87 results about "Muscle activation" patented technology

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 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 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 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 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 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.

The invention relates to the technical field of biomedical engineering and rehabilitation engineering, and discloses a functional electrical stimulation method and system based on physiological simulation and adaptive closed-loop feedback. The method comprises the following steps: calculating an ideal muscle activation curve based on a target trajectory; space-time asynchronous cooperative coding is executed, and a bionic stimulation pulse sequence is generated by simulating time asynchronism, space and intensity modulation and a multi-muscle cooperative time sequence; when stimulation is applied, the actual kinematics state and the muscle physiology state are monitored in real time through a sensor; and dynamically adjusting the internal parameters of the time-space asynchronous cooperative coding (T-S-C coding) based on the evaluation results of the trajectory error and the fatigue index to form a self-adaptive closed loop. According to the method, bionic coding and intelligent closed loop are combined, so that the problems of stiff movement, easy fatigue of muscles and poor control precision caused by traditional FES are fundamentally solved, and smooth, lasting and accurate personalized movement function rehabilitation is realized.

The application provides a muscle force prediction method under functional electrical stimulation, which can be applied to the field of electrical stimulation technology. The muscle force prediction method comprises the following steps: obtaining muscle fiber length information and muscle fiber velocity information of a target object; processing electrical stimulation parameters by using a neural activation model to obtain a neural activation value; the neural activation model represents the correlation between the predetermined electrical stimulation parameters and the predetermined neural activation value; the neural activation value represents the neural activation degree of the electrical stimulation parameters on the nerve; processing the neural activation value by using a muscle activation model to obtain a muscle activation value; the muscle activation model represents the correlation between the predetermined neural activation value and the predetermined muscle activation value; the muscle activation value represents the activation degree of the target muscle under the condition that the nerve of the target muscle is in the neural activation degree; processing the muscle fiber length information, the muscle fiber velocity information and the muscle activation value by using a muscle force prediction model to predict muscle force information of the target muscle.

The invention discloses a gait muscle force and activation real-time prediction method based on a physical constraint model, and belongs to the technical field of sports biomechanics and artificial intelligence. The method comprises the following steps: 1, inputting only hip, knee and ankle joint angles as input data; 2, a model backbone is a Transform; 3, multi-task loss constraint model training; 4, adjusting model parameters; and 5, calculating muscle activation. According to the invention, force and activation of multiple muscles can be output in real time only by inputting a joint angle sequence; the joint moment constraint is added in the loss, so that the prediction meets the biomechanical law, the precision, robustness and interpretability are considered, the traditional static optimization or dynamic optimization method can be replaced, and the process can be carried out in multiple scenes.

The invention discloses a multi-modal-based immersive self-adaptive treadmill rehabilitation training method and system, and the method comprises the steps: constructing a multi-modal synchronous data stream through synchronously collecting real-time posture data, surface electromyogram signals and treadmill state data of a user; further analyzing the gait quality, muscle activation and interaction state of the user in real time, and generating a multi-dimensional state vector; based on a preset self-adaptive strategy table, the state vector is compared with an expected threshold value of the current training stage, and a cooperative adjustment instruction aiming at the speed / gradient of the treadmill, the dynamic characteristics of the virtual scene and audio feedback is generated through intelligent decision; finally, closed-loop self-adaptive control is formed through multi-channel real-time feedback execution, and the training stage is automatically transited when conditions are met. According to the method, deep interaction and self-adaptive linkage of the physical training environment and the virtual immersion scene are realized, safer and more personalized rehabilitation training experience rich in sense of participation can be provided, and the accuracy and effect of rehabilitation training are effectively improved.

This invention relates to the field of lower limb rehabilitation data recognition and monitoring technology, specifically disclosing a method for recognizing lower limb rehabilitation movement postures and an intelligent ankle bracelet for preventing VTE, comprising: Step 1, real-time acquisition of three-dimensional spatial motion data and recognition of lower limb motion data, extracting kinematic parameter features; Step 2, identification of movement types based on kinematic parameter features, acquisition of movement pattern consistency scores, and determination of whether rehabilitation training is qualified; Step 3, acquisition of surface electromyographic signals of the rehabilitation participant's leg-raising force exertion movement during rehabilitation training and simultaneous acquisition of lower limb motion data, extraction of force exertion data, evaluation and analysis to generate a muscle activation efficiency score; Step 4, establishment of a mapping model based on historical hemodynamic data, inputting the extracted force exertion data into the mapping model to output a VTE prevention efficiency score; Step 5, calculation of the movement pattern consistency score, muscle activation efficiency score, and VTE prevention efficiency score through movement evaluation analysis to generate a movement evaluation quality coefficient.

