757 results about "Pulsed wave" patented technology

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention relates to a motion state detection method and device based on a multi-modal sensor, and the method comprises the following steps: carrying out the inertial parameter extraction of an original acceleration signal in the motion process of a human body through a three-axis acceleration sensor, and obtaining a motion acceleration feature vector; the original pulse wave signals in the movement process are monitored in real time, and a cardiovascular physiological feature sequence is obtained; performing time-frequency domain conjoint analysis on the time sequence myoelectricity feature data set, the motion acceleration feature vector and the cardiovascular physiological feature sequence by adopting an adaptive wavelet transform technology to obtain a multi-dimensional feature fusion matrix; and performing dynamic segmentation on the multi-dimensional feature fusion matrix through a hierarchical clustering algorithm to obtain motion state feature subspaces, thereby solving the problem that a traditional motion monitoring method mainly depends on a single type of sensor, although the motion condition of a human body can be reflected to a certain degree, the motion state feature subspaces cannot be monitored. However, the technical problem of lack of comprehensive understanding of complex motion states is solved.

Disclosed are a method and system for controlling a robotic arm pressing force based on a pulse wave signal. The method comprises: acquiring a pulse wave signal of a target; acquiring an interaction force between an end of a robotic arm and the target, and determining an expected pressing force according to a mapping relation between a pressing effect and the pulse wave signal; establishing an admittance control model to convert the expected pressing force into an expected trajectory of the robotic arm; and determining, on the basis of an error between an actual trajectory and the expected trajectory of the robotic arm, a control quantity of the subsequent movement of the robotic arm by using a PID controller, so as to achieve the tracking of the expected trajectory. The present invention achieves a low-impedance and compliant interaction effect when the target of pressing is moving.

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 relates to the field of computer medical engineering, and discloses a blood pressure continuous measurement abnormity early warning method and system based on AI data analysis, and the method comprises the steps: collecting a pulse wave signal, triaxial accelerometer data, an ambient light intensity signal and a skin conductance signal through a sensor on a preset wearable device; generating multi-scale physiological features; systolic pressure and diastolic pressure are calculated, and blood vessel stiffness is calculated; calculating the main arteriosclerosis risk level; when the main arteriosclerosis risk level is higher than a preset risk level, a user is prompted through the wearable device. According to the invention, the pulse wave, the triaxial accelerometer, the ambient light intensity and the skin conductance signal are synchronously acquired and analyzed to obtain the systolic pressure, the diastolic pressure dynamic trend and the blood vessel stiffness, and the main arteriosclerosis risk level can be accurately quantified. In addition, a'physiological parameter-angiosclerosis-risk decision 'closed-loop management mechanism is established, and automatic and active health management of the user is realized.

The invention relates to the technical field of medical diagnosis, and discloses a blood oxygen monitoring system fused with sleep rhythm modeling, which comprises the following steps: identifying a sleep stage conversion moment through a photoelectric volume pulse wave signal, detecting a blood oxygen saturation valley value in a window after conversion, and calculating response lag duration; according to the method, the physiological rhythm is converted into a natural probe for the compensatory ability of the respiratory system, dual diagnosis of sleep staging and respiratory function is realized through a single-path signal, and meanwhile, body movement interference is converted into an observation window for the respiratory stress recovery ability. The diagnosis reliability in a home monitoring scene is remarkably improved, the system completes core analysis at an edge node, and a respiratory function evaluation report which can be directly used for clinical decision making is output.

