417 results about "Biosignal" patented technology

The invention discloses a pneumatic-biological signal feedback cooperative control method based on an anti-gravity rehabilitation treadmill, and the method comprises the steps: synchronously collecting sEMG, IMU and plantar pressure signals, and completing the time-space registration through clock synchronization, time warping and extended Kalman filtering; muscle activation degree, pressure center offset and joint angular velocity are extracted to construct multidimensional features, and gait phase switching is predicted through LSTM-CNN; the TS fuzzy system generates a preliminary control instruction according to expert rules, and Q-learning takes pressure balance and motion smoothing as reward functions to optimize the weight reduction proportion and the treadmill speed online; a waist annular air cavity is driven to be inflated to form a pressure supporting field so as to lift a pelvis and guide a lower limb gait mechanical mode, so that the muscle activation degree, the joint angular motion trail and the pressure center migration characteristic are dynamically influenced, a servo motor tracks phase nodes in a feedforward-feedback mode, and risk early warning and training parameter self-adaptive closed-loop rehabilitation are achieved.

The invention provides a diabetic neuropathy dynamic early warning method based on a multi-modal biological signal, and the method comprises the steps: integrating a multi-modal sensor through an intelligent wearable device, and constructing a personalized prediction model in combination with a deep learning algorithm; through an artificial intelligence algorithm, diabetic neuropathy (DN) development risk prediction and unperceptual hypoglycemia real-time early warning are realized. According to the time-frequency domain characteristics and clinical data of the multi-mode signal, the time window of the DN from the progress to the serious stage can be predicted; recognizing an unperceptual hypoglycemia risk by combining ECG and HR features with a blood glucose trend prediction model, and triggering grading intervention measures; based on the individual physiological response mode of the patient, the algorithm threshold is dynamically optimized, and the accuracy of prediction and early warning is improved. According to the method, HR, BP, ECG and other multi-dimensional signals are integrated, and the prediction precision is improved; continuous monitoring and instant feedback are supported; the self-adaptive algorithm adapts to individual differences of patients, and the limitation of a traditional general model is overcome.

The invention relates to a child attention evaluation and training method and system, and the method comprises the steps: obtaining a brain wave mode, an eyeball movement track, an emotion activation level and autonomic nervous system function data of a child in real time, and generating multi-dimensional biological signal data; based on the multi-dimensional biological signal data, generating evaluation task data; generating dynamic task parameters according to biological signal data and behavior performance data in the evaluation task data; generating an attention feedback signal based on the dynamic task parameters; according to the attention feedback signal, a training task integrating work memory, suppression control and cognitive flexibility is constructed, and cognitive function training data is generated; and based on the cognitive function training data, synchronously sharing the attention performance data of the children to the parent terminal and the teacher terminal, generating life situation intervention suggestions, and iteratively optimizing evaluation parameters according to a long-term effect evaluation result. Therefore, the accuracy of children attention evaluation and training can be improved.

The invention relates to the technical field of medical instruments, and discloses a minimally invasive surgery system based on artificial intelligence. Comprising a target identification and positioning module, a multi-mode sensing module, a self-adaptive control module, a man-machine interaction module and a scene adaptation module. The target recognition and positioning module positions an operation target in real time through image recognition and a deep learning algorithm; the multi-mode sensing module collects the stress and movement track of an instrument and physiological signals of a patient. The self-adaptive control module adjusts movement parameters of the instrument according to the data; the man-machine interaction module displays data and provides an operation interface; the scene adaptation module adjusts the instrument form and function according to the operation type. In addition, the system also comprises an operational stability evaluation module, an instrument life prediction module and a biological signal fusion module. The system can achieve precise positioning, real-time monitoring and intelligent control, and the precision, safety and operation convenience of minimally invasive surgery are improved.

