CN116612893A - Method and device for identifying bipolar affective disorder and wearable device - Google Patents
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Abstract
The application discloses a method and a device for identifying bipolar affective disorder and wearable equipment, wherein the method comprises the following steps: after obtaining the double-phase emotion-related data of the target user, comparing each double-phase emotion-related data with a preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, and the double-phase emotion-related data comprises individual behavior data, environment interaction data and body index data; and when the bipolar emotion related data meets the bipolar emotion recognition index, determining that the type of the mental disorder of the target user is bipolar emotion disorder. The method fully utilizes different association degrees between various types of factors and different mental disorders, so that the recognition result of the bipolar disorder can be clearly distinguished from other types of mental disorders, and the accuracy of the recognition result of the bipolar disorder is effectively improved.
Description
Technical Field
The application relates to the technical field of health monitoring, in particular to a method for identifying bipolar affective disorder. The application also relates to a device for identifying the bipolar disorder and a wearable device.
Background
In the existing bipolar disorder recognition process, various physical indexes or physiological indexes are generally used as consideration factors for recognizing bipolar disorder, 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 bipolar disorder patient. However, the presentation forms 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 of recognition exists between the recognition result of the bipolar disorder and other types of mental disorders, and the accuracy of the recognition result of the bipolar disorder is further influenced.
Therefore, how to improve the recognition accuracy of bipolar disorder is a problem to be solved.
Disclosure of Invention
The application aims to solve the technical problem of providing a method for identifying bipolar affective disorder, a device for identifying bipolar affective disorder and a wearable device, so as to solve the problem that the accuracy of the identification result of bipolar affective disorder is affected due to the risk of confusion between the identification result of bipolar affective 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 bipolar disorder, the method comprising:
obtaining double-phase emotion related data of a target user, wherein the double-phase emotion related data comprises individual behavior data, environment interaction data and body index data;
comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes;
and determining that the type of the mental disorder of the target user is bipolar affective disorder in response to the bipolar affective related data meeting the bipolar affective recognition index.
In some embodiments, comparing each of the two-phase emotion related data with a preset two-phase emotion recognition index includes:
comparing the bi-phase emotion-related data with the index score;
the determining that the type of the mental disorder of the target user is a bipolar affective disorder in response to the bipolar affective associated data satisfying the bipolar affective recognition indicator comprises:
and determining that the type of the mental disorder of the target user is bipolar disorder in response to the bipolar emotion-related data being the same as the index score or the difference between the bipolar emotion-related data and the index score being less than a predetermined threshold.
In some embodiments, the individual behavioral data includes at least one of:
periodic statistical data of behavior rule data;
periodic statistics of 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 periodic statistics of the behavior rules data include behavior stability data and/or behavior volatility data.
In some embodiments, the physiological index data includes high frequency power spectral energy values in heart rate variability data; the comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index comprises the following steps:
obtaining high-frequency power spectrum energy 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 power spectrum energy value along with each preset time point based on the high-frequency power spectrum energy values respectively corresponding to the preset time points;
comparing the target change curve with a preset double-phase emotion recognition reference curve, wherein the double-phase emotion recognition reference curve represents a change curve of a high-frequency power spectrum energy value corresponding to a double-phase emotion disorder patient along with each preset time point;
the bi-phase emotion related data satisfies the bi-phase emotion recognition index, including:
and the similarity between the shape of the target change curve and the shape of the bipolar emotion recognition reference curve reaches a preset similarity threshold value.
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 the bi-phase emotion-related data of the target user includes:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and extracting the data of the multi-mode original data to obtain the double-phase emotion related data.
