79 results about "RESPIRATORY RHYTHMS" patented technology

The invention relates to the technical field of biomedicine, and discloses a female private rehabilitation training system integrating a breathing method and neural motor control, and the system comprises a data collection module which is used for collecting physiological data of a user; the personalized training scheme generation module is used for generating a personalized nerve-muscle-fascia training scheme; the nerve-muscle synchronous resonance training module is used for constructing a time sequence matching model and generating an adaptive training dynamic control instruction; the space-time adaptability training module is used for adjusting training duration, intensity and stimulation frequency; the multi-dimensional nerve-muscle feedback fusion module is used for constructing a feedback response model; and the fascia synchronous activation and relaxation module is used for coordinating the tension relationship between the fascia and the muscle system. According to the method, feature matching is carried out on an electric activation heat map matrix through the convolutional neural network, analysis of a myoelectricity response state transition probability is carried out in combination with a hidden Markov model, and stimulation parameters and respiratory rhythm are optimized in real time according to a training process.

The invention belongs to the technical field of intelligent rehabilitation equipment control, and relates to a multi-parameter feedback regulation and control method for postoperative lung function rehabilitation training, which comprises the following steps: acquiring an electrocardiosignal, a respiration signal and a limb movement signal of a patient; identifying a heart rate fluctuation form of the electrocardiosignal to generate an abnormal fluctuation time period, and analyzing a time difference between an inspiration phase and an expiration phase of the respiratory signal to generate a starting point of respiratory rhythm disorder; judging a cardiopulmonary risk level; the exercise intensity of rehabilitation training equipment is automatically adjusted based on the cardiopulmonary risk level, and meanwhile a regulation and control mode is dynamically selected according to the proportion change of the chest fluctuation amplitude and the abdomen fluctuation amplitude; when the heart rate recovery rate is lower than a preset reference and the respiratory rhythm stabilization time exceeds a critical value, gradually improving the exercise intensity; calculating a motion intensity correction coefficient and triggering a motion posture correction instruction; and generating a rating report containing the cardiopulmonary function fitness. According to the invention, the problem of lack of comprehensive quantitative evaluation of rehabilitation efficiency in the prior art is solved.

The invention discloses a thoracic surgery postoperative complication assessment method and system based on multi-dimensional analysis, and relates to the technical field of thoracic surgery postoperative complication. Key sign signals such as heart rate fluctuation amplitude Cxv, respiratory rhythm drift coefficient Chx, pulse waveform disturbance degree Cmd and epidermal temperature change rate Cbt of a postoperative patient are continuously collected and denoised through a postoperative sign data acquisition module, and a structured sign data set TW is formed. By means of the method, tiny physiological fluctuation of a postoperative patient within 24 hours can be captured, early risk signals are prevented from being neglected, and the time sensitivity of complication recognition is improved from the source. A dynamic sliding window regression technology is introduced to carry out trend modeling on the sign data, so that a miniature instability section can be accurately extracted; through the improvement, the system can capture precursor signals of potential infection and bleeding earlier, and the possibility of early intervention is improved.

The invention belongs to the field of intelligent driving interaction, discloses an HUD interaction method and system combining DMS and ADAS functions, and constructs a multi-mode state sensing system by obtaining a real-time image of a driver and steering wheel grip data. According to the method, eye, pupil and head posture recognition based on deep learning is complementary with a grip distribution matrix, so that fatigue behaviors, distraction behaviors and abnormal holding behaviors can be detected earlier and more accurately, and the precision and robustness of driver state recognition are effectively improved. According to the method, after the fatigue degree of the driver is analyzed, the cognitive load of the driver is dynamically evaluated in a finer-grained mode by constructing the composite indexes based on the eyelid opening degree, the sight line deviation, the grip variation coefficient and the respiratory rhythm. The evaluation mode can reflect the attention, mental load and information processing capability of the driver in real time, so that the system can generate a prompt strategy which better conforms to human factors according to the current state of the driver.

