397 results about "RR interval" patented technology

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention discloses an oxygenerator remote control system and method based on the Internet of Things, and belongs to the technical field of intelligent medical equipment and remote health management, and the method comprises the steps: S1, collecting multi-dimensional physiological data of a user in real time through a multi-source physiological signal collection module, a heart rate variability index and a body movement signal; s2, based on the multi-dimensional physiological data, processing is performed through a preset physiological state prediction model, and a unified physiological stress index is solved; s3, determining a predictive oxygen supply flow rate value in combination with the physiological stress index and a preset user basic oxygen supply flow rate; s4, according to the predictive oxygen supply flow velocity value, a control signal for a physical execution component of the oxygen generator is generated so as to dynamically adjust oxygen supply parameters, and conversion from passive compensation to active prediction is achieved.

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention belongs to the cross technical field of biomedical engineering and nerve regulation technology. According to the brain-heart linkage transcranial strong alternating current stimulation feedback control method and system, heart rate variability characteristics are obtained according to electroencephalogram signals, electroencephalogram signal characteristics are obtained according to the electroencephalogram signals, and a dynamic coupling index is determined according to the electroencephalogram signals and electrocardiosignals; when the heart rate variability characteristic is greater than or equal to a corresponding heart rate characteristic baseline threshold value, the electroencephalogram signal characteristic is greater than or equal to a corresponding electroencephalogram characteristic baseline threshold value, and the dynamic coupling index is greater than or equal to a set threshold value, judging that the emotional disorder is improved, and keeping the intensity of the stimulation current unchanged; otherwise, increasing the intensity of the stimulation current to a set threshold value until the emotional disorder is improved. Through combined calculation of the electroencephalogram signals and the electrocardiosignals, dynamic monitoring and accurate evaluation of the process of treating the emotional disorder through transcranial strong alternating current stimulation are achieved, and the accuracy of electrical stimulation is guaranteed.

The invention discloses an electrocardiosignal-based sleep apnea detection method and system and a storage medium. The method comprises the following steps: acquiring a target electrocardiosignal to be analyzed, performing R peak identification on the target electrocardiosignal, calculating an RR interval between two adjacent R peaks, and acquiring a voltage amplitude of the electrocardiosignal at the R peak position as an R peak amplitude; on the basis of the RR interval, the RR interval is converted into an instantaneous heart rate sequence, resampling operation is carried out on the heart rate sequence, interpolation processing is carried out on non-uniform sampling points obtained after resampling so as to obtain a continuous and equally-spaced heart rate curve, and signals of a breathing-related frequency band are extracted through filtering to serve as electrocardio derived breathing signals; and splicing the RR interval, the R peak amplitude and the electrocardio-derived respiration signal to form a multi-channel feature, and inputting the multi-channel feature into a trained sleep respiration detection model to obtain sleep apnea state judgment information. Therefore, the apnea state can be detected more comprehensively, and the accuracy of sleep apnea detection is improved.

The invention relates to the technical field of interactive systems, and particularly discloses a game motor home interactive demonstration control system which comprises a sensing interaction module, an intelligent scene engine, a multi-mode output module, a cooperative control module and a safety monitoring module. The perception interaction module integrates physiological, action and environment sensors, deeply analyzes explicit instructions and implicit demands (such as inferring emotions through a heart rate variability rate) of a user in combination with a Transform model, and generates a structured interaction intention label; according to the physiological, action and environment multi-dimensional data fusion mechanism, the system can accurately capture the user state, an accurate basis is provided for follow-up scene adjustment, and the fitting degree of interaction response is remarkably improved; the intelligent scene engine constructs a user immersion model through deep reinforcement learning, calculates an immersion index based on parameters such as heart rate, micro-expression and action angular velocity, and dynamically triggers a high immersion, balance or guide mode.

The invention relates to the cross technical field of biomedical engineering and psychotherapy, in particular to an automatic control system for classified exposure therapy of phobia based on naked eye 3D. The system comprises an electroencephalogram signal feature extraction module, a heart rate variability feature extraction module, a skin electric response feature extraction module, an eye movement feature extraction module, a physiological load state feature extraction module, an exposure therapy process feature extraction module, a first feature fusion module, a second feature fusion module and a stimulation parameter adaptive control module. The method combines multi-mode biological signal real-time analysis and naked eye 3D stimulation parameter self-adaptive adjustment, and is suitable for clinical psychological treatment mechanisms and psychological health intervention scenes.

