38 results about "Physiological stress" patented technology

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

The invention relates to the technical field of intelligent aquaculture and automatic processing, in particular to an appearance characteristic-based hippocampus intelligent grading and pond-dividing culture method and system, and the method comprises the following steps: S1, collecting sparse visual data and multi-source physiological data of hippocampus; s2, generating a three-dimensional digital twinborn model based on the sparse visual data; calculating a growth state grading index based on the three-dimensional digital twinborn model; s3, calculating a physiological stress index based on the multi-source physiological data; s4, comparing the physiological stress index calculated in real time with a preset safety stress threshold value and a critical stress threshold value, and generating a hierarchical adaptive correction strategy; s5, according to a grading self-adaptive correction strategy, controlling a flexible mechanical arm to execute fishing and pond separation operation; according to the method, the appearance and physiological data of the hippocampus are synchronously collected in a non-contact mode, the technical problems that the real condition of the hippocampus cannot be evaluated and damage is possibly caused due to target stress response caused by traditional physical intervention are solved, and the authenticity and effectiveness of original data are guaranteed.

The invention provides a community open space psychological recovery effect assessment method and system based on multi-modal perception. The method comprises the following steps: acquiring a physiological signal, a movement track, eyeball movement data and a panoramic image of a user; analyzing the image, extracting environmental element space distribution parameters, and calculating an environmental information entropy value; according to the physiological signal, calculating physiological stress deviation as a first type of error, analyzing the matching degree of a moving track and an environment structure as a second type of error, and combining eye movement characteristics and visual attraction distribution to calculate attention deviation as a third type of error; inputting the three types of errors and entropy values into an evaluation model, and outputting a recovery efficiency index and a multi-dimensional index representing error mitigation; and associating the environment parameters with the multi-dimensional indexes, explaining and extracting an environment intervention critical value and a recovery effect function, and generating a space optimization evaluation report in combination with dynamic indexes. According to the method, quantitative evaluation and accurate optimization decision support of the psychological recovery effect of the open space of the community are realized.

The invention discloses an oxygenerator remote control system and method based on the Internet of Things, and belongs to the technical field of intelligent medical equipment and remote health management, and the method comprises the steps: S1, collecting multi-dimensional physiological data of a user in real time through a multi-source physiological signal collection module, a heart rate variability index and a body movement signal; s2, based on the multi-dimensional physiological data, processing is performed through a preset physiological state prediction model, and a unified physiological stress index is solved; s3, determining a predictive oxygen supply flow rate value in combination with the physiological stress index and a preset user basic oxygen supply flow rate; s4, according to the predictive oxygen supply flow velocity value, a control signal for a physical execution component of the oxygen generator is generated so as to dynamically adjust oxygen supply parameters, and conversion from passive compensation to active prediction is achieved.

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.

Carotenoid compositions are described for management, treatment, and / or prevention of stress by reducing cortisol levels and / or improving distribution and / or density of macular pigment and thus improving overall health status. More particularly, a subject in need thereof for the method has disturbed macular pigment optical density or increased levels of cortisol as markers of psychological and physiological stress. Methods are also described of administering macular carotenoid compositions in daily doses of at least or about 0.005 mg / kg body weight of lutein, and at least or about 0.001 mg / kg body weight of meso-zeaxanthin and zeaxanthin isomer along with at least one food grade excipient and evaluating overall health status. The composition may be derived from plant extract containing xanthophylls and / or xanthophylls esters. The composition also reduces cortisol levels in the blood and relieves physiological stress, thus improving overall health status of a subject in need thereof.

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 invention relates to the field of load assessment, and discloses a personnel workload real-time assessment method and system based on multi-channel physiological feature fusion, electronic equipment and a storage medium, and the method comprises the following steps: obtaining multi-channel physiological data when a worker executes a task, the multi-channel physiological data comprises electroencephalogram data, myoelectricity data and electrocardio data. According to the method, three types of key physiological signals including electroencephalogram data, myoelectricity data and electrocardio data are synchronously collected, the electroencephalogram signals can accurately reflect cognitive loads such as the attention concentration degree and the fatigue state of people, and the myoelectricity signals can reflect the muscle tension degree and the operation fatigue degree; the electrocardiosignals can capture physiological stress indexes such as heart rate and heart rate variability, the cognitive and physiological dimensions of workload are comprehensively covered, and the integrity and accuracy of an evaluation result are remarkably improved by combining data processing means such as wavelet analysis and a dynamic entropy weight method.

