22 results about "Activity index" patented technology

The invention belongs to the technical field of aquaculture and food engineering, and relates to a fish anesthesia recovery activity prediction method based on acoustic signal time-frequency domain feature fusion. The method comprises the following steps of: acquiring acoustic respiratory vibration signals in real time in a fish anesthesia recovery process, performing 0.1-50Hz band-pass filtering, denoising and other pretreatment on the acoustic respiratory vibration signals, and extracting ZCR and Gammatone characteristic spectrums; meanwhile, fish swimming data are collected by adopting a YOLOv11 visual tracking algorithm, and activity grading standards are established by calculating activity indexes; zCR and Gammatone maps are used as double-branch input data of a deep learning model, corresponding activity grading data are used as labels, and a multi-category classification prediction model is constructed. According to the method, the anesthesia recovery state of the fish can be accurately predicted, and excessive stress and death loss in the transportation process are avoided; a non-invasive and intelligent detection means is provided for activity monitoring in the aquatic product transportation process, and the market application prospect is wide.

The invention belongs to the technical field of medical rehabilitation, and discloses a rehabilitation treatment monitoring system based on multi-modal sensing and AI evaluation. A multi-dimensional skeleton key point confidence evaluation mechanism is established by integrating three types of sensing data of visual skeleton tracking, an inertial measurement unit and pressure distribution, and potential tracking interruption risk frames and error recognition candidate frames are recognized in real time. And a multi-modal fusion correction model is adopted to carry out accurate compensation on error recognition, missing data are reconstructed in combination with a skeleton motion mode memory bank and a bidirectional time sequence prediction algorithm, and a continuous and complete all-day skeleton motion sequence is generated. On the basis of the calculation, the time-phased activity level index and the rehabilitation action completion degree score of the patient are calculated, and an objective and quantitative rehabilitation evaluation basis is provided. According to the invention, the stability and continuity of skeleton tracking in a complex rehabilitation environment are improved, so that a medical team can obtain a real and complete all-day activity portrait of a patient, accurately position a rehabilitation bottleneck, formulate a personalized treatment scheme and promote functional recovery of the patient.

The invention provides a heart in-vitro perfusion and static preservation system based on a heart supply transfer box, and the system comprises a data acquisition module which is used for carrying out the basic state inspection of a box body, the environment detection of the box body, the environment adjustment, and the heart detection and positioning; the intelligent filling module is used for automatically filling and monitoring medicines; the activity monitoring module is used for calculating a heart electrophysiological activity index, an epicardial injury parameter and a lactic acid uptake ratio to obtain a heart activity index; the intelligent evaluation module is used for constructing a coupling model, outputting a heart supply real-time quality score and injury trend prediction, and carrying out heart abnormity monitoring and alarming; the energy consumption management module is internally provided with a solid-state high-specific-energy lithium battery pack and is used for performing energy consumption prediction and optimization; and the security monitoring module is used for data compression, hierarchical uploading and remote encryption control. The method can standardize the operation of the donor heart preservation process, greatly reduces personal errors, improves the quality and availability of remote heart transfer and preservation, reduces the risk of organ scrap, and relieves the contradiction between supply and demand.

