CN116631629A - Method and device for identifying depressive disorder and wearable device - Google Patents
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Abstract
The application discloses a method and a device for identifying depressive disorder and wearable equipment, wherein the method comprises the following steps: after obtaining the depression emotion-related data of the target user, comparing each depression emotion-related data with a preset depression identification index, wherein the depression identification index comprises index categories related to depression emotion disorders and index scores used for representing the association degree between each index category and the depression emotion disorder, and the depression emotion-related data comprises individual behavior data, environment interaction data and body index data; and when the depression emotion related data meets the depression emotion recognition index, determining that the type of the mental disorder of the target user is the depression emotion disorder. The method fully utilizes different association degrees between various types of factors and different mental disorders, so that the identification result of the depressive mood disorder can be clearly distinguished from other types of mental disorders, and the accuracy of the identification result of the depressive mood disorder is effectively improved.
Description
Technical Field
The application relates to the technical field of health monitoring, in particular to a method for identifying depressive disorder. The application also relates to a device for identifying the depressed mood disorder and a wearable device.
Background
In the existing depressed mood disorder recognition process, various physical indexes or physiological indexes are generally used as consideration factors for recognizing depressed mood disorders, for example, a user is analyzed based on physiological monitoring data such as respiratory data, heartbeat data and the like to determine whether the user is a depressed mood disorder patient. However, the manifestations of different types of mental disorders are relatively close, and the degree of distinction between the corresponding physical indexes or physiological indexes is relatively small, so that the risk of confusion in recognition exists between the recognition result of the depressive mood disorder and other types of mental disorders, and the accuracy of the recognition result of the depressive mood disorder is affected.
Therefore, how to improve the recognition accuracy of depressive mood disorders is a problem to be solved.
Disclosure of Invention
The application aims to solve the technical problem of providing a method for identifying depressive disorder, a device for identifying depressive disorder and a wearable device, so as to solve the problem that the accuracy of the identification result of the depressive disorder is affected due to the risk of confusion between the identification result of the depressive disorder and other types of mental disorder.
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 a depressive mood disorder, the method comprising:
obtaining depression mood related data of a target user, wherein the depression mood related data comprise individual behavior data, environment interaction data and body index data;
comparing each depression emotion related data with a preset depression identification index, wherein the depression identification index comprises index categories related to depression emotion disorders and index scores used for representing the association degree between each index category and the depression emotion disorders, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes;
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data meeting the depressive mood recognition index.
In some embodiments, the comparing each of the depressed mood-related data with a preset depressed identification index comprises:
comparing the depressed mood-related data to the index score;
the determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data satisfying the depressive mood recognition indicator comprises:
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data being the same as the index score or a difference between the depressive mood-related data and the index score being less than a predetermined threshold.
In some embodiments, the individual behavioral data includes at least one of:
behavior rule data;
daily mood data;
sleep index data;
the body index data includes at least one of:
activity level data;
physiological index data;
the environmental interaction data includes at least one of:
social degree data;
and (5) activity range data.
In some embodiments, the behavior rules data includes behavior stability data and/or behavior volatility data.
In some embodiments, the physiological index data comprises high frequency energy power spectral density values in heart rate variability data; the comparing each depression emotion related data with a preset depression identification index comprises the following steps:
obtaining high-frequency energy power spectrum density values respectively corresponding to the target user at a plurality of preset time points, wherein the preset time points comprise corresponding time points of the target user in the process of executing the activity task, before executing the activity task and after executing the activity task;
obtaining a target change curve of the high-frequency energy power spectral density value along with each preset time point based on the high-frequency energy power spectral density values respectively corresponding to the preset time points;
comparing the shape of the target change curve with the shape of a preset depression identification reference curve, wherein the depression identification reference curve represents a change curve of a high-frequency energy power spectral density value corresponding to a depressed mood disorder patient along with each preset time point;
the depressed mood-related data satisfies the depressed mood-identifying index, comprising:
the similarity between the shape of the target variation curve and the shape of the depression identification reference curve reaches a predetermined similarity threshold.
In some embodiments, the physiological index data comprises galvanic skin orientation response 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;
a limb language emotion index.
In some embodiments, the obtaining depression mood-related data of the target user comprises:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and carrying out data extraction based on the multi-mode original data to obtain the depression emotion related data.
