213 results about "Respiratory rate" patented technology

The invention belongs to the technical field of sleep quality analysis, and particularly relates to a sleep quality analysis system and method based on a medical bed sensor, so as to solve the technical problem that an existing method excessively depends on duration statistics and neglects sleep continuity and periodic rhythmicity. The analysis method comprises the following steps: S1, acquiring sign signal data acquired by a sensor on a medical bed in a sleep cycle, and dividing the sign signal data into a plurality of continuous time slices; s2, extracting physiological indexes of respiratory rate, heart rate and body movement amplitude in each time slice, and calculating variation depth of the corresponding time slice; s3, generating a variation depth baseline; calculating an unsteady state disturbance factor according to the occurrence frequency and the duration of the micro-awakening disturbance fragment; and S4, generating a sleep quality comprehensive score. According to the analysis method, the comprehensiveness and objectivity of the evaluation result are improved, and the sleep health condition is reflected more truly.

The invention belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

A non-contact, non-invasive health monitoring device and method utilizing advanced artificial intelligence (AI) and machine learning techniques. This system captures real-time image data of a user's face using a high-resolution camera and processes the data to extract physiological signals, including Photoplethysmography (PPG, iPPG, and rPPG) and Ballistocardiography (BCG and iBCG). By leveraging facial landmark detection and deep learning models such as Convolutional Neural Networks (CNNs) and Transformers, the device predicts vital signs such as heart rate, respiratory rate, blood pressure, and oxygen saturation, alongside wellness metrics like stress levels and metabolic health. The device employs robust feature construction and signal processing modules to ensure accurate metrics under varying conditions, with error margins below 5%. Outputs are displayed in real time and integrated with external systems using standardized healthcare protocols. Applications include telehealth, fitness monitoring, public health screening, and automotive safety systems.

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 personal life support equipment in a high-altitude environment and a cooperative control method thereof, and relates to the technical field of personal life support equipment, and the equipment comprises a respiration sensor which is arranged on a mask and is used for detecting respiration frequency and phase; the blood oxygen and heart rate sensor is worn on the wrist of the user and used for detecting the blood oxygen saturation degree and the heart rate; the body temperature sensor is attached to the chest of the user and used for detecting the core body temperature; the automatic oxygen supply device is used for providing oxygen; the intelligent heating device comprises partition heating pieces attached to the chest and the four limbs of the user and is used for partition heating; the microcontroller unit is used for formulating an optimal life support strategy according to the sensor data and intelligently and cooperatively controlling the working states of the automatic oxygen supply device and the automatic heating device according to the optimal life support strategy; the sensor data comprises respiratory frequency and phase, oxyhemoglobin saturation, heart rate and core body temperature, and dynamic oxygen supply and body temperature adjustment can be cooperatively controlled.

A human body breathing and heartbeat signal estimation method based on a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) is characterized by comprising the following steps of (1) obtaining an intermediate frequency signal of a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) system, (2) carrying out distance dimension FFT and phase extraction and unwrapping on a Chirp intermediate frequency signal channel by channel, and (3) carrying out phase extraction and unwrapping on the Chirp intermediate frequency signal channel by channel. (3) calculating signal energy of interested breathing and heartbeat frequency bands in the phase time sequence under all distances, and weighting amplitudes of all distance dimension FFT under all distances to realize accurate positioning of a human body target, and (4) regarding the phase time sequence under the accurate distance and the adjacent distance of each channel as potential multipath signals, and performing fusion enhancement on the signals to realize accurate positioning of the human body target. And (5) processing the enhanced phase time sequences of the channels on the basis of multivariate variational mode decomposition, separating and estimating respiratory signals and heartbeat signals of the human body, and calculating an average respiratory rate and an average heart rate in an observation time period.