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 is suitable for the technical field of intelligent wearable devices, and provides a blood pressure dynamic calibration method and device of an intelligent wearable device and the wearable device. According to the blood pressure dynamic calibration method of the intelligent wearable device, the dual-wavelength pulse wave signal collected by the dual-wavelength PPG sensor is obtained, differential processing is conducted on the dual-wavelength pulse wave signal, the purified pulse wave signal is obtained, the signal-to-noise ratio of the purified pulse wave signal is calculated, and when the signal-to-noise ratio is higher than the threshold value, the blood pressure dynamic calibration of the intelligent wearable device is completed. Extracting a pulse wave characteristic value from the purified pulse wave signal, iterating the vascular elasticity coefficient according to the pulse wave characteristic value to obtain an iterative vascular elasticity coefficient, and outputting a first calibrated blood pressure value according to the pulse wave characteristic value and the iterative vascular elasticity coefficient. According to the embodiment of the invention, motion artifacts are effectively suppressed through differential processing of the dual-wavelength PPG sensor, the signal-to-noise ratio of signals is increased, and the problem that the blood pressure measurement error is large due to noise interference and blood vessel elastic fixation when a traditional intelligent wearable device is in a motion state is solved.

The invention relates to the technical field of medical monitoring, and discloses a portable blood oxygen signal OSA intelligent detection method which comprises the following steps: acquiring a pulse wave signal through a blood oxygen sensor, generating an amplitude difference sequence and a period difference sequence in parallel, analyzing rhythm synchronism of the amplitude difference sequence and the period difference sequence, and judging a respiratory effort event when a real-time relation index deviates from a reference value. By capturing a pulse wave time sequence-amplitude decoupling phenomenon, direct perception of a specific mode process of an invalid respiratory effort signal is realized, a traditional hysteretic monitoring mode depending on blood oxygen decline is avoided, and meanwhile, the system has the internal capability of distinguishing physiological fluctuation and specific mode struggling of the signal, and is convenient to use. And a brand new solution is provided for early screening of light and moderate OSA.

The invention discloses an auxiliary stroke rehabilitation method and device based on a multi-source physiological signal wearable device. The method comprises the steps that the pulse waveform variation rate and sublingual vein feature parameters of a subject are monitored through a self-powered five-hidden monitoring wrist strap; dynamically evaluating the internal organ imbalance index of the subject according to the pulse waveform variation rate and the sublingual vein characteristic parameters; generating personalized rehabilitation adjustment parameters according to the internal organ imbalance index of the subject; synchronously acquiring an electroencephalogram signal of a subject and a surface electromyogram signal of a target muscle, and generating an acupoint activation time sequence map of preset hand actions; the acupoint activation time sequence map is optimized according to the personalized rehabilitation adjustment parameters so as to adjust acupoint selection, stimulation intensity and stimulation time sequence; and presenting a built-in foot yangming stomach meridian virtual acupuncture scene to the subject through the VR guide instrument. According to the apoplexy rehabilitation method, electroencephalogram and myoelectricity cooperative decoding, personalized parameter adjustment, VR and a viscera imbalance traditional Chinese medicine diagnosis method are integrated into a rehabilitation scheme, and the apoplexy rehabilitation effect is improved.

The invention discloses an integrated pediatric vital sign intelligent monitoring and early warning method and system, and relates to the technical field of biomedical signal processing. The method is used for solving the problems of large monitoring interference and early warning lag in a dynamic environment. The method comprises the following steps: firstly, synchronously collecting a multi-modal signal to generate a motion intensity envelope signal and an R wave peak value interval sequence, carrying out orthogonal projection denoising on a photoelectric volume pulse wave signal by utilizing motion envelope based on a recursive least square algorithm, and outputting a reconstructed blood oxygen waveform; then, calculating a cross correlation coefficient of a heart rate trend and a motion envelope, quantifying a physiological coupling degree to generate an independent heart rate anomaly index, and accurately identifying a non-motion related risk; finally, the real-time blood oxygen value is analyzed, the index is used for dynamically increasing the low oxygen threshold value so as to tighten the alarm boundary, physiological parameter linkage grading early warning is achieved, and the recognition sensitivity of the compensation period risk is improved while interference resistance is guaranteed.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention discloses a non-contact blood pressure prediction method based on multi-modal fusion. The method comprises the following steps: firstly, carrying out preprocessing such as space-time alignment and face region cutting on visible light video and infrared video sequences; then, obtaining multi-scale and multi-channel depth feature representations from the visible light image and the infrared light image through an independent feature extraction module, and generating adaptive weights for two modals in a space-channel dimension by using a dynamic weighted fusion mechanism based on L1 / L2 mixed norm evaluation and Softmax normalization combination; fusing into a uniform feature vector according to a complementary principle; finally, fitting and reconstruction of rPPG signals are carried out based on the fusion features, the reconstructed pulse wave signals are input into a blood pressure prediction network at the tail end, and high-precision non-contact estimation of systolic pressure and diastolic pressure is achieved. According to the method, visible light and infrared light videos are cooperatively analyzed, and the problem that an existing face-based video method is sensitive to illumination conditions is successfully solved.

