33 results about "Physiological model" patented technology

A method of monitoring a subject with an opto-physiological sensor system is described herein. The method comprises obtaining a model of the optical properties of at least one body tissue type to be monitored, wherein the model of the optical properties comprises a definition of static (DC) and dynamic (AC) components of transmitted optical power and a definition of a source-detector separation related to a normalised path length for an illumination source of the opto-physiological sensor: obtaining an indication of at least one physiological property of the subject from a wearable device worn by the subject: obtaining an indication of at least one physical variable from the wearable device worn by the subject; determining, using the opto-physiological model, how the at least one physical variable affects the at least one physiological property; and determining a corrected value for the physiological property based on the determination of how the at least one physical variable affects the at least one physiological property.

The invention relates to the technical field of program control, in particular to a program control system and method based on a pressure distribution test. Comprising a multi-dimensional pressure data acquisition unit, a control logic decision unit and a cooperative control unit. Through the combination of the high-resolution flexible sensor array and the deep learning inversion model, the spine curvature and the shoulder-hip proportion of the user can be automatically identified, the shoulder, the waist, the hip and other skeleton protruding parts can be accurately tracked, a unique and accurate personalized physiological model basis is provided for subsequent analysis, and the user experience is improved. A'ideal pressure-bearing database 'constructed based on clinical medical knowledge is embedded, a'digital human' pressure state sensed in real time is compared with a matched'health template ', a dynamic weight mechanism with clinical significance is introduced, and a'pressure-bearing difference value' aiming at preventing pressure sores and maintaining spine health is calculated; the purpose of converting an adjustment target from fuzzy'more comfortable 'into quantitative physiological index optimization with definite health orientation is achieved.

This invention relates to a method for synchronous prediction of human thermal injury based on a dynamically coupled physiological model. First, a refined, segmented, multi-layered human physiological thermal model is constructed. Based on this model, a set of coupled biological heat transfer equations is established, embedding an active thermoregulation mechanism mediated by the human central nervous system. This set of equations is solved by deeply and dynamically coupling skin temperature, core temperature, and physiological parameters such as blood perfusion rate, metabolic rate, and sweat evaporation. Subsequently, by setting the initial and boundary conditions of a fire scene operation, the coupled equations are solved in one go using numerical methods. During the same solution process, the system synchronously and in parallel performs segmented skin burn risk assessment and overall human heat stress risk assessment. This invention overcomes the limitations of traditional models in segmented prediction and coupled active thermoregulation, achieving effective synchronous early warning of skin burn risk and overall heat stress risk in various parts of the human body under extreme thermal environments.

A testing device and testing method of a hydrocephalus shunt valve. It relates to the technical field of medical device testing. The long-term reliability and adaptability of the shunt valve can be simulated in a complex physiological environment. The testing device of the hydrocephalus shunt valve comprises a controller, a body position simulation device, an ICP pressure chamber, an IPP pressure chamber, a backflow pump, a flow sensor and a circulating pipeline. The body position simulation device comprises a support, a pair of long arms simulating the human torso and a pair of short arms simulating the human head and neck, and a motor one driving the pair of long arms to swing and a motor two driving the pair of short arms to swing. The present application combines the in-vitro hardware testing platform with different patient physiological models, can reproduce physiological characteristics such as basic body position posture change, body temperature circadian rhythm fluctuation and long-term applicability, and realizes the in-vitro testing of the shunt valve quickly, at low cost and with high authenticity. The present application can quantify the performance of the shunt valve in a real physiological environment.

The invention relates to the technical field of medical equipment and signal processing, in particular to a cerebral infarction thrombectomy postoperative intracranial pressure management method and system based on fNIRS-TCD combined monitoring, and the method comprises the steps: firstly, synchronously collecting fNIRS cerebral blood oxygen concentration change data and TCD cerebral blood flow velocity data through a head-mounted device, and calculating a pulsation index; then, on the basis of a Bayesian data assimilation framework, constructing the data into a real observation vector, combining a state vector predicted by an intracranial physiological model and an observation vector predicted by an observation model, calculating a Kalman gain through an iterative integrated Kalman algorithm, and further correcting the state vector in real time; and obtaining state estimation containing precise intracranial pressure. And finally, the system carries out three-level early warning according to the real-time intracranial pressure data in combination with brain blood oxygen and pulsation index changes, and carries out stepped individual management from observation, drug intervention to emergency rescue according to the three-level early warning. According to the invention, the intracranial pressure management accuracy is improved.

