655results about "Diagnostic signal processing" patented technology

In one embodiment, a medical system includes a catheter configured to be inserted into a chamber of a heart of a living subject, and including multiple electrodes configured to capture electrical activity from electrical activation signals propagating in tissue of the chamber, a display, and processing circuitry configured to automatically select bipolar signals to be captured into an electro-anatomical map from respective electrode pairs of the multiple electrodes responsively to an alignment of the respective electrode pairs with a direction of propagation of the electrical activation signals, and render the electro-anatomical map to the display.

The embodiment of the invention provides a breathing machine inspiration detection method and device and a computer readable storage medium, and the method comprises the steps: collecting first pressure data and first flow data in a current breathing period, and calculating a target flow baseline according to the first pressure data, the first flow data and a preset fitting formula; calculating a virtual traffic baseline according to the target traffic baseline and the first traffic data; if it is judged that the inspiration triggering state is not reached or the virtual inspiration flow change rate in the inspiration triggering state is smaller than a preset flow change threshold value, second pressure data and second flow data of the next respiratory cycle are continuously collected, and the virtual inspiration flow change rate is calculated; and judging that inspiration is detected until the detected virtual inspiration flow change rate is greater than or equal to the preset flow change threshold value. According to the invention, the phenomena of inspiration detection delay, group delay and the like can be avoided, the difference between detected inspiration and actual inspiration is reduced, and the sensitivity of inspiration detection is improved.

The invention discloses a brain power supply imaging method and system based on a physical information neural network. The method comprises the following steps: establishing a three-layer boundary element method head model based on a Maxwell equation set under quasi-static approximation and calculating a lead matrix; carrying out filtering, artifact suppression and standardization processing on the multi-channel electroencephalogram signals; inputting the standardized signal into a neural network encoder to extract a time sequence feature tensor, estimating the source current density based on the feature tensor, and performing forward calculation by using the lead matrix to obtain a predicted scalp potential; constructing a mixed loss function consisting of a data fidelity item, a physical consistency item and a physical heuristic regularization item, and performing network training and optimization by taking minimization of the loss function as a target; and when the training meets a convergence condition, outputting a visualization result of the brain endogenous current density spatial distribution. According to the method, electromagnetic physical constraints are introduced, so that the physical consistency, interpretability and stability of electroencephalogram inversion are improved.

The invention relates to the technical field of medical monitoring, and provides a high-precision sleep electrocardio continuous monitoring system and method.The high-precision sleep electrocardio continuous monitoring system and method.A modular closed-loop framework is adopted, and synchronous acquisition of electrocardio, movement and respiration signals is achieved through a multi-modal sensing and high-precision synchronous acquisition module; the signal quality multi-dimensional traceability diagnosis module performs feature extraction and noise classification on the input signal; the dynamic traceability filtering processing module intelligently calls a corresponding filtering algorithm to perform signal purification according to the noise type; and the intelligent output and self-adaptive resource management module outputs high-quality electrocardiosignals and realizes dynamic optimization of system power consumption. By establishing a complete signal quality evaluation system and an intelligent processing mechanism, the whole process optimization of the sleep electrocardiosignals from collection to output is realized, and the accuracy of monitoring data and the cruising ability of the system are remarkably improved.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively improved.

The invention discloses a children language cognition rehabilitation assisting system and method based on a self-adaptive co-creation mechanism, and belongs to the field of medical artificial intelligence and psychological rehabilitation engineering. The system comprises five core modules, the modules are connected in a closed-loop mode in a data flow mode to form a sustainable and optimized interaction system, a semantic vector, an emotion vector and a task vector of child voice are fused, a fusion state vector is constructed through a dynamic weight generation network and a cross-modal attention mechanism, and the fusion state vector and the task vector are integrated. And generating a co-creation control instruction by adopting an attention-enhanced gating circulation unit and a strategy network, and further synthesizing an interactive voice signal with emotional expressive force. Synchronous fusion and linkage feedback of semantic understanding and emotion recognition can be achieved, the interaction strategy is dynamically adjusted according to the real-time state of children, strategy network parameters are optimized through online self-learning, and the naturalness, coherence and individual adaptability of rehabilitation interaction are improved.

