56 results about "Abnormal breathing" patented technology

The invention relates to the technical field of intelligent medical monitoring, and discloses a breathing abnormity identification method, system and device and a medium, and the method comprises the steps: obtaining a multi-modal original data set; extracting a spectrum feature of the audio signal, a periodic feature of the physiological motion signal and a change feature of the environmental monitoring data from the multi-modal original data set, and constructing a multi-dimensional feature matrix based on the extracted features; performing cross-modal fusion processing on the multi-dimensional feature matrix by adopting a multi-channel convolutional network to generate a joint feature vector; and in combination with a pre-constructed knowledge graph, identifying an abnormal feature mode from the joint feature vector through a density clustering algorithm, screening an abnormal mode in combination with a medical diagnosis rule, and outputting a breathing abnormality identification result. According to the method, the defects that a single signal is sensitive to noise and a non-stationary signal is insufficient in analysis capability are overcome; meanwhile, a medical knowledge graph and a density clustering algorithm are combined, non-pathological breathing modes are screened out, and the accuracy and clinical credibility of complex breathing anomaly recognition are remarkably improved.

The invention discloses a child abnormal breathing early warning and remote cooperation system for a family environment, and relates to the technical field of breathing data processing, and the child abnormal breathing early warning and remote cooperation system comprises a data acquisition module, a first data calculation module, a first data judgment module, a second data judgment module, a second data calculation module, a third data judgment module and a data output module. The system sequentially performs primary risk assessment, hidden risk mining and potential decompensation index calculation on the basis of a multi-modal signal processing technology by synchronously acquiring a breathing sound signal, a non-contact thoracoabdominal movement signal and a contact thoracoabdominal movement signal, so that graded early warning is realized, and a remote cooperation mechanism is automatically triggered according to the early warning level. According to the method, the hidden risk in the early stage of respiratory compensation of children can be effectively identified, and advanced early warning and timely intervention of abnormal respiration can be realized in a family environment.

The invention discloses a breath anomaly detection method and system based on gas monitoring, and relates to the technical field of breath anomaly detection.The breath anomaly detection method comprises the steps that mouth and nose gas signals in the breath process of a user are collected and preprocessed, and gas component separation is conducted through a microfluidic gas separation module; and performing differential analysis on separated gas components, calculating fluctuation of nasal cavity and oral cavity gas components, performing breathing mode recognition through 1D SNN, and establishing a personalized breathing mode library in combination with long-term health data of a user. Compared with the prior art, the method has the advantages that more accurate breathing mode recognition and anomaly detection are realized by combining differential analysis of multi-dimensional gas components and a deep learning model with a personalized breathing mode library, so that a more intelligent health management scheme is provided, and particularly, the method has remarkable advantages in coping with complex personalized differences and long-term health trends.

The invention relates to the field of breathing mode anomaly detection, and provides a breathing mode anomaly recognition method and system based on time series data mining, and the method comprises the steps: collecting and preprocessing an original breathing signal, and obtaining breathing time series data; processing the breathing time sequence data by adopting a multi-scale feature extraction network to obtain a multi-scale breathing feature vector; performing sample amplification on the multi-scale respiration feature vector and the marked sample through a generative adversarial network to obtain an amplified training sample set; carrying out model training on the amplified training sample set through a decision making system, and carrying out anomaly detection on a to-be-detected respiration feature vector to obtain an anomaly detection result and an uncertainty evaluation result; and outputting graded early warning information based on an abnormal detection result and uncertainty assessment in combination with clinical risk assessment. According to the method, high-precision detection, dynamic risk assessment and graded early warning of the abnormal breathing mode are realized while the feature extraction process is ensured to be highly matched with the difference of individual breathing cycles.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

The invention relates to the technical field of neonatal respiration monitoring, and discloses a neonatal abnormal respiration early warning system. A physiological parameter acquisition module of the system acquires physiological parameter information of a target newborn in real time, and an analysis instruction is triggered after a physiological parameter abnormal signal is obtained; the breathing mode recognition module recognizes breathing mode characteristics and analyzes the breathing mode characteristics to obtain a breathing mode evaluation result; the abnormal feature extraction module extracts abnormal breathing features and analyzes the abnormal breathing features to obtain an abnormal feature evaluation value; the early warning grade evaluation module receives the result and the evaluation value, and obtains a low-risk early warning signal and a high-risk early warning signal through evaluation; the early warning signal generation module selects an early warning type according to the received signal, and obtains parameters of sound early warning and acousto-optic early warning which should be triggered. According to the system, dynamic monitoring and graded early warning of the neonatal breathing abnormity are achieved through multi-module cooperation, and support is provided for timely intervention of the neonatal breathing abnormity.

