300 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 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 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 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.

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 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.

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 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.

The invention provides an intelligent health monitoring LED lighting device integrated with a biosensor, and relates to the technical field of intelligent health monitoring. The intelligent health monitoring system comprises an intelligent biological signal acquisition system, a multi-modal data analysis and processing system, an integrated control adjustment decision system and an automatic illumination optimization feedback system, a total database is arranged in the intelligent health monitoring system, and the total database comprises a biological signal database, a health index database and a storage database. According to the system, through a wavelet envelope extraction algorithm and Hilbert transformation, the device recognizes instantaneous phase characteristics of biological signals, collection is triggered only at a phase turning point, data redundancy is reduced, and energy consumption is reduced. And high-precision signal acquisition is ensured by combining dynamic threshold adjustment and anti-motion artifact filtering.

The present disclosure relates to a device and method for generating avatar lip-sync animation based on multimodal biosignals, The device comprises a multimodal data collection unit configured to collect data including biosignal data including brain waves when a user imagines speaking and image data; a preprocessing unit configured to preprocess the multimodal data; a feature extraction unit configured to extract feature vectors including the user's biosignal feature and facial feature from the preprocessed multimodal data; an avatar generation unit configured to generate an avatar; a lip-sync reconstruction unit configured to predict the mouth shape and facial movement when the user imagines speaking by inputting the extracted feature vectors to a pre-prepared lip-sync reconstruction model; and a lip-sync animation implementation unit for implementing an avatar lip-sync animation by applying the mouth shape and facial movement predicted by the lip-sync reconstruction unit to the avatar generated by the avatar generation unit.

The present application relates to the technical field of medical monitoring and nursing, in particular to a kind of anesthesia perioperative patient emotion real-time monitoring and active pacification system, including multidimensional biological signal perception module, space-time associated emotion prediction engine, neural feedback type active pacification module, surgical event linkage unit and medical intelligent decision terminal.Multidimensional biological signal perception module fuses electromyogram subconscious emotion capture, saliva cortisol microfluidic rapid detection and non-contact physiological monitoring technology, realize the synchronous acquisition of subjective emotion and objective biomarker;Space-time associated emotion prediction engine is based on the time sequence characteristics of surgical event and patient historical data, advance prediction emotional fluctuation risk;Neural feedback type active pacification module is through transdermal electrical stimulation and multi-sensory collaborative output, forms emotion regulation closed loop;Surgical event linkage unit and operating room equipment real-time interaction, automatically trigger pre-pacification mechanism at key operation node.

Distinctive elements of the design relate to a smart wearable device that can be used to monitor biosignals. Diagonal line shading (hatching) is included in the figures to illustrate semi-transparent surfaces. 1.1) Perspective view; 1.2) Perspective view; 1.3) Front view; 1.4) Back view; 1.5) Right side view; 1.6) Left side view; 1.7) Top view; 1.8) Bottom view.

Concentration development device 100, comprising: an optical detection unit, including one or more lenses 201, 202 capable of containing sensitive elements, these sensitive elements being configured to detect a user-supplied biological signal according to at least three operating modes; three switches for toggling between the different operating modes; a plurality of lighting units configured to indicate each of the operating modes by emitting a predetermined light signal; an information processing unit using artificial intelligence. Figure for the abstract: Fig 1

The present invention relates to a smart ring device for electrocardiography. The smart ring device comprises: a first electrode which is in contact with a finger of a user while worn on the finger; an insulating spacer surrounding the first electrode; a circuit board which is disposed outside the insulating spacer and connected to the first electrode; an arc-shaped insulating cover covering the circuit board; a second electrode which is exposed to the outside of the insulating cover while connected to the circuit board and can come into contact with the body of the user; a third electrode which is coupled to the outside of the insulating spacer and connected to the circuit board, and can come into contact with the body; and a signal processing unit which is mounted on the circuit board and processes biosignals transmitted from the first, second, and third electrodes. The smart ring device for electrocardiography of the present invention having the above configuration is in the form of a ring and thus very convenient to carry, and in particular, can simultaneously collect electrical signals from three different parts of the body through the three electrodes, and thus is able to obtain more accurate electrocardiography data.