The invention discloses a non-invasive wearable vascular arteriosclerosis assessment system and method based on multi-mode pulse wave signal fusion, and belongs to the field of vascular function assessment. The system comprises a distributed acquisition front end, a butterfly-shaped flexible substrate, an integrated double-accelerometer, a four-wavelength LED (530nm / 660nm superficial blood vessel measurement), a 810nm / 940nm deep artery measurement device, a double-photodiode, a double-impedance electrode and a pressure sensor. The central integrated control end processes a multi-mode signal and realizes pressure-LED current closed-loop control; the upper computer terminal extracts the multi-modal pulse wave time difference of the limbs and the neck, the local and regional pulse wave conduction velocity and the blood oxygen hemoglobin change parameters induced by the standardized posture, the dimension is reduced to 30 through principal component analysis, then the parameters are input into the random forest regression model, and the vascular elasticity index is output. Signal stability is improved through flexible fitting, single-source limitation is solved through multi-modal fusion, signal quality is guaranteed through closed-loop control, sensitivity is enhanced through dynamic physiological excitation, and accuracy and universality of arteriosclerosis assessment are improved.

The invention discloses a pressure adjusting method and system for a pressure air gun for drying, and relates to the technical field of pressure air gun adjusting.The method comprises the steps that pressure distribution data and drying effect parameters of the surface of a dried object are collected in real time; the drying effect parameters are input into a reinforcement learning and quantum annealing collaborative decision model, a dynamic pressure adjusting strategy is generated, and the dynamic pressure adjusting strategy comprises control parameters of a pressure value, a waveform frequency and a nozzle direction; an air gun is controlled to output a target pressure waveform according to the dynamic pressure adjusting strategy, the target pressure waveform is generated based on the 3D geometrical characteristics of the drying object, and the impedance of an internal flow channel of the air gun is adjusted in real time through a magnetorheological fluid strategy; in the drying process, three-section mode switching is triggered according to the real-time water content and surface roughness data, chaos frequency modulation pulse waves are output in the initial stage to peel off water, fractal diffusion waves are switched in the middle stage to achieve uniform coverage, and nanometer resonance waves are adopted in the final stage to complete drying.

The invention discloses a method and device for predicting average arterial pressure based on a full-connection neural network, and relates to the technical field of biomedicine based on artificial intelligence. The method comprises the steps that blood pressure pulse wave signals of each patient are collected, and S-G filtering processing is conducted on the blood pressure pulse wave signals; constructing a blood pressure pulse wave envelope line through a double-Gaussian fitting algorithm, and estimating the average arterial pressure of each patient according to the blood pressure pulse wave envelope line; respectively calculating relevancy between the multiple human body physiological parameters of each patient and the average arterial pressure; the human physiological parameter variables with the relevancy larger than a preset value serve as the input quantity of a full-connection neural network, the predicted value of the average arterial pressure serves as the output quantity of the full-connection neural network, a training sample set and a verification sample set are constructed, and the full-connection neural network is trained; and predicting the average arterial pressure of the patient by using the trained full-connection neural network. The accuracy of the non-invasive blood pressure measuring equipment can be improved.

