57 results about "Biosensor array" patented technology

The invention discloses a wearable emotion regulation system and method integrated with an artificial intelligence algorithm, and belongs to the technical field of intelligent wearing, system hardware comprises an Arduino microcontroller, a biosensor array, an audio output module, a man-machine interaction module and a communication module; software comprises a multi-mode emotion recognition model, a self-adaptive music recommendation engine and a self-healing logic controller, implementation steps include data acquisition and preprocessing, emotion state recognition, music interaction strategies and closed-loop feedback adjustment, the system is integrated to clothes, and the system has specific sensor layout and a safety mechanism. The method can be used in the fields of depression adjuvant therapy, motion monitoring and the like.

The invention relates to the technical field of metabolite monitoring, and discloses a continuous multi-metabolite monitoring method based on multi-modal cross calibration. The method comprises the following steps: acquiring a multi-source metabolite signal sequence containing an electrochemical signal, an optical signal and a thermodynamic signal through an implantable biosensor array; performing time sequence segmentation on the multi-source metabolite signal sequence to generate metabolite concentration time sequence fragments; and synchronously inputting the metabolite concentration time sequence fragment into a metabolite dynamic response model and a metabolite interaction characteristic model, and respectively generating a first metabolite concentration track and a second metabolite concentration track. According to the method, by means of multi-modal signal fusion and cross calibration, in combination with time sequence segmentation and multi-model analysis, dynamic changes and interaction of metabolites can be comprehensively captured, the integrity and accuracy of metabolite monitoring are improved, and the method is suitable for continuous multi-metabolite monitoring scenes.

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 provides a micro-fluidic biosensor array, and relates to the technical field of sewage detection, the micro-fluidic biosensor array comprises a flow channel layer, a first detection layer and a second detection layer which are sequentially stacked from top to bottom; a flow channel structure is arranged in the flow channel layer, a micro-column array is arranged in the flow channel structure, and the micro-column array is used for generating a gradient resistance field for flowing liquid; the first detection layer is provided with a liquid inlet, the liquid inlet is used for receiving liquid discharged from the flow channel layer, the first detection layer comprises a three-dimensional interdigital electrode array and a micropore array modified by a quantum dot labeled antibody probe, and the three-dimensional interdigital electrode array is used for capturing and dissolving organic matter molecules; the second detection layer comprises a fluorescence detection module and an electrochemical detection module, the fluorescence detection module is used for carrying out cell counting and fluorescence signal acquisition on target microorganisms captured by the micropore array, and the electrochemical detection module is used for realizing directional enrichment and concentration detection of heavy metal ions. By means of the micro-fluidic biosensor array, sewage detection of multiple types of parameters can be achieved.

The invention provides an underground coal mine ventilation control method and device, electronic equipment and a storage medium, and belongs to the technical field of mine ventilation. The method comprises the steps that the type and concentration characteristics of harmful gas in a target area are determined according to data collected by a biosensor array; determining time sequence data of the harmful gas according to the concentration of the harmful gas, and determining a target concentration change rate of the harmful gas according to the time sequence data; according to the concentration of the harmful gas, the target concentration change rate and the current underground acoustic wave characteristics of the coal mine, the target air volume of the target area is determined; according to the target air volume of the target area, the ventilation resistance of the target area and the current opening degree of the ventilation valve, the opening degree of the ventilation valve is controlled, and the ventilation air volume is determined. According to the coal mine underground ventilation control method and device, the electronic equipment and the storage medium, the ventilation quantity can be accurately and timely adjusted.

