43 results about "Breath rate" patented technology

This invention discloses a feedback-based pulmonary rehabilitation assessment and treatment device, relating to the field of medical device technology. It includes a bicycle assembly connected to an expiratory mask and a detection wristband. The detection wristband is worn on the patient's wrist, and the expiratory mask is worn over the mouth and nose. The bicycle assembly is equipped with sensors and a resistance component, which is controlled by a central processing unit. The expiratory mask has a data acquisition module that collects the patient's respiratory rate. The patient's heart rate is then controlled by adjusting the resistance of the bicycle assembly. Through this technical solution, the sensor assembly can detect the resistance of the bicycle assembly in real time. The respiratory rate and the heart rate detected by the wristband are then assessed, and the central processing unit controls the resistance to help the patient achieve a stable respiratory rate and heart rate.

The utility model relates to a health monitoring system. The system comprises indoor equipment equipped with a sensor module, a processor in communication connection with the sensor module, and an alarm system. The embodiment of the utility model discloses breakthrough integration of radar technology and artificial intelligence in common household equipment (ceiling fan). The system is composed of a radar module and a decorative antenna housing which are seamlessly integrated, and a fan is converted into cautious and unnoticeable health monitoring equipment. The radar module remotely transmits signals to a monitored area, captures signals reflected from an individual body, and provides continuous real-time data about vital signs such as heart rate and respiratory rate. After the information is processed by the artificial intelligence processor, the system can detect abnormal conditions, and an alarm conveyed to the user through the user interface is triggered. The innovative system can adapt to different living environments and has deployment flexibility, and a new era of comprehensive, continuous and user-friendly health monitoring is created.

The invention relates to an intelligent low-altitude economic scene passenger emotion pacifying method and system based on a large model. The method comprises the following steps: constructing a multi-modal emotional state data set of low-altitude travel passengers; wherein the multi-mode emotional state data set comprises passenger face video sequence data, in-cabin voice and audio data, heart rate variability time sequence data, respiratory frequency time sequence data and external situation context data; performing emotional depth feature extraction based on multi-source space-time signals on the multi-modal emotional state data set to obtain multi-source emotional features; emotion recognition is carried out based on the multi-source emotion features; based on the emotion recognition result, constructing a pacifying strategy generation model; and utilizing a pacifying strategy generation model to intelligently pacify the emotion of the passenger, and evaluating and feeding back the pacifying effect in real time. According to the emotion and specific inducements recognized in real time, pacifying measure suggestions which conform to safety specifications, fit scenes and are natural in language can be generated, and timeliness and specialty of passenger cabin service are improved.

The present application relates to the technical field of psychophysiological monitoring, and discloses a video psychophysiological index analysis method and system; the method comprises the following steps: positioning facial key points and partitioning from a video, extracting remote optoelectronic plethysmography pulse wave and action unit breathing characteristics, outputting heart rate and breathing signals through Kalman filtering; obtaining a heartbeat interval through peak value detection, calculating heart rate variability indicators and breathing frequency, and normalizing by establishing an individual baseline in an initial calm period; fusing emotional characteristics and normalized indicators to form an observation vector, inferring a stress state through an unsupervised model and a forward algorithm; detecting index mutation points to identify stimulus events, and adopting a gated recurrent unit network to predict excessive stress risk and grade early warning; storing multi-session data into a database, quantifying desensitization rate, tolerance and recovery capacity through linear regression, weighted scoring and model updating. The present application realizes contactless monitoring, real-time stress evaluation and early warning, and supports quantitative analysis of cross-training effects.

This invention relates to the field of multi-organ dysfunction (MOD) early warning technology, specifically to an MOD early warning method for patients with severe burns. The method includes the following steps: acquiring heart rate, mean arterial pressure, respiratory rate, blood oxygen saturation, lactate concentration, and urine output per unit time; calculating the rate of lactate change at adjacent time points to form a sequence; comparing the rate of change across time intervals to obtain the difference in change; combining heart rate and blood oxygen saturation to calculate circulatory load; and counting the number of early warning triggers that meet risk conditions. In this invention, by calculating the rate of change of lactate concentration at adjacent time points and combining it with interval direction statistics, tissue metabolic changes show trend characteristics; the rate of change of urine output combined with mean arterial pressure forms a record of renal perfusion changes, providing a continuous characterization of perfusion status; heart rate and blood oxygen saturation are used to calculate circulatory load; multiple physiological signals are linked for judgment; and recurring abnormalities are expressed through the number of triggers. This improves the stability and predictability of MOD risk identification in critically burned patients.