The invention relates to a non-invasive cardiac output function detection method and device, and belongs to the technical field of biological measurement. According to the method, on the basis of pulse wave related data, electrocardiowave related data and biological impedance and biological reactance related data which are obtained through detection, the bioelectrical impedance method is adopted to calculate and obtain the cardiac output of the bioelectrical impedance method, and the pulse wave method is adopted to calculate and obtain the cardiac output of the pulse wave method. And according to the impedance signal confidence coefficient and the pulse wave signal confidence coefficient, carrying out weighted average on the cardiac output of the bioelectrical impedance method and the cardiac output of the pulse wave method, and taking the weighted average as a cardiac output detection result. The detection device comprises a pulse wave detection system, an electrocardiogram detection system, a biological impedance and reactance detection system and a host used for data processing and result display. The reliability of cardiac output detection is remarkably improved while portability is guaranteed, and accurate, continuous and portable detection of non-invasive cardiac output is achieved.

The embodiment of the invention discloses a method and system for non-contact identification of nine types of constitutions in traditional Chinese medicine, and the method comprises the steps: collecting N face videos, where N is an integer greater than 10; the face video is processed through a remote photoelectric volume pulse wave tracing method, and a remote photoelectric volume pulse wave signal is obtained; carrying out heart rate variability characteristic analysis on the remote photoelectric volume pulse wave signal to obtain a heart rate variability characteristic; constructing an identification model, and inputting the heart rate variability characteristics into the identification model for training to obtain an optimal identification model; and inputting a to-be-identified heart rate variability feature into the optimal identification model to obtain a traditional Chinese medicine nine-type constitution identification result corresponding to the to-be-identified heart rate variability feature. The problem that the traditional Chinese medicine constitution cannot be scientifically, accurately and conveniently predicted in the prior art is solved.

The invention relates to a device for performing electric field treatment on a digestive tract. The device comprises a catheter, a support body, an electrode array film and a pulse generator, the pulse generator comprises a current sensor for sensing current flowing through the electrode pair; in the initial time period, the voltage amplitude of the back pulse in the first sequence of pulse waves is larger than the voltage amplitude of the front pulse, and when the current sensor senses that the current is equal to or exceeds the lower limit value IS (MIN) of the set range, the absolute value US of the voltage amplitude of each pulse in the second sequence of pulse waves is equal in the next time period. The amplitude of the output voltage of the pulse generator is controlled through feedback of the current sensor, and the therapeutic instrument is safer for a human body during treatment.

The invention provides a non-invasive blood pressure monitoring method based on electrocardiosignal and volume pulse wave characteristics. The method comprises the steps that electrocardiosignals and volume pulse wave signals are collected; preprocessing, and inputting into a feature extraction module network to obtain a monitoring result; the feature extraction module network comprises two branches, namely an electrocardio branch and a volume pulse wave branch; each of the two branches comprises a feature extraction layer and a plurality of levels of structure re-parameterization modules; the later-stage structure re-parameterization module is connected with the previous-stage structure re-parameterization module through the down-sampling module; the structural re-parameterization modules corresponding to all levels are subjected to feature fusion through feature fusion modules, and the output of the previous feature fusion module is input into the next feature fusion module and fused with the output of the structural re-parameterization modules of the two branches of the next level. According to the method, richer physiological signal features can be captured, the robustness and generalization ability of the model are enhanced through cross-modal information complementation, and the detection accuracy is remarkably improved.