The invention discloses a cognitive impairment risk intelligent matching intervention system based on multi-modal data, and belongs to the technical field of cognitive impairment, and the cognitive impairment risk intelligent matching intervention system specifically comprises the following steps: collecting a tactile interaction sequence and a face video data stream when a user executes a cognitive intervention task, and constructing an original multi-modal data set; extracting tactile operation track features and reconstructing a heart rate variability sequence, and generating a synchronous multi-mode feature set; identifying a difference interval of state fluctuation and extracting behavior and physiological features in the interval to form an instant physical and mental load parameter set; in combination with an interaction efficiency index in the task execution record, generating a joint state feature vector, and outputting a personalized adaptation instruction through a pre-trained sub-state-parameter association model; and constructing an adaptive strategy model by using the multi-modal features and an adaptive instruction to realize millisecond-level dynamic adjustment from the real-time multi-modal features to task parameters. According to the method, real-time perception and accurate matching of the cognitive emotion load of the user are realized, and the individuation degree and instantaneity of intervention are improved.

The invention discloses a thoracic surgery postoperative drainage monitoring data processing method and a thoracic surgery postoperative drainage monitoring data processing system. The method comprises the following steps: synchronously acquiring thoracic respiration dynamic characteristics of a patient and fluid dynamic characteristics of a thoracic closed drainage system through a data acquisition module; the central processing server establishes a time sequence mapping relationship between the two, performs feature fusion analysis, and calculates to obtain a thoracic cavity-pipeline coupling efficiency index; performing logic judgment on the postoperative recovery state of the patient on the basis of the index, and identifying a chest-pipeline fluid transmission state including breathing work limitation, drainage channel blockage and pleural cavity air leakage; and dynamically generating a grading equipment response strategy including adjusting training intensity and triggering pipeline maintenance early warning or analgesia intervention according to an identification result. According to the method, accurate recognition and closed-loop management of postoperative complications are achieved by quantitatively evaluating the linkage relation between respiration driving and drainage response.

The invention relates to the field of medical treatment, and discloses a method for treating chronic insomnia by percutaneous magnetic stimulation of stellate ganglion block, which comprises the following steps: preprocessing: determining the initial position of stellate ganglion, tissue initial parameters and initial stimulation parameters through multi-modal image positioning and initialization to obtain reference data; dynamic sensing: collecting neck tissue displacement data, temperature field data and electromyographic signals in real time, and calculating spatial position variation of stellate ganglions and time-varying parameters of tissue electromagnetic characteristics; and modeling optimization: constructing a time-varying electromagnetic field distribution model based on the reference data, the spatial position variable quantity and the time-varying parameters. The method comprises the following steps: acquiring neck tissue displacement, a temperature field and an electromyographic signal in real time through reference data, capturing a stellate ganglion space position change and tissue electromagnetic property time-varying rule, constructing a time-varying electromagnetic field distribution model, and solving target stimulation parameters adaptive to a real-time tissue state through an optimal control algorithm.

The invention relates to the technical field of multi-parameter physiological signal monitoring, in particular to an intelligent laryngeal mask and respiration monitoring system which comprises a laryngeal mask body and a multi-parameter optical fiber measuring system. The optical fiber sensing unit is embedded into the laryngeal mask main body and comprises an acoustic sensing part and an environment sensing part with a functional coating; the signal processing unit demodulates and couples an original signal, combines physical parameters of the coating and inputs the original signal into a multi-parameter coupling correction model, and decouples independent and accurate multiple parameters; and the target thickness of the coating is determined through acoustic correction error minimization and comprehensive calculation that the sensitivity of each environmental parameter reaches the standard. The system can generate an abnormal ventilation alarm and feed back to control the breathing machine. Multi-parameter synchronous monitoring can be achieved, coating interference is eliminated, and breathing management safety and practicability are improved. In the actual application process, the abnormal breathing function caused by abnormal breathing machine state or abnormal laryngeal mask position of a user can be found in time, the purpose of early discovery and early intervention can be achieved, and malignant events are avoided.

