394 results about "Biofeedback" patented technology

The invention relates to a digital acupuncture dynamic control optimization method. According to the technical scheme, the method comprises the steps that real-time physiological data, including the heart rate, the oxyhemoglobin saturation, the skin temperature and the muscle tension, of a patient are collected through a wearable device and a sensor; performing cleaning, denoising and feature extraction on the multi-modal physiological data by using a data preprocessing algorithm; a personalized digital acupuncture model is established, and the physical condition and acupoint sensitivity of a patient are dynamically evaluated; adjusting acupuncture output parameters, control frequency, pulse width and current intensity according to real-time feedback of a patient by adopting a self-adaptive fuzzy control algorithm through the obtained personalized digital acupuncture model; multi-channel biofeedback equipment including EEG, EMG and ECG is integrated, and instant response of a patient is monitored; through a closed-loop control algorithm, biological feedback data are input into the control model in real time, and dynamic adjustment of acupuncture and moxibustion parameters is controlled; a deep reinforcement learning algorithm is introduced into acupuncture therapy.

The invention discloses an oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence, which belongs to the technical field of medical auxiliary systems and comprises a special data acquisition and monitoring module, a deep reinforcement learning analysis module, a biological feedback analysis module, a personalized rehabilitation plan generation module and the like. The system can monitor physiological parameters and biological feedback signals of a patient in real time, generate a personalized rehabilitation plan and provide real-time monitoring and early warning functions, in addition, the system further comprises a plurality of auxiliary modules such as a local database, a patient interaction module and an intelligent early warning and emergency intervention module, the reliability and practicability of the system are further improved, and the system is suitable for popularization and application. According to the system, the defects of a traditional rehabilitation nursing method can be overcome, the rehabilitation precision and effectiveness are improved, the participation degree and confidence of a patient are enhanced, and more comprehensive and efficient rehabilitation nursing service is provided for the oral and maxillofacial head and neck tumor patient.

The invention provides a motion fear intervention device based on virtual reality. The motion fear intervention device comprises a VR scene generation module; the subjective evaluation module presents the TSK scale to the user to obtain a TSK scale score of the user, and obtains a TSK grade corresponding to the TSK scale score according to the TSK scale score; the biological signal acquisition module is used for acquiring biological feedback of a user and calculating a comprehensive exercise fear grade according to the following formula: the comprehensive exercise fear grade = alpha * TSK grade + beta * HRV score + gamma * GSR score + delta * pupil change score + belonging to * EMG score; the dynamic intervention module is used for generating scene parameters in real time according to the comprehensive exercise fear level and inputting the scene parameters into the VR scene generation module to generate a corresponding scene; the dynamic intervention module is also used for providing forward excitation. According to the method and the device, the currently displayed VR hierarchical scene is adjusted through biological feedback, and closed-loop feedback adjustment of motion scene stimulation is realized.

The invention relates to the technical field of remote medical monitoring, in particular to a medical patient remote monitoring system which comprises a health monitoring module, a disease course prediction module, a physiological regulation module, a medicine management module, a behavior analysis module, a therapy customization module, a dose adjustment module and a system comprehensive management module. According to the invention, accurate physiological monitoring is realized through a data fusion algorithm and a noise filtering technology, the accuracy and reliability of data are ensured, the recurrent neural network model can provide more accurate disease development trend analysis, early diagnosis and timely intervention are facilitated, and the accuracy and reliability of the system are improved. A self-adaptive control algorithm and a biological feedback technology enable a patient to more effectively adjust the physiological state by themselves, the treatment initiative is improved, and the safety and effectiveness of drug treatment are improved through a mode recognition algorithm and drug interaction analysis of drug management. Through deep data mining and personalized treatment strategies of the behavior analysis module and the therapy customization module, a more comprehensive and customized treatment scheme is provided for the patient.

