Personality detection method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a personality detection method and device, electronic equipment, a storage medium and a program product, and the personality detection method comprises the steps that state data of a target object is acquired, and the state data comprises human body heart rate variability data of the object detected through a millimeter wave radar in the task state that the object executes a detection task; and processing the state data of the target object by using a personality detection model to obtain predicted personality information of the target object, the personality detection model being obtained by training through a supervised method. According to the method, a reliable basis can be provided for personality detection by obtaining the state data including the human body heart rate variability data. The human body heart rate variability data are detected through the millimeter wave radar, and the authenticity, accuracy and reliability of the obtained data can be guaranteed. By using the personality detection model to process the state data, the rule of the state data can be mined by means of the model, and reliable prediction of personality information is realized.

Description

Technical Field

[0001] The present disclosure generally relates to the field of data processing technology, and more specifically, to a personality detection method, device, electronic device, storage medium and program product. Background Art

[0002] Coronary heart disease (CHD), also known as coronary atherosclerotic heart disease, is a common cardiovascular disease in middle-aged and elderly people. As a typical physical and mental disease, CHD not only affects the patient's physical health, but also has a significant impact on his or her mental state. In recent years, CHD has gradually become the "number one killer" threatening people's health. Its high incidence and high mortality have brought huge social and economic burdens. Patients' families not only face huge economic pressure, but also need to invest a lot of time and energy in care. Studies have shown that type A personality and type D personality are of great significance in the field of detecting CHD. Risk assessment can identify high-risk groups early, develop personalized treatment and rehabilitation plans, and improve treatment effects and patient compliance. Understanding personality types can help doctors and psychologists develop comprehensive treatment plans, combine psychological and behavioral interventions, and improve overall health levels. In addition, through systematic testing and research on these personality types, the accumulated data can reveal the complex relationship between psychological factors and CHD, provide a scientific basis for prevention and treatment strategies, and promote the comprehensive prevention and management of cardiovascular diseases.

[0003] A common way to distinguish personality is through scale testing, which usually involves using a series of questionnaires to quantitatively evaluate personality traits. Scale testing methods have significant reference value because they can systematically capture and quantify the performance of individuals in different personality dimensions. However, inevitably, during the scale testing process, some subjects may fill in vague or even deceptive behaviors, which may be due to a variety of reasons. For example, the subjects may want to show their idealized side, so they tend to choose options that are generally recognized by society when answering the questionnaire; or, they may be unwilling to answer questions involving personal sensitive information frankly because of concerns about privacy. Such behavior will lead to data bias, which will have a certain erroneous impact on the final evaluation results. Summary of the invention

[0004] The present disclosure provides a personality detection method, device, electronic device, storage medium and program product, which are used to solve at least one of the above problems.

[0005] According to a first aspect of an embodiment of the present disclosure, a personality detection method is provided, the personality detection method comprising: acquiring state data of a target object, wherein the state data comprises human heart rate variability data of the object obtained by detecting through a millimeter wave radar in a task state in which the object performs a detection task; using a personality detection model to process the state data of the target object to obtain predicted personality information of the target object, wherein the personality detection model is trained by a supervised method.

[0006] Optionally, the detection task includes at least one of the following: a resting state detection task, an abdominal breathing state detection task, and a passive stress state detection task, wherein the passive stress state detection task includes at least one of the following: a passive stress state detection task after rest, and a passive stress state detection task after abdominal breathing, and the passive stress state detection task is a task of inducing a stress response of the corresponding object through emotion-inducing materials.

[0007] Optionally, the status data further includes questionnaire data, and the questionnaire data is obtained by the subject answering a preset questionnaire, and the preset questionnaire includes at least one scale related to personality detection.

[0008] Optionally, the questionnaire data includes the score of each scale of the at least one scale related to personality detection, wherein the using of the personality detection model to process the state data of the target object to obtain the predicted personality information of the target object includes: performing time-frequency domain feature extraction processing on human heart rate variability data corresponding to each detection task to obtain time domain features and frequency domain features corresponding to each detection task; performing fusion processing on the time domain features and frequency domain features corresponding to each detection task and the scores of each scale to obtain a fusion vector; and inputting the fusion vector into the personality detection model to obtain the predicted personality information.

[0009] Optionally, the questionnaire data includes the score of each scale in the at least one scale related to personality detection, and the training sample of the personality detection model includes the state data and real personality information of the reference object, wherein the personality detection model is trained by the following steps: acquiring the state data of the reference object; using the personality detection model to be trained, processing the state data of the reference object, and obtaining the predicted scale score and predicted personality information of the reference object, wherein the predicted scale score is a predicted value of the score of the reference object answering the at least one scale related to personality detection; determining the weighted cross entropy loss function according to the predicted personality information and the real personality information of the reference object. value as the first loss value, wherein the weighted cross entropy loss function is obtained by adding a personality category weight to the cross entropy loss function, and the personality category weight is negatively correlated with the proportion of the personality category corresponding to the real personality information of the reference object in all training samples; according to the predicted scale score of the reference object and the scores of each corresponding scale in the status data, the value of the correlation loss function is determined as the second loss value, wherein the correlation loss function is the difference between a preset constant and a preset correlation coefficient; according to the first loss value and the second loss value, a total loss value is determined; based on the total loss value, the parameters of the personality detection model to be trained are adjusted to obtain the personality detection model.

[0010] Optionally, the at least one scale related to personality testing includes at least one of the following: Type A Behavior Type Questionnaire, Type D Personality Questionnaire, Trait Coping Style Questionnaire, Self-Rating Depression Scale, Self-Rating Anxiety Scale, Anger and Anger Expression Scale, Perceptual Social Support Scale, and Indulgent Reaction Scale; and / or the preset questionnaire also includes a psychosomatic disease questionnaire.

[0011] According to a second aspect of an embodiment of the present disclosure, a personality detection device is provided, comprising: an acquisition unit, configured to acquire state data of a target object, wherein the state data comprises human heart rate variability data of a corresponding object detected in a task state, and the human heart rate variability data is obtained by using millimeter wave radar detection; and a processing unit, configured to use a personality detection model to process the state data of the target object to obtain predicted personality information of the target object, wherein the personality detection model is obtained by training using a supervised method.

[0012] Optionally, the detection task includes at least one of the following: a resting state detection task, an abdominal breathing state detection task, and a passive stress state detection task, wherein the passive stress state detection task includes at least one of the following: a passive stress state detection task after rest, and a passive stress state detection task after abdominal breathing, and the passive stress state detection task is a task of inducing a stress response of the corresponding object through emotion-inducing materials.

