108 results about "Skin conductance" patented technology

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 nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention discloses a sedation depth monitoring method based on a graph convolutional neural network, and relates to the technical field of sedation monitoring, and the method comprises the steps: collecting a multi-source biomarker of a patient, carrying out the filtering and artifact removal of an electroencephalogram signal, and generating preprocessed electroencephalogram data; performing attention pooling processing on the node-level feature tensor, calculating a sedation depth index, and generating a pain interference factor according to a connection edge weight of the dynamic function connection graph; and when the pain interference factor exceeds a preset pain threshold value and the sedation depth index meets a preset condition, generating a sedation optimization instruction, and when the virtual pharmacokinetic node displays metabolic abnormality, generating an infusion rate adjustment instruction. According to the method, through a dynamic function connection diagram construction link, the time-varying intensity of the skin conductance reaction signal and the rugosa electromyographic signal is used as a dynamic weight, and accurate quantification of the pain-sedation coupling effect is achieved.

The invention provides a depression assessment titration optimization method and system based on a professional label, and the method comprises the steps: collecting the heart rate variability, skin conductance, ambient light intensity, wrist body movement and voice data of a user according to a fixed period through a wearable device, inputting the data of each period into a unified assessment model trained by a professional medical label, the time sequence fusion network processes and outputs a depression state evaluation result containing the total score and the contribution value of each dimension; generating a standardized evaluation conclusion based on the multi-cycle prediction result, and accurately associating the original data with the standardized conclusion; and continuously optimizing model parameters according to an association result by utilizing an incremental learning technology, gradually generating a personalized evaluation model adaptive to individual characteristics of the user, and realizing progressive improvement of evaluation precision. According to the invention, the accuracy and individuation degree of dynamic monitoring of the depression state are improved.

The invention provides a group psychological data fluctuation early warning method based on VR emotion data processing, which comprises the following steps: acquiring multi-modal physiological signals such as electroencephalogram, heart rate variability and skin conductance of a user and behavior data such as voice, expression and virtual trajectory in real time, performing sliding window processing and feature extraction, fusing into multi-dimensional features, inputting the multi-dimensional features into an emotion recognition model, and performing emotion recognition on the emotion recognition model; generating an individual emotion dynamic sequence; a dynamic heterogeneous graph is constructed by combining the social relation and the emotion similarity, the emotion influence intensity between nodes is judged by using a dual-channel graph attention mechanism, and a propagation path is traced through an integral gradient method, so that interpretable group emotion diffusion mode recognition is realized; according to the method, a typical propagation mode and a time sequence convolutional network are combined, a group emotion intensity evolution trend is predicted, when fluctuation exceeds a threshold value, an emotion propagation path thermodynamic diagram is automatically generated and early warning is pushed, and the interpretability of an emotion propagation path and prediction and response efficiency of group psychological fluctuation are improved.

This invention discloses a multimodal emotion analysis method and system, comprising: performing heart rate variability analysis on collected vital sign signals to extract time-domain features, frequency-domain features, and nonlinear features corresponding to the analyzed heart rate variability signals, and fusing the time-domain features, frequency-domain features, and nonlinear features to generate heart rate variability features corresponding to the vital sign signals; calculating the power spectrum and power spectrum entropy corresponding to the collected electroencephalogram (EEG) signals to extract EEG features corresponding to the EEG signals based on the power spectrum and power spectrum entropy; extracting skin conductance features corresponding to the collected electroskin conductance (ESC) signals; inputting the heart rate variability features, the EEG features, and the ESC features into a pre-trained emotion recognition model, and outputting several emotion analysis results; obtaining an emotion regulation scheme matching each emotion analysis result to output the corresponding emotion regulation strategy, thereby improving the accuracy of emotion analysis and regulation strategies.