The invention relates to the technical field of veterinary epidemic disease detection, and discloses a veterinary epidemic disease remote monitoring and intelligent diagnosis system, which comprises a multi-mode biosensor array including an implantable miniature biochip, a wearable flexible physiological monitoring patch and an environment sensing sensor cluster, the implantable miniature biochip is used for being implanted into a key tissue organ area in an animal body and collecting microscopic physiological data such as cellular metabolism signals and local immune reaction marker concentration in real time, and the wearable flexible physiological monitoring patch is used for being attached to the body surface of the animal. By means of the multi-mode biosensor array and the edge intelligent preprocessing unit, animal physiology and environment data are accurately monitored in real time. The multi-modal biosensor array covers the implantable miniature biochip, the wearable flexible physiological monitoring patch and the environment sensing sensor cluster, and can comprehensively collect in-vivo microscopic physiological data, body surface macroscopic physiological parameters and feeding environment information of animals.

The invention provides a pet health monitoring system based on computer vision and biosensing. The pet health monitoring system comprises an input layer, a processing layer and an output layer, the input layer is a multi-modal data acquisition module, and the multi-modal data acquisition module comprises a biosensor array unit and a visual acquisition unit; the visual acquisition unit is used for capturing visual information of behaviors and appearances of pets, converting the visual information into digital signals and outputting the digital signals; the processing layer comprises an edge calculation module, and the edge calculation module is used for data transmitted by the biosensor array unit and the visual acquisition unit. According to the pet health monitoring system based on computer vision and biosensing, the multi-modal data acquisition module, the edge calculation module and the intelligent interaction module are utilized, compared with traditional manual observation, the abnormal behavior detection accuracy reaches 92%, early disease early warning can be achieved 24-48 hours in advance, and the pet health monitoring system based on computer vision and biosensing has the advantages of being high in practicability and the like. And precious time is won for timely treatment of pets.

The invention relates to the technical field of patches, in particular to an intelligent temperature control medicine sustained release patch based on Mongolian medicine wrapping therapy and a preparation method thereof.The intelligent temperature control medicine sustained release patch comprises a flexible temperature control layer which comprises a graphene heating film, a miniature temperature control chip and a temperature sensor; the medicine slow release layer is connected with the flexible temperature control layer and comprises a medicine insertion groove and medicine-carrying hydrogel arranged in the groove; the breathable waterproof layer is connected with the drug sustained release layer and comprises a polyurethane porous membrane and a degradable non-woven fabric; the detection module is connected with the flexible temperature control layer and comprises a micro biosensor array arranged on the graphene heating film; the control module is respectively connected with the flexible temperature control layer and the detection module and is used for increasing the temperature of the graphene heating film when the drug release is judged to be unqualified according to the release performance characteristic value, or secondarily judging the eligibility of the drug release according to the change rate of the release rate. The treatment effect and the use convenience of the patch are improved.

The invention provides methods for associating volatile compounds or mixtures of volatile compounds with combinatorial response patterns of Olfactory Receptors (OlfRs), a biosensor array comprising a plurality of biosensors, the use of said biosensor and of the method of the invention to screen for diseases, a molecule identified by a method of the invention as well as the use se of said molecule to modify the behavior of an insect.

The invention discloses an underground water pollution rapid detection device based on a biosensor array, and relates to the technical field of detection devices.The underground water pollution rapid detection device comprises a crawling pipeline, a biosensing detection module, a sampling module and a liquid feeding module, the sampling module, the liquid feeding module and the biosensing detection module are independently assembled and are in butt joint through standard interfaces, and maintenance and cleaning are convenient; meanwhile, the problems of independent work and connection fault of a traditional split device module are solved, the liquid conveying module can automatically convey sample liquid to the detection module, seamless connection is achieved in the whole process, time loss caused by independent work of the traditional split device module is avoided, redundant structures are reduced, a crawling pipeline serves as a common carrier, and the detection efficiency is improved. The modules are distributed along the axis instead of being stacked, the overall size is reduced, and the device is more suitable for being deployed in a narrow space.

The invention discloses a differential low-noise integrated biosensor array for cell culture detection and a preparation method thereof, and the differential low-noise integrated biosensor array integrates an ISHFET device, an REHFET device and an EXHFET device, so that extracellular action potential signals and local field potential signals can be detected during in-vitro cell culture; therefore, interference of factors such as tissue trauma and immune response on cell activity signal detection is avoided, and good reliability is realized; the ISHFET device and the REHFET device can form a differential sensing unit, so that the effect of reducing the detection error is realized; the EXHFET device not only can detect extracellular action potential signals, but also can independently detect local field potential signals, so that more diversified cell activity signals can be detected, and more data support is provided for in-vitro cell culture research. The method is widely applied to the technical field of semiconductors.

