78 results about "Respiratory sounds" patented technology

The invention discloses a child mouth breathing monitoring method, device and equipment and a medium, and the method comprises the steps that a non-contact sensor group is used for synchronously collecting multi-mode physiological data of a target child in the sleep period, and the multi-mode physiological data at least comprises a face thermal imaging video stream, a thoracic and abdominal micro-motion signal and an environment audio signal; the multi-modal physiological data is processed, multi-modal features related to respiration are extracted respectively, and the multi-modal features comprise thermodynamic change features of mouth and nose areas extracted based on a face thermal imaging video stream, respiratory effort waveforms extracted based on thoracic and abdominal micro-motion signals, and the respiratory effort waveforms extracted based on the thoracic and abdominal micro-motion signals; extracting a sound source spatial position and a spectrum feature of breathing sound based on the environment audio signal; inputting the multi-modal features into a pre-trained mouth breathing recognition model, and outputting a judgment result of the functional mouth breathing event of the target child within a specific time period; and generating a mouth breathing monitoring report of the target child based on the judgment result.

The invention particularly relates to a respiratory disease diagnosis device and method, and the device comprises a respiratory sound signal collection unit which is used for obtaining a current respiratory sound signal; the breathing sound signal preprocessing unit is used for performing segmentation processing and denoising processing on the current breathing sound signal to obtain a preprocessed breathing sound signal; the feature extraction unit is used for extracting multi-modal features of the preprocessed breath sound signals; and the detection unit is used for inputting the multi-modal features into a preset deep learning network model to obtain a respiratory system disease detection result. Therefore, through multi-modal feature extraction, dynamic fusion and an efficient classification strategy, the problems of relatively low feature identification degree, relatively low disease classification accuracy and the like in the process of researching disease diagnosis based on the breath sound signals in related technologies are solved, and the detection performance of chronic respiratory system diseases based on the breath sound signals is remarkably improved.

The invention discloses a remote recognition system based on spatial respiratory tract abnormal sound, and relates to the technical field of sound recognition. The multi-mode sensor array is used for collecting breathing sound in a non-contact mode and obtaining sound source space information of the breathing sound, the cross infection risk is eliminated, and the limitation that a traditional stethoscope can only collect sound signals is broken through. The spatial audio processing module performs preset audio processing on the breathing sound to obtain a breathing sound signal, and obtains sound source control characteristics based on the sound source spatial information; the feature extraction module extracts time-frequency joint features and nonlinear dynamic features from the breath sound signals, and performs feature fusion on the time-frequency joint features and the nonlinear dynamic features and sound source control features to obtain multi-dimensional feature vectors; and the abnormal sound recognition model performs abnormal sound type recognition and spatial positioning on the multi-dimensional feature vector. Therefore, the environmental sound interference can be effectively solved, the type and position of the breathing sound can be intelligently identified, detected and positioned, the identification accuracy of the breathing sound is effectively improved, and meanwhile, the dependence on medical staff is also reduced.

The invention relates to the technical field of medical artificial intelligence, in particular to a difficult airway glottis feature intelligent identification method and system.The method comprises the steps that a breathing sound signal of a patient is collected through a miniature microphone embedded in a laryngeal mask, and a time-frequency graph is obtained through preprocessing; time-frequency domain feature self-adaptive extraction is carried out by adopting a three-channel parallel architecture, and a ring-shaped cartilage compression sound and a secretion interference sound are separated through a double-path noise feature learning network; capturing glottis features of different time scales from microcosmic to macroscopic by using a multi-scale glottis feature pyramid fusion technology; time sequence modeling is carried out through a BiGRU network and a time attention mechanism, and a glottis state sequence is identified; according to the method, a Cormack-Lehane grading result is determined based on a double-branch decision network and Bayesian decision reasoning, laryngoscope model recommendation is given, the difficult airway prediction accuracy rate is improved to 91.7% and is improved by 25%-30% compared with a traditional method, anesthesia related complications can be effectively reduced, and the success rate of first intubation is improved.

