548 results about "Respiratory rate" patented technology

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

Systems and methods relating to a millimeter wave radio-frequency (RF) sleep monitoring device are described. The device includes a surface transparent to RF signals through which a radar device enclosed in a housing transmits RF signals. An antenna array is enclosed within the housing and is oriented at a non-zero angle with respect to a vertical axis of the device. The antenna array receives the reflected RF signals through the surface. The reflected RF signals are used to generate sleep monitoring data such as a respiratory rate or motion data associated with a user.

The invention relates to a fatigue driving detection method based on facial recognition analysis, and the method comprises the following steps: S1, video collection: during the driving of a vehicle, a camera collects the facial video of a driver at regular time, and after the continuous driving of the driver reaches a set time length, the camera automatically starts video collection to increase the monitoring of a fatigue state; s2, data extraction: extracting heart rate monitoring data, blood oxygen monitoring data, pulse wave detection data and respiratory rate detection data of a driver; s3, performing data analysis and fatigue judgment according to the data obtained in the step S1 and the step S2; and S4, outputting a result and warning. The method has the advantages that fatigue driving detection is realized through a non-contact facial recognition analysis method, and fatigue driving is judged through cooperation of multiple physiological indexes, so that the analysis method is more accurate and reliable.

The invention relates to a human motion data processing method, and belongs to the technical field of data processing. Comprising the following steps: controlling a microwave radar to emit a detection signal, and detecting a heart rate signal and a respiration signal of a human body; performing clustering analysis on the data according to the collected heart rate and respiratory rate, and extracting heart rate, respiratory rate change, duration and exercise intensity characteristics; according to the extracted heart rate change, duration, exercise intensity and other characteristics, the exercise stage is recognized, and according to the neural network model library, an energy consumption result is output in combination with data analysis. According to the human motion data processing method provided by the invention, the microwave radar is combined with the intelligent wearable device, so that the detection precision of heart rate and respiration signals is remarkably improved, multi-modal data fusion is realized, personalized heart rate prediction is performed, motion stage division is optimized in combination with acceleration data, and the accuracy of motion data processing is improved. The accuracy of motion mode recognition is improved, and personalized motion suggestions can be provided for the user.

Systems, methods, and non-transitory computer-readable media containing instructions are disclosed for detecting and utilizing facial skin micro-motion. In some non-limiting embodiments, detection of facial skin micro-motion occurs using a speech detection system that may include a wearable housing, a light source (coherent light source or incoherent light source), a light detector, and at least one processor. The one or more processors may be configured to analyze light reflections received from the facial region to determine facial skin micromotion, and extract meanings from the determined facial skin micromotion. Examples of meanings that may be extracted from the determined facial skin micromotion may include words spoken by the individual (silent or voiced), an identity of the individual, an emotional state of the individual, a heart rate of the individual, a respiratory rate of the individual, or any other biometric, emotion or speech related indicator.

The invention particularly relates to a quartered sleep state monitoring method and a quartered sleep state monitoring system based on a non-contact vibration sensor. The quartered sleep state monitoring method and the quartered sleep state monitoring system are particularly suitable for realizing non-sensitive sleep quality evaluation through an intelligent bed in a family environment. Comprising the following steps that 1, vibration signals of a human body in the sleep process are continuously collected through a non-contact vibration sensor; step 2, preprocessing the vibration signal, including denoising and segmentation processing, to obtain time domain vibration data; step 3, performing frequency domain transformation on the time domain vibration data, and extracting frequency domain features including respiratory rate, heart rate and heart variability parameters; 4, time alignment is carried out on the frequency domain features and sleep staging labels synchronously obtained through a polysomnography, and a training data set is constructed; an XGBoost classification model based on time-frequency domain feature fusion is provided, fundamental frequency and harmonic energy proportions of respiratory signals are extracted through short-time Fourier transform, and quartering classification is achieved in combination with the LF / HF ratio of heart rate variability.