A first electric connector arrangement of an electronic apparatus that is repeatedly connectable with and releasable from a first electric counter-connector arrangement of a bio-signal data processing apparatus. A second electric connector arrangement of the electronic apparatus is connectable with a second electric counter-connector arrangement of the bio-signal electrode arrangement in a releasable manner. A battery provides electric power for operation of the bio-signal data processing apparatus in response to an electric connection through the first electric connector arrangement between the electronic apparatus and the bio-signal data processing apparatus. The first electric connector arrangement includes separate electric connectors, which have multiple pins. The pins are for electric power supply and data transfer. The separate electric connectors and their counter-connectors of the bio-signal data processing apparatus have structural shapes matched with each other for attaching them together based on contact friction in the releasable manner.

According to one aspect of the present invention, there is provided a device for monitoring biological signals, comprising a main body portion and an electrode portion detachably coupled to the main body portion, the electrode portion including two or more electrodes for acquiring the biological signals and a circuit portion for electrically connecting the electrodes to the main body portion.

A method that selects high-quality data within recorded signals from patient monitoring devices, where portions of the signals may be corrupted by noise and should therefore be excluded. Signals are compared to models of expected signal characteristics, and portions of the signals that do not match the models may be excluded. Some models may check for expected relationships between signals from different devices. One such model identifies feature points in two signals from two different devices and calculates the time difference between each feature point in one signal and the earliest subsequent feature point in the other signal; data is excluded if this time difference exceeds an expected range. For example, an expected relationship between electrocardiogram and blood pressure signals is that the R-wave peak should be followed by a blood pressure peak within an expected delay time (the pulse transit time); this check can exclude invalid ECG / BP data.

The invention discloses a multi-unmanned aerial vehicle cooperative inspection system, and relates to the fields of a biological signal identification technology, an unmanned aerial vehicle technology, a bird-like bionic algorithm and multi-unmanned aerial vehicle cooperative management. The system comprises five modules: a biological signal multi-mode acquisition and identification module, an environment perception and multi-machine safety evaluation module, a bird-like bionic path planning and flight control module, a global communication and data processing module, and a ground management and control and cooperative scheduling module. Compared with the prior art, the method has the remarkable advantages that optical limitation is broken through through multi-mode identification, and the omission ratio of biological hidden dangers is reduced; the bird-like bionic algorithm and multiple machines cooperate to improve the inspection efficiency and shorten the period; a dynamic evaluation and collision avoidance mechanism guarantees multi-machine safety; the environmental adaptability is enhanced through three-dimensional meteorological capture and bionic control; the integrated function can be flexibly adapted to multiple industries, and the cross-scene application cost is reduced.

A vital signs measuring device includes a posture measuring unit attachable within a pentahedral space of a body, a biosignal measuring unit detachably connected to the posture measuring unit and capable of measuring at least one of the body's vital signs, a wireless communication unit that transmits data calculated from the biosignal measuring unit externally when the posture measuring unit determines the body to be in a specific posture, and a power supply unit that supplies power to the posture measuring unit, biosignal measuring unit and wireless communication unit. This enables vital signs data to be obtained that is stable and reliable for specific body postures.

This invention discloses a method for accurate heart rate calculation based on multi-source multimodal fusion, relating to the fields of biosignal processing and health monitoring technology. The method includes: simultaneously acquiring multi-source array data such as biosignals, environmental information, and motion status, and constructing a time-aligned dataset; preprocessing and extracting features from the data to obtain multi-dimensional features such as pulse peak interval, R-wave interval, environmental interference level, and exercise intensity level; identifying the current dynamic scene based on features and adaptively assigning heart rate calculation weights to photoelectric and electrocardiogram signals; calculating two preliminary heart rate values ​​in parallel and performing weighted fusion; combining individual feature calibration and a time-series trend model to perform anomaly judgment and correction on the preliminary heart rate values, and outputting the final heart rate value and confidence score. This method, through multi-source fusion and scene-adaptive weighting, achieves multi-scene adaptive, anti-interference, and personalized accurate heart rate monitoring.