The invention discloses an external diaphragm pacemaker regulation and control method and device based on a respiratory radar. The method comprises the following steps: acquiring a chest radar echo signal of a target; analyzing the radar echo signal to obtain a target breathing signal; analyzing the target breathing signal to obtain breathing parameters, and generating a synchronizing signal; the treatment parameters are adjusted by adopting the breathing parameters and the synchronizing signals; the treatment parameters are adopted to output control signals; and regulating and controlling the external diaphragm pacemaker based on the control signal. The breathing state of the target is detected and sensed in real time through the breathing radar, key parameters are extracted, and the opportunity and intensity of electrical stimulation are dynamically adjusted based on the key parameters, so that personalized synchronization with the natural breathing rhythm is realized, and the treatment efficiency and the user comfort are improved.

The invention relates to a neonatal respiratory distress risk assessment construction method, which comprises the following steps of: implementing non-intrusive micro-disturbance operation on a neonatal, and detecting respiratory rhythm phase drift, respiratory driving force low-frequency suppression and physiological tremor window change under disturbance so as to identify a pre-instability signal regulated and controlled by a respiratory system; collecting nerve and respiration feedback path data, analyzing the time difference between respiration instruction conduction and actual respiration starting, and identifying an abnormal decoupling phenomenon; based on multiple times of perturbation induction and feedback acquisition results, continuously monitoring the micro-adaptive recovery capability of the respiratory system of the newborn, and identifying the hidden fatigue trend of the respiratory system by judging progressive extension of respiratory frequency recovery time and gradual upward movement of respiratory power consumption; the respiratory redundancy regulation capability in the stress regulation process is monitored, premature intervention and repeated activation of an auxiliary breathing mode or a respiratory muscle group are judged, and the redundancy regulation critical compression phenomenon of a respiratory regulation system is analyzed to complete evaluation construction of the neonatal respiratory distress risk.

The invention relates to the field of intelligent medical treatment, in particular to a percutaneous lung channel acupoint electrical stimulation optimization system based on artificial intelligence, which comprises a multi-source data acquisition module (M1), a dynamic modeling module (M2) and a self-adaptive electrical stimulation module (M3). M1, collecting lung channel acupoint skin impedance, infrared thermal imaging, respiratory rhythm and cough frequency data in real time, and generating multi-source fusion features through space-time alignment; m2, based on a deep learning model, combining with a preset database to dynamically generate an electrical stimulation parameter combination, and ensuring parameter safety through impedance calibration and constraint optimization; and M3, adjusting the stimulation intensity according to the parameters, and setting an impedance threshold to trigger power-off protection. The traditional Chinese medicine meridian theory and the AI algorithm are fused, accurate acupoint positioning, personalized electrical stimulation and dynamic safety regulation are achieved, and the respiratory system disease treatment effect is improved.

The invention relates to the technical field of auxiliary medical treatment, in particular to an oxygen generator on-demand pulse oxygen supply control method based on respiratory rhythm perception, and the method comprises the steps: obtaining a plurality of respiratory pressure sequences, and dividing the respiratory pressure sequences into an expiratory phase and an end-expiratory transition section; determining a signal confidence coefficient according to the performance of the expiratory phase and the end-expiratory transition section in the respiratory pressure sequence; a regularization item of physiological rationality constraint is introduced into the regression loss function, and time sequence smooth constraint is applied to a model cell state updating process to obtain an optimized respiratory rhythm prediction model; taking the three-dimensional feature vector as input data of a respiratory rhythm prediction model to obtain an end-expiratory stationary section duration ratio of a next respiratory cycle; and determining a pre-judgment window period from the beginning of inspiration according to the duration proportion of the end-expiratory stationary section of the next respiratory cycle, and controlling pulse oxygen supply of the oxygen generator by taking the pre-judgment window period as a pulse trigger countdown parameter of the oxygen generator, thereby realizing accurate synchronization of the oxygen supply action and the inspiration action.