The invention relates to the technical field of breathing machine off-line evaluation, in particular to a breathing machine intelligent off-line evaluation system based on multiple parameters, which is characterized in that a data acquisition unit acquires breathing and physiological parameters of a patient in real time; the data analysis unit extracts a respiratory rate instantaneous value and a blood oxygen saturation continuous measurement value through preprocessing, inputs the respiratory rate instantaneous value and the blood oxygen saturation continuous measurement value into an intelligent analysis model composed of a deep residual network and a time convolution network, fuses a physiological hysteresis compensation coefficient to generate an offline index, and outputs a preliminary offline risk index; the data processing unit is embedded into a clinical rule base through a decision tree algorithm, generates a risk correction factor through a taboo check factor, allocates differentiation weights according to severity levels, corrects initial risk deviation and generates a comprehensive offline risk index, and the clinical rule base is continuously optimized through online learning, so that the offline risk assessment accuracy is improved; and a reliable basis is provided for clinical decision-making.

The invention relates to the technical field of millimeter wave radar vital sign detection, and discloses a millimeter wave radar vital sign signal processing system and method. According to the method, distance dimension processing and phase extraction are carried out on millimeter wave radar echo signals, heartbeat signals are enhanced through phase difference, and the time domain sparse characteristic of random motion interference is highlighted; then, a sparse optimization model combining a time domain and a frequency domain is constructed, differential sparse constraints are applied to a breathing frequency band, a heartbeat frequency band and a heartbeat harmonic frequency band in the frequency domain, and two-norm morphological constraints are applied to sparse peaks of random motion interference in the time domain; and finally, the model is solved to realize separation of interference and vital sign signals. Complex hardware configuration is not needed, weak heartbeat signal error suppression can be effectively avoided, the stability and accuracy of respiratory rate and heart rate estimation in a random human body motion scene are remarkably improved, and the method is suitable for a non-contact vital sign monitoring scene.

The invention discloses an inpatient alarm bracelet system and method based on multi-parameter vital sign monitoring, and relates to the technical field of inpatient alarm bracelets. Matching corresponding vital sign parameter initial weights from a preset disease-parameter sensitivity mapping library, wherein the vital sign parameters comprise heart rate, respiratory rate, oxyhemoglobin saturation, body temperature and heart rate variability; the method comprises the following steps: establishing an individualized physiological fluctuation baseline model based on vital sign data collected at the initial stage of wearing in combination with a resting state time period identified by an acceleration sensor, and generating a dynamic reference interval; collecting photoelectric volume pulse waves, electrocardiosignals, body temperature and acceleration data, judging whether motion artifacts exist or not by analyzing the spectrum correlation between physiological signals and motion signals, and generating a signal quality mark; and calling the initial weight and the dynamic reference interval under the condition that the signal quality mark indicates that the data is valid.

A self-regulating oxygen delivery system (100) for COPD, comprising: an oxygen source designed to provide medical oxygen; a sensor module configured to record physiological parameters such as oxygen saturation (SpO2), respiratory rate and pulse rate; a microcontroller-based control unit that is functionally connected to the sensor module and the oxygen source; and an intelligent flow control mechanism controlled by the microcontroller, the system automatically adjusts the oxygen flow in real time based on the recorded physiological parameters to maintain optimal oxygen saturation levels.

This invention relates to the field of depression monitoring and intervention technology, and discloses a depression monitoring and intervention system based on an internet hospital and heart rate variability (HRV). The system includes: a multimodal physiological signal acquisition module for simultaneously acquiring raw HRV signals, skin conductance fluctuations, and respiratory rate data; a signal processing module for preprocessing and feature extraction of the raw signals to obtain multidimensional feature vectors; and a depression level inference module that constructs a multilayer perceptron neural network, taking as input the multidimensional feature vector containing multidimensional HRV features, skin conductance fluctuations, and respiratory rate statistics, and outputting probability values ​​for no depression, mild depression, and moderate to severe depression levels. This invention establishes an intelligent management mechanism for internet hospitals for the first time, collecting multidimensional physiological data of patients with depression in real time through wearable monitoring units. Based on the dynamic trends of physiological data characteristics, it can serve as an auxiliary diagnostic tool for psychiatry, especially suitable for early screening and remote follow-up of depression, reducing the consumption of medical resources.