The invention discloses a throat mucosa intelligent state evaluation and inflammation early warning management system and method. The method comprises the following steps: synchronously acquiring a throat mucosa image, breath sound / cough sound / sounding signals, airflow data and body temperature or environment relative humidity data, evaluating image definition, shielding degree and coverage rate, and outputting acquisition guide superposition information and triggering re-acquisition when the image does not reach the standard; reliability is calculated for the multi-modal features, weight fusion output state vectors and confidence coefficients are determined in combination with quality gating, and cross-modal consistency verification is executed to trigger re-sampling or strategy adjustment; generating a risk index based on the change of the sliding time window, and outputting an early warning level by adopting hysteretic classification; and writing the threshold, the weight and the sampling and guiding strategy into a parameter library and a fusion model configuration area to form a closed loop. According to the system, improper electroencephalogram fusion and multi-terminal collaborative optimization can be selected, and daily scene evaluation stability and early warning reliability are improved.

The invention discloses an intelligent nasopharyngeal catheter and a breath sound monitoring system.The intelligent nasopharyngeal catheter comprises a nasopharyngeal catheter body, a first acoustic sensing part and an optical fiber, the nasopharyngeal catheter body comprises a first body and a second body, and the first body is connected with the second body; the cross sectional area of the second body is gradually increased from one end, close to the first body, of the second body to one end far away from the first body; at least part of the optical fiber is embedded in the first body, the first acoustic sensing part is arranged on the optical fiber and located in the first body, the first acoustic sensing part is used for converting vibration and / or pressure fluctuation of breathing sound waves of a patient into optical signals, and the optical fiber is used for being connected with a demodulation host so that the demodulation host can receive the optical signals from the first acoustic sensing part. Therefore, the breathing signal can be collected through the first acoustic sensing part, and the breathing signal of the patient can be transmitted to the demodulation host, so that the breathing condition of the patient can be monitored in real time to guarantee the safety of the patient.

The present application relates to the technical field of health monitoring, in particular to a kind of infant asphyxia risk autonomous monitoring method and system based on laryngeal respiratory sound, comprising: utilize high sensitivity MEMS microphone to collect the airflow and respiratory sound signal of infant larynx, and utilize micro accelerometer to monitor the thoracic movement data of infant synchronously;The airflow and respiratory sound signal are denoising, and the denoising respiratory sound signal is obtained;The denoising respiratory sound signal is separated according to respiratory event Signal, and is enhanced and combined into respiratory sound data set;Respiratory sound feature is extracted in respiratory sound data set, and asphyxia anomaly classification model is constructed based on respiratory sound feature by machine learning model, and dynamic baseline model that adapts individual respiratory difference is established according to respiratory sound feature.The present application utilizes the hardware device of high sensitivity MEMS microphone and micro accelerometer and artificial intelligence model to combine, realizes the use of laryngeal respiratory sound signal in home environment and autonomously completes infant asphyxia risk identification and early warning.

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 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 provides an AI-based child respiratory system diagnosis assistance method and system, and relates to the technical field of diagnosis assistance, and the method comprises the steps: collecting multiple layers of breathing sound signals through a bionic sensing array, building an airflow track model, carrying out the signal enhancement, and generating an enhanced breathing sound signal. Generating three-dimensional breathing sound propagation data by using the acoustic reconstruction network, evaluating a breathing function in combination with airflow dynamics and acoustic parameters, tracking abnormal breathing sound and generating a characteristic distribution diagram; symptom evolution analysis is carried out through a medical knowledge inference engine, real-time monitoring data is output, accurate auxiliary diagnosis is achieved, and the diagnosis efficiency and accuracy of children respiratory system diseases are improved.

The laryngeal mask structure comprises a mask head, the top of the mask head is fixedly connected with a ventilation catheter, the end, away from the mask head, of the ventilation catheter is provided with a catheter connector, the upper end of the ventilation catheter is fixedly sleeved with a mounting plate, and flexible piezoelectric materials are fixedly arranged at the bottom of the mask head. An intelligent data acquisition unit is fixedly arranged on the mounting plate, a silica gel cover is fixedly arranged on the outer side of the cover head, an air injection pipe is fixedly arranged at the upper end of the cover head, and a connector is arranged at the side end of the silica gel cover. The breathing sound of a patient is preliminarily collected through a flexible piezoelectric material, collected breathing sound data are transmitted to an intelligent data collection unit through a wire, a data collector in the intelligent data collection unit collects data subjected to filtering output again, the collected breathing sound data are transmitted to a computer through a wire, and the computer is connected with the intelligent data collection unit through a wire. Doctors can diagnose according to the breathing sound data on the computer without replacing equipment, so that the use is convenient.