The invention discloses a continuous blood pressure measuring method based on a remote photoelectric volume method and a radar sensor. In order to solve the problem that a traditional blood pressure measurement method cannot meet the non-inductive and continuous measurement requirement, the system uses a camera to collect face pulse wave signals, uses a radar sensor to collect chest pulse wave signals, uses Fwave and Fphase as input feature sets, uses real blood pressure values (diastolic pressure DBP and systolic pressure SBP) as labels, trains a machine learning model, and obtains a blood pressure measurement result. And forming a mapping relationship between the Fwave and Fphase feature sets and the corresponding DBP and SBP, and calculating a time difference between the two. The system is optimized in links of signal acquisition, processing, calculation, estimation and the like, adopts an advanced algorithm to extract pulse wave features and uses a machine learning model to map blood pressure values, has the advantages of non-contact, continuous monitoring, convenience and the like, can provide reliable data support for cardiovascular health monitoring, and meets clinical and family medical care requirements.

The invention relates to the technical field of pulse condition sensors, and particularly provides a pulse condition collection method, medium, device and product, the method can comprise the steps that in the process that a bionic touch pressure pulse condition sensor collects the pulse condition of a target object, the pulse wave amplitude change rate collected by the bionic touch pressure pulse condition sensor is obtained; under the condition that it is confirmed that the pulse wave amplitude change rate is smaller than a change rate threshold value, based on a preset pressure index, the pressing force of the bionic touch pressure pulse condition sensor is adjusted, pulse signals of the target object in different stages are obtained, and the stages comprise the floating stage, the middle taking stage and the sinking taking stage; wherein the pressing forces at different stages are different. According to some embodiments of the application, accurate collection of the pulse condition of the target object can be realized.

Systems and methods are disclosed for blood pressure analysis. In some examples, a system receives sensor data from a sensor during a time period to monitor a blood pressure wave signal in the sensor data. The system inflates a cuff (worn by a patient) with a gas until a characteristic of the pulse wave signal crosses a characteristic threshold from a first side to a second side at a first time within the time period. The cuff is at a first pressure at the first time. The system deflates the cuff until the characteristic of the pulse wave signal crosses the characteristic threshold from the second side to the first side at a second time within the time period. The cuff is at a second pressure at the second time. The system identifies a blood pressure value corresponding to the patient as being between the first pressure and the second pressure.

The invention discloses a blood oxygen detection and calculation method based on infrared light and visible light, and relates to the technical field of blood characteristic measurement. The method comprises the steps that physiological index information and pulse wave detection information of all bracelet wearers are obtained, the physiological index information comprises at least one of body fat information and age information, and the pulse wave detection information comprises infrared light pulse wave signals and visible light pulse wave signals; based on the physiological index information, clustering the pulse wave detection information to obtain a plurality of pulse wave detection groups; based on the infrared light pressure value and the visible light pressure value of each piece of pulse wave detection information in the pulse wave detection group, determining the light absorption abnormity degree of each piece of pulse wave detection information; based on the light absorption anomalies, screening out a target training data set from the pulse wave detection groups; and based on the target training data set, training the target model to obtain a blood oxygen detection model, and realizing blood oxygen detection calculation through the blood oxygen detection model. The accuracy of blood oxygen detection calculation can be improved.