The invention relates to the field of data anomaly analysis, in particular to an environment anomaly analysis method based on sheep breeding environment monitoring data, and the method comprises the steps: obtaining an initial prediction error of a sheep house environment through a long and short term memory prediction model; analyzing the relative humidity change characteristics to obtain a conventional operation false alarm inhibition factor; modeling is carried out on the environmental stress degree and the cumulative influence to obtain a persistent risk amplification factor; correcting the initial error by fusing a conventional operation false alarm suppression factor and a persistent risk amplification factor to obtain a final risk score; the environmental anomaly assessment is obtained by performing joint assessment on the dynamic relative risk and the physiological stress risk, so that the problems that conventional operation is easy to report mistakenly and persistent anomaly is easy to report omission are solved.

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 discloses an early warning method and system for physiological stress of onboard personnel, relating to the field of intelligent health monitoring in aviation. The method includes: conducting ground tests on onboard personnel before boarding to construct a personalized physiological baseline threshold library; during flight, collecting physiological data of onboard personnel in real time via wearable devices and simultaneously acquiring flight parameters, transmitting this data to a ground station; the ground station assessing the stress level of onboard personnel using a locally deployed large-scale physiological model combined with the stored personalized physiological baseline threshold library; and triggering a graded control mechanism when the stress level exceeds a safety threshold. This invention can improve the physiological stability of onboard personnel in complex low-altitude airflow environments, enhance the ergonomics of low-altitude manned aircraft, and quantitatively assess the stress state of onboard personnel in low-altitude environments.

A system and method for interactive three-dimensional visualization of physiological stress effects on the human body uses biomarker correlation algorithms to determine physiological responses to physical trauma and toxic stress events. The system receives stress event data, calculates biomarker levels including cortisol, blood pressure, blood sugar, and inflammatory markers, and identifies affected organ systems including cardiovascular, nervous, endocrine, digestive, immune, and reproductive systems. Three-dimensional anatomical models are modified with visual characteristics depicting stress-induced changes based on the biomarker data, including color alterations, geometric deformations, and animated effects. A temporal progression interface enables visualization of immediate, short-term, and long-term physiological effects. Spatial audio synchronized with visual animations provides immersive auditory feedback. Cross-platform rendering modules optimize visualization for mobile devices, desktop computers, tablets, virtual reality headsets, and augmented reality glasses. The system provides for real-time interactive manipulation of anatomical visualizations, particularly for pediatric toxic stress education related to corporal punishment.

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.

A system and method for assessing stress levels in real-time is described The stress level is assessed based on circadian rhythms, using a wearable device. The wearable device measures Resting Heart Rate (RHR) of a user and correlates changes in the RHR with circadian zones to evaluate the stress. Stress information along with sleep, activity, and nap zones is visually represented on an interface of the wearable device. A scoring mechanism is used for adjusting the user's stress score based on a time of day and physiological data, for enhancing personal management of health and stress.

This invention relates to the field of data processing technology, specifically to a multi-source data management system for monitoring the physiological stress index of heat endurance. The system includes: an acquisition module for acquiring the PSI value, body temperature data, heart rate data, and user attribute data of each test subject; a data processing module for calculating the high-risk coefficient of each test subject based on their body temperature and heart rate data, and calculating the display percentage of each test subject on the integrated display screen of the thermal stress index monitoring device based on their corresponding high-risk coefficient, PSI value, and user attribute data; and a display module for determining the display position and display area of ​​each test subject on the integrated display screen of the thermal stress index monitoring device based on their corresponding display percentage, and displaying the corresponding test subject information according to the display position and display area. This application solves the technical problem that display methods with the same display percentage cannot effectively highlight test subjects requiring priority attention.

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 Smart Interactive Shirt System is an advanced wearable biosensing platform engineered for real-time, multi-modal monitoring and predictive management of pain, fatigue, and physiological stress. Embedded within a flexible textile, the system integrates 15 specialized IoT sensors—spanning biochemical, biomechanical, thermal, and neurophysiological domains—to deliver continuous high-resolution insight into the wearer's physiological state. Key innovations include plasmonic nano-optical modules for real-time cortisol and CRP detection, EMG-based neuromuscular sensing, and thermopile arrays for deep-tissue inflammation mapping. A hybrid AI engine combining convolutional and recurrent neural networks (CNN-LSTM) processes incoming data locally, enabling early prediction of acute fatigue and pain episodes 10–15 minutes before physiological onset. The system incorporates triboelectric nanogenerators (TENGs) that harvest kinetic energy from body motion, minimizing reliance on external power sources and supporting extended autonomous operation. Validated through pilot deployments in elite European sports organizations, the shirt also features screen-printed electronic circuits, vibrotactile feedback mechanisms, and seamless Bluetooth Low Energy (BLE) integration with mobile interfaces. This invention marks a significant advancement over conventional wearables by offering proactive, data-driven physiological management, setting a new benchmark in intelligent, AI-powered textile systems.