71 results about "Electrodermal response" patented technology

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

The invention discloses a five-dimensional dynamic emotion visual chemotherapy healing method and system, and relates to the technical field of emotion visual chemotherapy healing, and the method comprises the steps: employing a multi-sensor fusion method to collect physiological data, psychological assessment questionnaire results and historical emotion data of a user, and obtaining a basic emotion feature vector of the user; mapping the emotional state of the user to a five-dimensional emotional space based on the basic emotional feature vector, and initializing a particle set; collecting multi-mode biological signals of electroencephalogram, heart rate variability, electrodermal response, body temperature and voice emotion recognition of the user, and processing to obtain a feature vector reflecting the current emotion state of the user; the geometric morphology and kinetic parameters in the particle set are dynamically adjusted, a visualization engine is used for rendering particles, a two-way feedback adjustment mechanism is constructed, particle behaviors are adjusted according to the emotional state of the user, and the emotional adjustment ability of the user is enhanced; and designing an interactive emotion regulation game.

The invention discloses a self-adaptive music playing intelligent headphone and a system thereof, and relates to the technical field of headphones, the headphone comprises a head frame and earmuffs, the head frame comprises an outer shell, a fixing frame is fixedly mounted in the outer shell, and slideways are fixedly mounted at two ends of the fixing frame; a movable connecting rod is arranged in the slide way, and a limiting groove and a limiting block are arranged at one end of the connecting rod; the earmuff comprises an earmuff shell, a connecting piece used for being connected with the connecting rod is fixedly installed in the earmuff shell, and a touch screen control module is installed on one side of the outer portion of the earmuff shell. According to the system, the mood state of the user is automatically recognized and the emotional state of the user is judged by integrating various sensors (such as a heart rate sensor and a skin electric reaction sensor), so that the accuracy of emotion recognition is improved, subtle emotion changes can be captured, more appropriate music recommendation is provided, and the use effect of equipment is improved.

The invention discloses an intelligent drug delivery pain management bracelet, and relates to the fields of wearable equipment, transdermal drug delivery and biological signal processing. The bracelet is integrated with a multi-mode biosensor which comprises a temperature sensor, a pressure sensor, a heart rate sensor, a blood oxygen sensor and a skin electric response sensor, and physiological data related to pain can be collected in real time. Through an embedded microcontroller and a machine learning algorithm, a pain intensity evaluation model is constructed, and real-time evaluation of the pain state of a patient is realized. Based on the pain intensity and the physiological state of a patient, a drug release strategy is dynamically adjusted through an intelligent algorithm, and drugs are released painlessly and accurately through a micro-needle transdermal drug delivery technology, so that the patient obtains the optimal treatment effect. And the functions of data storage, historical analysis and remote doctor monitoring can be expanded. The hand ring is convenient to operate, comfortable to use and suitable for various pain management scenes such as chronic pain and postoperative pain, and the personalized level of pain treatment and patient compliance are remarkably improved.

The invention discloses a hearing aid health analysis method based on a physiological signal monitoring function. The method comprises the following steps: collecting physiological signal data of a user in real time through a multi-mode biosensor array integrated on a hearing aid body; the hearing aid body comprises a digital signal processor and a wireless communication module; the physiological signal data at least comprises a brain wave signal, a skin electric response signal and a temporal artery pulse signal; performing motion artifact elimination processing on the physiological signal data through an adaptive filtering algorithm; performing feature extraction on the preprocessed physiological signal by using an embedded health analysis engine to generate a composite health feature vector containing a sympathetic nerve activity index, cognitive load measurement and a cardiovascular function index; and dynamically generating a health risk assessment report based on an association relationship between the composite health feature vector and a preset hearing scene, the bone conduction vibration unit and the audio synthesis module of the hearing aid body output graded health alarm signals.

