89 results about "Physiologic States" patented technology

The invention relates to the technical field of biomedical engineering and neuroscience, and discloses an in-ear vagus nerve stimulation control method, system and device based on scalp electroencephalography, and the method comprises the steps: collecting a scalp electroencephalogram signal, a body movement signal and a pulse signal of a to-be-detected user; performing preprocessing and frequency domain separation on the signal; calculating a sleep coupling score, and dividing a physiological state according to the sleep coupling score; dynamically adjusting the intensity and frequency of intra-ear vagus nerve stimulation of the user according to the physiological state, and generating feedback data; in-ear vagus nerve stimulation is performed and the dynamic adjustment parameters are updated based on the feedback data. According to the method, accurate sleep recognition is realized through multi-source signal parallel processing and SCS modeling, and the classification precision is remarkably improved; lSTM prediction and biphase modulation are combined to optimize stimulation parameters, sleep time is shortened, and side effects are reduced; a GPU acceleration and low-power-consumption framework is adopted, and efficient self-adaptive closed-loop regulation and control are achieved.

The invention discloses a personalized sleep intervention and first aid method and system based on multi-modal data fusion. The method comprises the steps that physiological index data and motion data of a user are collected in real time through wearable equipment; generating a sleep quality score based on the physiological index data in combination with a preset sleep quality evaluation model; generating a personalized sleep intervention scheme according to the sleep quality score and the motion data; the sleep intervention module is started by manually triggering or automatically detecting the insomnia state of the user; according to the physiological index data monitored in the sleeping process, the indexes exceeding the standard are recorded, and a prompt is given out after waking up the next day, or a first-aid mode is triggered. The personalized dynamic sleep intervention scheme is generated by quantifying the physiological indexes of the user, and the limitation of a single sensor is broken through; sleep intervention can be triggered in multiple modes, the intervention intensity can be dynamically adjusted according to the real-time physiological state, the first-aid mode can be automatically started, and first-aid delay is avoided.

The invention discloses an AI-based personalized pain management system and method, and the system comprises a multi-dimensional data collection module which is used for collecting multi-dimensional monitoring data including user physiological state data, motion data and location information data, and bio-electricity signal sensing data in real time; the pain type identification module is used for performing feature extraction on the physiological state data and the bio-electricity signal sensing data and identifying the pain type and the pain level of the user in combination with basic information of the user; the pain trend prediction module is used for performing time sequence analysis based on the pain type, the pain level and the multi-dimensional monitoring data, and predicting the pain trend of the user in a future preset time period; and the feedback evaluation module is used for outputting a pain management evaluation report of the user according to the pain type, the pain level and the pain trend. According to the invention, accurate monitoring, assessment and intervention of pain can be realized, so that pain management is more intelligent and personalized, and help is provided for improving the life quality of a user.

The invention provides a multi-mode adaptive wave simulation control system and method for a deep sleep physiotherapy cabin. Belongs to the technical field of fluid mechanics and health intervention. The method comprises the steps that multi-modal physiological parameter collection is conducted on a user on a deep sleep physiotherapy cabin, a real-time physiological data set is generated, the current sleep stage of the user is deduced through a time-frequency analysis and machine learning model on the basis of the data set, a sleep stage label is generated, a pre-trained wave mode generator is dynamically called according to the sleep stage label, and the deep sleep physiotherapy cabin is obtained. An initial set of wave parameters matching the current sleep stage is generated. By sensing the physiological state of the user in a non-contact mode, deducing the sleep stage and dynamically generating and adjusting the wave modes such as sleep guiding and deep sleep maintaining, water fluctuation accurately adapts to the physiological requirements of the user in different sleep stages, a traditional static wave mode is abandoned, highly-personalized sleep adjusting experience is provided for the user, and the sleep quality is effectively improved.

The invention relates to a working parameter adjusting method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring gas pressure and gas flow rate output by a breathing machine and collected by a breathing machine sensor; determining breathing information of the target user according to the gas pressure and the gas flow rate; the breathing information comprises at least one of breathing frequency, real-time tidal volume and breathing quality data; determining physiological state information of the target user according to the breathing information and a physiological state threshold value; and adjusting working parameters of the breathing machine based on the physiological state information. By analyzing the gas pressure and flow velocity data collected by the respirator sensor in real time, the breathing frequency, tidal volume and other key parameters of a user can be accurately determined, the breathing state of a patient is intelligently evaluated, the working parameters of the respirator are automatically adjusted according to the evaluation result, and the flexibility of the working parameters is improved.