The invention relates to the field of breath sound monitoring, and discloses a tracheal intubation device and a breath sound monitoring system.The tracheal intubation device comprises a tracheal catheter used for being inserted into an airway of a patient; the first sound detection piece is an optical fiber sound sensor, the first sound detection piece is installed on the tracheal catheter, the first sound detection piece is used for detecting breathing sound wave information in the airway of a patient, and the first sound detection piece is used for being in communication connection with the monitoring host so as to transmit the detected breathing sound wave information to the monitoring host. Therefore, the first sound detection piece is installed on the tracheal catheter, the first sound detection piece can be directly located in the internal environment of the airway of the patient, the accuracy of monitoring breathing sound and sound wave information of the patient through the tracheal intubation device can be improved, and the first sound detection piece transmits the breathing sound and sound wave information to the monitoring host in real time; the medical staff can monitor the breathing condition of the patient in real time through the monitoring host, find out abnormal breathing of the patient in time and carry out treatment.

The invention discloses a breathing equipment data processing method which comprises the following steps: synchronously collecting multichannel physiological signals through a built-in sensor of breathing equipment, carrying out noise processing and data standardization, and segmenting according to a time window to form data fragments; extracting breathing amplitude, frequency and morphological variability features of each data fragment, and combining to generate a multi-dimensional dynamic feature vector sequence; constructing a breathing state modeling system based on a hidden Markov model, training model parameters through a Baum-Welch algorithm, and calculating a prospective risk transition score by using a forward algorithm; and establishing an adaptive threshold strategy based on the risk transition score, carrying out structured labeling on the original respiration data, and generating processing data containing a prospective risk early warning label. Technical support can be provided for intelligent upgrading and personalized treatment of respiratory treatment equipment, and the accuracy, predictability and clinical practicability of respiratory anomaly detection are improved.

The invention discloses an intelligent ward risk assessment method and system, a product and a medium. The method comprises the following steps: synchronously monitoring breathing audio and mattress pressure signals of a patient, and identifying discrete action events with remarkable amplitude and duration from pressure data; further judging whether the breathing of the patient is abnormal or not before and after the action events occur, for example, the breathing rhythm is interrupted for a long time or the breathing cycle obviously deviates from a baseline; when the two conditions of the violent action and the abnormal breathing cooperatively occur, an effective cooperative event record is confirmed; according to the amplitude and the duration of the action in the event and the severity of the abnormal breathing, weighted summation is carried out through a preset risk weight, and a single event risk score is calculated; and accumulating all event risk scores in an evaluation period to obtain a comprehensive risk score, comparing the comprehensive risk score with a risk threshold, and outputting a risk level. By implementing the technical scheme provided by the invention, the accuracy of clinical compound action risk early warning of the patient is improved.

The present disclosure provides a method for assessing cardiorespiratory health. The method comprising: generating mechanical movement data associated with a torso of a subject's body using a plurality of ballistocardiography (BCG) sensors; filtering the mechanical movement data to extract respiratory signal waveforms and heart signal waveforms; decomposing the respiratory signal waveforms into thoracic, diaphragmatic, and abdominal breathing components based on waveform characteristics and anatomical location of the plurality of BCG sensors; analyzing phase differences between the thoracic, diaphragmatic, and abdominal breathing components over time to quantify a degree of synchrony or asynchrony; evaluating relative contributions of thoracic, diaphragmatic, and abdominal breathing components to an overall breathing effort; identifying a breathing abnormality based on the analysed phase differences and the evaluated relative contributions; and detecting a potential cardiorespiratory condition based on the identified breathing abnormality.

