632 results about "Body movement" patented technology

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 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 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.

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 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 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.

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 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 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.

Monitoring object movement comprises attaching an ultra-wide band (UWB) sensor to an object and transmitting, by the UWB sensor in a radar mode, a plurality of UWB pulses. The UWB sensor in the radar mode receives a plurality of reflected UWB pulses and determines over a time a channel response (CIR) based on the plurality of transmitted and reflected UWB pulses. The CIR is indicative of the object movement.

The invention belongs to the technical field of sleep quality analysis, and particularly relates to a sleep quality analysis system and method based on a medical bed sensor, so as to solve the technical problem that an existing method excessively depends on duration statistics and neglects sleep continuity and periodic rhythmicity. The analysis method comprises the following steps: S1, acquiring sign signal data acquired by a sensor on a medical bed in a sleep cycle, and dividing the sign signal data into a plurality of continuous time slices; s2, extracting physiological indexes of respiratory rate, heart rate and body movement amplitude in each time slice, and calculating variation depth of the corresponding time slice; s3, generating a variation depth baseline; calculating an unsteady state disturbance factor according to the occurrence frequency and the duration of the micro-awakening disturbance fragment; and S4, generating a sleep quality comprehensive score. According to the analysis method, the comprehensiveness and objectivity of the evaluation result are improved, and the sleep health condition is reflected more truly.

The invention belongs to the technical field of robot motion control, and particularly relates to a quadruped robot control method based on error symbol robust integral feedback, which combines an RISE control mechanism with a constraint-based optimization method, and realizes high-precision trajectory tracking and strong-robustness control under the condition that a system model has relatively large uncertainty. According to the method, RISE controllers are designed in a position subsystem and a posture subsystem respectively, quadratic programming (QP) optimization distribution of ground contact force is fused, control input is dynamically adjusted while the physical feasibility of foot end force is ensured, and closed-loop stable control over the whole-body movement of the quadruped robot is achieved. The control strategy has an asymptotic error convergence characteristic, and can be widely applied to quadruped robot motion tasks with severe load change and frequent interference.

The invention relates to the field of human-computer interaction, and discloses a human-computer interaction robot facial expression implementation control method, which comprises the following steps: dynamically capturing the face, sound and posture of a user in a human-computer interaction scene, and generating an initial emotion perception sequence; performing hierarchical cross mapping on the initial emotion perception sequence, and performing combinatorial analysis on facial expressions, voices and intonations and body movement features to form an emotion matching vector; based on the emotion matching vector, utilizing a priority regulation model to predict the emotion trend of the user at the next moment, and identifying an expression enhancement point and an emotion attenuation area; mapping the generated facial micro-expression adjusting instruction to a robot facial driving unit, and performing real-time correction and conflict resolution on an expression action sequence; and reversely fusing a user fixation point, facial muscle micro-motion and voice emotion which are acquired in real time into an emotion matching vector, and dynamically updating a perception weight and expression regulation and control parameters. The method has the advantage of improving the emotion matching degree of the robot expressions.

A system and method for training, assessing, and providing feedback on cardiopulmonary resuscitation (CPR) performance based on at least one video of a CPR training session performed by a trainee on a non-mannequin training object. A preprocessing module is configured to process the at least one video to generate a standardized video. A marking module is configured to use pose estimation to mark points for body movements during the CPR training session based on the standardized video, and a computing module configured to compute body movement parameters for CPR based on the marked points. A classification module implements a machine learning model that classifies CPR compressions on the non-mannequin training object based on the computed body movement parameters, thereby generating compression classifications, wherein the machine learning model is trained to extract CPR-specific features. An editor module maps metrics over the standardized video based on the compression classifications and generates a feedback video based on the mapped metrics. An analysis module identifies deviations from CPR guidelines based on the feedback video and generates analysis results based on the deviations. A feedback module provides performance feedback to the trainee based on the analysis results.

An avatar animation generation method includes obtaining video data of a physical object; extracting posture information of the object based on the video data, wherein the posture information indicates a body posture and an expression posture presented by the object in the video data; performing 3D reconstruction on the object based on the posture information to obtain motion data representing a body motion of an avatar and expression data representing a facial expression of the avatar, wherein the motion data is obtained through reconstruction based on the body posture, and wherein the expression data is obtained through reconstruction based on the expression posture; and obtaining animation data of the avatar through synthesis based on an appearance resource of the avatar, the motion data, and the expression data, wherein the animation data indicates that the avatar wears the appearance resource, presents the facial expression, and performs the body motion.

