Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B10/00—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements

Definitions

• the present invention relates to a mental disorder analysis device.
• a technique is known for analyzing the emotions felt by others in response to a speaker's words (see, for example, Patent Document 1).
• a technique is also known for analyzing changes in a subject's facial expression over a long period of time and estimating the emotions felt during that time (see, for example, Patent Document 2).
• a technique is also known for identifying the factor that most influenced changes in emotions (see, for example, Patent Documents 3 to 5).
• a technique is also known for comparing a subject's usual facial expression with their current facial expression and issuing an alert if the facial expression is gloomy (see, for example, Patent Document 6).
• a technique is also known for comparing a subject's normal (expressionless) facial expression with their current facial expression to determine the degree of the subject's emotions (see, for example, Patent Documents 7 to 9). Furthermore, a technique is also known for analyzing organizational emotions and the atmosphere felt by individuals within a group (see, for example, Patent Documents 10 and 11).
• the present invention aims to make it possible to determine whether the subject of analysis is in a state related to a mental disorder.
• a memory unit stores a mental disorder learning model that is trained using training videos including patients diagnosed with a mental disorder as training data;
• An acquisition unit that acquires a video to be analyzed that includes a person to be analyzed;
• an analysis unit that applies the mental disability learning model to the analysis target video to analyze whether the analysis target has the mental disability;
• a mental disorder analyzer is obtained.
• This disclosure makes it possible to reduce the burden of interviews when assessing mental disorders.
• FIG. 1 is a diagram showing an overall system according to an embodiment of the present invention.
• FIG. 1 is an example of a functional block diagram according to an embodiment of the present invention.
• FIG. 2 is a diagram illustrating an example of a functional configuration of a video analysis device according to an embodiment of the present invention.
• FIG. 2 is a diagram illustrating an example of a functional configuration of a mental disorder analysis device according to an embodiment of the present invention.
• FIG. 13 is a diagram showing the flow of a mental disorder device.
• FIG. 13 is a diagram showing the flow of a mental disorder device.
• the present disclosure has the following configuration.
• a memory unit that stores a mental disorder learning model that has been trained using training videos including patients diagnosed with a mental disorder as training data;
• An acquisition unit that acquires a video to be analyzed that includes a person to be analyzed;
• an analysis unit that applies the mental disability learning model to the analysis target video to analyze whether the analysis target has the mental disability;
• Psychological disorder analyzer [Item 2] 2.
• the mental disorder analysis apparatus according to claim 1,
• the learning video is a recording of the first subject and the second subject undergoing a diagnosis process for the mental disorder by an expert,
• the first subject is a person who has been previously diagnosed with the mental disorder
• the second subject is a person who has not been previously diagnosed with the mental disorder.
• Psychological disorder analyzer [Item 3] 2.
• the video to be analyzed includes at least facial expressions and voices of the person to be analyzed.
• the mental disorder analysis apparatus according to claim 1 is a video about a conversation that the subject is having with another person.
• Psychological disorder analyzer [Item 5] 2.
• the mental disorder analysis apparatus according to claim 1, The video to be analyzed was acquired using an online session.
• Psychological disorder analyzer [Item 6] 2.
• Psychological disorder analyzer The mental disorder analyzer.
• Methods of analysis of mental disorders [Item 9] A memory unit that stores a mental disorder learning model that has been trained using training videos including patients diagnosed with a mental disorder as training data; An acquisition unit that acquires a video to be analyzed that includes a person to be analyzed; an analysis unit that applies the mental disability learning model to the analysis target video to analyze whether the analysis target has the mental disability; Psychiatric Disorder Analysis System.
• the present invention relates to a system that analyzes video recordings of a subject communicating and performs an analysis to determine whether or not the subject is in a particular mental state (such as, but not limited to, a mental disorder).
• This system generates a mental disability learning model that is trained using training videos that include patients diagnosed with mental disorders as training data, and applies the mental disability learning model to videos that include the subject of analysis to analyze whether the subject of analysis has a mental disorder or not.
• the generation of the mental disability learning model and the analysis of the subject of analysis are carried out using a video analysis device, which will be described later.