The invention relates to the technical field of active ingredient extraction, and particularly provides an extraction method for classifying and screening active ingredients, raw materials are subjected to low-temperature freeze-drying pretreatment by adopting supercritical CO2, and treatment is performed for 4-6 hours under the conditions of minus 40-minus 20 DEG C and 8-12 MPa, so that the active ingredients are reserved to the greatest extent; through gradient ultrasonic-assisted extraction of ultrapure water, a 50% ethanol solution and a 95% ethanol solution, step-by-step separation of water-soluble, alcohol-soluble and fat-soluble components is realized; carrying out multi-dimensional purification by using a composite chromatographic column containing a 0.2 mu m ceramic membrane layer, a macroporous adsorption resin layer and a sephadex layer; a two-channel biosensor array is adopted to synchronously detect a cell response signal and a molecule binding signal, and components with the activity intensity reaching 80% or above are screened; and finally, carrying out vacuum rotary evaporation concentration and spray drying to obtain a finished product, and completing system purity verification through a high performance liquid chromatography-mass spectrometry technology to form a complete quality control system.

A method and device for performing DNA sequencing and extracting structural information from unknown nucleic acid strands. The device includes a microwell structure, where identical DNA strands are immobilized within the microwell structure on a surface of a micro-bead, an active electrode or a porous polymer. The device further includes a CMOS-integrated semiconductor integrated circuit, where the CMOS-integrated semiconductor integrated circuit includes metal layers on a silicon substrate, where the metal layers form an active electrode biosensor. In addition, a sensing electrode is formed by creating openings in a passivation layer of the CMOS-integrated semiconductor integrated circuit to hold a single bead, on which the DNA strands are immobilized.

The invention relates to the technical field of disease analysis and prediction, and discloses a disease analysis method based on a multi-target sensor array, which comprises the following steps: deploying a biosensor array, immersing the biosensor array into a biological sample to be detected, collecting biomarker parameters in the biological sample to be detected, pre-processing the collected biomarker parameters, and analyzing the pre-processed biomarker parameters in the biological sample to be detected. The method comprises the steps of preprocessing biomarker parameters, analyzing the preprocessed biomarker parameters through a biological parameter detection model, outputting the analyzed biomarker parameters to the biological parameter detection model through a disease monitoring model, performing disease prediction, and optimizing the biological parameter detection model and the disease monitoring model through performance indexes. A health warning threshold value is set according to personal signs, safety and privacy protection is set according to disease privacy levels, and data encryption is set; the high-precision acquisition of biomarker parameters and the precise prediction of the disease probability are realized through the biosensor.

A semiconductor device includes a first gate electrode, an insulating part, a source electrode, a drain electrode, and a contact part. The insulating part is on one surface of the first gate electrode. The source electrode is connected to the insulating part. The drain electrode is connected to the insulating part. The contact part is between the source electrode and the drain electrode and on the insulating part. The contact part contains an atomic layered material. The contact part has a second part contactable with a sample. The second surface is opposite to a first surface facing the insulating part. A surface of the insulating part, the surface facing the contact part, has an uneven structure with respect to the first gate electrode.

Provided herein are devices and methods used to produce tattoo biosensors that are based on spatially controlled intracutaneous gene delivery of optical reporters driven by specific transcription factor pathways for a given cytokine or other analyte. The biosensors can be specific to a given analyte, or more generically represent the convergence of several cytokines into commonly shared intracellular transcription factor pathways. These biosensors can be delivered as an array in order to monitor multiple cytokines. Biosensor redeployment can enable chronic monitoring from months to years. The tattooed biosensor array of the present invention includes endogenous reporter cells, naturally tuned to each patient's own biology and can be used to reliably measure the state of a patient in real-time.