The invention discloses a sleep state monitoring method and system, relates to the technical field of sleep monitoring, and solves the technical problems that standard setting lacks crowd adaptability and abnormal index analysis granularity is low. Specific physiological index standards and sleep quality parameters are set for different crowds, physiological feature differences of different age groups are met, monitoring deviation of special crowds is reduced, judgment accuracy is improved, multi-dimensional layering is carried out for respiratory rate and heart rate abnormity, a two-dimensional and double-record type judgment system is constructed, and the judgment accuracy is improved. By combining four types of combinations including duration standard-reaching and frequency standard-reaching combinations, duration standard-reaching and frequency substandard combinations, and scenes such as enough coverage amount and poor quality, and poor quality and enough amount, layering results of respiration / heart rate abnormity are associated, so that comprehensive coverage of a normal state and an abnormal state is realized; it is ensured that the sleep quality conclusion can truly reflect the physiological indexes, the sleep structure and the complete sleep state of subjective feeling.

This invention belongs to the technical field of sleep quality analysis, specifically relating to a sleep quality analysis system and method based on sensors in a medical bed, to address the technical problem of existing methods relying too heavily on duration statistics while neglecting the continuity and circadian rhythm of sleep. The analysis method includes: S1, acquiring vital sign signal data collected by sensors in the medical bed within a sleep cycle and dividing it into multiple continuous time segments; S2, extracting physiological indicators such as respiratory rate, heart rate, and body movement amplitude within each time segment, and calculating the variability depth of the corresponding time segment; S3, generating a variability depth baseline; calculating non-steady-state disturbance factors based on the frequency and duration of micro-awakening disturbance segments; and S4, generating a comprehensive sleep quality score. The analysis method of this invention improves the comprehensiveness and objectivity of the assessment results, more realistically reflecting sleep health status.

The present application relates to the technical field of vital sign monitoring, in particular to a patient vital sign real-time monitoring and intervention response system for smart tumor ward, which comprises a skin thermal state recognition module, a communication abnormal period screening module, a vital sign event screening module, an intervention task generation module and an intervention response execution state construction module.In the present application, through joint analysis of temperature trend and communication abnormal period, the timing correspondence relationship between vital sign fluctuation and channel interruption is established, the data section of closed loop interruption risk is screened, the continuous change trend of body temperature, heart rate and respiratory rate is combined, the abnormal section and task state are labeled, the nursing terminal activation condition and control equipment number are matched, the hierarchical generation, state identification and response matching of the task are completed, the vital sign recognition is changed from static index to trend linkage, the task scheduling is extended from artificial driving to closed loop control, and the continuity of vital sign perception, the identification ability of communication interference and the connection efficiency of intervention execution in the ward scene are enhanced.

A method and system are provided for estimating the respiratory rate of a subject using a wearable real-time heart rate monitor. The method includes receiving heart rate data indicating the subject from the real-time heart rate monitor; determining a heartbeat annotation of the heart rate based on the received data; filtering the heartbeat annotation to remove outliers from the heart rate; determining an IBI sequence based on the filtered heartbeat annotation; determining the power spectral density of the determined IBI sequence; inputting the power spectral density, a lower limit estimate of the respiratory rate, and an upper limit estimate of the respiratory rate into a respiratory rate estimation algorithm; using the respiratory rate estimation algorithm to determine the peak energy and maximum frequency energy of the power spectral density; and outputting an estimated respiratory rate by fusing the peak energy and maximum frequency energy using the respiratory rate estimation algorithm.

A physiological information display apparatus includes a heart rate acquisition unit configured to acquire a heart rate of a subject, a respiration rate acquisition unit configured to acquire a respiration rate of the subject, a hemodynamics calculation unit configured to calculate a hemodynamic parameter of the subject, and a respiratory variation of the hemodynamic parameter, based on a plurality of the hemodynamic parameters calculated in a predetermined period, and a display controller configured to output the respiratory variation to a display. The hemodynamics calculation unit is configured to calculate a heart rate in one respiration. The display controller is configured to output a setting screen for setting a length of the predetermined period. The hemodynamics calculation unit is configured to acquire setting information, set the length of the predetermined period based on the acquired setting information; and calculate the respiratory variation in the set predetermined period.