The invention discloses a VR emotion quantitative management method and system based on physical and psychological interaction, and relates to the field of psychotherapy, and the method comprises the steps: collecting the heart rate variability data, the electrodermal response data and the EEG alpha wave power data of a user in a resting state, and obtaining the EEG alpha wave power data of the user based on the heart rate variability data and the electrodermal response data; generating color, density and physical boundary parameters of the initial emotion energy field; and rendering a dynamic emotion energy field in the VR environment according to the initial emotion energy field parameters, and detecting the distance change between the user and the dynamic emotion energy field through a field density growth algorithm. According to the method, heart rate variability, electrodermal response and EEG alpha wave data are fused by adopting an S-shaped curve function to generate emotion energy field parameters, accurate mapping of physiological states and virtual environments is established, emotion recognition sensitivity is improved, treatment efficiency is improved, field density attenuation rate is dynamically controlled innovatively through parasympathetic nerve activation degree, and the treatment effect is improved. And the synergistic effect of behavior intervention and nerve regulation is realized.

The invention discloses a foreign language learning and homework doing system and a foreign language learning and homework doing method integrating education artificial intelligence, augmented reality (AR) technology and biological signal perception. A multi-mode biosensor (electrodermal response, heart rate, eye movement and the like) is integrated through customized AR intelligent glasses, and a closed loop of'interest scene triggering-biological signal sensing-personalized content pushing-self-adaptive review planning based on a forgetting curve and real-time emotion data 'is realized in combination with an interest matching algorithm and a dynamic learning engine of an intelligent foreign language learning APP. The system utilizes an artificial intelligence technology to accurately analyze the emotional state of a user, adopts a bidirectional long short-term memory network (Bi-LSTM) personalized forgetting curve algorithm, integrates foreign language words, grammar and other homework contents into an augmented reality or virtual interest scene, and supports on-school students to complete homework tasks and off-school users to autonomously learn. The system covers hardware architecture design, AI algorithm implementation and a data interaction process, foreign language learning and homework doing are completed during playing, the learning efficiency is remarkably improved, and the system has high innovativeness and practicability.

The invention discloses a postoperative pain degree sensor and an ovum extraction operation pain monitoring system, and belongs to the field of postoperative health monitoring and intelligent nursing equipment. The sensor comprises a flexible substrate, and a skin electric response sensing unit, a temperature sensing unit, a myoelectricity sensing unit, a micro-pressure sensing unit, a signal processing circuit and a wireless communication module which are integrated on the flexible substrate, the monitoring system comprises an edge processing module, a pain index evaluation module, an interaction terminal and a data recording module. The system evaluates the postoperative pain level based on the multi-channel physiological signals, real-time quantification and alarm prompt of the pain state are achieved, and the postoperative nursing response efficiency is improved. The scheme is suitable for assisting pain management after reproductive ovum extraction, and has the advantages of high sensitivity, low delay, structural flexibility and system closed loop.

According to the intelligent green light phototherapy glasses and the intelligent adaptation method thereof, a narrow-band green light LED light source with the central wavelength being 530 + / -5 nm is adopted, and individualized pain management is achieved in combination with heart rate variability, electrodermal response and pupil dynamic monitoring. A multi-source sensor and a micro control panel are arranged in the glasses, physiological data are collected in real time, the illumination intensity, frequency and irradiation mode are dynamically adjusted through an artificial intelligence algorithm, and a closed-loop phototherapy control system is constructed. The device supports Bluetooth / Wi-Fi connection, interacts with a mobile terminal APP, and realizes data recording, curative effect evaluation and remote monitoring. The system has multiple safety mechanisms such as constant-current driving, temperature feedback, over-illumination protection and posture detection, and long-term wearing safety is ensured. Through the regulation effect of green light on pain neural pathways, a safe, portable and intelligent chronic pain non-drug treatment means is provided under the condition that daily activities are not interfered, and high compliance and a remarkable analgesic effect are achieved.

