50 results about "Heart rate variability" patented technology

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention discloses a virtual reality fire drill and training method and system, and the method comprises the steps: building a three-dimensional fire scene based on a BIM model and fluid dynamics, resolving the fire spreading rate and the smoke concentration in real time, and generating a dynamic disaster evolution model in combination with explosive parameters; collecting the heart rate variability and the action track of the user through the wearable device, and calculating a physiological stress index and a device operation deviation degree; smoke suppression, explosion delay and fire behavior correction instructions are triggered based on fire behavior and physiological data association; a cooperative avoidance strategy is generated in combination with the path overlap ratio and the fire-fighting role and is fed back to the fire behavior correction module; aligning the spatio-temporal data to evaluate the contribution degree and the cooperative influence weight of the fire extinguishing operation on fire behavior inhibition, and outputting a multi-dimensional evaluation matrix; generating a three-dimensional defect thermodynamic diagram by using a space-time convolutional network, marking a high-risk area and feeding back the high-risk area to a disaster model, and realizing adaptive difficulty iteration; according to the invention, through multi-source data fusion and dynamic regulation and control, the drill authenticity and the emergency disposal efficiency are improved.

The invention discloses an intelligent driving school mode virtual driving scene generation method based on an AI model, relates to the technical field of virtual driving scene generation, and is used for solving the problems of feedback lag and poor individualized adaptability of a training scheme caused by difficulty in dynamically adapting to the current stress level and operation habit of a driver. The method comprises the steps of monitoring the instantaneous speed and direction control amplitude of a simulated vehicle in real time, calculating a longitudinal G value and a steering change rate, collecting physiological data of a tested driver to analyze heart rate variability and cognitive load degree, generating driving behavior characteristics and stress characteristics by using an LSTM model, and judging whether to trigger a specific scene or not through a support vector machine based on the characteristics. The method comprises the following steps of: acquiring response time and misoperation frequency, generating a risk response score through fusion function joint quantification, and judging a driving level grade according to a score threshold value, thereby realizing screening and marking of tested drivers, dynamically adapting to actual performance, and improving pertinence and effectiveness of virtual training.

The invention provides a teenager depression auxiliary diagnosis model training method and system based on multi-modal data, and relates to the technical field of mental health. According to the method, firstly, a VR intelligent interaction scene is constructed, and physiological data of juvenile depression and physiological data of a normal contrast subject in the VR intelligent interaction scene are synchronously collected; data preprocessing and feature extraction are conducted on the electroencephalogram data, the heart rate variability data and the eye movement data, a single-mode depression recognition model is obtained through classifier training, and then a multi-mode depression recognition model and a cross-mode depression recognition model are obtained. The teenager depression auxiliary diagnosis model obtained through machine learning is helpful for early discovery and timely treatment of teenager depression.

The invention provides a virtual reality training system and method for fireman stress disorder adjuvant therapy, the fireman training system and method based on virtual reality are provided by utilizing the development of the virtual reality technology and the heart rate variability monitoring technology, the virtual reality environment is selected and adjusted in combination with multiple dimensions, and the fireman stress disorder adjuvant therapy effect is improved. On one hand, the fire-fighting task processing capability of the firefighter can be trained, and on the other hand, the post-traumatic stress disorder of the firefighter can be treated in an auxiliary manner, so that the firefighter is helped to overcome the fear of trauma and high pressure in the fire-fighting task, and the post-traumatic stress disorder is prevented and treated in an auxiliary manner; the cure rate of the stress disorder of the group of firemen is effectively improved, and the prevalence rate of the stress disorder is reduced. Moreover, according to the scheme, through multi-level personalized adjustment, it is ensured that the adjuvant therapy training scheme better meets the unique requirements of each fireman according to individual characteristics, individual differences, psychological states and physiological states, and the training effect is effectively improved.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention relates to the technical field of early detection of neurological diseases, and discloses an application of a portable three-dimensional eye tracker in cognitive function assessment, comprising: a portable three-dimensional eye tracker used for collecting eye movement data, including fixation time, glancing trajectory, review frequency and eye jump speed; the electroencephalogram acquisition equipment is used for acquiring electroencephalogram data, and the electroencephalogram data comprises alpha wave energy E alpha, beta wave energy E beta and theta wave energy E theta; the heart rate variability monitoring equipment is used for collecting heart rate data, and the heart rate data comprises the ratio RLF / HF of low frequency (LF) to high frequency (HF). The accuracy of cognitive function evaluation is greatly improved through multi-modal data fusion, real-time monitoring and personalized feedback are provided, early discovery of cognitive impairment is facilitated, and the device is high in portability, supports long-term monitoring and testing anytime and anywhere, and is suitable for early detection and continuous evaluation of Alzheimer's diseases.

