38 results about "Muscle movement" patented technology

The invention relates to the technical field of signal processing, and provides an electromyogram data-based muscle movement analysis method, device and equipment and a storage medium, the electromyogram data-based muscle movement analysis method comprises the following steps: using an adaptive filter to pre-process original electromyogram signals, and dynamically filtering noise signals; segmenting the electromyographic signal after the noise signal is filtered by using a sliding window, calculating the energy of the signal in each window, and detecting the zero-crossing rate of the signal in each window; performing wavelet threshold denoising on the original electromyographic signals to obtain wavelet domain features; fusing the signal energy, the zero-crossing rate and the wavelet domain features to form a feature vector; and inputting the feature vector into a pre-trained classification model for analysis to obtain a muscle motion state. The signal energy, the zero-crossing rate and the wavelet are fused with the features for analysis, real-time effective analysis of the muscle motion state is achieved, and the accuracy of muscle motion state analysis under the dynamic condition is improved.

The invention discloses a multi-link collaborative evaluation and feedback system for upper limb rehabilitation training, and belongs to the technical field of medical rehabilitation, and the system comprises a data collection module which is used for obtaining the related data of the upper limb of a patient in the rehabilitation training process, and the related data comprises the motion posture, the muscle activity condition and the joint activity degree; the data processing module responds to the data acquisition module and is used for preprocessing the acquired related data, including filtering and denoising the related data, acquiring feature information in the preprocessed related data and sending the feature information to the data acquisition module; the feature information comprises movement tracks of joints, contraction intensity and time of muscles and coordination of upper limb movement. Multi-dimensional data such as action postures, muscle movement conditions and joint movement ranges in upper limb rehabilitation training are collected, preprocessing and feature extraction are carried out through the data processing module, the upper limb rehabilitation condition of a patient can be comprehensively and accurately evaluated, and a basis is provided for formulating a personalized rehabilitation scheme.

The invention discloses a scoliosis detection method based on muscle movement and body posture information. The method comprises the following steps: acquiring a corresponding electromyographic signal, a near-infrared signal and an inertial signal for a target spine; inputting the electromyographic signals, the near-infrared signals and the inertial signals into a trained graph neural network model to obtain an end cone of each section of spine; determining the central point of the spine according to the end cone of each section of spine, and calculating the Cobb angle of the scoliosis bending section; and based on the Cobb angle, determining the lateral bending section information of the target spine. According to the method, the accuracy of predicting the scoliosis angle by the neural network model is improved, and early low-cost screening of scoliosis is facilitated.

The invention provides a muscle motion anomaly analysis method for a rehabilitation process, and relates to the field of muscle motion anomaly detection.The muscle motion anomaly analysis method comprises the steps that firstly, the asymmetric cooperative offset rate between channels is extracted based on a muscle cooperative offset tensor, and a normalized cooperative offset matrix is constructed in combination with a time weight factor and a channel pair weight factor; deriving time sequence curve enhanced offset trend perception; then, dynamically integrating abnormal response by adopting an energy disturbance matrix and an accumulated deviation embedding structure, constructing an asymmetric collaborative modeling path based on a self-adaptive adjacency matrix and a disturbance operator matrix, and fusing a tensor interaction structure and channel deviation mapping to generate a deviation field coupling matrix; and finally, a deviation sensitivity response vector and a deviation intensity tensor are generated in combination with the standard template, a Gaussian disturbance kernel function, a sign function and a channel direction weight are introduced to calculate an abnormal scoring index, and model training and optimization updating are completed through a multi-layer sensing network and a cross entropy loss function.

Embodiments of the present invention provide a muscle movement state detection method, device, electronic device and storage medium, which relate to the field of medical instruments. Applied to electronic equipment, the electronic device pre-stores multiple muscle positions and a weight value for each muscle position. The method includes: determining a target muscle position from multiple muscle positions. Within a preset time period, collecting electromyographic signals of the target muscle position on the healthy side of the human body to obtain a healthy side signal sequence. And collecting electromyographic signals of the target muscle position on the affected side of the human body to obtain an affected side signal sequence. Calculate and obtain the shortest distance between the healthy side signal sequence and the affected side signal sequence. Repeat the above steps until the shortest distance at each muscle position is obtained. According to the shortest distance at each muscle position and the weight value of the muscle position, obtain a detection result. In this way, based on the detection results of the muscle movement state, it helps doctors to be more accurate in assessing the degree of rehabilitation.