The invention discloses a forearm motion posture recognition method based on flexible pressure and inertial sensing. The forearm motion posture recognition method comprises a recognition device used for obtaining inertial sensing data and muscle acting force signals of a forearm in a motion posture. Three-axis inertial motion data and muscle force signal data of the forearm of a human body are collected, after data preprocessing, kinematics features and muscle activation features are extracted from a time domain, a frequency domain and a space domain respectively, then the features are subjected to multi-level matching with a pre-constructed standard motion feature library, and a motion feature library is obtained. And finally, high-precision action recognition is realized through a decision-level fusion algorithm.

Embodiments of the present disclosure relate to the technical field of biomedical engineering, and provide a movement assistance method, device and apparatus, an electronic device, and a storage medium. The method comprises: acquiring, in real time, target foot features of a subject based on a current activity type, wherein the target foot features comprise: a plurality of target foot actions and moments corresponding to the target foot actions; on the basis of a preset movement relationship feature model, determining a first correspondence describing a target foot action and the current activity type as a target correspondence, and determining a target waiting duration, a target auxiliary muscle group, and a target stimulation duration described in the target correspondence; and determining a stimulation moment on the basis of a moment corresponding to the target foot action and the target waiting duration, and assisting muscle activation of the subject by providing, at the stimulation moment, a stimulation signal of which the duration is the target stimulation duration to the target auxiliary muscle group, thereby achieving movement assistance.

The techniques described herein relate to computerized methods and systems for integrating with a knowledge system. In some embodiments, a user interaction system may include a speech input device wearable on a user and configured to receive an electronic signal indicative of a user's speech muscle activation patterns when the user is speaking. In some embodiments, the electronic signal may include EMG data received from an EMG sensor on the speech input device. The system may include at least one processor configured to use a speech model and the electronic signal as input to the speech model to generate a text prompt. The at least one processor may use a knowledge system to take an action or generate a response based on the text prompt. In some embodiments, the system may provide context to the knowledge system.

This invention discloses a method for analyzing upper limb surface electromyography (sEMG) signals by integrating deep and wide learning, belonging to the field of upper limb surface EMG signal analysis technology. The method includes the following steps: acquiring multi-channel EMG signals to establish a complete sEMG dataset; designing the same number of convolutional neural network branches with residual structures for the acquired multi-channel sEMG signals; inputting the output features of the residual convolutional neural network structure of each channel into its respective wide learning sub-model; calculating weights through pseudo-inverse regression; obtaining a contribution index by normalizing the absolute value; stabilizing the contribution index through cross-validation; analyzing the muscle activation weights corresponding to upper limb movements; and validating the representation of muscle activation degree. This achieves the collaborative extraction of multi-channel muscle features and the quantification of muscle activation contribution, thereby solving the problem of insufficient representation ability of traditional muscle co-activation indices for the real neuromuscular control process, and realizing high-precision, interpretable quantification of muscle co-activation relationships.

The present application belongs to the technical field of underwater bionic robots, and particularly relates to a bionic duty cycle gait control method for an elastic robotic fish, a program, an equipment and a storage medium. The present application is improved based on a traditional Hopf oscillator, a CPG oscillator simulating the red muscle activation mode of fish is constructed, a CPG network model based on the improved Hopf central pattern generator is constructed according to the structure of the elastic robotic fish, and actuator control mapping is completed; a proportional-integral-derivative (PID) CPG closed-loop controller is constructed, and through continuous iteration of the motion cycle, the actual maximum angle of each joint gradually approaches the maximum angle expected value. According to the physiological characteristics of the red muscle activation of fish, the present application constructs a general gait control method for bionic robotic fish, which can effectively reduce the power consumption of the elastic robotic fish and improve the efficiency.

The invention provides a microemulsion with muscle activating, protecting and repairing effects and a preparation method thereof, and belongs to the technical field of medical treatment, the microemulsion comprises the following raw materials by mass: 1-8% of an oil phase, 4-10.5% of a surfactant, 0.1-0.2% of a preservative, 1-3.5% of a safflower extract, 0.1-0.5% of polyacrylamide, 0.02-0.05% of ocimum gratissimum oil, and the balance of water. 0.05 to 1.28 percent of cinnamon oil, 0.5 to 1 percent of carbomer 980 and water; wherein the oil phase comprises olive oil and a mixture of another one or two grease substances, and the grease substances comprise one of lanolin, isopropyl myristate, glyceryl monostearate, menthol and vitamin E succinate; the traditional Chinese medicine composition is simple to prepare, convenient to use and high in skin permeability, has definite effects of activating, protecting and repairing muscles, and has moisturizing and moistening effects.