According to another aspect of the present application there is provided a device for identifying depression-related mental disorders, the device comprising:
a depressed emotion-related data obtaining unit configured to obtain depressed emotion-related data of a target user, the depressed emotion-related data including individual behavior data, environmental interaction data, and body index data;
a depression recognition index comparison unit, configured to compare each depression emotion related data with a preset depression recognition index, where the depression recognition index includes index categories related to a depression emotion disorder and index scores for characterizing a degree of association between each index category and the depression emotion disorder, and the index categories include an individual behavior index, an environmental interaction index, and a physical index;
a depressed emotion determining unit for determining that the type of mental disorder of the target user is a depressed emotion disorder in response to the depressed emotion-related data satisfying the depressed emotion recognition 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 depression emotion related data of a target user are obtained, the depression emotion related data are compared with preset depression identification indexes, the depression identification indexes comprise index categories related to the depression emotion disorders and index scores used for representing the association degree between the index categories and the depression emotion disorders, and the depression emotion related data comprise individual behavior data, environment interaction data and body index data; the index category comprises individual behavior indexes, environment interaction indexes and body indexes; and determining that the type of the mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data meeting the depressive mood recognition index. According to the method, the depression emotion related data of different forms or dimensions such as individual behavior data, environment interaction data and body index data are taken as consideration factors when the depression emotion disorder is identified, so that the identification process of the depression emotion disorder not only considers the influence of a single factor or the same category factor of a body or a physiological condition, but also considers the influence of multiple types of factors related to the depression emotion of different dimensions and different forms, different association degrees between the types of factors and different mental disorders are fully utilized, index scores corresponding to the factors are used as reference standards of the depression emotion related data, the identification result of the depression emotion disorder can be clearly distinguished from other types of mental disorder, and the accuracy of the depression emotion disorder identification result is effectively improved.
Drawings
FIG. 1 is a flow chart of a method of identifying a depressive mood disorder provided in one embodiment of the present application;
fig. 2 is a block diagram of a unit of an apparatus for recognizing a depressed mood disorder in accordance with an embodiment of the present application;
fig. 3 is a schematic logic structure diagram of a wearable device according to an embodiment of the present application.
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 depressed emotion recognition scene, the application provides a method for recognizing depressed emotion disorder, which aims at improving the accuracy of a depressed emotion recognition result. The application also provides a device and wearable equipment for identifying the depressive disorder, 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 a depressive disorder, whose application body may be a computing device application for identifying a depressive disorder of a user, which may be running in a wearable device or in a server for identifying a mental disorder. Fig. 1 is a flowchart of a method for identifying a depressive disorder 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 a depressive disorder provided in this embodiment includes the following steps:
s101, obtaining depression emotion related data of a target user.
The step is used for obtaining depressive emotion related data, wherein the depressive emotion related data refers to various types of data related to depressive emotion, each type of depressive emotion related data can be taken as a consideration factor when identifying depressive emotion disorder, and in the embodiment, the depressive emotion related data comprises individual behavior data, environment interaction data and body index data with different dimensions and different forms. Wherein the individual behavioral data may be one or more of the following: behavior rule data, daily mood data, and sleep index data; the body index data includes one or more of the following: activity level data; physiological index data; the environmental interaction data includes one or more of the following: social degree data; and (5) activity range data.
Wherein, the behavior rule data in the individual behavior data comprises 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 disturbed, and the disorder phenomenon shows a depression risk, i.e. the smaller the IS value, the greater the risk of psychological problems for the user. IV represents behavioral volatility, with a range of values between 0 and 2, with a higher value favoring fragmentation of behavioral patterns, again indicating that there is no stable pattern of behavior, i.e., the more fragmented patterns, the greater the risk of psychological problems. 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).
The activity level data represent the energy consumption condition of the target user, and one of the obvious symptoms of the depressive disorder is mental retardation, particularly the reduction of the quantity of exercise, and further the reduction of the energy consumption. In the present embodiment, the activity level data of the target user can be obtained by the motion sensor calculation based on the following formula:
wherein, A represents the coefficient size of VM3, B represents the coefficient size of Gender, v represents the coefficient size of BMI, delta is a constant of a formula, VM3 is a total acceleration (unit is g, the range is generally between 0.6 and 1.8 g) similar to the intercept on the y axis of the equation, gender represents Gender, female is encoded as 0, male is encoded as 1, BMI represents weight (kg)/(height (m))2, the range is generally between [18.5 and 24.9], weight is thinner than the value, and weight is fat than the value.