• Video analysis device Basic functions of the video analysis device Example of hardware configuration Video acquisition method Analysis flow 2. Model generation Overview Model generation method Other model generation methods 3. Analysis of subjects of analysis Acquisition of videos Application of analysis model Acquisition of numerical values Setting and judging thresholds Output format Examples of subjects of analysis
• the video analysis device of the present embodiment is a system that analyzes and evaluates a unique emotion (feelings caused by one's own or other's words and actions, such as pleasantness or unpleasantness, or the degree of pleasantness) of an analysis target among a plurality of people in an environment where the plurality of people are engaged in a video session (hereinafter, one-way and two-way sessions are both referred to as online sessions) that is different from that of other people.
• An online session is, for example, an online conference, an online class, an online chat, etc., and terminals installed in a plurality of locations are connected to a server via a communication network such as the Internet, and video images can be exchanged between the plurality of terminals through the server.
• the video images handled in the online session include facial images and voices of users using the terminals.
• the video images also include images such as materials shared and viewed by a plurality of users. It is possible to switch between a facial image and a document image on the screen of each terminal to display only one of them, or to divide the display area to display a facial image and a document image simultaneously. It is also possible to display an image of one of the plurality of people in full screen, or to display some or all of the images of the users divided into small screens. It is possible to specify one or more of the plurality of users who participate in the online session using a terminal as the analysis target.
• the leader, facilitator, or manager of the online session designates one of the users as the analysis target.
• the organizer of the online session is, for example, a lecturer of an online class, a chairperson or facilitator of an online conference, or a coach of a session for coaching purposes.
• the organizer of the online session is usually one of the multiple users who participate in the online session, but may be a different person who does not participate in the online session. It is also possible to designate all participants as the analysis target without designating an analysis target. It is also possible for the leader, facilitator, or manager of the online session (hereinafter collectively referred to as the organizer) to designate one of the users as the analysis target.
• the organizer of the online session is, for example, a lecturer of an online class, a chairperson or facilitator of an online conference, or a coach of a session for coaching purposes.
• the organizer of the online session is usually one of the multiple users who participate in the online session, but may be a different person who does not participate in the online session.
• the video analysis device when a video session is established between multiple terminals, at least a video image acquired from the video session is displayed.
• the displayed video image is acquired by the terminal, and at least a facial image contained in the video image is identified for each predetermined frame unit.
• An evaluation value for the identified facial image is then calculated.
• the evaluation value is shared as necessary.
• the acquired video image is stored in the terminal, analyzed and evaluated on the terminal, and the results are provided to the user of the terminal. Therefore, even if a video session contains personal information or confidential information, for example, the video itself can be analyzed and evaluated without providing the video itself to an external evaluation agency, etc.
• the results can be visualized and cross-analysis can be performed by providing only the evaluation result (evaluation value) to an external terminal.
• the video analysis device includes user terminals 10 and 20 having at least an input unit such as a camera unit and a microphone unit, a display unit such as a display, and an output unit such as a speaker, a video session service terminal 30 that provides two-way video sessions to the user terminals 10 and 20, and an evaluation terminal 40 that performs part of the evaluation of the video sessions.
• user terminals 10 and 20 having at least an input unit such as a camera unit and a microphone unit, a display unit such as a display, and an output unit such as a speaker, a video session service terminal 30 that provides two-way video sessions to the user terminals 10 and 20, and an evaluation terminal 40 that performs part of the evaluation of the video sessions.
• Each functional block, functional unit, and functional module described below can be configured by, for example, hardware, DSP (Digital Signal Processor), or software provided in a computer.
• DSP Digital Signal Processor
• the computer when configured by software, the computer is actually configured with a CPU, RAM, ROM, etc., and is realized by the operation of a program stored in a recording medium such as a RAM, ROM, hard disk, or semiconductor memory.
• a series of processes by the system and terminal described in this specification may be realized by using any of software, hardware, and a combination of software and hardware.
• a computer program for realizing each function of the information sharing support device 10 according to this embodiment can be created and implemented in a PC or the like.