The invention relates to a diabetes early subtype recognition and risk prediction system based on continuous blood glucose monitoring. The system comprises a data preprocessing module, a subtype recognition module, a physiological modeling module, a residual prediction module and a fusion prediction module. The data preprocessing module is used for interpolating and standardizing the continuous blood glucose monitoring data; the subtype recognition module is used for extracting a typical daily curve, performing clustering analysis and outputting a metabolic subtype tag; the physiological modeling module is used for constructing and fitting a glucose-insulin kinetic model and outputting personalized physiological parameters; the residual prediction module is used for training the LSTM network and predicting the residual of the dynamic model; and the fusion prediction module is used for dynamically fusing output of the two types of models and generating a final blood glucose prediction result. According to the method, subtypes of the early stage of diabetes can be recognized, a prediction analysis method with high precision and high interpretability can be provided for different subtypes, and individualized dynamic mode recognition and hybrid prediction based on continuous blood glucose monitoring (CGM) data are achieved.

The application discloses a kind of cardiac transmembrane potential imaging methods based on 12 lead electrocardiogram joint physiological model and attention mechanism, to solve the serious ill-conditioned problem caused by information asymmetry between 12 lead signal and TMP, reconstruct cardiac transmembrane potential from 12 lead ECG signal.In order to obtain the dynamic physiological information of BSP and TMP, the application introduces self-attention mechanism to capture the long-term, periodic dynamic activation process characteristics of cardiac electrophysiology, extract the similarity within the sequence, calculate the attention score, then multiply the score with the input sequence, extract the key segment in the sequence, i.e.the segment that is expected to be "noticed".Meanwhile, the application regards the inverse problem as the potential feature mapping of TMP on BSP, captures the dynamic characteristics of inverse relationship by introducing cross-attention module, learns the inverse feature mapping relationship from a large number of data pairs using neural network, and solves the ill-posed problem of inverse problem.

The invention provides an intermittent heart rate gating ankle rehabilitation robot system and a training method, and belongs to the technical field of rehabilitation engineering, man-machine interaction and motion control. The training method comprises the steps that real-time heart rate data of a patient is collected and processed, and the maximum heart rate is estimated; parameters such as a double-threshold ratio and a state duration time threshold for switching the high power mode and the low power mode are set; automatically switching a power assisting mode according to the heart rate and the duration time, and triggering a protection mechanism when the heart rate exceeds a safety boundary; multi-type patient physiological models are established in a virtual simulation environment, and optimal PID control parameters are matched; issuing the verified prescription to a rehabilitation execution mechanism for execution; heart rate records are monitored in real time during training, and data of the whole process are traced. According to the intermittent heart rate gating ankle rehabilitation robot system and the training method, people with ankle joints injured or needing rehabilitation are helped to conduct more effective and safer training.

The application provides a CAR-T cell therapy toxicity management optimization method and system, relates to the technical field of artificial intelligence, and comprises the following steps: acquiring clinical data of a blood tumor patient receiving a CAR-T cell therapy; performing missing value interpolation and standardization processing on baseline characteristic information and process monitoring data to obtain a standardized data set; obtaining a calibrated virtual patient physiological model according to a preset tumor cell kinetics model, an immune cell kinetics model and a toxicity reaction model; taking the calibrated virtual patient physiological model as an interactive environment to obtain a correlation database; constructing a deep Q network intelligent agent with a maximum reward function as a target to obtain an optimized deep Q network intelligent agent; and inputting the process monitoring data into the optimized deep Q network intelligent agent to obtain a target strategy, so that the individualized CAR-T treatment strategy balancing the therapeutic effect and toxicity can be automatically output by inputting the real-time monitoring data of the patient.