The invention belongs to the field of human body detection devices, and provides a wearable system for evaluating pain, which comprises wearable equipment in communication connection with a cloud system. The wearable device is used for continuously collecting physiological signals when a patient wears the wearable device. The cloud system is provided with a hierarchical evaluation module, a correction module and an intervention control module, the hierarchical evaluation module extracts physiological features in a long-term time window, extracts skin electroreaction mutation, a myoelectricity activity peak value and skin temperature rapid change in a short-term time window, and deduces an acute pain level in combination with instant self-evaluation information of a patient; the correction module is used for correcting the acute score based on the mapping relation between the long-term stability characteristic and the acute pain level and an individualized threshold value; the intervention control module generates individualized electrical stimulation control parameters according to the correction result and issues the individualized electrical stimulation control parameters to the wearable device, and the electrical stimulation execution unit outputs a stimulation signal through the percutaneous electrode to relieve pain of the patient and uploads a feedback signal to the cloud end to form a closed loop.

The invention discloses an intelligent monitoring system for tumor nursing. The intelligent monitoring system comprises a wearable sensing terminal group, a patient terminal, a cloud platform intelligent analysis center and a medical care terminal. The wearable terminal is used for collecting multi-modal physiological and behavior data such as electroencephalogram, near-infrared brain blood oxygen, heart rate variability, body movement, electrodermal response and gaits; the patient terminal is used for recording subjective symptoms and completing cognition and exercise task evaluation. The cloud platform intelligent analysis center constructs a multi-parameter individualized baseline through data fusion, noise suppression and time alignment, comprehensively analyzes real-time deviation, electroencephalogram abnormality, cognitive deviation and gait change by using a short-term acute early warning model and a long-term trend early warning model, and generates a risk level. The digital twinborn visualization module is used for dynamically displaying the state of the patient, and the medical care terminal performs clinical response based on the graded alarm board. According to the invention, extra-hospital multi-mode continuous monitoring, individualized trend identification and intelligent early warning can be realized, and the real-time performance of follow-up management of brain tumor patients is improved.

The invention discloses a music interaction method used in games, electronic equipment and a storage medium, and relates to the field of music interaction in games, and the method comprises the following steps: establishing a character information fingerprint database in one-to-one correspondence with players; a brainwave, heart rate and electrodermal response collecting device is arranged, and physiological signals of a player in the game process are collected in real time; establishing internal and external dual modes of the game scene, and obtaining dynamic parameter information of the game scene; according to the physiological signal of the player in the game process and the dynamic parameter information of the game scene, establishing a music interaction dynamic adjustment model in the game; obtaining and recording the optimal somatosensory of music interaction in the game, and establishing an optimal somatosensory library of the music interaction in the game; and establishing a feedback evaluation system, and optimizing music interaction in the game of the player. The method has the advantages that real-time behaviors, physiological states and game scene data of players in a game are deeply analyzed, and player personalized adaptation-game scene dynamic response is generated in real time through AI.

This invention relates to the field of depression monitoring and intervention technology, and discloses a depression monitoring and intervention system based on an internet hospital and heart rate variability (HRV). The system includes: a multimodal physiological signal acquisition module for simultaneously acquiring raw HRV signals, skin conductance fluctuations, and respiratory rate data; a signal processing module for preprocessing and feature extraction of the raw signals to obtain multidimensional feature vectors; and a depression level inference module that constructs a multilayer perceptron neural network, taking as input the multidimensional feature vector containing multidimensional HRV features, skin conductance fluctuations, and respiratory rate statistics, and outputting probability values ​​for no depression, mild depression, and moderate to severe depression levels. This invention establishes an intelligent management mechanism for internet hospitals for the first time, collecting multidimensional physiological data of patients with depression in real time through wearable monitoring units. Based on the dynamic trends of physiological data characteristics, it can serve as an auxiliary diagnostic tool for psychiatry, especially suitable for early screening and remote follow-up of depression, reducing the consumption of medical resources.

