130 results about "Muscle activation" patented technology

The invention provides a musculoskeletal disease risk assessment and early warning method and system based on multi-source data, and relates to the technical field of assessment and early warning of muscles and skeletons. Joint angles, myoelectricity and plantar load data are collected, a joint feature vector sequence consistent in time sequence is constructed, the limitation of a traditional single monitoring means is effectively made up, and the risk assessment and early warning of musculoskeletal diseases is achieved. K-means clustering is carried out on a joint angle sequence to form a standard attitude set with physiological significance, and a transition edge weight based on angle change, muscle activation and load fluctuation weighted calculation is constructed, so that quantitative expression of physiological costs of different attitude transfer paths is realized, a risk transition region is conveniently identified, and the risk transition rate is improved. According to the method, the sudden transition behavior in the movement process can be captured by calculating the deviation between the predicted value and the true value of the latent variable, and effective early warning of the musculoskeletal disease risk is realized by combining the edge weight strength of the sudden transition behavior in the directed graph and considering the transition strength and the abnormal behavior degree at the same time.

The invention discloses a double-arm robot autonomous control system and method based on teleoperation and visual features. The system comprises a teleoperation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a track deviation detection and takeover module and a hybrid control interface module. Human demonstration is completed through teleoperation, multi-modal information of images, tracks, clamping jaws and muscle activation is collected, perception features are fused by adopting multi-view space-time alignment and a cross-view attention mechanism, strategy learning is completed in combination with an end-to-end large model, and in the autonomous operation stage, the multi-view space-time alignment and the cross-view attention mechanism are combined with the end-to-end large model. The risk of current operation is predicted and evaluated through track deviation detection and collision probability, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the whole system are improved, the robot control mode of seamless switching between man-machine cooperation and autonomous and teleoperation is achieved, and the method is suitable for object control tasks in complex and highly variable environments.

The invention discloses a pneumatic-biological signal feedback cooperative control method based on an anti-gravity rehabilitation treadmill, and the method comprises the steps: synchronously collecting sEMG, IMU and plantar pressure signals, and completing the time-space registration through clock synchronization, time warping and extended Kalman filtering; muscle activation degree, pressure center offset and joint angular velocity are extracted to construct multidimensional features, and gait phase switching is predicted through LSTM-CNN; the TS fuzzy system generates a preliminary control instruction according to expert rules, and Q-learning takes pressure balance and motion smoothing as reward functions to optimize the weight reduction proportion and the treadmill speed online; a waist annular air cavity is driven to be inflated to form a pressure supporting field so as to lift a pelvis and guide a lower limb gait mechanical mode, so that the muscle activation degree, the joint angular motion trail and the pressure center migration characteristic are dynamically influenced, a servo motor tracks phase nodes in a feedforward-feedback mode, and risk early warning and training parameter self-adaptive closed-loop rehabilitation are achieved.

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

The invention discloses a control method and system of a lower limb exoskeleton power assisting device for iron tower climbing. The method comprises the steps that electromyographic signals are processed, and muscle activation degree indexes are calculated; the two-channel LSTM neural network realizes motion intention online prediction; iron tower cross arm structure edge identification and safe climbing path generation; designing an asymmetric impedance function and optimizing joint stiffness damping parameters; solving a metabolic consumption evaluation model and a minimum energy consumption assistance curve; and real-time calculation is realized through three-stage control period setting and hardware acceleration. Based on the method, all the modules of the exoskeleton power assisting device operate cooperatively; data are accurately collected through the sensor, the power and execution module is flexibly adjusted according to instructions, efficient operation is ensured through the control and operation module, stable electric power is provided through the energy module, and therefore the safety and efficiency of iron tower climbing operation are remarkably improved, and the burden of operators is relieved.