The invention relates to the technical field of health monitoring, in particular to an infant suffocation risk autonomous monitoring method and system based on laryngeal breathing sound, and the method comprises the steps: collecting airflow and breathing sound signals of the throat of an infant through a high-sensitivity MEMS microphone, and synchronously monitoring thoracic motion data of the infant through a miniature accelerometer; performing noise reduction processing on the airflow and the breathing sound signal to obtain a denoised breathing sound signal; performing signal separation on the denoised breathing sound signals according to a breathing event, and enhancing and combining the denoised breathing sound signals into a breathing sound data set; extracting breath sound features from the breath sound data set, constructing a suffocation anomaly classification model based on the breath sound features through a machine learning model, and establishing a dynamic baseline model adapted to individual breath differences according to the breath sound features; hardware devices of a high-sensitivity MEMS microphone and a micro accelerometer are combined with an artificial intelligence model, and infant asphyxia risk identification and early warning are autonomously completed in a home environment by using laryngeal respiration sound signals.

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.

The application discloses an obstructive sleep apnea device and method; the device comprises a mouthpiece carrier, an electrode stimulation module and a control unit, the electrode stimulation module comprises an integrated flexible circuit board transmission link, the transmission link is integrated with a stimulation electrode with a nickel-titanium alloy support strip on the inner side of the mouthpiece carrier, and the stimulation electrode can apply electric stimulation to the genioglossus muscle or hypoglossal nerve; the external control unit is provided with a microcontroller and an acoustic sensor; the method first collects an environmental background signal, processes the signal to generate a signal determination threshold, then collects a human respiratory acoustic vibration signal in real time, processes the signal to generate a respiratory sound energy value, compares the two to determine an abnormal respiratory event; after determining the abnormality, a gradient output electric stimulation is output, and a dynamic feedback mechanism is combined to adjust the stimulation intensity; when the stimulation intensity is zero and there is no abnormal signal, it is determined that the intervention is completed and the standby state is entered; the application can adapt to environmental noise, avoid current mutation discomfort, closed-loop control intervention intensity, and is suitable for sleep apnea auxiliary intervention.

An artificial intelligence-based system and method for scalable screening of individuals for respiratory infection, such as COVID-19. The system is trained to distinguish distinct latent features of cough sounds produced by a COVID-19 infected person from cough sounds produced by patients suffering from any other respiratory infection or involuntary cough sounds produced by a healthy person. Cough sound samples from individuals can be remotely collected and evaluated by the system for likelihood of the COVID-19 infection. Additionally, images of affected body parts, biomarkers, metadata, and other respiratory sound samples can also be used for screening.

A smart bed control method and system, and a smart bed. The control method comprises: by means of a sound signal acquisition device, acquiring, at predetermined time intervals, N groups of respiratory sound signals generated by a subject, wherein the subject is on a smart bed (S102); by means of a snore detection device, detecting whether a snore signal is present in the N groups of respiratory sound signals (S104); and when it is detected that, among the N groups of respiratory sound signals, there are more than M groups in which a snore signal is present, controlling the smart bed to perform an anti-snoring action, where M is an integer less than or equal to N (S106).

The invention relates to the field of medical diagnosis, in particular to a central airway stenosis degree evaluation method and system based on deep learning, and the method comprises the steps: obtaining breath sound simulation data of the body surface of a patient; denoising and windowing the breath sound simulation data, and extracting features to obtain breath sound pathological features; the patient is examined to obtain the airway stenosis degree and airway stenosis partitions, a preset basic network is adjusted according to the breath sound simulation data, the breath sound pathological features, the airway stenosis degree and the airway stenosis partitions, and an adjusted network structure is obtained; constructing an initial breath sound analysis model according to the adjusted network structure, and training by using breath sound simulation data to obtain a standard breath sound analysis model; acquiring to-be-diagnosed breath sound simulation data of a to-be-diagnosed patient, and diagnosing the to-be-diagnosed breath sound simulation data by using the standard breath sound analysis model to obtain an illness state diagnosis result of the to-be-diagnosed patient. The reliability of the diagnosis result is improved.