The invention discloses a carbon dioxide-starch biosynthesis intelligent regulation and control chip system based on multi-mode AI. The system comprises a photonic crystal biosensor array used for monitoring physical and chemical parameters such as a pH value, a dissolved oxygen concentration and a carbon dioxide concentration in a reaction process in real time; the 16 * NPU array is used for efficiently calculating and processing the data of the multi-modal sensor and predicting the reaction dynamic state in real time; the 4 * Cortex-M7 treatment core is used for controlling the reaction process in real time and adjusting reactor conditions; and a neural symbol system-based multi-modal reaction regulation and control algorithm which is used for optimizing reaction dynamics and realizing cooperative regulation of 2000 + reaction parameters. All parameters in the reaction process are optimized through real-time multi-mode data monitoring (including enzyme activity, reaction temperature, pH value and the like). The enzyme activity can be accurately adjusted, so that the purity of the final product is remarkably improved, and the purity of the starch can be stabilized at 99% or above, while the purity of the starch prepared by the traditional method is only 89.7%.

The invention provides a wireless sensor system for monitoring fetus intrauterine activities, and belongs to the technical field of collection and monitoring of biological measurement signals, and the system comprises a signal collection module which is used for synchronously collecting abdomen multi-dimensional biological field signals through a heterogeneous biosensor array, and generating an original data stream; the model construction module is used for extracting fetal movement associated feature subspaces and constructing a space-time cause and effect diagram; the intelligent decision-making module is used for determining a fetal movement credibility index and determining an abnormal level through a preset multi-agent decision-making system; the parameter updating module is used for sending the fetal movement credibility index and the abnormal level to a hospital verification node and obtaining feedback data corrected by a doctor so as to update parameters of the space-time cause and effect graph and a preset fetal movement knowledge graph; and the parameter correction module is used for projecting a holographic fetal movement map on the flexible electronic skin interface and acquiring an electromyographic signal adjusted by a pregnant woman to correct the fetal movement credibility index and the abnormal level. And the credibility and reliability of fetal movement monitoring are improved.

This invention relates to the fields of medical device technology, biosignal processing, and artificial intelligence-assisted diagnostic technology. Specifically, it relates to a non-invasive dynamic monitoring system and method for gastric function based on quantum sensing and multimodal artificial intelligence. The system includes a quantum dot biosensor array, a multimodal signal fusion processor, an artificial intelligence dynamic evaluation algorithm, and a cloud-based collaborative diagnostic platform. The quantum sensor uses graphene quantum dot material and achieves 128-channel parallel acquisition through capacitive coupling. In this invention, by setting up the quantum dot biosensor, the sensitivity reaches the single-photon level, the resolution is 0.1μV, the frequency response range is 0.01-100Hz, and the anti-interference capability is 90dB common-mode rejection ratio, thus improving signal acquisition accuracy and anti-interference capability.

This invention relates to the field of metabolite monitoring technology and discloses a multimodal cross-calibrated method for continuous multi-metabolite monitoring. The method acquires multi-source metabolite signal sequences containing electrochemical, optical, and thermodynamic signals through an implanted biosensor array; it performs time-series segmentation of the multi-source metabolite signal sequences to generate metabolite concentration time-series segments; and it synchronously inputs these metabolite concentration time-series segments into a metabolite dynamic response model and a metabolite interaction feature model to generate a first metabolite concentration trajectory and a second metabolite concentration trajectory, respectively. This method, by leveraging multimodal signal fusion and cross-calibration, combined with time-series segmentation and multi-model analysis, can comprehensively capture the dynamic changes and interactions of metabolites, improving the completeness and accuracy of metabolite monitoring, and is suitable for continuous multi-metabolite monitoring scenarios.