The present invention relates to an operating method of a lung monitoring system operated by at least one processor, the operating method comprising the steps of: acquiring a plurality of electrical impedance tomography (EIT) images of the chest of a patient and a plurality of respiratory sound intensity images generated on the basis of respiratory sound signals acquired from the chest of the patient; mapping, on the basis of a plurality of pieces of respiratory interval information acquired by binning a respiratory signal of the patient, an EIT image and a respiratory sound intensity image corresponding to each respiratory interval to each other; and inputting, into a respiratory anomaly prediction model, at least one pair of the EIT image and the respiratory sound intensity image included in each respiratory interval, to acquire a prediction result for respiratory anomalies of the patient.

Embodiments disclosed herein improve digital stethoscopes and their application and operation. A first method detects of a respiratory abnormality using a convolution. A second method counts coughs for a patient. A third method predicts a respiratory event based on a detected trend. A fourth method forecasts characteristics of a future respiratory event. In a fifth embodiment, a base station is provided for a digital stethoscope.

The invention provides a radar non-inductive respiration monitoring system and method based on a passive wrinkled airbag, and the system comprises a sleep monitoring pillow and a respiration recognition and abnormity monitoring subsystem, the respiration recognition and abnormity monitoring subsystem comprises a signal monitoring and coupling module which processes original monitoring data, and transmits the processed data to the sleep monitoring pillow; a human body respiration direct measurement signal and an air bag coupling amplification signal are obtained; the state recognition and inversion module is used for carrying out in-place state recognition, sleeping posture recognition and airbag state inversion on the coupled double-domain fusion respiration candidate data to obtain state constraint data; the respiration reconstruction and feature extraction module is used for performing screening and feature extraction on the double-domain fusion respiration candidate data to obtain respiration feature data; and the abnormity identification and confirmation module is used for identifying the apnea candidate event and the abnormal event, obtaining an abnormity judgment result and carrying out graded alarm pushing. According to the invention, through the non-inductive pillow type structure and radar-air bag double-domain fusion perception, breathing abnormity intelligent monitoring with high robustness and low false alarm rate is realized.

The present application relates to the technical field of sleep intervention, in particular to an intelligent sleep instrument and a sleep instrument control method, the system comprising a respiratory mutation recognition module, a slow wave initiation linkage module, a pharyngeal cavity contraction evaluation module, an intervention section positioning module and a delay trigger control module. In the present application, the extreme points densely appearing in the continuous cycle are accurately positioned as the key nodes of respiratory abnormalities through the identification of the mutation of nasal respiratory flow, and the slow wave initiation stage closely related to the respiratory mutation is screened out and marked as the linkage period in combination with the power curve change trend of the electroencephalogram signal. The pharyngeal cavity state is dynamically evaluated through the change of acoustic echo in the period, the intelligent postponement and delay processing of the intervention behavior are realized through the comparison of the trigger point of the intervention action and the boundary of the platform section, the accuracy of the sleep intervention timeliness and the adaptability of the intervention execution are improved, and the respiratory event control effect and the sleep continuity are significantly improved.

The invention discloses a breathing difficulty degree quantitative evaluation method and system based on artificial intelligence and a medium. The method comprises the steps that breathing sound signals, basic information and medical history information of a wearer are obtained; extracting time domain features and frequency domain features in the breath sound signals based on a preset machine learning model, and analyzing and evaluating in combination with the basic information and the medical history information to obtain a current breath condition level; according to the historical breathing data and the current breathing condition grade of the wearer, whether abnormal breathing exists or not is evaluated; if yes, the current breathing condition grade is corrected according to the motion information of the wearer in the period corresponding to the breathing sound signals, and the current dyspnea grade of the wearer is obtained. The breathing sound signals are collected in real time and analyzed and evaluated by the preset machine learning model, and the historical breathing data and the motion information are combined for analysis and correction, so that the accuracy of the evaluated dyspnea grade is improved, and the breathing condition of a wearer in daily life is accurately monitored in real time from multiple dimensions.

The application discloses a mesh nebulizer. The nebulization control method of the mesh nebulizer comprises the following steps: acquiring breathing data of a user; determining a breathing state of the user and an end time point of exhalation of each breathing cycle based on the breathing data; the breathing state comprises stable breathing and abnormal breathing; and controlling a running state of a nebulization sheet based on the breathing state and the end time point of exhalation. According to the breathing state of the user and the end time point of exhalation, the running state of the nebulization sheet is accurately controlled, so that the drug utilization rate is improved, and intelligent and personalized nebulization treatment is realized.