According to another aspect of the present application there is provided an apparatus for identifying bipolar disorders, the apparatus comprising:
the system comprises a double-phase emotion-related data acquisition unit, a target user acquisition unit and a target user acquisition unit, wherein the double-phase emotion-related data comprises individual behavior data, environment interaction data and body index data;
a bi-phase emotion recognition index comparison unit, configured to compare each bi-phase emotion related data with a preset bi-phase emotion recognition index, where the bi-phase emotion recognition index includes an index category related to a bi-phase emotion disorder and an index score for characterizing a degree of association between each index category and the bi-phase emotion disorder, and the index category includes an individual behavior index, an environmental interaction index, and a physical index;
and the bipolar emotion disorder determining unit is used for determining that the type of the mental disorder of the target user is bipolar emotion disorder in response to the bipolar emotion related data meets the bipolar 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 obtaining the double-phase emotion related data of a target user, comparing each double-phase emotion related data with a preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to the double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, the double-phase 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 bipolar emotion related data meets the bipolar emotion recognition index, determining that the type of the mental disorder of the target user is bipolar emotion disorder. According to the method, the bipolar affective related data of different forms or dimensions such as the individual behavior data, the environment interaction data and the body index data are taken as consideration factors when the bipolar affective disorder is identified, so that the identification process of the bipolar affective 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 depression emotion of 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 bipolar affective disorder can be clearly distinguished from other types of mental disorder, and the accuracy of the identification result of the bipolar affective disorder is effectively improved.
Drawings
FIG. 1 is a flow chart of a method for identifying bipolar disorders provided in one embodiment of the present application;
FIG. 2 is a block diagram of a device for identifying bipolar disorder according to one 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 double-phase emotion recognition scene, the application provides a method for recognizing double-phase emotion disorder in order to improve the accuracy of a double-phase emotion recognition result. The application also provides a device and a wearable device for identifying bipolar affective 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 bipolar disorder, where an application body of the method may be a computing device application for identifying bipolar disorder of a user, where the computing device application may be running in a wearable device or in a server for identifying mental disorder. Fig. 1 is a flowchart of a method for identifying bipolar 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 bipolar disorder provided in this embodiment includes the following steps:
s101, obtaining the double-phase emotion-related data of the target user.
The step is used for obtaining the double-phase emotion-related data, wherein the double-phase emotion-related data refers to various types of data related to double-phase emotion, the double-phase emotion-related data of each type can be taken as a consideration factor when identifying double-phase emotion disorder, and in the embodiment, the double-phase 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: periodic statistical data of behavior rule data, periodic statistical data of 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.
Bipolar disorder is a mental disorder that repeatedly circulates or alternates between mania and depression, for example, the episode pattern of bipolar disorder may be that the mania episode lasts for more than one week, the depressive mood disorder episode lasts for more than two weeks, while simple depressive mood disorder does not occur, that is, the behavior rule data and daily mood data of a patient with depressive mood disorder tend to be relatively stable, and as time goes on, it does not occur the repeated switching. Therefore, the embodiment takes whether the stable behavior rule data and the daily mood data are provided as important basis for distinguishing the depressive mood disorder and the bipolar mood disorder, and takes the periodic statistical data of the behavior rule data and/or the periodic statistical data of the daily mood data as necessary bipolar mood related data according to the important basis. Wherein the behavior rules 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, i.e. the user's circadian behavioral pattern IS disturbed, which represents a risk of depression, i.e. the smaller the IS value, the greater the risk of mental disorder for the user. IV represents behavior volatility, with a range of values between 0 and 2, with a higher value favoring fragmentation of the behavior pattern, again 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 representing the active or passive mood state of the target user, and since the voice/text or the limb language can intuitively express the contrast condition of the depressed or manic mood state of the user, in this embodiment, the periodic statistical data of the voice text mood index (for example, the proportion of the voice or text data of the negative energy or the passive mood) in the daily mood data and the periodic statistical data of the limb language mood index (for example, the proportion of the passive mood or the manic mood in the limb language) are preferentially regarded as the bi-phase emotion related data.
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).
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 the bi-phase emotion-related data in this embodiment.
The environmental interaction data are used for representing the social state of the target user, and one of important characteristics of the bipolar affective disorder is little social contact or no social contact, so that the environmental interaction data can be used as one of the bipolar affective 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 this embodiment, the bi-phase 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 different types of double-phase emotion related data.
S102, comparing each piece of double-phase emotion related data with a preset double-phase emotion recognition index, and determining that the type of the mental disorder of the target user is double-phase emotion disorder when the double-phase emotion related data meets the double-phase emotion recognition index.
After obtaining the relevant data of the double-phase emotion such as the individual behavior data, the environment interaction data and the body index data of the target user in the above steps, the step is used for comparing the relevant data of each double-phase emotion with the preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to the double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, and the index categories comprise individual behavior indexes (behavior rule periodic statistics indexes, daily mood periodic statistics indexes and sleep indexes), environment interaction indexes (social degree indexes and activity range indexes) and body indexes (activity level indexes and physiological indexes), namely, the index categories are consistent with the categories of the relevant data of the double-phase emotion, and the index scores can be used as influence thresholds of each index category on the double-phase emotion disorder.