The invention discloses an intelligent nursing behavior monitoring system, and relates to the field of intelligent nursing. Comprising a multi-mode sensing terminal which collects multi-dimensional data of nursing operation through a three-view synchronous collection module and a biosensor array; the edge intelligent analysis node is used for carrying out behavior identification and risk assessment on the collected data by adopting a containerized processing architecture; the block chain evidence storage platform is used for constructing an operation evidence chain which cannot be tampered through a Hyperledger Fabric alliance chain architecture; and the feedback optimization engine adopts a double-model operation framework to be matched with a federated learning framework to realize self-optimization and scene self-adaption of the system. According to the method, the technical problem of space and time information processing separation is effectively solved by adopting a space-time diagram convolutional network technical means, so that deeper and more global understanding and modeling of nursing actions are realized, the recognition precision and robustness of complex and fine actions are greatly improved, and the false alarm rate and the missing report rate are reduced.

This invention relates to the fields of digital therapy, intelligent medical devices, and applied artificial intelligence technologies, and more specifically to a closed-loop pain management method and system integrating diagnostic assistance and interference-resistant sensing. It includes: a wearable device comprising: a neuromodulation signal generator configured to generate neuromodulation stimulation pulses; a biosensor array physically integrated with the neuromodulation signal generator configured to sense physiological data indicating the user's autonomic nervous system activity; a controller configured to execute a time-division multiplexing protocol; a processor; and a non-transitory computer-readable storage medium associated with the processor for generating a diagnostic support report. This invention enables simultaneous effective neural stimulation and high-fidelity ANS signal acquisition on a single wearable device.

The invention relates to the technical field of pain state prediction, and discloses a method for constructing a patient pain state prediction model based on brain electric signals. The method comprises the following steps: acquiring an original data stream of brain electric signals of a patient through a multi-channel biosensor array, synchronously recording pain event labeling information, and generating a standardized signal sequence after preprocessing; extracting multi-modal features of time domain stability, frequency domain resonance and space domain connectivity, and constructing a feature-label mapping table; training the graph convolutional neural network model, and obtaining an initial prediction model in combination with a node embedding technology; dynamically adjusting parameters through an adaptive filtering algorithm to generate an optimization model; and in combination with the real-time electric signal flow and the physiological context information, outputting a pain level evaluation report, and automatically matching a clinical intervention protocol library to form a personalized scheme. According to the method, multi-modal features and a dynamic optimization mechanism are integrated, and prediction accuracy and real-time performance are improved.

The invention provides an emotion monitoring system based on a hybrid architecture, and the system comprises a multi-mode biosensor array which is used for collecting a physiological monitoring signal of a target person; the edge calculation unit is used for determining preliminary emotion classification information according to the physiological monitoring signals and a lightweight model; the cloud server is used for determining an emotion monitoring result according to the preliminary emotion classification information and a GNN-Transform mixed architecture model; wherein the GNN-Transform hybrid architecture model comprises a plurality of layers of GNN-Transform co-processing modules, and the GNN-Transform hybrid architecture model comprises a plurality of layers of GNN-Transform co-processing modules; each layer of GNN-Transform coprocessing module is used for completing feature optimization and determining an attention sampling focus of the next layer according to a feature optimization result of the current layer; and the lightweight model is obtained by the lightweight of the GNN-Transform hybrid architecture model. According to the method, a GNN-Transform hybrid architecture is provided, time-space association among all modals is modeled through a neural network, and meanwhile, multi-modal time sequence data is processed by utilizing a self-attention mechanism of Transform, so that deep fusion of cross-modal features is realized, and the emotion monitoring precision is improved.

A foot covering comprising an elongated fabric body. The foot covering includes a biosensor system that includes a biosensor array integrated into the elongated fabric body, and a microprocessor that is configured to removably electronically couple to the biosensor array and removably physically couple to the elongated fabric body.