The invention provides a sleep detection model training method and a sleep detection method.The training method comprises the steps that according to the segment signal-to-noise ratios of a plurality of initial respiration signal segments and a plurality of corresponding heart beat signal segments in the sleep period and a preset signal-to-noise ratio threshold value, the heart beat signal segments are selected from the initial respiration signal segments, acquiring a plurality of respiratory signal training fragments with the fragment type of abnormal respiration and the fragment type of normal respiration, and then taking the fragment types, the first signal fluctuation characteristics and the second signal fluctuation characteristics corresponding to the plurality of respiratory signal training fragments as sleep respiratory abnormality fragment training samples; and training a plurality of machine learning models according to the sleep breathing anomaly fragment training samples of the plurality of object individuals so as to obtain a plurality of sleep detection models for detecting sleep breathing anomaly fragments. The proportion of the fragment types corresponding to the training samples is balanced, and the accuracy of the sleep detection model obtained through training can be improved.

The invention discloses a sleep quality monitoring and abnormal breathing early warning system based on a generative adversarial network, and particularly relates to the technical field of intelligent health monitoring. The system comprises a signal acquisition module, a data processing module, a generative adversarial network model library and an early warning module, the signal acquisition module is used for acquiring a breathing vibration signal, a body movement signal, a blood oxygen signal, a photoelectric volume pulse wave signal and a wrist body movement signal in parallel through a non-contact mattress piezoelectric sensor and an intelligent bracelet; the data processing module generates comparison data through a first processing method and a second processing method, and outputs a comprehensive feature vector after integration processing; a discriminator of the generative adversarial network model library adopts a multi-task learning architecture and outputs an abnormal index; and the early warning module realizes third-level early warning according to the abnormal index. Through multi-mode signal fusion processing and a non-contact monitoring mode, the accuracy, the reliability and the user experience of sleep breathing abnormity monitoring are effectively improved.

To provide a support surface moving device that can give the user a good breathing condition. [Solution] A support surface moving device that moves a support surface that supports the user's body comprises a respiratory abnormality detection unit that detects respiratory abnormalities of the user on the support surface, and a movement control unit that controls the movement of the support surface. The support surface has an upper body support section that supports the upper body of the user on the support surface, and the movement control unit moves the upper body support section based on a respiratory abnormality index that indicates the number of times the respiratory abnormality of the user has been detected by the respiratory abnormality detection unit within a predetermined period.

The invention relates to a breathing physical therapy device, belongs to the technical field of medical rehabilitation equipment, and solves the problem that existing breathing training equipment cannot intelligently adjust resistance according to the real-time breathing effort degree of a user. The device comprises a mouth-nose mask, an airflow resistance adjusting valve, a diaphragm myoelectricity sensor and a pressure difference sensor, a control module monitors the pressure difference rising rate and the current myoelectricity integral value in real time in the treatment stage based on a myoelectricity integral reference value calculated in the calibration stage, and accordingly resistance adjusting instructions including resistance increasing, resistance reducing and emergency resistance reducing instructions are generated; and an instruction priority and execution logic are set, so that timely intervention is ensured when the breathing of the user is abnormal. The device is mainly used for respiratory muscle strength training and rehabilitation treatment of patients with chronic respiratory diseases, and the training safety and the individuation level can be effectively improved.

The invention discloses a breathing abnormity monitoring method and system based on electroencephalogram and pressure collaborative awareness, and relates to the field of biological data processing.The method comprises the steps that electroencephalogram signals and chest surface muscle mechanical vibration signals, synchronously collected in the breathing stage, of a user are obtained, and data preprocessing is conducted respectively; inputting the preprocessed electroencephalogram signal and the preprocessed chest vibration signal into a trained breathing type recognition model, and firstly, respectively inputting an electroencephalogram branch and a chest vibration branch in a double-branch structure to carry out feature extraction to obtain an electroencephalogram feature and a chest vibration feature; the electroencephalogram features and the chest vibration features are spliced through a splicing layer and then input into an ECA module for feature fusion to obtain fusion features, the fusion features pass through a full-connection classifier to obtain a corresponding breathing type, and if the breathing type is one of enzootic pneumonia, tachypnea, shortness of breath and apnea, it is determined that breathing is abnormal. The method solves the problem that characteristic similar states such as shallow and slow breathing and apnea are easy to misjudge.

