25 results about "Physiological stress" patented technology

A flight simulation system for enabling g-force training includes a seating system, a sensor arrangement, and a controller. The seating system is configured for accommodating a human occupant and for providing thereto physically simulated flight conditions corresponding to predetermined real flight conditions, the predetermined real flight conditions including g-forces, wherein the physically simulated flight conditions include application of non-g forces to the human occupant corresponding to the g-forces, and wherein the g-forces are considered sufficient to provide g-force induced physiological stress to the human occupant. The sensor arrangement is configured for providing real-time feedback data of predetermined physiological parameters of the human occupant, in operation of the flight simulation system with the human occupant accommodated in the seating system, wherein the predetermined physiological parameters are indicative of the g-force induced physiological stress. The controller is configured for controlling the seating system to provide the physically simulated flight conditions.

The present application relates to the field of livestock and poultry intelligent breeding and environmental control, in particular to a sheep house environment adaptive regulation system for Nanjiang yellow sheep breeding, comprising a data acquisition device, an environment control device, a feeding execution device and a controller; the controller receives real-time environmental data of a target environment and real-time physiological behavior data of a target breeding object, generates a physiological stress load value through a time integral algorithm, and inputs the physiological stress load value and the real-time physiological behavior data into a neural network model containing a time sequence processing structure to output an effective feeding capacity, a nutrient absorption efficiency and an expected feed conversion rate; when the effective feeding capacity is lower than a standard feeding capacity, a target intervention combination strategy composed of an environment compensation weight and a feeding adjustment weight is generated with the condition of maximum expected feed conversion rate, and then a feeding end control instruction and an environment control end control instruction are generated and closed-loop feedback adjusted to realize self-adaptive closed-loop control of the sheep house environment and the Nanjiang yellow sheep.

This invention discloses a method, device, equipment, and medium for monitoring high physiological stress based on rPPG. First, a video sequence containing the user's face is acquired, and the region of interest (ROI) sequence is extracted and input into a deep learning network model. A spatiotemporal encoder extracts preliminary spatiotemporal physiological feature sequences from these sequences. Then, relying on an adaptive frequency-aware decoder, multi-scale parallel processing and dynamic feature fusion are performed on these preliminary spatiotemporal physiological feature sequences to output feature representations of abnormal high-frequency rhythmic events. Finally, a state determiner, combined with the abnormal high-frequency rhythmic event identification results, directly outputs a binary classification result indicating whether the user is in a state of high physiological stress. This invention effectively solves the problem of abnormal high-frequency rhythmic signal loss caused by existing fixed filters, achieving high-precision identification of abnormal high-frequency rhythmic events and accurately determining the user's state of high physiological stress.

The invention belongs to the technical field of aircraft control, and particularly relates to a simulation test system and method for an aircraft obstacle avoidance algorithm. The invention discloses a simulation test system and method for an aircraft obstacle avoidance algorithm. The system comprises a virtual environment modeling device, a dynamics resolving device, an obstacle avoidance logic processing device, a physiological feature modeling device, a stress parameter mapping device, a comprehensive evaluation feedback device and a data monitoring device. According to the method, kinematic parameters are mapped into physiological stress parameters such as gravity overload and motion sickness, and an obstacle avoidance track is fed back and corrected according to a physiological comfort threshold, so that an obstacle avoidance path conforms to the tolerance limit of a human body on the premise of ensuring safety. According to the method, deep fusion of control engineering and aeronautical medicine is realized, obstacle avoidance assessment extends to the field of physiological tolerance from physical safety, the scientificity of simulation assessment of the manned aircraft is improved, and the risk of personnel disability caused by violent maneuver in real flight is reduced.