The invention relates to the technical field of signal analysis, in particular to an acute pain assessment method and system based on an EDA signal, and the method comprises the following steps: collecting a skin electrical activity EDA signal of a subject; calculating an individual time-varying sympathetic nerve activity TVSymp curve; the method comprises the following steps: constructing a priori knowledge base containing a standardized pain response sympathetic nerve SpainSymp template; inputting the EDA signal and the individual TVSymp curve into a PCVAE model, fusing features and decoupling the features into pain common features and individual specific features; carrying out pain intensity classification, and reconstructing and generating a standardized SpainSymp curve of which individual specific characteristics are removed; and outputting the pain intensity classification result and the dynamic change trend of the standardized SpainSymp curve in real time. By adopting the method, the pain-induced physiological reaction and individual baseline variation can be effectively decoupled and separated from the mixed EDA signal, the interference of individual difference is avoided, and the method has the characteristics of high accuracy and strong real-time performance.

The invention discloses a sympathetic cervical spondylosis autonomic nerve regulation enhancing method based on a brain-computer interface, and relates to the technical field of nerve regulation and repair. According to the method, by synchronously collecting electroencephalogram and skin conductance signals and conducting time-frequency domain conjoint analysis and Lempel-Ziv complexity evaluation, accurate quantitative evaluation on sympathetic nerve activity and nerve disorder degree is achieved, double-model collaborative decision-making of machine learning and deep reinforcement learning is utilized, personalized nerve regulation parameters are intelligently generated, and the method has the advantages of being high in accuracy and high in accuracy. High-targeting and safe intervention is implemented through double-target hierarchical regulation and control aiming at neck sympathetic nerves and vagus nerves, and stimulation parameters are dynamically optimized in a closed-loop manner by adopting a genetic algorithm on the basis of real-time feedback of heart rate variability, skin conductance and autonomic nerve balance indexes, so that the stimulation accuracy is improved. Therefore, the sympathetic cervical spondylosis autonomic nerve regulation efficiency and effect are enhanced.

The evaluation method of thermoregulatory function is characterized by comprising: a process of acquiring an autonomic nerve index of a subject; a process of acquiring a sweating index of the subject; and an evaluation process of evaluating a thermoregulatory function of the subject using the autonomic nerve index of the subject and the sweating index of the subject. Preferably, the autonomic nerve index comprises at least one of a heartbeat variation index and a pulse variation index, and the sweating index comprises at least one of a skin conductance and a sweating amount.

The invention provides an intelligent relieving system and method for phantom pain after breast resection based on biofeedback, develops a non-drug and non-invasive intelligent system, recognizes the phantom pain state in real time through multi-dimensional biofeedback, dynamically adjusts an intervention strategy, and solves the problems of high dependency and poor universality of a traditional method. Specifically, through multi-modal signal fusion, sEMG (surface electromyography), heart rate variability (HRV) and skin conductance (EDA) data are integrated, and the accuracy of magic pain recognition is improved. Through an adaptive algorithm, feedback parameters (such as electrical stimulation intensity and virtual reality scenes) are dynamically optimized based on an AI analgesia intervention model (such as LSTM), and personalized intervention is realized. According to the invention, a dynamic adjustment mechanism is integrated for magic pain for the first time, and the limitation of a traditional single intervention mode is broken through based on a self-adaptive adjustment strategy of a multi-modal signal. The occurrence rate of postoperative chronic pain is reduced, and consumption of medical resources is reduced. An intelligent tool is provided for nursing science, and the development of'precise nursing 'is promoted.

The application discloses a blood pressure monitoring device based on multi-source physiological parameter adaptive control, comprising a blood pressure measurement module, an electrocardiogram measurement module and a processing module. The blood pressure measurement module comprises a microcontroller unit and an air pump and a deflation valve controlled thereby. The processing module acquires multi-source physiological parameters and controls the air path accordingly: when the skin conductance response is high, the deflation time is shortened to reduce the stress stimulus of the patient; when the pulse pressure difference of the initial measurement is large, the deflation frequency is increased to adapt to the rapid change of blood pressure and avoid false hypotension; when the electrocardiogram QT interval is long, the inflation time is prolonged to ensure measurement accuracy. The application dynamically adjusts the inflation and deflation physical process through multi-source physiological feedback, significantly improving the measurement accuracy and adaptability of the device under complex physiological conditions.