[0013] Optionally, the status data further includes questionnaire data, and the questionnaire data is obtained by the subject answering a preset questionnaire, and the preset questionnaire includes at least one scale related to personality detection.

[0014] Optionally, the questionnaire data includes the score of each scale of the at least one scale related to personality detection, and the processing unit is further configured to: perform time-frequency domain feature extraction processing on the human heart rate variability data corresponding to each detection task to obtain the time domain features and frequency domain features corresponding to each detection task; perform fusion processing on the time domain features and frequency domain features corresponding to each detection task and the scores of each scale to obtain a fusion vector; and input the fusion vector into the personality detection model to obtain the predicted personality information.

[0015] Optionally, the questionnaire data includes the score of each scale in the at least one scale related to personality detection, and the training sample of the personality detection model includes the state data and real personality information of the reference object, wherein the personality detection model is trained by the following steps: acquiring the state data of the reference object; using the personality detection model to be trained, processing the state data of the reference object, and obtaining the predicted scale score and predicted personality information of the reference object, wherein the predicted scale score is a predicted value of the score of the reference object answering the at least one scale related to personality detection; determining the weighted cross entropy loss function according to the predicted personality information and the real personality information of the reference object. value as the first loss value, wherein the weighted cross entropy loss function is obtained by adding a personality category weight to the cross entropy loss function, and the personality category weight is negatively correlated with the proportion of the personality category corresponding to the real personality information of the reference object in all training samples; according to the predicted scale score of the reference object and the scores of each corresponding scale in the status data, the value of the correlation loss function is determined as the second loss value, wherein the correlation loss function is the difference between a preset constant and a preset correlation coefficient; according to the first loss value and the second loss value, a total loss value is determined; based on the total loss value, the parameters of the personality detection model to be trained are adjusted to obtain the personality detection model.

[0016] Optionally, the at least one scale related to personality testing includes at least one of the following: Type A Behavior Type Questionnaire, Type D Personality Questionnaire, Trait Coping Style Questionnaire, Self-Rating Depression Scale, Self-Rating Anxiety Scale, Anger and Anger Expression Scale, Perceptual Social Support Scale, and Indulgent Reaction Scale; and / or the preset questionnaire also includes a psychosomatic disease questionnaire.

[0017] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: at least one processor; and at least one memory storing computer executable instructions, wherein when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute a personality detection method according to an exemplary embodiment of the present disclosure.

[0018] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to execute a personality detection method according to an exemplary embodiment of the present disclosure.

[0019] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising computer instructions, which, when executed by at least one processor, prompt the at least one processor to execute a personality detection method according to an exemplary embodiment of the present disclosure.

[0020] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0021] According to the personality detection method and device, electronic device, and storage medium disclosed in the present invention, by executing the specified detection task, different reactions of the object can be stimulated, and these reactions can be objectively reflected from the physiological data of the object, reducing the influence of the subjective factors of the object on the detection results. After a large number of studies, it is found that relatively single physiological data such as blood pressure and heart rate do not differ significantly between objects of different personalities, and human heart rate variability data (referring to the change of heartbeat intervals), a physiological data with dynamic characteristics, has obvious differences between objects of different personalities. By obtaining state data including this data, a more reliable basis can be provided for personality detection. In addition, by using millimeter wave radar to detect human heart rate variability data, electrodes, patches or other devices that directly contact the skin are not required, thereby reducing interference to the target object, reducing the pressure and discomfort of the target object, having non-invasive characteristics, and being able to monitor the target object in its natural state, which helps to ensure the authenticity, accuracy and reliability of the acquired data. Finally, by using the personality detection model obtained by supervised training to process the state data, the law of the state data can be mined with the help of the model, so that the state data can be processed to achieve reliable prediction of personality information.

[0022] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0024] Figure 1 is a flow chart of a personality detection method according to an exemplary embodiment of the present disclosure;

[0025] Figure 2 is a flowchart of a rest state detection task according to a specific embodiment of the present disclosure;

[0026] Figure 3 is a flowchart of an abdominal breathing state detection task according to a specific embodiment of the present disclosure;

[0027] Figure 4 is a flowchart of a passive stress state detection task after rest according to a specific embodiment of the present disclosure;

[0028] Figure 5 is a flowchart of a passive stress state detection task after abdominal breathing according to a specific embodiment of the present disclosure;

[0029] Figure 6 is a block diagram of a personality detection device according to an exemplary embodiment of the present disclosure;

[0030] Figure 7 is a block diagram of an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation methods described in the following examples do not represent all implementation methods consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the attached claims.

[0033] It should be noted that the phrase "at least one of the items" in the present disclosure includes three types of parallel situations: "any one of the items", "a combination of any number of the items", and "all of the items". For example, "including at least one of A and B" includes the following three parallel situations: (1) including A; (2) including B; (3) including A and B. Another example is "executing at least one of step 1 and step 2" which means the following three parallel situations: (1) executing step 1; (2) executing step 2; (3) executing step 1 and step 2.

[0034] Hereinafter, a personality detection method and apparatus, an electronic device, and a storage medium according to exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0035] Figure 1 is a flow chart of a personality detection method according to an exemplary embodiment of the present disclosure. The personality detection method can be executed by an electronic device with corresponding computing capabilities.

[0036] Reference Figure 1 , in step S101, the status data of the target object is obtained.

[0037] The state data includes human heart rate variability (HRV) data of the object obtained by detecting through the millimeter wave radar when the object is in a task state of performing a detection task.

[0038] By executing the specified detection task, different reactions of the object can be stimulated. These reactions can be objectively reflected in the physiological data of the object, reducing the influence of the subjective factors of the object on the detection results. After a lot of research, it was found that relatively single physiological data such as blood pressure and heart rate do not differ significantly between objects of different personalities. The human heart rate variability data (referring to the changes in the heartbeat interval), a physiological data with dynamic characteristics, has obvious differences between objects of different personalities. By obtaining the state data including this data, a more reliable basis can be provided for personality detection. It should be understood that when executing the detection task, the heart rate signal of the object is directly detected, and the interval duration between two adjacent heartbeats can be calculated based on this, and the interval duration sequence can be obtained as the human heart rate variability data.

[0039] Specifically, the core characteristics of Type A Personality include high competitiveness, a sense of time urgency, and irritability. High competitiveness refers to the strong achievement motivation and competitive consciousness that individuals show in life and work. They always desire success, pursue excellence, set high standards, and are not only strict with themselves, but also have high expectations of others. The sense of time urgency is manifested in the fact that individuals always feel that time is not enough, like to complete tasks quickly, and find it difficult to relax. They often handle multiple tasks at the same time, pursue high efficiency, and feel extremely uneasy about procrastination. Irritability refers to the tendency of individuals to be impatient and angry, especially when encountering obstacles or delays. They have a low tolerance for frustration and often show impatience and irritability. This personality trait often makes individuals nervous and anxious when facing pressure and challenges, which in turn affects their mental health and interpersonal relationships.