The invention relates to the technical field of data processing, and provides a driver state monitoring method and device and an intelligent safety belt. The method comprises the following steps: collecting a physiological signal and a behavior signal of a driver through a multi-mode sensor group; performing feature extraction on the physiological signals to obtain real-time physiological state parameters of the driver; the physiological state parameters at least comprise a heart rate value and a breathing frequency value; performing feature extraction on the behavior signal to obtain a real-time behavior state parameter of the driver; the behavior state parameters at least comprise a body movement intensity value and a pressure distribution mass center position; determining current state information of the driver according to the physiological state parameters and the behavior state parameters; and executing an early warning operation corresponding to the current state information.

A mechanism for guiding the length of a multitude of different segments across a multitude of breathing cycles. A recorded length of each of the multitude of segments is obtained in a sample breathing cycle. The recorded length of each segment, along with a sample breathing rate and a desired breathing rate, is used to define a target length for each segment in each of the multitude of breathing cycles. Each target length is proportional to the corresponding recorded length.

The invention discloses a feedback type lung rehabilitation evaluation therapeutic apparatus, and relates to the technical field of medical instruments, the feedback type lung rehabilitation evaluation therapeutic apparatus comprises a bicycle assembly, the bicycle assembly is connected with an expiration mask and a detection bracelet, the detection bracelet sleeves the wrist of a patient, the expiration mask is worn at the mouth and nose position, and the bicycle assembly is provided with a sensor and a resistance assembly. The resistance assembly is controlled through the central processing unit, a data acquisition module is arranged on the expiration mask, the data acquisition module acquires the respiratory rate of a patient, and then the heart rate of the patient is controlled by controlling the resistance of the bicycle assembly. According to the technical scheme, the resistance of the bicycle assembly can be detected in real time through the sensor assembly, then evaluation is conducted through the respiratory rate and the heart rate detected by the detection bracelet, and then the resistance is controlled through the central processing unit so that a patient can achieve the stable respiratory rate and heart rate.

The present application relates to the technical field of health data monitoring, in particular to a health monitoring method based on data analysis and a smart watch, which obtains physiological parameters such as heart rate, blood pressure, blood oxygen and respiratory rate of a user, analyzes the interaction between the physiological parameters, judges whether the physiological parameters are abnormally high or low, and obtains the individual health state distribution result. Based on the dynamic analysis of physiological parameters and combined with multi-dimensional data measurement, the present application accurately depicts the change characteristics of the health state, avoiding the errors caused by single data point judgment. By measuring the interaction between different physiological parameters, the health state can be more comprehensively evaluated, and the judgment of individual health state is not affected by the fluctuation of single physiological index. With the help of multi-dimensional coordinate space, the distance between the health state and the steady state interval is calculated, so that the evaluation of health state change is more quantitative, and the identification ability of abnormal fluctuation is improved.

Techniques for gathering and analyzing respiratory information concerning a subject are disclosed. The techniques may involve determining a plurality of metrics concerning respiration by a subject over time. Metrics concerning respiration by the subject may be compared to determine one or more respiratory features of the subject. Such respiratory feature(s) may then be used to identify at least one respiratory condition which the subject exhibits or is at risk of exhibiting.

The invention relates to the technical field of rehabilitation training, in particular to a neural rehabilitation progress prediction method based on multimode data fusion. According to the method, the intensity adaptation factor is analyzed through the respiratory rate increase situation, and the suitability between the training intensity and the physiological tolerance of the patient is analyzed; based on the myoelectricity value periodic change and the joint angle deviation degree, the muscle fatigue degree and the motion nonstandard degree are quantified, and the execution quality of each training item is obtained; and in combination with a training effect index obtained by the strength adaptation factor, analyzing change trend mechanical energy prediction. Through multi-dimensional data collaborative analysis, trend prediction is carried out on more comprehensive and objective quantitative evaluation of the neural rehabilitation process, the rehabilitation management accuracy is effectively improved, and reliable data support is provided.