• a computer-readable recording medium in which such a computer program is stored can also be provided.
• the recording medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, etc.
• the above computer program may be distributed, for example, via a network, without using a recording medium.
• the evaluation terminal acquires video from a video session service terminal, identifies at least the facial images contained in the video for each predetermined frame, and calculates an evaluation value for the facial images (details will be described later).
• the video session service provided by the video session service terminal (hereinafter, sometimes simply referred to as "this service") enables two-way communication with user terminals 10 and 20 using images and audio.
• This service is capable of displaying video images captured by the camera unit of the other user terminal on the display of the user terminal, and outputting audio captured by the microphone unit of the other user terminal from the speaker.
• This service is also configured to enable both or any of the user terminals to record video images and audio (collectively referred to as "video images, etc.”) in the memory unit of at least one of the user terminals.
• the recorded video information Vs (hereinafter, referred to as “recorded information”) is cached in the user terminal that started the recording, and is recorded only locally in one of the user terminals. If necessary, the user can view the recorded information himself or share it with others within the scope of use of this service.
• Fig. 3 is a block diagram showing an example of the configuration according to this embodiment.
• the video analysis device of this embodiment is realized as a functional configuration of a user terminal 10. That is, the user terminal 10 includes, as its functions, a video image acquisition unit 11, a biological reaction analysis unit 12, a peculiar determination unit 13, a related event identification unit 14, a clustering unit 15, and an analysis result notification unit 16.
• the video acquisition unit 11 acquires video from each terminal, which is obtained by photographing multiple people (multiple users) with a camera equipped in each terminal during an online session.
• the video acquired from each terminal may or may not be set to be displayed on the screen of each terminal.
• the video acquisition unit 11 acquires video from each terminal, including video that is being displayed and video that is not being displayed on each terminal.
• the bioreaction analysis unit 12 analyzes changes in bioreactions for each of multiple people based on the video images (whether or not they are being displayed on the screen) acquired by the video image acquisition unit 11.
• the bioreaction analysis unit 12 separates the video images acquired by the video image acquisition unit 11 into a set of images (a collection of frame images) and audio, and analyzes changes in bioreactions from each.
• the bioreaction analysis unit 12 analyzes the user's facial image using frame images separated from the video acquired by the video acquisition unit 11, thereby analyzing changes in bioreactions related to at least one of facial expression, eye movement, pulse rate, and facial movement.
• the bioreaction analysis unit 12 also analyzes changes in bioreactions related to at least one of the user's speech content and voice quality by analyzing the audio separated from the video acquired by the video acquisition unit 11.
• the change in the user's emotions is analyzed by analyzing the change in the user's bioreaction.
• the emotion analyzed is, for example, the degree of comfort/discomfort.
• the bioreaction analysis unit 12 quantifies the change in the bioreaction according to a predetermined standard, thereby calculating a bioreaction index value that reflects the content of the change in the bioreaction.
• the analysis of changes in facial expressions is performed, for example, as follows. That is, for each frame image, the facial area is identified within the frame image, and the identified facial expressions are classified into multiple categories according to an image analysis model that has been trained by machine learning in advance. Then, based on the classification results, it is analyzed whether a positive or negative facial expression change has occurred between consecutive frame images, and to what extent the facial expression change has occurred, and a facial expression change index value according to the analysis results is output.
• Analysis of changes in eye line is performed, for example, as follows. That is, for each frame image, the eye area is identified from within the frame image, and the direction of both eyes is analyzed to analyze where the user is looking. For example, it is analyzed whether the user is looking at the face of the speaker currently being displayed, at the shared material currently being displayed, or looking outside the screen. It may also be possible to analyze whether the eye line movement is large or small, and whether the movement is frequent or infrequent. Changes in eye line are also related to the user's level of concentration.
• the biological response analysis unit 12 outputs an eye line change index value according to the results of the analysis of changes in eye line.
• the analysis of changes in pulse rate is performed, for example, as follows. That is, for each frame image, the facial area is identified within the frame image. Then, using a trained image analysis model that captures the numerical values of facial color information (G in RGB), changes in the G color of the face surface are analyzed. The results are arranged along the time axis to form a waveform that represents changes in color information, and the pulse rate is identified from this waveform. When a person is nervous, their pulse rate increases, and when they feel calm, their pulse rate decreases.