The invention discloses a physiotherapy instrument control system and method based on artificial intelligence, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting an electromyographic signal in real time, carrying out band-pass filtering and TK energy operator processing, and outputting a muscle activation feature vector; based on the muscle activation feature vectors, spatial-temporal features are extracted through a convolutional neural network, and a physiotherapy parameter combination is constructed; a PID controller is adopted to adjust the physical therapy parameter combination in real time, and optimized physical therapy parameters are output; performing electromagnetic thermal coupling simulation on the optimized physiotherapy parameters through finite element analysis to obtain a physiotherapy instrument control instruction; and updating the weight of the convolutional neural network by adopting a stochastic gradient descent algorithm according to the physiotherapy efficiency evaluation report, and outputting an optimization execution instruction. PID parameters are optimized through a quantum annealing algorithm, stimulation parameters are dynamically adjusted in combination with myoelectricity beta wave band phase locking, and treatment parameters and physiological response are synchronized.

The invention belongs to the technical field of exercise rehabilitation, and particularly discloses an exercise rehabilitation effect quantitative evaluation method which comprises the following steps: S1, a data acquisition unit acquires data of a patient in an exercise process through fusion of a multi-source sensor; s2, muscle electrical activity signals in the movement process of the patient are collected through bio-electricity signals, and the activation degree and the fatigue condition of muscles in rehabilitation movement are known; s3, acquiring muscle electrical activity signal data according to the acquired multi-source fusion data and the bio-electricity signals, and performing rehabilitation evaluation on the model; s4, according to the output of the evaluation model, a quantitative rehabilitation effect evaluation report is generated, and the report content comprises numerical values of various quantitative indexes, comparative analysis of rehabilitation targets and comprehensive scores of rehabilitation effects; an exercise rehabilitation effect quantitative evaluation system comprises a data acquisition system and a rehabilitation evaluation module. The method solves the problems that a traditional evaluation method is high in subjectivity and single in data, and has the advantages of being objective, comprehensive and accurate in quantification.

The invention discloses an action state evaluation method and system based on a time-varying muscle synergistic effect. According to the method, a muscle synergistic effect matrix of standard walking actions, a standard reference range of a scaling coefficient and a time delay parameter and a classifier for action state recognition are constructed, then real-time surface electromyogram signals of a target individual are collected in a real-time stage, and the muscle synergistic effect matrix is extracted; calculating a second similarity index with a muscle synergistic effect matrix of a standard walking action; and inputting the second similarity index into the classifier, if the second similarity index is identified as a'moving 'state, performing an action state evaluation process, otherwise, re-collecting the real-time surface electromyogram signal of the next window. According to the method, the time-varying muscle synergistic effect model is adopted, and the time dimension is introduced, so that the dynamic change in the whole muscle activation process can be more accurately captured; meanwhile, the muscle activation process is modeled from the source, whether the action is completed or not is reflected, and more attention is paid to physiological rationality and control strategies.

The invention discloses an anti-resistance exercise training system for old people based on intelligent wearable equipment, and relates to the technical field. The problems that in the prior art, a dynamic action monitoring error correction mechanism is lacked, exercise intensity adjustment is not personalized and real-time, interaction and data feedback functions do not exist, and equipment portability and wearing adaptability are insufficient are solved. The system collects data in real time through the whole body position intelligent wearable device, corrects non-standard actions through the action recognition feedback module, dynamically regulates the intensity through the exercise intensity evaluation module, reduces the risks of muscle strain, cardiovascular burden and the like, takes standardized action guidance, personalized intensity adaptation and data visualization as the core, ensures target muscle activation, and improves the training efficiency. The training effect is quantified, the operation is simple, the use threshold is reduced through voice guidance, the boring feeling is relieved through personalized plans and summary reports, the training achievement feeling and compliance of the old people are enhanced, the muscle strength can be improved through whole body position training, and the anti-resistance exercise requirement of the old people is met.