The invention discloses a five-dimensional dynamic emotion visual chemotherapy healing method and system, and relates to the technical field of emotion visual chemotherapy healing, and the method comprises the steps: employing a multi-sensor fusion method to collect physiological data, psychological assessment questionnaire results and historical emotion data of a user, and obtaining a basic emotion feature vector of the user; mapping the emotional state of the user to a five-dimensional emotional space based on the basic emotional feature vector, and initializing a particle set; collecting multi-mode biological signals of electroencephalogram, heart rate variability, electrodermal response, body temperature and voice emotion recognition of the user, and processing to obtain a feature vector reflecting the current emotion state of the user; the geometric morphology and kinetic parameters in the particle set are dynamically adjusted, a visualization engine is used for rendering particles, a two-way feedback adjustment mechanism is constructed, particle behaviors are adjusted according to the emotional state of the user, and the emotional adjustment ability of the user is enhanced; and designing an interactive emotion regulation game.

The invention relates to the field of traditional Chinese medicine pulse condition collection, and provides a traditional Chinese medicine six-channel identification cognition method and system based on heart rate variability, and the method comprises the steps: obtaining heart rate variability data of a detected object, and extracting parameters such as time frequency from the heart rate variability data; constructing a hierarchical feature extraction network, inputting parameters such as time frequency into the hierarchical feature extraction network, and distributing weights for the parameters such as time frequency based on a qi-blood-body fluid theory to obtain a fusion feature vector; inputting the fused feature vector into a particle swarm optimization algorithm, and performing feature selection by adopting a six-channel transmission constraint function and a syndrome affinity particle update strategy to obtain an optimized feature combination; the optimized feature combination is converted through the semantic mapping relation between the heart rate variability parameters and the pulse condition descriptors, and pulse condition feature parameters are obtained; and performing syndrome classification calculation based on the pulse condition characteristic parameters, and outputting six-channel syndrome types. According to the invention, automatic identification conversion from physiological signals to traditional Chinese medicine syndromes is realized, and the precision and reliability of six-channel syndrome identification are improved.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.

A biometric wearable device (e.g. finger ring or smart watch) has optical sensors to measure body oxygenation level, hydration level, glucose level, heart rate, heart rate variability, and / or blood pressure. Light from light emitters is transmitted through body tissue and changes in the light are analyzed. The angles and / or vectors along which the light is transmitted through body tissue can be automatically changed by the device in order to scan different tissue regions and / or different tissue depths.

The invention relates to the field of biosensors, and discloses a cardiovascular data monitoring method for the cardiovascular medicine department. According to the method, electrocardio, respiration and blood oxygen signals are synchronously collected through a non-invasive terminal, sleep apnea events are analyzed and recognized in a combined mode, the electrocardio signals are deeply analyzed to detect cardiovascular abnormalities, and time sequence characterization of the function state of the autonomic nervous system is generated based on heart rate variability; and further constructing a time sequence causal reasoning model, quantifying the triggering effect of the respiratory event on the cardiovascular event, generating a comprehensive night cardiovascular risk layering index, and triggering graded early warning. According to the invention, non-sensitive, long-time-history and high-precision night cardiovascular risk dynamic assessment and causal mechanism analysis are realized.

The invention discloses an infant state recognition method and system based on traditional Chinese medicine five-tone monitoring analysis, and the method comprises the steps: synchronously collecting the crying sound, physiological signals and behavior videos of an infant, extracting the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate of the physiological signals, and the facial expression and limb movement features, and carrying out the recognition of the state of the infant through the Mel-frequency cepstral coefficient of the audio, the heart rate variability, galvanic skin response and respiratory rate. Performing structured integration by using a multi-modal feature fusion model; in combination with the five-tone theory of traditional Chinese medicine, a corresponding relation between audio features and five-organ states is established, and the robustness of five-tone and five-organ mapping is improved through fuzzy reasoning and a Bayesian mechanism; the system dynamically adjusts the weight coefficient of each mode, adapts to the individual and emotion historical trend, achieves more accurate emotion recognition and physiological evaluation, achieves cross-mode and multi-layer information fusion, improves the accuracy and interpretation ability of infant emotion and five-internal-organ state recognition, and provides a scientific basis for clinical evaluation and health management.