The invention relates to the cross technical field of signal processing and artificial intelligence, in particular to a multi-source noise removal method and system based on DAE, and the method comprises the steps: 1, carrying out the data collection and feature extraction of multi-source noise and pure signals; step 2, constructing a de-noising recognition knowledge base based on feature analysis; step 3, constructing a deep denoising auto-encoder model based on a knowledge base; step 4, hierarchical training and optimization guided by a mixed loss function of the deep denoising model; step 5, denoising processing of a target signal and output evaluation based on a discrimination model; according to the invention, noise data in multiple fields such as electromagnetism, remote sensing and biological signals are integrated, a dynamic mixing strategy and a data enhancement technology are adopted, a training set which highly simulates a real environment is constructed, and a unique cross-scene adaptation module can perform adaptive adjustment according to signal characteristics of different application scenes; the problem that a traditional method is poor in scene adaptability is solved.

The invention belongs to the field of intelligent rehabilitation medical instruments, and particularly relates to a brain-computer interface-based multi-modal child sensory integration training device, which comprises a brain-computer interface head ring, an intelligent touch floor and AR interactive glasses, the brain-computer interface head ring is connected with a biological signal acquisition module, the intelligent touch floor is connected with a motion trail analysis unit, the AR interactive glasses are connected with a universe scene engine, and the biological signal acquisition module, the motion trail analysis unit and the universe scene engine are connected with a digital twin generator. The digital twin generator is connected with a dynamic mode switching decision tree, and the dynamic mode switching decision tree is connected with a multi-mode feedback actuator; the brain-computer interface head ring is located on the head of the child and collects electroencephalogram, myoelectricity and electrocardiosignals through a biological signal collection module; and the biological signal acquisition module sends a signal to the digital twin generator through wireless transmission. According to the invention, the training efficiency can be improved, the evaluation dimension can be expanded, potential safety hazard early warning can be realized, and the compliance can be enhanced.

The invention belongs to the technical field of resource management, and relates to a cloud computing-based intelligent agricultural resource optimization scheduling system, which comprises a biological signal acquisition module used for acquiring biological signals of crops in a target area; the environment data acquisition module is used for acquiring environment parameters outside crops; the dynamic demand analysis module is used for fusing the biological signals and the environmental parameters, generating dynamic demand indexes and judging whether a resource cooperation request is triggered or not; the block chain scheduling management module is used for receiving the resource cooperation request and calling surplus resources of the target farm to execute operation; the contribution point management module is used for generating verifiable contribution points and dynamically adjusting the exchange weight; and the intelligent verification module is used for comparing the deviation between the chain record and the local sensor data, triggering an automatic verification mechanism and correcting the account book record. The method can solve the problems that the contribution degree of participants lacks a credible quantitative standard, the benefit distribution contradiction inhibits the resource sharing willingness between farms.

The invention relates to the technical field of intelligent medical rehabilitation evaluation, and discloses a biological index detection system for orthopedic rehabilitation evaluation. According to the system, biological signals of a patient are collected through a multi-sensor array of a biological signal sensing module to generate an original data stream; the quality self-adaptive evaluation module carries out real-time quality analysis on the data and generates a quality parameter set; the signal intelligent processing module dynamically adjusts a signal processing strategy according to the quality parameter set and then outputs optimized signal data; the feature depth extraction module is used for mining biological features from the optimized signal data and generating feature weight distribution; the rehabilitation dynamic scoring module calculates a rehabilitation evaluation index based on the feature weight distribution; and the intervention strategy generation module automatically adjusts an evaluation strategy according to the rehabilitation evaluation index and outputs a personalized evaluation report. According to the invention, through real-time quality monitoring and adaptive signal processing, the accuracy and reliability of orthopedic rehabilitation evaluation are improved.