The invention discloses a real-time monitoring-based implant operation pain self-adaptive regulation and control method and system, and belongs to the field of biomedical engineering. The method comprises the following steps: establishing a tooth position-myoelectricity mapping relation according to an implant tooth position, and deploying bilateral myoelectricity electrodes to establish an individualized myoelectricity baseline; event-related myoelectricity response and skin conductance response are calculated, and pain side positioning is carried out; evaluating the anxiety level, and quantifying and correcting the pain intensity; based on the corrected pain intensity index and anxiety level index, performing state comprehensive judgment by adopting fuzzy logic; self-adaptive regulation and control including cognitive intervention, physical intervention, drug intervention and joint regulation and control are executed according to the state judgment result, and hierarchical upgrading is carried out according to the regulation and control effect. According to the invention, scientific distinguishing between pain and anxiety, accurate positioning of pain side identification and closed-loop self-adaptive regulation and control are realized.

The invention relates to the technical field of evaluation of heart rehabilitation curative effects of breast cancer chemotherapy patients, in particular to a method and system for evaluating the heart rehabilitation curative effects of the breast cancer chemotherapy patients, and the method comprises the following steps: continuously obtaining heart rate variability data and skin conductance data of the breast cancer chemotherapy patients; based on the heart rate variability data and the skin conductance data, the activation state of the autonomic nervous system of the patient is judged; according to the activation state of the autonomic nervous system of the patient, quantifying the intensity and duration of the internal emotional state of the patient, and generating an emotional energy index; when the conventional physiological indexes of the patient fluctuate, determining a physiological fluctuation attribution result in combination with the emotional energy index to distinguish whether the physiological fluctuation is derived from heart function change or emotional influence; and evaluating the cardiac rehabilitation effect of the patient based on the physiological fluctuation attribution result. The accuracy of heart rehabilitation curative effect evaluation can be improved.

This invention discloses a neurophysiological behavior-driven multimodal intelligent learning guidance system and method. The system achieves closed-loop control by constructing a cognitive-emotional-task adaptive adjustment space: 1) In the cognitive dimension, it integrates EEG neural oscillations and frontoparietal network connections, fNIRS cerebral oxygen metabolism indicators, and eye-tracking data to construct a multimodal cognitive load assessment model; 2) In the emotional dimension, it combines the nonlinear characteristics of skin conductance signals with facial expression recognition to quantify learners' motivation levels and emotional valence in real time; 3) In the task dimension, it parameterizes task difficulty and interaction form as moderating variables. During system operation, the system locates the learner's position on the cognitive-emotional state plane in real time and dynamically adjusts task dimension parameters through a nonlinear mapping strategy. This invention effectively utilizes the decoupling ability of multimodal signals and the interaction of intention to achieve precise adaptive delivery of teaching content.

An electrodermal activity (EDA) sensor has one or more pairs of permeable electrodes, configured to be located on skin of a user, a voltage source operable to apply a voltage across the electrodes, and a port electrically coupled to the one or more pairs of electrodes to transmit readings from the one or more pairs of permeable electrodes.

The application discloses a wearable anesthesia depth monitoring device and a monitoring method thereof, and relates to the technical field of anesthesia monitoring.The method comprises the following steps: collecting electroencephalogram signals, blood oxygen saturation, integrated heart rate and skin conductance signals of a user through a head-mounted module and a wristband module, sending data to a cloud server in combination with physiological characteristic information and surgical characteristic information, and configuring an anesthesia monitoring precision coefficient.According to the precision coefficient, anesthesia depth recognition is performed by using multi-modal physiological signals, anesthesia depth monitoring results are generated, and real-time display is performed.The application solves the technical problems that existing anesthesia depth monitoring devices rely on a single signal, have insufficient accuracy and real-time performance, have poor portability, and cannot adapt to the multi-scene requirements of resource-limited environments, and achieves the technical effects of improving the accuracy, real-time performance and portability of anesthesia depth monitoring by means of multi-modal physiological signal collection and individualized monitoring precision configuration, and meeting the multi-scene application requirements.