The invention belongs to the technical field of medical robot control, and discloses a physiotherapy robot self-adaptive control method and system based on multi-mode biological feedback, and the method comprises the steps: carrying out the preprocessing of a biomechanical sensing signal obtained in advance, carrying out feature extraction and dynamic weight fusion on the preprocessed biomechanical sensing signal by utilizing wavelet transform and frequency domain transform to obtain a joint feature; establishing a system dynamics model based on a Lagrange equation, generating a time-varying impedance trajectory model by utilizing a joint state acquired in real time and combining a rolling optimization algorithm, and dynamically adjusting impedance parameters of the time-varying impedance trajectory model according to joint features to generate a safe motion trajectory; and performing double closed-loop control on the physiotherapy robot according to the safe movement track and a biomechanical sensing signal obtained in real time to obtain a control signal of the physiotherapy robot, and realizing joint motor driving of the physiotherapy robot based on the control signal. The device can be flexibly changed according to individual differences of patients and rehabilitation processes.

The invention relates to the field of pilot psychological competency evaluation, and discloses a civil aviation pilot psychological competency evaluation method and system fused with biofeedback, and the method comprises the following steps: S1, collecting the physiological data and behavior data of a pilot in a baseline test situation, and building individualized psychological load baseline data; s2, in the virtual situation, collecting multi-modal data, including physiological data and behavior data, of the pilot in real time; S3, analyzing the data collected in real time through a multi-modal data fusion model, and generating a psychological state score of the pilot; s4, providing real-time feedback and situation difficulty adjustment based on the psychological state score; and S5, generating a personalized psychological competency assessment report. The psychological state analysis of multi-modal data fusion is matched with the real-time physiological data acquisition, so that the dynamic monitoring and accurate evaluation effects on the psychological load of the pilot are realized, and the defect that the psychological state of the pilot is difficult to dynamically capture in real time in the prior art is overcome.

The invention discloses a self-adaptive intestinal function adjusting system based on biological feedback, and relates to the technical field of intestinal function adjustment, the self-adaptive intestinal function adjusting system comprises a signal acquisition module, a data analysis module, an adjustment feedback module and a user interaction module; the signal acquisition module adopts a multi-sensor fusion algorithm and a multi-source sensor to acquire intestinal function state information; wherein the multi-source sensor comprises a pressure sensor, a peristaltic sensor and a nerve sensor. The intestinal function self-adaptive adjusting system based on biological feedback can comprehensively collect biological signals of intestinal tracts, including pressure, peristalsis, nervous system signals and chemical substance secretion signals; the multi-dimensional monitoring mode can reflect the real state of the intestinal function more accurately, the limitation of a single sensor is avoided, and a rich and accurate data basis is provided for subsequent analysis and adjustment.

The invention relates to the technical field of data acquisition control, in particular to a breeding environment temperature control system for a pig farm, and the system comprises a temperature acquisition module which collects the temperature data of each monitoring point in the pig farm in real time, and converts the temperature data into a temperature value sequence. According to the method, temperature data of a plurality of monitoring points in a pig farm are collected in real time, a discrete temperature data sequence is converted into basic environment information such as average temperature and temperature difference, and trend and anomaly analysis is carried out on the basis, so that the opportunity and demand of temperature adjustment are determined, the future temperature change trend of the environment is predicted, and a corresponding regulation and control plan is generated; on the basis of prediction, real-time biological feedback information, such as heart rate and body temperature, of pigs is fused, dynamic evaluation is carried out on the comfort level of individual animals, and an environment temperature regulation and control strategy is further optimized by taking the comfort level of the animals as a benchmark, so that the regulation of the environment temperature is more accurately matched with the growth requirements of the pigs, and personalized and intelligent control of the breeding environment is realized.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to the technical field of self-adaptive electrical stimulation pelvic floor treatment systems, in particular to a self-adaptive electrical stimulation pelvic floor treatment system combined with reinforcement learning and a control method of the self-adaptive electrical stimulation pelvic floor treatment system combined with reinforcement learning, and the self-adaptive electrical stimulation pelvic floor treatment system comprises a micro electrical stimulation device which is used for applying electrical stimulation to pelvic floor muscles; the multi-mode biological feedback sensor array is used for collecting physiological signals of a patient; the data processing module is connected with the multi-mode biological feedback sensor array and used for processing the collected physiological signals; the reinforcement learning controller is connected with the data processing module and the micro electrical stimulation device and is used for dynamically adjusting electrical stimulation parameters based on the processed physiological signals; the human-computer interaction interface is connected with the reinforcement learning controller and is used for receiving doctor input and displaying treatment information; through real-time self-adaptive control and personalized parameter adjustment, a target muscle group can be stimulated more accurately, so that muscles are stimulated in an optimal state all the time, and the pelvic floor muscle strength improvement degree of a patient reaches 68.5% and is far higher than that of a traditional method.