The invention relates to the technical field of image processing and biological signal detection, discloses a facial physiological detection method and system based on signal quality driven ROI selection, and aims to solve the problem of signal degradation of a traditional fixed geometric ROI in a complex scene. The method comprises the following steps: collecting a user face video stream through a camera and preprocessing the user face video stream; detecting a face bounding box and dividing the face bounding box into a plurality of sub-regions; calculating the signal-to-noise ratio, the periodic intensity and the motion artifact interference degree of each sub-region; screening an optimal sub-region according to a weighted fusion formula to generate a dynamic ROI mask; extracting a pure rPPG signal from the dynamic ROI mask coverage area; detrending and band-pass filtering are carried out on the rPPG signals, and physiological parameters such as the heart rate and the blood oxygen saturation degree are extracted. The system comprises a face video acquisition module, a face region positioning and segmentation module, a signal quality evaluation module, a dynamic ROI selection module, an rPPG signal extraction module and a physiological parameter estimation module. According to the technical scheme, signal degradation caused by local shielding, illumination abrupt change or attitude offset can be effectively avoided, the signal-to-noise ratio and the stability of the rPPG signal are remarkably improved, and the universality and the robustness of the method are enhanced.

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 provides an intelligent motion evaluation and damage early warning method and system based on multi-source biological signal fusion, and is applied to the technical field of medical data processing. The method comprises the following steps: acquiring a multi-modal biological signal acquired based on a multi-source sensing node; carrying out self-adaptive preprocessing on the multi-modal biological signals, eliminating motion artifacts by adopting an improved spectral subtraction formula, realizing multi-rate synchronization through a Lagrange kernel, triggering node vibration by virtue of signal quality evaluation to prompt electrode reattachment, and generating preprocessed signal features; and constructing cross-modal fusion features by using multi-scale cavity convolution and the like, and outputting results such as a motion mode and the like. A target depth model is trained in combination with signal optimization and data enhancement and the like, a motion pattern classification result, joint load estimation data and a damage risk scoring result are processed based on the target depth model, damage early warning information is generated, and accurate and dynamic motion evaluation and damage early warning are achieved.

A device 100 for developing the concentration, the device 100 comprising: an optical sensor unit, the optical sensor unit comprising a plurality of one or more lenses 201, 202 capable of holding sensitive elements, the plurality of sensitive elements being configured to detect a user-provided biological signal in at least three operating modes; three switches for switching between the plurality of operating modes; a plurality of illumination units configured to indicate each of the plurality of operating modes by emitting a predetermined light signal; a processing unit for processing information using artificial intelligence.

The invention provides a relaxation and decompression system for relieving juvenile depression based on traditional Chinese medicine five-tone music, and relates to the technical field of five-tone relieving, the relaxation and decompression system comprises an equipment layer and a cloud service layer, the equipment layer comprises an intelligent bracelet module and a multi-mode equipment output module, a user interaction sub-module, an optical heart rate sensing sub-module, a skin electric response sensing sub-module, a body temperature sensing sub-module and a questionnaire survey sub-module are arranged in the intelligent bracelet module, the multi-modal equipment output module comprises an audio conduction unit and a wearable acupuncture point stimulation unit, and a phototherapy control sub-unit is arranged in the wearable acupuncture point stimulation unit. The cloud service layer comprises a traditional Chinese medicine five-tone generation module, a cloud intelligent analysis module and a user feedback module; by introducing the mechanism of five voices of traditional Chinese medicine and combining the theory of traditional Chinese medicine, a three-dimensional synergistic mode of music, acupoint stimulation and phototherapy is provided, and a relieving control scheme is continuously adjusted through real-time biological signal feedback, so that the depressive symptom of teenagers is effectively relieved, and the pressure is relieved.

The invention relates to the technical field of image enhancement, in particular to a rhizosphere microflora distribution analysis method based on image enhancement, which comprises the following steps: acquiring a continuous microscopic image sequence of a rhizosphere microenvironment, converting the continuous microscopic image sequence into a gray data space, and constructing a standard input frame set for optical flow calculation. According to the method, the stability score of each feature is calculated by using the signal-to-noise ratio stability measurement function, and the long-life-cycle feature is screened and reserved, so that the random imaging noise of the short life cycle can be filtered fundamentally, and the topological structure of the target community is obtained; and mapping the topological structure back to an original image space by using an inverse persistence mapping algorithm to be fused with a continuous microscopic image sequence, and finally, remarkably enhancing weak biological signals while keeping original form details, thereby realizing high signal-to-noise ratio analysis of microflora spatial and temporal distribution in a complex rhizosphere environment.