The present invention discloses a whole-cell biosensor array, comprising a 96-well plate on which 14 whole-cell biosensors are placed, with 3-4 of each whole-cell biosensor. Each whole-cell biosensor comprises: a host cell; a recombinant vector located within the host cell, the recombinant vector comprising a vector and a promoter inserted into the vector, wherein the promoters within the 14 whole-cell biosensors are recAp, dnaKp, grpEp, katGp, fabAp, yibTp, soxSp, glgSp, oxyRp, uvrAp, ompFp, spyp, pspAp, and leuAp, as well as an application of the whole-cell biosensor array. The present invention can indicate the moldy state by monitoring the headspace organic volatile components of food raw materials without sample pretreatment, has low cost, high accuracy, and is easy to operate.

The invention relates to the field of whole-cell biosensors, and provides a whole-cell biosensor array and an early warning model for monitoring the mildewing degree and aflatoxin content of corn peanuts. The Escherichia coli cell comprises a promoter related to the response of a volatile organic compound released by Aspergillus flavus pollution, the promoter is operably connected with a reporter gene, and an expression product of the reporter gene is used for generating a bioluminescence signal which can be detected. Through the whole-cell biosensor and the array thereof, related volatile organic compounds after corn peanuts are infected by aspergillus flavus can be accurately responded, so that rapid and high-sensitivity monitoring on aspergillus flavus mildew and toxin pollution is realized. Besides, the whole-cell biosensor array is combined with a machine learning regression model, so that the aspergillus flavus infection degree can be quantified, and the aflatoxin content can be predicted.

The application discloses a groundwater pollution rapid detection device based on a biosensor array, and relates to the technical field of detection devices.The device comprises a crawling pipeline, a biosensing detection module, a sampling module and a liquid feeding module.The sampling module, the liquid feeding module and the biosensing detection module are independently assembled and are connected through standardized interfaces, which facilitates maintenance and cleaning, and simultaneously solves the problem of independent work and connection faults of traditional split-type equipment modules.The liquid feeding module of the application can automatically feed sample liquid to the detection module, and the whole process is seamlessly connected, thereby avoiding time loss caused by independent work of modules in traditional split-type devices, reducing redundant structures, and reducing the overall volume by arranging the modules along an axis instead of stacking them, which is more suitable for deployment in narrow spaces.

The invention relates to the technical field of vegetable heavy metal detection, and discloses a continuous detection method based on organic fertilizer heavy metals in vegetables. The method comprises the following steps: collecting a heavy metal ion concentration signal of a mixed solution of a vegetable sample and an organic fertilizer through a biosensor array, and generating original electrochemical response data; performing baseline correction and noise filtering on the original data, and extracting a characteristic response peak value of each heavy metal ion; calculating initial concentration values of the various heavy metal ions according to a mapping relation between the characteristic response peak values and a preset standard curve; and inputting the initial concentration value into a dynamic weighted fusion model, and generating a corrected heavy metal concentration distribution table in combination with vegetable growth cycle data and an organic fertilizer application record. The method can be used for synchronously detecting various heavy metals, simplifying the process, improving the data accuracy, reflecting the concentration change under different growth and fertilization conditions, and is suitable for detecting heavy metals in agricultural vegetables and assisting quality safety management and control.

A system for the rapid detection of viral infections in blood samples, consisting of: a microfluidic processing module configured to receive a microliter-scale whole blood sample and process it through sequential steps, including plasma separation, lysis of viral particles, and biomarker extraction; a biosensor array unit comprising a plurality of nanostructured sensor elements functionalized with pathogen-specific ligands, wherein the biosensor array is integrated into the microfluidic module to enable real-time detection of viral nucleic acids or antigens; a temperature-controlled amplification module embedded in the microfluidic channel and configured to perform isothermal nucleic acid amplification using recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), or helicase-dependent amplification (HDA); a signal acquisition subsystem comprising optical, electrochemical, or piezoelectric transducers coupled to the biosensor array, the subsystem converting molecular interaction data into digital signals; a machine learning processor operatively connected to the signal detection subsystem and configured to classify the infection status using pre-trained classification models with confidence scoring and anomaly marking; a wireless communication interface operatively connected to the machine learning processor to disseminate the results remotely, with the entire system housed in a portable enclosure operable via a graphical user interface or touchscreen.