The invention discloses an intelligent lower limb rehabilitation training device based on multi-modal feedback. Comprising an adjustable exoskeleton support capable of dynamically adjusting exoskeleton driving torque, and a multi-mode feedback system installed outside the adjustable exoskeleton support and capable of capturing the motion intention of a patient. The central controller can calculate the optimal motion trail of the patient according to the data fed back by the multi-mode feedback system; the force feedback actuator can provide assistance or resistance in real time according to the muscle force of the patient; by arranging the multi-mode feedback system, the action starting of a patient can be predicted, and the action intention and the muscle activation intensity of a user can be recognized; the hip, knee and ankle joint angles and plantar pressure of the patient during movement are measured in real time, the training intensity is dynamically adjusted according to the muscle force state and the movement intention of the patient, and the training individuation is optimized; compared with traditional single-machine training, the device can avoid secondary injury of muscles and reduce the risk of muscle injury.

The invention discloses an elbow joint rehabilitation training evaluation method and system, and the method comprises the following steps: S1, collecting multi-cycle motion data and surface myoelectricity data when a patient executes a specified rehabilitation motion in a standard state through an inertia measurement unit and a surface myoelectricity sensor which are disposed on the upper limb of the patient, based on this data, establishing a personal motion baseline template comprising a standard angle-time curve and a standard muscle activation level; s2, in the rehabilitation training process, motion data and surface myoelectricity data are collected in real time, and the following operations are executed: S21, a joint motion range and a motion smoothness index are extracted from the motion data; s22, muscle activation intensity and collaborative shrinkage are extracted from the surface myoelectricity data; according to the method, individuation of evaluation standards, diversification of monitoring dimensions and dynamic weight adjustment are achieved, the accuracy and safety of rehabilitation training can be effectively improved, and the method has important clinical application value.

The invention relates to the technical field of rehabilitation medical treatment, in particular to a hand rehabilitation training system based on virtual reality and gesture interaction, and the system comprises a gesture sensing module which collects a hand movement track, a muscle electric signal, grip pressure and a joint movement angle; the VR rehabilitation scene construction module constructs a rehabilitation training virtual scene, simulates a hand interaction behavior and a force feedback effect through an adaptive physical engine and a neural action mapping algorithm, and outputs simulation data; the intelligent evaluation center evaluates the action standard degree and the muscle activation efficiency based on an improved CNN-GRU model, identifies an action compensation mode and abnormal force generation, and outputs an evaluation report; the interactive training unit renders a motion state and a training process in real time, and generates personalized training guidance in combination with voice, visual and tactile prompts; and the personalized regulation and control unit predicts a rehabilitation trend and a stage target through a reinforcement learning algorithm, and dynamically adjusts training parameters and scene difficulty. Therefore, the problems of low interaction precision, incapability of sharing and the like in the prior art are solved.

The present disclosure describes devices, methods and systems for modifying or altering gait kinematics via sensory augmentation and / or modifying muscle activation patterns via augmented motor learning to slow the progression of and / or reduce the pain associated with knee OA, particularly during gait (e.g., walking, running, stair climbing, etc.). Sensors can be used to measure gait parameters and characteristics and muscle activation patterns. Stimulation can be provided to the individual in order to promote learning new gait kinematics and muscle activation patterns.

The invention discloses an interaction control method and system based on virtual reality, and relates to the technical field of monitoring analysis, and the system comprises a multi-mode motion capture module which is used for capturing limb space coordinate data, joint rotation angle data and muscle activation signals corresponding to a target user; the action intention analysis module comprises a time sequence convolutional neural network model, the time sequence convolutional neural network model comprises an input layer and an output layer, the input layer is used for receiving the skeleton joint point sequence, the angular velocity data and the myoelectricity feature vector of the multi-modal action capture module, and the output layer is used for extracting spatial and temporal features through three-layer expansion causal convolution. Connecting a classifier to generate initial action data corresponding to the target user; the correction module is used for constructing a human body joint angle constraint library, judging the initial action data corresponding to the target user and outputting a correction instruction; and the environment adaptation module is used for adjusting the environment in the virtual scene. The method has the effect of improving the interaction efficiency.