The invention discloses a control method and system for a full-spectrum brain therapeutic apparatus, and relates to the technical field of brain therapeutic apparatuses, and the method can obtain key physiological data such as body temperature, heart rate, blood pressure and respiratory rate of a patient, and carries out the physiological data fusion through employing an improved weighted average algorithm and a D-S evidence theory. And inputting the fused data into a physiological status evaluation model to dynamically evaluate the physiological status of the patient, thereby determining a targeted treatment target and optimal control parameters, and controlling the full-spectrum brain therapeutic apparatus to work according to the control parameters. According to the method, intelligence and adaptivity of the treatment process are enhanced, and more reliable and effective treatment experience is provided for the patient.

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 invention provides a method and device for determining animal heart rate and respiration information, and the method comprises the steps: obtaining the body type and posture of an identified target animal, the body type and posture of the target animal being identified based on a millimeter wave signal corresponding to the target animal; based on the body type and posture of the target animal, performing filtering processing on each frame of millimeter wave signal which is continuously collected to obtain a millimeter wave signal after filtering processing; detecting and tracking a thoracic and abdominal region of the target animal based on the filtered millimeter wave signal; based on the tracked thoracic and abdominal region of the target animal, extracting physiological signal features of the target animal from the filtered millimeter wave signals by adopting a physiological signal feature extraction mode corresponding to the posture of the target animal, the physiological signal characteristics comprise a periodic amplitude change signal corresponding to respiration and a periodic amplitude change signal corresponding to heart rate; and determining the heart rate and the respiratory rate of the target animal based on the physiological signal characteristics of the target animal.

1. The name of the design product: millimeter wave radar breath and heart rate monitor. 2. The use of the design product: for non-contact breath and heart rate monitoring of static human body, and data transmission to the cloud and remote equipment for interaction, thereby realizing online real-time monitoring, remote sharing and abnormal alarm of breath, heart rate, sleep, body movement, in-bed / off-bed and other data of the monitored population in the ward or family. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The application provides an emergency method and device for pre-hospital medical treatment, which is applied to a medical treatment platform of an ambulance, acquires first patient data of equipment, wherein the equipment is instrument equipment on the ambulance, and the first patient data includes one or more of body temperature, blood pressure, heart rate and respiratory rate of the patient; obtains first symptom parameters according to the first patient data; matches a first treatment mode corresponding to the first symptom parameters from a preset model library; and shows the first treatment mode to medical staff, so that the medical staff pre-treats the patient according to the first treatment mode. By using the above method, the ambulance is used to match the corresponding treatment mode according to the patient data during the time before entering the hospital, and the medical staff pre-treats the patient according to the treatment mode, so that the occurrence of the omission of the patient data is reduced.

To provide a biological information generation system, a biological information generation method and a program capable of accurately measuring biological information such as a respiration rate and a heart rate of a person to be measured.SOLUTION: The Doppler sensor control section 30 controls the position of the Doppler sensor 15 so that the Doppler sensor 15 is arranged at one of a plurality of predetermined candidate positions. For at least one candidate position, based on the Doppler data indicating the measurement result by the Doppler sensor 15 disposed at the candidate position, the reliability calculator 36 calculates the reliability of the biological information obtained from the Doppler data. The decision unit 38 decides the measurement position from among the plurality of candidate positions based on the reliability calculated for at least one candidate position. The biological information generation unit 40 repeatedly executes a process of generating biological information of the measurement subject based on Doppler data indicating the measurement result by the Doppler sensor 15 arranged at the measurement position.SELECTED DRAWING: Figure 3

Provided are a biological information generation system, a biological information generation method, and a program that make it possible to accurately measure biological information such as the respiration rate and heart rate of a subject. A Doppler sensor control unit (30) controls the position of a Doppler sensor (15) such that the Doppler sensor (15) is disposed at one of a plurality of predetermined candidate positions. A reliability calculation unit (36) calculates, for at least one candidate position, the reliability of biological information obtained from Doppler data indicating a measurement result measured by the Doppler sensor (15) disposed at the candidate position, on the basis of the Doppler data. A determination unit (38) determines the measurement position from among the plurality of candidate positions on the basis of the reliability calculated for the at least one candidate position. The biological information generation unit (40) repeatedly executes processing for generating biological information of the measurement subject on the basis of Doppler data indicating a measurement result measured by the Doppler sensor (15) disposed at the measurement position.

