236 results about "Mental condition" patented technology

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

The present invention relates to a system (10) for monitoring a physiological state and / or a mental state and / or an emotional state of a user and providing a personalized breathing exercise to the user, the system including a physiological monitoring device (12) configured to detect a physiological parameter of the user and a breathing monitoring device (14) configured to detect a breathing parameter of the user. The system (10) is configured to determine a target physiological index based at least on the detected physiological parameter, determine a target breathing exercise to be executed by the user based at least on the target physiological index, determine a target breathing index to be achieved by the user during execution of the breathing exercise, compare the target breathing index with the breathing parameter, which is detectable as the user is executing the breathing exercise, and determine a deviation therebetween, and adapt the target breathing exercise based at least on the determined deviation. The invention also relates to a method and a monitoring program for executing the method.

An image processing framework for multi-class classifying a subject into cognitive normal, mild cognitive impairment and Alzheimer's disease (AD) conditions is developed. In one realization of the framework, an AD_Net model, which is an attention-enhanced convolution neural network (CNN) formed by embedding a Convolutional Block Attention Module (CBAM) into a CNN having a Visual Geometry Group 19 (VGG19) architecture, processes an image volume of the subject's brain to generate a plurality of AD_Net feature maps and a first plurality of scores that predict respective likelihoods of the three conditions. To enhance the prediction accuracy, a multilayer perception model formed with a plurality of fully connected layers processes the plurality of AD_Net feature maps and a plurality of influencing factors of AD, such as age, gender, geriatric depression scale score, Mini-Mental State Examination score and clinical dementia rating score, to generate a second plurality of scores that predict the respective likelihoods.

Disclosed herein are system, method, and device embodiments for determining the mental state of a driver using gaze tracking and object detection. Specifically, an Advanced Driver Assistance System may determine if the driver's gaze is associated with spatial locations of objects of interest in the surrounding environment. The surrounding environment may be represented with a 360-degree, three-dimensional model generated by the external-facing cameras and other sensors. The driver's gaze direction may be represented by a heatmap derived based on the driver's eye movements. Whether or not the driver's gaze is associated with a spatial location of an object may be used as evidence about the driver's mental state. The system may perform appropriate actions based on the mental state including generating alerts, engaging mechanical systems, and adjusting parameters in ancillary safety systems to increase or decrease tolerance. The mental state may be tracked throughout a driving session and stored in a driver profile.

According to the intelligent awakening system and method based on sleep monitoring, on the basis of wearable sleep monitoring equipment, by analyzing the sleep stage of a user in real time and combining strategies such as blue light awakening and vibration awakening, the user is awakened at a proper time, so that interference to sleep is reduced, and the sleep monitoring effect is improved. And the comfort of the awakening process and the mental state of the user are improved. Multi-channel physiological signals such as electroencephalogram signals, electro-oculogram signals and mandibular electromyographic signals are used, the advanced signal processing technology is combined, the sleep stage of a user can be accurately analyzed in real time, awakening is carried out at a proper time point, especially sudden awakening is avoided in the deep sleep (N3) stage and the rapid eye movement sleep (REM) stage, and the sleep state of the user is improved. And the sleep inertia is effectively reduced.

The invention relates to the technical field of image processing, in particular to a mental patient auxiliary analysis method and device based on facial expression recognition, and the method comprises the steps: collecting a plurality of face images when a patient communicates with a doctor; obtaining a plurality of suspected Chinese character chuan-shaped pattern regions in the eyebrow region and a suspected Chinese character chuan-shaped pattern region sequence; acquiring a pupil area of each face image to obtain a plurality of facial expression change moments; obtaining the possibility that the expression of the patient is angry at each facial expression change moment; obtaining a plurality of anger stages of the patient; obtaining the time point when the doctor menses the keyword of the patient every time, obtaining the anger initial stage of each anger stage of the patient, and further obtaining the control ability of the patient on the own emotion in the conversation process; and the severity of the mental problem of the patient is obtained. The face change of the patient is analyzed, a doctor is assisted to judge the mental state of the patient, and the diagnosis and treatment effect is improved.