The physiological index includes one or more of the following: heart rate variability (Heart Rate Variability, HRV) data, galvanic skin directional response data (Electro Dermal Orienting Reactivity, EDOR), galvanic skin activity (Electrodermal Activity, EDA) data.
The HRV can reflect the variability of heart rate variation, reflect the relationship between the magnitude of sympathetic activity and parasympathetic activity in the autonomic nervous system and balance coordination thereof, can be used as an index of the adaptability of body functions to environmental variation, pressure and emotion intensity, and has better heart rate variability, and the heart can adapt to the influence caused by environmental stimulation, emotion and pressure more quickly, namely the body has better adaptability to stress stimulation and strong compression resistance; on the contrary, the worse the adaptability of the organism, the higher the risk of anxiety, depression and cardiovascular diseases of the individual with lower heart rate variability, the larger the pressure, mood swings, sleep failures and other conditions can reduce HRV.
EDOR is a physiological measurement method for assessing an individual's response to an environmental stimulus, the skin conductivity of which varies with the activity of the sympathetic nervous system, and thus is typically measured by varying skin conductivity, for example by providing a user with a stimulus (such as sound, light or an image) and measuring its skin conductivity, which rises if the user responds strongly to the stimulus, and which is less responsive to the stimulus (e.g. low electrical skin reactivity) if the skin conductivity does not vary significantly or varies less, and which may occur in some psychotic disorders, such as depression, depression patients prone to suicide typically exhibit low electrical skin reactivity. Based on this, EDOR can be used as depression emotion-related data in the present embodiment.
Skin electrical activity refers to a change in the electrical conductivity of sweat glands on the skin surface due to activation caused by stress or other stimuli, for example, when the body is stimulated (e.g., needled, shocked, etc.) or emotion changes, sympathetic branches of the autonomic nervous system are awakened, blood vessels in the skin contract and relax, sweat glands of the body are activated to change, sweat is secreted, sweat contains water and electrolyte, passes through pores into the skin surface, increases skin conductivity, decreases skin resistance, skin electrical activity (Electrodermal activity, EDA).
The environmental interaction data are used for representing the social state of the target user, and one of important characteristics of the depressive mood disorder is little social contact or no social contact, so that the environmental interaction data can be used as one of the depressive mood related data. The social degree data in the environment interaction data can be data such as user call times, call time, information sending times, social frequency and the like; the activity range data may be characterized by the position variation of the user, and in this embodiment, may be obtained by calculating the following formula:
where Location Variance represents the position variation, lat represents latitude, long represents longitude, if Location Variance is smaller, it indicates that the user is in a relatively fixed position for a long time, for example, two points of a company and a family are in a line, and no unnecessary going out exists.
In the present embodiment, depression emotion-related data of the target user can be obtained by: acquiring multi-mode raw data based on a wearable device worn by a target user, wherein the multi-mode raw data comprise a pulse wave sensor, a skin sensor, an acceleration sensor, an angular velocity sensor, a GPS sensor, an oxygen sensor, a blood pressure sensor, a voice sensor and other sensors used for acquiring the multi-mode raw data, such as a pulse wave sensor, a skin sensor, an acceleration sensor, an angular velocity sensor, a GPS sensor, an oxygen sensor, a blood pressure sensor, a voice sensor and other monitoring modules, which are arranged on the wearable device, and acquiring the multi-mode raw data, such as PPG signals, a time point of getting up, a time point of falling asleep, social data, limb actions, geographic positions and the like of the user; and carrying out data extraction based on the multi-mode original data to obtain the depression emotion related data of different types.
S102, comparing each depressive emotion related data with a preset depressive emotion recognition index, and determining that the type of the mental disorder of the target user is the depressive emotion disorder when the depressive emotion related data meets the depressive emotion recognition index.
After obtaining the data related to the depressed emotion such as the individual behavior data, the environmental interaction data, and the body index data of the target user in the above steps, the present step is used to compare each of the above depressed emotion related data with a preset depressed identification index, where the depressed identification index includes index categories related to the depressed emotion disorder and index scores for characterizing the degree of association between each index category and the depressed emotion disorder, and the index categories include an individual behavior index (behavior rule index, daily mood index, sleep index), an environmental interaction index (social level index, and activity range index), and a body index (activity level index, physiological index), that is, the index categories are consistent with the categories of the above depressed emotion related data, and the index scores can be used as the influence threshold of each index category on the depressed emotion disorder.