The invention belongs to the technical field of medical rehabilitation instruments and biological signals, and particularly relates to a multi-mode fusion intelligent rehabilitation evaluation system based on EMG and PPG. The system comprises a flexible multi-mode sensor array module used for collecting surface electromyogram signals EMG and photoelectric volume pulse wave signals PPG and capturing muscle activation intensity and blood perfusion dynamics in real time; the self-adaptive contact enhancing module is used for driving the airbag assembly to dynamically adjust the contact pressure between the embedded pressure sensor and the skin through the micro air pump; the edge calculation unit module is used for carrying out localization processing on the collected original signals, quantitatively evaluating the collaboration of muscle activity and blood flow fluctuation and judging whether muscle contraction drives local blood flow change or not or whether vessel limitation reversely inhibits neuromuscular activation or not; and the lightweight classification model module is used for processing the time-frequency characteristics of the EMG and the waveform fragments of the PPG, and outputting rehabilitation state labels after screening key collaborative characteristics through an attention mechanism.

The invention relates to the technical field of exoskeleton gait classification, in particular to an exoskeleton auxiliary gait recognition method and system based on neuromuscular signals, and the method comprises the steps: carrying out the signal segmentation of an electroencephalogram signal and an electromyographic signal according to a periodic gait time period set, and obtaining an electroencephalogram signal segment set and an electromyographic signal segment set, and calculating a power spectral density feature value set of the electroencephalogram signal fragments, calculating a muscle activation time period set and a root mean square value set of the electromyographic signal fragments, and inputting the exoskeleton torque data, the power spectral density feature value set and the muscle activation time periods into a gait classification model for gait classification to obtain a predicted gait. The human gait recognition accuracy of the exoskeleton gait assisting technology can be improved.

The invention relates to a closed-loop control method suitable for personalized lower limb exercise training, belongs to the technical field of exercise rehabilitation, and designs a closed-loop control method for dynamically adjusting lower limb training load according to muscle activation degree by combining surface myoelectricity characteristics with a personalized training model and a closed-loop control algorithm. According to the method, the resistance load can be adjusted in real time according to the muscle state, and finally the muscle activation level in the training process is maintained in an expected range. In the pre-training stage, sEMG data are collected to establish a personalized mapping model, and response characteristics of individual muscles are quantified; in the formal training stage, the muscle state is monitored in real time, the reference load is predicted through the model, and a closed-loop control algorithm is adopted to generate dynamic adjustment amount to adjust resistance output in real time. The invention further provides a closed-loop control system suitable for personalized lower limb exercise training.

The invention relates to the technical field of artificial intelligence, and discloses a digital human driving method based on artificial intelligence, comprising the following steps: step 1, acquiring a voice signal, a facial electromyographic signal, a pupil size signal and a hand motion signal of a student through a wearable device, the wearable device comprising a helmet built-in sensor and a hand motion capture device; step 2, converting the voice signal in the step 1 into a fundamental frequency feature vector, converting the facial electromyographic signal into a 28-dimensional muscle activation degree vector, converting the pupil size signal into a diameter change rate scalar, and converting the hand motion signal into a six-degree-of-freedom pose matrix; according to the method, the multi-mode behavior recognition engine is adopted to fuse micro expression analysis, voice stress detection and skeleton motion capture technologies, the technical effect of recognizing the tiny abnormal reaction of the student in real time is achieved, and compared with a visual behavior recognition scheme based on a preset script in the prior art, the defects that deviation correction response is delayed and the safety knowledge memory effect is poor are overcome.

The invention relates to a multi-modal information feedback regulation and control electrical stimulation method and system, and the system comprises an electrical stimulation device, a multi-modal information feedback regulation and control upper computer, and a data collection device. According to the method, inertia information, hand distributed pressure data and skin impedance data in the muscle action process are fully considered, and features are extracted from the data to determine initial stimulation parameters such as channels, frequencies, pulse widths and amplitudes. In addition, when stimulation is carried out according to the initial electrical stimulation parameters, distributed pressure data and multi-channel inertial data of the hand are collected in real time, then an actual muscle activation graph is obtained in combination with a machine learning model, and the actual muscle activation graph is fused with the ideal muscle activation graph and first spatial data extracted from skin impedance data respectively, so that the actual muscle activation graph is obtained. And the electrical stimulation parameters are dynamically adjusted. And multi-modal information feedback regulation and control electrical stimulation is realized.