[0040] Type D Personality, or "troubled personality", is a psychological trait whose core characteristics include persistent negative emotions and social inhibition. Persistent negative emotions refer to individuals who often experience negative emotions such as anxiety, depression, anger, and irritability. These emotions are not only strong but also persistent, and they are particularly sensitive to stress and frustration in life. Social inhibition is manifested in individuals avoiding and suppressing their own emotions and thoughts in social situations, being afraid to express their true selves, and worrying about being rejected or criticized by others, so they tend to avoid social interactions and emotional expression. This personality trait often makes individuals feel isolated and helpless when facing social and emotional challenges, which in turn affects their mental health and interpersonal relationships.

[0041] According to relevant research, human heart rate variability data can be used as an important indicator to distinguish between type D personality and type A personality, because human heart rate variability data reflects the functional state of the autonomic nervous system, and its changes can reveal the differences in psychological and physiological stress responses. Specifically, time-frequency domain features can be extracted from human heart rate variability data, and these time-domain features and frequency-domain features can be used to quantify the changes in heartbeat intervals from different angles. Studies have found that individuals with overall lower frequency domain features usually show higher sympathetic nervous system activity and poor parasympathetic nervous system regulation, which is consistent with the high stress, tension and hostility characteristics of type A personality; while type D personality individuals are often accompanied by negative emotions and social inhibition. These emotional states also lead to a decrease in frequency domain features, but the different frequency domain features of the two (such as but not limited to frequency domain features of different frequency bands) still have regular differences. Therefore, by measuring and analyzing changes in heartbeat intervals, an objective physiological means can be provided to achieve the distinction between different personality types.

[0042] In addition, traditional monitoring methods such as electrocardiogram and pulse wave monitoring require the use of electrodes, patches or other monitoring devices that directly contact the skin, which are easy to interfere with the object and may affect the accuracy of the test results. Millimeter wave radar is a device that emits electromagnetic waves with a frequency in the millimeter wave band to the target and works by receiving the electromagnetic waves reflected by the target. Millimeter wave radar also has the characteristics of high precision and high resolution. It can capture subtle changes in heartbeat and breathing by detecting and analyzing tiny chest movements, thereby obtaining human heart rate variability data. Millimeter wave radar does not require electrodes, patches or other devices that directly contact the skin, thereby reducing interference with the target object, reducing the pressure and discomfort of the target object, and has non-invasive characteristics. It can monitor the target object in its natural state, which helps to ensure the authenticity, accuracy and reliability of the acquired data. This non-contact detection is especially suitable for people with sensitive skin, skin diseases or infants who are not suitable for wearing traditional monitoring equipment. Non-contact technology also has the advantage of reducing the spread of germs in public health and clinical environments, improving safety and hygiene levels.

[0043] Continue to refer to Figure 1 In step S102, the personality detection model is used to process the state data of the target object to obtain the predicted personality information of the target object.

[0044] The personality detection model is trained through supervised methods. It can use the model to mine the patterns of state data, thereby processing the state data and achieving reliable prediction of personality information.

[0045] Optionally, the training samples of the personality detection model include the state data and real personality information of the reference object, and the real personality information is obtained by following up the reference object. Follow-up refers to a method of observation in which the hospital regularly understands the changes in the patient's condition and guides the patient's rehabilitation by communication or other means. In other words, the present disclosure selects the objects that have been followed up, such as patients with coronary heart disease follow-up, and regular physical examination personnel who are not sick, and of course other personnel whose stable real personality can be determined by follow-up, and these objects are used as reference objects. Taking patients with coronary heart disease follow-up as an example, different personalities, especially type A and type D personalities, have different effects on the prognosis of coronary heart disease, and the reference object has detailed information on the treatment plan and prognosis after long-term follow-up, and often has a relatively stable judgment on its personality type, so it is possible to obtain more reliable real personality information of the reference object, which can basically eliminate the influence of human factors (for the scheme using scale detection, such as subjective factors when the detection object answers, and human analysis of the scale evaluator) on personality evaluation. Using the real personality information obtained in this way as the personality label in training can obtain high-quality training data, which helps to improve the accuracy of the trained personality detection model from the source. It should be understood that for reference subjects who do not suffer from coronary heart disease, although there is no information such as treatment plan and prognosis, through long-term follow-up, relatively stable personality detection results can be obtained, thereby significantly reducing the error of a single personality test and ensuring sample quality. As an example, the real personality information can include type A personality information and type D personality information.

[0046] Next, a personality detection method according to an exemplary embodiment of the present disclosure is further introduced.

[0047] Optionally, the detection task includes at least one of the following: a resting state detection task, an abdominal breathing state detection task, and a passive stress state detection task, wherein the passive stress state detection task includes at least one of the following: a passive stress state detection task after rest, and a passive stress state detection task after abdominal breathing, and the passive stress state detection task is a task of inducing a stress response of a corresponding object through emotion-inducing materials. By adopting at least one of the above detection tasks, human heart rate variability data in different states can be obtained, thereby enriching the physiological description of the target object.

[0048] As an example, each detection task can be a short-term task (such as 5 minutes) or a long-term task (such as 24 hours), and the present disclosure does not impose any limitation on this.

[0049] Short-term tasks are mainly used to evaluate the short-term response of the autonomic nervous system and reflect the dynamic balance of the sympathetic and parasympathetic nerves in a short period of time. In the frequency domain feature selection of human heart rate variability data, total power (Total Power, TP, frequency band ≤ 0.4 Hz), very low frequency band (VLF, frequency band 0.0033-0.04 Hz) power, low frequency band (LF, frequency band 0.04-0.15 Hz) power, high frequency band (HF, frequency band 0.15-0.4 Hz) power, the ratio of low frequency band power to high frequency band power (LF / HF), low frequency band power normalization value (LFnorm), and high frequency band power normalization value (HFnorm) can be used. Among them, high frequency band power can reflect the tension of parasympathetic nerves. Low frequency band power can reflect the joint action of sympathetic and parasympathetic nerves. The power of the very low frequency band usually reflects slower physiological changes, involving aspects such as temperature regulation, hormone regulation, chronic stress and immune system activity. It is related to the long-term regulation of the autonomic nervous system. Especially under chronic stress or disease conditions, the power of the very low frequency band may decrease, indicating abnormal autonomic nervous function. The ratio of the low frequency band power to the high frequency band power reflects the balance of the sympathetic and parasympathetic nerves. The unit of the normalized value of the low frequency band power (LFnorm) and the normalized value of the high frequency band power (HFnorm) is (nu). The calculation method is to use the absolute value of the measured low frequency band power or high frequency band power, divided by the difference obtained by subtracting the very low frequency band power from the total power, and then multiplied by 100.

