5297results about "Sleep inducing/ending devices" patented technology

The present invention relates to a system and method for providing adaptive digital therapy, enhancing the emotional well-being of users through the analysis and translation of expressive and physiological states. Aspects of the invention are directed to identifying the neuroplastic window via user inputs, with or without the aid of psychedelics. This invention is particularly suited for supporting psychoactive therapies involving compounds such as 5-HT2A agonists, dissociatives, and empathogens, as well as applications beyond psychoactive treatments. By utilizing multi-modal deep learning methods, the system captures a wide array of user inputs, including text, speech, vision, symbols, and brain waves, to form a dynamic user representation. This representation is then used to provide real-time, tailored generative communications informed by established psychological therapeutic frameworks. The invention features a closed-loop feedback system through an access module, enabling continuous data relay, patient response metric derivation, and transmission of emotional optimization communications. The system also introduces novel communication methods, including symbolic language input, to articulate complex experiences and extends its capabilities to include natural emotional states, broadening its therapeutic applicability.

Provided are systems, devices, and methods for energy delivery to a patient to treat a medical condition. A system for delivering energy to a patient includes: a treatment device having one or more energy delivery elements. The treatment device can deliver energy, such as ultrasound energy, to a target location of the patient via the one or more energy delivery elements. The energy delivery to the target location treats a medical condition of the patient.

The invention relates to the technical field of neural rehabilitation training, and discloses a neural feedback rehabilitation training method and system. According to the method, electroencephalogram, near-infrared, myoelectricity and heart rate variability signals are acquired based on a multi-modal nerve-physiological signal acquisition device, and a nerve-physiological signal sequence is generated and input into a fusion processing system. And carrying out denoising, artifact removal and feature extraction on the data sequence to generate standardized feature data. And based on the standardized data, identifying the neural state of the user by utilizing a graph neural network, and generating personalized neural state trend data by adopting an attention mechanism in combination with historical data. Based on trend data and a training target, reinforcement learning is adopted to generate a personalized training strategy, training is implemented through an immersive interaction system, user behaviors and neural feedback are recorded, the training strategy is dynamically adjusted and uploaded to a cloud end, and a cloud-side collaborative optimization neural state model and algorithm are adopted, so that the intelligence and accuracy of rehabilitation training are improved.

The invention discloses a comprehensive regulation and control method and system based on multi-mode electromagnetic and ultrasonic stimulation, and the method comprises the steps: collecting brain wave signals in real time through a multi-electrode EEG system, synchronously collecting skin electroreaction and heart rate variability signals, preprocessing the collected signals, and carrying out the feature extraction; the extracted signal features are subjected to multi-modal feature fusion through a CNN, and the multi-modal features are subjected to time sequence dependency analysis through a long-short-term memory network to generate a real-time emotional state quantization vector and identify a current emotional state category. Through synchronous acquisition and feature fusion of multi-electrode EEG, EDA and HRV signals, a real-time brain function connection network is constructed, accurate recognition and stimulation strategy making are performed on emotion regulation and control nodes based on graph theory analysis, the accuracy and reliability of emotion recognition are improved, personalized regulation and control of individual emotion states are realized, and the emotion regulation and control efficiency is improved. The cognitive function and language expression ability of an individual can be improved, and movement coordination disorder caused by emotional fluctuation is reduced.

The invention discloses a cognitive function rehabilitation training system and method based on virtual reality, and the system comprises a cognitive function test unit which is used for carrying out the cognitive impairment test evaluation of a patient based on a virtual reality module, and generating a cognitive function evaluation report of the patient; the cognitive training unit is used for generating a personalized cognitive training scheme based on the cognitive function evaluation report, carrying out targeted cognitive training in a virtual reality environment, and collecting physiological data and training performance data of the patient in the cognitive training process; the feedback unit is used for comprehensively analyzing the physiological data and the training performance data and generating a staged feedback report; and the rehabilitation effect tracking unit is used for analyzing rehabilitation effects of the patient in different time periods based on the periodic feedback report and optimizing a subsequent cognitive training scheme based on an analysis result. And the interestingness and interactivity of rehabilitation training are improved.