A method of monitoring stress of a user includes receiving a plurality of time-series data inputs from a plurality of biometric sensor electrodes of a wearable computing device. The time-series data inputs includes continuous electrodermal activity data and at least one of heart rate data, skin temperature data, and heart rate variability data. The method also includes processing the time-series data inputs using a plurality of filtering techniques in sequence. Further, the method includes selecting a model from a plurality of models based on types of data inputs received as the time-series data inputs to calculate an indicator of a physiological response of the user at a certain time. Thus, the selected model is tailored to use all of the time-series data inputs in the calculation of the indicator of the physiological response. Further, the method includes controlling a function of the device when the indicator of the physiological response exceeds a threshold.

A system and a method for objectively assessing an experience undergone by an individual. The system comprises: a first sensing device, configured to detect a heart rate (HR) of the individual; a second sensing device, configured to detect a heart rate variability (HRV) of the individual; a third sensing device, configured to detect a body temperature (BT) of the individual; a fourth sensing device, configured to detect an electrodermal activity (EDA) of the individual; and a processing device, configured to: determine a current HR of the individual; determine a current HRV of the individual; determine a current BT of the individual; determine a current EDA of the individual; perform a comparison of the current HR, the current HRV, the current BT and the current EDA with respective recommended maximum thresholds; and provide a result of the comparison, in the form of a graphical representation and / or a summary indicator.

This invention discloses an intelligent exercise assessment and early warning system based on multi-source biosignal fusion, belonging to the field of exercise risk early warning technology. It collects multi-source data, including electromyography (EMG), heart rate, blood oxygen, skin conductance, and acceleration, from various wearable sensors to assess muscle fatigue, cardiovascular load, exercise status, and environmental factors. After preprocessing, the system uses quantitative models such as the muscle fatigue correction index and the cardiovascular load dynamic coefficient to calculate a comprehensive exercise risk early warning index by integrating physiological state and environmental factors. This results in a normalized risk classification, corresponding to low, medium, and high risk levels and marked with different color icons. This system overcomes the limitations of single signals, considering the synergistic effects of multi-dimensional physiological indicators and the influence of environmental factors, improving the accuracy of assessment in complex scenarios. It can effectively intervene before risks develop and is suitable for scenarios such as mass fitness and professional training. It is integrated into lightweight wearable devices without interfering with the exercise process.

The present application relates to the technical field of health monitoring, in particular to a cancer survivor work pressure monitoring system based on big data, which comprises a physiological parameter analysis module, a stress cause analysis module, a work load distribution module and an abnormal stress monitoring module. In the present application, the sensitivity and accuracy of data analysis are improved through the difference analysis of heart rate fluctuation, skin conductance and blood pressure range, the data points with large fluctuations are screened and classified, the efficiency of abnormal data capture and classification is optimized, the stress cause identification accuracy and distribution law insight are improved, the key stress factors are determined through sensitivity sorting, the stress evaluation is more scientific and quantitative, the priority dynamic adjustment is realized in load classification and grouping, the work pressure distribution is optimized, the abnormal stress point monitoring and grading judgment are strengthened, the stress abnormality grade fine monitoring is realized, the data quantification and dynamic monitoring capability are enhanced, the stress management efficiency is improved, and the targeted data support is provided for personalized health intervention.