[0050] The normal range of power spectrum recorded while lying still for 5 minutes is:

[0051] Total power: 3466±1018ms2 / Hz.

[0052] Extremely low frequency band power: 1170±416ms2 / Hz.

[0053] Normalized value of low-frequency band power: 54±4nu.

[0054] High frequency band power: 975±203ms2 / Hz.

[0055] Normalized value of high frequency band power: 29±3nu.

[0056] The ratio of low-frequency band power to high-frequency band power: 1.5~2.0.

[0057] Long-term tasks are used to evaluate the comprehensive situation of autonomic nervous activity throughout the day. They can capture circadian rhythms and long-term physiological changes, and can reflect the overall comprehensive situation of the subject. The selection of statistical indicators of human heart rate variability can include total power, ultra-low frequency power (ULF, frequency band ≤ 0.003Hz), extremely low frequency band power, low frequency band power, and high frequency band power.

[0058] In addition to human heart rate variability data, the status data may also include questionnaire data, which is obtained by the subject answering a preset questionnaire, and the preset questionnaire includes at least one scale related to personality testing. Although scale testing has the risk of data bias, it is still a relatively mature quantitative assessment method for personality traits. By combining human heart rate variability data with strong objectivity and authenticity with questionnaire data including scale test results, multi-dimensional information can be provided, thereby conducting a comprehensive and integrated analysis and obtaining a more accurate and detailed personality analysis result. By adopting this multi-level, non-invasive assessment method, personality traits can be more effectively identified and analyzed, which not only brings possibilities for personalized mental health intervention, but also provides a scientific basis for personalized disease treatment.

[0059] As an example, the questionnaire data may include the test results of each scale, such as the score of each scale, and the personality detected by each scale, and may also include the answers given by the subject in each scale to the specified questions. When multiple scales are used, the corresponding test results may be concatenated into a vector as the questionnaire data. Of course, the questionnaire data may also include the answer data of other preset questionnaires other than the scales, and the present disclosure does not limit this.

[0060] Optionally, the questionnaire data includes the scores of each scale in at least one scale related to personality detection, and step S102 includes: performing time-frequency domain feature extraction processing on the human heart rate variability data corresponding to each detection task to obtain the time domain features and frequency domain features corresponding to each detection task; fusing the time domain features and frequency domain features corresponding to each detection task and the scores of each scale to obtain a fusion vector; inputting the fusion vector into the personality detection model to obtain predicted personality information. By fusing the time domain features and frequency domain features corresponding to the human heart rate variability data with the scores of each scale, a fusion vector containing rich information can be obtained, which facilitates the personality detection model to perform comprehensive analysis and processing on the overall information, thereby learning the implicit relationship between different elements, and helping to improve the accuracy of the personality detection results. As an example, the fusion process can be a splicing process, that is, splicing these data into a fusion vector, or using a fusion model to process these data to obtain a fused feature vector, and the present disclosure does not limit this.

[0061] Optionally, the questionnaire data still includes the scores of each scale mentioned above, and the personality detection model is trained by the following steps: obtaining the status data of the reference object; using the personality detection model to be trained, processing the status data of the reference object, and obtaining the predicted scale score and predicted personality information of the reference object, wherein the predicted scale score is the predicted value of the score of at least one scale related to personality detection answered by the reference object; determining the value of the weighted cross entropy loss function as the first loss value according to the predicted personality information and the real personality information of the reference object, wherein the weighted cross entropy loss function is obtained by adding the personality category weight to the cross entropy loss function, and the personality category weight is negatively correlated with the proportion of the personality category corresponding to the real personality information of the reference object in all training samples; determining the value of the correlation loss function as the second loss value according to the predicted scale score of the reference object and the scores of each corresponding scale in the status data, wherein the correlation loss function is the difference between a preset constant and a preset correlation coefficient; determining the total loss value according to the first loss value and the second loss value; adjusting the parameters of the personality detection model to be trained based on the total loss value to obtain the personality detection model.

[0062] This embodiment specifically describes the loss function used in the training process. On the one hand, based on the conventional comparison between the prediction result (i.e., the predicted personality information) and the sample label (i.e., the real personality information), the cross entropy loss function is used, and by adding a personality category weight that is negatively correlated with the proportion of the real personality category (i.e., the personality category corresponding to the real personality information) in all training samples, that is, the calculated value of each training sample is calculated using the existing cross entropy loss function, and then multiplied by the corresponding personality category weight, and the obtained product is used as the new calculated value of each training sample, and then the average of the new calculated values ​​of all training samples is calculated as the first loss value. This processing method can weaken the influence of a single training sample of a personality category with a high proportion on the first loss value, so that when there is a significant difference in the number of samples of different personality categories, the influence of training samples of different categories can be balanced. On the other hand, considering the correlation between human heart rate variability data and scale scores, the personality detection model is made to output the predicted scores of each scale in the training stage, and the correlation measure (i.e., the value of the preset correlation coefficient) between the predicted scale scores and the actual scale scores (i.e., the scores of each scale contained in the state data) is calculated, and then the difference between the preset constant and the correlation measure is obtained as the second loss value, which can guide the model to make the predicted scale scores closer to the actual scale scores during the training process, so that the model can learn the correlation between human heart rate variability data and scale scores, which helps to improve the accuracy of personality prediction. It should be understood that in the inference stage, that is, when the trained personality detection model is used for personality detection, there is no need to output the predicted scale scores. By using the first loss value and the second loss value in combination, the advantages of the above two aspects can be taken into account, thereby significantly improving the objectivity and accuracy of personality prediction.

[0063] As an example, the personality category weight is, for example, but not limited to, the inverse of the proportion of the corresponding real personality category in all training samples; the preset correlation coefficient used in the correlation loss function is, for example, but not limited to, the Pearson correlation coefficient, the canonical correlation coefficient, and the cosine similarity; the preset constant used in the correlation loss function is, for example, but not limited to, 1; when determining the total loss value, it is, for example, but not limited to, calculating the weighted sum of the first loss value and the second loss value, so as to flexibly adjust the influence of the first loss value and the second loss value by adjusting the weight.