It should be noted that, for any two different types of mental disorders (such as a depressive mood disorder and an anxiety 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 between the index categories is higher, and another mental disorder is not associated with the index categories), and in the case that the index categories are the same or partially the same, at least one or more index categories correspond to different index scores, that is, there is a distinction between the degree of association between the same index category and the different types of mental disorders, for example, for the index categories related to the depressive mood disorder, the behavior rule index, the daily mood index, the sleep index, the activity level index, the physiological index, the social level index, and the activity range index, the index categories related to the anxiety disorder are the same as the index categories, however, the association between the depressive mood disorder and the index categories is significantly higher than the association between the anxiety disorder and the index categories. As another example of this, for example,
the step of comparing each depressive emotion-related data with a preset depressive recognition index specifically means that the above-mentioned depressive emotion-related data are compared with index scores, that is, each depressive emotion-related data in all the depressive emotion-related data are respectively compared with index scores of the corresponding index categories, and when the depressive emotion-related data are the same as the index scores or the difference between the depressive emotion-related data and the index scores is smaller than a predetermined threshold, the type of mental disorder of the target user is determined to be a depressive emotion disorder.
In this embodiment, the above-mentioned physiological index data further includes a high-frequency energy power spectral density value in heart rate variability (Heart Rate Variability, HRV) data, and parameters that can be used for analyzing the HRV are generally classified into time domain parameters (e.g., SDNN, rMSSD, NN, SDANN, etc.) and frequency domain parameters (e.g., LF, HF, LF/HF, etc.), where HF is the high-frequency energy power spectral density value. For different types of mental disorders and normal users, the shape of the change curve of the high-frequency energy power spectral density value with time (typically, the preset time points before, during and after the activity) is different, for example, the shape of the curve corresponding to the anxiety disorder and the depressive mood disorder is different, so based on the characteristics, in another embodiment, the comparing each depressive mood related data with the preset depressive recognition index may further refer to: obtaining high-frequency energy power spectrum density values respectively corresponding to a target user at a plurality of preset time points, wherein the plurality of preset time points comprise corresponding time points of the target user in the process of executing an activity task (such as a stress test task), before executing the activity task and after executing the activity task; obtaining a target change curve of the high-frequency energy power spectral density value along with each preset time point based on the high-frequency energy power spectral density values respectively corresponding to the preset time points; comparing the shape of the target change curve with the shape of a preset depression identification reference curve, wherein the depression identification reference curve represents the change curve of the high-frequency energy power spectral density value corresponding to the depressed mood disorder patient along with each preset time point; and when the similarity between the shape of the target change curve and the shape of the depression identification reference curve reaches a preset similarity threshold, determining that the depression emotion-related data meets the depression emotion identification index.
After obtaining the depression emotion related data of the target user, comparing each depression emotion related data with a preset depression identification index, wherein the depression identification index comprises index categories related to the depression emotion disorder and index scores used for representing the association degree between each index category and the depression emotion disorder, the depression emotion related data comprise individual behavior data, environment interaction data and body index data, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes; and when the depression emotion related data meets the depression emotion recognition index, determining that the type of the mental disorder of the target user is the depression emotion disorder. According to the method, the individual behavior data, the environment interaction data, the body index data and other depression emotion related data in different forms or dimensions are taken as consideration factors when the depression emotion disorder is identified, so that the identification process of the depression emotion disorder not only considers the influence of a single factor or the same category factor of the body or the physiological condition, but also considers the influence of multiple types of factors related to the depression emotion in different dimensions and different forms, different association degrees between the various types of factors and different mental disorders are fully utilized, the identification result of the depression emotion disorder can be clearly distinguished from other types of mental disorder, and the accuracy of the identification result of the depression emotion disorder is effectively improved.
The first embodiment provides a method for identifying a depressive disorder, and correspondingly, another embodiment of the present application also provides a device for identifying a depressive disorder, 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 referred to 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 units of an apparatus for identifying a depressive disorder according to the embodiment, and as shown in fig. 2, the apparatus includes:
a depressed emotion-related data obtaining unit 201 for obtaining depressed emotion-related data of a target user, the depressed emotion-related data including individual behavior data, environmental interaction data, and body index data;
a depression recognition index comparing unit 202, configured to compare each depression emotion related data with a preset depression recognition index, where the depression recognition index includes index categories related to the depression mood disorder and index scores for characterizing a degree of association between each index category and the depression mood disorder, and the index categories include an individual behavior index, an environmental interaction index, and a physical index;
a depressed emotion determining unit 203 for determining that the type of mental disorder of the target user is a depressed emotion disorder in response to the depressed emotion-related data satisfying the depressed emotion recognition index.