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 invention relates to a motion breathing rhythm optimization method, system and device based on somatosensory synchronization of smart clothing and a medium, and the method comprises the steps: collecting human motion posture data in real time through a sensor assembly, extracting the main frequency and phase features of a motion rhythm through a time-frequency mixed analysis method, and recognizing the type and intensity of a current motion; according to the current motion type and intensity, calling a matched inspiration and expiration ratio parameter in a preset respiration database, predicting a starting point of a next period by combining the phase characteristics, and generating a target respiration rhythm template containing a phase compensation amount; generating a pneumatic tactile waveform parameter group based on the target respiratory rhythm template, controlling the working time sequence and the pressure gradient of a flexible pneumatic assembly of the intelligent garment according to the pneumatic tactile waveform parameter group, and forming simulated respiratory fluctuating waves propagating along the direction of the intercostal muscles on the body surface of the human body. Therefore, implicit accurate regulation and control of the motion respiratory rhythm are achieved through the synergistic effect of pneumatic tactile feedback and an intelligent algorithm.

The invention relates to an adaptive breathing phase estimation method, which comprises the following steps of: firstly, acquiring data through a breathing flow sensor, preprocessing a breathing signal by adopting a Kalman filter, removing noise interference in the breathing signal, and ensuring that the acquired breathing data is accurate and reliable; further, the extreme value of the lung volume curve is calculated, then potential phase conversion points are recognized, and division of breathing phases is achieved; then, through a real-time anomaly detection mechanism, non-rhythm respiration data is quickly rejected, and only rhythm respiration data is reserved for estimating a respiration phase. According to the algorithm, the safety of the breathing assisting system in the breathing training process can be ensured, and human body confrontation caused by control logic confusion is avoided; and finally, estimating the respiratory rhythm change of the human body based on a prediction algorithm of physiological features, and completing the accurate recognition of the respiratory phase. The method provided by the invention can effectively adapt to human respiratory rhythm changes, and can provide powerful support for real-time control and stable operation of a respiratory assist system.

This invention provides a method and system for risk warning of dysphagia based on multi-signal analysis. The method includes: acquiring surface electromyography (EMG) signals, bioimpedance signals, laryngeal vibration signals, and neck microenvironment temperature and humidity signals from the wearer's neck using a flexible wearable sensing unit; using the respiratory phase determined by the neck microenvironment temperature and humidity signals as a time reference, performing time alignment and preprocessing on the multimodal physiological signals to obtain multi-channel time-series data; extracting and fusing features from the multi-channel time-series data to generate a discriminative fusion feature vector; inputting the vector into a pre-trained classification model for real-time inference to identify swallowing function states, including effective swallowing, ineffective swallowing, and suspected aspiration; and generating a graded risk warning signal in response to the identified abnormal states. This invention solves the problems of motion artifact interference and time alignment through multimodal signal fusion and respiratory rhythm calibration, achieving accurate and continuous monitoring of dysphagia risk.

The application discloses a kind of unmanned plane overhead wounded detection and priority recommendation method and system for medical service training and storage medium, it is related to computer vision technical field.For realizing the quick identification of wounded in training scene and priority treatment decision.First, obtain the overhead video stream of training area, carry out ground texture suppression and illumination difference processing to continuous frame, construct ground structure gradient field and detect human body profile curvature abnormal section, generate profile confidence chart;Subsequently, according to the confidence chart positioning head and shoulder area, extract pitch attitude, bleeding distribution and limb movement characteristics, obtain motion frequency index;Respiratory rhythm is estimated based on head and shoulder area and pitch attitude, and is fused with bleeding proportion and motion frequency, constructs life-behavior joint parameter vector;Finally, input preset weight matrix to calculate the severity of illness emergency degree score of joint parameter vector, divide treatment priority and generate spatial distribution chart, realize the comprehensive quantitative evaluation and visual management of wounded state.