The invention discloses a cognitive impairment risk intelligent matching intervention system based on multi-modal data, and belongs to the technical field of cognitive impairment, and the cognitive impairment risk intelligent matching intervention system specifically comprises the following steps: collecting a tactile interaction sequence and a face video data stream when a user executes a cognitive intervention task, and constructing an original multi-modal data set; extracting tactile operation track features and reconstructing a heart rate variability sequence, and generating a synchronous multi-mode feature set; identifying a difference interval of state fluctuation and extracting behavior and physiological features in the interval to form an instant physical and mental load parameter set; in combination with an interaction efficiency index in the task execution record, generating a joint state feature vector, and outputting a personalized adaptation instruction through a pre-trained sub-state-parameter association model; and constructing an adaptive strategy model by using the multi-modal features and an adaptive instruction to realize millisecond-level dynamic adjustment from the real-time multi-modal features to task parameters. According to the method, real-time perception and accurate matching of the cognitive emotion load of the user are realized, and the individuation degree and instantaneity of intervention are improved.

The invention relates to the field of biological signal processing and emotion calculation, and discloses a multi-mode mental stress assessment method and system based on machine learning, and the method comprises the steps: collecting a brain wave signal and a heart rate variability signal of a user; using a CNN-LSTM model to perform multi-modal depth feature extraction on the spectrogram of the brain wave signal and the time sequence of the heart rate variability signal to obtain a brain wave signal depth feature vector and a heart rate variability signal depth feature vector; splicing the brain wave signal depth feature vector and the heart rate variability signal depth feature vector to form a high-dimensional multi-modal feature vector representation; and classifying the multi-modal feature representation by using a support vector machine, and outputting a pressure state judgment result. The real-time pressure state of the user can be accurately predicted, and the technical problem that an existing pressure detection method is not high in detection precision is solved.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

The embodiment of the invention provides an information adjustment method, system and device for a virtual reality scene, and relates to the technical field of virtual reality, the method is applied to a management device in a virtual reality system, and the system further comprises a physiological monitoring device and a virtual reality device. The method comprises the steps of obtaining parameter information of heart rate variability associated parameters when a target object is in a current virtual reality scene, and obtaining current first information; on the basis of the parameter information corresponding relation, determining parameter information which specifies presentation parameters and corresponds to the current first information, and obtaining second information; and sending the second information to the virtual reality equipment, so that the virtual reality equipment adjusts parameter information of a specified presentation parameter of the current virtual reality scene into the second information. Visibly, the information of the virtual reality scene is adjusted, so that the target object can more quickly adapt to the stimulation source in the virtual reality scene, and the possibility that the target object generates the conflict psychology is reduced.

The invention discloses an exoskeleton robot abnormal working condition automatic response and risk avoiding decision-making method, and relates to the technical field of exoskeleton, and the method comprises the steps: deploying a plurality of types of sensors to synchronously collect environment data, human physiological data and exoskeleton operation data, and carrying out the preprocessing to remove interference; through fusion of abnormal harmonic detection and a finite-state machine, dangerous working conditions such as wind speed abrupt change are identified, and grades are judged; monitoring eye movement and heart rate variability data in real time, quantifying cognitive load and grading; a decision rule base is constructed, and joint locking and other adaptive risk avoiding decisions are generated in combination with working condition types, grades and cognitive loads; the controller drives the execution mechanism to execute a decision, continuously monitors data at a frequency, dynamically adjusts the decision or recovers a normal mode. According to the method, dangerous working conditions are accurately identified through multi-source data fusion, adaptive risk avoiding measures are automatically triggered, a power assisting strategy is dynamically adjusted in combination with cognitive loads, and operation flexibility and physical output are balanced.

The invention belongs to the technical field of information, and discloses an intelligent sleep environment adjusting method based on artificial intelligence, and the method comprises the steps: synchronously collecting the heart rate variability, respiratory rate and body temperature data of a user, and carrying out the classification and recognition of a sleep state through a long-short-term memory network and a random forest algorithm, and obtaining a sleep state label; judging a deep sleep state according to the sleep state label and a corresponding body temperature data change trend, and further obtaining indoor environment parameters and calculating a deviation value with an ideal sleep environment; a reinforcement learning algorithm is adopted to construct a personalized environment adjustment strategy model, and when the environment parameter deviation exceeds a personalized threshold value, a multi-dimensional environment optimization control scheme is automatically generated. According to the invention, accurate identification of the sleep state of the user and intelligent adjustment of the environmental parameters are realized, the sleep quality is effectively improved, and a comfortable and healthy personalized sleep environment is created for the user.

