812 results about "Body movement" patented technology

The invention provides an AI-based virtual digital human interaction system, and relates to the technical field of data processing, and the system is used for obtaining a user feature data set by performing feature extraction on an interaction data set, calculating an audio change rate and speech speed stability, obtaining speech emotion data, calculating syntactic complexity and counting the number of emotion words. Calculating a facial expression symmetry degree; obtaining facial emotion data; calculating body stability and identifying a visual staying area; obtaining attention state data; adjusting tone and speed of the virtual digital person according to the virtual digital person text, optimizing sentence pattern complexity and sentiment words in the virtual digital person text, adjusting changes of eyes, eyebrows and mouth corners of the virtual digital person, and adjusting information display sequence and visual focus guidance. According to the method, the interaction efficiency of the virtual digital human is improved by analyzing the facial expressions and the body actions of the user.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention discloses a sleep state monitoring and analyzing system based on multi-sensor fusion, and relates to the technical field of health monitoring, the sleep state monitoring and analyzing system comprises a multi-modal sensor module used for collecting multi-dimensional data related to a sleep state, the multi-dimensional data comprises a bio-electricity signal, a physiological parameter, body movement data and an environment parameter, and the multi-modal sensor module is used for collecting the multi-dimensional data; the multi-modal sensor module comprises a non-contact sensor, a flexible electronic skin sensor and a bio-electricity signal sensor, and the data fusion and processing module is used for carrying out preprocessing, dynamic self-adaptive fusion and federal learning modeling on collected original data. According to an existing contact type sleep state monitoring scheme, more flexible and accurate sleep state data are realized through a non-contact type monitoring sensor in cooperation with dynamic adjustment of sleep monitoring content and adjustment of weights of various sensors for monitoring the sleep state; and a more accurate and intuitive reference report is provided for the sleep state and the health state of the subsequent user.

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.

The invention provides an immersive concert experience system based on virtual reality, and belongs to the technical field of virtual reality, and the system comprises a dynamic adjustment module which is used for predicting the preference of a user for stage arrangement, light effects and music types through a machine learning model according to the historical preference data of the user, and outputting the preference to the stage arrangement, light effects and music types; dynamically adjusting visual elements and sound field parameters of the virtual scene and a refresh rate and a field angle of the virtual scene in combination with the user physiological indexes and the user head motion trail which are acquired in real time; the action capture and feedback module is used for capturing an interaction instruction sent to the dynamically adjusted virtual scene by the limb action of the user in real time; and the interaction feedback module is used for enabling the virtual performer to respond to the interaction instruction of the user in real time based on the expression recognition and voice synthesis technology, and generating personalized interaction feedback content. A user constructs an immersive music interaction field domain, and experience logic of a virtual music meeting is completely reconstructed.

The invention provides a life sleep sign monitoring method, and relates to the technical field of sign data monitoring, and the method comprises the following steps: collecting an original echo signal of a monitoring area, and extracting a respiration signal, a heartbeat signal and a body movement signal corresponding to a body movement event from the original echo signal; obtaining relevance characteristics of the respiratory signal and the heartbeat signal and adjusting characteristics of the body movement event on the respiratory signal and / or the heartbeat signal; according to the relevance features and the adjustment features, biological features representing individual physiological differences are generated; based on the biological characteristics, target identity probability distribution is obtained; and performing interference suppression processing on the original echo signal according to the target identity probability distribution so as to separate a target respiration waveform and a target heartbeat waveform of the target individual from the original echo signal. The method is suitable for practical application scenes such as multi-person same-bed monitoring and family bedroom monitoring, and the technical bottleneck that the monitoring precision is reduced along with the increase of the target number in the prior art is broken through.