The application discloses an anesthetic efficacy dynamic evaluation system and method based on artificial intelligence, relates to the technical field of anesthetic efficacy evaluation, and comprises a simulation prediction unit, a comparison correction unit, an abnormal backtracking unit and an output warning unit; the simulation prediction unit is used for collecting real-time physiological parameters of a patient through a medical monitoring device, constructing a virtual anesthetic mirror according to the real-time physiological parameters, generating a virtual physiological model through a drug metabolism dynamics algorithm, outputting anesthetic efficacy evaluation data and sending the anesthetic efficacy evaluation data to the comparison correction unit, and can reduce the occurrence of deep or shallow anesthesia caused by inaccurate evaluation, reduce the operation risk, guarantee the life safety of the patient, enable an anesthesiologist to know possible anesthesia state changes of the patient in advance, timely adjust an anesthesia scheme, such as adjusting the infusion rate, the dose and the like of an anesthetic, ensure the stability of the anesthesia state in the operation process, improve the smooth operation degree of the operation, and provide more comprehensive and detailed decision basis for the anesthesiologist.

The application provides a cross-user hand joint angle estimation method, system and computer device. Hand activity signal data of multiple users is acquired, a multi-user model is constructed according to the hand activity signal data, the multi-user model is calibrated through training data of a new user, and the calibrated multi-user model estimates the finger joint angle of the new user. The method, system and computer device utilize surface electromyography signals and finger joint angle data of multiple users to establish a multi-user model, then adopt a newly proposed adversarial transfer learning strategy, use part of the training data of the new user to calibrate the multi-user model, and the calibrated model can estimate the joint angle of the hand joint in a continuous motion process of a person in real time. Meanwhile, since a data-driven algorithm is applied, the complex implementation process of establishing an electromyography-to-physiology model is avoided.

The invention discloses a device and a method for testing a hydrocephalus diverter valve. Relates to the technical field of medical instrument testing. And a complex physiological environment and long-term reliability and adaptability of the diverter valve can be simulated. The hydrocephalus diverter valve testing device comprises a controller, and further comprises a body position simulation device, an ICP pressure chamber, an IPP pressure chamber, a reflux pump, a flow sensor and a circulation pipeline, and the body position simulation device comprises a support, a pair of long arms simulating the trunk part of the human body and a pair of short arms simulating the head and neck part of the human body; the first motor is used for driving the pair of long arms to swing; and the second motor is used for driving the pair of short arms to swing. The in-vitro hardware testing platform is combined with physiological models of different patients, physiological characteristics such as basic body position and posture changes, body temperature circadian rhythm fluctuation and long-term applicability can be reproduced, and rapid, low-cost and high-authenticity in-vitro testing of the diverter valve is achieved. The performance of the diverter valve in a real physiological environment can be quantified.

The invention relates to the technical field of medical simulation training, in particular to a diagnosis and treatment thinking dynamic simulation training method and system based on a computable physiological model, and the method comprises the steps: instantiating a virtual patient model defined by a physiological state vector, a clinical expression vector and a static parameter; receiving a user diagnosis and treatment operation instruction; according to the medical knowledge rule base, the influence of the coupling differential equation set dynamic calculation operation on the physiological state vector is solved; according to the updated physiological state, generating a corresponding clinical manifestation vector through a mapping function; the clinical manifestation is converted into a multi-mode feedback signal to be output; and forming an interactive training cycle, and performing multi-dimensional quantitative evaluation based on the complete operation sequence and the final state of the model. The system takes a computable model as a core, realizes continuous dynamic evolution and response of cases, and can effectively train and evaluate the high-order diagnosis and treatment thinking ability of clinicians.

The invention provides an intelligent control method and system for a model animal hog house, and belongs to the technical field of intelligent control, and the method comprises the steps: obtaining the current growth stage parameters of pigs in the hog house; calling a preset pig physiological model, and calculating a metabolic pollution production index and an environmental tolerance threshold value of the pig at the current stage based on the growth stage parameters; constructing an optimization function containing an energy consumption target and a growth target, and solving the optimization function by taking an environment tolerance threshold as a constraint condition to generate an optimal environment control parameter at the current moment; and according to the optimal environment control parameters, adjusting environment control equipment in the hog house, so that the environment parameters in the hog house are maintained in a range limited by the optimal environment control parameters. According to the method, the biological and environment coupling model is constructed, so that self-adaptive dynamic regulation and control based on the actual physiological needs of the pigs are realized, the problems of energy consumption waste and environment mismatching caused by a fixed control mode are effectively solved, and the operation energy consumption is remarkably reduced while the growth performance of the pigs is guaranteed.