FIELD: clinical gastroenterology.SUBSTANCE: intended to predict the risk of relapse of autoimmune hepatitis after discontinuation of immunosuppressive therapy. The patient undergoes a clinical and biochemical examination and the relapse risk probability index is determined using the formula: PI RR = –0.394×(IGA≥ 9) + 1.252×(AT) + 0.915×(a-SLA / LP) + 0.555×(a-LKM1) – 0.692×(IgG norm) + 0.316×(obesity), where PI RR is the prognostic index of recurrence risk; HAI is the histological activity index of more than 9 according to Knodell (binary indicator: HAI ≥ 9 corresponds to the multiplier “1”; HAI < 9 corresponds to the multiplier “0”); AB – the presence of autoantibodies (binary indicator: detection of at least 1 corresponds to the multiplier “1”; absence of autoantibodies corresponds to the multiplier “0”); a-SLA / LP – antibodies to soluble liver / kidney antigen (binary indicator: presence – corresponds to the multiplier “1”; absence – corresponds to the multiplier “0”); a-LKM1 – antibodies to liver and pancreas microsomes (binary indicator: presence corresponds to the multiplier “1”; absence corresponds to the multiplier “0”); IgG – immunoglobulin G (binary indicator: normal Ig G level corresponds to the multiplier “1”; elevated IgG level corresponds to the multiplier “0”); obesity (BMI ≥ 30) – (binary indicator: presence of BMI ≥ 30 corresponds to a multiplier of “1”; BMI < 30 corresponds to the multiplier “0”). With the value of PI RR < 0.93 – low probability of relapse, PI RR > 0.93 – high probability of relapse.EFFECT: increased accuracy in predicting the risk of recurrence of autoimmune hepatitis after discontinuation of immunosuppressive therapy.1 cl, 2 ex

The invention provides a dry-mixed mortar strength evolution modeling method and system. The method comprises the following steps: acquiring synergistic excitation effect data (including an activity index and a hydration heat release curve) of the dry-mixed mortar waste residues, and analyzing reaction kinetics to generate a waste residue-cement interaction characteristic matrix; a temperature field and an acoustic emission signal in the hydration process are monitored in real time through a thermoacoustic synchronous sensing array integrating a thermocouple and piezoelectric ceramics, and a thermoacoustic coupling space-time distribution diagram is output based on space-time relevance; fusing the interaction characteristic matrix and the thermoacoustic distribution diagram by using a graph neural network, and constructing a waste residue / cement / water molecule multi-topology interaction diagram; and simulating a collaborative excitation path in the graph, dynamically mapping the topological graph into intensity evolution curves of different ages by adopting an intensity trajectory prediction algorithm, and quantifying the contribution degree of the waste residue. Accurate prediction of the waste residue resource utilization efficiency is achieved, the waste residue mixing amount is optimized, and the prediction precision is improved.

An EEG-based dementia diagnosis assistance device according to the present embodiment comprises: an EEG data preprocessing unit for preprocessing a collected EEG data set of a test subject, the collected EEG data set being divided into time intervals by setting a window length and a shift length; an Hjorth parameter extraction unit for extracting, for each time interval, Hjorth parameters including an activity index, a mobility index, and a complexity index; and a dementia classification unit for classifying the test subject into one from among a Alzheimer's disease (AD) group, a frontotemporal dementia (FTD) group, and a cognitive normal (CN) group on the basis of the extracted Hjorth parameters. Therefore, AD, FTD, and CN subjects can be classified using Hjorth parameters composed of three major indicators of activity, mobility, and complexity through time domain analysis on EEG datasets.

Disclosed are methods for managing a treatment in an adult individual having systemic lupus erythematosus (SLE) undergoing therapy for lupus nephritis. In aspects, the method comprises detecting a level of each of neutrophil gelatinase associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), monocyte chemotactic protein 1 (MCP-1), adiponectin, hemopexin, and ceruloplasmin in a urine sample obtained from the individual, the levels of which are used to determine a score at a first time point and a second time point. Whether the individual is responding to the therapy, in particular an induction phase of a therapy, is determined based on a comparison of the first and second score. The therapy may then be continued or adjusted based on that determination.

The invention provides a preparation method and application of a dental pulp stem cell preparation based on AI activity prediction, and the method comprises the following steps: collecting historical preparation parameters and corresponding activity indexes of the dental pulp stem cell preparation, constructing a historical database, and carrying out data preprocessing and core parameter screening; according to the method, an activity prediction model comprising a first sub-model and a second sub-model is constructed, bidirectional function prediction is realized through two-stage training, multiple types of activity indexes and comprehensive scores can be predicted based on preparation parameters, and the preparation parameters can be reversely recommended according to activity requirements; and preparing a compound preparation which takes the dental pulp stem cells as a core and PDD-ALN as a synergistic enhancement carrier based on model output. The method can accurately guide the preparation of the dental pulp stem cell preparation, and is especially suitable for the development of periodontitis treatment drugs.