The application discloses a kind of idiopathic scoliosis rehabilitation multi-modal signal evaluation method and device.The idiopathic scoliosis rehabilitation multi-modal signal evaluation method includes: obtaining the muscle activation information of the person to be evaluated in the movement cycle;Actual time of reaching action peak in the movement cycle of the person to be evaluated is obtained;The displacement information of the person to be evaluated in the movement cycle is obtained;The movement amplitude information of the person to be evaluated in the movement cycle is obtained;Multi-modal rehabilitation confidence model is obtained;The muscle activation information, actual time of reaching action peak, displacement information and movement amplitude information are input into the multi-modal rehabilitation confidence model, so as to obtain rehabilitation confidence information.The application uses multi-modal information, can more comprehensively evaluate the effect of rehabilitation action;By characteristic parameter representation factor that influences rehabilitation action effect, mapping is established between mathematical model and clinical index, can more comprehensively, scientifically and accurately evaluate rehabilitation action.

Techniques for cuffless blood pressure monitoring with multiparameter correction are described and are implementable to reduce measurement inaccuracies in wearable cuffless blood pressure devices. In an example, a wearable device includes a sensor arrangement to collect physiological timing data indicative of a pulse propagation time along a cardiovascular pathway and a correction sensor that is configured to measure a correction parameter that impacts the pulse propagation time independent of a corresponding change to blood pressure, such as body temperature, skin temperature, perfusion index, hydration level, or muscle activation.  A processor of the wearable device is configured to process the physiological timing data to determine the pulse propagation time and generate a blood pressure measurement based on the pulse propagation time and measurements of the correction parameter.  Accordingly, the techniques described herein generate blood pressure measurements that account for physiological variables that have an independent impact on pulse propagation characteristics.

The application discloses a dual-arm robot autonomous control system and method based on remote operation and visual features, comprising a remote operation acquisition module, a multi-view visual perception module, a data synchronization and demonstration acquisition module, a strategy model training module, an autonomous strategy execution module, a trajectory deviation detection and takeover module and a hybrid control interface module. The application completes human demonstration through remote operation, and acquires multi-modal information such as images, trajectories, grippers and muscle activations. The perception features are fused by using multi-view space-time alignment and cross-view attention mechanism, and strategy learning is completed in combination with an end-to-end large model. In the autonomous operation stage, the risk of current operation is evaluated through trajectory deviation detection and collision probability prediction, the autonomous control proportion is dynamically adjusted, manual intervention is allowed when necessary, the safety and stability of the overall system are improved, a robot control mode of seamless switching of human-machine cooperation, autonomy and remote operation is realized, and the application is suitable for object operation tasks in complex and high-variable environments.

The invention discloses a multi-mode signal evaluation method and device for idiopathic scoliosis rehabilitation. The multi-mode signal evaluation method for idiopathic scoliosis rehabilitation comprises the following steps: acquiring muscle activation degree information of a person to be evaluated in a motion cycle; acquiring the actual time of the to-be-assessed person reaching the action peak value in the motion period; acquiring displacement information of the to-be-assessed person in the motion period; acquiring motion amplitude information of the to-be-assessed person in the motion period; acquiring a multi-modal rehabilitation confidence model; inputting the muscle activation degree information, the actual time for reaching the action peak value, the displacement information and the motion amplitude information into the multi-mode rehabilitation confidence model, so as to obtain rehabilitation confidence information. According to the method, multi-modal information is used, so that the effect of rehabilitation actions can be evaluated more comprehensively; the factors influencing the rehabilitation action effect are represented through the characteristic parameters, mapping is established between the mathematical model and the clinical indexes, and the rehabilitation action can be evaluated more comprehensively, scientifically and accurately.