In some embodiments, the comparing each of the depressed mood-related data with a preset depressed identification index comprises:
comparing the depressed mood-related data to the index score;
the determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data satisfying the depressive mood recognition indicator comprises:
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data being the same as the index score or a difference between the depressive mood-related data and the index score being less than a predetermined threshold.
In some embodiments, the individual behavioral data includes at least one of:
behavior rule data;
daily mood data;
sleep index data;
the body index data includes at least one of:
activity level data;
physiological index data;
the environmental interaction data includes at least one of:
social degree data;
and (5) activity range data.
In some embodiments, the behavior rules data includes behavior stability data and/or behavior volatility data.
In some embodiments, the physiological index data comprises high frequency energy power spectral density values in heart rate variability data; the comparing each depression emotion related data with a preset depression identification index comprises the following steps:
obtaining high-frequency energy power spectrum density values respectively corresponding to the target user at a plurality of preset time points, wherein the preset time points comprise corresponding time points of the target user in the process of executing the activity task, before executing the activity task and after executing the activity task;
obtaining a target change curve of the high-frequency energy power spectral density value along with each preset time point based on the high-frequency energy power spectral density values respectively corresponding to the preset time points;
comparing the shape of the target change curve with the shape of a preset depression identification reference curve, wherein the depression identification reference curve represents a change curve of a high-frequency energy power spectral density value corresponding to a depressed mood disorder patient along with each preset time point;
the depressed mood-related data satisfies the depressed mood-identifying index, comprising:
the similarity between the shape of the target variation curve and the shape of the depression identification reference curve reaches a predetermined similarity threshold.
In some embodiments, the physiological index data comprises galvanic skin orientation response 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;
a limb language emotion index.
In some embodiments, the obtaining depression mood-related data of the target user comprises:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and carrying out data extraction based on the multi-mode original data to obtain the depression emotion related data.
According to the device for identifying the depressive disorder, provided by the embodiment of the application, the depressive disorder identification process not only considers the influence of a single factor or the same category factor, such as a physical or physiological condition, but also considers the influence of multiple factors related to the depressive disorder in different dimensions and different forms by taking full advantage of different association degrees between the factors and the mental disorders, so that the depressive disorder identification result can be clearly distinguished from other types of mental disorders, and the accuracy of the depressive disorder identification result is effectively improved.
In the above embodiments, a method for identifying a depressive disorder and an apparatus for identifying a depressive disorder 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 multimodal raw data, such as a pulse wave sensor, a skin sensor, an acceleration sensor, an angular velocity sensor, a GPS sensor, an 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:
obtaining depression mood related data of a target user, wherein the depression mood related data comprise individual behavior data, environment interaction data and body index data;
comparing each depression emotion related data with a preset depression identification index, wherein the depression identification index comprises index categories related to depression emotion disorders and index scores used for representing the association degree between each index category and the depression emotion disorders, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes;
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data meeting the depressive mood recognition index.
In some embodiments, the comparing each of the depressed mood-related data with a preset depressed identification index comprises:
comparing the depressed mood-related data to the index score;
the determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data satisfying the depressive mood recognition indicator comprises:
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data being the same as the index score or a difference between the depressive mood-related data and the index score being less than a predetermined threshold.
In some embodiments, the individual behavioral data includes at least one of:
behavior rule data;
daily mood data;
sleep index data;
the body index data includes at least one of:
activity level data;
physiological index data;
the environmental interaction data includes at least one of:
social degree data;
and (5) activity range data.
In some embodiments, the behavior rules data includes behavior stability data and/or behavior volatility data.