The invention belongs to the technical field of intelligent medical treatment, and discloses a camera-free intelligent vital sign monitoring patient bed system which comprises a sickbed body, a vital sign monitoring module, a state sensing module, an abnormal behavior recognition module, an intelligent reminding module and a computer terminal. The sickbed body is used for sensing whether a patient is on the bed, the lying position and the body movement condition, the vital sign monitoring module is used for monitoring vital signs of the patient in real time, the state sensing module is used for judging the life state of the patient, and the abnormal behavior recognition module is used for recognizing whether the behavior of the patient is abnormal. The intelligent reminding module is used for reminding medical staff to pay attention to the condition of the patient, and the computer terminal is used for displaying the real-time state of the patient in each sickbed. According to the camera-free intelligent vital sign monitoring patient bed system, the vital signs, states and behaviors of the patient can be monitored in real time on the premise that the privacy of the patient is protected, and a nurse is reminded to deal with abnormal conditions in time.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on combination of electroencephalogram and millimeter wave radar, and aims to solve the problem that OSA judgment accuracy is insufficient due to complication. The method comprises the following steps: acquiring an electroencephalogram signal of a user based on a wireless EEG sensor, and acquiring a physiological signal of the user by using a millimeter wave radar; performing sleep staging analysis on the electroencephalogram signal, and detecting a micro-awakening event in combination with body movement data; the physiological signals are processed through a physiological signal processing model, physiological signal features of the user are extracted, and an apnea event, a hypopnea event, an arrhythmia event, a blood pressure fluctuation abnormal event and a periodic leg movement event of the user are monitored; calculating each OSA judgment index according to the event; and calculating OSA personalized scores according to the OSA judgment indexes, performing OSA severity grading, and generating an OSA severity grading report for assisting medical staff in diagnosis and treatment scheme formulation.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention discloses a non-contact sleeping posture detection and vital sign monitoring method based on a millimeter-wave radar, and the method employs a multi-transmitting and multi-receiving millimeter-wave radar, employs an innovative signal processing algorithm and a data processing algorithm, and carries out sleep monitoring through comprehensive consideration of frequency spectrum information and point cloud information. Comprising the steps of extraction of vital sign data such as respiration and heartbeat and judgment of in-bed, body movement and sleeping posture states. Breathing and heartbeat components are effectively extracted through cross-correlation entropy operation, then the signal-to-noise ratio of the breathing and heartbeat components is increased, then fast Fourier transform is carried out on cross-correlation entropy, and it is proved that the breathing and heartbeat frequency is effectively extracted.

The invention provides a result judgment and use method of a portable borborygmus monitor, and relates to the technical field of medical monitoring equipment.The method comprises the steps that abdominal borborygmus is collected through a piezoelectric ceramic sensor array in multi-channel triangular distribution, and breathing and body movement interference is eliminated in combination with a dynamic noise filtering algorithm; extracting borborygmus outbreak duration, interval period and time-frequency energy indexes, and constructing an activeness and rhythm disorder two-parameter evaluation system; dynamically adapting a reference threshold value by adopting an age piecewise function, and introducing a feeding time compensation factor to optimize an individualized criterion; the intestinal obstruction risk is judged through a time sequence matching algorithm and a multi-parameter classification model dual-verification mechanism; a three-dimensional dynamic map is generated based on a sound source localization algorithm and spectral analysis, and data integrity is guaranteed in combination with a priority transmission protocol. According to the method, spatial-temporal feature analysis and an intelligent decision model are fused, sensor anti-interference, individual adaptability and pathological early warning precision are considered, and the core pain points of high misjudgment rate and poor clinical compatibility of traditional equipment are solved.

The invention relates to a non-contact sleep monitoring method based on cross-modal compensation. In order to overcome the defects that vital sign radar is single in physiological information and prone to being interfered, the method is achieved through multi-modal fusion of a radar sensor, a video sensor and an audio sensor. Specifically, a radar sensor obtains heartbeat, respiration and body movement physiological information through chest mechanical waves, a video sensor obtains heartbeat, respiration and body movement physiological information through a photoplethysmography (PPG), an audio sensor receives sound and the like, feature parameters are extracted, a ReliefF algorithm is used for feature optimization, a machine learning algorithm (Subspace KNN, Bagged Tres and the like) is adopted for classification, and the feature parameters are extracted. And finally, decision-level fusion is carried out through a naive Bayes classifier, and a sleep staging result is output. Experimental results show that the method is robust in a complex environment, the accuracy and reliability of sleep staging can be remarkably improved, cross-modal compensation is achieved, and measurement is more continuous and accurate.