According to the individualized color vision abnormality image color reconstruction method based on the physiological model, continuous offset of an L / M visual cone absorption peak value is simulated on the wave number scale, spectral perception changes of color weakness / color blindness people on the physiological structure level are truly represented, and scientificity and interpretation of color vision abnormality simulation are remarkably improved; a color vision anomaly model for individuals is constructed, so that a simulation result not only conforms to a physiological mechanism, but also can reflect unique color vision features of each observer, and technical support is provided for individualized image enhancement and accurate visual evaluation; eEW normalization correction is carried out on abnormal cone cell response, so that the visual response of a simulated individual under isoenergetic white light is consistent with that of a standard observer, and the stability and naturalness of image simulation are facilitated; the GOG model is introduced in the mapping process between the RGB space and the XYZ space, Gamma response characteristics and channel nonlinearity of actual display equipment are fully considered, and the fitting precision from image pixels to visual response is effectively improved.

The invention belongs to the technical field of primary cell culture, and particularly relates to a bronchial epithelial cell physiological or pathological model and a construction method and application thereof.The construction method comprises the steps that primary HBECs are provided, an amplification culture medium is used for conducting infiltration type amplification culture on the HBECs in a cell culture plug-in, after the cells form a complete barrier, the amplification culture medium is removed, and the model is obtained; carrying out gas-liquid interface culture on the cells in the cell culture plug-in by adopting a differential culture medium so as to induce the differentiation of the HBECs to form a false multi-layer columnar epithelial tissue structure; the cell culture plug-in comprises a porous membrane substrate, and construction of a physiological model or a pathological model is realized by selecting the cell culture plug-in of the porous membrane substrate with different pore diameters. According to the method, the in-vitro differentiation period of the HBECs can be remarkably shortened, and the model culture efficiency is greatly improved. The bronchial epithelium model capable of accelerating physiological differentiation or having pathological phenotype prepared by the method provided by the invention has significant advantages in drug screening and disease research.

A method of predicting future blood glucose concentrations of an individual patient includes: selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected model having a plurality of model parameters whose values are to be determined; estimating values for each of the model parameters in the plurality of model parameters, a first subset of the model parameters having values estimated from a priori population data and a second subset of the model parameters having values personalized for the individual patient by applying a parameter estimation technique to a priori information and data for the individual patient to obtain a posteriori information; and; applying a nonlinear prediction technique to the selected model using the estimated values for each of the model parameters to obtain a predicted blood glucose concentration of the individual patient at a future time.

Systems, devices, and techniques are disclosed for providing personalized insulin dose recommendations for a patient. In some aspects, a method includes generating a physiological model for estimating glucose values of a patient based on historical data including a history of glucose values and a history of insulin doses over a window of time during which the patient ingests multiple meals. The method may also include estimating an effect of the plurality of meals on the glucose value of the patient using the physiological model and the historical data, and receiving a first meal size indication for a first meal ingested by the patient. The method may also include outputting a first meal-time bolus insulin dose recommendation based on the effect of the multiple meals on the glucose value of the patient and the first meal size indication.

The invention discloses an integrated hemodynamic monitoring sensor and a target-oriented liquid treatment method. The method comprises the following steps: acquiring an arterial pressure waveform corresponding to at least one cardiac cycle; based on the arterial pressure waveform, the stroke-by-stroke basic features of each cardiac cycle are recognized in real time, and the stroke-by-stroke basic features comprise stroke-by-stroke systolic pressure and stroke-by-stroke diastolic pressure; calculating a pulse-to-pulse pressure difference corresponding to each cardiac cycle; in a preset time window, based on all pulse-by-pulse pressure differences appearing in the time window, through a single analysis operation, a capacity reactivity index which does not depend on estimation of an absolute value of an output quantity per pulse is directly calculated; and, based on the volume reactivity indicator, generating intervention guidance information for the targeted fluid therapy. According to the method, a traditional calculation process is compressed, and a complicated step of estimating the output quantity per stroke is avoided, so that the calculation complexity and the dependence on a physiological model are remarkably reduced, and the real-time performance and the robustness of monitoring and the reliability of clinical guidance are improved.