The invention provides an operating room nursing operation intelligent training system and method, and the method comprises the steps: collecting a nursing operation track of a target user in a high-fidelity operation situation simulation environment matched with a target operation type in real time, generating a deviation map of the nursing operation track and a standard operation, and recognizing a space-time operation abnormality feature in the deviation map; determining a pressure load index of the target user when the emergency event occurs according to the combined characteristics of the various physiological signals of the target user on frequency change in combination with heart rate variability, and when the pressure load index exceeds a preset psychological pressure threshold value, guiding the target user to relieve psychological pressure and recording a physiological stress state track of the target user; and carrying out emergency response training on the target user based on the spatio-temporal operation abnormal features and the physiological stress state trajectory pushing special training strategy. By adopting the scheme of the invention, an adaptive intraoperative nursing emergency response capability training system can be constructed to perform dynamic intervention training on the intraoperative emergency response capability of nursing personnel.

The application relates to the technical field of duck breeding, and provides an intelligent marking and egg laying breeding device for breeding ducks, which comprises a net bed and an egg laying row nest, the egg laying row nest is arranged around the net bed, padding is arranged in the egg laying row nest, and the device further comprises independent egg laying nests, the independent egg laying nests are arranged in the egg laying row nest, and the device has the advantages that the two times of weighing when a duck enters and walks out of the independent egg laying nest are compared, when the difference between the two times of weighing is greater than or equal to a specified value, the duck is defined as having laid an egg, the pigment in a pigment spray tank can be sprayed on the duck through the perforation to mark the duck, the color on the duck can be observed in the daily feeding process to accurately lock the target duck that has not laid an egg for a long time, the duck cannot be caused to have persistent physiological stress and local infection, the duck that has low egg laying efficiency can be timely eliminated, and the feed cost and environmental pollution of a breeding farm can be reduced.

This application provides a coordinated feedback-integrated drum music teaching method, which includes: calculating the user's human biological index, human psychological parameters, and physiological stress index; constructing a user feature profile based on the human biological index, human psychological parameters, and physiological stress index; matching the user feature profile with a constructed drum music repertoire library to select drum music repertoire matching the user; and generating a drum music teaching plan for the user based on the selected drum music repertoire. This solution achieves comprehensive quantification of the user's state through multi-dimensional index calculation, systematic integration of environmental factors based on dynamic profile construction, and targeted adaptation of teaching objectives through a precise matching mechanism. It fundamentally solves the problems of insufficient matching accuracy, poor environmental adaptability, and vague teaching objectives in existing technologies, providing an innovative technical path for drum music teaching that integrates biomedical indicators, psychological characteristics, and environmental stress theory.

The application provides a veterinary traditional Chinese medicinal material characteristic map efficacy quality correlation determination method, relates to the fields of traditional Chinese medicine analysis and detection and intelligent sensing technology, and the application converts static chemical components in the characteristic map into active node networks with inherent metabolic properties and definite dynamic interaction rules by constructing a chemical component characteristic space based on pharmacological mechanisms, quantifies synergistic or antagonistic inhibitory relationships between key nodes by using data driving and model simulation methods, thereby establishing a dynamic correlation evaluation system from material basis to biological effects, and realizing scientific determination of internal quality and actual efficacy of the veterinary traditional Chinese medicinal material. The application introduces metabolic response disorder degree and physiological stress load index as dynamic feedback variables, and improves the accuracy of the evaluation model in responding to real-time biological state of the body.

The application discloses an inside-outside cooperative intelligent fire-fighting clothes monitoring system and relates to the technical field of fire-fighting monitoring.The system comprises a data acquisition component, a data processing component, a calculation and analysis component and a monitoring and alarming component.The data acquisition component is used to acquire external environment data, internal physiological data and equipment team correlation data of the fire-fighting clothes when the fire-fighting clothes is used.The calculation and analysis component comprises an environment risk unit, a physiological risk unit and a safety early warning unit.The application realizes dynamic control of the safety of firemen through the three units.The environment risk unit integrates temperature, toxic gas concentration and other parameters to output a comprehensive risk coefficient, thereby providing a reliable basis for subsequent evaluation.The physiological risk unit combines heart rate and other physiological parameters and an environment coefficient to output a physiological stress index, thereby accurately reflecting current physiological bearing capacity.The safety early warning unit integrates the two indexes and multidimensional parameters such as equipment and cooperation to output an early warning level, thereby effectively supporting risk control of inside-outside cooperative intelligent monitoring.