The invention discloses a transmission device for assisting a small animal to autonomously breathe hyperpolarized gas, which is characterized in that a sealed breathing mask is used for replacing a trachea cannula, and stable pressure of atmospheric pressure at the breathing mask is used for replacing periodic pressure change under high-pressure forced ventilation at the cannula; the animal spontaneous respiration gas amount is used for replacing the manually set fixed gas amount, and the state of inhaled gas is controlled under spontaneous respiration to replace fully controlled mechanical ventilation in the respiration process; the invention further discloses a method for assisting the small animal in autonomously breathing the hyperpolarized gas, the device can supply the gas amount required by autonomously breathing to the experimental animal according to the self-breathing state of the experimental animal, and the gas amount inhaled by the experimental animal each time is determined by the self-breathing state, so that the experimental animal can autonomously breathe in a free state; lung expansion in an abnormal breathing state can be avoided, so that a more accurate ventilation function and qi-blood exchange function evaluation result is obtained.

This invention provides a method and system for detecting child sleep in a smart cockpit of a new energy vehicle. The sleep detection method includes: collecting respiratory signals from a target child sitting in a child seat, and verifying respiratory interruptions based on the respiratory signals to identify abnormal respiratory events; in response to the abnormal respiratory event to be verified, acquiring the target child's body surface temperature distribution information and the target child's body posture relative to the child seat; performing correlation analysis between the temporal characteristics of the abnormal respiratory event and the temporal temperature change sequence of the mouth and nose area in the body surface temperature distribution information to generate a suffocation risk verification result; and controlling the child seat to perform corresponding posture adjustments based on the risk level of the suffocation risk verification result. The technical problem solved by this invention is that existing child seats, as passive safety devices, have limited functionality and cannot handle the risk of abnormal breathing that occurs when a child is sleeping while traveling in a vehicle.

The invention relates to a breath pattern classification and evaluation method for chest and abdomen tumor patients before radiotherapy. The method comprises the steps that respiratory movement signals of the chest and the abdomen of a patient are collected, and original signal data reflecting changes of chest and abdomen respiratory movement along with time are obtained; preprocessing and feature extraction are conducted on the original signal data, a plurality of breathing feature parameters are obtained, and the feature parameters comprise a chest breathing movement amplitude parameter, an abdomen breathing movement amplitude parameter, breathing frequency, a chest and abdomen breathing synchronism index and a chest and abdomen breathing movement phase difference; inputting the characteristic parameters into a breathing mode classification model based on machine learning, and identifying and classifying the breathing mode of the patient to determine that the breathing mode of the patient belongs to an abdominal breathing mode, a thoracic breathing mode, a mixed breathing mode or other abnormal breathing modes; and evaluating the breathing mode of the patient based on the classification result and the characteristic parameters. According to the invention, the safety and effectiveness of the radiotherapy process are improved.

Embodiments disclosed herein improve digital stethoscopes and their application and operation. A first method detects of a respiratory abnormality using a convolution. A second method counts coughs for a patient. A third method predicts a respiratory event based on a detected trend. A fourth method forecasts characteristics of a future respiratory event. In a fifth embodiment, a base station is provided for a digital stethoscope.

The invention discloses a pig early disease abnormal behavior recognition system based on attitude angle trend and trajectory analysis of a low-power-consumption Bluetooth ear tag, and the system comprises an attitude angle collection terminal module, a trajectory behavior reconstruction module, a high-frequency sampling trigger module, an edge recognition and preprocessing module, and a fusion judgment and early warning output module. And a cloud platform collaboration module. The attitude angle acquisition terminal is used for acquiring three-axis attitude angle data (a yaw angle Yaw, a pitch angle Pitch and a roll angle Roll) of a pig and reducing energy consumption through an intermittent sampling mechanism; the trajectory reconstruction module constructs a virtual trajectory point string based on Bluetooth signals such as RSSI and the like, and is used for identifying spatial abnormal behavior characteristics; when the system recognizes a low-frequency attitude anomaly or a trajectory disturbance trend, a high-frequency sampling window is automatically triggered, and slight or short-time behavior changes such as local wandering, rotation behaviors and the like are further recognized; and finally, whether the pigs have behavior symptoms such as neurological diseases, abnormal breathing or highly pathogenic infectious diseases or not is judged through a fusion algorithm. The system has the capabilities of high recognition precision, low power consumption, long endurance and large-scale deployment, and can be widely applied to pig farm abnormal behavior early warning and animal health management scenes.