A garment can include a pocket to hold a removable electronic device. The electronic device can sense body core temperature, heart rate, skin conductivity, blood oxygen level, and geo-spatial location of the wearer, and transmit this data wirelessly to a mapping application. A viewer of the application can track the location and the health status of the wearer. A leader at a search and rescue command post can easily monitor both the location and well-being of personnel through the mapping application while they are on a mission. An algorithm on the backend can determine a health status color based on the sensor data. The pocket design facilitates insertion and removal of the electronics, and ensures that sensors are positioned at optimal locations on the body.

This invention proposes a health monitoring method and device based on a smart bracelet. The method uses the smart bracelet as the executing entity and includes the following steps: S1: Acquiring a multimodal physiological signal set and an environmental interference data set of the monitored object. The multimodal physiological signal set includes electrocardiogram (ECG) signals, heart rate signals, blood pressure signals, blood oxygen saturation signals, skin conductance signals, and body temperature signals. The environmental interference data set includes motion state data, light intensity data, wearing contact pressure data, and ambient temperature data. S2: Preprocessing the multimodal physiological signal set and the environmental interference data set. This invention can achieve accurate, efficient, and personalized health monitoring, output health level and abnormality tracing information, and trigger early warnings when necessary. It is suitable for daily health management, exercise monitoring, and disease early warning scenarios for different groups such as children, adults, and patients with chronic diseases.

The invention relates to the technical field of intelligent early warning, in particular to a wrist-wearing type acute infarction disease heart symptom home early warning system which comprises a signal alignment module, a feature association module, a trend recognition module, a risk triggering module and an event feedback module, and based on an electric pulse signal waveform, the occurrence time node of an R wave is judged to serve as a reference. Synchronous acquisition and unified time sequence processing of multichannel signals are realized, electrocardiogram, skin conductance and micro blood flow data are integrated on the basis of the same cardiac reference point, a time window-oriented multi-factor dynamic comparison structure is constructed, the trend relationship among multiple parameters is identified through correlation analysis, and the accuracy of the comparison result is improved. A threatened characteristic period is accurately positioned by means of a common pattern screening mechanism, real-time monitoring and historical comparison are integrated in a feedback process, data cyclic utilization is enhanced, multi-source signal dynamic collaborative judgment is realized, the timeliness and stability of risk identification are further improved, and the high-confidence early warning requirement of home health monitoring is effectively met.

The invention discloses a heating temperature regulation and control system and method for a massage instrument, and relates to the technical field of massage instrument heating temperature control, and the system comprises a user data acquisition module, a massage site temperature regulation and control module, and a temperature curve construction and adaptive adjustment module. The user data acquisition module comprises an array pressure sensor and a skin electric signal sensor, receives pressure of massage positions of the massage instrument through the array pressure sensor, constructs and records pressure distribution diagrams of different massage positions, performs similarity judgment on the pressure distribution diagrams, divides the positions, and transmits the pressure distribution diagrams to the user data acquisition module; a skin electric signal sensor is used for receiving and recording a working surface skin electric signal in the current working process of the massage instrument, and a user database is established for file establishment based on a user id. According to the heating temperature regulation and control system and method for the massage instrument, the massage position can be recognized, the comfortable temperature of a user can be excavated, personalized massage instrument temperature regulation and control can be provided, and the practicability of the massage instrument in the actual use process is enhanced.

This invention discloses a coordinated detection circuit for exercise physiological parameters, including a physiological parameter power supply, a physical parameter power supply, a memory power supply, a skin conductance sensor, a photoelectric sensor, an attitude sensor, a memory, a communication module, and a battery charging module.

The invention discloses wearable pain relieving equipment and a remote monitoring system thereof. The wearable pain relieving equipment comprises a multi-mode physiological sensor array, a data fusion processing module, a control module, a transcutaneous electrical stimulation output module, a communication module and the remote monitoring system. The multi-mode sensor array collects myoelectricity, skin conductance, body surface temperature, heart rate, brain oxygen and other pain related physiological parameters, the data fusion module generates a pain estimated value, the control module dynamically adjusts the stimulation intensity based on a PID + differential compensation algorithm, and the transcutaneous electrical stimulation module outputs current to intervene in pain. The communication module uploads data to a remote monitoring system, and medical staff can monitor and remotely adjust parameters in real time through a cloud platform. The system realizes real-time monitoring, intelligent control and remote intervention of pain, has the advantages of personalized treatment, data safety, expandability and the like, and is suitable for scenes of postoperative pain management, chronic neuralgia and the like.