The invention discloses an artificial shoulder joint prosthesis design method and system, and the method comprises the steps: collecting the motion trail and muscle activation state of a shoulder joint in different motion modes, separating the feature frequency and amplitude of the shoulder joint motion, and extracting a dynamic motion feature set of the shoulder joint; inputting the dynamic motion feature set into a contact mechanical model based on a discrete element method, and simulating contact force and friction behaviors between the shoulder joint prosthesis and the skeleton to obtain contact mechanical characteristics of the prosthesis and the skeleton; and according to the contact mechanical characteristics, generating an initial structure of the prosthesis by using a topological optimization algorithm based on a generative adversarial network, generating a prosthesis topological structure with gradient material distribution, and obtaining an optimized prosthesis design scheme. By utilizing the embodiment of the invention, a prosthesis design scheme with gradient material distribution can be generated, the weight of the prosthesis is reduced, and the strength and durability of the prosthesis are enhanced.

The invention discloses a fitness quality evaluation method based on myoelectricity and images, which comprises the following steps: step 1, data acquisition: the acquired data comprises original myoelectricity data with time sequence information and time sequence image data of fitness actions; step 2, preprocessing the collected data; step 3, for the preprocessed myoelectricity data, extracting myoelectricity characteristics through a sliding window by adopting a time domain intermediate frequency value model, and then decomposing a muscle activation degree matrix into a collaborative structure factor matrix and an activation coefficient matrix through a non-negative matrix decomposition algorithm; for the preprocessed time sequence image data, adopting a BlazePose algorithm to extract 33 3D articulation points in the time sequence image data and calculating the Euler angles of the articulation points so as to obtain attitude angle features; and step 4, using a support vector machine to fuse the myoelectricity features and the attitude angle features to carry out action scoring. The method can effectively improve the fitness quality of the user.

A muscle activation sensing system includes a mechanical sensor mounted on a body part and is configured to detect a change in a muscle associated with the body part. A location of the muscle activation sensing system on the body part is determined. A control signal is derived based on the detected change in the muscle associated with the body part and the determined location. The control signal is outputted to a device.

Introduced here are computer-implemented platforms (also referred to as "muscle activation platforms") that are able to determine activated muscles of individuals performing physical activities. By predicting activated muscles, the muscle activation platforms are able to more accurately determine whether the individuals are performing the physical activities correctly.

This application relates to the fields of biomedical engineering and rehabilitation engineering technology, and discloses a functional electrical stimulation method and system based on physiological simulation and adaptive closed-loop feedback. The method includes: calculating an ideal muscle activation curve based on a target trajectory; performing spatiotemporal asynchronous co-coding to generate a biomimetic stimulation pulse sequence by simulating temporal asynchrony, spatial and intensity modulation, and multi-muscle synergistic timing; simultaneously applying stimulation while monitoring the actual kinematic state and muscle physiological state in real time using sensors; and dynamically adjusting the internal parameters of the spatiotemporal asynchronous co-coding (T-S-C coding) based on the evaluation results of trajectory error and fatigue index to form an adaptive closed loop. This application, by combining biomimetic coding with an intelligent closed loop, fundamentally solves the problems of motion stiffness, muscle fatigue, and poor control precision caused by traditional FES, achieving smooth, sustained, and precise personalized motor function rehabilitation.

The invention belongs to the technical field of underwater bionic robots, and particularly relates to a bionic duty ratio gait control method for an elastic robotic fish, a program, equipment and a storage medium. According to the method, improvement is carried out based on a traditional Hopf oscillator, a CPG oscillator simulating a red muscle activation mode of fish is constructed, a CPG network model based on an improved Hopf central pattern generator is constructed according to the structure of the elastic robotic fish, and actuator control mapping is completed; and a PID-CPG closed-loop controller is constructed, and the actual maximum angle of each joint gradually approaches the maximum angle expected value through continuous iteration of the motion period. The universal gait control method of the bionic robotic fish is constructed according to the physiological characteristics of fish red muscle activation, the power consumption of the elastic robotic fish can be effectively reduced, and the efficiency can be improved.