, IV) data. IS stands for behavioral stability, the smaller the interval IS, the more unstable the behavioral pattern IS, i.e. the behavioral pattern of the user IS disordered, and the disorder phenomenon represents the risk of mental disorder, i.e. the smaller the IS value IS, the greater the risk of mental problems of the user IS. IV represents behavior volatility, with a range of values between 0 and 2, with a larger value indicating a more preferential fragmentation of the behavior pattern, and also indicating that there is no stable pattern of behavior, i.e., the more fragmented the pattern, the greater the risk of developing mental disorders. In this embodiment, the behavior stability data can be obtained by calculation of the following formula:
behavior volatility data can be calculated by the following formula:
wherein N represents the total number of data, P represents the average daily data acquisition number, xh represents the average hourly value size, X represents the average of all data, xi represents the value size of each data point, and H represents the time (e.g., hours), which refers to the data size of acceleration.
The above daily mood data refers to data for characterizing the active or passive mood state of the target user, including one or more of the following: a phonetic text emotion index (e.g., a proportion of negative energy or negative emotion voice or text data), a positive and negative part-of-speech proportion (e.g., a proportion of negative energy words), a limb language emotion index (e.g., a proportion of positive emotion in a limb language, a proportion of negative emotion is represented).
The sleep index data includes a sleep time period, a number of wakefulness, a deep sleep time period, a fall-to-sleep time, a get-up time, a sleep period (e.g., sleep during a period of day or a period of night).
In this embodiment, the dysphoria-related data of the target user may be obtained by: acquiring multi-mode raw data based on a wearable device worn by a target user, for example, acquiring multi-mode raw data such as PPG signals, getting-up time points, falling asleep time points, social data, limb actions, geographic positions and the like of the user through a pulse wave sensor, a skin electric sensor, an acceleration sensor, an angular velocity sensor, a GPS sensor, an blood oxygen sensor, a blood pressure sensor, a voice sensor and other sensors which are arranged on the wearable device and are used for acquiring the multi-mode raw data; and carrying out data extraction based on the multi-mode original data to obtain the different types of related data of the dysthymia.
S102, comparing the dysthymia related data with preset dysthymia identification indexes, and determining that the type of the mental disorder of the target user is dysthymia when the dysthymia related data meets the dysthymia identification indexes.
After obtaining the data related to the dysthymia, such as the behavior rule data, the daily mood data, the sleep index data, and the like of the target user in the above steps, the method is used for comparing the data related to the dysthymia with preset dysthymia identification indexes, wherein the dysthymia identification indexes comprise index categories related to dysthymia and index scores used for representing the association degree between the index categories and the dysthymia, and the index categories comprise the behavior rule index, the daily mood index and the sleep index, namely, the index categories are consistent with the category of the data related to the dysthymia, and the index scores can be used as influence thresholds of the index categories on the dysthymia.
It should be noted that, for any two different types of mental disorders (such as dysthymic disorder and sleep disorder), the corresponding index categories may be the same or partially the same (partially the same means that some mental disorders are more prominent in some index categories, the degree of association with the index categories is higher, while another mental disorder is not associated with the index categories, for example, the degree of association between vital sign index and sleep disorder is higher, but there is no degree of association with dysthymic disorder, for example, sleep index and daily mood index, anxiety disorder, depressive mood disorder and bipolar disorder are all associated with the mood index, and in the case that the index categories are the same or partially the same, at least one or more index categories corresponding to different index scores exist, that is, the degree of association between the same index category and different types of mental disorder is different, for example, the index category of index related to dysthymic disorder is a behavior rule, a daily mood index, a sleep index, an index category related to the dysthymic disorder contains the above-mentioned index, however, the above-mentioned index related to the dysthymic disorder is associated with the anxiety disorder is recognized by using the score when the associated relation between the dysthymic disorder and the anxiety disorder is higher than the anxiety rule. For another example, dysthymic disorder is also called depressed mood and light depression, and patients with dysthymic disorder do not have many symptoms similar to those of patients with depressed mood disorder, and only some symptoms of depressed mood disorder are included, so that the corresponding index categories are only partial index categories (behavior rule index, daily mood index and sleep index) when identifying depressed mood disorder, and the correlation between the partial index categories and dysthymic disorder is weaker than the correlation of depressed mood disorder, and further, the index scores are also smaller.