The invention discloses a whole-body collaborative voice control system and method for a humanoid robot, relates to the technical field of robots, and aims to solve the problems of semantic understanding superficial layer, action planning localization and safety control lagging in existing humanoid robot voice control. Through closed-loop cooperation of the multi-mode intention understanding module, the whole-body action planning module, the dynamic balance security arbiter and the microkernel security scheduler, a unified control architecture is constructed. According to the system, natural language instructions are deeply analyzed into whole-body motion constraints through a semantic-action mapping knowledge base, anthropomorphic collaborative motion trails are generated, pre-security verification is conducted through a boundary model based on Lyapunov stability, and finally hard real-time execution and full-cycle security guarding are achieved through a microkernel security scheduler. According to the method, direct conversion from natural language to safe, stable and anthropomorphic whole-body motion is realized, and the task execution capability and the interaction naturalness of the humanoid robot in a complex scene are remarkably improved.

A medical imaging method for detecting a movement and a magnetic resonance imaging system, wherein the method includes: receiving, through a plurality of channels, a plurality of original first time domain signals recording a movement of an object under examination; transforming, based on a plurality of respiratory frequency components as bases, the plurality of first time domain signals into a vector matrix including representations of phases; computing an eigenvector based on the vector matrix; transforming the first time domain signals into second time domain signals based on the eigenvector, and removing a maximum energy term related to a respiratory movement from the second time domain signals; and determining whether a portion of a non-respiratory body movement is detected, and determining to execute a sequence based on only a first time domain signal in which a portion of a non-respiratory body movement is not detected.

The invention provides a classroom student concentration degree monitoring system and method based on computer vision. The system comprises a vision collection module, an edge calculation unit and a cloud analysis platform. A multi-dimensional sensing mode combining head posture analysis, eye state tracking, facial expression recognition and body movement detection is adopted to evaluate the concentration degree, through a dynamic feature fusion mechanism, the system can intelligently adjust the weight ratio of different modal features, the limitation of single-dimensional analysis in a traditional method is effectively solved, and the accuracy of the system is improved. The system adopts edge calculation and cloud analysis, ensures high efficiency of data processing, ensures reliability of complex algorithm operation, and has important value for improving classroom teaching quality and learning efficiency. According to the invention, through multi-modal visual analysis, behavior characteristics and physiological reactions of students in class are monitored in real time, the concentration level of the students is comprehensively evaluated, and objective teaching feedback and improvement basis are provided for teachers.

The invention discloses an instrument of an intelligent breathing and motion capture system based on six-character table health maintenance, and relates to the technical field of intelligent health maintenance equipment, the instrument comprises a hardware part and a software part; the hardware part comprises a mouth shape visual capture assembly, an action force and angle sensing assembly, an audio acquisition and feedback assembly and a main control and power supply assembly; the software part comprises a data acquisition module, a signal preprocessing module, a parameter analysis and judgment module, a real-time feedback module and a data storage and management module; according to the invention, through integration of the mouth-shaped visual capture assembly, the action force and angle sensing assembly, the audio acquisition and feedback assembly and the master control and power supply assembly, synchronous acquisition and processing of multi-source data are realized; the mouth shape change, the body movement and the audio pronunciation of the user can be accurately captured in real time, and powerful technical support is provided for standardized practice of six-character table health maintenance.

The invention provides an image AI-based intelligent behavior recognition and anomaly detection method, which comprises the following steps of: acquiring human body heat source contour data through thermal imaging equipment, acquiring breathing and body movement signals through millimeter wave radar, and fusing the human body heat source contour data and the breathing and body movement signals to obtain an abnormal behavior recognition result. And determining action continuity and micro-action characteristics of breathing and body movement based on the fused signal, determining an abnormal score based on the action continuity and the micro-action characteristics, and performing graded alarm. According to the method, thermal imaging and millimeter wave radar data are fused, and the problem that the false alarm rate of a traditional monitoring technology is high is solved.

The application provides application of SEBS in implanting a biological electrode base. The biological electrode based on the SEBS base can be implanted in biological tissues, and is used for continuously or discontinuously analyzing physiological parameters of the biological body. Compared with other flexible bases, the SEBS base has the characteristics of being thinner and more flexible, can reduce the interference of body movement on the electrical signal, reduce the volume range of the implanted tissue and reduce the inflammatory reaction, thereby improving the measurement accuracy and long-term stability, is expected to meet more implantation site requirements when used for implanting a sensor, and has a good application prospect.