The invention discloses an intelligent monitoring system for tumor nursing. The intelligent monitoring system comprises a wearable sensing terminal group, a patient terminal, a cloud platform intelligent analysis center and a medical care terminal. The wearable terminal is used for collecting multi-modal physiological and behavior data such as electroencephalogram, near-infrared brain blood oxygen, heart rate variability, body movement, electrodermal response and gaits; the patient terminal is used for recording subjective symptoms and completing cognition and exercise task evaluation. The cloud platform intelligent analysis center constructs a multi-parameter individualized baseline through data fusion, noise suppression and time alignment, comprehensively analyzes real-time deviation, electroencephalogram abnormality, cognitive deviation and gait change by using a short-term acute early warning model and a long-term trend early warning model, and generates a risk level. The digital twinborn visualization module is used for dynamically displaying the state of the patient, and the medical care terminal performs clinical response based on the graded alarm board. According to the invention, extra-hospital multi-mode continuous monitoring, individualized trend identification and intelligent early warning can be realized, and the real-time performance of follow-up management of brain tumor patients is improved.

The invention discloses a diet intervention method and system for crowds with cardiopulmonary health risks. The system comprises a database, an information acquisition module, a risk assessment module, a calculation module, a diet recommendation module, an auditing module and an information pushing module. The database comprises a personal information database, a suggestion database, a recipe database and a different-energy-level food intake database; the information acquisition module is used for acquiring intelligent bed sleep health data and personal information data in a database, and generating a personal user data set for the risk assessment module and the calculation module; wherein the sleep health data collected by the intelligent bed comprises but is not limited to the whole late heart rate, the respiration rate, the heart rate variability and the like; according to the invention, through combination of static and dynamic health data, through risk assessment and early warning, diet health management is carried out on crowds with cardiopulmonary health risks, and the problem that the public cannot effectively utilize knowledge to carry out self-health management actually is accurately solved.

The invention discloses an attention reminding algorithm for intelligent driving based on multiple modes, and relates to the technical field of intelligent driving and active safety, and the algorithm comprises the steps: firstly, collecting data in real time through a multi-mode sensor group, carrying out the alignment, and then calculating the quantization features, such as the fixation deviation degree, the steering wheel disturbance entropy and the heart rate variability; then, mapping scores by adopting an S-type function, analyzing a historical sequence by utilizing a long short-term memory network to generate a dynamic weight, and performing adjustment and weighted calculation on a single-mode score in combination with an environment complexity weight to obtain a comprehensive attention score; and finally, according to the continuous driving duration, calculating a linear decreasing dynamic reminding threshold value, executing dual logic judgment in combination with the environmental risk level, and triggering graded feedback through sound and light or vibration. According to the method, the problems of poor adaptability and high false alarm rate of traditional single-mode monitoring in a dynamic environment are effectively solved, the robustness of the system is improved, and adaptive evaluation and accurate early warning of driver distraction behaviors in a complex scene are realized.

The invention relates to the technical field of virtual reality (VR) interaction and artificial intelligence, and provides an emotion recognition method based on a multi-modal fusion neural network, which comprises the following steps: S1, acquiring multi-modal physiological signal data including electroencephalogram signals, eye movement data, heart rate variability signals and skin electric response signals; s2, performing filtering, slicing and normalization processing on the obtained original signal through an edge calculation preprocessing unit to generate multi-channel time sequence data; s3, inputting the multi-channel time sequence data into a multi-modal fusion neural network model which comprises a spatial feature extraction layer, a time sequence feature extraction layer, a cross-modal attention fusion layer and a classification output layer and is used for extracting deep features of a multi-modal signal and identifying an emotional state of a user; the emotional state of the user in the virtual reality environment can be accurately recognized in real time, the virtual scene is dynamically adjusted according to the emotional state, and the reality sense and the participation degree of user experience are improved.

The invention provides a light environment adaptive HUD adjusting method based on a man-machine flight mission, and belongs to the technical field of flight display control. Light intensity, color temperature and reflectivity data outside a cabin are collected, and eye movement signals, electroencephalogram signals and heart rate variability signals of a pilot are synchronously obtained; after registration, constructing a synchronous time sequence data set containing the light environment and the physiological state, and based on external light parameters, utilizing a gradient lifting decision tree model to identify the current light environment and obtaining a brightness reference interval; according to the invention, the HUD brightness and contrast are dynamically calculated and compensated in advance through real-time acquisition of external light parameters and a classification result of a light environment recognition model, so that the HUD can maintain clear symbols and stable contrast under various light conditions such as cloud crossing, backlight, strong reflection and night voyage; the problem of over-brightness or over-darkness caused by adjustment lag of a traditional fixed brightness curve is avoided, and information readability in a flight task is improved.