This invention relates to the field of intelligent agricultural robot technology, specifically to a fruit posture adaptation harvesting method, system, and equipment based on binocular vision. It avoids the high breakage rate problem caused by existing technologies that rely on fixed postures or simple path planning for harvesting. By accurately sensing the fruit's growth posture, dynamically calculating the target posture of the end effector, and adjusting it in real time, it can adapt to fruits in different growth stages without human intervention, reducing labor intensity and costs. It ensures precise alignment between the fruit and the harvesting direction of the end effector, achieving a harvesting success rate of over 95%, avoiding the poor adaptability of fixed-posture harvesting. Simultaneously, during harvesting, the harvesting force is applied along the physiological stress direction of the fruit stem, without lateral compression, pulling, or reciprocating vibration, thereby reducing the fresh fruit breakage rate to below 3%, meeting practical operational needs.

The application discloses a reinforcement learning reward function construction method and system based on multi-modal physiological signals, and belongs to the technical fields of automatic driving, artificial intelligence and human-computer interaction. The application solves the problem that the existing reward function design has a fundamental defect, that is, it is impossible to balance the task target and the physiological comfort of a driver or a passenger. The core technical idea lies in a two-stage decoupling architecture. In a first stage (an offline training stage), the purpose of the stage is to train a "physiological reward agent model". In a second stage (a reward generation and deployment stage), in the stage, real-time dependence on all multi-modal physiological signals and their acquisition devices is completely abandoned. Only the "physiological reward agent model" trained in the first stage is used to batch process any data set containing only vehicle state data and driving behavior data, infer the physiological stress level, and calculate the physiological reward according to the physiological stress level.

The invention, which relates to the technical field of data analysis, discloses a physical and mental quality testing method for flying students based on an artificial high-pressure environment, comprising: establishing a physical and mental quality testing database for flying students in an artificial high-pressure environment; based on the flight student physical and mental quality test database in the artificial high-pressure environment, evaluating the psychological pressure state indexes of the flight students in different artificial high-pressure environments and the physiological pressure state indexes of the flight students in different artificial high-pressure environments, and carrying out weighted calculation on the comprehensive operation capability indexes of the flight students in the artificial high-pressure environments; determining a standardized operation vector of the flight trainee in the man-made high-voltage environment; and judging whether the comprehensive operation capability index of the flight trainee in the man-made high-voltage environment is in the standardized operation vector range interval of the flight trainee in the man-made high-voltage environment or not. The method has the advantages that the physical and mental quality of the flight trainee in the high-pressure environment can be accurately evaluated, and the selection efficiency of a pilot is improved.

The present application relates to a kind of intelligent individual temperature adjustment method and equipment, belong to intelligent environment control and wearable technology field.The present application comprehensively controls the operating power of refrigeration device by PSI physiological stress index and the temperature and humidity data of the microenvironment obtained, so that refrigeration device operates in the power corresponding to the higher level data in PSI physiological stress index and the temperature and humidity data of microenvironment.The present application simultaneously considers PSI physiological stress index and the temperature and humidity data of microenvironment, and temperature adjustment based on the data of the two dimensions effectively solves the technical problems that fixed temperature threshold control cannot perceive individual physiological state, response lag, and easily lead to excessive refrigeration or heating in prior art, and further realizes accurate grading dynamic control according to real-time thermal stress level of human body, and significantly improves thermal comfort experience and individual adaptability.

This invention relates to a caregiver post-traumatic growth monitoring system and method based on multimodal situational awareness, comprising a sequentially functioning, closed-loop system: an independent sensing terminal, a role-situation recognition module, a situation-physiological stress decoupling module, a PTG semantic feature extraction module, an AIGC narrative reconstruction intervention module, and a closed-loop feedback unit. Through multimodal data acquisition and situational recognition, it achieves precise decoupling of physical fatigue and psychological stress; combining semantic analysis and generative AI technology, it generates personalized narrative reconstruction intervention content for different situations; and based on closed-loop feedback, it achieves dynamic strategy optimization. This invention, through the collaboration of an independent sensing terminal and multiple modules, accurately distinguishes between caregiver physical fatigue and psychological stress; combines semantic analysis and AIGC technology to generate personalized narrative interventions; and relies on closed-loop feedback and adaptive adjustment of the treatment cycle to achieve intelligent cognitive reassessment, comprehensively promoting post-traumatic growth.