The invention provides a traditional Chinese medicine acupuncture and respiratory training integrated rehabilitation apparatus. The rehabilitation apparatus comprises a controller; the respiration detection assembly is electrically connected with the controller through a transmission line; and the mounting assembly is arranged on the side surface of the controller. According to the traditional Chinese medicine acupuncture and respiratory training integrated rehabilitation apparatus, through mutual cooperation of the controller, the respiration detection assembly, the mounting assembly, the guide assembly, the driving assembly and other structures, when the rehabilitation apparatus is used, the respiration detection assembly collects respiration data in real time and transmits the respiration data to the controller, and after the controller analyzes the respiratory rhythm, the respiratory training is completed. Through a servo motor, a threaded rod and a threaded sleeve of the driving assembly, a sliding plate, a mounting ring and an acupuncture needle of the guiding assembly are driven to move accurately, automatic and accurate cooperation of acupuncture and respiratory rhythm is achieved, manual control is not needed, and the influence of human factors on the treatment effect is reduced; therefore, the problems that in the prior art, cooperation of acupuncture and respiratory rhythm depends on manual work, and accuracy is low are solved.

The invention discloses an AI sleep monitoring intelligent ring based on dynamic breath feedback and a health early warning system, and relates to the technical field of intelligent wearable equipment, and the AI sleep monitoring intelligent ring comprises an acquisition module which is used for acquiring photoelectric volume pulse waves, skin electrical activity and body temperature and body movement signals during sleep; the respiratory phase module extracts and constructs a time alignment frame of respiratory rhythm synchronization based on photoelectric volume pulse waves, the phase segmentation alignment and signal enhancement module performs specific filtering enhancement on multi-mode signals according to respiration, the enhanced signals are guided into the recognition module, a personalized health reference module is established by combining results, the physiological state is monitored in real time, and the recognition module is used for recognizing the physiological state. And when the reference is deviated, the health early warning module is triggered, the personalized respiration guiding module is started according to the abnormal type, the respiration rhythm is adjusted through tactile feedback, the guiding intensity is dynamically adjusted according to the intervention effect, progressive separation is achieved, new data is fed back to the personalized health reference model, and continuous self-adaptive updating is completed.

The application discloses a kind of multi-channel sensing fusion systems for exposure stress judgment, it is related to sensing fusion technical field, for solving the problem of insufficient accuracy of stress exposure state judgment, by the monitoring equipment corresponding to the working individual, the respiratory rhythm, the heart beat interval, the skin electricity response and the action trajectory such as multi-dimensional physiological and behavioral parameters in the working process are continuously collected, the multi-channel stress state observation basis is constructed, the feature extraction and abnormal discrimination of each channel data are carried out, the state mode is classified modeling using pattern recognition method, combined with cross-channel consistency analysis and causal order constraint, the abnormal response is comprehensively evaluated, when the evaluation result meets the preset stress exposure determination condition, combined with action trajectory characteristics, the stress exposure grade of working individual is graded determination, and according to the determination result, continuous monitoring or event record and information upload operation is selected, realize the automation, real-time judgment of stress exposure state.

This invention discloses an intelligent, adaptive local oxygen supply system and its control method, belonging to the technical field of oxygen supply auxiliary equipment. The system includes an oxygen supply module, a target tracking module, an adaptive air delivery module, and a control module. The target tracking module acquires the spatial coordinate information of the user's face or nasal cavity area in real time; the control module generates a pointing command based on the spatial coordinate information, driving the adaptive air delivery module to perform displacement compensation, ensuring the oxygen jet direction is towards the target area. Furthermore, the system, through a respiratory rhythm monitoring module in conjunction with a throttle switch, closes the throttle switch during the exhalation phase and accumulates pressure in the buffer pressurization container; during the inhalation phase, it triggers the throttle switch in advance to release pressurized oxygen based on the airflow flight time difference calculated by the infrared ranging module, achieving synchronous coverage of the oxygen jet and the inhalation peak. This invention can automatically adjust the air delivery direction according to changes in the user's position and match the respiratory rhythm for intermittent pressurized oxygen supply, effectively improving oxygen utilization.

