6057results about "Measuring/recording heart/pulse rate" patented technology

An electrocardiogram device configured to transmit at least one signal responsive to a wearer's cardiac electrical activity can include a disposable portion and a reusable portion configured to mechanically and electrically mate with each other. The disposable portion can include a base having at least one mechanical connector portion, a plurality of cables and corresponding external ECG electrodes, and a first plurality of electrical connectors associated with the plurality of cables. The reusable portion can include a cover having at least one mechanical connector portion, a second plurality of electrical connectors configured to electrically connect with the first plurality of electrical connectors of the disposable portion, and an output connector port configured to transmit at least one signal responsive to one or more signals outputted by the external ECG electrodes of the disposable portion.

A non-invasive physiological sensor system implemented as a smart watch or other wearable device includes an emitter for emitting light and a detector for collecting light after the light interacts with a tissue of a wearer. The system can measure light intensity at one or more radial distances to estimate attenuation parameter values that can be used to correct physiological parameter bias across subjects. In addition or alternatively, the system can determine whether a detector is positioned sufficiently proximate to an obstructing tissue (for example, an artery or vein) so as to cause inaccurate measurements by the detector.

A sensor system for monitoring patients is provided. The sensor system includes a wireless charging dock, one or more patient sensors, and a processing module. The patient sensor is configured to collect patient physiological data and send the data to the processing module. The processing module wirelessly transmits the patient physiological data to a patient monitor system. The wireless charging dock is wirelessly and removably coupled to the processing module to wirelessly provide power for the processing module. The wireless charging dock is magnetically coupled to the processing module.

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.

An optical physiological sensor configured to be secured to a user's nose includes a first prong configured to be positioned proximate an outside portion of the nose, a second prong configured to be positioned proximate an inside portion of the user's nose, a winged portion coupled to the first prong and configured to contact tissue of the user, one or more emitters configured to emit light of one or more wavelengths into the tissue, and one or more detectors configured to detect at least a portion of the light emitted from the one or more emitters after attenuation through at least a portion of the tissue. In some configurations, the winged portion comprises a width that is greater than a width of the second prong. In some configurations, at least a portion of the winged portion is curved toward the second prong.

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention discloses a sleep state monitoring and analyzing system based on multi-sensor fusion, and relates to the technical field of health monitoring, the sleep state monitoring and analyzing system comprises a multi-modal sensor module used for collecting multi-dimensional data related to a sleep state, the multi-dimensional data comprises a bio-electricity signal, a physiological parameter, body movement data and an environment parameter, and the multi-modal sensor module is used for collecting the multi-dimensional data; the multi-modal sensor module comprises a non-contact sensor, a flexible electronic skin sensor and a bio-electricity signal sensor, and the data fusion and processing module is used for carrying out preprocessing, dynamic self-adaptive fusion and federal learning modeling on collected original data. According to an existing contact type sleep state monitoring scheme, more flexible and accurate sleep state data are realized through a non-contact type monitoring sensor in cooperation with dynamic adjustment of sleep monitoring content and adjustment of weights of various sensors for monitoring the sleep state; and a more accurate and intuitive reference report is provided for the sleep state and the health state of the subsequent user.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a brain-computer interface driven emotion recognition and self-adaptive regulation and control system which comprises a multichannel nerve-peripheral coupling module, an emotion intensity probability mapping module and a closed-loop nerve regulation and control current module. The multi-channel nerve-peripheral coupling module is used for realizing overall quantification of central and peripheral emotional physiology; the emotion intensity probability mapping module is used for generating continuous emotion probabilities ranging from 0 to 1 through normalization and nonlinear mapping by utilizing emotion energy and combining eye movement fatigue and electroencephalogram entropy; and the closed-loop nerve regulation and control current module is used for dynamically adjusting the transcranial electrical stimulation intensity within the safety current upper limit according to the difference value between the emotion probability and the expected target. According to the invention, the recognition precision, the response speed and the use comfort are obviously improved.