To solve a problem of supporting individual health management by photographing a face, using artificial intelligence to analyze a health condition, a risk of disease and a mental state, and proposing appropriate advice and an improvement method.SOLUTION: A system includes means for: photographing a face; using artificial intelligence to analyze a health condition, a risk of disease and a mental state; and proposing appropriate advice and an improvement method.SELECTED DRAWING: Figure 1

The invention discloses a transcranial electrical stimulation control method and system for regulating mental fatigue, and belongs to the technical field of mental state regulation. The system comprises an EEG collecting and processing module and an electrical stimulation module which are in wireless communication to form a closed loop. The EEG collecting and processing module collects a forehead lobe three-lead EEG signal, after preprocessing (baseline drift and high-frequency interference are removed through FIR filtering, and eye movement artifacts are removed through combination of wavelet transform and Kalman filtering), the mental fatigue state is classified through an onboard lightweight convolutional neural network (CNN) algorithm, and a control command is generated; the electrical stimulation module receives the command, generates bipolar pulse width modulation transcranial direct current stimulation (tPWMDCs) current, and stimulates the prefrontal lobe. Local low-power-consumption reasoning of EEG signals is achieved, tPWMDCs avoid skin injury, a closed-loop system dynamically adapts to individual differences, portability, safety and accuracy are achieved, and the system is suitable for driving, office and other scenes.

The invention relates to the technical field of mental state monitoring, in particular to a mental state real-time monitoring system based on intelligent wearable equipment, which comprises a physiological signal analysis module, a voice data analysis module, a mental fluctuation evaluation module, a fatigue boundary judgment module and a mental state analysis module. According to the method, by extracting key physiological and voice signal features, the activity stability of the neural signal and the autonomic nervous system of an individual can be more accurately judged, the stability of the physiological signal is effectively evaluated, and by calculating the fluctuation trend of the mental state and combining physiological and voice data, the accuracy of the physiological signal is improved. Real-time changes of emotion fluctuation and mental load of an individual can be captured more comprehensively, so that mental state monitoring is more comprehensive and accurate, real-time evaluation of mental fatigue is performed according to physiological and voice signal changes, a more objective fatigue judgment basis is provided, the accuracy of mental health state evaluation is enhanced, and the mental health state evaluation efficiency is improved. And more accurate mental health state information is provided for individuals.

A computerized train driver behavioral analysis system for the automated analysis of behavioral characteristics of drivers of railway trains based on target and keypoint detection. A train operation status and position analysis portion monitors train position, speed, and acceleration. A standardized driver practice analysis portion compares actual driver behaviors and actions to standardized driver behaviors and actions. A driver mental state analysis portion automatically detects a driver's face with a human face detection model with automated target keypoint detection to detect predetermined keypoints to produce an electronic human face box and performs a computer analysis of eye and mouth statuses and makes an automated electronic determination whether the eyes and mouth are open or closed. The system thus produces a computerized judgment regarding behavioral characteristics of drivers based on the train operation status and position analysis, standardized driver practice analysis, and driver mental state analysis portions.

The invention discloses a multi-modal person mental state recognition method based on cross-modal synchronization and adaptive decision making. The method comprises the following steps: step 1, detecting and cutting an obtained face video, and extracting visual modal features of a cut image through a visual feature extraction module; 2, performing audio feature extraction on the original audio through an audio feature extraction module to obtain audio modal features; 3, the visual modal features and the audio modal features are synchronously fused through an audio and video cross-modal synchronization and audio and video feature extraction module; and step 4, adaptively fusing classification results of the first three module paths through an adaptive decision fusion module, simulating a collective perception and decision process, and finally obtaining a final mental state classification result. According to the invention, corresponding personnel mental states can be respectively obtained for different induction sources.

A method of adjusting mental state includes acquiring video data of a first individual, identifying a first set of features based on image, audio, and text data extracted from the video data, predicting a baseline mental state for the first individual based on the first set of features, identifying a target mental state, and simulating a predicted path from the baseline mental state to the target mental state. The predicted path is simulated using a multidimensional mental state model, a plurality of actions, and a computer-implemented machine learning model. The predicted path comprises one or more actions of the plurality of actions and corresponding changes to at least one of first and second dimensions of the multidimensional mental state model. The one or more actions of the predicted path are output to a second individual and are performable to adjust the baseline mental state of the first individual.