The invention discloses a non-invasive sleep monitoring and intervening system, which constructs a thoracic and abdominal ventilation displacement model through human body micro-motion phase calculation of millimeter wave radar, and realizes continuous and non-contact monitoring of respiratory rhythm, ventilation change and body motion state. The system further utilizes a time-frequency fusion feature and trend analysis method to identify and predict sleep disorder events such as snoring, apnea and hypopnea in real time, and generates acoustic disturbance signals meeting sound pressure and frequency constraints before and after the events are formed, so that the ventilation mode or sleeping posture of a user is promoted to be controllably and finely adjusted on the premise that the user is not awakened, and the sleep disorder events are prevented from being disturbed. Therefore, the abnormal event duration is shortened and the sleep quality is improved. The method is suitable for sleep monitoring equipment, bedside lamps, intelligent sound boxes or desktop type health equipment, and algorithms and intervention strategies can be further optimized through software upgrading.

The embodiment of the invention relates to the field of data processing and machine learning, and provides a respiratory rhythm recommendation method based on multi-modal data fusion and a related device.The method comprises the steps that electrocardiosignals of a to-be-detected user during CTA examination are obtained; performing heart rate state evaluation according to the electrocardiosignal to obtain a heart rate state evaluation result; if the heart rate state evaluation result indicates that the to-be-detected user is in a heart rate stable state, respiratory rhythm evaluation parameters of the to-be-detected user are obtained; and pushing the target rhythm information to the to-be-detected user in a voice, image or rhythm rhythm mode and the like, and guiding the to-be-detected user to carry out autonomous adjustment, so that respiration guiding closed-loop control from monitoring to intervention is realized, and the coronary artery CTA image quality and diagnosis stability are optimized.

The invention relates to the technical field of medical data analysis, in particular to an intelligent screening method for respiratory disorders. According to the method, the key feature point sequence is constructed by accurately recognizing the speed peak value and the change inflection point in the continuous respiratory airflow data, fine description of the dynamic structure of the respiratory rhythm can be achieved, and then the dynamic structure of the respiratory rhythm can be obtained according to the change trend of the time interval between the key points. Long-time respiration monitoring data are automatically segmented into a plurality of sections capable of reflecting different expressions such as rhythm stability, acceleration or deceleration, so that a context with physiological significance is provided for subsequent analysis, and the irregularity of the respiration rhythm in each section is quantized by utilizing an information entropy theory; according to the method, an objective index for measuring the rhythm stability can be obtained, and pseudo events caused by swallowing, body movement and the like can be effectively filtered out by judging whether the change of the rhythm entropy value before and after a suspected pause event is obvious or not and combining with confirmation of the airflow amplitude contraction trend before and after pause.

A computer-implemented method is provided to estimate a periodic feature, such as a period length or a period location, of a physiological rhythm, such as a cardiac rhythm or an autonomic respiratory rhythm. The computer-implemented method comprises receiving a signal representing the physiological rhythm, estimating a first set of periodic feature estimates based on the signal, generating a prior probability of the periodic feature based on at least a subset of the first set of periodic feature estimates, and estimating a second set of periodic feature estimates based on the first set and the prior probability. By using such a computer-implemented method, the accuracy of the signal is improved, in particular when the signal is obtained from an accelerometer arranged on the chest of a subject.