The application discloses a hearing aid health analysis method based on physiological signal monitoring function, comprising the following steps: collecting physiological signal data of a user in real time through a multi-modal biological sensor array integrated in a hearing aid body; the hearing aid body comprises a digital signal processor and a wireless communication module; the physiological signal data at least comprises an electroencephalogram signal, a galvanic skin response signal and a temporal artery pulse signal; the physiological signal data is subjected to motion artifact elimination processing through an adaptive filtering algorithm; a preprocessed physiological signal is subjected to feature extraction through an embedded health analysis engine, and a composite health feature vector comprising a sympathetic nerve activity index, a cognitive load metric and a cardiovascular function index is generated; a health risk assessment report is dynamically generated based on the association between the composite health feature vector and a preset hearing scene; and a graded health alarm signal is output through a bone conduction vibration unit and an audio synthesis module of the hearing aid body.

The invention relates to the technical field of virtual reality (VR) interaction and artificial intelligence, and provides an emotion recognition method based on a multi-modal fusion neural network, which comprises the following steps: S1, acquiring multi-modal physiological signal data including electroencephalogram signals, eye movement data, heart rate variability signals and skin electric response signals; s2, performing filtering, slicing and normalization processing on the obtained original signal through an edge calculation preprocessing unit to generate multi-channel time sequence data; s3, inputting the multi-channel time sequence data into a multi-modal fusion neural network model which comprises a spatial feature extraction layer, a time sequence feature extraction layer, a cross-modal attention fusion layer and a classification output layer and is used for extracting deep features of a multi-modal signal and identifying an emotional state of a user; the emotional state of the user in the virtual reality environment can be accurately recognized in real time, the virtual scene is dynamically adjusted according to the emotional state, and the reality sense and the participation degree of user experience are improved.

This invention relates to the fields of physiological signal processing and emotion recognition technology, and discloses a method for recognizing physiological and emotional states of diverse populations. The method includes: acquiring facial video streams, infrared thermal imaging, and electrodermal signals; performing spectral adaptive enhancement based on skin color to obtain a spectrally enhanced video stream; inputting the spectrally enhanced video stream into a spatially modulated attention network to obtain a remote photoplethysmography waveform; extracting temperature distribution features from infrared thermal imaging; extracting electrodermal response features from electrodermal signals; aligning the three signals spatiotemporally and constructing alternative features for the failed signal based on the signal-to-noise ratio; inputting the three signals and alternative features into a multimodal signal pattern recognition network for weighted fusion, outputting a fused physiological feature; inputting the fused physiological feature into a population-level verification network for population adaptive matching and individual baseline correction, outputting the final emotional state. This invention achieves robust, fair, and accurate emotion recognition for diverse populations.

The invention discloses a driver trust state identification and behavior verification method in pilot aided driving. Comprising the following steps: collecting physiological signal data and staged subjective evaluation data of a plurality of drivers under a pilot aided driving task; performing feature extraction and normalization processing on the physiological signals to obtain heart rate variability related features and skin electric response related features; carrying out dimensionality reduction fusion on the two types of features by adopting a grouping principal component analysis method, and calculating a heart rate variability comprehensive score and a skin electroreaction comprehensive score; the subjective trust score and the two physiological comprehensive scores are combined, and the comprehensive trust score of each driving task is calculated in a weighted mode; dividing the driving task into a high credibility state and a low credibility state based on a clustering algorithm; and corresponding vehicle operation data are extracted, behavior differences in safety, comfort and efficiency indexes in different states are analyzed, and the rationality of state division is verified through a statistical method. The objective quantification of the driver trust state and the verifiability of the behavior level are realized.