Disclosed are a method and a system for mental state perception and a computer-readable storage medium. The method includes: acquiring image sequences with timestamps and millimeter-wave radar raw data with timestamps; preprocessing the image sequences and the millimeter-wave radar raw data; analyzing the head region image sequences to obtain head vibration signal features; calculating face region image sequences obtained from preprocessing by using a remote photovolumetric pulse wave recording method to obtain a first heart rate; analyzing the original millimeter-wave radar data sequence to obtain a second heart rate and a breathing rate; fusing the first heart rate, the second heart rate and the breathing rate to obtain a fused heart rate and breathing rate; performing feature extraction on facial change information by a Transformer-like network; establishing a non-contact multi-modal mental perception model for prediction to obtain a predicted result of the mental state.

The invention discloses an intelligent seat adjusting method, device and equipment based on voiceprint recognition and a medium, and the method comprises the steps: obtaining human body sound source information collected by a sound wave analyzer, judging whether the human body sound source information meets voiceprint collection conditions or not, and if yes, sending a voiceprint collection instruction to obtain voiceprint information, performing adjustment necessity analysis on the voiceprint information to obtain voiceprint analysis information, and performing combined analysis on the voiceprint analysis information and the human body sound source information through an analysis rule to obtain seat dynamic adjustment strategy information; and generating a human body-seat coordination degree adjustment instruction according to the seat dynamic adjustment strategy information, and sending the human body-seat coordination degree adjustment instruction to the driving optimization assembly to adjust the height of the seat. According to the intelligent seat adjusting method, accurate recognition of the identity and the mental state of the user can be achieved by deeply mining the voiceprint features, intelligent adjustment of the seat in combination with the voiceprint analysis information and the mental state is achieved, the accuracy and the intelligent degree of seat adjustment are improved, and the use feeling of the user is improved.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient; identifying a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient; providing values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric; and receiving, from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient.

In at least some embodiments, the present disclosure describes an electroencephalography (EEG) device having a main band and at least one moveable band. The main band may include fixed EEG sensors positioned on the main band so as to contact a scalp of the wearer in at least two scalp zones. The at least one moveable band may include at least one moveable EEG sensor. The moveable band may move relative to the main band to adjust to a position of the moveable EEG sensor(s) on the scalp of the wearer in at least one adjustable zone. The EEG device may include a processing device to measure EEG measurements using the fixed and / or movable EEG sensors, and transmit the EEG measurements to an external computing device to determine a mental condition, a mental state or both of the wearer based on the EEG measurements.

Provided are systems, methods, computer-readable media, and techniques for assessing a mental condition, including: (A) obtaining, from a first source, first input data corresponding to a patient; (B) obtaining, from a second source, second input data corresponding to the patient; (C) analyzing the first input data and the second input data using one or more machine learning models to generate a plurality of patient scores for at least two mental conditions for the patient; (D) generating at least two patient scorecards (e.g., clinical rating scales) for the patient based at least in part on the plurality of patient scores, wherein each patient scorecard of the at least two patient scorecards corresponds to at least one of the at least two mental conditions, and wherein each patient scorecard of the at least two patient scorecards is at least partially complete; and (E) outputting or storing the at least two patient scorecards.

Systems and methods are provided for providing mental health intervention using a virtual environment. A spoken communication from a first user is conveyed to a second user in a virtual environment. Data about the first user is collected as the first user interacts with the second user. The collected data includes tone information from the spoken communication. A clinical parameter representing a mental state of the first user is determined from the collected data at a machine learning model. A prompt is provided to the second user, representing one of a suggested phrase, a suggested sentence, and a suggested topic of conversation, according to the determined clinical parameter.