The invention relates to the technical field of physiological parameter monitoring of intelligent wearable equipment, in particular to an intelligent ring health monitoring method based on multi-sensor cooperation and related equipment. The method comprises the following steps: acquiring motion, optical and temperature sensing data, determining a scene in combination with a multi-dimensional rule and a user preset log, executing differential data acquisition, and generating a health assessment result matched with the scene after signal noise reduction, feature extraction and fusion analysis. According to the invention, monitoring accuracy, low energy consumption and individuation can be considered, and the effectiveness of health monitoring is improved.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention relates to the field of wearable equipment, and discloses a data processing method for assisting old people based on a smart watch, and the method comprises the steps: building an old people individual physiological baseline library at a smart watch end, and carrying out the dynamic comparison of the dislocation difference between a real-time collection signal and the baseline library; performing behavior interference separation on the individual compensation sequence, and generating a health time sequence feature block after interference stripping based on a multi-scene embedding model; an event entropy triggering mechanism is introduced into the health time sequence feature block, and when the drift rate exceeds an individual self-adaptive threshold value, a candidate health abnormal event is automatically marked; calibrating a signal source corresponding to the candidate health abnormal event as a local feedback loop node, and performing coupling correction on a signal in a local feedback loop; and based on the health monitoring priority feedback map, in combination with the global circadian rhythm change trend and the accumulated weight of the abnormal event, generating a multi-level interaction prompt instruction. The method has the advantage of improving the accuracy of the health monitoring data of the old people.

The invention discloses a tumble detection method, wearable equipment and a storage medium, relates to the technical field of tumble detection, and discloses a tumble detection method applied to the wearable equipment, the wearable equipment comprises an acceleration sensor and at least one other sensor except the acceleration sensor, the tumble detection method comprises the following steps: acquiring acceleration data acquired by an acceleration sensor based on a time sequence; if the acceleration data accord with the free falling acceleration performance in the first time period and accord with the impact rebound acceleration performance in the second time period, it is judged that a falling impact event is detected; in response to the falling impact event, obtaining a falling detection result determined based on detection data collected by other sensors; and if the tumble detection result is that the tumble event exists, triggering a tumble event response process. On the basis, when falling impact is detected, detection and identification are carried out based on other sensor parameters, so that the falling detection accuracy of impact caused by falling is improved.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention provides a multi-source heterogeneous data integrated coronary heart disease accurate prediction method, which comprises the following steps: acquiring various physiological signals of a patient in real time, the physiological signals comprising ST segment change characteristics and basic cycle function parameters in electrocardiosignals, and obtaining a multi-source signal data set; according to the joint data set, analyzing instantaneous fluctuation characteristics of a vascular resistance index in a postprandial hyperlipemia window period, extracting a vascular resistance fluctuation amplitude from the instantaneous fluctuation characteristics, and if it is detected that the fluctuation amplitude exceeds a preset threshold range, marking the window as a high-risk time window; if the score value exceeds a preset threshold value, triggering a coronary heart disease early warning signal according to the comprehensive risk score value in combination with a feature mode of a coronary heart disease hidden period in historical data; and storing the current high-risk time window characteristics and the blood sugar and blood fat curve change trend through a triggered coronary heart disease early warning signal to obtain structured risk archive data.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention provides a driving fatigue monitoring auxiliary system based on Beidou satellite positioning and a multi-modal data fusion technology, which relates to the field of electric digital data processing and comprises a multi-modal information sensing and acquisition module, a data preprocessing and quality assurance module, a fatigue state recognition and evaluation module and an intelligent early warning and adaptive optimization module. The multi-modal information perception and acquisition module is responsible for acquiring driver states, driving behaviors and environment information in real time, and the data preprocessing and quality assurance module is responsible for performing space-time alignment, quality evaluation and feature standardization on multi-source data. The fatigue state recognition and evaluation module is responsible for fusing multi-dimensional features and judging fatigue levels and risk trends, and the intelligent early warning and self-adaptive optimization module is responsible for implementing hierarchical intervention and continuously optimizing system performance; according to the system, the accurate space-time reference provided by Beidou satellite positioning is utilized, effective fusion of multi-source heterogeneous data is realized, and the accuracy, the real-time performance and the individuation level of fatigue monitoring are remarkably improved.