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

The invention belongs to the technical field of biological calculation, and discloses an intelligent music regulation and control system and method based on electroencephalogram signal multi-modal feature recognition, electroencephalogram data are obtained through anti-interference collection and adaptive filtering processing, multi-modal feature vectors are constructed based on physiological feature extraction and information fusion, and the multi-modal feature vectors are extracted and subjected to multi-modal feature recognition. And accurate recognition of the emotional state and cognitive load evaluation are realized. A music-emotion mapping network is constructed through music feature analysis and emotion semantic matching, a personalized music regulation and control strategy is formulated, music parameter dynamic adjustment and audio stream reconstruction are realized, and a music output stream is generated. A closed-loop optimization feedback mechanism is formed by adopting electroencephalogram feedback collection and adjustment effect evaluation, user data security grading and privacy protection processing are implemented, a security specification database is constructed, the effect of a music output stream is continuously evaluated, and a comprehensive evaluation report is generated. Immediate emotion regulation is provided for the user, a healthier emotion management mode is established for the user through long-term data accumulation, and psychological toughness is improved.

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 provides a system and method for assisting rehabilitation in sleep, and the system comprises an electroencephalogram monitoring assembly, a heart rate and breath monitoring assembly, a myoelectricity monitoring assembly and a body movement monitoring assembly which are used for obtaining physiological signals of an object; the intervention component is used for performing predetermined intervention operation when being activated; the control component is used for determining a sleep stage according to the physiological signal of the object; when the sleep stage is a deep sleep stage, determining an intervention priority score according to a day rehabilitation training target, a focus position and a stability score of the current deep sleep stage of the object; when the intervention priority score is larger than or equal to a preset threshold value, the object is in the deep sleep period at present, and the stability of the current deep sleep period meets a preset stability condition, a corresponding intervention assembly is activated. By means of the technical scheme, accurate intervention can be achieved in the sleep process, and the rehabilitation process of a patient is accelerated by effectively connecting intervention in the sleep stage with a rehabilitation training target in the daytime.

Disclosed is a system for providing a sleep healthcare service using brainwave entrainment and autonomous nervous system control, and the system including: a user terminal configured to set a bedtime and a wake-up time, at the bedtime, output a sleep-inducing breathing method for enhancing the parasympathetic nervous system, while emitting light of non-melatonin-suppressing wavelengths, and at the wake-up time, insert and output an Alpha-Band or Beta-Band flicker for brainwave synchronization of alpha or beta waves, and output blue light to induce arousal; and a sleep healthcare server.

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The invention relates to the technical field of child cognitive training, and discloses an ADHD child cognitive training system. The system comprises a multi-modal sensing module, an attention evaluation module, a dynamic task generation module, a behavior feedback module and a self-adaptive adjustment module. The multi-modal perception module collects multi-dimensional behavior data, the attention evaluation module extracts attention fluctuation features, the dynamic task generation module generates a cognitive training task sequence, the behavior feedback module performs emotion state classification processing to generate feedback signals, and the self-adaptive adjustment module generates personalized training difficulty steps. According to the system and the method, through comprehensive data acquisition, precise attention assessment, dynamic task generation, real-time emotion feedback and difficulty adaptive adjustment, personalized and precise cognitive training of ADHD children is realized, the training effect is effectively improved, and the problems of lack of personalization, insufficient state monitoring, unscientific difficulty adjustment and the like of an existing training method are solved.

Systems and techniques are described for predicting one or more behaviors and generating one or more interventions based on the behavior(s). For instance, a system (e.g., an extended reality (XR) device) can obtain behavioral information associated with a user of the XR device and can determine, based on the behavioral information, a likelihood of the user engaging in a behavior. The system can determine, based on the determined likelihood exceeding a likelihood threshold, an intervention. The system can generate (e.g., display or otherwise output) the intervention. The system can determine, subsequent to generating the intervention, whether the user engaged in the behavior and can determine an effectiveness of the intervention based on whether the user engaged in the behavior. The system can send, to a server, an indication of the effectiveness of the intervention for use in determining interventions for one or more additional users.

The invention discloses a multi-modal emotion analysis method and system, and the method comprises the steps: carrying out the heart rate variability analysis of a collected vital sign signal, so as to extract a time domain feature, a frequency domain feature and a nonlinear feature corresponding to a heart rate variability signal obtained after the analysis, fusing the time domain feature, the frequency domain feature and the nonlinear feature to generate a heart rate variability feature corresponding to the vital sign signal; calculating a power spectrum and a power spectrum entropy corresponding to the collected electroencephalogram signals, and extracting electroencephalogram characteristics corresponding to the electroencephalogram signals according to the power spectrum and the power spectrum entropy; extracting skin electric characteristics corresponding to the collected skin electric signals; respectively inputting the heart rate variability feature, the electroencephalogram feature and the galvanic skin feature into a pre-trained emotion recognition model, and outputting a plurality of emotion analysis results; and acquiring an emotion regulation scheme matched with each emotion analysis result to output a corresponding emotion regulation strategy, thereby improving the accuracy of the emotion analysis and regulation strategy.