This invention provides a non-contact method, device, equipment, and storage medium for measuring trunk respiratory parameters. It synchronously acquires three-dimensional motion data of optical reflective markers during respiration, with these markers positioned at multiple pre-defined anatomical landmark locations on the subject's trunk. Based on the three-dimensional motion data, it determines the volume change of the spatial envelope formed by the optical reflective markers. According to a pre-established mapping relationship between the volume change and tidal volume, it converts the volume change into tidal volume. Based on the signal of the volume change over time, it extracts the respiratory rate. Finally, it calculates the total respiratory volume based on the tidal volume and the respiratory rate. By combining optical motion capture with spatial geometric modeling, it achieves non-contact, continuous, and high-precision measurement of trunk respiratory motion, effectively overcoming the shortcomings of traditional methods, such as reliance on patient cooperation, invasiveness, expensive equipment, and complex operation.

This method suppresses the decrease in accuracy of respiratory rate estimation caused by the subject's condition without requiring the addition of sensors. [Solution] The input interface 111 receives a waveform signal WF corresponding to the waveform of biological information with periodic changes from a sensor 30 attached to the subject 20. The processor 112 visualizes information corresponding to the estimated respiratory rate of the subject 20 on the visualization device 12 based on the waveform signal WF. The processor 112 extracts a plurality of feature quantities corresponding to the respiratory rate of the subject 20 from the waveform signal WF. The processor 112 obtains a plurality of preliminary estimates for the respiratory rate by applying frequency analysis processing to each of the plurality of feature quantities. The processor 112 excludes one of the plurality of preliminary estimates obtained from one of the plurality of feature quantities based on state information corresponding to the state of the subject 20, and determines the estimate based on at least one of the remaining plurality of preliminary estimates.

A cardiopulmonary management system includes a multi-sensor device and a controller. The multi-sensor device includes an elongated body having a first end and a second end, a first electrode disposed at the first end of the body, a second electrode disposed at the second end of the body, an electrical circuitry disposed near a middle of the body, and a sound sensor. The multi-sensor device is configured to be disposed on a skin of a subject and to measure cardiopulmonary parameters from the subject. The controller is configured to derive a score of sound change, heart rate change, thoracic impedance change, respiratory rate change, QRS wavelength change, QT interval change, electromechanical activation time change, and left ventricular systolic time change based on the measured cardiopulmonary parameters. The controller is further configured to control an operation of the system when the derived score is greater than a threshold.

The invention belongs to the field of respiratory system disease treatment, and provides application of a gymnadenia conopsea extract in preparation of a medicine for treating or preventing pulmonary edema. The embodiment of the invention proves that both the gymnadenia conopsea alcohol extract and the gymnadenia conopsea water extract can effectively improve indexes such as pulmonary edema respiratory rate, minute ventilation volume, rising tidal volume, inspiration peak flow, expiration peak flow and the like, and improve arterial blood-gas indexes and inflammatory factor level in alveolar lavage fluid. The invention provides the application of the gymnadenia conopsea alcohol extract and the gymnadenia conopsea water extract in the medicine for treating or preventing lung diseases, especially pulmonary edema for the first time, the traditional medicine gymnadenia conopsea is fully developed and utilized, and a safe and effective means is provided for treating the critical disease pulmonary edema.

The present application relates to the field of artificial intelligence and intelligent sleep technology, and discloses a health state evaluation model generation method and system based on multi-point body surface temperature and vital sign data during sleep. The method continuously collects body surface temperature data of multiple body contact areas during the user's sleep process, and synchronously collects data such as heart rate, respiratory rate, body movement, environmental temperature and bed surface temperature, to construct a multi-dimensional sleep physiological data set. On this basis, the multi-dimensional sleep physiological data is preprocessed, feature extraction and model analysis are performed, and the health state evaluation result and personalized health insight information of the user during sleep are generated. The staged evaluation can also be carried out in combination with the change of sleep stage, and the model is continuously optimized based on the long-term temperature adjustment behavior of the user. By using the present application, the accuracy, continuity and individual adaptability of sleep health state evaluation can be improved, and more accurate sleep health analysis basis is provided for the user.