In some embodiments, the physiological index data comprises high frequency energy power spectral density values in heart rate variability data; the comparing each depression emotion related data with a preset depression identification index comprises the following steps:
obtaining high-frequency energy power spectrum density values respectively corresponding to the target user at a plurality of preset time points, wherein the preset time points comprise corresponding time points of the target user in the process of executing the activity task, before executing the activity task and after executing the activity task;
obtaining a target change curve of the high-frequency energy power spectral density value along with each preset time point based on the high-frequency energy power spectral density values respectively corresponding to the preset time points;
comparing the shape of the target change curve with the shape of a preset depression identification reference curve, wherein the depression identification reference curve represents a change curve of a high-frequency energy power spectral density value corresponding to a depressed mood disorder patient along with each preset time point;
the depressed mood-related data satisfies the depressed mood-identifying index, comprising:
the similarity between the shape of the target variation curve and the shape of the depression identification reference curve reaches a predetermined similarity threshold.
In some embodiments, the physiological index data comprises galvanic skin orientation response 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;
a limb language emotion index.
In some embodiments, the obtaining depression mood-related data of the target user comprises:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and carrying out data extraction based on the multi-mode original data to obtain the depression emotion related data.
According to the wearable device provided by the embodiment, the individual behavior data, the environment interaction data, the body index data and other depression emotion related data in different forms or dimensions are taken as consideration factors when the depression emotion disorders are identified, so that the identification process of the depression emotion disorders considers the influence of a single factor or the same category factor of body or physiological factors, the influence of multiple types of factors related to the depression emotion in different dimensions and different forms is considered, different association degrees between the various types of factors and different mental disorders are fully utilized, the identification result of the depression emotion disorders can be clearly distinguished from other types of mental disorders, and the accuracy of the identification result of the depression emotion disorders 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.
Claims (10)
1. A method of identifying a depressive mood disorder, the method comprising:
obtaining depression mood related data of a target user, wherein the depression mood related data comprise individual behavior data, environment interaction data and body index data;
comparing each depression emotion related data with a preset depression identification index, wherein the depression identification index comprises index categories related to depression emotion disorders and index scores used for representing the association degree between each index category and the depression emotion disorders, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes;
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data meeting the depressive mood recognition index.
2. The method of claim 1, wherein said comparing each of said depressed mood-related data with a preset depression identification indicator comprises:
comparing the depressed mood-related data to the index score;
the determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data satisfying the depressive mood recognition indicator comprises:
and determining that the type of mental disorder of the target user is a depressive mood disorder in response to the depressive mood-related data being the same as the index score or a difference between the depressive mood-related data and the index score being less than a predetermined threshold.
3. The method of claim 1, wherein the individual behavioral data comprises at least one of:
behavior rule data;
daily mood data;
sleep index data;
the body index data includes at least one of:
activity level data;
physiological index data;
the environmental interaction data includes at least one of:
social degree data;
and (5) activity range data.
4. A method according to claim 3, wherein the behavioural law data comprises behavioural stability data and/or behavioural volatility data.
5. A method according to claim 3, wherein the physiological index data comprises high frequency energy power spectral density values in heart rate variability data; the comparing each depression emotion related data with a preset depression identification index comprises the following steps:
obtaining high-frequency energy power spectrum density values respectively corresponding to the target user at a plurality of preset time points, wherein the preset time points comprise corresponding time points of the target user in the process of executing the activity task, before executing the activity task and after executing the activity task;
obtaining a target change curve of the high-frequency energy power spectral density value along with each preset time point based on the high-frequency energy power spectral density values respectively corresponding to the preset time points;
comparing the shape of the target change curve with the shape of a preset depression identification reference curve, wherein the depression identification reference curve represents a change curve of a high-frequency energy power spectral density value corresponding to a depressed mood disorder patient along with each preset time point;
the depressed mood-related data satisfies the depressed mood-identifying index, comprising:
the similarity between the shape of the target variation curve and the shape of the depression identification reference curve reaches a predetermined similarity threshold.
6. A method according to claim 3, wherein the physiological index data comprises galvanic skin-oriented response data.
7. The method of claim 3, wherein the daily mood data comprises at least one of:
a phonetic text emotion index;
the ratio of positive and negative part of speech;
a limb language emotion index.
8. The method of claim 1, wherein the obtaining depression mood-related data for the target user comprises:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and carrying out data extraction based on the multi-mode original data to obtain the depression emotion related data.
9. A device for identifying depression-related mental disorders, said device comprising:
a depressed emotion-related data obtaining unit configured to obtain depressed emotion-related data of a target user, the depressed emotion-related data including individual behavior data, environmental interaction data, and body index data;
a depression recognition index comparison unit, configured to compare each depression emotion related data with a preset depression recognition index, where the depression recognition index includes index categories related to a depression emotion disorder and index scores for characterizing a degree of association between each index category and the depression emotion disorder, and the index categories include an individual behavior index, an environmental interaction index, and a physical index;
a depressed emotion determining unit for determining that the type of mental disorder of the target user is a depressed emotion disorder in response to the depressed emotion-related data satisfying the depressed emotion recognition index.