[0064] Optionally, at least one scale related to personality testing includes at least one of the following: Type A Behavior Type Questionnaire, Type D Personality Questionnaire, Trait Coping Style Questionnaire, Depression Self-Assessment Scale, Anxiety Self-Assessment Scale, Anger and Anger Expression Scale, Perceptual Social Support Scale, and Indulgent Reaction Scale. By using at least one of the above preset scales, the personality of the target object can be tested from different angles, thereby providing rich information.

[0065] Specifically, the Type A Behavior Pattern Questionnaire (TABP) is a questionnaire compiled by Professor Zhang Boyuan. It has 60 questions in total and is divided into three parts: the Time Hurriedness (TH, 25 questions), which indicates the sense of time rush, time urgency and doing things quickly; the Competition and Hostility (CH, 25 questions), which includes characteristics such as competitiveness, wariness or hostility and lack of patience; and the Lie Detector (L, 10 questions). The score is scored as yes (1) or no (0), and the score is the sum of CH and TH. The correlation between the peer-assessment and self-assessment of this scale is 0.57, and the retest reliability after a 2-month interval is 0.51.

[0066] The diagnostic criteria were determined based on the Chinese norms: 1-18 is type B, 19-26 is partial to type B, 27-28 is divided into intermediate type, 29-36 is partial to type A, and 37-50 is type A.

[0067] The Type D Personality Scale (DS14) was developed by Denollet et al. in 1998 to assess Type D personality. It was revised to DS14 in 2005. It consists of 14 questions, 7 of which are used to measure negative affectivity (NA) and social inhibition (SI), such as "I often feel unhappy", "I often make a fuss about trivial matters", "I feel very restrained in social interactions", etc. It uses a five-point scoring system, from 0 (very inconsistent) to 4 (very consistent).

[0068] The score range of NA and SI subscales is 0-28. When used, 10 points are used as the cutoff point. Subjects with NA and SI scores ≥ 10 are considered to have type D personality tendencies.

[0069] The Trait Coping Style Questionnaire (TCSQ) is the first self-compiled coping scale in China compiled by Professor Jiang Qianjin. It is used to evaluate the subjects' relatively stable coping strategies related to personality traits. The questionnaire has a certain consistency across situations and is related to physical and mental health. The scale includes 20 items, and the subjects make a five-level choice answer from 1 to 5 for each item. The scale includes two dimensions: negative coping (NC) and positive coping (PC).

[0070] The Self-Rating Depression Scale (SDS) is the most commonly used depression assessment scale in clinical practice and is one of the scales recommended by the U.S. Department of Education, Health and Welfare. The scale includes 20 items, reflecting emotional conditions (crying, depression, etc.), physical functions (such as loss of appetite, fatigue), mental dullness (confused mind, agitation), and psychological disorders (emptiness, self-deprecation, suicidal tendencies, etc.). It is scored on a four-level scale from 1 (none) to 4 (continuous). The depression index is calculated, ranging from 0.25 to 1.0. The higher the index, the more severe the depression.

[0071] The Self-Rating Anxiety Scale (SAS) is the most commonly used anxiety assessment scale in clinical practice, consisting of 20 items, with a four-level score from 1 (none) to 4 (continuous). When tested simultaneously with the Hamilton Anxiety Scale (HAMA), the total scores of the two scales had a Pearson correlation coefficient of 0.365 and a Spearman rank correlation coefficient of 0.341, indicating that their validity is good.

[0072] Regarding the anger and anger expression scale, anger is closely related to the occurrence and development of coronary heart disease. Many prospective studies have shown that anger can easily cause coronary heart disease and affect the prognosis of coronary heart disease. In order to evaluate the subjects' anger and anger expression, the anger subscale and the anger-in and anger-out subscales of the Type C Behavior Scale (CB) were selected. This scale was introduced to China by Professor Zhang Yao and is widely used in clinical practice. The anger scale contains 10 items, and the anger-in and anger-out scales each contain 6 items. Each question is answered according to a four-level score of 1 (almost never) to 4 (often).

[0073] Regarding the Perceived Social Support Scale (PSSS), social support is an important mediating factor affecting the relationship between psychological stress and physical and mental health. Social support includes objective, visible or actual support, as well as the individual's perceived respect, support and understanding. The Perceived Social Support Scale (PSSS) mainly assesses the social support perceived by the individual. The scale was adapted by Blumenthal et al. and introduced and revised by Jiang Qianjin et al. It measures the individual's subjective experience of respect, understanding and support from family, friends and other people. Domestic studies have shown that the social support scale has good reliability and validity. The scale contains 12 questions, divided into two subscales: support within the family and support outside the family. Specific items include "I share happiness and sorrow with some people", "My family can help me in a practical and specific way", and "I can discuss my problems with my friends". Each item uses a seven-level scoring method of 1-7.

[0074] Regarding the Ruminative Responses Scale (RRS), studies have shown that rumination is related to the generation and maintenance of a variety of negative emotions, especially anxiety, depression, and anger, which are closely related to rumination and are important cognitive factors in the stress coping process. In order to evaluate the rumination response of the subject, the present disclosure adopts the most commonly used rumination response scale. The scale is a subscale of the Ruminative Style Questionnaire (RSQ), which mainly assesses the subject's attention to the symptoms, meaning, causes, and results of the sad state, which is an important cognitive factor for the maintenance and strengthening of depressive emotions (Nolen-Hoeksema, 1991). The scale contains 22 items, each of which is scored according to 4 levels (1 never or occasionally; 2 sometimes; 3 often; 4 always), mainly involving the attention and evaluation of the symptoms, meaning, causes, and results of sad and depressive emotions.

[0075] Optionally, the preset questionnaire also includes a psychosomatic disease questionnaire. Since the subject's psychosomatic health status is related to personality, coping style, emotion, cognition, and will also affect the emotional state, in order to study the psychological mechanism by which type A behavior and type D personality affect the prognosis of coronary heart disease, it is necessary to understand the subject's psychosomatic health status. To this end, a psychosomatic disease questionnaire was compiled based on common typical psychosomatic diseases (hypertension, diabetes, coronary heart disease, ulcer disease, neurodermatitis, and allergies). The survey content mainly includes the subject's psychosomatic disease history and family psychosomatic disease history, and also investigates smoking and drinking. It should be understood that for questionnaires that include psychosomatic disease questionnaires, or other non-scale types that are only used to collect information, the collected information may not be involved in the processing of the personality detection model, and may only be saved as background information, or may be further extracted to obtain key data, and added to the questionnaire data to participate in the processing of the personality detection model. For the latter, a specific participation method is, for example, when obtaining the fusion vector introduced above, allowing the key data to participate in the fusion process, so that it is called a part of the model input; another example is to use the population to which the object belongs as key data, such as healthy people and people with psychosomatic diseases, and train personality detection models for different populations, and then determine the applicable personality detection model according to the key data of the target object when performing personality detection, and use the determined model to perform personality detection. The present disclosure does not limit the specific use of the information collected by the information collection questionnaire.