Wearable sensors and uses thereof are disclosed. Preferred sensors include surface EMG electrodes for measuring electrical muscle activity, by which muscle contraction is measured. Such muscle contraction can be used to determine a person's posture, especially when the contraction being measured is that of core muscles. An alert preferably is provided to a person in real time when an incorrect posture is detected. By providing such biofeedback, a person is trained in maintaining a good posture. Such posture training is believed to heal alleviate or even present lower back pain. Wearable sensors of the invention also can be used to measure contraction in muscles during exercise, including running and strength training, to gain information for use in improving such activities.

The invention discloses a remote proving method for a trusted data space, which relates to the technical field of computer and data security, and comprises the following steps: initializing a trusted environment and generating a dynamic identity key for a heterogeneous terminal containing a CPU (Central Processing Unit), a GPU (Graphic Processing Unit) and an FPGA (Field Programmable Gate Array) based on a hardware security module and a physical unclonable function, and generating a challenge value by a verifier by using a quantum random number; the method comprises the following steps of: firstly, transmitting to a proving party through quantum encryption and a neuromorphic photonic network, constructing proving by the proving party by adopting a tensor decomposition zero-knowledge proving protocol, finishing multi-stage verification on a verification party in combination with a space-time cause and effect graph and quantum signature aggregation, and finally realizing dynamic trust evaluation and adaptive strategy adjustment through a quantum Bayesian network, reinforcement learning and biological feedback. According to the method, the remote attestation performance of the trusted data space is improved, dynamic trust evaluation is realized by means of the quantum Bayesian network and biological feedback, attacks are effectively resisted, real-time requirements are met, trust is accurately evaluated, and the development of the trusted data space is promoted.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

The invention relates to the technical field of electromyographic signal monitoring, and discloses a guided monitoring method and system based on a wireless surface electromyographic analysis feedback instrument. The method comprises the following steps: acquiring electromyographic signal time sequence data and limb movement track space coordinate information when a target muscle group moves through a multi-channel wireless surface electromyographic sensor; muscle activation intensity and fatigue change features are extracted, a joint activity angle change sequence is calculated, and a muscle-joint collaborative activity feature matrix is generated through space-time alignment fusion; analyzing signal transduction delay and amplitude matching degree in the matrix, and evaluating the nerve driving coordination level; according to the deviation from the preset standard mode, generating a real-time biological feedback signal containing an action correction instruction and a muscle activation intensity adjustment parameter; and converting into a tactile vibration mode and a visual guidance mark to be output. According to the method, synchronous acquisition and fusion analysis of multi-dimensional motion information are realized, and visual guidance is provided through multi-modal feedback.

A portable therapeutic exercise device is disclosed for strengthening leg muscles in patients with severe muscular weakness after bedrest. The device features a lightweight, compact base with longitudinal rails and a moving carriage equipped with an adjustable footplate, cushion, and straps for secure engagement. Resistance cords provide variable tension, and a lumbar bolster with an adjustable strap stabilizes the base. A distance sensor and Bluetooth-enabled microcontroller relay real-time data to mobile devices for interactive visual biofeedback, including gaming applications. The carriage offers smooth linear motion via bearing wheels with adjustable spacers. Designed for use in hospital beds, chairs, or on the floor, the device provides a safe, adaptable, and engaging solution for rehabilitation exercises.