A method for biosignal data transformation can include: determining a set of biosignal data, determining a brain state representation (e.g., an embedding) based on the biosignal data, and determining a biomarker value based on the brain state representation. The method can optionally include training a model (e.g., training a model used to determine the brain state representation), and / or any other suitable steps. A system for biosignal data transformation can include a biosignal device and a computing system.

The invention provides a multi-mode biological signal dynamic fusion fitness guidance method and system. The method comprises the steps that a signal acquisition layer synchronously acquires dynamic force and static pressure through a flexible voltage capacitance sensor; in the feature processing layer, frequency domain energy distribution is extracted from voltage signals through short-time Fourier transform, static pressure gradient is obtained from capacitance signals through wavelet packet decomposition, time-frequency decomposition is carried out, and feature dimensionality reduction is carried out; the fusion modeling layer is used for eliminating time migration of sensor data through dynamic time warping to carry out time alignment and carrying out feature fusion based on a Transform attention mechanism; and the decision generation layer performs incremental learning and large model driven fine tuning to generate a personalized exercise prescription. Based on the technical scheme of the invention, the muscle state analysis precision is effectively improved through the dynamic time-frequency fusion of the dynamic and static signals; real-time dynamic feedback of the system is improved; the model power consumption is reduced, and the noise suppression rate is improved; and the personalized adaptation degree is improved by a dynamic threshold value.

Apparatus and methods detect sleep staging events. The apparatus (100) may be configured to obtain a facial biopotential signal measured between two electrodes connected to a user's face, which may be configured to form a transverse-ocular measurement vector. The biopotential signal may be measured by a biopotential measurement device comprising the two electrodes. The apparatus (100, 200) may be configured to derive, from the facial biopotential signal, a plurality of biosignals suggestive of sleep staging events. The apparatus may be configured to classify individual segments of the plurality of biosignals as belonging to one of a plurality of sleep stating events. The classification may involve using a trained machine learning model, such as a recurrent neural network, to predict sleep staging events for different segments or epochs of the plurality of biosignals.

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 training system for economic management teaching based on cloud service. The training system comprises a biological signal acquisition module, an electroencephalogram signal processing module and a dynamic difficulty adjustment module, the invention relates to the technical field of educational training. According to the training system for economic management teaching based on the cloud service, the cognitive load degree of a student is clearly judged by collecting electroencephalogram signals of the student, and corresponding intervention of the difficulty of an economic management teaching course is performed according to the cognitive load degree of the student, so that personalized teaching of the student can be realized, and the teaching efficiency is improved. In addition, the system can assist students in high-efficiency learning, achieves the purpose of better adapting to individual differences and teaching stage changes, and provides effective support for improving intervention accuracy, enhancing system adaptability and optimizing learning experience.

The invention relates to the technical field related to intelligent traffic driving safety, in particular to a dangerous driving behavior recognition method and system based on multi-modal data fusion. According to the method, driver behaviors and vehicle operation data are collected in real time through a vehicle-mounted sensing system, hardware-level synchronization of multi-source data is achieved based on GPS-PPS pulses, and a unified timestamp system is established; extracting multi-modal features of vision, vehicle dynamics and biological signals under a unified time sequence; cross-modal feature deep fusion is realized by using a graph neural network and an attention mechanism; performing risk quantification by adopting a secondary decision model in combination with an environment adaptive weight; and finally, according to the risk score, triggering graded early warning and linkage with vehicle control, and meanwhile, generating an event report and uploading the event report to a cloud platform to form closed-loop management. According to the method, the problem of misjudgment caused by data asynchronization in a traditional scheme is effectively solved, and the recognition accuracy and the system reliability in a complex environment are remarkably improved.