• the biological response analysis unit 12 outputs a pulse rate change index value according to the analysis results of changes in pulse rate.
• the analysis of changes in facial movement is performed, for example, as follows. That is, for each frame image, the facial area is identified from within the frame image, and the facial direction is analyzed to analyze where the user is looking. For example, it is analyzed whether the user is looking at the face of the currently displayed speaker, at the currently displayed shared material, or outside the screen. It may also be analyzed whether the facial movement is large or small, or whether the movement occurs frequently or infrequently. It may also be analyzed by combining the facial movement with the eye movement. For example, it may be analyzed whether the user is looking straight at the currently displayed speaker's face, looking up or down, or looking at an angle.
• the biological response analysis unit 12 outputs a facial direction change index value according to the analysis result of the change in facial direction.
• the speech content is analyzed, for example, as follows. That is, the biological response analysis unit 12 performs known speech recognition processing on the speech for a specified period of time (for example, about 30 to 150 seconds) to convert the speech into a string of characters, and then performs morphological analysis on the string of characters to remove words that are unnecessary for expressing the conversation, such as particles and articles. The remaining words are then vectorized, and an analysis is performed to determine whether a positive or negative emotional change has occurred, and to what extent the emotional change has occurred, and a speech content index value corresponding to the analysis results is output.
• a specified period of time for example, about 30 to 150 seconds
• Voice quality analysis is performed, for example, as follows. That is, the biological response analysis unit 12 identifies the acoustic characteristics of the voice by performing a known voice analysis process on the voice for a specified period of time (for example, about 30 to 150 seconds). Then, based on the acoustic characteristics, it analyzes whether a positive or negative voice quality change has occurred, and to what extent the voice quality change has occurred, and outputs a voice quality change index value according to the analysis results.
• a specified period of time for example, about 30 to 150 seconds
• the biological reaction analysis unit 12 calculates a biological reaction index value using at least one of the facial expression change index value, eye gaze change index value, pulse rate change index value, face direction change index value, speech content index value, and voice quality change index value calculated as described above.
• the biological reaction index value is calculated by weighting the facial expression change index value, eye gaze change index value, pulse rate change index value, face direction change index value, speech content index value, and voice quality change index value.
• the uniqueness determination unit 13 determines whether the change in the biological reaction analyzed for the subject of analysis is unique compared to the change in the biological reaction analyzed for other people other than the subject of analysis. In this embodiment, the uniqueness determination unit 13 determines whether the change in the biological reaction analyzed for the subject of analysis is unique compared to other people based on the biological reaction index values calculated for each of the multiple users by the biological reaction analysis unit 12.
• the uniqueness determination unit 13 calculates the variance of the biological reaction index values calculated for each of multiple individuals by the biological reaction analysis unit 12, and by comparing the biological reaction index value calculated for the analysis subject with the variance, determines whether or not the change in biological reaction analyzed for the analysis subject is unique compared to others.
• the following three patterns can be considered when changes in the analyzed biological reactions of the subject are unique compared to others. The first is when no particularly significant changes in biological reactions occur in others, but a relatively large change in biological reactions occurs in the subject. The second is when no particularly significant changes in biological reactions occur in the subject, but a relatively large change in biological reactions occurs in others. The third is when relatively large changes in biological reactions occur in both the subject and others, but the nature of the change differs between the subject and others.
• the associated event identification unit 14 identifies events occurring with respect to at least one of the subject, other people, and the environment when a change in a biological reaction determined to be unique by the uniqueness determination unit 13 occurs. For example, the associated event identification unit 14 identifies from video images the words and actions of the subject when a unique change in a biological reaction occurs in the subject. The associated event identification unit 14 also identifies from video images the words and actions of other people when a unique change in a biological reaction occurs in the subject. The associated event identification unit 14 also identifies from video images the environment when a unique change in a biological reaction occurs in the subject. The environment can be, for example, shared documents being displayed on the screen or things that appear in the background of the subject.