The present invention provides a semi-automatic respiratory sound labeling method, system, and device. The method comprises acquiring respiratory sound audio data; manually labeling a portion of the respiratory sound audio data into a plurality of respiratory sound cycles; calculating the division positions of the remaining respiratory sound cycles in the respiratory sound audio data based on the manually labeled respiratory sound cycles to obtain all respiratory sound cycles in the respiratory sound audio data; classifying each respiratory sound cycle by pathological characteristics using an automatic recognition model to obtain a confidence probability for the corresponding category; directly labeling the respiratory sound cycle as the corresponding category when the confidence probability of the classification is greater than a set threshold; otherwise, multiple independent manual judgments are performed; if all manual judgments are consistent, the respiratory sound cycle is directly labeled as the corresponding category; otherwise, the respiratory sound cycle is discarded. The present invention achieves automatic labeling of respiratory sound categories, saving the cost and time of manual labeling, with a short cycle time, high accuracy, and good economic benefits.

The invention relates to the technical field of respiration monitoring, in particular to a snore monitoring method and device for sleep apnea pathology identification. The method comprises the following steps: acquiring an infrared grayscale image and a breathing audio signal; determining a temperature expression value according to the gray level change of two continuous frames of infrared gray level images; analyzing to obtain inspiration duration and expiration duration; further determining the breathing depth of each actual breathing interval; then, according to the breathing audio signals of the intersection point of the inspiration process and the expiration process and the two end points on each actual breathing interval time sequence, an initial estimation audio signal is determined; further determining a snore signal and a noise signal, and determining the breathing change intensity of each actual breathing interval according to the amplitude change and breathing depth of all extreme points in the snore signal; according to the breathing change intensity, whether each actual breathing interval is abnormal snore or not is determined. The abnormal snore monitoring accuracy and reliability are improved.

The invention belongs to the field of artificial intelligence and non-contact measurement, and relates to a lung ventilation function measurement method and device based on a smartphone microphone. A built-in microphone of the smart phone is used for collecting breathing audio signals of a user in the measurement process, and a lung ventilation function true value is obtained through the lung function instrument to serve as reference data; dividing an inspiration phase and an expiration phase of the breathing audio signal based on a threshold method, and extracting an expiration phase audio clip; processing the expiration signal by using a self-adaptive convolution frequency domain enhancement network, dynamically adjusting a convolution kernel to adapt to different frequency bands by self-adaptive convolution, and enhancing low-frequency components by combining frequency domain enhancement with fast Fourier transform; the transformation type convolutional network models time sequence dependence through causal convolution and jump convolution, and the feature learning ability is enhanced through residual connection and dense connection; and finally predicting lung ventilation function parameters through full connection layer regression. According to the invention, the accuracy of lung ventilation function measurement based on the microphone of the smart phone is improved, and the user can conveniently carry out self-health monitoring.

The invention discloses an anesthesia period respiration monitoring method, device, system and equipment and a storage medium, and relates to the technical field of medical monitoring. The method is executed by signal processing equipment which is respectively in communication connection with a sound acquisition unit and a display, and specifically comprises the following steps: firstly, carrying out real-time denoising processing and preprocessing on an audio signal acquired by the sound acquisition unit in real time to obtain current frame audio data; and inputting the current frame of audio data into a breathing sound type identification model which is based on a deep learning algorithm and has completed pre-training, outputting to obtain a current breathing sound type identification result, and finally transmitting the current breathing sound type identification result to a display in real time for output and display. Therefore, the identified breathing sound type can be used as an index for reflecting whether the lung function state is normal or not and is output and displayed in real time, an anesthetist can know the lung function state of a patient in the operation anesthesia period at any time without using a stethoscope, the abnormal condition is responded in time, and the energy of the anesthetist is saved.