The invention provides a sudden disease early warning method and system based on heart rate variability and steering wheel grip strength, and belongs to the technical field of vehicle safe driving, and the method comprises the steps: obtaining heart rate variability detection information and steering wheel pressure detection information; determining that the heart rate variability is abnormal when judging that the heart rate variability detection information does not meet a first preset threshold value, and determining that the grip strength is abnormal when judging that the pressure detection information of the steering wheel does not meet a second preset threshold value; when the heart rate variability is abnormal and the grip strength is abnormal, sudden disease early warning information is sent out. The pathological state of the driver can be accurately recognized, and the defect that single heart rate monitoring is prone to motion interference is overcome. According to the invention, the monitoring response speed is improved, the false alarm rate is reduced, and the hardware cost is reduced compared with the traditional technology.

The invention discloses a personalized motion posture correction system and method based on multi-modal biomechanical feedback, and relates to the technical field of emotion recognition and intelligent feedback control. The system comprises the following components: a multi-modal biomechanical data acquisition module, an emotional state sensing module, a posture deviation analysis module, a dynamic feedback decision module and a multi-channel feedback output module. The multi-mode biomechanical data acquisition module acquires biomechanical parameters such as muscle electric signals, joint motion trails and gravity center distribution of a user in real time, and combines emotional related physiological signals such as heart rate variability and skin conductivity acquired by the emotional state sensing module and results of the motion scene recognition unit. According to the method, the personalized biomechanical baseline model is constructed, and the motion posture deviation type and degree of the user can be identified more accurately in a multi-modal data fusion and personalized modeling mode, so that targeted feedback and correction are provided.

This invention proposes a method and system for generating and controlling aromatherapy-inspired natural wind based on multi-parameter coupling, relating to the fields of indoor environmental regulation and intelligent control. It includes: constructing a simulated natural dynamic airflow field using three fans equipped with aromatherapy release devices; generating a random wind speed sequence by correcting an initial random wind speed based on preset wind speed gradient levels and hydrodynamic characteristics, and driving each fan to deliver air at preset time phase differences; controlling the gas release amount of the aromatherapy release devices based on fan wind speed information and environmental feedback; and collecting skin temperature and heart rate variability data of subjects to adjust the wind speed amplitude based on feedback. This invention improves indoor thermal comfort and enhances cognitive performance and overall indoor living experience by constructing a simulated natural dynamic airflow field combined with dynamic aromatherapy diffusion and skin temperature feedback regulation.

This application discloses the application of pyridostigmine in the preparation of drugs for the prevention and treatment of non-alcoholic fatty liver disease (NAFLD). Validation was conducted using animal models of NAFLD induced by three different etiologies: methionine-choline deficiency diet, high-fat diet, and leptin gene knockout. Pyridostigmine significantly improved vagal nerve activity in the model animals (e.g., improving high-frequency and baroreflex sensitivity in heart rate variability), effectively corrected serum lipid metabolism disorders (reducing LDL cholesterol), alleviated hepatocyte damage (reducing ALT and AST), and at the histopathological level, reduced hepatic steatosis, decreased inflammatory cell infiltration, and inhibited collagen deposition, thereby achieving the prevention and treatment of NAFLD.

A method for improving stability and / or absorption of one or more biologically active agents including preparing formulations wherein a biologically active agent is dispersed using a homogenizer and / or a nanofluidizer; and optionally: i) enriching the formulation; and / or ii) delivering the formulations to a subject whereby the biologically active agent is absorbed by said subject, and / or iii) testing the formulation to identify suitable dosing ranges using computational modeling of biomolecular pathways to determine at least one feature selected from absorption in a cell, saturation of a cell, and potential toxicity in a cell, and / or iv) testing the formulation by monitoring with a low impact, minimally intrusive heart rate variability monitoring to enable rapid determination of neurological and physiological effects of a dosage, establishing dosing levels of the biologically active agent, and / or v) defining the corresponding metabolic effects of the dosage of the biologically active agent on the subject.