The present invention provides a solution of a human-computer interaction method and system based on three-dimensional coordinate measurement, and a plurality of solutions for applying same to solve technical problems. An operation body and / or an operator performs input and operation interaction by means of a virtual keyboard and a virtual mouse, or integrates a display picture space and carries out various participatory and interactive direct operations and experiences on object components and objects in virtual and real scenes. Virtual handwriting input, body movement posture recognition, and lip recognition interaction methods are further provided, and virtual mode control and interaction methods such as gesture recognition and virtual touch screen interaction are supported. Contact is not needed and there is no need to touch the screen, and mobility is achieved. The present invention is suitable for the two-dimensional conventional computing field, and particularly suitable for three-dimensional space computing fields (comprising VR, MR, AR, meta-universe, virtual simulation, software games, three-dimensional graphics, three-dimensional animation, and other three-dimensional scenes). Human-computer interaction operations in work and life of people can be greatly facilitated and optimized.

Provided herein is a string shooting device, and more particularly, a string shooting device having that is wearable and operable by hand gestures or body movement. Embodiments include a wearable device for propelling a string including: a string shooting device; a band configured to attach to a body part of a user; and a holder, where the holder is secured by the band to the user; where, in response to the body part of the user being in a first position, the string shooting device is not actuated, and where in response to the body part of the user being in a second position, the string shooting device is actuated.

The invention relates to the technical field of game development and artificial intelligence, and discloses a Qt interactive game role action response control method fused with AI emotion recognition, which comprises the following steps: S1, acquiring multi-modal data such as facial expression, voice intonation and limb action of a player by using sensors such as a camera and a microphone on game equipment; s2, the collected multi-modal data are transmitted to an AI emotion recognition module, a deep learning algorithm is adopted to analyze and process the data, and the current emotion state of the player is recognized; and S3, obtaining current game scene information and other operation instructions input by the player through the Qt framework. According to the Qt interactive game role action response control method and system fused with AI emotion recognition, through the AI emotion recognition technology, a game role can sense the emotion state of a player and make the emotion interaction action matched with the game role, the emotion resonance between the game role and the player is enhanced, and the interaction experience and immersion of a game are greatly improved.

The invention discloses a multi-dimensional personality evaluation method for dynamic emotion changes, and relates to the technical field of personality evaluation, the system is composed of a plurality of functional modules, and the method comprises the following steps: step 1, recording physiological data in real time through a wearable device, capturing micro-reactions by using a computer vision technology, and determining whether the micro-reactions exist or not; the micro-reactions comprise facial micro-expressions, limb actions and voice and intonation changes; based on a preset dynamic simulation social scene, observing an emotion regulation strategy of the subject in pressure; step 2, using an LSTM model to carry out time sequence modeling on the multi-modal acquisition data, capturing dynamic evolution of emotions, and combining an attention mechanism to dynamically adjust the weight of each modal; based on six basic emotions and nine mental disorder tendencies, establishing a classification model, and capturing association between emotions and personalities; reinforcement learning is introduced, and model parameters are dynamically optimized according to feedback of a subject; and predicting the emotion trend of the subject in a period of time in the future through an autoregressive integral moving average model.

The invention relates to the technical field of service robots, and discloses an elderly care service robot based on an emotion adjustment system, and the robot comprises a multi-mode emotion recognition module which is used for carrying out the comprehensive perception of the emotional state of the elderly through combining various emotion signals of voice, facial expression and limb movement; the personalized emotion adjusting module is used for generating personalized emotion responses meeting the requirements of the old people; the emotion memory module is used for recording emotion changes, important life events and personal preferences of the elderly; the scene self-adaption module is used for sensing the living environment of the old people in real time and adjusting the emotional expression mode of the robot according to the environment information; the health intervention module is used for generating a health management scheme through long-term monitoring of the emotional state and health of the old people; and a safety monitoring module. According to the invention, the emotion state of the elderly is analyzed through the multi-modal fusion network, the emotion misjudgment probability is reduced, and the man-machine interaction experience and the emotion support effect of the elderly are remarkably improved.