The invention provides a wearable early-stage deterioration early warning system and application, and belongs to the technical field of medical auxiliary equipment. A first electrode group, a second electrode group, a photoelectric volume pulse wave sensor and an inertial measurement unit are integrated on a wearable vest of the system, and the wearable vest is used for synchronously collecting thoracic impedance, thoracic and abdominal movement impedance, oxyhemoglobin saturation and trunk movement data; the edge computing gateway carries out signal preprocessing; the feature extraction module is used for extracting respiratory rate, tidal volume trend, thoracico-abdominal respiration asynchronism index, blood oxygen trend and activity state; the hospital information system interface obtains a medication record; the context-aware risk assessment module fuses the multi-dimensional parameters and the clinical context to generate a risk score; and the early warning generation module triggers graded early warning according to the score and automatically integrates the graded early warning to the hospital clinical workflow. According to the method, the early-stage decompensation trend of the non-asthmatic respiratory failure can be continuously and specifically recognized, closed-loop management from risk recognition to intervention is realized, and the clinical response efficiency is improved.

The invention discloses a pediatric clinical breathing machine and a use method thereof, and belongs to the technical field of breathing machines. The pediatric clinical breathing machine comprises a host, a breathing pipeline and a data acquisition module; a main control module and an intelligent sensing module are arranged in the host, the breathing pipeline is a hollow pipeline, one end of the breathing pipeline is connected with a gas output port of the host, the other end of the breathing pipeline is provided with an interface in butt joint with the mouth and nose of a child patient, and the data acquisition module is used for acquiring breathing parameters and blood oxygen parameters of the child patient; the intelligent sensing module generates an adjusting instruction based on the breathing parameters and the blood oxygen parameters and uploads the adjusting instruction to the main control module, the main control module dynamically adjusts output airflow parameters of the gas output port based on the adjusting instruction, and the output airflow parameters comprise the tidal volume, the inspiration pressure, the breathing frequency and the inspiration-expiration ratio. Real-time detection and intelligent regulation and control of infant breathing parameters are achieved, and the problem that an existing infant breathing machine depends on manual parameter setting and is difficult to dynamically adapt to changes of a child patient is solved.

The present invention relates to a system and a method for monitoring patient risks by using bio-signals obtained in a non-contact type. According to the present invention, an apparatus for automatically collecting bio-signals comprises the steps of: periodically emitting radar toward a patient; measuring the heart rate and the respiration rate of the patient by using radar signals reflected from the patient; measuring the body temperature of the patient by using an infrared body temperature measurement sensor; estimating the blood pressure of the patient by using the heart rate; estimating the electrocardiogram and the oxygen saturation of the patient by using the heart rate and the blood pressure value; and predicting the risk of a severe disease of the patient by using the heart rate, the respiration rate, the blood pressure, the body temperature, the electrocardiogram, and the oxygen saturation. The apparatus may further comprise a step of delivering an alarm signal to an external terminal if the predicted risk is greater than a reference value.

1. Name of the product in this design: Vital Signs Monitor. 2. Purpose of this design: As a medical device, used to monitor a patient's respiratory rate, heart rate, and other vital signs. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

This utility model belongs to the field of experimental equipment technology, and discloses an ion monitoring device for a rabbit hepatic encephalopathy experimental model, including: an operating table with a monitoring system installed on it; the monitoring system includes a first base with a first fixing rod fixed in the center and two fixing clips on the first fixing rod; a tension transducer with its input end detachably connected to the fixing clips and a three-way valve detachably connected to the input end, with an endotracheal tube detachably connected to the three-way valve for monitoring respiratory rate; and a pressure transducer with its input end detachably connected to the fixing clip and a three-way valve detachably connected to the input end, with an arterial cannula detachably connected to the three-way valve for monitoring changes in arterial blood pressure, heart rate, and respiratory rate during the progression of rabbit hepatic encephalopathy (early, middle, progressive, and recovery phases), thereby increasing the scientific rigor and repeatability of the experimental operation.

The invention relates to the technical field of sports underwear, in particular to intelligent sports underwear, which is characterized in that a flexible sensor and an inertial sensor are respectively arranged in different areas of an underwear main body, so that dynamic information of chest and spine parts can be comprehensively reflected, and dynamic monitoring of respiratory rate and spine state is realized; therefore, the health monitoring precision and the scientific guidance level in the exercise process are improved.