[0076] Next, a specific embodiment of a detection task is introduced. In this specific embodiment, the detection task is a short-term task and includes detection tasks under four different states. In addition, there is a significant correlation between the two scale dimensions of type A personality and type D personality, but the connotations of the measured dimensions are very different. In order to distinguish different personalities more accurately, the subjects are divided into four groups according to the two dimensions of the type A behavior type questionnaire (i.e., type A and type B) and the two dimensions of the type D personality scale (i.e., type D and type d, where type d refers to non-type D): AD group, BD group, Ad group, and Bd group. It should be understood that the division into four groups here is to facilitate a brief distinction and description of the task performance of different objects, and it is not necessary to first determine the group to which the target object belongs based on the human heart rate variability data when executing the personality detection method of the exemplary embodiment of the present disclosure.

[0077] 1. Rest state detection task.

[0078] like Figure 2 As shown, the target subject is asked to lean on a high-back chair with armrests and close his eyes to rest for ten minutes, and then the millimeter-wave radar is used to detect and record his human heart rate variability data. After resting for five minutes, the human heart rate variability data is recorded for the second time.

[0079] In this task, the AD group was lower than the BD group in TP. The TP, VLF, and LF of the type D personality subjects were lower than those of the type D personality subjects.

[0080] 2. Abdominal breathing state detection task.

[0081] like Figure 3 As shown, the target subject is asked to lean on a high-back chair with armrests and close his eyes to rest for ten minutes. The target subject is guided to practice abdominal breathing seven times per minute. After practicing for one minute, the target subject is asked to take deep abdominal breaths for five minutes. The millimeter wave radar is used to detect and record the human heart rate variability data. After resting for five minutes, the above steps are repeated to record the second human heart rate variability data.

[0082] In this task, there was a significant difference in HF between the AD and BD groups under abdominal breathing. The HF of AD subjects was significantly lower than that of BD subjects, and it was marginally significant in LFnorm and HFnorm. The TP and HF of type D subjects were lower than those of type D subjects.

[0083] 3. Passive stress state detection task after rest.

[0084] like Figure 4 As shown, the target subject is asked to lean on a high-back chair with armrests and close his eyes to rest for ten minutes. The target subject is asked to watch the sadness emotion inducing video material for five minutes, and the millimeter wave radar is used to detect and record the human heart rate variability data. After resting for five minutes, the above steps are repeated, the sadness emotion inducing video material is replaced, and the human heart rate variability data is recorded for the second time.

[0085] In this task, the AD group showed marginal significance in LF / HF, LFnorm, and HFnorm, and was higher than the BD group in HFnorm, and lower than the BD group in heart rate change, LF / HF, and LFnorm. For young men, the LF / HF and LFnorm of type D personality subjects were higher than those of type D personality subjects, and the HFnorm of type D personality subjects was lower than that of type D personality subjects.

[0086] 4. Passive stress state detection task after abdominal breathing.

[0087] like Figure 5 As shown, the target subject is asked to lean on a high-back chair with armrests and close his eyes to rest for ten minutes. The target subject is instructed to practice abdominal breathing, seven times per minute. After practicing for one minute, the target subject is asked to take deep abdominal breaths for five minutes. The target subject is asked to watch the sad emotion inducing video material for five minutes. The millimeter wave radar is used to detect and record the human heart rate variability data. After resting for five minutes, the above steps are repeated, the sad emotion inducing video material is replaced, and the human heart rate variability data is recorded for the second time.

[0088] In this task, the heart rate changes of type D subjects before and after watching the video were greater than those of type D subjects.

[0089] Next, a personality detection method according to an exemplary embodiment of the present disclosure is introduced in conjunction with a specific embodiment.

[0090] This specific embodiment mainly includes the following steps:

[0091] 1. Data collection: Use millimeter-wave radar to collect the heart rate signal of the target object under different detection tasks.

[0092] 2. Signal processing: extract the time domain and frequency domain features of human heart rate variability data.

[0093] 3. Feature fusion: Map the time-frequency domain features with the scale scores to form a fusion vector.

[0094] 4. Personality detection: Use the trained personality detection model to process the fusion features and obtain the predicted personality traits.

[0095] Next, we will introduce them one by one.

[0096] 1. Data collection steps.

[0097] In order to stimulate different physiological responses of the target objects, the following multiple detection tasks are designed.

[0098] Resting state detection task (Task 1): Sit quietly and keep breathing naturally.

[0099] Abdominal breathing state detection task (Task 2): guide the target subject to perform deep abdominal breathing.

[0100] Passive stress state detection task (Task 3): The target subject’s stress response is induced by emotion-inducing materials (such as stressful pictures or sounds).

[0101] During each detection task, the millimeter-wave radar device is used to obtain the heart rate signal of the target object in a non-contact manner. The millimeter-wave radar detects the tiny chest wall displacement caused by the heart beat by emitting and receiving electromagnetic waves.

[0102] 2. Signal processing steps.

[0103] This step further includes 4 sub-steps.

[0104] 1) Preprocess the collected heart rate signal, specifically extract the heart rate time series {R(t)} from the original signal collected by the millimeter wave radar, and use a filter to remove noise and enhance the signal quality.

[0105] 2) The heart rate interval (RR interval) sequence is calculated using the following formula:

[0106] RR(n)=R(t n+1 )-R(t n ).

[0107] 3) Extract time domain features, specifically calculate the following time domain features.

[0108] Average heart rate interval:

[0109]

[0110] Standard Deviation of Normal to Normal intervals (SDNN):

[0111]

[0112] Root Mean Square of the Successive Differences (RMSSD):

[0113]

[0114] 4) Extracting frequency domain features, specifically, first performing Fast Fourier Transform (FFT) on the heart rate interval sequence RR(n), and then calculating the power of different frequency bands.

[0115] The fast Fourier transform of RR(n) can be expressed as:

[0116] PSD(f)=|F{RR(n)}| 2 .