The invention relates to the technical field of medical diagnosis, and discloses a blood oxygen monitoring system fused with sleep rhythm modeling, which comprises the following steps: identifying a sleep stage conversion moment through a photoelectric volume pulse wave signal, detecting a blood oxygen saturation valley value in a window after conversion, and calculating response lag duration; according to the method, the physiological rhythm is converted into a natural probe for the compensatory ability of the respiratory system, dual diagnosis of sleep staging and respiratory function is realized through a single-path signal, and meanwhile, body movement interference is converted into an observation window for the respiratory stress recovery ability. The diagnosis reliability in a home monitoring scene is remarkably improved, the system completes core analysis at an edge node, and a respiratory function evaluation report which can be directly used for clinical decision making is output.

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 discloses a text narrative-guided virtual reality motion sickness monitoring and relieving system, and relates to the technical field of virtual reality, the text narrative-guided virtual reality motion sickness monitoring and relieving system comprises an adaptability monitoring module, the adaptability monitoring module comprises a data acquisition unit, and the data acquisition unit is used for acquiring physiological information of a user in virtual reality training in real time; the data is transmitted to the adaptability evaluation unit; the dynamic adjustment module is used for receiving the sensing data acquired by the data acquisition unit and dynamically adjusting parameters of the virtual reality equipment according to the received data; by monitoring physiological information and perception data of a user in real time, signs of motion sickness can be rapidly recognized, the real-time monitoring is combined with dynamic adjustment of visual and vestibular stimulation in a VR environment, the risk of motion sickness caused by mismatching of sensory input and actual body movement can be effectively reduced, and the motion sickness can be rapidly recognized through the dynamic adjustment. The system can preventively adapt to individual differences of users and reduce motion sickness.

The invention relates to the technical field of respiratory training, in particular to a respiratory muscle training system based on thoracic induction, which comprises a thoracic dynamic monitoring module, a respiratory muscle state recognition module, a fatigue judgment and adjustment module, a training control module and a task construction module. According to the method, through combined extraction of capacitance sensing data of chest expansion change, a vertical displacement trend and chest pressure floating, the reduction precision of a real respiration traction process is effectively improved, a time axis is used as a unified reference, different types of sensing data are synchronously analyzed, the respiration traction judgment accuracy is remarkably improved, and the method is suitable for popularization and application. The probability of misjudgment caused by electromyography and body movement dislocation is eliminated, the capacity of capturing the participation state of respiratory muscles is enhanced, the variation trend between electromyography response and traction starting is tracked and captured through the time difference of continuous periods, the training execution accuracy is improved, the intervention targeting is recovered, and the training efficiency is improved. And leap-over improvement is realized in the aspects of multi-source information collaborative identification and training personalized adjustment.

A body-worn PAP system providing proper mask fitment during body movement, reduced leakage, and elimination of a need for a connecting hose to the blower unit, and a PAP blower adapted for use in a PAP system with a compact noise reduction system that does not utilize acoustic foams in the airflow path.

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

The invention relates to the technical field of intelligent wearable devices, and discloses a multi-modal sensing and motion state recognition method and device for an intelligent wearable device, and the method comprises the steps: carrying out the anatomical coordinate transformation of original motion data collected by a wrist wearable device, and obtaining an acceleration sequence and an angular velocity sequence; performing sliding window segmentation and multi-scale wavelet packet decomposition to obtain a potential conversion period window set and corresponding multi-scale time-frequency features; constructing a conversion period feature vector for describing a conversion relation between the wrist movement and the whole body movement; inputting the conversion period feature vector into a hierarchical conversion identification model for processing to obtain a conversion type label and a conversion confidence coefficient; body part association mapping and lower limb action state inference are executed, a lower limb state sequence and a whole body action conversion type in the conversion process are obtained, real-time efficient recognition of action conversion is achieved, limitation of a single model is avoided, and the high recognition rate under low calculation complexity is kept.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention relates to the technical field of integrated sleep management, in particular to a sleep data analysis management system based on artificial intelligence. The method comprises the following steps: segmenting a sleep cycle of a user based on brain wave data to obtain N sleep stages of the user; based on the body movement monitoring data, the pulse monitoring data and the respiration monitoring data, calculating to obtain a first sleep quality characteristic value of the sleep stage of the user, and based on the sleep posture category and a sleep quality weight parameter corresponding to the sleep posture category, determining a second sleep quality characteristic value of the sleep stage of the user; the sleep quality analysis index of the sleep cycle of the user is generated based on the sleep quality parameters of the sleep stage of the user, the sleep early warning signal is generated based on the sleep quality analysis index of the sleep cycle of the user, multi-dimensional data such as brain waves, body movement, pulse, breathing and sleeping postures can be combined, the sleep stage can be accurately divided, and the sleep early warning effect is improved. And sleep quality parameters are calculated through multi-modal data fusion, so that the accuracy and practicability of sleep monitoring are improved.