10. A wearable device, characterized in that the wearable device is executable to perform the method of any of claims 1-8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116831580A (en) * | 2023-08-31 | 2023-10-03 | 北京中科心研科技有限公司 | Method and device for identifying depressive disorder and wearable device |
| CN117711626A (en) * | 2024-02-05 | 2024-03-15 | 江西中医药大学 | Depression emotion evaluating method based on multidimensional factor |
| CN117727456A (en) * | 2023-12-28 | 2024-03-19 | 江苏智慧智能软件科技有限公司 | Obstetrical psychological assessment model modeling system and method based on artificial intelligence |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN211355518U (en) * | 2019-11-18 | 2020-08-28 | 北京工业大学 | Depression diagnosis device |
| CN113299358A (en) * | 2021-05-20 | 2021-08-24 | 北京大学深圳医院 | Negative emotion screening method, device and equipment based on assessment scale |
| CN113855021A (en) * | 2021-10-11 | 2021-12-31 | 北京工业大学 | Depression tendency evaluation method and device |
| CN114898861A (en) * | 2022-03-22 | 2022-08-12 | 合肥工业大学 | Multi-modal depression detection method and system based on full attention mechanism |
| CN115363585A (en) * | 2022-09-04 | 2022-11-22 | 北京中科心研科技有限公司 | Standardized group depression risk screening system and method based on habituation removal and film watching tasks |
| US20230037749A1 (en) * | 2020-11-30 | 2023-02-09 | Matrixcare, Inc. | Method and system for detecting mood |
| CN116110578A (en) * | 2022-12-26 | 2023-05-12 | 清华大学深圳国际研究生院 | Screening device for diagnosis of depression symptoms assisted by computer |
| CN116153496A (en) * | 2022-12-01 | 2023-05-23 | 兰州大学 | Neural network model training method and depression emotion detection method |
-
2023
- 2023-07-21 CN CN202310902873.0A patent/CN116631629A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN211355518U (en) * | 2019-11-18 | 2020-08-28 | 北京工业大学 | Depression diagnosis device |
| US20230037749A1 (en) * | 2020-11-30 | 2023-02-09 | Matrixcare, Inc. | Method and system for detecting mood |
| CN113299358A (en) * | 2021-05-20 | 2021-08-24 | 北京大学深圳医院 | Negative emotion screening method, device and equipment based on assessment scale |
| CN113855021A (en) * | 2021-10-11 | 2021-12-31 | 北京工业大学 | Depression tendency evaluation method and device |
| CN114898861A (en) * | 2022-03-22 | 2022-08-12 | 合肥工业大学 | Multi-modal depression detection method and system based on full attention mechanism |
| CN115363585A (en) * | 2022-09-04 | 2022-11-22 | 北京中科心研科技有限公司 | Standardized group depression risk screening system and method based on habituation removal and film watching tasks |
| CN116153496A (en) * | 2022-12-01 | 2023-05-23 | 兰州大学 | Neural network model training method and depression emotion detection method |
| CN116110578A (en) * | 2022-12-26 | 2023-05-12 | 清华大学深圳国际研究生院 | Screening device for diagnosis of depression symptoms assisted by computer |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116831580A (en) * | 2023-08-31 | 2023-10-03 | 北京中科心研科技有限公司 | Method and device for identifying depressive disorder and wearable device |
| CN116831580B (en) * | 2023-08-31 | 2023-12-01 | 北京中科心研科技有限公司 | Computing device application, device and wearable device for identifying depressive disorder |
| CN117727456A (en) * | 2023-12-28 | 2024-03-19 | 江苏智慧智能软件科技有限公司 | Obstetrical psychological assessment model modeling system and method based on artificial intelligence |
| CN117727456B (en) * | 2023-12-28 | 2024-05-24 | 江苏智慧智能软件科技有限公司 | Obstetrical psychological assessment model modeling system and method based on artificial intelligence |
| CN117711626A (en) * | 2024-02-05 | 2024-03-15 | 江西中医药大学 | Depression emotion evaluating method based on multidimensional factor |
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