To provide a muscle vibration detection sensor, an electric stimulation device, and a muscle vibration detection method capable of easily detecting a motion of a muscle with respect to an electric stimulation.SOLUTION: A muscle vibration detection sensor includes: a film whose physical property value changes due to deformation; and a circuit that outputs a detection signal obtained by detecting vibration of a muscle generated according to a motion of the muscle with respect to the electric stimulation, on the basis of a change in the physical property value of the film, when a device that gives the electric stimulation to the muscle gives the electric stimulation to the muscle.SELECTED DRAWING: Figure 1

The application provides a rehabilitation training difficulty self-adaptive system based on motion capture, and relates to the technical field of rehabilitation training.The rehabilitation training difficulty self-adaptive system based on motion capture comprises a signal processing module, a muscle movement acquisition module, an electric stimulation module, a self-adaptive adjustment module and a neuromuscular evaluation module.The output end of the signal processing module is connected with the self-adaptive adjustment module and the neuromuscular evaluation module.The output end of the muscle movement acquisition module is connected with the input end of the signal processing module.The output end of the electric stimulation module is connected with the input end of the muscle movement acquisition module.The output end of the self-adaptive adjustment module is connected with the input end of the electric stimulation module.Through the signal processing module, the muscle movement acquisition module, the electric stimulation module, the self-adaptive adjustment module and the neuromuscular evaluation module, the motion trajectory and the motion speed of the rehabilitation training can be conveniently acquired, and the difficulty of the rehabilitation training can be self-adapted, so that the switching is accurate.

1. The name of the design product: muscle stickers (1). 2. The use of the design product: for sports, stick to the body muscle movement area, stabilize the muscle, promote muscle volatile contraction. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view 1.

A muscle movement information-containing intraoral scanning data accurate acquisition method comprises the following steps: S1, collecting a single-frame texture map including an intraoral photo or a dental scanning image, marking an attached gum boundary by using a dental professional, creating a ground truth segmentation map, and performing creation and training of an attached gum area automatic segmentation model after performing variant on U-Net; s2, generating edge line data by utilizing Poi ntNet + + based on gypsum model scanning and artificially described edge lines, and creating and training an automatic edge line generation model; the model aims at automatically generating accurate edge lines for the intraoral scanning data obtained in the step S4 and guiding manufacturing of the movable restoration; s3, dynamically recording three-dimensional form information of intraoral muscles of the user in a motion state; s4, the obtained dynamic data and conventional static oral scanning data are guided by mark points to be elastically registered, and an intraoral data sequence with the complete form is obtained.According to the method, the implementation scheme that oral scanning is used for replacing impression taking can be achieved.

The invention provides a dynamic cupping muscle puller which comprises a soft cupping cup, a handle is combined with the top of the cup, so that after the cupping cup applies pressure to a cupping part and exhausts to generate vacuum negative pressure, muscles at the sucked and covered part are expanded, and a user holds the handle combined with the top of the cup by hand to pull the muscles into the cupping cup. By means of the multi-directional dynamic muscle pulling operation, the muscle at the suction cover part is pulled in a pulling mode or a twisting mode, through the multi-directional dynamic muscle pulling operation, the movement efficiency range of the muscle generated by cupping is greatly increased, and the cupping effect is effectively improved.

The invention discloses a bench press safety alarm system based on real-time monitoring, which relates to the field of bench press safety alarm and comprises a data acquisition module, a server, a biomechanics module, a physiological analysis module and a management alarm module. The method comprises the following steps: respectively carrying out quantitative analysis on a muscle state, a torque state and a body stable state of a bench press on the basis of muscle movement information, torque information and body movement information of the bench press to obtain a signal frequency drop abnormal value, an inter-muscle cross correlation coefficient, a muscle abnormal value and a bench press twisting stable value, and carrying out analysis to obtain a biological force state value of the bench press; carrying out numerical analysis on the received sweat information, respiration information, skin information and saliva information to obtain a physiological state value of a bench press person, and judging the comprehensive motion state of the bench press person based on the biological force state value and the physiological state value of the bench press person to obtain a corresponding alarm strategy corresponding to an abnormal bench press person. The fitness state of a bench press sporter can be accurately judged from multiple directions, and the bench press sporter can be reminded in time.