[0117] The power in the very low frequency band (VLF, 0.003-0.04Hz) can be expressed as:

[0118]

[0119] The power in the low frequency band (LF, 0.04-0.15Hz) can be expressed as:

[0120]

[0121] The power in the high frequency band (HF, 0.15-0.4Hz) can be expressed as:

[0122]

[0123] The ratio of low-frequency band power to high-frequency band power (LF / HF) can be expressed as:

[0124]

[0125] 3. Feature fusion.

[0126] On the one hand, it is necessary to obtain the scores of the target users in answering various scales, including: Type A behavior type questionnaire (score S A ), Type D Personality Questionnaire (score S D ), Trait Coping Style Questionnaire (score S C ), Self-rating Depression Scale (score S Dep ), Self-rating Anxiety Scale (score S Anx ).

[0127] On the other hand, it is necessary to construct physiological feature vectors (i.e., feature vectors of human heart rate variability data, which will not be explained one by one below). First, construct the feature vector X for each detection task i :

[0128]

[0129] Then the feature vectors of all detection tasks are merged into X:

[0130] X=[X 1 ,X 2 ,…,X M ],

[0131] Where M is the number of tasks.

[0132] By fusing these two features, we can form a fusion vector Z:

[0133] Z=[X,S A ,S D ,S C ,S Dep ,S Anx ].

[0134] 4. Personality test.

[0135] This step uses a deep neural network (DNN) as a personality detection model, which contains multiple fully connected layers and activation functions. The model input is the fusion vector Z, and the output is the predicted personality information.

[0136] The personality detection model is obtained through supervised training. Before starting the training, it is necessary to obtain a number of training samples (each training sample includes the status data of a reference object and the real personality information representing the real personality category of the reference object), and randomly divide these training samples into two data sets: a training set and a test set. The former is used to train the model, and the latter is used to test the trained model.

[0137] Next, we will introduce the loss function and parameter update algorithm used during training.

[0138] Regarding the loss function, in order to better adapt to the personality detection task, this specific embodiment designs a composite loss function that combines weighted cross entropy loss and correlation loss.

[0139] Regarding the weighted cross entropy loss, since there may be category imbalance in the training set, a weighted cross entropy loss function is introduced, which is defined as follows:

[0140]

[0141] in, is the true personality category y corresponding to the kth training sample (k) The category weights are used to balance the influence of different personality categories and are defined as:

[0142]

[0143] in, Represents category y (k) The prior probability of y is the proportion of samples of this category in the training set, that is, the sample with the true personality category y (k) The ratio of the number of samples in the training set to the total number of samples in the training set.

[0144] Regarding correlation loss, considering the correlation between physiological features and scale scores, this specific embodiment introduces correlation loss to improve the effectiveness of the model for multimodal data fusion. The correlation loss is defined as:

[0145]

[0146] in, S is the real scale score (k) (i.e., the scale score in the state data) and the predicted scale score predicted by the model The Pearson correlation coefficient between is calculated as follows:

[0147]

[0148] in, is the mean of the true scale scores, is the mean of the predicted scale scores.

[0149] Combining the above two loss functions, the total loss function is defined as:

[0150] L(θ)=L wCE (θ)+λL corr (θ).

[0151] Among them, λ is a trade-off parameter used to adjust the ratio between weighted cross entropy loss and correlation loss.

[0152] When updating the parameters, the back propagation algorithm is used to update the model parameters, with the goal of minimizing the total loss function:

[0153]

[0154] Where η is the learning rate, is the gradient of the total loss function with respect to the parameters.

[0155] After the parameters were adjusted, the test set was used for testing, and the model performance was evaluated using indicators such as accuracy, precision, recall, and F1 score. A high accuracy rate was achieved, proving the effectiveness of integrating physiological features with scale scores.

[0156] This specific embodiment proposes a personality category detection method based on millimeter wave radar and deep learning, which achieves objective and accurate prediction of personality category through non-intrusive physiological signal collection, feature extraction and fusion, and construction and application of deep learning models.

[0157] Figure 6 is a block diagram of a personality detection device according to an exemplary embodiment of the present disclosure. Figure 6 The personality detection device 600 includes an acquisition unit 601 and a processing unit 602.

[0158] The acquisition unit 601 can acquire the state data of the target object, wherein the state data includes the human heart rate variability data of the corresponding object detected in the task state, and the human heart rate variability data is obtained by using millimeter wave radar detection.

[0159] The processing unit 602 may use a personality detection model to process the state data of the target object to obtain predicted personality information of the target object, wherein the personality detection model is obtained by training using a supervised method.

[0160] Optionally, the detection task includes at least one of the following: a resting state detection task, an abdominal breathing state detection task, and a passive stress state detection task, wherein the passive stress state detection task includes at least one of the following: a passive stress state detection task after rest, and a passive stress state detection task after abdominal breathing, and the passive stress state detection task is a task that induces a stress response of the corresponding object through emotion-inducing materials.

[0161] Optionally, the status data also includes questionnaire data, which is obtained by the subject answering a preset questionnaire, and the preset questionnaire includes at least one scale related to personality detection.

[0162] Optionally, the questionnaire data includes scores of each scale in at least one scale related to personality detection, and the processing unit is further configured to: perform time-frequency domain feature extraction processing on human heart rate variability data corresponding to each detection task to obtain time domain features and frequency domain features corresponding to each detection task; perform fusion processing on the time domain features and frequency domain features corresponding to each detection task and the scores of each scale to obtain a fusion vector; and input the fusion vector into a personality detection model to obtain predicted personality information.

[0163] Optionally, the questionnaire data includes the score of each scale in the at least one scale related to personality detection, and the training sample of the personality detection model includes the state data and real personality information of the reference object, wherein the personality detection model is trained by the following steps: acquiring the state data of the reference object; using the personality detection model to be trained, processing the state data of the reference object, and obtaining the predicted scale score and predicted personality information of the reference object, wherein the predicted scale score is a predicted value of the score of the reference object answering the at least one scale related to personality detection; determining the weighted cross entropy loss function according to the predicted personality information and the real personality information of the reference object. value as the first loss value, wherein the weighted cross entropy loss function is obtained by adding a personality category weight to the cross entropy loss function, and the personality category weight is negatively correlated with the proportion of the personality category corresponding to the real personality information of the reference object in all training samples; according to the predicted scale score of the reference object and the scores of each corresponding scale in the status data, the value of the correlation loss function is determined as the second loss value, wherein the correlation loss function is the difference between a preset constant and a preset correlation coefficient; according to the first loss value and the second loss value, a total loss value is determined; based on the total loss value, the parameters of the personality detection model to be trained are adjusted to obtain the personality detection model.