The invention discloses an intelligent regulation and control system and method for tea fermentation, and relates to the technical field of tea processing, and the method comprises the following steps: S001, establishing a synchronous acquisition process, continuously recording a wind speed rhythm, a spraying rhythm, air pressure, humidity and leaf surface temperature, and generating a damp-heat cross plot according to a time sequence for identifying an overlapping relationship between wind flow and a humidifying rhythm; and S002, identifying a beat overlap region based on the damp and hot cross plot, setting a spray collection window and a spray stop buffer window in the beat overlap region, and generating phase pointer data for time sequence coordination by adjusting spray residence time and a diffusion range. According to the method, rhythm overlapping recognition is achieved through multi-parameter synchronous collection and a damp-heat cross plot, wind and fog dislocation regulation and control are achieved based on a phase pointer, evaporation stress is slowly released by combining measures such as fog particle preposition, temperature rise gentle slope and wind direction guiding, closed-loop control is constructed based on a breathing rhythm index, and self-adaptive steady-state regulation of the fermentation environment is achieved.

This invention discloses a multivariate fusion detection method for neonatal respiratory distress monitoring, belonging to the field of variable detection technology. It addresses the problem of inaccurately reflecting changes in true respiratory rhythm under scenarios with sudden changes in light intensity or frequent occlusion. By setting the light source observation time and dividing it into luminous and non-luminous phases, the method extracts the photoelectric response voltage to achieve temporal separation of ambient light and detection light. A photoelectric receiving occupancy level is constructed based on probe contact pressure, and the driving current of the light-emitting diode is compensated and adjusted based on this occupancy level. Ambient light irradiance data is collected under compensated driving conditions. Furthermore, the frequency of ambient light occlusion is analyzed by varying irradiance, and a joint judgment index is constructed by combining this with the respiratory waveform amplitude. This adaptively selects between conventional monitoring and compensated maintenance processing, achieving stable acquisition and quality improvement of respiratory signals under complex lighting and contact conditions.

The invention discloses an air control pressurization type cell culture instrument in the technical field of cell culture instruments. The air control pressurization type cell culture instrument comprises a culture cabin, a cabin cover and a pressure detection unit, the cabin cover is provided with a dynamic pressure simulation module which is used for generating periodic dynamic pressure for simulating the respiratory rhythm of a human body in the culture cabin; in the process of culturing the cells in the human body, the driving mechanism is used for driving the moving plate, the elastic element and the piston to reciprocate, the pressure in the culture cabin fluctuates within a certain range, and the periodic change of the in-vivo pressure intensity when the human body breathes is simulated, so that the cells grow in a state closer to the in-vivo state; a more reliable in-vitro cell model is provided for subsequent research for revealing physiological and pathological mechanisms of a human body; and in the culture process, the detection mechanism and the compensation mechanism are used for supplementing or discharging gas into the culture cabin, so that the air pressure change caused by cell respiration in the culture cabin can be automatically adjusted.

The application discloses a biofeedback device data processing method based on artificial intelligence and relates to the technical field of biofeedback data processing.The application realizes accurate decoupling of respiratory rhythm and autonomic nervous regulation signals in a nonlinear characteristic space through a latent space orthogonal decomposition mechanism, thereby avoiding the problem of HRV low-frequency band information loss caused by traditional frequency domain filtering;respiratory component estimation based on a variational autoencoder does not need to rely on external sensors, thereby reducing hardware cost and user burden;and through a dynamic orthogonal constraint and online fine-tuning mechanism, individual respiratory mode differences and frequency drifts are adaptively adapted, thereby guaranteeing the stability of long-term analysis.

The invention relates to the field of health monitoring, and discloses a chronic obstructive pulmonary disease patient cough detection system based on axial acceleration data combination. Comprising a data acquisition module configured to acquire multi-axial acceleration time sequence data through an accelerometer worn on the body surface of a user; the control module is configured to perform gradient filtering preprocessing on the multi-axial acceleration time sequence data so as to extract effective physiological signal features; constructing a deep learning model; performing feature extraction on the preprocessed acceleration time sequence data to generate a multi-dimensional feature vector; processing the multi-dimensional feature vector by adopting a joint probability verification mechanism, improving the confidence coefficient of cough recognition by combining probability distribution differences of a cough signal, a motion signal and a respiratory rhythm signal, and separating the cough signal from a non-cough physiological signal or a motion signal; a cough detection result is output, and the detection result comprises the cough occurrence time, the cough occurrence frequency and the clinical characteristics related to the respiratory rhythm and is used for illness state analysis and rehabilitation evaluation.