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 discloses an intelligent classroom instant feedback system and method based on multi-modal interaction, and the system comprises a multi-modal data collection module which is used for collecting the facial expression, voice and intonation, limb movement, eye movement track and classroom interaction text of a student; the data processing and analyzing module is used for carrying out preprocessing and feature extraction on the collected data; the state recognition and modeling module is used for fusing the multi-modal features and recognizing the learning state of the student; the instant feedback module is used for feeding back the recognition result to the teacher in a visual or voice form; the teaching intervention suggestion generation module is used for generating teaching strategy suggestions according to the recognition result; the data storage and management module is used for storing historical data and supporting subsequent analysis; according to the invention, fusion analysis of multi-modal data is realized, and the accuracy and robustness of student classroom state identification are improved; and a real-time feedback mechanism is provided, so that the teacher can timely master the learning condition of the student, and the teaching quality is improved.

A method for training an artificial intelligence (AI) model for allowing a user to intuitively control an electronic device includes positioning a plurality of sensors at a plurality of particular positions on a human body for sensing electric signals. The method also includes recording a first set of electric signals from each of the plurality of sensors in a continuous manner. At the same time, a first set of motion intents associated with a first sequence of body movement is also recorded in a continuous manner. An AI regression model is trained using a neural network to map the first set of electric signals to the first set of motion intents. In response to receiving a second set of electric signals from the plurality of sensors in a continuous manner, the AI regression model predicts a motion intent, causing the electronic device to perform an action.

The invention discloses an air conditioner control method, device and equipment in a sleep application scenario and a medium, and the air conditioner control method in the sleep application scenario comprises the steps that point cloud data of a user sleep area is continuously obtained through a millimeter wave radar, and the covering states of covering objects of all parts of a body are accurately recognized; the defects that a traditional infrared or temperature and humidity sensor cannot penetrate through the fabric, and local exposure is difficult to judge are overcome. Based on the recognition result, the system generates wind direction, wind speed and other wind feeling adjusting instructions in real time, air supply of the air conditioner is prevented from directly blowing exposed parts, and the thermal comfort is improved. The self-adaptive optimization of the strategy is realized by continuously monitoring the body movement frequency and the turn-over times of the user before and after adjustment, evaluating the adjustment effect and carrying out positive or negative weight adjustment on the control rule. The problems that in the prior art, due to the fact that the coverage state cannot be accurately perceived, air supply is uncomfortable, cold catching is likely to happen, and adjustment lags are solved, and the air conditioner use experience in the sleep scene is remarkably improved.

The invention discloses an undisturbed sleep monitoring method and a related device, and the method comprises the steps: carrying out the feature extraction of a first piezoelectric signal, obtaining a body movement feature, and enabling the first piezoelectric signal to be obtained through the superposition of a plurality of preprocessed original piezoelectric signals; frequency domain analysis is carried out on the second piezoelectric signals, frequency components corresponding to wave crests are recognized in a sleep heart rate related frequency band, a frequency pair corresponding to each moment is extracted, a frequency set is obtained, the frequency pair comprises a plurality of preorder frequency components in the first frequency sequence and the frequency of each frequency component, and the frequency set is obtained; the first frequency sequence is obtained by arranging frequency components in a descending order according to amplitude intensity; determining heart rate characteristics at each moment according to the frequency set; performing feature extraction on the acquired piezoresistive signals to obtain piezoresistive features; and determining the sleep state according to the body movement characteristics, the heart rate characteristics at each moment and the piezoresistance characteristics. The reliability and accuracy of sleep state recognition can be improved.

The invention discloses an integrated system and method with body movement control in a home environment, and the system comprises a home task construction module which is used for constructing a simulation environment, designing home tasks, labeling task data, defining task attributes, and obtaining an original home task data set; the data enhancement module is used for performing enhancement processing on the data set by adopting a supervised fine tuning SFT method and a direct preference optimization DPO method; the evaluation index module is used for setting indexes for evaluating system performance; the HLP module is responsible for decision making and planning adaptation of a tool, and generates an output O comprising a subtask and a selected bottom layer model m based on the input instruction I; the LLE module is used for converting the subtasks output by the HLP, the selected underlying model m and the inventory inv into model calling instructions, the selected underlying model m starts to execute work, error detection is carried out in real time in the execution process, the execution result is fed back to the HLP, and the execution process is recorded. The system has excellent performance.