[0164] Optionally, at least one scale related to personality testing includes at least one of the following: Type A Behavior Type Questionnaire, Type D Personality Questionnaire, Trait Coping Style Questionnaire, Self-Rating Depression Scale, Self-Rating Anxiety Scale, Anger and Anger Expression Scale, Perceptual Social Support Scale, and Indulgent Reaction Scale; and / or the preset questionnaire also includes a psychosomatic disease questionnaire.

[0165] Regarding the device in the above embodiment, the specific manner in which each unit performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0166] Figure 7 A structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown.

[0167] Reference Figure 7The electronic device 700 includes: at least one memory 701 and at least one processor 702, wherein the at least one memory 701 stores computer executable instructions, and when the computer executable instructions are executed by the at least one processor 702, the at least one processor is prompted to execute the target corresponding method as described in the above exemplary embodiment.

[0168] As an example, the electronic device 700 may be a PC, a tablet device, a personal digital assistant, a smart phone, or other device capable of executing the above-mentioned instruction set. Here, the electronic device 700 is not necessarily a single electronic device 700, but may also be any device or circuit capable of executing the above-mentioned instructions (or instruction sets) individually or in combination. The electronic device 700 may also be part of an integrated control system or a system manager, or may be configured as a portable electronic device 700 that is interconnected with a local or remote (e.g., via wireless transmission) interface.

[0169] In the electronic device 700, the processor 702 may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller or a microprocessor. As an example and not limitation, the processor 702 may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0170] The processor 702 may execute instructions or codes stored in the memory 701, which may also store data. Instructions and data may also be sent and received over a network via a network interface device, which may employ any known transmission protocol.

[0171] The memory 701 may be integrated with the processor 702, for example, by placing RAM or flash memory within an integrated circuit microprocessor or the like. In addition, the memory 701 may include a separate device, such as an external disk drive, a storage array, or any other storage device that can be used by a database system. The memory 701 and the processor 702 may be operatively coupled, or may communicate with each other, such as through an I / O port, a network connection, etc., so that the processor 702 can read files stored in the memory.

[0172] In addition, the electronic device 700 may further include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.) All components of the electronic device 700 may be connected to each other via a bus and / or a network.

[0173] According to an exemplary embodiment of the present disclosure, a computer-readable storage medium storing instructions may also be provided, wherein the instructions, when executed by at least one processor, cause the at least one processor to execute the target corresponding method as described in the above exemplary embodiment. Examples of computer-readable storage media here include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer programs and any associated data, data files and data structures in a non-transitory manner and provide the computer programs and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above-mentioned computer-readable storage medium can be run in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. In addition, in one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system, so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0174] According to an exemplary embodiment of the present disclosure, a computer program product may also be provided, including computer instructions, which, when executed by at least one processor, execute the target corresponding method as described in the above exemplary embodiment.

[0175] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

[0176] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A personality detection method, characterized in that: The personality detection method comprises: Acquire state data of the target object, wherein the state data includes human heart rate variability data of the object obtained by detecting through a millimeter wave radar when the object is in a task state of performing a detection task; The state data of the target object is processed using a personality detection model to obtain predicted personality information of the target object, wherein the personality detection model is obtained by training using a supervised method.

2. The personality detection method according to claim 1, characterized in that: The detection task includes at least one of the following: a resting state detection task, an abdominal breathing state detection task, and a passive stress state detection task, wherein the passive stress state detection task includes at least one of the following: a passive stress state detection task after rest, and a passive stress state detection task after abdominal breathing. The passive stress state detection task is a task that induces a stress response of the corresponding object through emotion-inducing materials.

3. The personality detection method according to claim 1 or 2, characterized in that: The state data also includes questionnaire data, and the questionnaire data is obtained by the subject answering a preset questionnaire, and the preset questionnaire includes at least one scale related to personality detection.

4. The personality detection method according to claim 3, characterized in that: The at least one scale related to personality testing includes at least one of the following: Type A Behavior Type Questionnaire, Type D Personality Questionnaire, Trait Coping Style Questionnaire, Self-Rating Depression Scale, Self-Rating Anxiety Scale, Anger and Anger Expression Scale, Perceptual Social Support Scale, and Indulgent Reaction Scale; and / or The preset questionnaire also includes a psychosomatic disease questionnaire.

5. The personality detection method according to claim 3, characterized in that: The questionnaire data includes a score of each scale in the at least one scale related to personality detection, wherein the using the personality detection model to process the state data of the target object to obtain the predicted personality information of the target object includes: Perform time-domain feature extraction and processing on the human heart rate variability data corresponding to each detection task to obtain the time-domain features and frequency-domain features corresponding to each detection task; The time domain features and frequency domain features corresponding to each detection task and the scores of each scale are fused to obtain a fusion vector; The fusion vector is input into the personality detection model to obtain the predicted personality information.

6. The personality detection method according to claim 3, characterized in that: The questionnaire data includes the score of each scale in the at least one scale related to personality detection, and the training sample of the personality detection model includes the state data and real personality information of the reference object, wherein the personality detection model is trained by the following steps: Acquiring status data of the reference object; Using the personality detection model to be trained, processing the state data of the reference subject, obtaining a predicted scale score and predicted personality information of the reference subject, wherein the predicted scale score is a predicted value of the score of the reference subject answering the at least one scale related to personality detection; Determine, according to the predicted personality information and the real personality information of the reference object, a value of a weighted cross entropy loss function as a first loss value, wherein the weighted cross entropy loss function is obtained by adding a personality category weight to the cross entropy loss function, and the personality category weight is negatively correlated with a proportion of the personality category corresponding to the real personality information of the reference object in all training samples; Determining a value of a correlation loss function as a second loss value according to the predicted scale score of the reference object and the scores of each corresponding scale in the status data, wherein the correlation loss function is a difference between a preset constant and a preset correlation coefficient; Determining a total loss value according to the first loss value and the second loss value; Based on the total loss value, the parameters of the personality detection model to be trained are adjusted to obtain the personality detection model.

7. A personality detection device, characterized in that: The personality detection device comprises: an acquisition unit configured to acquire state data of a target object, wherein the state data includes human heart rate variability data of a corresponding object detected in a task state, and the human heart rate variability data is obtained by using a millimeter wave radar detection; The processing unit is configured to use a personality detection model to process the state data of the target object to obtain predicted personality information of the target object, wherein the personality detection model is trained by a supervised method.

8. An electronic device, characterized in that: include: at least one processor; at least one memory storing computer executable instructions, Wherein, when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the personality detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to perform the personality detection method according to any one of claims 1 to 6.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by at least one processor, the at least one processor is prompted to perform the personality detection method according to any one of claims 1 to 6.

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