The invention discloses an emotional pressure relieving and monitoring method combined with a virtual reality technology, and the method comprises the following steps: S1, providing a virtual environment for a user through a virtual reality device, and enabling the user to interact with the environment through wearing a virtual reality head-mounted device and using a controller; s2, collecting physiological data of the user in real time through a biosensor, wherein the physiological data comprises heart rate, brain wave, electrodermal response and oxyhemoglobin saturation; s3, inputting the physiological data in the S2 into a long-short-term memory network model for processing, and predicting emotional changes; s4, adjusting the virtual reality environment in real time to adapt to the current emotional state of the user based on the emotional state analysis and prediction result of the long-short-term memory network model; and S5, repeating the steps S2 to S4 to monitor the emotion of the user. According to the method, solid technical support is provided for emotion pressure management and monitoring based on the virtual reality technology, and a traditional static emotion regulation method can be effectively replaced.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention discloses a self-adaptive music synthesis sleep aiding method and system based on physiological feedback. The method comprises the following steps: acquiring physiological data of a user; preprocessing the physiological data; evaluating the sleep state of the user based on the preprocessed physiological data; defining a music parameter space according to the sleep state; optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while music is played, physiological reactions of the user are continuously monitored, music parameters are adjusted in real time based on the physiological reactions, and feedback circulation is formed; analyzing a long-term sleep pattern of the user based on the data obtained in the feedback loop; according to the long-term sleep mode, updating a music parameter space and an optimization strategy; security monitoring is executed, and intervention is carried out when an abnormal condition is detected; according to the invention, a plurality of key indexes such as sleep time, deep sleep proportion, sleep quality and the like are obviously improved.

A stuffed, interactive children's toy for assisting individuals in self-calming and / or self-regulating exercises. The toy may include one or more weights, a microphone, a motor, one or more light emitting diodes (LEDs), and / or one or more speakers. The toy optionally further includes one or more buttons corresponding with one or more emotional states. For example, a red button, a yellow button, and / or a green button. Upon pressing the button, the toy may deliver either a random and / or a preprogrammed intervention. The toy may be used in combination with a children's book. In another example, the toy may be in communication with a mobile application (app) via a Bluetooth connection. In yet another example, the toy may be in communication with a website and / or a remote-control device. The app may be used to record emotional data, physical date, and / or to assign preprogrammed stimuli to the one or more buttons.

The invention discloses a data-driven self-adaptive cognitive competence training method and a data-driven self-adaptive cognitive competence training device. The method comprises the following steps: collecting initial cognitive competence data of a user; establishing a mapping relationship between the training tasks and the cognitive ability dimensions, and associating a plurality of predefined training tasks to the corresponding cognitive ability dimensions respectively; based on the user cognitive competence vector, selecting a training task matched with the current cognitive competence level of the user from a training task library through an adaptive recommendation algorithm; providing the training task matched with the current cognitive ability level of the user for the user to execute; and updating the user cognitive ability vector according to the behavior feedback data. According to the technical scheme, the training task is finely matched through the multi-dimensional cognitive vector, the difficulty is adjusted in real time, and the load is prevented from being too high or too low; and by utilizing behavior data closed-loop updating, a capability increasing curve can be accurately quantified, task sorting and rhythm are dynamically optimized, and a learning motivation is stimulated.

The present disclosure describes systems, devices, and methods of benefiting a cognitive state or function of a brain of a subject. The systems can include a portable system comprising a single headset device to be worn by a subject, comprising a pair of glasses configured to provide a visual stimulus to the subject, and a pair of supra-aural headphones configured to provide an auditory stimulus to the subject.

The invention provides a biological health system, a physiological state regulation and control method, wearable equipment and a storage medium, and the biological health system comprises a detection assembly which is configured to collect a biological signal of a user in real time; a processing component configured to calculate a physiological state regulation parameter based on the phase of the biological signal, and generate a stimulation signal based on the physiological state regulation parameter; and the output component is configured to output the stimulation signal, so that the coupling effect of the biological signal and the stimulation signal regulates and controls the physiological state of the user. The biological signal of the user is acquired in real time, and the personalized physiological state regulation and control parameter of the user is calculated based on the phase of the biological signal to generate the stimulation signal in real time, so that the physiological state of the user is regulated and controlled through the coupling effect of the stimulation signal and the biological signal.