The application discloses a kind of pilot trust state in piloting auxiliary driving identification and behavior verification method;Including: collecting the physiological signal data and phased subjective evaluation data of multiple drivers under piloting auxiliary driving task;Characteristic extraction and normalization processing are carried out to physiological signal, and heart rate variability related feature and galvanic skin response related feature are obtained;Two kinds of features are respectively reduced dimension and fused using grouping principal component analysis method, and heart rate variability comprehensive score and galvanic skin response comprehensive score are calculated;Subjective trust score and two physiological comprehensive scores are combined, and the comprehensive trust degree score of each driving task is weighted calculated;Driving task is divided into high and low trust degree state based on clustering algorithm;Extract corresponding vehicle operation data, analyze the behavior difference in safety, comfort, efficiency index under different states, and verify the rationality of state division by statistical method.The objective quantification of pilot trust state and the verifiability of behavior level are realized.

The application relates to the technical field of medical devices and particularly discloses a pain dynamic evaluation and treatment linkage system based on three-dimensional somatosensory interaction, which comprises the following modules: a human body modeling module for constructing a three-dimensional human body dissection model; a somatosensory interaction module for receiving pain area marking instructions and pain intensity scores; an infrared sensing module for monitoring human body infrared thermal imaging data of a patient in real time; a physiological monitoring module for monitoring heart rate variability indexes and skin electric reaction indexes of the patient based on wearable devices; a region marking module for obtaining a calibrated pain area of the patient based on the pain area marking instructions and abnormal temperature zones in the human body infrared thermal imaging data; a pain prediction module for predicting a pain intensity prediction curve in a future period based on the pain intensity scores, the heart rate variability indexes, the skin electric reaction indexes and the calibrated pain area of the patient; and a treatment linkage module for generating treatment parameter control instructions based on the pain trend prediction curve and a multi-dimensional pain characteristic vector, so that precise and dynamic treatment intervention is realized.

The invention discloses a bioelectricity signal detection device, a bioelectricity signal detection method and an electrode detection circuit of the bioelectricity signal detection device, and relates to the technical field of electrical signal detection.The electrode detection circuit of a bioelectricity signal detection module comprises a skin electricity detection circuit and a nerve electricity detection circuit, the skin electric detection circuit comprises a skin electric reaction chip and a skin electric reaction electrode group; the skin electric reaction positive electrode and the skin electric reaction negative electrode are respectively connected with the skin electric reaction chip through a first branch circuit; the neural electric detection circuit comprises a neural electric chip and a neural electric electrode group; the two neural electric positive electrodes and the two neural electric negative electrodes are connected with the neural electric chip through second branches respectively; each second branch is provided with a first switch, and each first switch is connected with the skin electric reaction chip through a third branch. Through cooperation of the third branch circuit and the first switch, the electroneurographic signal positive electrode and the electroneurographic signal negative electrode are used for skin electric response signal detection, and the accuracy of judgment of physiological state information of a user is improved.

The invention discloses a cancer rehabilitation period patient and caregiver physiological health collaborative monitoring and intervention system, which comprises a multi-source physiological data acquisition module, a physiological interaction modeling module, a collaborative health intervention module and a closed-loop feedback regulation and control module, the multi-source physiological data acquisition module synchronously acquires heart rate variability, electrodermal response and body movement acceleration data of a patient and a caregiver through a wearable device and outputs a physiological stress index, a physiological load index, a physiological synchronization index and a rhythm matching index, and the physiological interaction modeling module constructs a double-person physiological state interaction model based on cross-correlation analysis. Key intervention nodes are recognized, the collaborative health intervention module determines the optimal intensity ratio and time sequence cooperation parameters of intervention of the two parties through a collaborative optimization algorithm, the closed-loop feedback regulation and control module executes intervention and monitors the effect, and when deviation exceeds a threshold value, a feedback signal is transmitted back to trigger scheme adjustment; a complete technical link of two-person physiological data collaborative acquisition, interactive modeling, collaborative intervention and closed-loop feedback is realized.