The invention discloses a respiratory rehabilitation training management system based on an applet, and relates to the technical field of medical health, and the system comprises a data collection module which collects the physiological data of a user in real time through a sensor at an applet end, and comprises a respiratory rate, thoracic and abdominal movement displacement, airflow pressure and a physiological state calculation module; according to physiological data collected in real time, the actual breathing flow of each patient is dynamically calculated through a thoracico-abdominal movement-airflow dynamics combined equation. According to the respiratory rehabilitation training management system based on the applet, by adopting a thoracico-abdominal movement-airflow dynamics combined equation and a lightweight LSTM network, delay between physiological feedback and training instructions is reduced, the real-time performance and individuation of rehabilitation training are ensured, through local real-time processing and feedback, the problem of traditional cloud delay is avoided, and the system is suitable for popularization and application. And the training compliance and the rehabilitation effect of the patient are effectively improved.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The pelvic floor muscle self-adaptive rehabilitation training system comprises a data acquisition unit, a data processing unit, a type identification unit, a degree evaluation unit and a training adjustment unit, and is used for acquiring rectus abdominis data, gluteus maximus data, respiratory rate data and the like of a user in a pelvic floor muscle rehabilitation training process as compensation identification data; then, the type recognition unit can process the compensation recognition data to obtain unconscious compensation types of the user in pelvic floor muscle rehabilitation training, the compensation types comprise abdominal muscle compensation, hip compensation and suffocation compensation, and meanwhile, the degree evaluation unit can evaluate the severity of the compensation types, so that the degree of the pelvic floor muscle rehabilitation training is improved. Finally, the training adjusting unit can generate a multi-modal strategy according to different compensation types and different severity degrees, the multi-modal strategy is used for guiding pelvic floor muscle rehabilitation training of the user, intervention on compensation is achieved, it is guaranteed that the user conducts complete rehabilitation training on pelvic floor muscles, invalid training and even injury are avoided, and the rehabilitation effect is enhanced.

The invention provides an electrocardiogram data analysis method, device, medium and equipment based on multi-modal fusion, which are applied to early warning of acute coronary syndrome (ACS). The method comprises the following steps: acquiring time sequence data of electrocardiosignals and ballistocardiogram signals as well as structural data such as heart rate, respiratory rate and oxyhemoglobin saturation by using a portable device, and preprocessing the data; constructing a double-branch global and local interactive feature extraction model, respectively extracting local and global features of the electrocardiosignal and the ballistocardiogram signal, and simultaneously bridging the isomerism of the two signal types; after feature mapping is carried out on the structural data, inputting the structural data and features of the time series data into a feature fusion model; and finally inputting the fused features into a disease early warning model, outputting an ACS risk probability, and comparing the ACS risk probability with a preset threshold value to realize real-time monitoring and early warning. The method is of great significance to prevention of ACS and guarantee of life health of high-risk groups with cardiovascular and cerebrovascular diseases.

Technologies directed to respiration monitoring based on noise channel state information (CSI) data are described. A method includes receiving CSI data representing channel properties of a wireless channel used by a first wireless device and a second wireless device located in a geographical region. The method generates a set of CSI samples by sampling the CSI data and removes one or more outlier CSI samples using a sparse outlier process, and removes a cluster of outlier samples using a cluster outlier process. The method determines Fast Fourier Transform (FFT) data for each channel subcarrier index and a signal-to-noise ratio (SNR) value for each channel subcarrier index, and identifies a first number of channel subcarrier indexes having highest SNR values to obtain a subset of FFT data, which represents a breathing spectrum. The method determines a respiratory rate of a user in the geographical region from the subset of the FFT data.

The invention provides a fatigue driving monitoring method and system and a storage medium, and the method comprises the steps: obtaining a DMS video stream, carrying out the feature extraction of the DMS video stream, and obtaining a face ROI region; inputting the face ROI region into a regression model to extract skin brightness change characteristics of the driver, and obtaining multiple vital signs of the driver based on the skin brightness change characteristics; the vital signs at least comprise heart rate, respiratory rate, oxyhemoglobin saturation, blood pressure and heart rate variability; the vital signs are judged according to a preset time period and multiple vital sign threshold ranges, and when at least two of the multiple vital signs exceed the corresponding vital sign threshold ranges, it is judged that the driver is in the fatigue driving state. According to the method provided by the invention, multiple vital signs are detected by depending on one independent regression model, and the accuracy and timeliness of fatigue driving detection are improved.