The invention relates to the technical field of sleep respiratory disease analysis, and discloses a method for analyzing obstructive sleep apnea. The method comprises the following steps: acquiring an original physiological signal flow which is output by a multi-channel sleep monitoring device and comprises a respiratory waveform, blood oxygen fluctuation, an electrocardio rhythm and a sound vibration signal; and then, carrying out adaptive window function segmentation and multi-resolution conversion on the original signal flow to generate a standardized multi-modal signal sequence. State decoding is carried out on the sequence through a hidden Markov model, and steady state physiological mode features and transient abnormal mode features are extracted. And fusing the steady state features and clinical archive data of the patient, calculating an apnea risk index, and forming an initial evaluation report. Meanwhile, a dynamic evolution path of transient abnormal mode characteristics is monitored, and a real-time pathology indicator in the signal is detected. And finally, a risk weight coefficient in the initial evaluation report is adjusted according to the real-time pathology indicator, and a more accurate optimization evaluation report is generated.

The invention relates to a flexible electronic skin integrated dust-breath bimodal detection system and detection method. A bimodal sensing module, a signal processing module, a wireless transmission module, a data application module, an abnormity early warning module and a power supply are integrated on a composite flexible substrate; the bimodal sensing module monitors a dust concentration characteristic signal of a working place and a breathing frequency characteristic signal of a worker in real time; the signal processing module receives the signals and carries out noise reduction and data fusion processing to obtain a data set D; the wireless transmission module transmits the data set D to the data application module in real time, performs data real-time display, historical data storage, report generation and data export functions, and supports synchronization to an occupational health management platform; meanwhile, the data application module can process data in the data set D and feed back a processing result to the abnormity early warning module, and the abnormity early warning module correspondingly triggers an early warning signal according to feedback data; according to the invention, the target can be continuously monitored, and a high-precision detection result is obtained.

The invention belongs to the technical field of medical equipment, and particularly discloses a pregnancy monitoring method and system based on multi-mode signals, and the method comprises the following steps: synchronously collecting multi-mode physiological signals of a fetus and a maternal body; based on the synchronously collected multi-modal physiological signals, multi-channel feature extraction is carried out; on the basis of the extracted multi-channel features, screening out a feature subset with the most discriminant ability by using feature engineering; and inputting the feature subset into a pre-trained machine learning model, and outputting starting and ending of single fetal movement time, duration, fetal movement type and strength and maternal physiological features. By adopting the technical scheme, multi-mode synchronous acquisition is realized, high-sensitivity and high-specificity detection on fetal movement events and physiological characteristics and states of a mother are realized by adopting a characteristic engineering and machine learning model, non-invasive and harmless real-time monitoring on health conditions of the fetus and the mother is realized, and when the mother or the fetus is abnormal, intelligent remote alarm is realized, so that the safety of the fetus is improved. And a guarantee can be better provided for the health of fetuses and maternal bodies.

The invention discloses a sleep state self-adaptive layered regulation and control method and platform based on an environment regulation window, and relates to the technical field of self-adaptive control, and the method comprises the steps: carrying out the sleep state analysis according to a physiological data sequence, obtaining a real-time sleep stage, carrying out the scheduling task distribution, and generating a function unit scheduling sequence; performing data acquisition priority management and control on the wearable device to obtain real-time sleep associated data; generating a sleep environment adjustment strategy by taking the real-time sleep stage as an environment adjustment constraint; after updating the sleep environment of the user, activating an environment adjusting window; and operating a hierarchical scheduling architecture by taking an environment adjustment window as a time constraint, and circularly performing adaptive control updating. The technical problem that in the prior art, due to the fact that the sleep environment is difficult to adjust accurately in real time according to the sleep state of the user, the sleep quality of the user is low is solved, and the technical effects that the sleep state of the user is accurately judged, the sleep environment is dynamically and adaptively adjusted, and the sleep quality is improved are achieved.

The invention discloses a sleep emotion state recognition-oriented multi-mode sensing signal analysis method and system, and relates to the technical field of sleep emotion data processing. According to the scheme, a progressive data processing method is constructed, and emotion-sleep state recognition and self-adaptive intelligent feedback regulation and control are achieved. The core process comprises the steps of ensuring the time sequence consistency of original data through multi-mode signal transmission time sequence interference analysis, adaptive selection of fixed clock calibration alignment and multi-mode signal dynamic resynchronization, then extracting and fusing deep features of sleep emotion multi-mode signals by using a deep network, and further completing emotion-sleep state recognition. And finally, sleep emotion collaborative intelligent regulation and control are driven according to a sleep-emotion state recognition result, and a physiological safety monitoring mechanism is introduced, so that the reliability and safety of the regulation and control process are ensured, and the effect of improving the emotion-sleep state recognition and intelligent feedback regulation and control accuracy is achieved.