The invention relates to the technical field of biomedical engineering. The invention discloses a triggering method and device of magnetic resonance imaging equipment based on multi-modal signal fusion, terminal equipment and a storage medium. The accuracy of triggering the magnetic resonance imaging equipment to collect images can be improved. The method comprises the following steps: acquiring a first morphological characteristic of a heart vibration signal of a target object, a second morphological characteristic of a jugular vein pulse signal and a third morphological characteristic of a photoelectric volume pulse wave signal; constructing a heterogeneous graph based on the first morphological feature, the second morphological feature and the third morphological feature; performing global graph feature vector extraction processing on the heterogeneous graph by adopting a heterogeneous graph attention network to obtain a target global graph feature vector; and performing time prediction processing on the target global map feature vector by adopting a trigger control model to obtain target time, and triggering magnetic resonance imaging equipment to collect images when the target object is in the middle and later period of cardiac diastole based on the target time.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention belongs to the technical field of biological signal processing and monitoring, and particularly relates to a multi-mode-based non-contact heart rate intelligent monitoring method which comprises the steps that a ballistocardiogram signal of a user is collected through a piezoelectric film sensor, meanwhile, a photoelectric volume pulse wave signal of the user is collected through a photoelectric sensor, and a multi-mode physiological signal is formed; performing space-time alignment processing on the multi-modal physiological signal, eliminating low-frequency breathing interference and motion noise by adopting an adaptive filtering algorithm, and outputting a pre-processed signal; inputting the preprocessed signal into a space-time coupling feature analysis engine, extracting time sequence features through a bidirectional long-short-term memory network, and extracting spatial features through a full convolutional network; adopting a multi-head self-attention mechanism to carry out dynamic weight fusion on the time sequence features and the spatial features to obtain fusion features; and calculating a real-time heart rate value based on the fused features, and judging a heart rate abnormal state. The problem that the heart rate monitoring difficulty is large is well solved.

The invention relates to a continuous noninvasive physiological parameter detection method and device, and belongs to the technical field of physiological parameter detection. According to the method, on the basis of photoelectric volume pulse wave detection data under multiple wavelengths, absorbance related characteristic parameters and pulse wave related characteristic parameters are extracted, a hemoglobin concentration detection result is obtained through calculation of a hemoglobin concentration calculation model, and the absorbance related characteristic parameters comprise the adjacent surface difference, the relative difference, the adjacent surface ratio, the relative ratio and the progressive surface ratio. Extracting based on a normalized absorbance fluctuation amplitude curve; the pulse wave characteristic parameters comprise rising branch maximum slope, rising time, falling time, dicrotic wave slope, dicrotic amplitude ratio, rising branch time ratio, low-high frequency energy ratio and harmonic component ratio, and extraction is carried out based on normalized pulse waves. The detection device for implementing the method comprises a probe, a data processing circuit and the like. The accuracy of the noninvasive hemoglobin concentration detection result can be effectively improved.

The invention provides a pulse condition modeling enhancement system based on image recognition. The pulse condition modeling enhancement system comprises a pulse position marking module, a pulse image acquisition module, a pulse wave tracking module, a pulse condition structure modeling module and a pulse condition modeling enhancement module. The invention belongs to the technical field of image recognition pulse condition modeling, and particularly relates to a pulse condition modeling enhancement system based on image recognition, which comprises the following steps of: acquiring a pulse image sequence through three-pulse position pre-labeling and high-frame-rate image acquisition; pulse wave propagation features are extracted by adopting an image processing method combining motion amplification and phase encoding, a structured pulse condition model is further constructed, and automatic identification and visual expression of common traditional Chinese medicine pulse conditions are realized by fusing multi-dimensional feature vectors, self-encoding compression and multi-label classification.

The invention provides a flexible wearable multi-physiological-parameter monitoring device, and relates to the technical field of wearable medical instruments.The garment body comprises a flexible conductive fiber layer, a sensing center layer and an elastic protection layer which are attached to the human body; the sensor module in the sensor central layer is connected with the control module, and the power supply module is connected with the sensor module and the control module; the sensor module collects bioelectricity signals of a human body and transmits the bioelectricity signals to the control module, and the control module analyzes and processes the bioelectricity signals to obtain physiological parameters; a sensor in the sensor module is made of a flexible electronic material, the sensor module comprises a body temperature acquisition module, a blood pressure and blood oxygen acquisition module and an electrocardio acquisition module, and the blood pressure and blood oxygen acquisition module acquires a blood pressure signal and a blood oxygen signal by utilizing a photoelectric volume pulse wave tracing method. The equipment is high in integration level, comprehensive and dynamic monitoring of human health indexes is achieved, the accuracy of physiological parameter reading and monitoring result obtaining is improved, the wearing comfort is improved, and the equipment is suitable for the outdoor environment.