In addition, when different types of mental disorders are identified, the required accuracy of the same index category is different, so that different indexes in the same index category need to be selected.
The step of comparing the dysthymia related data with a preset dysthymia identification index specifically means that the dysthymia related data is compared with index scores, namely, all the dysthymia related data are respectively compared with index scores of index categories corresponding to the dysthymia related data, and when the dysthymia related data are the same as the index scores or the difference value between the dysthymia related data and the index scores is smaller than a preset threshold value, the type of the mental disorder of a target user is determined to be the dysthymia disorder.
After obtaining the related data of the dysthymia of the target user, comparing the related data of the dysthymia with preset dysthymia identification indexes, wherein the dysthymia identification indexes comprise index categories related to the dysthymia and index scores used for representing the association degree between the index categories and the dysthymia, the related data of the dysthymia comprise behavior rule data, daily mood data and sleep index data, and the index categories comprise behavior rule indexes, daily mood indexes and sleep indexes; and when the dysthymia related data meets the dysthymia identification index, determining that the type of the mental disorder of the target user is dysthymia. According to the method, the dysthymia related data of different forms or dimensions such as the behavior rule data, the daily mood data and the sleep index data are taken as consideration factors when the dysthymia is identified, so that the effect of a single factor or the same category factor, namely the physical or physiological condition, is considered in the dysthymia identification process, the effect of multiple types of factors related to the dysthymia of different dimensions and different forms is considered, different association degrees between the various types of factors and different mental disorders are fully utilized, the dysthymia identification result can be clearly distinguished from other types of mental disorders, and the accuracy of the dysthymia identification result is effectively improved.
The above-mentioned embodiments provide a method for identifying dysthymia, and correspondingly, another embodiment of the present application provides a device for identifying dysthymia, and since the device embodiments are substantially similar to the method embodiments, the description is relatively simple, and details of relevant technical features should be found in the corresponding descriptions of the method embodiments provided above, and the following descriptions of the device embodiments are merely illustrative.
Referring to fig. 2 for understanding the embodiment, fig. 2 is a block diagram of a unit of an apparatus for identifying dysthymia according to the present embodiment, and as shown in fig. 2, the apparatus includes:
a dysthymia-related data obtaining unit 201 for obtaining dysthymia-related data of a target user, the dysthymia-related data including behavior rule data, daily mood data, and sleep index data;
a dysthymia recognition index comparing unit 202 for comparing each of the dysthymia-related data with a preset dysthymia recognition index including index categories related to dysthymia and index scores for characterizing a degree of association between each index category and dysthymia, the index categories including a behavior rule index, a daily mood index, and a sleep index;
a dysthymia determining unit 203 for determining that the type of mental disorder of the target user is a dysthymia in response to the dysthymia related data matching the dysthymia identification index.
In some embodiments, the comparing each of the dysthymia-related data with a preset dysthymia identification index comprises:
comparing the dysphoria-related data to the index score;
the determining that the target user is a dysthymia patient in response to the dysthymia-related data matching the dysthymia identification index comprises:
and determining that the type of mental disorder of the target user is dysthymia in response to the dysthymia-related data being the same as the index score or a difference between the dysthymia-related data and the index score being less than a predetermined threshold.
In some embodiments, the behavior rules data includes behavior stability data and/or behavior volatility data.
In some embodiments, the daily mood data includes at least one of:
a phonetic text emotion index;
the ratio of positive and negative part of speech;
the proportion of body language positive and negative.
In some embodiments, the sleep index data includes at least one of:
sleep duration;
number of wakeups;
deep sleep duration;
time to fall asleep;
the time of getting up;
sleep period.
In some embodiments, the obtaining the dysphoria-related data of the target user comprises:
acquiring and obtaining multi-mode original data of the target user based on the wearable equipment worn by the target user, and extracting the data of the multi-mode original data to obtain the related data of the target user, wherein the related data of the dysphoria is obtained.