The invention relates to the technical field of medical instruments, and discloses an intelligent device for monitoring the cardio-pulmonary function of a heart failure patient, the intelligent device comprises a multi-mode sensor module, an AI analysis module, a biological feedback module and a wearable carrier, the multi-mode sensor module is used for collecting heart rate, respiratory rate, oxyhemoglobin saturation and body movement data in real time, and the AI analysis module is used for analyzing the heart rate, the respiratory rate, the oxyhemoglobin saturation and the body movement data; monitoring modes can be dynamically switched in rest, motion and sleep scenes; the AI analysis module is used for detecting abnormity based on real-time data and predicting a heart failure deterioration risk in combination with historical data; and the biological feedback module is used for providing personalized intervention in a vibration or audio mode according to the analysis result. The heart rate, the respiration rate, the blood oxygen saturation and the body movement data are collected at the same time through the multi-mode sensor module, sleep apnea or periodic respiration is recognized in combination with the AI analysis module, the defect of separation of cardiopulmonary function monitoring and sleep mode analysis in the prior art is overcome, and therefore early intervention and treatment adjustment are supported.

The invention discloses an intelligent driving fatigue evaluation method and system, and relates to the technical field of automobile safety, and the method comprises the steps: obtaining the physiological data of a driver in a driving scene; the physiological data comprises myoelectricity data, heart rate data and respiratory rate data; calculating a driving fatigue evaluation index according to the physiological data; the driving fatigue evaluation indexes comprise physical load, alertness and cognitive load; and according to the driving fatigue evaluation index, calculating a fatigue driving comprehensive evaluation index by adopting a weight method. According to the method, psychological fatigue and physiological fatigue are comprehensively considered, and from three dimensions of physical load, alertness and cognitive load, indexes such as an electromyographic signal root-mean-square value, an electromyographic signal integral value, a heart rate mean value, a root-mean-square value of a whole-course adjacent NN interval difference, a ratio of heart rate variability low-frequency power to heart rate variability high-frequency power and heart rate breathing cross consistency are synthesized; the driving fatigue evaluation is carried out by adopting a weight method, so that the accuracy and objectivity of the driving fatigue evaluation can be effectively improved.

The invention relates to the technical field of health management and medical information, and discloses an all-dimensional intelligent cardiopulmonary health management system and method and electronic equipment, and the system comprises a biological sensing module which is used for continuously collecting key physiological indexes of the heart rate, the oxyhemoglobin saturation, the respiratory rate and the motion state of a user; the data processing module is used for storing the collected physiological data and historical exercise records of the user through a central database of a cloud computing platform; the deep learning analysis module is a prediction model built based on a deep learning framework and is used for analyzing the physiological data of the user and generating a personalized exercise plan; the real-time monitoring module is used for detecting the physiological state of the user and automatically adjusting the exercise intensity or type when the physiological state exceeds a preset threshold value. The technical scheme that the multi-mode biosensor is combined with the real-time monitoring module is adopted, and the technical effect of comprehensively monitoring the cardiopulmonary function state of the user in real time is achieved.

The invention relates to a breathing heart rate detection method and device based on an FMCW radar, and belongs to the technical field of signal processing.The breathing heart rate detection method based on the FMCW radar comprises the steps that FMCW radar detection signals are preprocessed, and thoracic cavity phase signals are obtained; performing time sequence data decomposition on the thoracic cavity phase signals, and determining a target signal from the thoracic cavity phase signals after time sequence data decomposition based on the reference range of the respiratory frequency and the heartbeat frequency of the target population; performing multi-scale decomposition on the target signal, and separating a target respiration signal and a target heartbeat signal from the target signal; and based on a spatial spectrum estimation algorithm, determining the breathing frequency and the heartbeat frequency of the to-be-detected person from the target breathing signal and the target heartbeat signal. According to the method, the respiratory heart rate detection is realized, the influence of noise on the detection result is reduced, and the accuracy of the respiratory heart rate detection is further improved.