A system and method for objectively determining interpersonal compatibility by correlating synchronized multimodal biometric responses to humorous stimuli. The system presents categorized media content to a user while simultaneously capturing asynchronous data streams, including facial video, audio vocalizations, and galvanic skin response (GSR). A synchronization engine aligns these streams, applying a latency correction algorithm to correlate delayed physiological arousal peaks (phasic SCR) with antecedent visual micro-expressions (Action Units 6 and 12). By validating visual smiles with physiological arousal, the system distinguishes genuine Duchenne responses from social masking. These validated responses are fused via a tensor network into a multidimensional “Humor Response Vector.” Compatibility is determined by calculating the cosine similarity between users' vectors in a latent space, providing a quantification of psychological alignment that transcends self-reported bias. Embodiments include privacy-preserving edge extraction and homomorphic encryption of profile vectors.

This product provides a necklace-type terminal and data processing system that allows users to optimize their performance in real time during exercise, enabling more effective training. [Solution] The necklace-type terminal includes an acceleration sensor that detects the wearer's movements, a skin electroreaction sensor that detects the wearer's biological data, an environmental sensor that acquires data on the wearer's external environment, a current location acquisition module that acquires the wearer's current location, a data collection unit that collects data from the acceleration sensor, the skin electroreaction sensor, the environmental sensor, and the current location acquisition module, and a control unit that acquires the results of the analysis of the data collected by the data collection unit and provides feedback on the wearer's training.

A system for interactive audience engagement during motivational speeches and live events. The system includes: a plurality of wearable audience engagement devices configured to be worn by the audience, each device including at least one biometric sensor selected from the group consisting of a photoplethysmography (PPG) sensor, a galvanic skin response (GSR) sensor, and a micro-expression detection camera, and further comprising an inertial measurement unit (IMU), a directional microphone, and a low-power wireless communication module; a central analysis and control server communicatively connected to the plurality of wearable devices via a wireless communication protocol, the server comprising a real-time data acquisition interface, a feature extraction engine, and a sentiment inference module executing one or more machine learning models trained to classify emotional and cognitive engagement states at the individual and group levels; a speaker feedback interface configured to receive processed audience participation metrics from the central server and provide real-time feedback to the speaker via a haptic, visual, or audible signaling mechanism; and an ambient synchronization subsystem consisting of intelligent lighting fixtures, programmable acoustic emitters, and projection systems, all networked to the central server and configured to dynamically adjust ambient conditions based on detected audience mood states.

The invention discloses a human body emotion recognition system and method based on a double-flow residual space-time fusion model, and relates to the technical field of artificial intelligence. Comprising a human body peripheral physiological information acquisition module, a human body peripheral physiological information transmission module, a human body peripheral physiological information preprocessing module, a human body emotion classification and recognition module and a human body emotion information application module which are connected in sequence, the human body peripheral physiological information acquisition module is used for acquiring peripheral physiological data of a user in real time, wherein the peripheral physiological data comprises photoelectric volume pulse waves, skin electrical response and temperature; the human body peripheral physiological information transmission module is used for transmitting the collected peripheral physiological data of the user to a local server or a cloud server; the human body peripheral physiological information preprocessing module is used for sequentially carrying out storage, extreme value removal, filtering, interpolation, normalization, sliding window segmentation and label calibration on the collected peripheral physiological data of the user.

This invention discloses a bioelectrical signal detection device, detection method, and electrode detection circuit, relating to the field of electrical signal detection technology. The electrode detection circuit of the bioelectrical signal detection module includes a skin conductance detection circuit and a neural conductance detection circuit. The skin conductance detection circuit includes a skin conductance response chip and a skin conductance response electrode group. The positive and negative electrodes of the skin conductance response are each connected to the skin conductance response chip through a first branch. The neural conductance detection circuit includes a neural conductance chip and a neural conductance electrode group. Two positive and two negative neural conductance electrodes are each connected to the neural conductance chip through a second branch. Each second branch is equipped with a first switch, and multiple first switches are each connected to the skin conductance response chip through a third branch. This invention utilizes the cooperation of the third branches and the first switches to use the positive and negative electrodes of the neural conductance signal for skin conductance response signal detection, improving the accuracy of judging the user's physiological state information.