It should be noted that, for any two different types of mental disorders (for example, bipolar affective disorder and depressive mood 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 category), 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, that is, the degree of association between the same index category and different types of mental disorder is differentiated, for example, the index categories related to bipolar affective disorder are behavior rule periodic statistics index, daily mood periodic statistics index, sleep index, activity level index, physiological index, social level index, and activity range index, and the index categories related to depressive mood disorder are the same, however, the association between bipolar affective mood disorder and the above-mood disorder is significantly higher than the association between depressive mood disorder and the above-mentioned index categories, so that the score is used when identifying bipolar affective disorder is higher than the score used when identifying bipolar affective disorder for the same index category.
In addition, when different types of mental disorders are identified, the required precision of the same index category is different, so that different indexes in the same index category need to be selected, for example, when bipolar affective disorder is identified, the requirement on physiological index is finer, and therefore heart rate variability (Heart Rate Variability, HRV) data and/or galvanic skin orientation response data (Electro Dermal Orienting Reactivity, EDOR) with higher precision are adopted as bipolar affective related data; in the case of anxiety disorder, the accuracy requirement for physiological index is low, so that the data of skin electric activity (Electrodermal Activity, EDA) is used as anxiety related data, the skin electric activity refers to the change of electric conduction capacity of sweat glands on the surface of skin caused by activation caused by stress or other stimuli, for example, when the body is stimulated (such as acupuncture, electric shock and the like) or emotion is changed, sympathetic nerve branches of the autonomic nervous system are awakened, blood vessels in the skin shrink and relax, sweat glands of the body are activated to change, sweat is secreted, and the sweat contains water and electrolyte and enters the surface of the skin through skin pores, so that the conductivity of the skin is increased, and the skin resistance is reduced.
The step of comparing each piece of double-phase emotion related data with a preset double-phase emotion recognition index specifically means that the double-phase emotion related data are compared with index scores, namely, each piece of double-phase emotion related data in all pieces of double-phase emotion related data are respectively compared with index scores of corresponding index categories, and when the double-phase emotion related data are identical to the index scores or the difference value between the double-phase emotion related data and the index scores is smaller than a preset threshold value, the type of mental disorder of a target user is determined to be double-phase 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 (generally, the preset time points before, during and after the activity) is different, for example, the shape of the curve corresponding to the depressive mood disorder or the anxiety disorder is different from that of the bipolar mood disorder, so based on the characteristics, in another embodiment, the comparing each bipolar mood-related data with the preset bipolar mood recognition index may also 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 double-phase emotion recognition reference curve, wherein the double-phase emotion recognition reference curve represents the change curve of the high-frequency energy power spectral density value corresponding to the double-phase emotion disorder patient along with each preset time point; when the similarity between the shape of the target change curve and the shape of the biphase emotion recognition reference curve reaches a preset similarity threshold value, determining that the biphase emotion-related data meets the biphase emotion recognition index.
After obtaining the double-phase emotion related data of a target user, comparing each double-phase emotion related data with a preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to the double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, the double-phase 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 bipolar emotion related data meets the bipolar emotion recognition index, determining that the type of the mental disorder of the target user is bipolar emotion disorder. According to the method, the bipolar affective related data of different forms or dimensions such as the individual behavior data, the environment interaction data and the body index data are taken as consideration factors when the bipolar affective disorder is identified, so that the identification process of the bipolar affective 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 depression emotion of 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 bipolar affective disorder can be clearly distinguished from other types of mental disorder, and the accuracy of the identification result of the bipolar affective disorder is effectively improved.