To support treatment of a patient.SOLUTION: An information processing device 2 includes: an acquisition unit 100 that obtains psychosomatic information about at least either the physical or mental condition of a patient; and an output unit 102 that outputs a notification to a supporter who supports treatment of the patient based on the psychosomatic information, the notification including recommended action information about an action that the supporter is recommended to do.SELECTED DRAWING: Figure 5

The invention relates to the technical field of brain-computer interaction, in particular to a patient mental state assessment system for a clinician, which comprises a semantic manifold calibration module, a state trajectory projection module, a geometric curvature resolving module and an emotion transformation quantification module. According to the method, electroencephalogram data are collected and deeply analyzed, high-dimensional neural activity characteristics are converted into a visual three-dimensional state track in combination with titer and awakening degree information, continuous dynamic monitoring of the mental state of a patient is achieved, then the emotion conversion process is quantitatively analyzed according to the geometric curvature change rate of the state track, and therefore the mental state of the patient can be rapidly and accurately monitored. According to the method, rapid emotion fluctuation which is difficult to perceive in a traditional evaluation mode can be accurately recognized and marked, objective and fine data support is provided for clinical diagnosis, and the accuracy and instantaneity of mental state evaluation are remarkably improved.

To periodically and easily evaluate ADL of a user in a nursing care office.SOLUTION: An evaluation support system for supporting an evaluation of a physical function or a cognitive function necessary for a person to autonomously perform a living motion, or an evaluation of a mental state of a person or an ability necessary for a person to perform a social activity, wherein a plurality of types of images graphically representing scales of the evaluation are selected for each type of living motion performed by a person to be evaluated, and a sum of numerical values corresponding to the scales of the images selected for each type of living motion is used as an evaluation value.SELECTED DRAWING: Figure 2

A system predicts the mental state of a user using a variety of contact or contactless sensors that measure heart rate, breathing or other data of the user. The mental state may be flow, overload, underload, stress, attention, motivation, valence, arousal, etc. While performing a game-playing task the difficulty of the task is adjusted based upon the predicted mental state of the user. A classifier for classifying a mental state is trained using event marking and ground truth data. Computer usage generates event markers. Game playing is used to simulate a mental state. Interruptions of a user are prevented when the user enters a flow, focused, or effortful attention state by classifying that state and indicating physically or electronically that the user should not be interrupted. Interruptions of a user are also prevented when the user enters a mind wandering state by classifying or predicting that state and indicating physically or electronically that the user should not be interrupted. A cost of interrupting is calculated and displayed. Users may also be coached regarding mental state. Advertisements to the user may vary based upon a classified mental state of the user.

Systems, methods, and apparatus for controlling a device based on the detection of transient oscillatory or pseudo-oscillatory bursts are disclosed. For example, a method can comprise detecting one or more transient oscillatory or pseudo-oscillatory bursts from an ongoing neural signal recording of a subject. The method can also comprise extracting one or more burst features from the one or more transient oscillatory or pseudo-oscillatory bursts detected within a detection period. The method can also comprise predicting a thought generated or conjured by the subject or a change in mental state evoked by the subject by applying at least one of a machine learning algorithm and a feature threshold to the one or more burst features extracted within the detection period. An input command associated with the prediction can be transmitted to the device in order to control the device.

A mental state estimation device 1X mainly includes an observation data acquisition means 15X, a first mental state acquisition means 16X, and a second mental state estimation means 17X. The observation data acquisition means 15X acquires observation data obtained by observing a target person. The first mental state acquisition means 16X acquires an estimation result of a first mental state of the target person. The second mental state estimation means 17X estimates a second mental state of the target person, which is a mental state correlated with the first mental state, based on the observation data and the estimation result of the first mental state.

The invention provides a crane operating handle safety monitoring method and system. The method comprises the following steps: creating a safety monitoring object; setting monitoring nodes; acquiring handle operation habit data; obtaining mental state data of the driver; analyzing the fatigue state of the driver; setting a dynamic threshold value of handle operation; analyzing whether the handle operation habit data exceeds a threshold value or not by utilizing an HMM; and analyzing the judgment result in combination with the mental state data of the driver, and judging whether an abnormal condition exists or not. By establishing a comprehensive handle safety monitoring system, intelligent monitoring of handle operation is achieved, the problem of false alarm in handle operation safety monitoring is effectively solved by combining the mental state of a driver while the grip strength, the moving speed and the displacement of the handle are considered, the intelligent level and safety of handle safety monitoring are remarkably improved, and the safety of the handle operation is improved. And a more stable and reliable operation environment is provided for the operation of the crane handle.