The application belongs to the technical field of smart home, wearable device, biological signal processing and artificial intelligence control, and specifically discloses a self-adaptive audio adjustment sleep-aiding method based on sleep state recognition, which comprises the following steps: a non-invasive touch interaction mode is introduced to simplify the starting process of the sleep-aiding process; key physiological indexes such as the heart rate and body movement of a user are continuously monitored, and subtle changes in the data are analyzed to determine the physiological state of the user and the reaction of the user to environmental changes, so that the change in the acoustic environment is ensured to be always within the comfortable range that can be accepted by the user; the amplitude of the volume adjustment is controlled to be below the perception threshold of the user, and a special disturbance observation window is set to evaluate the physiological influence of each fine adjustment operation, so that inappropriate adjustment can be found and cancelled in time before substantial interference is caused. The application significantly improves the reliability of the user experience and sleep-aiding effect.

The invention relates to the technical field of wearing control, and discloses an intelligent sleeping bag temperature and humidity self-adaptive control method and system based on multi-source data, and the method comprises the steps: obtaining multi-modal data of user physiology, environment and system state; simulating and predicting a future physiological state vector of the user through a physiological digital twin module; evaluating an energy supply capability vector of the system through an energy management twin module; the cooperative control decision module receives the two vectors and generates a final control sequence by taking minimization of physiological discomfort as a target under energy constraint; adjusting the temperature and humidity in the sleeping bag according to the sequence; the system comprises a multi-modal data acquisition module, a physiological digital twinning module, an energy management twinning module, a cooperative control decision module and a physical execution module. According to the invention, parallel prediction and evaluation of user demands and system capabilities are realized through the double twin modules, optimal balance of comfort and energy efficiency is realized through collaborative decision, and the problems of hysteresis and lack of energy consumption management of a traditional control method are solved.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying a value of a heart rate variability metric that indicates a variation in a heart waveform of a patient; identifying a value of a brain activity metric that indicates a type of electrical activity represented by a brain waveform of the patient; providing values for a collection of metrics to a computational model, the values for the collection of metrics including the value for the heart rate variability metric and the value for the brain activity metric; and receiving, from the computational model as a result of having provided the values for the collection of metrics to the computational model, an indication of mental state of the patient.

The invention discloses an ear acupoint closed-loop ultrasonic stimulation system and method based on sleep electroencephalogram signal feedback, and belongs to the technical field of ultrasonic stimulation nerve regulation and control. The ear acupoint closed-loop ultrasonic stimulation system comprises an electroencephalogram acquisition module, a regulation and control module, an ultrasonic transmitting module and a dynamic association module; the electroencephalogram acquisition module is used for acquiring electroencephalogram signals within limited time specified by a user according to specified sampling frequency, amplifying and filtering the acquired signals and sending the processed signals to the regulation and control module; the regulation and control module judges the current sleep stage of the user, drives the ultrasonic transmitting module, performs variable parameter closed-loop ultrasonic stimulation on the user, and automatically adjusts stimulation parameters and duration according to the current sleep stage; and the dynamic association module sets a multi-threshold triggering mechanism based on the real-time electroencephalogram characteristic parameters and discriminates a specific physiological status. According to the invention, accurate acupoint stimulation can be realized, a real-time feedback mechanism is provided, and the accuracy and effectiveness of stimulation can be ensured.

The invention provides a BCG sleep signal medical calibration and analysis method based on semi-supervised deep learning, and relates to the technical field of biomedical signal processing, and the method comprises the steps: dynamically adjusting the signal collection sensitivity through a high-sensitivity flexible piezoelectric sensor, optimizing adaptive gain control in combination with the body weight of a user and mattress material parameters, and generating a de-noised BCG time domain sequence; extracting a dynamic signal complexity coefficient (DSCC) and a physiological state confusion index (PSCI), and quantizing a signal nonlinear characteristic and a cardiac and respiratory coupling relationship; constructing a semi-supervised deep learning model fusing a convolutional neural network and a graph attention mechanism, and training a feature extraction network by using limited label samples and augmented unlabeled data; establishing a topological correlation model based on a medical calibration database, and analyzing the time-frequency domain characteristics of the BCG signal in combination with a persistent coherence algorithm; and integrating the electronic medical record data of the user, and generating a personalized health assessment report through an attention mechanism and transfer learning. And the screening precision is improved through dynamic signal adaptation, semi-supervised learning and data optimization.