• the clustering unit 15 analyzes the degree of correlation between a change in a biological reaction determined to be unique by the unique determination unit 13 (for example, one or more combinations of gaze, pulse rate, facial movement, speech content, and voice quality) and an event occurring when the unique change in a biological reaction occurs (an event identified by the related event identification unit 14), and if the correlation is determined to be at a certain level or above, the clustering unit 15 clusters the person or event being analyzed based on the results of the correlation analysis.
• a change in a biological reaction determined to be unique by the unique determination unit 13 for example, one or more combinations of gaze, pulse rate, facial movement, speech content, and voice quality
• an event occurring when the unique change in a biological reaction occurs an event identified by the related event identification unit 14
• the clustering unit 15 clusters the subject of analysis or the event into one of multiple pre-segmented classifications according to the content of the event, the degree of negativity, the magnitude of correlation, etc.
• the clustering unit 15 clusters the subject of analysis or event into one of multiple pre-segmented classifications according to the content of the event, the degree of positivity, the magnitude of correlation, etc.
• the analysis result notification unit 16 notifies the person who designated the subject of analysis (the subject of analysis or the organizer of the online session) of at least one of the changes in the biological reaction determined to be specific by the specificity determination unit 13, the events identified by the related event identification unit 14, and the classifications clustered by the clustering unit 15.
• the analysis result notification unit 16 notifies the analysis subject of his/her own words and actions as an event occurring when a unique change in the analysis subject's biological reaction that differs from that of other people occurs (any of the three patterns described above; same below). This allows the analysis subject to understand that when he/she behaves in a certain way, he/she has different emotions than other people. At this time, the analysis subject may also be notified of the unique changes in the biological reaction identified for the analysis subject. Furthermore, the analysis subject may also be notified of the changes in the biological reaction of other people for comparison.
• the analysis result notification unit 16 also notifies the organizer of the online session of events that occur when a unique change in the biological reaction occurs in the analysis subject that is different from that of others, along with the unique change in the biological reaction. This allows the organizer of the online session to know what events are influencing what emotional changes as a phenomenon unique to the specified analysis subject. It then becomes possible to take appropriate measures for the analysis subject depending on the content of the information obtained.
• the analysis result notification unit 16 also notifies the organizer of the online session of events occurring when a unique change in the biological reaction of the analysis subject occurs that is different from that of others, or the clustering results of the analysis subject. This allows the organizer of the online session to understand the behavioral tendencies unique to the analysis subject and predict possible future behaviors and conditions, etc., depending on which category the specified analysis subject has been clustered into. It then becomes possible to take appropriate measures for the analysis subject.
• the system of this embodiment includes a memory unit that stores a mental disability learning model trained using training videos including patients diagnosed with mental disorders as teacher data, an acquisition unit that acquires videos including the subject of analysis, and an analysis unit that applies the mental disability learning model to the videos to analyze whether the subject of analysis has a mental disorder or not.
• the mental disorder learning model uses videos showing the behavior, facial expressions, speech, and other characteristics of patients diagnosed with mental disorders as training data. This allows the model to learn the characteristics and patterns of mental disorders and become capable of analyzing the presence or absence of mental disorders in new video data.
• the learning video according to this embodiment is a recording of the first and second subjects undergoing the mental disorder diagnosis process by an expert.
• the first subject is a person who has been diagnosed in advance with a mental disorder
• the second subject is a person who has not been diagnosed in advance with a mental disorder.
• the first subject was a person who had already been diagnosed with a mental disorder before the video was taken.
• the video of this subject serves as the primary training data for the model to learn the defining characteristics of the mental disorder. From the video of the first subject, it is possible to learn typical symptoms, reactions, facial expressions, speech, and other characteristics of the mental disorder.
• the second subject was not diagnosed with a mental disorder before the video was taken.
• the video of this subject helps the model learn normal reactions and behavioral characteristics. It is also essential to prevent overfitting and to improve the model's ability to learn normal behavioral characteristics and determine the absence of a mental disorder.