Disclosed are a method and system for monitoring movement. The system includes: a muscle movement sensor, a movement capturing device and a processing device, wherein the muscle movement sensor is configured to acquire muscle movement information of a moving part of a living body in the case that the muscle movement sensor is fixed onto the moving part; the movement capturing device is configured to acquire whole movement information of the living body; and the processing device is configured to determine a movement parameter of a muscle of the moving part based on the muscle movement information and the whole movement information.

The present invention is an electric motor capable of contracting itself to transmit its mechanical force in the form of traction to another attached object. This type of motor behaves similarly to a muscle and can be manufactured to its exact size and shape to replace it, thus enabling mechanical limb prostheses equipped with it to restore to the user almost all the motor skills of their lost limb, unlike other types of electric motors which only restore a portion of them. This motor consists of an assembly of contraction units, each comprising a solenoid attached to the outer fibrous sheath by central fibers, and a permanent magnet facing it, also attached in the same way.When an electric current passes through the solenoid, it becomes magnetic. The two attract each other, and their attractive force is transmitted via the fibers to the coating, causing it to contract. This force can then be transmitted to another object attached to the coating. A special damping system is also present. Everything relies on a clever interplay of mechanical tension coupled with a driving force source derived from the properties of electromagnetism to successfully reproduce the subtleties of muscle movement. This device is useful in improving the capabilities of active limb prostheses, in robotics for enhancing the capabilities of android robots, and is also likely to find applications in other fields.

The invention discloses a rehabilitation exercise posture monitoring and evaluating method and system, and relates to the technical field of exercise monitoring, and the method comprises the steps: obtaining motion feature data, including joint motion data, muscle motion data and spatial position data, corresponding to a target object in real time through a sensor module worn by the target object; performing space-time alignment on the joint movement data, the muscle movement data and the spatial position data; matching a corresponding action judgment rule according to the current rehabilitation stage of the target object; and calculating and outputting an abnormal action probability and an abnormal action type according to the joint movement data, the muscle movement data and the spatial position data based on the action judgment rule, and correspondingly generating movement posture evaluation data. The method can solve the problem that in the prior art, due to the fact that monitoring means are limited in the rehabilitation exercise process, scientific proceeding of rehabilitation exercise cannot be promoted.

The present application relates to the technical field of rehabilitation nursing, and discloses a method and system for tracking the rehabilitation progress of a patient, the method comprising obtaining the patient's affected tissue information, matching associated muscles from a preset skeletal muscle model based on the affected tissue information to define the muscles to be monitored, and then determining the pasting positions of several monitoring elements; obtaining the patient's initial information on the contralateral muscles, the first-order treatment time, and the contralateral muscle monitoring information to create a muscle movement monitoring form; based on the muscle movement monitoring form and the preset muscle maintenance movement amount form, evaluating the affected-side movement loss and the contralateral movement loss of the patient's muscles to be monitored; generating a rehabilitation training plan based on the affected-side movement loss and the contralateral movement loss and sending it to the patient; the present application has the effect of improving the patient's rehabilitation training efficiency.

There is provided an information processing apparatus including: a model generation unit that generates, by simulating contraction of a plurality of muscle fibers which make up a muscle, a muscle model that simulates a movement of the muscle; and a simulation unit that simulates, by using the muscle model to simulate movements of a plurality of muscles which are included in a human face, a movement of the human face.

The present invention relates to a muscle nerve stimulation method of an electric muscle stimulation (EMS) algorithm for artificially applying electricity to body muscles to induce muscle movement of a body without pain, the method comprising an electrical stimulation application step of outputting electrical energy from an electrical stimulation terminal of an EMS device such that electricity is applied to a body in contact with the electrical stimulation terminal at a designated frequency of a 21-27% duty cycle per cycle.

1. The name of the product of this design: Pre-cut muscle energy patch. 2. Purpose of the product with this appearance design: When used for exercise, it is attached to the muscle movement area around the knee to stabilize the muscles and promote muscle volatility and contraction. 3. The design key point of this appearance design product lies in the combination of shape and pattern. 4. The picture or photo that best illustrates the key points of the design: Main view of Design 1. 5. This design product is a thin product, and the rear view, left view, right view, top view and bottom view of design 1, and the rear view, left view, right view, top view and bottom view of design 2 are omitted. 6. Designate Design 1 as the base design. 7. Other situations that need explanation: Part A and Part B marked in the main view of Design 1 and the main view of Design 2 are both hollow designs.