The invention relates to the technical field of medical instruments, and discloses an intelligent device for monitoring the cardio-pulmonary function of a heart failure patient, the intelligent device comprises a multi-mode sensor module, an AI analysis module, a biological feedback module and a wearable carrier, the multi-mode sensor module is used for collecting heart rate, respiratory rate, oxyhemoglobin saturation and body movement data in real time, and the AI analysis module is used for analyzing the heart rate, the respiratory rate, the oxyhemoglobin saturation and the body movement data; monitoring modes can be dynamically switched in rest, motion and sleep scenes; the AI analysis module is used for detecting abnormity based on real-time data and predicting a heart failure deterioration risk in combination with historical data; and the biological feedback module is used for providing personalized intervention in a vibration or audio mode according to the analysis result. The heart rate, the respiration rate, the blood oxygen saturation and the body movement data are collected at the same time through the multi-mode sensor module, sleep apnea or periodic respiration is recognized in combination with the AI analysis module, the defect of separation of cardiopulmonary function monitoring and sleep mode analysis in the prior art is overcome, and therefore early intervention and treatment adjustment are supported.

The invention relates to the field of psychotherapy, and discloses a pain psychotherapy simulation system based on a virtual reality technology, which is used for solving the problem that when a female patient experiences a physiological cycle, a patient biofeedback signal collected by the system can be interfered, and comprises the following steps: combining a head-mounted display, a handle and other control equipment; the method comprises the steps that a patient enters an immersive virtual world through equipment, physiological data of the patient are obtained, the physiological data comprise heart rate data, skin conductivity and brain wave data, an initial disease pain index is calculated according to the physiological data of the patient, a regulatory factor is calculated according to physiological period information, and the initial disease pain index is calculated according to the regulatory factor; the actual disease pain index is calculated according to the initial disease pain index and the regulatory factor, and psychological intervention is performed according to the actual disease pain index, so that the accuracy of evaluating the state of the treated part of the patient by the system is effectively improved, and the use experience of the patient is improved.

A wearable device may be worn in an ear of a user. The wearable device may include a biometric sensor that collects biometric information from the ear, and an environmental sensor that collects environmental information from the ear. The wearable device may receive the biometric information from the biometric sensor, and the environmental information from the environmental sensor, and send biofeedback determined from the biometric information, and an environmental condition determined from the environmental information, wirelessly to a communication device. Other embodiments are disclosed.

The invention discloses a vocal cord problem recognition and feedback system for the ophthalmology and otorhinolaryngology department. The vocal cord problem recognition and feedback system comprises a sound collection module, an image collection module, a biological feedback module, a data processing module, an AI diagnosis module and a rehabilitation guidance module. The sound acquisition module comprises a microphone array and a self-adaptive noise reduction unit; the image acquisition module is provided with an endoscope camera and an image enhancement processor; the biological feedback module integrates a laryngeal myoelectricity sensor and a three-dimensional motion simulator; the data processing module executes multi-modal feature extraction and fusion; through mutual cooperation of the sound acquisition module, the image acquisition module, the biological feedback module and the data processing module, data can be accurately acquired, the early canceration detection rate is improved and the misdiagnosis rate is reduced through multi-modal fusion, the data acquired by the multi-modal structure is analyzed through the data processing module, the model is combined with a weekly updated disease map, and the early canceration detection rate is improved. And the recurrence prediction accuracy is improved.

The invention relates to the technical field of electric acupuncture apparatuses, and discloses a method and a system for automatically adjusting stimulation parameters of an electric acupuncture apparatus. According to the method, body surface electromyographic signals, skin impedance and capillary hemodynamic parameters of a user are collected in real time through a sensor set, feature extraction and analysis are conducted through a time sequence prediction model of a Transform architecture, and physiological feature data are generated. And then, constructing a user physiological state characterization model by adopting a multi-modal neural perception fusion algorithm, and carrying out collaborative optimization on the electrical stimulation parameters through an adaptive quantum particle swarm optimization algorithm to generate a stimulation parameter combination matched with the real-time neuromuscular response characteristics of the user. The system drives electrical stimulation output according to the parameter combination, monitors physiological data changes in real time, realizes closed-loop dynamic balance of stimulation parameters and biological feedback signals through the dynamic parameter compensation module, and improves the treatment effect and safety.