The invention relates to the technical field of intelligent bed early warning, and discloses an intelligent bed integrated AI monitoring and early warning method and system, and the method comprises the steps: collecting sleep-related data, and carrying out formatting and quantity stiffening processing to obtain target sleep data; constructing a sleep stage thermodynamic diagram, analyzing the sleep cycle characteristics of the intelligent bed user, analyzing the body movement frequency condition of the intelligent bed user in the sleep process by using the target sleep data, and analyzing the overall sleep characteristics of the intelligent bed user; analyzing the sleep quality of the intelligent bed user, and when the sleep quality is poor, analyzing cardiovascular health characteristics and respiratory system health characteristics of the intelligent bed user so as to analyze preliminary health state characteristics of the intelligent bed user; and collecting sleep adjustment related data after the intelligent bed user uses the adjustment equipment, analyzing the health state adjustment characteristics of the intelligent bed user, and performing sleep health early warning on the intelligent bed user in combination with the preliminary health state characteristics. According to the invention, the early warning reliability of the intelligent bed integrated AI can be improved when the number of influence factors is large.

The invention relates to the technical field of inertial sensing attitude monitoring, and particularly discloses a body motion attitude monitoring method and system based on an inertial sensing technology, and the method comprises the steps: collecting the original data of a body motion attitude, outputting an attitude quaternion after fusion and an angular velocity vector after calibration, carrying out the dynamic attitude feature extraction, and carrying out the calibration. And outputting a body motion posture feature vector, carrying out stability margin modeling, obtaining a stability margin scalar, determining a tumble probability update value, carrying out tumble evolution prediction, combining the tumble probability update value, outputting a tumble probability and a probability change trend, obtaining a tumble early warning level, analyzing adaptive calibration parameters and carrying out feedback. The problems that in the prior art, multi-sensor data cannot be effectively integrated, so that the information utilization efficiency is low, and the potential of multi-modal data fusion is not fully exerted are solved, and the monitoring accuracy can be continuously optimized.

The invention provides an intelligent pillow control method and system, and belongs to the technical field of intelligent control. The method comprises the steps that an FPGA obtains a user sleeping posture data set collected by a pressure sensor array; and determining a current sleep stage based on the user sleep posture data set and a pre-trained sleep stage recognition model. According to the user sleeping posture data set, the historical sleeping posture data set and a preset cervical vertebra supporting curve, a first correction target parameter matrix corresponding to the current cervical vertebra curvature deviation value of the user is determined; wherein the first correction target parameter matrix comprises first correction target parameters corresponding to air bags in different areas of the intelligent pillow; the first correction target parameter is the airbag pressure adjustment amount dynamically constrained by the receptor dynamic frequency; and based on the current sleep stage and the first correction target parameter matrix, determining a corresponding airbag adjustment parameter group, operating an airbag driving module according to the airbag adjustment parameter group, performing airbag pressure adjustment, and updating the first correction target parameter matrix according to the real-time body movement frequency.

The invention discloses an intelligent accompanying robot system based on multi-modal interaction and a control method, which adopt a dual-chip collaborative framework to realize multi-modal emotion interaction. According to the equipment, a quantized and optimized lightweight face recognition and voice detection model is locally operated in an off-line manner through an ESP32-S3 chip, and facial expressions and voice emotions of a user are analyzed in real time; meanwhile, an STM32F103RCT6 chip is adopted to be responsible for peripheral control, and the peripheral control comprises OLED expression display, body movement driven by a servo motor and a music feedback module, so that visual and dynamic emotion feedback is achieved. A task polling mechanism is utilized, face and voice recognition tasks are efficiently and alternately processed under limited computing resources, and low power consumption and high responsiveness of the system are ensured. The interactive naturalness and the user immersion of the intelligent toy are improved, and the intelligent toy has a good personalized emotion regulation effect and is suitable for the fields of child accompanying, psychological counseling, special crowd assistance and the like.