The application discloses a method and system for electrocardiosignal pattern recognition for sleep respiratory state discrimination, and relates to the technical field of apnea prediction, and comprises the following steps: acquiring a chest electrode stress state and constructing a dynamic baseline, mapping the electrocardiosignal based on the dynamic baseline to distinguish dynamic noise segments, implementing rhythm rearrangement based on the mapping result to obtain a corrected electrocardiosignal, introducing a body position evolution direction correlation constraint in combination with contact impedance changes, forming an electrocardio expression result under the constraint of a body position stable window, and reconstructing a risk trigger path based on the result to realize continuous rhythm risk judgment. The application realizes effective recognition of electrocardiosignal patterns under the interference of body position changes by establishing a correlation between electrode stress and contact impedance changes, and improves the analysis stability and physiological state discrimination accuracy of electrocardiosignals in a complex sleep environment.

The invention belongs to the technical field of smart home, wearable equipment, biological signal processing and artificial intelligence control, and particularly discloses a sleep state recognition-based adaptive audio adjustment sleep-aiding method, which comprises the following steps of: simplifying the starting process of a sleep-aiding process by introducing a non-intrusive touch interaction mode; by continuously monitoring key physiological indexes such as heart rate and body movement of the user and analyzing subtle changes of the data, the physiological state of the user and the response to environmental changes are judged, and it is ensured that the changes of the acoustic environment are always in a comfortable area which can be accepted by the physiology of the user; the volume adjustment amplitude is controlled below the perception threshold value of the user, and the special disturbance observation window is set up to evaluate the physiological influence of each fine adjustment operation, so that improper adjustment can be found and revoked in time before substantive interference is caused. The user experience and the reliability of the sleep aiding effect are remarkably improved.

Method and apparatus for personal health navigation using a wearable device having at least one sensor, including receiving an input from an individual indicating a personal health goal, determining a goal physiological state for the individual based on the personal health goal; receiving at least one of a first physiological measurement of the individual from the at least one sensor or an existing physiological profile of the individual; determining a current physiological state for the individual based on at least one of the first physiological measurement or the existing physiological profile of the individual; determine a personalized route for the individual, the personalized route comprising a sequence of physiological states transiting from the current physiological state to the goal physiological state through a plurality of intermediate physiological states; and providing an instruction indicative of a recommended user action to the individual.

The invention relates to the technical field of enuresis nursing equipment, and discloses a night intelligent urine monitoring and nursing wearable system for enuresis children, which comprises a wearable diaper unit, a physical sign monitoring module, an intelligent interaction terminal, a data fusion analysis module and an intervention strategy generation module. According to the invention, through multi-modal data fusion of the distributed capacitive humidity sensor array and physiological sign monitoring, high-precision capture and urine volume estimation of the whole enuresis event process are realized; on this basis, sleep analysis and historical data are combined to generate a dynamic and personalized hierarchical intervention strategy, so that the accuracy and effectiveness of alarm are improved, false alarm and missing alarm are reduced, and meanwhile, comprehensive and associated data support including urine parameters and physiological states is provided for clinical diagnosis; the comprehensive effects of auxiliary treatment and improvement of child sleep quality and nursing efficiency are achieved.

Systems and methods for identifying physiological states of human subjects are disclosed herein. In one embodiment, a system receives, from one or more sensors, physiological signals of a human subject. The system processes the physiological signals to extract feature signals as time series. The system identifies change points in the feature signals. The system identifies critical change points in the feature signals by applying a voting process to the change points in the feature signals. The system partitions the feature signals into segments based on the critical change points. The system detects a predetermined physiological state (an anomalous physiological state) of the human subject by applying clustering and probabilistic analysis to the segments. In response to detecting the predetermined physiological state, the system automatically takes an action to assist the human subject.

Disclosed are sensors that measure various both biological conditions and mental conditions of the trainer and on the user and adds an immersion controller into the user's system that essentially uses the exercise devices as actuators that are adjusted in order to keep both the user's physiological state and mental state tracking the trainer's physiological state and mental state.