The invention discloses a safe medication system and a safe medication method for elderly patients, which can monitor whether the elderly take medicine according to medical advice or not and monitor the health state of the elderly. The physiological data of the patient is monitored by the wearable end, so that the health state of the patient can be roughly judged by combining data such as heart rate, electrocardio, respiratory rate and blood pressure, and whether the patient is in danger or not can be found in time; and meanwhile, whether the drug effect of the patient is exerted and whether the patient has drug safety can be known by monitoring the physiological data for a period of time after drug use. According to the invention, the medical advice is uploaded or issued through the medical care terminal, and the medication information in the medical advice is analyzed into the medicine type counting quantity and the medication mode of the medication, so that the medication plan of the patient is planned, and then the patient is prompted to take the medication on time according to the plan. And the medicines are distributed through the intelligent terminal module, and the intelligent terminal module communicates through the system, so that the medicines can be distributed according to the control of the medical care terminal, the management terminal and the system to prevent the patient from eating by mistake, and the medication safety is greatly improved.

The invention relates to the technical field of exercise risk analysis and early warning, and discloses an exercise risk analysis and early warning system. The exercise risk analysis and early warning system comprises a patient data collection module, a patient exercise risk analysis module, a patient exercise risk early warning module and a data visual storage and display module. According to the system, whether the patient can do exercise or not is preliminarily judged by combining medical records of the patient, the heart rate, the respiratory rate, the body temperature and the stability analysis result of the patient in the exercise process are considered in the process of calculating the # imgabs0 #, the smaller the value of the # imgabs1 # is, the more indicating that the heart rate fluctuates during rehabilitation exercise, and the lower the value of the # imgabs1 # is, the lower the value of the # imgabs1 # is, the more indicating that the patient can do rehabilitation exercise. The more stable the change condition of the step length and the respiratory rate is, the lower the risk degree of the rehabilitation exercise is reflected, so that the patient can visually check the own condition, and the patient can visually see the own rehabilitation condition by checking the # imgabs2 # calculated after the rehabilitation exercise is performed each time.

The invention discloses an artificial intelligence-based obstructive sleep apnea judgment method, which comprises the following steps of: S1, acquiring the respiratory rate, the heart rate and the oxygen saturation of a user in a sleep period, and constructing a physiological data set; s2, filtering, denoising and cleaning the collected original physiological data set to remove noise and abnormal values; s3, key physiological features are extracted from the preprocessed physiological data set; s4, training a judgment model by utilizing improved deep learning, and performing comprehensive analysis by the judgment model according to the extracted physiological features to judge whether obstructive sleep apnea occurs or not; s5, the system monitors the sleep state of the user in real time, and provides instant feedback or alarm when obstructive sleep apnea is detected; and S6, the system performs analysis according to the physiological features of different users, and provides judgment results and health suggestions for individuals. The accuracy and real-time performance of obstructive sleep apnea judgment are improved, and accurate sleep apnea monitoring and judgment are achieved.

The invention discloses an all-directional monitoring and emergency treatment system for patients in the emergency department, which belongs to the technical field of diagnosis and monitoring of emergency surgery and comprises a data input and acquisition module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, wherein the data input and acquisition module is used for acquiring emergency treatment features including external stimulation inducements and ages of a current patient before emergency treatment; the monitoring and evaluation module is used for acquiring an epilepsy risk evaluation value, a comprehensive evaluation value and a feedback adjustment value, and collecting all-dimensional monitoring characteristics of a current patient, including amygdalia activeness, a brain activity reference level, abnormal discharge intensity, heart rate, respiratory rate, systolic pressure value and diastolic pressure value, and the monitoring and evaluation module is used for acquiring and outputting the epilepsy risk evaluation value, the comprehensive evaluation value and the feedback adjustment value; the early warning module is responsible for carrying out early warning and inputting an adjusted first-aid strategy to the first-aid execution module when the epilepsy risk assessment value, the comprehensive assessment value and the feedback adjustment value are in a continuous monitoring rising trend on the visualization module, and comprehensive and real-time monitoring and accurate assessment of the illness state of a patient and timely adjustment of first-aid measures are achieved.