• Step 1 The first and second subjects are asked to talk to the expert and the conversation is recorded, and both video (visual information) and audio (auditory information) data are acquired.
• the "diagnostic criteria for depression” are as follows. That is, depression is diagnosed based on the patient's self-reported symptoms, clinical observation, and standard diagnostic criteria (e.g., DSM-5, ICD-10, etc.). For example, a specialist will ask the patient whether the following symptoms are present: 1. Low mood or depressed mood 2. Loss of interest or pleasure 3. Fatigue or loss of energy 4. Feelings of worthlessness or excessive or inappropriate guilt 5. Poor concentration or difficulty making decisions 6. Insomnia or hypersomnia 7. Increased or decreased appetite, resulting in weight changes 8. Thoughts of self-harm or suicide thoughts or plans
• the expert may evaluate the presence or absence and degree of the above-mentioned symptoms of depression through conversation with the patient, or may label the subject.
• information such as the patient's daily life and level of activity, past medical history, family medical history, medications being used, and other treatments may be collected.
• Multimodal AI is used to analyze the features of all subjects' conversations and generate a learning model.
• multimodal AI it is possible to make the most of the information obtained from both video and audio to generate a highly accurate mental disorder learning model.
• the model according to this embodiment may be generated, for example, in the following manner.
• Supervised learning Videos of patients with mental health diagnoses are paired with the diagnoses (labels) and the model learns by extracting features from the videos and associating them with the labels.
• Transfer learning Using existing video and face recognition models as a basis, we train models that are specific to mental disorders. We use models that have been pre-trained with a large amount of general video data, and then fine-tune them with a small amount of mental disorder data.
• Deep learning Using neural networks to capture deep features of image and audio data in videos.
• CNN Convolutional Neural Network
• RNN Recurrent Neural Network
• LSTM Long Short Term Memory
• the subject of analysis is recorded engaged in everyday conversations.
• the recording may be of an everyday online meeting. It may mimic everyday conversations with friends and family, or a conversation with a computer-generated agent.
• the agent general everyday questions or questions that elicit deeper emotions may be programmed, and the subject's reactions may be observed.
• the acquired video is input into the multimodal AI.
• information is acquired from both the video's visual data (e.g., facial expressions and body language) and audio data (e.g., speaking style and word choice).
• the features and patterns contained in the subject's video are analyzed to determine whether there are signs or characteristics of a mental disorder.
• the AI outputs the results of the analysis as numerical data.
• This numerical value may be, for example, between 0 and 1, and the closer to 1 the value is, the stronger the characteristics of a mental disorder are.
• This numerical value is used as an index showing the probability and degree of a mental disorder.
• ⁇ Threshold setting and judgment> Based on a preset threshold, the system judges whether the subject of the analysis is likely to have a mental disorder. For example, if the value is 0.7 or higher, it is judged that there is a high probability that there are signs of a mental disorder.
• This threshold is set based on previous research and clinical experience, and is expected to be updated according to the times and progress of research. Ultimately, this system can detect mental disorders at an early stage.
• the above-mentioned determination result is output in a predetermined format.
• the output format include a PDF report, a notification by e-mail, a display on a dashboard of a human resources cloud service, an API link, etc.
• the recipients of the output include a doctor, a counselor, or other specialist, the subject, his/her family, a management department of an organization such as a company or school, an insurance company, a rehabilitation facility, etc.
• Examples of diseases that can be treated by this system include depression, bipolar disorder, schizophrenia, generalized anxiety disorder, obsessive-compulsive disorder, panic disorder, social anxiety disorder, PTSD, borderline personality disorder, antisocial personality disorder, dependent personality disorder, avoidant personality disorder, attention-deficit hyperactivity disorder, autism spectrum disorder, alcohol use disorder, drug use disorder, Alzheimer's disease, dementia with Lewy bodies, vascular dementia, anorexia nervosa, bulimia nervosa, insomnia, sleep apnea syndrome, hypoactive sexual desire disorder, sexual dysfunction, conversion disorder, dissociative disorder, samatoform disorder, pseudo-disorder caused by others, and pseudo-disorder caused by self.

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