The invention discloses a postoperative delirium prediction system and method based on electroencephalogram signals, and relates to the technical field of biomedical engineering.The system uses EEG equipment and a heart rate monitor to obtain brain wave signals and heart rate data, the signals are processed through filtering and FFT conversion, and brain wave power data are generated; calculating a delirium risk assessment index ZWF by obtaining a heart rate variability index HRV and brain wave power data through calculation, and performing preliminary assessment; when it is preliminarily assessed that the patient does not have the delirium risk, the medication history of the patient is obtained through a hospital information system (HIS), and the answer and performance of the patient are recorded according to a short mental state examination scale (MMSE) to calculate a cognitive function score (RZ); and comprehensively calculating a comprehensive delirium risk index ZFX based on the data, and performing secondary evaluation. The system finally provides targeted postoperative intervention suggestions for patients through two times of risk assessment in combination with drug history and cognitive functions, and aims to effectively reduce the risk of postoperative delirium.

The invention discloses a driving fatigue relieving system based on a brain-computer interface, and the system comprises an electroencephalogram signal collection module which is used for collecting the electroencephalogram data of a driver in real time; the electroencephalogram signal analysis module is connected to the electroencephalogram signal acquisition module and is used for processing and analyzing the electroencephalogram data received from the electroencephalogram signal acquisition module so as to judge the mental state of the driver; and the execution application module is connected to the electroencephalogram signal analysis module and is used for executing a corresponding auxiliary strategy based on an analysis result of the electroencephalogram signal analysis module. According to the driving fatigue relieving system based on the brain-computer interface, a direct interaction control channel between the brain of the driver and the external equipment is constructed, so that the mental state of the driver is monitored and intervened in real time, and a new technical means is provided for improving driving safety and reducing traffic accident risks.

To provide a system that generates a problem corresponding to a learning level and answer contents of a child, proposes a learning method based on a physical condition and mental state of the child on that day, describes an evidence of an answer, and realizes an educational application and service for children in which generative AI for supporting further learning is incorporated.SOLUTION: In a data processing system including a data processing device 12 and a smart device 14, a specific processing section 290, when a child is struggling with an arithmetic problem, generates a problem corresponding to a his / her level to advance learning, and proposes a learning method based on a physical condition and mental state of the child on that day. The specific processing section records the answer contents of the problem solved by the child and the process of answering the problem, proposes the next learning contents on the basis of it, and supports efficient learning. The specific processing section further incorporates a function of eliminating violent contents and pornography, and provides an environment that can be used by children, parents, and educational institutions without anxiety.SELECTED DRAWING: Figure 2

The invention discloses a composition for improving pet fur quality and a preparation method thereof, and belongs to the field of pet nutritional supplements. The invention provides a composition for improving the quality of pet fur. The composition is prepared by blending an extract, prepared fish oil microcapsules, lecithin and palatability materials, wherein the extract is prepared from Chinese herbal medicines (semen cuscutae, fructus ligustri lucidi, radix astragali, radix polygoni multiflori preparata, radix glycyrrhizae, radix ginseng and radix angelicae sinensis) through semi-bionic combination and low-temperature ultrahigh-pressure supercritical extraction. The traditional Chinese medicine and the modern technology are combined, the unexpected beneficial effects are achieved, the glossiness and the smoothness of pet fur can be remarkably improved, the mental state of pets can be improved, hair slip can be effectively reduced, and the hair is full and fluffy. The composition is high in safety and can be eaten for a long time.

The invention discloses an information data processing system based on brain wave signals, and particularly relates to the field of medical signal processing and brain science application, and the system comprises a data importing and filtering module which supports the input of an original electroencephalogram file and carries out band-pass filtering on the original electroencephalogram file; the bad track detection module is used for automatically detecting and marking bad tracks based on a local outlier factor algorithm, and comparing local density differences between each channel and surrounding channels; the artifact suppression module is used for correcting non-engraving plate transient artifacts in the electroencephalogram; the data restoration and interpolation module is used for carrying out interpolation on the detected bad track and restoring the overall channel layout; the frequency band and ratio characteristic module is used for calculating energy, relative power and ratio of delta, theta, alpha, SMR, beta and betaH frequency bands; the spectrum-space-time characterization module is used for constructing the features into two-dimensional tensors; and the judgment module is used for outputting the mental state category and the attention score. The mental state can be identified based on the juvenile electroencephalogram.