The invention relates to the technical field of data processing, and discloses a hydrocephalus assessment method and system based on a bladder pressure dynamic monitoring device. The hydrocephalus assessment method based on the bladder pressure dynamic monitoring device comprises the steps that wrist sympathetic nerve and lumbosacral electromyographic signals are collected and filtered; extracting a specific frequency band power spectrum integral value and a root-mean-square value to fuse and calculate an equivalent bladder pressure value; calculating a detrusor muscle activity index, a compliance index and a night fluctuation coefficient to form a pressure mode; obtaining pressure mode calculation improvement rate judgment reactivity before and after liquid discharge; and calculating a comprehensive improvement index through a weighting algorithm to determine a surgical indication level. The problem that in the prior art, continuous non-invasive monitoring and objective quantitative evaluation cannot be conducted on the bladder function of an iNPH patient is solved, and the accuracy of cerebrospinal fluid drainage test effect evaluation is improved.

The application provides a heart ex vivo perfusion and static preservation system based on a donor heart transport box, which comprises the following modules connected with each other: a data acquisition module for box basic state inspection, box environment detection, environment adjustment, and heart detection and positioning; an intelligent perfusion module for automatic drug perfusion and monitoring; an activity monitoring module for calculating a heart electrophysiological activity index, an epicardial injury parameter, and a lactate uptake ratio to obtain a heart activity index; an intelligent evaluation module for constructing a coupling model, outputting a donor heart instant quality score and an injury trend prediction, and performing heart abnormality monitoring and alarm; an energy consumption management module for built-in solid high-energy lithium battery pack and energy consumption prediction and optimization; and a safety monitoring module for data compression, hierarchical uploading, and remote encryption management and control. The application can standardize the heart preservation process operation, greatly reduce human errors, improve the quality and availability of remote heart transport and preservation, reduce the risk of organ rejection, and alleviate the contradiction between supply and demand.

The invention discloses an intelligent counting and marking method of a plugboard type hair follicle hair transplanting device, and relates to the technical field of hair follicle detection and counting, and the method comprises the steps: installing a micropore array plugboard, collecting an image, extracting the characteristics of hair follicle deviation, closure degree and definition of each hole position, and building a hair follicle data set; then, according to the hair density of the patient and transplantation requirements, the distance between the inserting plates is automatically adjusted, and personalized adaptation is completed; then, through multi-angle image acquisition, extracting a hair follicle structure map, a vascular ring feature map and a hair papilla map, evaluating hair follicle activity, calculating a hair follicle activity index, and comparing the hair follicle activity index with a threshold value to determine a hair follicle quality grade; and finally, de-weighting hair follicle entries and generating an intraoperative implantation list to help a doctor to quickly select hair follicles in a hair implantation stage and facilitate post-operation charging. The method can effectively solve the technical problem that manual counting of hair follicles by a hair transplantation doctor is tedious and is easy to make mistakes, and can guarantee the success rate of a hair transplantation operation.

The invention discloses a rice seedling blast disease intelligent analysis system based on remote sensing data analysis, and relates to the technical field of agricultural remote sensing and plant disease monitoring, the system comprises a physiological data acquisition module, a physiological feature extraction module and a disease severity calculation and severity grading module; wherein the physiological data acquisition module realizes in-situ non-invasive acquisition of the spectral reflectivity of rice seedling leaves, the maximum photochemical efficiency of a light system II and the water transport rate of stalks through various sensors such as a hyperspectral sensor and a chlorophyll luminoscope; the physiological feature extraction module fuses multiple parameters to calculate a leaf surface structure health index and a vascular function activity index; the disease grading module obtains an in-vivo disease load index through weighting, and outputs different disease grades in combination with a K-means clustering threshold value; the system solves the problems that traditional rice seedling blast diagnosis is high in subjectivity and hysteresis quality, and early-stage accurate recognition of diseases is achieved.