A method of predicting future blood glucose concentrations of an individual patient includes: selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected model having a plurality of model parameters whose values are to be determined; estimating values for each of the model parameters in the plurality of model parameters, a first subset of the model parameters having values estimated from a priori population data and a second subset of the model parameters having values personalized for the individual patient by applying a parameter estimation technique to a priori information and data for the individual patient to obtain a posteriori information; and; applying a nonlinear prediction technique to the selected model using the estimated values for each of the model parameters to obtain a predicted blood glucose concentration of the individual patient at a future time.

The invention discloses a portable lung function monitoring method and device based on artificial intelligence, and relates to the technical field of medical monitoring. The method comprises the following steps: acquiring sparse time sequence data of at least one physiological parameter, and performing inverse problem solving on a preset respiratory system physiological model of which the control equation is a partial differential equation by using a first neural network model so as to identify model parameters representing individual dynamic characteristics in real time; performing forward prediction by using a pre-trained second neural network model based on the identified model parameters and the current physiological state, and generating a rolling prediction sequence of a complete space-time physical field quantity representing the lung function state; a closed-loop correction mechanism is introduced, a physical residual error of a control equation is calculated based on a rolling prediction sequence, and small-step optimization is performed on the prediction sequence according to the physical residual error so as to perform self-adaptive correction. According to the method, the problems that the monitoring dimension of existing portable equipment is single, and long-term prediction of a pure data driving model is unstable are solved.

The embodiment of the application provides a leaf light-nitrogen matching method based on a photosynthetic physiological model, which comprises the following steps: S1, data acquisition: acquiring characteristic parameters of target rice leaves under a set environmental condition; S2, constructing a photosynthetic physiological model based on the characteristic parameters and the physiological and morphological adaptability change rule of different genotypes of rice leaves to effective radiation light; S3, inputting the characteristic parameters into the photosynthetic physiological model, acquiring light-nitrogen spatial distribution characteristic parameters of the cross section of the leaves, and quantifying the light-nitrogen matching degree of the leaves based on the parameters; and S4, dynamically outputting adaptive response results of the light-nitrogen distribution mode of the leaves by changing external light environment parameters input into the photosynthetic physiological model. The method realizes nondestructive and accurate quantification of the matching degree of the spatial distribution of light resources and nitrogen resources on the cross section of a single leaf of C3 crops such as rice, and provides support for screening of adaptive rice varieties for "global dimming".

A method of determining a contrast scanning protocol and a computer device are provided. The method includes: obtaining a target scanned object physiological model of a target scanned object, inputting the target scanned object physiological model and a plurality of candidate contrast scanning protocols into a trained contrast simulation model to obtain enhancement effect parameters of the plurality of candidate contrast scanning protocols, and determining a recommended contrast scanning protocol from the plurality of candidate contrast scanning protocols according to the enhancement effect parameters of the plurality of candidate contrast scanning protocols.

This specification discloses an audio-based authentication method applied to a head-mounted device containing air conduction and bone conduction sensors. The method involves acquiring air conduction and bone conduction signals, upsampling the bone conduction signal, and constructing a similarity matrix based on the audio features of the two types of signals after sampling rate matching. A path algorithm is then used for temporal alignment. Finally, based on a physiological model of bone conduction signal propagation, delay compensation is applied to the processed bone conduction signal to obtain bone conduction and air conduction signals with aligned sampling rate and pronunciation timing. Features are then extracted from these signals for identity verification. This three-level signal alignment process enables cross-device signal features to be processed within the same feature space, resolving the problem of inaccurate authentication caused by device heterogeneity.

The invention provides a CAR-T cell therapy toxicity management optimization method and system, and relates to the technical field of artificial intelligence. Clinical data of a hematologic tumor patient receiving a CAR-T cell therapy is acquired; missing value interpolation and standardization processing are carried out on the baseline feature information and the process monitoring data, and a standardized data set is obtained; obtaining a calibrated virtual patient physiological model according to a preset tumor cell kinetic model, an immune cell kinetic model and a toxic reaction model; taking the calibrated virtual patient physiological model as an interaction environment to obtain an associated database; constructing a deep Q network agent with a maximized reward function as a target, and obtaining an optimized deep Q network agent; and inputting the process monitoring data into the optimized deep Q network agent to obtain a target strategy, so that the individualized CAR-T treatment strategy for balancing the curative effect and the toxicity can be automatically output by inputting the real-time monitoring data of the patient.