The present invention aims to create the ideal conditions for a user to consume and interact with digital content in a simple, effective and distraction-free manner. In order to achieve this aim, it provides the possibility of accessing the content by various means, with particular emphasis on physical media such as content discs or release discs. After accessing the content, the user experiences simplified navigation, free from distractions or interruptions. In addition to providing access to the content, the device has mechanisms to measure the user's level of attention and focus, as well as their stress and emotional state, while they are using the device. Some of these mechanisms are embedded and operate by means of an infrared camera mounted on the main device. Other mechanisms can be included by means of connecting peripherals such as an EEG, a heart rate sensor and a galvanic skin response sensor. The data resulting from these measurements are combined and used to provide feedback to the user and to personalise their content consumption experience. Finally, the user's focus and sense of immersion can be enhanced by means of using a pair of virtual reality glasses as a peripheral. These glasses may or may not have an integrated EEG and when connected to the device, provide new possibilities for viewing and interacting with the content.

The invention relates to a vehicle-mounted anti-carsickness device and a control method thereof. The device comprises a mask body, a dosing module, an electromagnetic valve, an exhaust fan, an induction module, a control module and a power module. The mask body is provided with an air inlet hole and an exhaust hole which are connected with the electromagnetic valve and the exhaust fan respectively and used for achieving air circulation adjustment. The drug delivery module adopts a pharmacological unit containing peppermint essential oil and an activated carbon filtering unit, and is detachably connected with the mask body through a modular structure, so that drug delivery and air purification are realized. The sensing module is arranged on the wrist of a wearer, physiological data are collected through the skin electric response sensor and the heartbeat frequency sensor, the control module calculates the carsickness index MSI, the working states of the electromagnetic valve and the exhaust fan are regulated and controlled according to the index, and personalized dynamic intervention is achieved. The device is compact in structure and convenient to use, has high real-time response capability and carsickness relieving effect, and is suitable for various vehicle-mounted environments.

The invention relates to the field of acupuncture physiotherapy evaluation and diagnosis, in particular to an evaluation method for acupuncture treatment insomnia curative effect quantification based on physiological sign signal analysis. The method comprises the steps that physiological sign signals are collected in real time and subjected to normalization processing, and normalized electroencephalogram signals, electrocardiogram signals and skin electric response signals are obtained; based on the normalized electroencephalogram signal, electrocardiogram signal and skin electric response signal, obtaining brain wave characteristics of each frequency band, dynamic interaction characteristics of the electrocardiogram signal and change rate of the skin electric response signal, and performing multi-dimensional signal characteristic fusion; based on the fused signal features, a curative effect score is calculated. The problems that a traditional curative effect evaluation method is easily influenced by subjective emotions of a patient, and accurate curative effect quantification is difficult to provide are solved; the problems that traditional linear regression or simple weighted average models are mostly adopted, the curative effect cannot be accurately evaluated in real time, and the dynamic adaptive capacity to individual differences is insufficient are solved.

The invention relates to an audience emotion analysis method and device based on multi-modal data, equipment and a medium thereof. The method comprises the following steps: screening a plurality of historical reference films of a target type from a historical film library, and fusing audience skin electrical response and electrocardio data to establish a type film emotion reference curve; collecting same-mode physiological data of a to-be-tested film to generate a to-be-tested film emotion curve, comparing the continuous time periods when the recognition values of the two curves are continuously lower than the reference, and positioning the continuous time period when emotion arousing is weak. By adopting the method, the emotional valley in the movie can be accurately positioned by using objective physiological data, and the defect that traditional subjective evaluation cannot quantify time-period play is overcome.