The invention relates to the field of biological waste treatment, and discloses a hermetia illucens age identification and intelligent feeding linkage kitchen waste treatment system, which comprises a data acquisition module, a data processing and activity index calculation module, a data processing and activity index calculation module, a data processing and activity index calculation module, a data processing and activity index calculation module, a data processing and activity index calculation module, and a data processing and activity index calculation module, the activity index is used for representing the activity state of the insect population and the adaptive capacity to the environment; the insect age identification module is based on a Transform model detected in real time and introduces the activity index to serve as an attention guiding signal so as to output insect age distribution, individual counting and health state labels; a feeding linkage control module; and a cloud management and self-learning module. By introducing multiple environmental factors such as temperature, humidity, gas concentration and the like and insect group activity information and combining with the improved deep learning model, the insect age can be identified more accurately, so that identification errors caused by factors such as illumination and insect body overlapping in a traditional method are effectively reduced.

The invention belongs to the technical field of elderly nursing, and relates to an elderly nursing activity index fusion recommendation method and system. Comprising a data acquisition module which is deployed in each key area of an elderly nursing place, comprises a plurality of sensors and a data input interface, and is used for acquiring multi-dimensional data such as physiological indexes, psychological states, activity ability and social communication of the elderly, adding timestamps to the acquired data, and sending the data to the elderly nursing place; preliminarily classifying the data and then transmitting the data to a data processing module through a data transmission network; the data transmission network is used for transmitting the data acquired by the data acquisition module; according to the method, comprehensive evaluation of the individual condition of the old people is realized by fusing multi-dimensional indexes such as physiology, psychology, activity ability and social communication, the accuracy and pertinence of old people nursing activity recommendation are improved, and the limitation of single-index recommendation is avoided. A dynamic adjustment mechanism is established, and a recommendation scheme can be optimized in time according to the real-time state and the nursing effect of the old people.

The invention provides a system for evaluating lower limb strength and walking gait influence of a patient, which relates to the technical field of gait evaluation and comprises a data acquisition module, a synchronous control module and a data processing and analyzing module. The data acquisition module comprises a constant-speed muscle force test unit, a surface electromyogram signal acquisition unit and an optical motion capture unit; the synchronous control module is in communication connection with the data acquisition module; the data processing and analyzing module is configured to receive and time align the lower limb muscle strength data, the lower limb muscle electrophysiological data and the lower limb kinematics data; extracting a lower limb strength index, a myoelectricity activity index and a gait parameter from the aligned data; and based on the extracted indexes and parameters, establishing a correlation model among the lower limb strength indexes, the myoelectricity activity indexes and the gait parameters, and outputting an evaluation result. The method can generate remarkable effects from data islands to association fusion, from subjective experience to objective quantification, and from phenomenon description to pathogenesis revelation.

The invention provides a group identification method, a group identification system and related equipment. The method comprises the following steps: acquiring access information of a group from a first platform; filtering the access information of the group based on a regular expression rule base to obtain the access information of an initial abnormal group; adding the corresponding initial abnormal group through the access information of the initial abnormal group; determining an activeness index of the initial abnormal group; and determining whether the initial abnormal group is an abnormal group based on the activeness index. Wherein the regular expression rule base is constructed based on uniform resource locator format features and group identification modes of known abnormal groups. The method can improve the efficiency of identifying the abnormal group.