The invention relates to the technical field of vehicle intelligent lighting, in particular to an intelligent headlamp control method and system based on driving intention prediction. The method comprises the steps that environment data, driver biological signals and vehicle dynamic data are collected through a multi-source sensor; predicting a future short-time driving intention and confidence by adopting a space-time diagram convolutional network; correcting illumination parameters in real time according to the eye movement gazing area of the driver and the heart rate variability; initiating a conservative wide-angle lighting mode when the intention confidence is below a threshold; a stroboscopic warning light pattern is triggered in response to an ADAS system pre-collision signal. The system comprises a data acquisition unit, a processing unit and an execution layer, and cooperative adjustment of the deflection angle, the brightness and the irradiation range of the headlamp is achieved. The problems of response lag and blind area risk of a traditional self-adaptive headlamp are solved, and the illumination timeliness of a blind area of a curve and the safety of a low-visibility working condition are remarkably improved.

The application discloses an ion conductive hydrogel and a preparation method and application thereof, relates to the technical field of functional hydrogel and sensors, and discloses the ion conductive hydrogel which comprises a gel-forming monomer, a crosslinking agent, an inorganic salt and a photoinitiator; the gel-forming monomer comprises acrylamide and zwitterionic monomer sulfobetaine methacrylate; the crosslinking agent is N, N'-methylene bisacrylamide; the photoinitiator is 2-hydroxy-4'-(2-hydroxyethoxy)-2-methylpropiophenone; and the solvent is deionized water. Based on the ion conductive hydrogel, an ion conductive hydrogel electrode patch can be prepared; by means of a wearable device, the ion conductive hydrogel electrode patch can be adhered to a human heart part, safe and comfortable heart rate variability data acquisition of the human body can be realized, and then the detection of CAN can be realized.

Devices and methods for stimulation and modulation of brain activity using transcranial photobiomodulation (PBM) or electroencephalogram or heart rate variability (HRV) biofeedback, and devices and methods for analyzing and monitoring evoked electroencephalogram (EEG) electrical activity before and / or after neurocognitive testing using event-related potentials (ERP) and evoked and stimulated EEG oscillations (ERO) measurements in a human user. Electroencephalography (EEG) and photobiomodulation (PBM) devices in the form of wearable devices with headphones for stimulating the brain with PBM or using biofeedback training with simultaneous EEG monitoring, as well as a description of a method for testing a person's cognitive and physiological state during cognitive testing using the provided devices, are described. PBM light is pulsed to stimulate an increase in target brain activity by providing additional energy to mitochondria and activating other photosensitive brain processes. PBM stimulation is combined with an EEG sensor to detect EEG responses during cognitive testing using event-related potentials (ERP) and evoked and stimulated EEG oscillations (ERO) for the assessment of the user's cognitive state and treatment of functional outcomes.

The invention discloses an out-of-control braking system and method for a micro-detonation surgical robot based on energy step. The system comprises a multi-modal data acquisition module, a risk monitoring and braking control module and a micro-detonation physical braking module. The multi-modal data acquisition module comprises a hardware state data acquisition module, a motion state data acquisition module, a physiological state data acquisition module and a data storage-fusion-transmission chip; the risk monitoring and braking control module comprises a communication module, a data fusion module and a control circuit; the braking method comprises the steps that robot mechanical arm current, output shaft torque, magnetic field intensity, translation speed, acceleration component, point cloud density, depth, and patient blood oxygen saturation, heart rate variability and blood pressure variability are collected; and risk levels are divided according to the comprehensive risk quantized value, the hardware risk quantized value, the exercise risk quantized value and the physiological risk quantized value for braking. According to the invention, out-of-control risk monitoring, graded response and hardware-level physical damage brake control of the robot are realized.

This invention discloses a method and system for adjusting the color temperature of a display. The method includes: real-time acquisition of ambient light parameters and user physiological characteristic parameters, wherein the ambient light parameters include at least the ambient color temperature value and illuminance value, and the physiological characteristic parameters include at least the duration of continuous eye use and heart rate variability index; generating a first color temperature adjustment reference value based on the ambient light parameters, and generating a second color temperature adjustment reference value based on the physiological characteristic parameters; inputting the first and second reference values ​​into a color temperature fusion decision model to dynamically calculate a target color temperature value; and gradually adjusting the current color temperature of the display to the target color temperature value according to a smooth transition curve, wherein the slope of the transition curve is adjusted in real time according to the heart rate variability index. The system includes an environmental perception module, a physiological monitoring module, a dual-path reference generation module, a color temperature decision module, and a smooth adjustment execution module. This invention integrates environmental and physiological characteristics to achieve personalized, eye-friendly, and smooth adaptive color temperature adjustment, effectively alleviating visual fatigue.