The first embodiment provides a method for identifying bipolar affective disorders, and correspondingly, another embodiment of the present application also provides a device for identifying bipolar affective disorders, and since the device embodiments are basically similar to the method embodiments, the description is relatively simple, and the details of the 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 an understanding of the embodiment, fig. 2 is a block diagram of a unit of an apparatus for identifying bipolar disorder according to the embodiment, and as shown in fig. 2, the apparatus includes:
a bi-phase emotion-related data obtaining unit 201 for obtaining bi-phase emotion-related data of a target user, the bi-phase emotion-related data including individual behavior data, environmental interaction data, and body index data;
a bi-phase emotion recognition indicator comparing unit 202 for comparing each of the bi-phase emotion related data with a preset bi-phase emotion recognition indicator, wherein the bi-phase emotion recognition indicator includes an indicator category related to bi-phase emotion disorder and an indicator score for representing a degree of association between each indicator category and bi-phase emotion disorder, and the indicator category includes an individual behavior indicator, an environmental interaction indicator and a physical indicator;
a bipolar affective disorder determining unit 203, configured to determine that the type of mental disorder of the target user is bipolar affective disorder in response to the bipolar affective related data satisfying the bipolar affective recognition indicator.
In some embodiments, comparing each of the two-phase emotion related data with a preset two-phase emotion recognition index includes:
comparing the bi-phase emotion-related data with the index score;
the determining that the type of the mental disorder of the target user is a bipolar affective disorder in response to the bipolar affective associated data satisfying the bipolar affective recognition indicator comprises:
and determining that the type of the mental disorder of the target user is bipolar disorder in response to the bipolar emotion-related data being the same as the index score or the difference between the bipolar emotion-related data and the index score being less than a predetermined threshold.
In some embodiments, the individual behavioral data includes at least one of:
periodic statistical data of behavior rule data;
periodic statistics of 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 periodic statistics of the behavior rules data include behavior stability data and/or behavior volatility data.
In some embodiments, the physiological index data includes high frequency power spectral energy values in heart rate variability data; the comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index comprises the following steps:
obtaining high-frequency power spectrum energy 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 power spectrum energy value along with each preset time point based on the high-frequency power spectrum energy values respectively corresponding to the preset time points;
comparing the target change curve with a preset double-phase emotion recognition reference curve, wherein the double-phase emotion recognition reference curve represents a change curve of a high-frequency power spectrum energy value corresponding to a double-phase emotion disorder patient along with each preset time point;
the bi-phase emotion related data satisfies the bi-phase emotion recognition index, including:
and the similarity between the shape of the target change curve and the shape of the bipolar emotion recognition reference curve reaches a preset similarity threshold value.
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 the bi-phase emotion-related data of the target user includes:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and extracting the data of the multi-mode original data to obtain the double-phase emotion related data.
According to the device for identifying bipolar affective disorder, provided by the embodiment of the application, the bipolar affective related data of different forms or dimensions such as the individual behavior data, the environment interaction data and the body index data are taken as the consideration factors when the bipolar affective disorder is identified, so that the effect of a single factor or the same category factor of the body or the physiological condition is considered in the bipolar affective disorder identification process, the effect of multiple types of factors related to depression emotion of different dimensions and different forms is considered, different association degrees between the types of factors and different mental disorders are fully utilized, the recognition result of the bipolar affective disorder can be clearly distinguished from other types of mental disorder, and the accuracy of the bipolar affective disorder recognition result is effectively improved.
In the above embodiments, a method for identifying bipolar affective disorder and an apparatus for identifying bipolar affective 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, and 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 oxygen sensor, a blood pressure sensor, a voice sensor, and other monitoring modules, are mounted on the wearable device. 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 double-phase emotion related data of a target user, wherein the double-phase emotion related data comprises individual behavior data, environment interaction data and body index data;
comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes;
and determining that the type of the mental disorder of the target user is bipolar affective disorder in response to the bipolar affective related data meeting the bipolar affective recognition index.
In some embodiments, comparing each of the two-phase emotion related data with a preset two-phase emotion recognition index includes:
comparing the bi-phase emotion-related data with the index score;
the determining that the type of the mental disorder of the target user is a bipolar affective disorder in response to the bipolar affective associated data satisfying the bipolar affective recognition indicator comprises:
and determining that the type of the mental disorder of the target user is bipolar disorder in response to the bipolar emotion-related data being the same as the index score or the difference between the bipolar emotion-related data and the index score being less than a predetermined threshold.
In some embodiments, the individual behavioral data includes at least one of:
periodic statistical data of behavior rule data;
periodic statistics of 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 periodic statistics of the behavior rules data include behavior stability data and/or behavior volatility data.