A computer-implemented method (100) for detecting physiological stress of a user of a virtual reality environment, wherein the method comprises: Display (310) virtual objects at object positions in the virtual reality environment for the user; Instruction (320) to the user to interact with one of the displayed virtual objects; Capturing (330) at least one parameter of the user’s movement in response to the instruction, wherein the at least one parameter of the movement includes response time or movement speed or movement direction; Determine (340) a measure of physiological stress based on the captured at least one parameter of the user's movement, wherein the determination (340) includes comparing the captured parameters of movement with a reference value associated with a virtual object and / or its object position and / or the user; Obtain (350) historical information relating to previously determined measures of physiological stress and compare a newly determined measure with the historical information to provide a personal baseline measure, including checking or flagging erroneous or deviant values; Identify a target part of the user's body to which no measure of physiological stress is associated and adjust the object position so that the interaction requires the use of the target part; Detect a lack of movement of the target part as an indication of significant physiological stress; Assigning (370) the measure of physiological stress to a body part of the user based on the object position of one of the displayed virtual objects; and Adapting the virtual reality environment based on the measure of physiological stress and the user's body part, wherein the adaptation of the virtual reality environment involves adjusting the object position of one of the displayed virtual objects.

The invention provides an intelligent deep physiotherapy cabin self-adaptive sleep assisting system and method based on environmental perception. Belongs to the technical field of intelligent health and active sleep intervention. The method comprises the following steps: synchronously acquiring environment disturbance parameters and user physiological stress data through a deployed multi-mode environment sensing array and a physiological signal acquisition module; and establishing an environment and physiological causal mapping model based on a dynamic coupling algorithm, and generating a real-time correlation map of an environment pressure index and a nerve stress index. By constructing the environment and physiological causal mapping model, the environment disturbance is accurately associated with the human body stress state, so that the wave motion adjustment strategy closely fits the real demand of the user, the problem of adjustment failure caused by traditional control logic splitting is avoided, and the pertinence of sleep assistance is remarkably improved.

The invention discloses a military psychological stress disorder assessment method, and relates to the technical field of stress disorder assessment, and the method comprises the following steps: building a stress response judgment index framework with cardiovascular and central nervous systems as the core, including three dimensions of physiological, psychological and cognitive ability and operation ability, formulating the grading standard of each index, according to the military psychological stress disorder assessment method, a military stress response multi-dimensional comprehensive assessment mode is established, the structural conference time of military doctors can be shortened through self-assessment scale assessment, and the psychological stress assessment condition of patients on self-stress symptoms can be preliminarily known; the physiological stress level and the cognitive stress level of the patient are judged in combination with physiological and cognitive function measurement and evaluation of the patient, the method is more objective and accurate, and meanwhile the stress level of the patient can be comprehensively judged according to the psychological stress level, the physiological stress level and the cognitive stress level.

The remote evaluation and monitoring method and system based on the rehabilitation treatment of the elderly according to the application belong to the technical field of remote evaluation and monitoring of the rehabilitation treatment of the elderly, comprising S1, collecting kinematic data and physiological data of a patient; S2, establishing a physiological safety baseline model for the patient according to standard test data, and obtaining a preset standard motion template; S3, calculating a kinematic deviation index based on the kinematic data and the standard motion template, and calculating a physiological stress index based on the physiological data and the physiological safety baseline model; S4, using the physiological stress index to perform nonlinear correction on the kinematic deviation index to generate a risk-adjusted deviation; S5, generating an adaptive evaluation score based on the risk-adjusted deviation; S6, determining a system risk level according to the physiological stress index, and performing closed-loop intervention corresponding to the system risk level, wherein the adaptive evaluation score generated by the application is more comprehensive, objective and humanized.