The invention discloses an acupuncture intervention method after a pancreatic cancer operation, and aims to solve the problem that autonomic nerve regulation based on heart rate variability in an acupuncture stimulation closed loop is difficult to objectize and safely optimize. Heart rate variability time-frequency and time-domain features and respiratory coherence are extracted based on synchronous compression wavelets, a reliability score is generated, short-time heart rate variability and uncertainty are predicted through a neural state space model, a hierarchical control barrier function is generated through a context gating network, a safe MPC is constructed, and real-time quadratic programming solution is carried out. The heart rate variability enters the target domain and the stimulation parameter change rate and the energy consumption are reduced under the condition that safety constraints such as current, pulse width, charge quantity and impedance are met by combining safety verification and self-adaptive updating, and the reliability and the robustness of closed-loop control are improved.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

The invention discloses a method for constructing a health influence prediction model based on multi-source air data coupling, and relates to the technical field of air quality health monitoring, and the method comprises the steps: obtaining multi-source air environment data in a selected space as an environment index, and obtaining a multi-source physiological signal of a person in the selected space as a physiological index; on the basis of the feature vectors of the environment indexes and the physiological indexes, the coupling relation between the air environment features and the physiological features is constructed, modeling is conducted on the nonlinear coupling relation between the air features and the physiological features through a time sequence deep learning model, and the action rule of air pollution accumulative exposure on dynamic changes of the physiological indexes is captured. Through feature extraction and cross-modal coupling, the comprehensive influence rule of air pollution on the blood pressure, blood oxygen, body temperature, heart rate, heart rate variability and blood glucose physiological indexes of the human body can be revealed, and dynamic prediction of health risks can be achieved through a deep learning model.

A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.

The invention discloses a VR emotion quantitative management method and system based on physical and psychological interaction, and relates to the field of psychotherapy, and the method comprises the steps: collecting the heart rate variability data, the electrodermal response data and the EEG alpha wave power data of a user in a resting state, and obtaining the EEG alpha wave power data of the user based on the heart rate variability data and the electrodermal response data; generating color, density and physical boundary parameters of the initial emotion energy field; and rendering a dynamic emotion energy field in the VR environment according to the initial emotion energy field parameters, and detecting the distance change between the user and the dynamic emotion energy field through a field density growth algorithm. According to the method, heart rate variability, electrodermal response and EEG alpha wave data are fused by adopting an S-shaped curve function to generate emotion energy field parameters, accurate mapping of physiological states and virtual environments is established, emotion recognition sensitivity is improved, treatment efficiency is improved, field density attenuation rate is dynamically controlled innovatively through parasympathetic nerve activation degree, and the treatment effect is improved. And the synergistic effect of behavior intervention and nerve regulation is realized.

The invention discloses a cognitive load assessment method based on fusion of behavior characteristics and heart rate variability in a classroom video. According to the method, face and behavior videos of students in a real classroom are collected, behavior characteristics such as eye movement tracks, sitting postures and facial expressions of the students are extracted through a computer vision algorithm, heart rate variability indexes are predicted in combination with a remote photoplethysmography technology, and a time sequence characteristic sequence is formed. And then, a time sequence deep learning model is adopted to carry out joint modeling on the multi-modal time sequence characteristics, and the cognitive load scale level is taken as a supervision signal to construct a classification model to realize cognitive load level prediction. The method has the advantages of non-contact, automation, high adaptability and the like, and can be applied to personalized teaching monitoring and intelligent teaching feedback.

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.

The invention relates to the technical field of medical instruments, in particular to a multi-mode biological feedback ear vagus nerve self-adaptive stimulation device and method.The device comprises a host, a stimulation electrode, a signal collection bracelet and an electroencephalogram collection module, and the host comprises core hardware configuration and a power management system; the signal acquisition bracelet comprises an ECG module, a PPG module, a GSR module, a six-axis acceleration sensor and the like. The ear vagus nerve stimulation parameter can be dynamically adjusted through real-time analysis of multi-mode data such as electrocardio, electroencephalogram, heart rate variability, respiration-heart rate synchronism and emotional state, personalized precise treatment is achieved, meanwhile, the wearing comfort and the durability of the device are improved, and the application range is wide. The traditional Chinese medicine composition can be used for auxiliary treatment of various diseases such as inflammatory bowel disease, epilepsy, chronic insomnia, depression, chronic pain, anxiety disorder, post-traumatic stress disorder and autonomic nerve dysfunction, and the functions of a nervous system are adjusted and the symptoms of a patient are improved by accurately stimulating the vagus nerve in the auricular concha area.