Embodiments are directed to in-bed haptic systems including a pneumatic pad configured for placement between a user and a bed. The pneumatic pad can include an array of actuation cells, where each actuation cell in the array of actuation cells is configured to actuate in response to fluid being introduced into the actuation cell. The system can include an input device that includes a touch sensitive display and is configured to display a set of haptic sequences that can be applied to the array of actuation cells in a user interface of the input device and receive an input at the touch sensitive display of a selected haptic sequence. The system can include a control system that receives an indication of the selected haptic sequence and actuates actuation cells of the array of actuation cells using the selected haptic sequence and the pressure measurements.

The invention discloses a neurological rehabilitation training and evaluation method based on electroencephalogram and electromyography coupling analysis, which comprises the following steps: designing an electroencephalogram and electromyography signal acquisition experiment normal form, and acquiring an electroencephalogram signal and an electromyography signal of a subject; preprocessing the electroencephalogram signals and the electromyographic signals; electroencephalogram signal features and electromyographic signal features are extracted, and the signal features are classified by adopting a classification model; carrying out frequency band decomposition on the classified electroencephalogram signal features and electromyographic signal features to obtain intrinsic mode function components of different frequency bands, and calculating the coupling strength between the intrinsic mode function components by adopting a transfer entropy function; based on the coupling strength obtained through calculation as a feedback signal, the electroencephalogram signal beta rhythm and the electromyographic signal coupling strength value are analyzed, rehabilitation training of a patient is assisted, rehabilitation evaluation is carried out according to the transfer entropy value before and after rehabilitation training, electroencephalogram and electromyographic coupling analysis is introduced into a rehabilitation training evaluation link, and rehabilitation evaluation is carried out according to the transfer entropy value before and after rehabilitation training. The current neuromuscular rehabilitation effect is judged, and the next round of rehabilitation training is assisted.

The invention relates to the technical field of natural language processing and image processing, and discloses an emotional music adjusting method and terminal based on dynamic perception and multi-modal fusion. The method comprises the following steps: acquiring multi-dimensional feature data of a user, processing an unstructured text through a dynamic sensing field based on a large language model, and carrying out dynamic weight distribution on the multi-dimensional feature data to generate fusion features; collecting facial image data of a user, extracting facial emotion features through a visual converter model, and mapping the facial emotion features to a music feature space; the features of the two modes of text and image, namely fusion features and facial emotion features, are input into a music generation module, and personalized treatment music is generated through feature fusion coding, music structure planning and a layered decoding architecture. And music generation parameters are adjusted through a dynamic feedback mechanism to form closed-loop control. According to the method and the computer terminal, full-process intelligent processing from user feature capturing to emotional music generating is achieved, and the treatment effect and the user experience are improved.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

A system and a method provide closed-loop sedation, anesthesia, or analgesia by monitoring EEG and automatically adjusting delivery of sedative, anesthetic, and / or analgesic drugs to maintain a desired or predetermined level of cortical at all echelons of care. The system and the method further monitor a subject's cortical activity to detect occurrence of burst suppression which can be indicative of unsafe depth of anesthesia or sedation. Further, the system and the method provide for alteration or cessation of administration of anesthesia or sedation based on the occurrence of burst suppression to mitigate harm to the subject.

Described herein are wearable sensory stimulation and monitoring devices that can be worn around the back of the neck along and in contact with the spine. Apparatuses described herein stimulate one or more senses including auditory, tactile or olfactory. Apparatuses are also described that allow for sensing and monitoring of physiologic parameters such as heart rate, blood pressure and movement. The device also has features that allow for easy attachment or integration to articles worn on the head such as augmented reality and virtual reality accessories to provide visual stimulation and entertainment.

A wireless implantable neuromuscular stimulator includes an antenna for producing an induced current in response to being disposed in an electromagnetic field. The antenna includes a substrate having an upper surface and a lower surface. An upper coil including a plurality of coil turns is disposed on the upper surface of the substrate. A lower coil including a plurality of coil turns is disposed on the lower surface of the substrate. The upper and lower coils are electrically connected to each other in parallel. The parallel connection can be facilitated by a plurality of connectors that extend through the substrate and electrically connect the upper coil to the lower coil. In one example configuration, connectors connect each coil turn of the upper coil to a corresponding turn of the lower coil.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.