The invention relates to the technical field of medical data mining, in particular to a neonatal jaundice four-level early warning system and method based on multi-modal analysis. The method comprises the steps of firstly obtaining feeding record data; further obtaining a bilirubin increment-day age-growth increment change curved surface according to the growth increment of the historical neonates of the same day age and the distribution of the corresponding bilirubin increment; historical predicted bilirubin is further obtained based on the change curved surface; further obtaining network prediction bilirubin based on the pre-trained neural network; and finally, according to the difference between the historically predicted bilirubin and the network predicted bilirubin, in combination with the difference between the historically predicted feeding interval and the network predicted feeding interval, four-level early warning of jaundice is carried out. According to the scheme, the influence of breast feeding is considered, the physiological model and the neural network are fused, dynamic personalized prediction and multi-source comparison of bilirubin are realized, the problems of poor model generalization and early warning lag in traditional jaundice monitoring are solved, and the accuracy and timeliness of jaundice risk identification are improved.

The invention discloses a far-infrared intelligent Dan moxibustion cooperative system based on big data analysis and an intelligent Dan moxibustion bed, and the method comprises the steps: firstly, generating a homogenized acupoint distribution map through heat radiation scanning, combining an analysis algorithm with a physiological model for comparison, precisely marking a thermal anomaly region, and extracting a treatment point coordinate set; secondly, dynamically calculating a temperature gradient curve based on priority ranking and sensor feedback, and optimizing treatment parameters; and finally, fusing distance sensor data and a posture calibration system, determining an accurate treatment distance, and integrating duration control and vector data to form a continuous collaborative treatment sequence, so as to ensure treatment precision and adaptability. According to the method, through integrated innovation of heat energy distribution analysis, dynamic parameter adjustment and posture calibration, the accuracy and personalized adaptive capacity of acupoint treatment are remarkably improved, and an efficient solution is provided for accurate intervention of heat energy abnormity of a human physiological system.

The application provides a terminal blood glucose detection data consistency alignment and fusion method and related equipment. The method comprises the following steps: obtaining original collection data of at least one blood glucose detection terminal, corresponding environment parameters, individual physiological difference parameters of a target user, and historical blood glucose change feedback data; inputting the individual physiological difference parameters into a physiological model to generate a theoretical blood glucose change curve; matching the original collection data and the theoretical blood glucose change curve to construct a blood glucose time offset correction model; inputting the original collection data and the corresponding environment parameters into the blood glucose time offset correction model to obtain a time delay amount of the original collection data; mapping the original collection data to a standard time axis based on the time delay amount to generate aligned blood glucose data; performing multi-dimensional feature extraction on the aligned blood glucose data, calculating corresponding confidence weights, and obtaining a fusion blood glucose optimal estimation value and a confidence interval at a current moment. Reliable blood glucose detection results can be stably output.

This invention discloses a method and system for optimizing the layout of optic nerve electrode arrays. The method first constructs a static physiological model of the random distribution of optic nerve fibers. Then, it uses multiphysics simulation software to establish optic nerve and electrode models to obtain the potential distribution. A Python master script initializes the covariance matrix adaptive evolution strategy algorithm and introduces constraints related to biosafety and electrode manufacturing processes. Automated coupled modeling is achieved through a Python interface, mapping the potential distribution to an axon model for neurodynamic simulation and fitness evaluation. Finally, the algorithm iteratively updates and outputs the globally optimal set of electrode spatial distribution parameters. This invention deeply integrates three-dimensional finite element electric field simulation with neurodynamic models, and through automated algorithms, globally optimizes under strict constraints, resolving the contradiction between "high coverage" and "low damage" in optic nerve prosthesis design, significantly improving the electrical stimulation coverage and design efficiency of optic nerve prostheses.

Systems, devices, and techniques are disclosed for providing personalized insulin dosage recommendations for a patient. In some aspects, a method includes generating a physiological model for estimating glucose values of the patient based on historical data, the historical data comprising glucose value history and insulin dosage history for a time window during which the patient consumed a plurality of meals. The method also may include estimating, using the physiological model and the historical data, an impact of the plurality of meals on the glucose values of the patient, and receiving a first meal size indication of a first meal for consumption by the patient. The method may also include outputting a first meal bolus insulin dose recommendation based on the impact of the plurality of meals on the glucose values of the patient and the first meal size indication.