This invention discloses a multimodal emotion analysis method and system, comprising: performing heart rate variability analysis on collected vital sign signals to extract time-domain features, frequency-domain features, and nonlinear features corresponding to the analyzed heart rate variability signals, and fusing the time-domain features, frequency-domain features, and nonlinear features to generate heart rate variability features corresponding to the vital sign signals; calculating the power spectrum and power spectrum entropy corresponding to the collected electroencephalogram (EEG) signals to extract EEG features corresponding to the EEG signals based on the power spectrum and power spectrum entropy; extracting skin conductance features corresponding to the collected electroskin conductance (ESC) signals; inputting the heart rate variability features, the EEG features, and the ESC features into a pre-trained emotion recognition model, and outputting several emotion analysis results; obtaining an emotion regulation scheme matching each emotion analysis result to output the corresponding emotion regulation strategy, thereby improving the accuracy of emotion analysis and regulation strategies.

The invention discloses a flight student pressure assessment system based on fusion of heart rate variability and multi-modal data, and relates to the technical field of flight training. The data acquisition module is used for acquiring heart rate variability data, electroencephalogram data, galvanic skin data and behavior data of a flight student, and constructing a data set after preprocessing; the feature extraction module is used for extracting features from the data set and comprises the steps of extracting time domain features and frequency domain features from heart rate variability, calculating phase synchronism of different electroencephalogram channel signals based on a phase locking value, extracting energy features of each frequency band and decomposing a skin electric signal on different time scales based on wavelet transform; and extracting a baseline value and fluctuation amplitude characteristics. According to the method, the limitation of a single index is broken through by integrating the heart rate variability physiological signal and the limb movement behavior data; the system assists students in cognitive pressure, personalized guidance of coaches and mechanism optimization training, promotes flight training to be changed from experience driving to data driving, and provides early warning measures for high-risk flight students.

The invention relates to the technical field of operator safety management, and discloses a non-contact physiological status rapid screening method and system and a medium, and the method comprises the steps: collecting an RGB three-channel space-time image sequence of a facial region of interest; carrying out illumination correction on the RGB three-channel space-time image sequence by adopting an adaptive gamma correction algorithm, carrying out end-to-end noise reduction processing on the illumination correction image sequence by utilizing a one-dimensional convolutional neural network auto-encoder noise reduction algorithm, and separating a remote photoelectric volume pulse wave tracing pulse signal; calculating physiological indexes of heart rate, heart rate variability and oxyhemoglobin saturation based on the remote photoplethysmography pulse signal; and performing grading judgment on the physiological indexes based on the physiological index grading standard, determining a final post suitability judgment grade in combination with the wooden barrel effect model, and generating a grading early warning signal. According to the method, accurate and rapid evaluation of the physiological state of the operator in a complex environment can be realized, the individual health degree can be comprehensively and objectively considered, and decision support is provided for reasonable distribution of posts and efficient configuration of personnel.

According to the intelligent green light phototherapy glasses and the intelligent adaptation method thereof, a narrow-band green light LED light source with the central wavelength being 530 + / -5 nm is adopted, and individualized pain management is achieved in combination with heart rate variability, electrodermal response and pupil dynamic monitoring. A multi-source sensor and a micro control panel are arranged in the glasses, physiological data are collected in real time, the illumination intensity, frequency and irradiation mode are dynamically adjusted through an artificial intelligence algorithm, and a closed-loop phototherapy control system is constructed. The device supports Bluetooth / Wi-Fi connection, interacts with a mobile terminal APP, and realizes data recording, curative effect evaluation and remote monitoring. The system has multiple safety mechanisms such as constant-current driving, temperature feedback, over-illumination protection and posture detection, and long-term wearing safety is ensured. Through the regulation effect of green light on pain neural pathways, a safe, portable and intelligent chronic pain non-drug treatment means is provided under the condition that daily activities are not interfered, and high compliance and a remarkable analgesic effect are achieved.