Provided is a system using smart contact lenses for measuring biosignals, the system comprising: smart contact lenses for measuring biosignals or injecting drug into the human body; and an accessory device for communicating with the smart contact lenses and supplying power thereto, wherein the smart contact lenses and accessory device are each provided with at least one electrode for connecting to the human body, and transmit and receive electrical signals between each other using the human body as the medium, and the smart contact lenses are provided with a power reception circuit for receiving operational power from the drug injection device.

The invention relates to the technical field of intelligent interaction, and discloses an intelligent game interaction method for an intelligent park, which comprises the following steps of: 1, acquiring behavior data of players through a plurality of sensors; step 2, constructing a dynamic Hamiltonian optimization model containing a physiological signal coupling item, and solving the quantum state representation through a quantum optimization algorithm to generate a multi-target optimization parameter; 3, based on the optimization parameters, carrying out cooperative adjustment on music rhythm and task difficulty in game interaction; and step 4, performing incremental updating on the dynamic Hamiltonian optimization model according to the adjusted player feedback data. According to the method, the space-time registration algorithm of the millimeter wave radar, the visual sensor and the biological signal is adopted, high-fidelity representation of the human body motion mode in the quantum Hilbert space is achieved, and quantization input with definite physical significance is provided for subsequent optimization.

The invention discloses a power grid dispatcher cognitive load dynamic adjustment system and method based on multi-modal biofeedback, and the method comprises the steps: carrying out the real-time quantification of the cognitive load of a power grid dispatcher through fusing the multi-modal biofeedback signals, such as electroencephalogram, eye movement and galvanic skin; and a human-computer interface information presentation mode and a task allocation strategy are dynamically adjusted according to the load level. The system specifically comprises a biological signal acquisition module, a cognitive load calculation engine module, a dynamic adjustment execution module and a closed-loop feedback adjustment module. According to the method, the problems of information overload, poor individual adaptability, high misoperation risk and the like in a traditional power grid dispatching system can be effectively solved, the efficiency and safety of dispatchers in complex fault handling are improved, and the time sensitivity requirement of power control is met.

The invention relates to the technical field of sonar imaging, and discloses a marine organism distribution detection system and method based on sonar imaging, and the method comprises the steps: setting a plurality of depth layers, and collecting an acoustic echo signal of each depth layer; marine organism block sparse observation is carried out based on the three-dimensional acoustic echo signals, an observation matrix is obtained, and an effective biological signal block set is calculated; calculating space coordinates and reflection intensity of each biological target according to the effective biological signal block set, obtaining biological positioning data of each depth layer, and obtaining a three-dimensional acoustic image of each depth layer through three-dimensional imaging; the biological density index, the Shannon-Vaniner diversity index and the vertical migration speed of the scattering coefficient in each ecological layer are calculated, the biological detection result of each depth layer is output, data collection is carried out on the multiple depth layers, the detection reliability in the low-signal-to-noise-ratio marine environment is improved, and the detection efficiency is improved. And marine organism populations and distribution characteristics of different depth layers can be accurately identified.

The invention provides an extreme motion environment prediction method and system based on AR glasses, a medium and a product, and relates to the technical field of extreme motion safety protection, and the method comprises the steps: collecting environment data in a preset distance range of a front environment of a target object in an extreme motion state through a multi-mode environment sensing module of target AR glasses; inputting the obtained current extreme motion type and environmental data into an edge calculation module of the target AR glasses to obtain a danger prediction result output after the edge calculation module calls an extreme motion danger model corresponding to the current extreme motion type to carry out danger prediction analysis on the environmental data; performing motion state analysis on the target object by using a biological signal acquisition module of the target AR glasses to obtain biological signal parameters; and generating safety auxiliary information according to the danger prediction result and the biological signal parameters, and presenting the safety auxiliary information to the target object by using a multi-mode feedback module of the target AR glasses.