The invention provides a sleep monitoring model training method, a sleep monitoring method and equipment. The method is applied to radar signal processing. The method comprises the following steps: acquiring a data set S1 and a data set S2; a pure radar feature extractor M3 is trained by using a pre-trained teacher network model M2 and the data set S1, the teacher network model M2 fuses the radar data and the pulse wave data in the data set S1 and outputs fused feature data, and the pure radar feature extractor M3 performs feature extraction on the radar data in the data set S1 and outputs radar feature data; training a bimodal sleep monitoring model M4 and a pure radar modal sleep monitoring model M5 by using the data set S2, and determining first sleep stage and / or respiratory event information by a first recognition layer according to fusion feature data output by the teacher network model M2, and the second identification layer determines second sleep stage and / or respiratory event information according to the radar feature data output by the pure radar feature extractor M3. According to the invention, the sleep monitoring task is efficiently completed at low cost.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

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 invention relates to the technical field of biomedical engineering and computer vision, in particular to a non-contact physiological signal extraction method and system based on frequency self-adaption and illumination noise perception.The method comprises the following steps of multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, multi-mode video stream collection and spatio-temporal data preprocessing, illumination-noise perception mask generation and feature filtering, and non-contact physiological signal extraction. Frequency adaptive gating and frequency domain feature enhancement, depth time attention feature re-calibration, physiological signal regression and closed loop optimization; the method has the beneficial effects that a lightweight end-to-end deep learning network architecture is constructed by systematically fusing three core modules of illumination-noise perception mask, frequency adaptive gating and depth time attention, and the defects that a traditional physical model depends on artificial prior and is poor in anti-interference performance and high in reliability are overcome. And the one-sidedness caused by high calculation complexity and difficulty in distinguishing the signal and noise of the existing deep learning model is avoided, and the weak physiological signal can be recovered from the face video more accurately and robustly.

The invention provides a pain assessment system and method based on multi-modal physiological signals. The system comprises a multi-modal signal acquisition module, a signal preprocessing module, a multi-modal feature extraction module, a deep fusion analysis module, an individualized calibration module and a result output and early warning module. By synchronously collecting and analyzing multi-dimensional data such as facial expressions, sound features, physiological signs and behavior responses and combining deep learning and multi-modal information fusion technologies, objective quantitative evaluation and real-time monitoring of the pain degree are achieved, and accurate decision support is provided for clinical pain management.

The invention discloses a multi-dimensional personality evaluation method for dynamic emotion changes, and relates to the technical field of personality evaluation, the system is composed of a plurality of functional modules, and the method comprises the following steps: step 1, recording physiological data in real time through a wearable device, capturing micro-reactions by using a computer vision technology, and determining whether the micro-reactions exist or not; the micro-reactions comprise facial micro-expressions, limb actions and voice and intonation changes; based on a preset dynamic simulation social scene, observing an emotion regulation strategy of the subject in pressure; step 2, using an LSTM model to carry out time sequence modeling on the multi-modal acquisition data, capturing dynamic evolution of emotions, and combining an attention mechanism to dynamically adjust the weight of each modal; based on six basic emotions and nine mental disorder tendencies, establishing a classification model, and capturing association between emotions and personalities; reinforcement learning is introduced, and model parameters are dynamically optimized according to feedback of a subject; and predicting the emotion trend of the subject in a period of time in the future through an autoregressive integral moving average model.