The application discloses a lung function evaluation method and system based on bidirectional cross attention. The breath sound of a subject is collected, and the original audio is preprocessed to obtain standardized audio frames. Multi-scale time-frequency feature extraction is performed on the standardized audio frames to obtain a breath sound time-frequency feature sequence. The time-frequency feature sequence and a learnable hidden vector are input into a BiXT encoder for bidirectional cross attention encoding to obtain a breath sound time sequence position feature and a pathological semantic global feature, respectively. Spatial attention and channel attention enhancement of position information and semantic information are performed through a double attention module to realize feature fusion. Frame-level abnormal event detection, sequence-level breath sound classification and lung function state evaluation are performed based on the fused features. Model training is completed. The application considers global context modeling, feature fine extraction and multi-task joint output, which helps to improve the accuracy of breath abnormality detection, breath sound classification and lung function evaluation, and enhances the result interpretability.

The present invention discloses a method and device for monitoring sleep respiratory status by synergizing audio with thermal imaging video. The method comprises the following steps: synchronously collecting sleep audio and facial thermal imaging video, proposing a new feature of the audio segment, wavelet information entropy, and combining it with the Mel-frequency cepstral coefficient as a feature for distinguishing snoring segments from non-snoring segments; framing the facial thermal imaging video to obtain multiple frames of images, proposing an adaptive measurement region positioning algorithm based on centroid mapping, locating the image measurement region, acquiring the respiratory signal from this region, and denoising it; extracting the time-frequency features of the denoised respiratory signal, combining it with the audio features, training an SVM classifier, monitoring the respiratory status, and distinguishing between normal and abnormal breathing. By integrating the different representations of sleep respiratory status in audio and thermal imaging video, the present invention provides a detailed, accurate, and comfortable monitoring method for sleep respiratory status.

The utility model belongs to the technical field of medical health, and particularly relates to a respiration monitoring and early warning device which comprises an elastic waistband. A contact belt is fixedly connected to the inner side wall of the elastic waistband; the middle part of the contact belt is fixedly connected with an inflating block; a mounting box is mounted on the side wall of the elastic waistband; the inflating block is in sliding fit with the side wall of the mounting box; a hollow telescopic rod is fixedly connected to the middle part of the mounting box; compressed air can push the output end of a hollow telescopic rod, respiration of a wearer is concrete through a top plate, convenience is brought to monitoring, air in a pressing type inflation pipe can be released through an early warning device by means of an early warning assembly when the wearer stops breathing, an alarm is given out to remind the wearer and surrounding people, and therefore the wearer can stop breathing. And the length of the elastic waistband can be adjusted through the arrangement of the storage assembly to adapt to wearers with different physiques, so that the detection and early warning stabilization effect of the breath monitoring and early warning device is enhanced.

The invention relates to an automatic intervention device for preventing an upper airway from collapsing in sleep. The automatic intervention device comprises a physiological parameter monitoring module, a control module, a pneumatic driving module and a lower jaw advancing execution module which are connected in sequence, according to the device, by monitoring the oxyhemoglobin saturation in real time, the pneumatic driving module is automatically triggered when abnormal breathing is detected, so that an expandable driving body in the execution module is inflated and deformed, the lower jaw of a user is moved forwards instantly and accurately to open the airway, and the device is automatically reset after returning to normal; the problem of temporomandibular joint discomfort caused by continuous forward movement of a traditional oral appliance is solved, meanwhile, pure pneumatic and non-conductive design is adopted, air serves as a working medium, electrical risks near the mouth and the nose are thoroughly eradicated, and safety, comfort and reliability in unattended scenes such as family sleep are remarkably improved.