This invention discloses a method for processing and analyzing VR emotion regulation task monitoring data based on multimodal data, belonging to the field of VR emotion monitoring technology. The method includes: collecting multi-source physiological time-series data such as heart rate variability, skin conductance response, EEG rhythm, and eye movement trajectory through a VR scene emotion association mapping gateway; eliminating data sampling frequency differences and time offsets through multi-source physiological time-series alignment processing; then filtering key feature subsets through high-dimensional feature dimensionality reduction and distillation; utilizing emotion fluctuation gradient tracking to analyze the dynamic fluctuation patterns of analytical features with VR scene changes; establishing a mapping relationship between analytical features and VR scene parameters; generating an emotion-scene association feature matrix; and finally, completing comprehensive processing and analysis of monitoring data based on this matrix to output dynamic emotion change association data. The method achieves deep integration of multimodal data and VR emotion regulation tasks through step-by-step feature extraction, fluctuation tracking, and scene mapping processes, improving the accuracy and targeting of data processing.

The invention discloses a ship cabin healthy environment dynamic regulation and control method and system based on multi-modal data fusion and deep learning. The method comprises the steps that multi-modal environment data and user physiological data (such as heart rate, body movement and skin conductance) are collected through a multi-sensor network; preprocessing the data; extracting space and time features by using a hybrid model of a convolutional neural network and a long-short-term memory network, and generating a fusion feature vector; generating a user health state score based on a deep reinforcement learning framework; environment parameter adjustment amounts (such as temperature, humidity, air purification intensity and illumination adjustment amount) are calculated through a fuzzy logic controller; and realizing closed-loop regulation and control by controlling the environment equipment through the actuator network. The problems of inaccurate health risk assessment and regulation response lag caused by single data and simple processing of an existing environment regulation system are solved, real-time, accurate and adaptive dynamic regulation is realized, and the safety and comfort of the cabin health environment are improved.

The invention discloses a portable electrocardio skin conductivity bimodal emotion discrimination system, and belongs to the technical field of biomedical signal detection. Comprising a core control main board, a data analysis and evaluation module and a control module, wherein the core control main board is used for collecting electrocardio and skin conductance signals; the data analysis and evaluation module is used for processing the collected electrocardio and skin conductance signals and judging emotions; the data analysis and evaluation module is matched with the core control main board and controls the core control main board to collect the electrocardio and skin conductance signals; by the adoption of the system and a corresponding method, the system is simple in structure, small in size and integrated in design, meets the portable application requirement, supports real-time signal processing and diagnosis, is high in reliability and high in reliability, and can be applied to the field of wireless communication. The method is suitable for rapid and accurate emotion-related mental disease detection in a dynamic clinical environment.

The invention provides a software usability defect detection method and device, a storage medium and electronic equipment, and relates to the technical field of computers.The method comprises the steps that multi-modal data generated when a user operates a user interface of software is collected, and the multi-modal data comprises an operation log, an eye movement track and a skin conductance signal; calculating the absolute change rate of the skin conductance signal in a set time window and the relative change rate of the relative resting reference; if the absolute change rate is greater than a first threshold value and the relative change rate is greater than a second threshold value, judging that the user is in an abnormal state; in response to the judged abnormal state, performing fusion analysis on the eye movement track of the occurrence time period of the abnormal state and the operation log to obtain a fusion analysis result; and under the condition that the fusion analysis result shows that at least one of the eye movement track and the operation log is abnormal, determining that the user interface has the usability defect. The accuracy of usability defect judgment can be improved.