The invention relates to the technical field of intelligent health equipment, in particular to a massage chair intelligent adjusting system fusing biological feedback signals and a control method thereof, and the system comprises a multi-mode biological signal acquisition array, a signal conditioning and feature calculation unit, a physiology-massage coupling model, a mechanical-electrical integration execution assembly and a closed-loop correction loop; according to the method, multi-dimensional quantification of fatigue, muscular tension and stress level of the user is completed through synchronous acquisition and feature extraction of multi-modal physiological signals, optimal massage excitation parameters adaptive to the physiological state of the user are solved in combination with a physiology-massage coupling model, and dynamic optimization of the massage process is achieved through a closed-loop correction loop. According to the method, personalized generation and full-process closed-loop regulation and control of the massage scheme are achieved, the real-time physiological state of the user is accurately adapted, and the effectiveness and comfort of the massage effect are effectively improved.

The invention belongs to the field of physiological health regulation and health care, and particularly discloses an acoustic physiological health promotion system, which comprises a multi-source signal acquisition module; a physiological state feature extraction module; an acoustic strategy generation module; a multi-dimensional sound field processing engine; a dynamic feedback unit; real-time physiological signals and physique data of a user are acquired through the multi-source signal acquisition module, a quantitative physiological state is extracted through physiological state features, a dynamic acoustic strategy is generated in combination with a circadian rhythm model, sound wave interference is eliminated by the multi-dimensional sound field processing engine, and target frequency band energy is accurately adjusted. And the dynamic feedback unit optimizes the output in real time, so that accurate matching between acoustic intervention and the real-time physiological state of the user is effectively realized, and the physiological health regulation intervention effect is improved.

The invention discloses an intelligent bed APP system suitable for aging based on a large language model, and belongs to the technical field of intelligent home control and intelligent health old-age care. The system comprises a user interaction layer, a multi-mode perception layer, a heterogeneous data layer, an intelligent algorithm layer and a closed-loop feedback layer, physiological state data of a user are collected through a non-intrusive sensor, and combined analysis is performed on touch or voice input of the user and the physiological state data based on a large language model. And a structured control instruction is generated to drive a bed body execution mechanism and environment equipment to execute corresponding operation, so that self-adaptive adjustment of the bed body state and comfort parameters is realized. The intelligent bed system improves the interaction intelligence level and the use experience suitable for the aged, and is suitable for home-based care for the aged and intelligent health management scenes.

The invention discloses a traditional Chinese medicine guide operation action adaptation generation method for psychological rehabilitation, and belongs to the technical field of traditional Chinese medicine guide operations. S2, constructing a traditional Chinese medicine guidance knowledge graph; s3, generating a personalized action sequence; s4, reinforcement learning safety fine tuning; and S5, carrying out AR interaction and privacy protection. According to the method, through real-time, dynamic and personalized guide action generation, an intervention scheme can always fit the current psychological and physiological state of an individual, so that the effects of emotion regulation, pressure relief and sleep quality improvement are more remarkable and stable; the closed-loop online fine tuning mechanism enables the system to continuously learn in each breath and each motion range of the user, so that the training safety is ensured, and individualized experience is continuously accumulated.

The invention discloses an AI sleep monitoring intelligent ring based on dynamic breath feedback and a health early warning system, and relates to the technical field of intelligent wearable equipment, and the AI sleep monitoring intelligent ring comprises an acquisition module which is used for acquiring photoelectric volume pulse waves, skin electrical activity and body temperature and body movement signals during sleep; the respiratory phase module extracts and constructs a time alignment frame of respiratory rhythm synchronization based on photoelectric volume pulse waves, the phase segmentation alignment and signal enhancement module performs specific filtering enhancement on multi-mode signals according to respiration, the enhanced signals are guided into the recognition module, a personalized health reference module is established by combining results, the physiological state is monitored in real time, and the recognition module is used for recognizing the physiological state. And when the reference is deviated, the health early warning module is triggered, the personalized respiration guiding module is started according to the abnormal type, the respiration rhythm is adjusted through tactile feedback, the guiding intensity is dynamically adjusted according to the intervention effect, progressive separation is achieved, new data is fed back to the personalized health reference model, and continuous self-adaptive updating is completed.