The invention discloses a remote evaluation monitoring method and system based on rehabilitation treatment of old people, and belongs to the technical field of remote evaluation monitoring of rehabilitation treatment of old people, and the method comprises the steps: S1, collecting kinematics data and physiology data of a patient; s2, establishing a physiological safety baseline model for the patient according to the standard test data, and obtaining a preset standard motion template; s3, based on the kinematics data and the standard motion template, calculating a kinematics deviation index; calculating a physiological stress index based on the physiological data and the physiological safety baseline model; s4, performing nonlinear correction on the kinematics deviation index by using the physiological stress index to generate deviation after risk adjustment; s5, generating an adaptive evaluation score based on the deviation after risk adjustment; and S6, according to the physiological stress index, the system risk level is judged, and closed-loop intervention corresponding to the system risk level is executed. The self-adaptive evaluation score generated by the method is more comprehensive, objective and humanized.

The invention belongs to the technical field of intelligent health monitoring and exercise rehabilitation equipment, relates to an old people fitness method and system with an intelligent chip, and aims to solve the problem that traditional equipment is insufficient in the aspects of multi-dimensional data fusion, personalized health response model construction and self-adaptive intervention scheme generation. The method comprises the following steps: acquiring and analyzing multi-modal biomechanical and physiological data, and extracting feature vectors; constructing a dynamic health response model based on an adaptive neural fuzzy inference system, and representing a nonlinear mapping relationship between the mechanical load and the physiological stress; and according to the user health target reverse solution model, generating an optimal biomechanical instruction (such as adjusting the resistance of the fitness ball) and personalized nutrition supplement suggestions. According to the technical scheme, health management from popularization to personalization and from passivity to initiative is achieved, and the scientificity and effectiveness of fitness of the old people are effectively improved.

The present application relates to the field of data anomaly analysis, and more particularly to an environment anomaly analysis method based on sheep breeding environment monitoring data, which comprises: obtaining initial prediction error of sheep shed environment through a long short-term memory prediction model; obtaining a conventional operation false alarm suppression factor by analyzing the change characteristics of relative humidity; obtaining a persistent risk amplification factor by modeling the environmental stress degree and cumulative influence; obtaining a final risk score by correcting the initial error by fusing the conventional operation false alarm suppression factor and the persistent risk amplification factor; and obtaining an environment anomaly evaluation by jointly evaluating the dynamic relative risk and the physiological stress risk, thereby solving the problems of easy false alarm in conventional operation and easy missed alarm in persistent anomaly.

The invention relates to the technical field of artificial intelligence and physiological status monitoring, in particular to a physiological stress status monitoring method in a limited diet or specific diet mode, and the method specifically comprises the following steps: collecting physiological index data of a subject in a diet intervention period, marking the physiological index data, and dividing the collected data; aligning the multi-source asynchronous physiological index time sequence data by using a dynamic time warping algorithm based on mutual information, and outputting a regular physiological index matrix; constructing a physiological stress state monitoring model, inputting the normalized physiological index matrix into the model for classification probability calculation, then calculating the total loss of the model, and training the model; and inputting the physiological index data of a new subject into the trained model, outputting the probability corresponding to each physiological stress state category, and selecting the category with the highest probability value as a final result. The physiological stress state monitoring method provided by the invention is more accurate and comprehensive and meets clinical requirements in a limited diet or specific diet mode.

This invention belongs to the field of intelligent driving technology and discloses a method and system for early warning of driver emotional state based on multimodal data fusion. The method includes: acquiring and analyzing the driver's facial video stream to obtain facial data; extracting features from the facial data to obtain micro-expression features; acquiring and standardizing the driver's physiological signals to obtain standard physiological signals; analyzing the standard physiological signals to obtain asymmetry indicators; using micro-expression features and asymmetry indicators as inputs to a cross-modal fusion model to obtain the current emotional state and the predicted time of emotional outburst; and classifying corresponding warning levels based on the current emotional state and the predicted time of emotional outburst. This invention accurately identifies the driver's psychological state at the critical point of outburst by capturing internal representations such as micro-expressions and physiological stress, predicts the time of emotional outburst in advance, and curbs the escalation of emotions before dangerous driving behavior occurs through graded warnings, effectively avoiding accidents.