The invention relates to a slit grating-assisted high-quality factor micro-ring resonant cavity sensor, which is connected with an input light source and a detector, and comprises a sensor chip module provided with an input optical fiber, an input grating coupler, a slit grating micro-ring resonant cavity sensor, an output grating coupler and an output optical fiber, eight inclination angles are formed between the input optical fiber and the input grating coupler and between the output grating coupler and the output optical fiber; the slit grating micro-ring resonant cavity sensor comprises an upper channel straight waveguide and a sensing ring. The slit grating-assisted micro-ring resonant cavity sensor is introduced, a sensing test shows that the quality factor (Q value) is higher, the higher quality factor (Q value) means a sharper harmonic peak and lower energy loss, the sensor can accurately detect extremely weak chemical / biological signals in combination with high sensitivity, and the signal-to-noise ratio and the measurement precision are greatly improved.

The invention relates to the technical field of animal behavioral experiments, and discloses an animal behavioral experiment multi-scene simulation system. According to the invention, through cooperative control of multiple environment modules and seamless switching of a dynamic scene, continuous switching of natural scenes such as illumination, sound, smell, terrain and the like is accurately simulated; on the basis of real-time biological signal detection and intelligent feedback, environment adjustment or scene switching is automatically triggered according to animal behaviors / physiological states, and closed-loop self-adaptive adjustment is formed; in combination with an intelligent decision and an AI scene generation engine, self-definition and iterative optimization of a complex script are supported; the parameter coupling controller simulates the interaction effect between environmental factors, the fault-tolerant mechanism and the effect evaluator guarantee the data reliability, the closed-loop verification loop efficiently establishes a causal model of environmental parameters and behavior response, and a high-authenticity, self-adaptive and reproducible experimental platform is provided for animal behavior research.

The invention belongs to the technical field of medical rehabilitation instruments and biological signals, and particularly relates to a multi-mode fusion intelligent rehabilitation evaluation system based on EMG and PPG. The system comprises a flexible multi-mode sensor array module used for collecting surface electromyogram signals EMG and photoelectric volume pulse wave signals PPG and capturing muscle activation intensity and blood perfusion dynamics in real time; the self-adaptive contact enhancing module is used for driving the airbag assembly to dynamically adjust the contact pressure between the embedded pressure sensor and the skin through the micro air pump; the edge calculation unit module is used for carrying out localization processing on the collected original signals, quantitatively evaluating the collaboration of muscle activity and blood flow fluctuation and judging whether muscle contraction drives local blood flow change or not or whether vessel limitation reversely inhibits neuromuscular activation or not; and the lightweight classification model module is used for processing the time-frequency characteristics of the EMG and the waveform fragments of the PPG, and outputting rehabilitation state labels after screening key collaborative characteristics through an attention mechanism.

The present disclosure relates to methods and system for acquiring and analyzing biosignals or physiological signals of a person sitting in a vehicle and predicting (in real-time) time-varying attention, engagement level or alertness level using the biosignals. The biosignals may be acquired using one or more clusters of electrodes together with a wearable user device or from a sensing device that is embedded in the seat of the vehicle. In some embodiments, the biosignals may be utilized to predict restedness level of the subject, to monitor or predict physiological state of the subject, to detect a distress situation and to adapt a vehicle control accordingly. In some other embodiments, the biosignals can be transformed into communication, for example, speech signals or instructions for the vehicle. One or more actions may be triggered based on the analysis of the biosignals including engaging the person, generating alerts, or adapting the vehicle control.

A bio-signal detection apparatus includes an analog front end unit includes a plurality of analog front end circuits that process a detected signal; an analog-to-digital conversion unit that converts an output signal of the analog front end unit to a digital code and a multiplexer (MUX) that outputs a processed signal by the analog front end circuits to the analog-to-digital conversion unit; wherein the analog digital conversion unit comprises: a comparator, a successive-approximate analog-to-digital converter (SAR ADC) that converts output signal of the analog front end circuit and form MSB side j-bit of the digital code, and a single slope ADC that converts the output signal of the analog front end circuit to k bit of the digital code.