The invention discloses an intelligent early warning method and device based on multi-parameter physiological monitoring, an intelligent bracelet and equipment. The intelligent early warning method based on multi-parameter physiological monitoring comprises the steps that motion state information and environment information of a user are collected; after the high-precision data acquisition mode is started, the data acquisition frequency is improved, and physiological data of a user is acquired in real time based on the data acquisition frequency; the method comprises the following steps: acquiring physiological data of a user, monitoring the physiological data in real time to determine a physiological state of the user, performing early warning judgment operation to determine whether the physiological data meets an alarm condition, generating an early warning judgment result, and triggering remote alarm reminding of a corresponding level to a target object based on the early warning judgment result. The multi-level monitoring and judging mechanism has good monitoring accuracy, remote alarm reminding of the corresponding level can be generated according to different early warning and judging results, and the problem that the user cannot be rescued in time when the user is in a static state during sleeping and has a sudden health risk is effectively prevented.

The application discloses a personalized massage prescription generation system based on multi-modal perception and semantic mapping, and belongs to the technical field of medical intelligence and modernization of traditional Chinese medicine. The system comprises a multi-modal perception and feature extraction module, a semantic analysis and knowledge reasoning module, an intelligent prescription decision engine module and a self-adaptive compliant control and execution module. The method realizes the full-process intelligentization of massage treatment through four core steps of multi-modal data fusion, semantic analysis of traditional Chinese medicine chief complaint, intelligent prescription generation and closed-loop adaptive execution. The application realizes the digitization of the logic of traditional Chinese medicine syndrome differentiation and treatment through semantic mapping of "syndrome-position-method", realizes the accurate assessment of the patient's body state and physiological state through multi-modal space-time fusion, realizes safe and adaptive massage execution based on the closed-loop control of the muscle fatigue index, solves the problems of blind execution, lack of personalization and large safety hazards of traditional massage equipment, and promotes the standardized inheritance and grass-roots popularization of traditional Chinese medicine massage skills.

The invention discloses a multi-mode VR interaction system and method for depression rehabilitation, and the system comprises a signal collection device which is used for synchronously collecting physiological signals of a user; the reinforcement learning device is used for determining scene adjustment parameters based on the user physiological signals; the intelligent interaction device is used for adjusting at least one of the visual sense, the touch sense and the smell sense of the user based on the scene adjustment parameters, and the reinforcement learning device comprises a state recognition unit; a strategy optimization unit; and a scene parameter adjustment unit. According to the system, accurate monitoring and dynamic regulation and control of the physiological state of a patient are achieved, the rehabilitation effect is improved, ruminant thinking is reduced, a data-driven mediation effect model is constructed, and a brand-new and all-around technical solution is provided for depression positive breathing rehabilitation.

Embodiments of the present application provide a method, system, device and equipment for recognizing physiological state of an animal, and relate to the technical field of data processing. In the recognition method, each video frame collected by an image collection device within a predetermined analysis period is analyzed and processed to determine target behavior characteristics and sleep characteristics of a target animal; radar data of a radar sensor within the predetermined analysis period is analyzed and processed to determine physiological characteristics of the target animal; based on the target behavior characteristics, sleep characteristics and physiological characteristics, comprehensive characteristics of the target animal are constructed; and the constructed comprehensive characteristics are input into a first recognition model pre-trained for physiological state recognition to obtain a physiological state recognition result of the target animal within the predetermined analysis period. Through the present solution, the recognition accuracy of the physiological state of the animal can be improved.

A system for assessing the physiological state of a subject, comprising: a task delivery module configured to communicate to a subject at least two sets of information, each set of information relating to a cognitive task requiring a spoken response from the subject; a response detection module configured to record the respective spoken responses from the subject as an audio signal, the response detection module comprising a microphone; an analysis module configured to analyze the audio signals corresponding to the respective spoken responses recorded by the response detection module to determine from the respective spoken responses one or more characteristics indicative of the physiological state of the subject, compare said characteristics from the respective spoken responses, and determine the physiological state of the subject based on said comparison.

To provide a novel configuration with which it is possible to execute processes according to the sleep state of a user and the present physical state of the user.SOLUTION: The information processing device includes: a sensing unit that outputs a signal according to the motion of a user; a sleep state measurement unit that measures the sleep state of the user on the basis of the output from the sensing unit; a user state measurement unit that measures, on the basis of the output from the sensing unit, a user state indicating the present physical state of the user; and a process execution unit that executes a prescribed process on the basis of at least one of the sleep state of the user measured by the sleep state measurement unit and the user state measured by the user state measurement unit.SELECTED DRAWING: Figure 13