A test platform for simulating patient conditions. In accordance with methods, systems, platforms and modules, the controller of a ventilator, may be tested. Where the ventilator manages automated mechanical ventilation controllers' maintenance of peripheral oxygen saturation (SpO2) for a simulated patient, an analogue blood oxygen output signal that is responsive to the fraction of inspired oxygen (FiO2) and the peak inspiratory pressure ventilator settings of the ventilator is tested by a test platform controller. Where the ventilator manages end-tidal carbon dioxide (EtCO2) of a simulated patient, operation of the test platform controlled by the platform controller of the test platform in providing carbon dioxide generation, lung compliance (CL) and airway resistance (RAW) states while providing an EtCO2 level that is responsive to changes in tidal volume and respiratory rate changes of the patient is tested.

The application discloses a kind of multi-camera-based animal vital sign monitoring method, medium, equipment, it is related to animal vital sign monitoring technical field, method includes: using DLC posture estimation network to train animal multi-view video, obtain key point information;According to the key point information of main visual angle, the centroid motion of animal is analyzed and compared with motion threshold, determine animal state, in the continuous image sequence of relative static state time period, according to key point information, obtain region of interest;Corner point is detected in region of interest, and corresponding motion signal is acquired by tracking corner point;Correlation analysis is carried out to motion signal, and sorting is carried out based on information gain, and vital sign signal is obtained by fusing motion signal higher than set threshold;Power spectrum is used to analyze vital sign signal, obtain the energy distribution of vital sign signal, and the value of respiratory rate and heart rate is calculated.The application can effectively reduce artificial intervention, and has the role of promoting to animal experiment method innovation and data analysis.

The present application relates to the field of on-site monitoring technology, in particular to a monitoring method and system for on-site execution information of child first aid transfer based on edge network, comprising: controlling a first monitoring terminal to collect first vital sign data of a target child in a first aid site and on-site video of the first aid site; the first vital sign data includes heart rate, blood oxygen and respiratory rate; uploading the first vital sign data and the on-site video to an edge computing node, so that the edge computing node generates a first aid priority label according to the first vital sign data, and determines a video transmission strategy according to the first aid priority label; the present application collects the vital sign data and the on-site video of the target child in real time through the control of the first monitoring terminal, and uploads them to the edge computing node, so as to ensure that the first aid personnel can obtain the key physiological information and the on-site situation in time, and improve the timeliness of the first aid decision.

To provide a measuring device capable of outputting a respiratory rate by guaranteeing reliability, while suppressing a measuring time.SOLUTION: A measuring device includes: an imaging part for acquiring a dynamic image by imaging an organism; a respiratory rate calculation part for calculating a respiratory rate by detecting a vibration cycle of a respiratory signal included in a time change of multiple pixel values included in the dynamic image; a reliability calculation part for calculating reliability of the respiratory rate from a comparison result between the respiratory signal and a noise signal included in the time change; and an output part for outputting the respiratory rate, when the reliability is equal to or more than a threshold.SELECTED DRAWING: Figure 2

The invention relates to intelligent bedding, in particular to an intelligent hot compress pillow based on pressure sensing and an intelligent temperature control method thereof. Common hot compress pillows are large in temperature fluctuation, neglect in environment temperature, easy to overcharge and overadjust, need to be manually opened, closed and controlled in temperature, and easy to scald at low temperature. The invention provides an intelligent hot compress pillow based on pressure sensing and an intelligent temperature control method thereof.The intelligent hot compress pillow comprises a pillow inner, an electric heating piece, a heating control system with a controller, a temperature sensor, a physical sign sensing assembly for measuring the heart rate and the respiration rate and a piezoresistive sensing assembly for measuring pressure, and the controller is connected with the electric heating piece through a power supply line; the sensor and the sensing assembly are both connected with the controller, the physical sign sensing assembly and the piezoresistive sensing assembly are arranged at the bottom of the pillow inner, the piezoresistive sensing assembly comprises a base layer and a plurality of pressure detection sensors, and the pressure detection sensors form partitioned pressure detection. The hot compress pillow is automatically opened / closed for heating through pressure, environment temperature is introduced to judge seasons for intelligent heating, temperature rise is efficient and safe, and low-temperature scalding can be prevented.