The invention provides a wearable wireless stethoscope early warning device for monitoring acute attack of an asthma patient, the wearable wireless stethoscope early warning device comprises a vest, a first binding belt and a second binding belt, the first binding belt and the second binding belt are arranged on two sides of the vest, two adjusting belts are arranged above the vest, and the two adjusting belts are connected with the vest; a miniature sound signal sensor, a miniature lung sound discriminator and a signal microprocessor are arranged in the vest, the miniature sound signal sensor is used for collecting breathing sound signals, and the miniature lung sound discriminator is used for recognizing dry rale, wet rale and normal breathing sound. The breathing sound of an asthma patient, especially a child patient, is monitored in real time, abnormal breathing sound characteristics are accurately recognized, timely early warning of acute attack is achieved, by means of wireless transmission and a remote diagnosis technology, a doctor can remotely obtain illness state information of the patient, timely diagnosis and treatment guidance is provided for the patient, and the patient experience is improved. The risk caused by acute attack of asthma is reduced, and the health management level of patients is improved.

The invention discloses a children pneumonia screening method and system based on multi-modal deep learning, and is applied to the technical field of artificial intelligence assisted medical diagnosis. Comprising the following steps: acquiring respiration audio data and structured clinical medical history data of a to-be-screened child; converting the respiratory audio data into a time-frequency graph, and inputting the time-frequency graph into a pre-trained feature extractor to extract a high-dimensional acoustic feature vector; screening key clinical indexes in the structured clinical medical history data based on a feature selection algorithm, and inputting the key clinical indexes into a pre-trained multi-layer perceptron model to obtain clinical feature vectors; and carrying out weighted splicing on the high-dimensional acoustic feature vector and the clinical feature vector, and inputting the spliced high-dimensional acoustic feature vector and the clinical feature vector into a classification multilayer perceptron model to obtain the pneumonia prediction probability of the to-be-screened child. According to the method, the problem of data scarcity is solved through a two-stage transfer learning strategy, and the accuracy, specificity and generalization ability of children pneumonia screening in different clinical environments are remarkably improved by simulating the comprehensive diagnosis and treatment thinking of clinicians.

The application provides a medical and home non-invasive ventilator based on multi-channel regulation and a control method thereof, and belongs to the technical field of clinical medical instruments. The ventilator comprises a mask breathing sound collecting device for collecting mask breathing sound signals of a user, a chest wall breathing sound collecting device for collecting chest wall breathing sound signals of the user, a pneumatic measuring device for collecting pressure and flow signals of the ventilator, an acoustic signal processor connected with the mask breathing sound collecting device, the chest wall breathing sound sensor device and the pneumatic measuring module device, for quantifying the filtered mask breathing sound signals, the chest wall breathing sound signals, the pressure and flow signals, and generating a confidence degree, and a fusion controller for adjusting the operation mode of the ventilator according to the confidence degree. The medical and home non-invasive ventilator based on multi-channel regulation and the control method thereof can obtain more stable phase determination and more timely automatic titration control under complex working conditions.

The invention relates to the technical field of pathological characteristic sound detection, and provides a Velcro rale detection method and system based on respiratory auscultation sound, and the method comprises the steps: firstly determining the collection position of lung sound, obtaining the lung sound data of a subject, carrying out the preprocessing, carrying out the initial feature extraction of a preprocessed respiratory sound data set, and obtaining a frequency spectrum representation; then, a pre-trained double-flow multi-view Velcro rale detection model is used for processing: the Velcro rale characteristic acoustic biomarker features represented by the frequency spectrum are enhanced, feature fusion is performed on the enhanced frequency spectrum representation, and multi-view fusion advanced feature representation is obtained; time domain feature representation is extracted from the preprocessed breath sound data set; and finally, integrating the multi-view fusion advanced feature representation and the time domain feature representation to form multi-dimensional time-frequency joint representation, and outputting a detection result. According to the method, the time-frequency distribution mode of the Velcro rale is fully considered, the Velcro rale is accurately and reliably detected, and a non-invasive, economical and efficient means is provided for early screening of IPF.