The invention relates to a self-adaptive movement method and system based on an intelligent rehabilitation device, and relates to the field of intelligent rehabilitation devices.The method comprises the steps that biomechanical data are collected; extracting multi-dimensional motion features from the biomechanical data, and combining a preset multi-stage standard motion template library to obtain a current motion state; identifying a current motion phase based on the current motion state; matching a standard motion template of a corresponding stage from a multi-stage standard motion template library according to the current motion stage; combining the current motion state with a standard motion template to obtain a motion parameter deviation risk value; when the motion parameter deviation risk value exceeds a preset risk threshold value, generating a motion adjustment strategy according to the standard motion template and the motion parameter deviation risk value; and performing adaptive motion adjustment on the intelligent rehabilitation device based on the motion adjustment strategy. The method has the effect of adapting to the actual motion states of the user in different stages.

The invention relates to the technical field of intelligent walking assisting equipment, and discloses a blind person intelligent guiding walking assisting method and system based on deep learning. The intelligent guiding power-assisted walking method for the blind is applied to guiding power-assisted electronic equipment, and specifically comprises the following steps: S101, receiving a starting signal sent by a user terminal, starting a multi-source sensor array to collect environment data in parallel, executing a Kalman-Transform space-time alignment algorithm, establishing a multi-modal data space-time unified coordinate system, and establishing a multi-modal data space-time unified coordinate system; when gait phase changes are collected and detected, a multi-source sensor array is activated and dynamically triggered for high-precision scanning, and a threat evaluation model is generated to calculate an obstacle collision probability point cloud matrix; and S102, processing the obstacle collision probability point cloud matrix through Point VoxelNet to generate a three-dimensional environment skeleton. The cross-modal attention mechanism of the millimeter wave radar and the vision improves the rain and fog weather recognition accuracy, and in addition, the dynamic threat evaluation module is combined with the double-threshold early warning strategy, so that the collision false alarm rate is effectively reduced.

The invention relates to a self-adaptive laryngeal mask intubation system and method based on anesthesia depth monitoring feedback. The method comprises the following steps: acquiring flow velocity data of gas in a laryngeal mask microfluid channel in real time and denoising to obtain smooth flow velocity data; detecting the deviation between each data point in the smooth flow velocity data and the target flow velocity in real time, and determining the data point of which the deviation is greater than a preset threshold value as an abnormal point; calculating the adjustment amount of the gas parameter according to the abnormal type of the abnormal point and a preset adjustment logic; generating an adjusted flow velocity control signal in combination with safety constraints; and then calculating a comprehensive trend index of the physiological data of the patient fused with the adjusted flow rate control signal, determining a drug delivery amount by adopting a fuzzy control algorithm, and adjusting the flow rate of the drug in the microfluid channel according to the drug delivery amount. By adopting the method, real-time deviation detection and dynamic adjustment can be performed in combination with the anesthesia depth in a complex physiological environment, and accurate mapping of the flow velocity value and the anesthesia depth state of a patient is ensured.

The invention discloses an early warning method and device based on nursing requirements, a medium and a smart wearable product, and relates to the technical field of smart home / smart home, and the early warning method based on nursing requirements comprises the steps: detecting the physiological feature data of a target user through a millimeter wave radar, and obtaining the physiological feature data of the target user; detecting first temperature and humidity data and posture data of the target user through an auxiliary sensor module; and based on the physiological feature data, the first temperature and humidity data and the posture data, predicting a nursing demand value of the target user through a nursing prediction model, generating early warning information when the nursing demand value is greater than a preset threshold, and sending the early warning information to at least one terminal device. The method is different from a traditional single sensor detection scheme, through multi-dimensional data fusion, the prediction accuracy is improved, the false alarm rate is reduced, the problem of nursing response delay existing in a traditional scheme is solved, timely response in a complex environment is ensured, and the use experience of a user is improved.