According to the device for identifying dysthymia, provided by the embodiment, the dysthymia related data of different forms or dimensions such as the behavior rule data, the daily dysthymia data and the sleep index data are taken as the consideration factors when the dysthymia is identified, so that the influence of a single factor or the same type of factors such as the physical or physiological condition is considered in the dysthymia identification process, the influence of multiple types of factors related to the dysthymia in different dimensions and different forms is considered, different association degrees between the types of factors and different dysthymia are fully utilized, the dysthymia identification result can be clearly distinguished from other types of dysthymia, and the accuracy of the dysthymia identification result is effectively improved.
In the above embodiments, a method for identifying dysthymia and an apparatus for identifying dysthymia are provided, and in addition, another embodiment of the present application further provides a wearable device, which may be a wearable bracelet, a helmet, or the like, on which a sensor for acquiring multi-mode raw data, such as a pulse wave sensor, a skin sensor, an acceleration sensor, an angular velocity sensor, a GPS sensor, an blood oxygen sensor, a blood pressure sensor, a voice sensor, and other monitoring modules, are mounted. Since the wearable device embodiment is substantially similar to the method embodiment, the description is relatively simple, and the details of the relevant technical features may be found in the corresponding description of the method embodiment provided above, and the following description of the wearable device embodiment is merely illustrative. The wearable device embodiment is as follows:
fig. 3 is a schematic diagram of the wearable device provided in the present embodiment.
As shown in fig. 3, the wearable device provided in this embodiment includes, in addition to various sensors and other monitoring modules for acquiring multi-mode raw data: a processor 301 and a memory 302;
the memory 302 is used to store computer instructions for data processing which, when read and executed by the processor 301, perform the following operations:
acquiring dysphoria related data of a target user, wherein the dysphoria related data comprises behavior rule data, daily mood data and sleep index data;
comparing each piece of the related data of the dysthymia with a preset dysthymia identification index, wherein the dysthymia identification index comprises index categories related to dysthymia and index scores used for representing the association degree between each index category and the dysthymia, and the index categories comprise behavior rule indexes, daily mood indexes and sleep indexes;
and determining that the type of mental disorder of the target user is dysthymia in response to the dysthymia-related data matching the dysthymia identification index.
In some embodiments, the comparing each of the dysthymia-related data with a preset dysthymia identification index comprises: comparing the dysphoria-related data to the index score;
the determining that the target user is a dysthymia patient in response to the dysthymia-related data matching the dysthymia identification index comprises:
and determining that the type of mental disorder of the target user is dysthymia in response to the dysthymia-related data being the same as the index score or a difference between the dysthymia-related data and the index score being less than a predetermined threshold.
In some embodiments, the behavior rules data includes behavior stability data and/or behavior volatility data.
In some embodiments, the daily mood data includes at least one of:
a phonetic text emotion index;
the ratio of positive and negative part of speech;
the proportion of body language positive and negative.
In some embodiments, the sleep index data includes at least one of:
sleep duration;
number of wakeups;
deep sleep duration;
time to fall asleep;
the time of getting up;
sleep period.
In some embodiments, the obtaining the dysphoria-related data of the target user comprises:
acquiring and obtaining multi-mode original data of the target user based on the wearable equipment worn by the target user, and extracting the data of the multi-mode original data to obtain the related data of the target user, wherein the related data of the dysphoria is obtained.
By using the wearable device provided by the embodiment, the dysthymia related data of different forms or dimensions such as the behavior rule data, the daily mood data and the sleep index data are taken as the consideration factors when the dysthymia is identified, so that the influence of a single factor or the same category factor of the physical or physiological condition is considered in the dysthymia identification process, the influence of multiple types of factors related to the dysthymia of different dimensions and different forms is considered, different association degrees between the various types of factors and different mental disorders are fully utilized, the dysthymia identification result can be clearly distinguished from other types of mental disorders, and the accuracy of the dysthymia identification result is effectively improved.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
1. Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer readable media, as defined herein, does not include non-transitory computer readable media (transmission media), such as modulated data signals and carrier waves.
2. It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
While the application has been described in terms of preferred embodiments, it is not intended to be limiting, but rather, it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the spirit and scope of the application as defined by the appended claims.