The invention provides an emotion synchronous visual design system based on multi-modal biological feedback, and aims to dynamically adjust visual design elements by analyzing physiological signals of a user in real time, so that the emotion experience of the user is enhanced. According to the system, various biological feedback sensors such as heart rate, skin conductance and brain wave sensors are utilized, an advanced data processing algorithm is integrated, and the emotional state of a user is accurately captured and analyzed. And then, the system changes visual elements such as colors, shapes and layouts in real time through a dynamic visual design adjustment module so as to match and respond to emotional changes of the user. In addition, the system further comprises a real-time feedback and interaction module and a personalized learning and optimization module so as to provide personalized visual experience and continuously optimize user interaction. The system not only improves the interactivity and attraction of the user interface, but also deepens the emotional relation between the brand and the consumer, and has important commercial application value and market potential.

The invention provides a biofeedback type electrical stimulation regulation and control device based on multi-mode physiological signal monitoring and a control method, the regulation and control device comprises an electronic component layer, a circuit layer, a man-machine interaction part and an adhesion layer, physiological signal acquisition and electrical stimulation regulation technologies are utilized, and a multi-source biosensor and an intelligent processing module are integrated, so that the regulation and control of the biofeedback type electrical stimulation regulation and control device is realized. Dynamic monitoring and real-time analysis of physiological signals are achieved, self-adaptive adjustment of personalized parameters is conducted based on signal characteristics, and a non-invasive biological feedback regulation and control system is formed. Through integrated multi-mode detection and intelligent regulation and control functions, a non-invasive and convenient regulation and control means is provided for wearers with abnormal nerve or physiological states; a portable and comfortable attached structure is adopted, the device can be worn at the Neiguan point for a long time, physiological signal continuous monitoring and electrical stimulation closed-loop feedback adjustment are achieved, and the personalized health management requirement of a user is met.

The invention discloses a respiratory therapy system and method based on biofeedback, and belongs to the field of medical instruments, and the therapy method comprises the following specific steps: S1, collecting and recording multiple paths of user physiological parameters; s2, respiratory rhythm extraction is carried out according to the characteristics of the multi-path data; s3, analyzing each piece of feature data to obtain the confidence coefficient of each piece of data, calculating the total confidence coefficient, and rejecting non-ideal data; according to the invention, by realizing coordination of electrical stimulation between diaphragm and abdominal muscles, the effect of synchronizing with breathing of a user is achieved, the expiration ability is improved, the recovery of respiratory muscle functions of a patient is promoted, the muscle strength and endurance are gradually improved, the dyspnea symptom is relieved, the exercise ability and exercise endurance are improved, and the life quality of the patient is improved.

The invention provides an anesthetic gas concentration detection and early warning method, and relates to the technical field of medical treatment. The anesthetic gas concentration detection and early warning method specifically comprises the following steps: S1, setting biofeedback equipment; s2, biological feedback signal processing and analysis; s3, performing gas concentration adjustment and biological feedback closed-loop control; s4, implementing a double-layer early warning mechanism; s5, performing predictive early warning and deep nerve regulation and control; s6, anesthetic gas management and non-invasive feedback enhancement; and S7, context awareness and personalized adjustment are carried out. Through signal processing, intelligent closed-loop control, comprehensive multi-dimensional monitoring and personalized dynamic adjustment, the anesthetic gas concentration detection and early warning method has the remarkable advantages in the aspects of ensuring the safety of a patient, improving the anesthetic precision, reducing side effects and achieving efficient management, the anesthetic management process can be optimized, and the anesthetic gas concentration detection and early warning method has the advantages that the anesthetic gas concentration detection and early warning effects are improved. And a solid guarantee is provided for safe operation of the operating room.

A system for guiding a user with heart rate variability feedback may include a computing device comprising a heart rate sensor, one or more processors, and memory having stored thereon computer program code that, when executed by the one or more processors, is configured to cause the one or more processors to receive user input, responsive to receiving the user input, cause an audio output device to output an audio content associated with a default breathing pattern, dynamically receive interbeat interval data from the heart rate sensor, dynamically extract one or more characteristics from the interbeat interval data, cause the display to indicate a dynamic visual pattern based on the one or more characteristics, dynamically determine a second breathing pattern based on the one or more characteristics, and dynamically cause the audio content to change based on the second breathing pattern.