The invention relates to the technical field of medical hyperbaric oxygen chamber control, in particular to a stepped pressurization mode for a medical hyperbaric oxygen chamber, which comprises the following steps of: acquiring a heart rate variability basic value, a respiratory rate baseline value and individual physiological characteristic parameters of body surface area of a user, analyzing preset target treatment pressure, and determining the target treatment pressure; and generating a staged pressurization rate set containing the multi-stage pressure increasing rate and the corresponding target pressure sub-value through a preset stepped pressurization strategy model. After pressurization is started, the real-time heart rate variability and respiratory rate of a user are collected in real time, the pressurization rate of the current stage is adjusted according to dynamic physiological data in each pressure increasing stage, and after the pressure in the cabin reaches a target pressure sub-value, the next stage is started until the target treatment pressure is reached. According to the mode, stage pressurization parameters are customized by combining individual physiological characteristics, and the pressurization rate is dynamically regulated and controlled by real-time physiological data to adapt to the physiological state of a user.

The invention provides a monitor vital sign early warning method based on artificial intelligence, and the method comprises the steps: continuously obtaining vital sign data from a monitoring device, the vital sign data including heart rate, blood pressure, respiratory rate and oxyhemoglobin saturation, and obtaining a continuous sign data stream; extracting vital sign characteristics in a preset time period from the vital sign data stream, analyzing dynamic relevance among vital signs in the preset time period, and determining a mutual influence mode among the vital signs; if the dynamic relevance exceeds a preset relevance threshold value, identifying the delay time of each physical sign change according to a mutual influence mode between the physical signs to obtain a physical sign change amplitude; and continuously recording an early warning result, updating a mutual influence mode between the signs according to newly obtained sign data, and performing re-evaluation to obtain a newest sign correlation intensity value and a time sequence deviation value.

Foley type catheter embodiments for sensing physiologic data from a urinary tract of a patient are disclosed. The system includes the catheter and a data processing apparatus and methods for sensing physiologic data from the urinary tract. Embodiments may also include a pressure sensor having a pressure interface at a distal end of the catheter, a pressure transducer at a proximal end, and a fluid column disposed between the pressure interface and transducer. When the distal end is residing in the bladder, the pressure transducer can transduce pressure impinging on it into a chronological pressure profile, which can be processed by the data processing apparatus into one or more distinct physiologic pressure profiles, for example, peritoneal pressure, respiratory rate, and cardiac rate. At a sufficiently high data-sampling rate, these physiologic data may further include relative pulmonary tidal volume, cardiac output, relative cardiac output, and absolute cardiac stroke volume.

Embodiments propose a system and method for providing feedback information based on response information to a viewer of a musical performance through artificial intelligence. A method according to one embodiment receives image information about a musical performance viewer from a camera device mounted on a musical performance stage, and receives biometric information about the musical performance viewer from a sensing device worn on the musical performance viewer. And the biological characteristic information of the music performance includes heart rate data, body temperature data, respiratory rate data, and skin conductance data, and obtaining motion information about the audience of the music performance by using a motion analysis model based on a first neural network. Based on the motion information and the biometric information, response information of the audience of the musical performance is determined by a response analysis model using a second neural network, and response information of the audience of the musical performance is determined based on the response. The method may include determining feedback information about music performance, and sending the feedback information about the music performance to a command terminal. The feedback information on the musical performance may include feedback information on lines and songs included in each of a plurality of scenes constituting the musical performance.

According to the present disclosure, a measuring apparatus and an operating method thereof are presented which measure a heart rate and a breathing rate of a pet by using a radar sensor, and then confirm whether a health status of the pet is abnormal based on the measured data, and when it is confirmed that the health status of the pet is abnormal, transmit a notification message to an electronic terminal of a pet owner to support the pet owner to more rapidly respond to health abnormality of the pet.

This invention relates to the field of state recognition technology, specifically a method and system for health status recognition of thoracic surgery patients. The method includes the following steps: acquiring postoperative heart rate, blood pressure, and respiratory rate data; normalizing and constructing a recovery rhythm sequence; using a variational autoencoder to map and calculate the residuals to extract the maximum deviation; calibrating the unstable intervals; determining the synchronous transition critical point; constructing a recovery sequence; identifying abnormal chains; and outputting the state result. In this invention, by constructing the original recovery rhythm sequence and introducing a latent space prediction mechanism, and using differential calculation of residual vectors combined with a fluctuation tolerance threshold to calibrate the unstable intervals, rhythm abnormalities are more accurately judged. By extracting directional consistency and average dwell time indicators from the recovery sub-paths composed of continuous states, and combining this with standard path matching to form a potential abnormal chain identification method, this invention effectively solves the problems of inaccurate identification caused by multi-parameter disconnection, missed transition nodes, and lack of temporal trend in traditional methods.