The invention relates to the technical field of brain-computer interaction, in particular to a patient mental state assessment system for a clinician, which comprises a semantic manifold calibration module, a state trajectory projection module, a geometric curvature resolving module and an emotion transformation quantification module. According to the method, electroencephalogram data are collected and deeply analyzed, high-dimensional neural activity characteristics are converted into a visual three-dimensional state track in combination with titer and awakening degree information, continuous dynamic monitoring of the mental state of a patient is achieved, then the emotion conversion process is quantitatively analyzed according to the geometric curvature change rate of the state track, and therefore the mental state of the patient can be rapidly and accurately monitored. According to the method, rapid emotion fluctuation which is difficult to perceive in a traditional evaluation mode can be accurately recognized and marked, objective and fine data support is provided for clinical diagnosis, and the accuracy and instantaneity of mental state evaluation are remarkably improved.

The invention is suitable for the technical field of biological measurement, and particularly relates to an intelligent body fat scale and a multi-frequency bioelectrical impedance measurement data fusion method thereof, and the method comprises the steps: carrying out the multi-frequency bioelectrical impedance measurement of a target measurement object, and obtaining a plurality of groups of bioelectrical impedance data sequences under different frequency bands; performing feature analysis on each bioelectrical impedance data sequence, determining a data analysis result corresponding to each frequency band, and determining data reliability corresponding to each frequency band according to the data analysis result; the fusion weight corresponding to each frequency band is determined based on the data reliability, and the limitation of a traditional fixed weight or simple average fusion method is overcome; and according to the fusion weight corresponding to each frequency band, weighting adjustment is performed on the corresponding bioelectrical impedance data sequence to obtain the adjustment data, and fusion processing is performed based on each adjustment data to obtain the measurement data, so that the technical advantages of multi-frequency measurement are fully exerted, and the overall competitiveness of the body fat scale product is enhanced.

This invention discloses a fall detection method based on millimeter-wave radar, comprising the following steps: S1: The millimeter-wave radar continuously transmits frequency-modulated continuous wave signals and receives echo signals reflected by the human body within the monitoring area; S2: The echo signals are processed to generate human point cloud time-series data containing distance, orientation, velocity, and micro-motion information; S3: Multi-dimensional feature vectors representing human posture and motion state are extracted from the human point cloud time-series data; S4: The multi-dimensional feature vectors are input into a primary fall recognition model to calculate a preliminary fall risk probability value; S5: If the preliminary fall risk probability value exceeds a first set threshold, it is determined to be a suspected fall event, and a secondary precise authentication process is immediately initiated; the secondary precise authentication process includes: initiating non-contact vital sign monitoring and retrieving the user's recent behavioral dynamic baseline model for comparative analysis. This invention enables more accurate fall detection.

The invention relates to the technical field of digital medical treatment, and discloses an intelligent chronic disease screening method based on multi-source data fusion, which comprises the following steps: firstly, calculating a sensor credibility reference value based on the historical performance and real-time state of each sensor; performing quality evaluation on the physiological signal according to the credibility reference value, and identifying and marking a signal disturbance interval; then, performing frequency domain analysis and environment interference correlation verification on the marked disturbance interval, and dynamically adjusting the weight coefficient of each sensor; carrying out weighted fusion on the multi-source physiological signals by adopting the adjusted weight coefficient, and carrying out baseline drift correction on low-quality signals; and finally, extracting physiological feature parameters from the fusion signal, matching the physiological feature parameters with a health state pattern library, and outputting a chronic disease screening evaluation result. The problems that in a traditional monitoring system, data quality evaluation standards are not uniform, fusion strategies are rigid, and continuous optimization capacity is lacked are solved, and the accuracy and reliability of chronic disease screening are remarkably improved.

The invention discloses a multi-dimensional night urination behavior monitoring system based on rhythm features, and relates to the technical field of biomedical signal processing, and the system specifically comprises a data acquisition module, a rhythm feature extraction module, a urine volume dynamics modeling module, a clustering monitoring module and a real-time feedback module; collecting individual data in real time through IoT equipment and preprocessing the individual data; calculating a nocturnal urination frequency index, a nocturnal urination time concentration ratio, a urination interval rhythm variation coefficient and a deviation index based on the individual data, and performing rhythm feature extraction; performing quadratic polynomial least square fitting on the night accumulated urine volume, and calculating a urine volume acceleration index by using an obtained second derivative to quantify a urine volume generation trend; the method comprises the following steps: constructing and preprocessing a night urine multi-dimensional digital phenotypic vector matrix, fitting a Gaussian mixture model based on an expectation maximization algorithm of a Bayesian information criterion, automatically mapping individuals into four types of subtypes according to cluster center features, and outputting individual subtype labels and confidence coefficients; and obtaining a comprehensive risk score through normalized risk assessment.