The invention discloses a geological operation-oriented operator mental fatigue real-time monitoring method and device. The geological operation-oriented operator mental fatigue real-time monitoring method comprises the following steps: S1, determining the layout of a measured target and a millimeter wave radar; s2, millimeter wave radar intermediate frequency echoes are obtained, after a measured target is positioned through a distance domain, complex phases of corresponding distance information are extracted, phase unwrapping and steady state correction are carried out, and a continuous phase sequence representing chest micrometric displacement is obtained; s3, IIR digital band-pass filtering is applied to the continuous phase sequence, and the continuous phase sequence is decoupled into a breathing channel and a heartbeat channel; s4, extracting time domain and frequency domain features of heart rate variability (HRV) and respiratory rhythm from the respiratory channel and the heartbeat channel in the sliding time window to form a multi-dimensional vital sign feature vector set; s5, taking the multi-dimensional vital sign feature vector set as input, performing incremental learning by using OS-ELM, and judging and outputting a mental fatigue level in real time; s6, implementing hierarchical risk management, and triggering early warning and intervention measures; and S7, locally storing and visually displaying the fatigue level and the event log, and synchronizing the fatigue level and the event log to a remote management platform.

The invention provides an intelligent early warning intervention system for chronic obstructive pulmonary diseases, and relates to the technical field of medical instruments. Comprising a core host, the core host is integrated with an active multi-modal sensor arm and an integrated drug delivery arm, a patient can be actively guided to enter a standardized test posture, and high-quality multi-modal physiological data such as lung function, blood oxygen and breath sound are synchronously collected; an intelligent medicine bin and an automatic delivery mechanism are arranged in the intelligent medicine box, and accurate taking, delivery and auxiliary inhalation of medicine can be completed after early warning is triggered. The standardized seat assists in fixing the sitting posture of a patient through a physical guiding structure, and the wearable monitoring vest achieves respiratory rhythm monitoring and auxiliary sputum excretion. According to the system, all the modules are cooperated through the central controller, and a complete entity closed loop from standard monitoring to intelligent early warning to automatic intervention is constructed. The system effectively solves the problems that household lung function monitoring data is unreliable and intervention is difficult to execute after early warning, and the timeliness, safety and patient compliance of chronic disease management are remarkably improved.

The present application discloses a method and device for regulating respiratory function by targeting the cervical spinal cord. Specifically, the present application provides a novel method for regulating respiratory rhythm by alternating electrical stimulation and mechanical ventilation to induce diaphragm and / or intercostal muscle movement, thereby achieving recovery and reconstruction of spontaneous breathing in patients with impaired respiration.

This invention discloses a functional prototype of an external head-mounted odor pickup device for an artificial nose, belonging to the interdisciplinary field of analytical detection technology and wearable devices. It adopts an integrated hat-and-glasses design architecture. The overall structural layout uses a modular design, integrating multiple modules into the hat, with the glasses section housing the air intake pipe. The concealed airway system utilizes the structure from the temples to the nose pads as the air intake pipe, delivering gas from the nose pads to the brim air chamber via micro-tubes, and then expelling it from the back of the hat. The optical path adaptive air chamber dynamically adjusts the position of the optical signal receiver using a micro-actuator, combined with a control algorithm to achieve intelligent adjustment of the optical path. The periodic sampling and signal processing method monitors the respiratory cycle using a respiratory airflow sensor, performing gas sampling and detection during inhalation and airway cleaning during exhalation, achieving a detection cycle synchronized with the respiratory rhythm. This system achieves accurate detection and long-term dynamic monitoring of the wearable electronic nose over a wide concentration range.