The invention discloses a non-contact respiration detection method. First, an access point (AP), a base station (STA) and a capture device are arranged in a target area, and channel state information is obtained by capturing a beamforming feedback frame (BFI) periodically sent from the STA to the AP. And then, decompressing and extracting amplitude signals and time information in the BFI frame, performing phase-space reconstruction on the extracted representative subcarrier signals of the antenna combination, and extracting and evaluating the optimal antenna combination through dynamic characteristics. Thirdly, performing Hampel filtering on the subcarrier amplitude signals of the optimal antenna combination to remove abnormal values, performing phase-space reconstruction and dynamic feature extraction on all subcarriers, and selecting the optimal subcarrier by using a K-means clustering algorithm; and then, Savitzky-Golay filtering and band-pass filtering are carried out on the selected optimal subcarrier signal, and linear interpolation is carried out through principal component analysis (PCA) dimension reduction to obtain an equally-spaced sampling signal. And finally, performing fast Fourier transform (FFT) on the processed signal, and estimating the respiratory rate through an innovative peak detection algorithm. The invention provides a non-contact respiration detection method by utilizing a phase space reconstruction and dynamic feature extraction technology, and compared with a traditional method, the method can better capture nonlinear dynamic features of signals and more comprehensively evaluate dynamic features of each subcarrier, so that the subcarrier most suitable for respiration detection is selected, and the detection accuracy is improved. The breathing signal and the interference signal are effectively distinguished, and the accuracy and robustness of non-contact breathing detection based on the Wi-Fi signal are remarkably improved.

The invention discloses an animal quarantine method, and aims to solve the problems of high omission ratio, early warning lagging and lack of environment optimization of the traditional quarantine means. According to the method, multi-dimensional biological signals such as the heart rate, the respiratory rate, the body temperature and the acceleration of an animal are collected in real time through an ear tag type wearable device, and the anomaly detection precision is improved through a self-adaptive sampling mechanism; meanwhile, the environment monitoring module collects the temperature, humidity and ammonia gas concentration of the farm, and dynamically regulates and controls the temperature, humidity and ammonia gas concentration in case of abnormality. And the edge computing node adopts a long-short-term memory network to fuse and analyze data, outputs an epidemic disease risk probability, and optimizes the model through self-learning. The embodiment shows that the method can perform early warning and environment optimization in the incubation period, the quarantine time is shortened to 5-10 minutes per head, the omission ratio is lower than 5%, and an efficient and accurate epidemic disease prevention and control solution is provided for the breeding industry.

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 discloses a breathing machine output gas monitoring method, system and device and a storage medium. The breathing machine output gas monitoring method comprises the steps that airway pressure information, gas flow information and oxygen concentration information of breathing pipeline gas are obtained; determining tidal volume, respiratory rate and inspiration and expiration ratio according to the gas flow information; performing air leakage compensation on the tidal volume according to the airway pressure information to obtain a real tidal volume; the airway pressure information, the inspiration and expiration ratio, the real tidal volume, the respiratory rate and the oxygen concentration information are displayed. The breathing machine output gas monitoring comprehensiveness and accuracy are improved, and the breathing machine output gas monitoring method can be widely applied to the technical field of breathing machines.

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.

The invention discloses intelligent pasture livestock health monitoring equipment. The equipment comprises a vital sign monitoring mechanism worn on the legs of livestock, a behavior monitoring mechanism worn on the neck of the livestock and an environment monitoring mechanism vertically extending and arranged on the pasture ground; the vital sign monitoring mechanism comprises a vital sign monitoring fixing ring, a vital sign monitoring supporting shell with an upward opening is connected to the vital sign monitoring fixing ring, and a plurality of body temperature monitoring sensors are connected into the vital sign monitoring supporting shell; a pulse monitoring through hole penetrating in the radial direction of the physical sign monitoring fixing ring is formed in the side face of the physical sign monitoring fixing ring, and a pulse monitor is arranged at the pulse monitoring through hole; the behavior monitoring mechanism comprises a conventional monitoring camera, a high-speed monitoring camera and an infrared monitoring camera; the equipment can accurately collect various physiological data of livestock, such as body temperature, heart rate, respiratory rate and the like, as well as behavior data, such as activity amount, ingestion time, rumination times and the like, so that the accuracy and reliability of the data are ensured, and an accurate basis is provided for evaluating the health condition of the livestock.