The invention discloses a breath sound classification method and device, a storage medium and a computer program product, and relates to the technical field of acoustic signal processing. The breath sound classification method is applied to the head-mounted equipment, and comprises the following steps: acquiring a bone conduction audio stream collected by a bone conduction sensor in the head-mounted equipment, and extracting frequency domain features of the bone conduction audio stream; a breathing sound classification model obtained through pre-training is adopted to process the frequency domain features, a breathing sound classification result is obtained, the breathing sound classification result is at least used for representing whether the bone conduction audio stream contains breathing sound or not, and the breathing sound classification model comprises a backbone network and a classifier; the backbone network comprises at least two layers of time sequence convolutional network structures which are connected in sequence, and each layer of time sequence convolutional network structure comprises a time sequence one-dimensional convolutional network module and a time sequence two-dimensional convolutional network module which are connected in sequence. According to the method, a set of complete bone conduction breath sound analysis process is realized, and the breath sound classification accuracy in a complex environment is improved.

This invention discloses a respiratory monitoring device and its working method, relating to the field of pulmonary function monitoring technology. The respiratory monitoring device includes a flexible chest strap and a flexible shoulder strap. One end of the flexible shoulder strap is sewn to the flexible chest strap. A chest rise and fall acquisition mechanism is installed on one side of the front end of the flexible chest strap, and a lung sound acquisition mechanism is installed on the other side of the front end of the flexible chest strap. A control box is installed on the outer wall of the flexible shoulder strap, and a wire is installed at the upper end of the control box. A respiratory sound acquisition mechanism is installed at one end of the wire. This solution solves the problems of existing pulmonary function monitoring devices being unable to perform real-time monitoring, only being able to monitor pulmonary function through inhaled and exhaled gases, having a single measurement parameter, and low accuracy.

A system screens, diagnoses, or monitors sleep disordered breathing of a patient. The system may include a nasal cannula, a conduit connected to the nasal cannula at a first end, an adaptor configured to receive a second end of the conduit and / or a portable computing device. The adaptor may be configured to position the second end of the conduit in proximity with a microphone of the portable computing device. Optionally, a processor may generate an indicator to guide placement of the adaptor for use. Such positioning may, in use, permit the microphone to generate a patient breathing sound signal via the adaptor for processor(s) of the device. The processor(s) may then process the breathing sound signal. The process may include detecting SDB events from an extracted and / or de-rectified loudness signal. The process may include computing a metric of severity of a respiratory condition of the patient using detected SDB events.

The invention relates to a multi-modal fusion decision-making remote medical guidance first-aid transfer method and device. The method comprises the following steps: acquiring a real-time data stream including heart rate, oxyhemoglobin saturation, body temperature, respiratory rate, blood pressure, posture, limb activity and gait through wearable equipment of a patient terminal; acquiring an on-site video stream through a camera carried on a first-aid personnel terminal; acquiring moan sound, breathing sound, environmental noise and voice information of a patient or an accompanying person through a microphone; inputting the on-site video stream, the voice information and the real-time data stream into a trained multi-mode emotion recognition neural network for real-time analysis, and reasoning a preliminary analysis result, a potential risk and a first-aid transfer scheme of the patient; the first-aid transfer scheme comprises on-site emergency treatment measures, transfer postures, transfer routes and calling of specific medical staff. According to the invention, the traffic risk and the disease deterioration probability of the patient are comprehensively considered, and the efficiency and safety of first-aid transfer are improved.

The present application relates to the field of old-age service management, and particularly relates to a smart old-age service management system based on the Internet of Things, which is provided with an observation and collection module, which comprises an image collection unit for collecting infrared images of a monitoring target and an audio collection unit for collecting respiratory sound volumes of the monitoring target; a behavior analysis module, which is used to identify respiratory behavior characteristics based on the infrared images of the monitoring target every monitoring period, analyze respiratory state abnormality characteristic values for the monitoring target based on the respiratory behavior characteristics, and divide respiratory state abnormality tendency categories of the monitoring target; and a behavior analysis module, which is used to adaptively analyze the monitoring target according to the respiratory state abnormality tendency categories. The present application analyzes the characteristic state presented by the monitoring target during night sleep, discovers the sleep abnormality of the monitoring target in time, issues a warning prompt information so as to enable a caretaker to check in time, ensures the sleep health of the monitoring target, and reduces the risk of occurrence of other health problems.