The application provides a biomarker combination model for evaluating endoscopic activity of pediatric Crohn's disease and a construction method and application thereof, and belongs to the technical field of biological medicine. + CD69 + The biomarker combination model of the proportion of T cells combined with the level of C-reactive protein can effectively evaluate the endoscopic activity of pediatric Crohn's disease patients through simple peripheral blood detection, avoids the invasiveness, high cost and potential risk of endoscopy, and is particularly suitable for pediatric patients who need frequent monitoring. The model has good diagnostic efficiency and can accurately distinguish patients in the endoscopic active phase from patients in the remission phase, thereby making up for the inconsistency between the clinical activity index and the endoscopic activity. The model provides a simple and objective non-invasive evaluation tool for the treatment decision, efficacy monitoring and long-term follow-up of pediatric Crohn's disease, and has important clinical application value and popularization prospect.

To evaluate a degree of motion sickness.SOLUTION: An acquisition part acquires an active index value showing a degree of the activity of the sympathetic nerve or parasympathetic nerve of a user, calculated from vital data of the user. An evaluation part evaluates a degree of motion sickness of the user based on a distribution degree relating to the time sequence of the activity index value.SELECTED DRAWING: Figure 1

The application belongs to the technical field of medical rehabilitation, and discloses a rehabilitation treatment monitoring system based on multi-modal sensing and AI evaluation; three types of sensing data, namely visual skeleton tracking, inertial measurement unit and pressure distribution, are integrated to establish a multi-dimensional skeleton key point confidence evaluation mechanism, and potential tracking interruption risk frames and error recognition candidate frames are recognized in real time. A multi-modal fusion correction model is used to accurately compensate for the error recognition, and the missing data is reconstructed by combining a skeleton motion mode memory bank and a bidirectional time sequence prediction algorithm to generate a continuous and complete all-day skeleton motion sequence. Based on this, the patient's time period activity index and rehabilitation action completion score are calculated to provide objective and quantitative rehabilitation evaluation basis. The application improves the stability and continuity of skeleton tracking in a complex rehabilitation environment, enables the medical team to obtain the real and complete all-day activity image of the patient, accurately locates the rehabilitation bottleneck, formulates a personalized treatment plan, and promotes the functional recovery of the patient.

The present invention discloses a virtual screening method for drug molecules based on binding mode analysis, which belongs to the field of artificial intelligence drug discovery. PLIP is used to calculate the binding mode between the target to be screened and the small molecule, and the binding mode is encoded. The DBSCAN algorithm is used to cluster the binding modes to identify groups of compounds with similar binding modes. In the screening stage, two key indicators are introduced: an energy index is formed based on the hydrogen bond compensation number and the AutoDock Vina score, and an activity index is used based on the correlation between the relevant residues in the binding mode and the alpha carbon atom of the key site, which is used to evaluate the binding stability and characteristic activity between the target and the small molecule, and to perform multi-target frontier analysis on the candidate molecules. The method of the present invention significantly improves the efficiency and accuracy of virtual screening, enhances the detailed analysis of intermolecular interactions, is suitable for small-scale and large-scale molecular screening, reduces the cost and time of drug research and development, enriches the diversity of virtual screening results, and provides more potential candidate molecules for subsequent experiments.

The invention provides an inflammatory bowel disease patient psychological intervention management system, which comprises a medical care terminal, a patient terminal and a management terminal, and is characterized in that the medical care terminal, the patient terminal and the management terminal are in communication connection; the patient end comprises a disease evaluation module which is used for providing different disease scoring systems according to disease types of patients to evaluate disease stages of the patients, and the disease types of the patients comprise ulcerative colitis and Crohn's disease. The different disease scoring systems comprise an improved Mayo scoring system suitable for ulcerative colitis patients and a simplified Crohn disease activity index scoring system suitable for Crohn disease patients; and the scheme guidance module is used for generating a corresponding guidance scheme according to the disease type and the disease stage of the patient. According to the invention, different disease scoring systems can be provided according to the types of the patients and disease stages of the patients can be accurately evaluated, so that corresponding personalized guidance schemes are automatically generated, the patients are helped to carry out more targeted treatment and psychological intervention, and the treatment compliance and treatment effect of the patients are improved.