The invention provides a terminal data management method and system for medicine sales. According to the method, medicine purchasing historical data, electronic health record data and allergy taboo data of customers are integrated to construct a medicine adaptive sign map, and the map establishes a dynamic association relationship between medicine indications and sign parameters. And acquiring heart rate and respiratory rate parameters of the customer in real time, and matching the heart rate and respiratory rate parameters with the physical sign parameters in the map to generate a physical sign parameter matching result. And generating a personalized recommendation instruction containing an initial recommended drug list and taboo prompts in combination with taboo rules and curative effect data in the drug indication library. According to the real-time change trend of the heart rate and the respiratory rate, the drug priority of the recommendation list is dynamically adjusted, finally, an updated recommendation drug list is generated and sent to the sales terminal equipment, and accurate drug recommendation based on the real-time health state is achieved. According to the technical scheme provided by the invention, the security and accuracy of terminal data management can be improved.

The invention discloses an intelligent animal husbandry and veterinary diagnosis system based on machine learning, and particularly relates to the technical field of intelligent animal husbandry diagnosis. The livestock body surface temperature, the livestock chewing data and the livestock movement data are processed to obtain the real body temperature, the rumination time, the movement state and the movement speed of the livestock, and the livestock heart rate, the respiratory rate, the real body temperature, the rumination time and the movement speed are input into a livestock sign abnormity recognition model for abnormity recognition. The behavior data of the livestock is obtained through the multi-angle camera, the abnormal image data set is marked after a series of processing, the received livestock sign abnormal data set and the received livestock behavior abnormal image data set are input into the livestock health intelligent diagnosis model for health diagnosis, and the livestock illness state diagnosis efficiency and accuracy can be effectively improved.

The invention relates to the technical field of riding safety monitoring, in particular to a riding monitoring method, device and system and an electric vehicle. The method is applied to the electric vehicle and comprises the steps that physical sign information of a user in a riding task is obtained, and the physical sign information at least comprises the body temperature, the heart rate and the breathing frequency; vehicle state information of the target vehicle is obtained, the riding behavior level of the user is determined based on the vehicle state information, and the behavior level represents the intense degree of riding the target vehicle by the user; the vehicle state information at least comprises a vehicle body inclination angle, a driving speed, braking times and a braking speed; the riding risk level of the user is determined based on the physical sign information and the behavior level, an early warning instruction is generated when the riding risk level meets the set requirement, and the early warning instruction is used for conducting risk prompting on the user and / or conducting deceleration on the target vehicle. By the adoption of the scheme, the safety of a user in the riding process can be improved.

The invention discloses an interactive nursing teaching system for nurses and patients, relates to the technical field of nursing teaching, and aims to solve the problems that the traditional teaching is difficult to create a highly tense and complex emergency room atmosphere, students cannot deeply experience the pressure caused by time urgency, and serious consequences possibly caused by slightly poor information transmission. The nursing teaching system comprises a scene simulation module, an interaction module, an intelligent feedback module and a case management module. The scene simulation module utilizes virtual reality and augmented reality technologies to restore the environment of an emergency room and dynamically simulate patient condition changes, and the condition changes comprise dynamic adjustment of the heart rate, the blood pressure, the respiratory rate, the pain degree and the consciousness state of a patient in real time according to different emergency diseases; the interaction module comprises a voice interaction device and an action interaction device. The system has the advantages that the adaptation period of students entering clinical practice, especially emergency room work, is effectively shortened, and the nursing work efficiency and quality are guaranteed.