This invention discloses a method for locating epileptogenic zones based on high-frequency oscillations and connectivity, comprising the following steps: 1. Acquiring SEEG data and selecting representative channels; 2. Filtering to acquire 54-200Hz signals and selecting baseline and target data; 3. Acquiring normalized high-frequency energy; 4. Calculating time and energy coefficients to obtain a high-frequency epileptogenicity index; 5. Filtering to acquire 12-45Hz signals; 6. Calculating nonlinear regression analysis; 7. Calculating total intensity; 8. Defining and calculating the connectivity high-frequency epileptogenicity index and evaluating performance. This invention can accurately locate epileptogenic zones in patients with different seizure patterns, and has potential application value in epilepsy treatment.

This invention belongs to the field of food quality evaluation and provides a method for evaluating the synergistic effect of umami based on electroencephalogram (EEG) monitoring technology. This invention mainly addresses the problem that in traditional sensory evaluation methods, psychological or physiological factors such as subjective judgment of sensory personnel, forced selection or scoring, individual differences in umami perception and sensitivity may affect the accuracy of sensory experimental results. This invention discloses methods for umami sample selection and intensity assessment, EEG signal acquisition experimental paradigms, quantitative evaluation and statistical analysis, feature extraction and response difference analysis, etc. Taking the human response to umami signals as the research object and using EEG signal detection and analysis as the main method, it explores the response mechanism of typical umami compounds in the human brain, providing a new theoretical basis for the quantitative evaluation of human umami intensity, and is worthy of widespread application.

The present disclosure relates to a method for calculating the calibration sensitivity of a sensor for insertion into the body and, more particularly, to a method for calculating calibration sensitivity, wherein a biometric value of a user can be accurately calibrated by overcoming an error in a biometric value measured through a sensor for insertion into the body, or an error in a reference biometric value measured through a biometric information measurement device, by storing past sensitivities and using at least one of the past sensitivities and a currently calculated sensitivity to calculate a calibration sensitivity of the sensor for insertion into the body, and the calibration sensitivity of the sensor for insertion into the body can be accurately calculated, even if there is an error in the reference biometric value or the reference biometric value temporarily deviates from the range of normal biometric values of the user, by determining whether the reference biometric value used to calculate the calibration sensitivity is within an allowable range.

The application discloses a Parkinson gait evaluation method and system based on a millimeter wave radar signal. The method comprises the following steps: acquiring a millimeter wave radio frequency signal in an environment, performing data processing on the signal to obtain a distance spectrum of position change of a Parkinson patient's body and a Doppler spectrum reflecting the degree of change of the Parkinson patient's body; extracting a peak value at each moment from the distance spectrum through a filtering method, calculating a cross-correlation coefficient of the Doppler spectrum as an index of gait pattern matching, and dividing the Doppler spectrum according to each step; calculating gait features according to the filtered distance spectrum peak value and the divided Doppler spectrum; inputting the normalized gait features into a trained machine learning model for feature evaluation to obtain a UPDRS-III score of the patient. The application realizes non-invasive measurement and evaluation of Parkinson gait features in a low-cost manner, is convenient to operate, and is accurate in evaluation effect, thereby providing quantitative data and diagnosis reference for doctors.

The invention discloses a target state parameter determination method and device, a storage medium and an electronic device, and relates to the technical field of smart home, and the target state parameter determination method comprises the steps: obtaining a target face video stream of a target object outputted by an image collection module, obtaining a light supplementing state signal output after the light supplementing module supplements light to the space where the target object is located; determining a light supplement condition corresponding to each frame of image in the target face video stream according to the light supplement state signal, and determining a reference light intensity offset of a forehead area and a nose root area in each frame of image according to the light supplement condition corresponding to each frame of image; and according to the reference light intensity offset of the forehead region and the nose root region in each frame of image, pixel-by-pixel correction is performed on the pixel brightness value of each frame of image to obtain a corrected video stream, and a target state parameter of the target object is determined according to the corrected video. By adopting the technical scheme, the problem that the state parameters of the target object cannot be accurately determined is solved.