The invention discloses a multi-mode non-contact intelligent early warning monitoring method and system, and relates to the technical field of intelligent medical monitoring. Four core physical sign data including body temperature, heart rate, respiratory rate and body movement are collected in a non-contact mode; filtering and denoising are carried out by adopting an LMS adaptive filtering and moving average filtering combined algorithm; extracting each physical sign characteristic parameter; the early warning level is judged through the improved BP neural network intelligent early warning model; when early warning is triggered, local sound-light alarm is started, and early warning information is synchronously pushed to the multiple terminals. The system not only realizes multi-sign non-contact, real-time and accurate acquisition, strong anti-interference capability, accurate early warning judgment and multi-terminal linkage alarm, is suitable for vital sign monitoring of scenes such as hospital wards and pension institutions, but also solves the problems that traditional contact monitoring is poor in comfort level, easy to cross infection and poor in monitoring effect. And the problems of weak multi-mode fusion capability, poor anti-interference performance and low early warning precision of the existing non-contact monitoring are solved.

The invention discloses an apnea syndrome evaluation method based on a smart watch. According to the method, a photoelectric volume pulse wave signal and a three-axis acceleration signal in the sleep period of a user are collected through a built-in sensor of the smart watch, and filtering and window segmentation preprocessing is carried out on the signals. Extracting multi-dimensional features such as heart rate, heart rate variability, body movement index and respiratory rate from each analysis window, and constructing a feature vector sequence; and analyzing the feature vector by using a pre-trained machine learning classification model, outputting the apnea event probability and performing binary classification. And finally, quantitatively analyzing the event sequence, calculating an apnea hypopnea index and dividing risk levels. According to the method, the signal processing technology and the machine learning algorithm are combined, intelligent identification and risk assessment of the sleep apnea event are achieved, and an effective solution is provided for portable monitoring.

The invention provides a state prediction method and device for a traumatic patient, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring state parameters of a traumatic patient, wherein the state parameters comprise multiple of gender, age, systolic pressure, diastolic pressure, noninvasive blood pressure average value, heart rate, respiratory rate and oxyhemoglobin saturation; standardizing the state parameters to obtain input parameters; inputting the input parameters into a prediction model to obtain a first prediction probability output by the prediction model; wherein the first prediction probability is the probability that the traumatic patient has hemorrhagic shock, the probability that the traumatic patient needs to be infused with erythrocyte products of more than a set unit, the probability that the traumatic patient needs to receive an operation within a set time or the probability that the traumatic patient has poor prognosis. According to the invention, the shock probability, the probability of infusing red blood cell products, the probability of needing operations or the probability of poor prognosis of the traumatic patient can be predicted according to the state parameters of the traumatic patient.

This invention belongs to the field of particulate matter toxicity assessment for environmental health, and discloses a health risk assessment method based on the respiratory tract deposition rate of particulate matter of different sizes and its application. The method collects the concentrations of toxic benzene series compounds and naphthalene in human exhaled breath and atmospheric particulate matter of different sizes in the environment; detects human tidal volume and respiratory rate to obtain minute ventilation. Then, using the ICRP model combined with Python software to process the data, the respiratory tract deposition fractions of benzene series compounds and naphthalene in different regions of the human respiratory tract are obtained. Finally, the respiratory tract deposition amount is obtained based on the respiratory tract deposition amount formula, and the deposition rate of benzene series compounds and naphthalene in the human respiratory tract is obtained by dividing the respiratory tract deposition amount by time. This method is simple, fast, and highly accurate, effectively exploring the deposition rate of toxic components in different particle sizes of particulate matter in the main parts of the human respiratory tract and corresponding health risk assessment, which is of great significance for health exposure to environmental pollutants.