In some embodiments, the physiological index data includes high frequency power spectral energy values in heart rate variability data; the comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index comprises the following steps:
obtaining high-frequency power spectrum energy 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 power spectrum energy value along with each preset time point based on the high-frequency power spectrum energy values respectively corresponding to the preset time points;
comparing the target change curve with a preset double-phase emotion recognition reference curve, wherein the double-phase emotion recognition reference curve represents a change curve of a high-frequency power spectrum energy value corresponding to a double-phase emotion disorder patient along with each preset time point;
the bi-phase emotion related data satisfies the bi-phase emotion recognition index, including:
and the similarity between the shape of the target change curve and the shape of the bipolar emotion recognition reference curve reaches a preset similarity threshold value.
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 the bi-phase emotion-related data of the target user includes:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and extracting the data of the multi-mode original data to obtain the double-phase emotion related data.
By using the wearable device provided by the embodiment, the bipolar affective related data of different forms or dimensions such as the individual behavior data, the environment interaction data and the body index data are taken as consideration factors when the bipolar affective disorder is identified, so that the identification process of the bipolar affective disorder considers the influence of a single factor or the same category factor of the body or the physiological condition, considers the influence of multiple types of factors related to depressed emotion of different dimensions and different forms, fully utilizes different association degrees between the various types of factors and different mental disorders, enables the identification result of the bipolar affective disorder to be clearly distinguished from other types of mental disorder, and effectively improves the accuracy of the identification result of the bipolar affective disorder.
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 bipolar disorder, the method comprising:
obtaining double-phase emotion related data of a target user, wherein the double-phase emotion related data comprises individual behavior data, environment interaction data and body index data;
comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index, wherein the double-phase emotion recognition index comprises index categories related to double-phase emotion disorder and index scores used for representing the association degree between each index category and the double-phase emotion disorder, and the index categories comprise individual behavior indexes, environment interaction indexes and body indexes;
and determining that the type of the mental disorder of the target user is bipolar affective disorder in response to the bipolar affective related data meeting the bipolar affective recognition index.
2. The method of claim 1, wherein comparing each of the bi-phase emotion-related data with a preset bi-phase emotion recognition index, comprises:
comparing the bi-phase emotion-related data with the index score;
the determining that the type of the mental disorder of the target user is a bipolar affective disorder in response to the bipolar affective associated data satisfying the bipolar affective recognition indicator comprises:
and determining that the type of the mental disorder of the target user is bipolar disorder in response to the bipolar emotion-related data being the same as the index score or the difference between the bipolar emotion-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:
periodic statistical data of behavior rule data;
periodic statistics of 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, characterized in that the periodic statistics of the behavior rules data comprise behavior stability data and/or behavior volatility data.
5. A method according to claim 3, wherein the physiological index data comprises high frequency power spectral energy values in heart rate variability data; the comparing each piece of the related data of the double-phase emotion with a preset double-phase emotion recognition index comprises the following steps:
obtaining high-frequency power spectrum energy 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 power spectrum energy value along with each preset time point based on the high-frequency power spectrum energy values respectively corresponding to the preset time points;
comparing the target change curve with a preset double-phase emotion recognition reference curve, wherein the double-phase emotion recognition reference curve represents a change curve of a high-frequency power spectrum energy value corresponding to a double-phase emotion disorder patient along with each preset time point;
the bi-phase emotion related data satisfies the bi-phase emotion recognition index, including:
and the similarity between the shape of the target change curve and the shape of the bipolar emotion recognition reference curve reaches a preset similarity threshold value.
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 obtaining the bipolar emotion-related data of the target user comprises:
acquiring multi-mode original data based on wearable equipment worn by the target user;
and extracting the data of the multi-mode original data to obtain the double-phase emotion related data.
9. An apparatus for identifying bipolar disorders, said apparatus comprising:
the system comprises a double-phase emotion-related data acquisition unit, a target user acquisition unit and a target user acquisition unit, wherein the double-phase emotion-related data comprises individual behavior data, environment interaction data and body index data;
a bi-phase emotion recognition index comparison unit, configured to compare each bi-phase emotion related data with a preset bi-phase emotion recognition index, where the bi-phase emotion recognition index includes an index category related to a bi-phase emotion disorder and an index score for characterizing a degree of association between each index category and the bi-phase emotion disorder, and the index category includes an individual behavior index, an environmental interaction index, and a physical index;
and the bipolar emotion disorder determining unit is used for determining that the type of the mental disorder of the target user is bipolar emotion disorder in response to the bipolar emotion related data meets the bipolar 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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