The invention relates to the field of intelligent creation of personalized music, and discloses an intelligent creation system of personalized music, comprising a non-residual music gene modeling module for constructing a structured feature tensor; the user state sensing and trajectory planning module is used for acquiring physiological state data of a user in real time and planning a target emotion parameter trajectory sequence for guiding the emotion state of the user; the constraint optimization generation module is used for generating an optimal music feature tensor sequence by solving a dynamic constraint optimization problem; and the multi-modal synthesis and presentation module synthesizes the music feature tensor sequence into audio synchronous with the virtual playing action and outputs the audio. According to the method, a scheme of combining user physiological state perception and predictive emotion trajectory planning is adopted, so that dynamic and prospective guidance of user emotion is realized; the music is converted into an active and personalized biological feedback tool from a passive listening medium, so that the effectiveness of the music in emotion regulation application is remarkably improved.

The invention discloses a concentration-fatigue cooperative regulation and control method and system based on electroencephalogram bimodal feedback, and the method comprises the steps: collecting first electroencephalogram data of a student in a resting state before training, and calculating a mean value of the ratio of a theta wave to an alpha wave as a fatigue resting state reference value; after training is started, second electroencephalogram data of the trainee are collected, a concentration index is calculated according to the ratio of the beta wave to the sum of the beta wave, the theta wave and the alpha wave, and a fatigue index is calculated according to the ratio of the theta wave to the alpha wave; calculating a dynamic threshold value according to the fatigue resting state reference value; and generating a corresponding risk prompt according to whether the concentration index and the fatigue index deviate from their thresholds. According to the method, the limitation of traditional single-dimensional biofeedback is broken through, the safety and efficiency of complex task training are remarkably improved, the limitation of the traditional single-dimensional biofeedback is broken through, and a quantitative neurocognitive enhancement scheme is provided for special operation training.

The invention relates to the field of pelvic floor muscle recovery treatment, in particular to a female pelvic floor muscle recovery treatment device and an adjusting structure thereof.The female pelvic floor muscle recovery treatment device comprises a movable base, a control box is arranged on the surface of the movable base, a hydraulic rod is installed on one side of the control box, and a recovery supporting plate is installed at the top of the hydraulic rod; a massage repairing device capable of massaging female pelvic floor muscles is installed on the upper surface of the repairing supporting plate, a movable structure is installed in the repairing supporting plate, and a lifting adjusting structure capable of freely moving is arranged above the repairing supporting plate. The top of the lifting adjusting structure is provided with an abdomen repairing structure capable of repairing female abdomen muscle tissue, and the matrix type layout of the massage vibrators can stimulate pelvic floor muscle groups in a partitioned mode, so that the biological feedback effect is enhanced. The leg repairing structure drives a sliding ball to move in an arc groove through an electric telescopic rod, switching between resistance training and passive stretching is achieved, and lower limb venous thrombosis is effectively prevented.

The invention provides a pelvic floor muscle function exercise method and system based on remote digital guidance, and relates to the technical field of remote rehabilitation guidance, and the method comprises the steps: arranging a multi-channel micro sensor array at a preset position of a pelvic floor muscle group, and collecting electromyographic signals and tissue deformation data in resting and contraction states; generating a biomechanical model through Fourier transform and multi-dimensional mapping calculation; performing convolutional neural network analysis on the model and a standard physiological database to generate functional damage positioning data; the remote medical data center calculates a neural pathway activation parameter based on the injury positioning data, generates a training instruction and converts the training instruction into a biological feedback control signal; collecting real-time biomechanical data in the training process, calculating a muscle group synergy efficiency value and generating a fatigue characteristic curve; training parameters are adjusted in real time through wavelet transformation and deep reinforcement learning network analysis, a training scheme iteration closed loop is formed, and precise remote guidance of pelvic floor functional rehabilitation is achieved.

Disclosed are devices, systems and methods for acquiring, analyzing, and utilizing neurofeedback to promote brain coherence. Neurofeedback is a form of biofeedback that allows an individual to regulate his / her brain activity by providing a visual metaphor of brain function, thereby making it accessible for manipulation. In some embodiments of the present technology, a system includes a brain signal detection device wearable by a subject and a computer device including a display and a brain-computer interface (BCI) configured to monitor brain signals and display visual, auditory, and / or tactile stimuli to the subject according to a neurofeedback threshold-based protocol to deliver brain signal coherence between the left and right hemispheres of a subject's brain.