The invention discloses a wearable cardiovascular pressure early warning system, which relates to the technical field of wearable medical health monitoring and comprises wearable equipment, and a signal acquisition module, a signal processing module, a posture self-adaptive module and a communication module which are integrated in the wearable equipment. The signal acquisition module comprises a photoelectric volume pulse wave sensor and an inertial measurement unit and is used for acquiring physiological signals and motion data of a user; the signal processing module is used for extracting pulse wave characteristics based on the signals and judging the motion state and the body position state of the user in combination with data of the inertial measurement unit so as to realize pulse-by-pulse blood pressure estimation and cardiovascular pressure trend analysis; the posture self-adaption module identifies user postures through deep learning and dynamically selects a compensation algorithm according to different postures, so that the signal accuracy is improved. According to the invention, continuous, non-invasive and dynamic monitoring of cardiovascular pressure is realized, and long-term and convenient health state monitoring and risk early warning service can be provided for users.

The invention discloses a multi-modal data fusion and intelligent analysis high-precision sleep monitoring system and method. The system comprises a data acquisition layer, a transmission layer, an analysis layer and an application layer, the data acquisition layer transmits data acquired by a sensor to the transmission layer step by step through BLE or Wi-Fi, the transmission layer divides the data acquired by the sensor into two paths through a Wi-Fi module and transmits the data to a main control device and a cloud server in the analysis layer, and the main control device and the cloud server process the data. The data collection layer comprises a data collection module, the data collection module comprises a physiological data collection unit, an environment data collection unit and a subjective data collection unit, the data collection layer is used for collecting data of multiple sources such as physiological data, environment data and subjective data, and the data collection layer is used for collecting data of multiple sources such as the physiological data, the environment data and the subjective data. The deep learning and big data analysis technology is applied, high-precision recognition of the sleep state and precise evaluation of health risks are achieved, and a personalized sleep improvement scheme is provided.

The invention relates to the technical field of biological feature recognition, and particularly provides an anti-migration PPG recognition method based on rate perception and a state space model. The method comprises the following steps: performing physiological feature front-end extraction on an original single-channel PPG signal to obtain a high-dimensional shallow feature sequence; performing double-flow cooperative processing on the high-dimensional shallow-layer feature sequence, and distributing the high-dimensional shallow-layer feature sequence to two parallel branches, namely a control flow branch and a data flow branch; in the control flow branch, an amplitude spectrum and an instantaneous physiological rate curve are obtained; in the data stream branch, acquiring a deep global feature sequence with rate invariance; obtaining multi-scale refinement features based on the high-dimensional shallow feature sequence and the deep global feature sequence; according to the multi-scale refinement features, a final biological feature recognition result is obtained, the method can actively sense the physiological rate change, and efficient nonlinear modeling can be achieved with the extremely low parameter quantity.

This disclosure provides a method, apparatus, electronic device, and storage medium for animal language conversion, relating to the field of artificial intelligence technology, specifically machine learning, deep learning, and natural language processing. The specific implementation involves: acquiring multimodal data related to the animal, including animal vocal data, animal behavioral data, and animal physical characteristics data; preprocessing the multimodal data to obtain fused multimodal data; identifying the animal's current emotion based on the fused multimodal data to obtain an emotion recognition result; and performing semantic mapping and language translation on the emotion recognition result to convert the animal language into human language, obtaining a language conversion result. This disclosure can accurately identify the animal's current emotional state and convert it into human language, thereby achieving deeper emotional communication and understanding between animals and humans, and improving the accuracy and efficiency of cross-species communication.

The invention relates to the field of physiological parameter detection and medical diagnosis measurement, in particular to a multi-mode psychological state quantitative evaluation method and device and a medium thereof. The method comprises the following steps: acquiring multi-source physiological and behavior signals and situation metadata, performing clock synchronization and equipment identifier mapping processing, performing quality feature extraction, artifact detection and slice completion processing, and generating a quality passing fragment set; de-noising normalization, time windowing and feature stack construction, modal gating and attention weighting processing are executed, and unified time sequence embedding is generated; through domain alignment parameter estimation, double-domain drift correction and multi-task inference, a psychological state quantitative index and uncertainty thereof are output; and finally, generating a structured evaluation report and updating parameters through consistency comparison, online self-distillation updating and individual baseline refreshing. According to the method, the anti-interference performance, the individual suitability and the result credibility of psychological state assessment in a natural scene are effectively improved.