The application relates to a muscle movement recognition method and a surface electromyogram signal collection device, and the method comprises the following steps: collecting historical surface electromyogram signals by using the surface electromyogram signal collection device; processing the historical surface electromyogram signals to obtain a surface electromyogram signal feature subset; constructing a convolutional neural network model; training the convolutional neural network model by using the surface electromyogram signal feature subset to obtain a muscle movement recognition model; and recognizing muscle movement according to the muscle movement recognition model. The device comprises an electromyogram signal sensor, a low-pass filter, an operational amplifier, an analog-to-digital converter and a wireless transmission module. The surface electromyogram signal feature subset is obtained by processing historical surface electromyogram signals, and the muscle movement recognition model is obtained by training the surface electromyogram signal feature subset; therefore, the muscle movement can be recognized, and the recognition process is faster and more accurate.

The invention relates to the technical field of maxillofacial muscle group function evaluation, in particular to a maxillofacial muscle group function dynamic capturing method for a patient with dysphagia, which comprises the following steps: acquiring a rate slow expression based on age, weakness degree obtained by body data evaluation and a displacement rate of a monitoring point; acquiring displacement shrinkage performance based on the volume and the spacing of the tongue bones and the displacement of the monitoring points; acquiring a muscle movement sequence expression based on the actual movement sequence number of the muscle movement event corresponding to each monitoring point and the actual time interval of the adjacent muscle movement event; on the basis of the rate slow expression and the displacement shrinkage expression, obtaining the overall movement expression of the single-swallowing monitoring point; and acquiring swallowing ability expression based on the integral movement expression of the single swallowing monitoring point and the muscle movement sequence expression so as to acquire the maxillofacial muscle group function expression of the patient. According to the invention, accurate evaluation of the maxillofacial muscle group function of the patient can be realized.

A system for diagnosing or treating bruxism. The system includes a wearable device configured to be coupled to a head of a patient to move as the patient expresses bruxism. The wearable device includes an energy harvester configured to detect muscle movement and generate electrical signals in response to the detected muscle movement and a signal processor configured to process the electrical signals from the energy harvester to provide processes electrical signals indicative of bruxism.

The present invention provides a method for extracting motion characteristic data, comprising: obtaining kinematic data of a muscle to be measured; establishing a finite element model of the muscle to be measured based on a differential voltage signal value of electrical impedance imaging of the muscle to be measured and anatomical prior information of a cross-section of the muscle to be measured; extracting time domain information and frequency domain information of each of the conductivity data of multiple signal nodes from the finite element model; performing correlation calculation based on the time domain information, the frequency domain information, and the kinematic data to obtain a motion participation degree of each signal node, wherein the motion participation degree is used to characterize the degree to which each signal node participates in muscle movement; obtaining motion characteristic data based on the motion participation degree of each signal node, the finite element model, and the conductivity data, wherein the motion characteristic data is used to characterize the spatial position distribution of multiple key muscle units in the muscle to be measured, so as to determine the morphological characteristics of the muscle to be measured based on the spatial position distribution.

The application discloses a closed-loop electric stimulation control system based on multi-modal vibration and model prediction, comprising: initializing the system; obtaining personalized parameters and stimulation parameters and determining a basic electric stimulation intensity in combination with a preset function; collecting high-frequency vibration signals and low-frequency vibration signals in real time, performing signal preprocessing, obtaining piezoelectric film signals and MEMS accelerometer signals and analyzing to determine corresponding muscle movement intensity characteristics; weighting and fusing the muscle movement intensity characteristics to obtain a basic muscle movement intensity; predicting and solving the basic muscle movement intensity to obtain a predicted muscle movement intensity; determining an optimal electric stimulation intensity based on the predicted muscle movement intensity, the basic electric stimulation intensity and the basic muscle movement intensity; applying stimulation to a preset position and repeatedly updating the optimal electric stimulation intensity until the system ends running. The application dynamically optimizes the electric stimulation intensity through multi-modal vibration signal analysis and model prediction, and realizes personalized closed-loop electric stimulation control.

This invention discloses a muscle electrical stimulation device and method. The device includes: an electrical stimulation component, which is a flexible structure with electrode contacts on its surface, and can be flexibly fitted onto the muscle to be stimulated; a control module, wiredly connected to the electrical stimulation component, which has multiple preset electrical stimulation programs and executes the corresponding programs according to different muscle sites, controlling the electrical stimulation component to apply electrical stimulation to the muscle; a power supply unit, connected to the control module, providing power to the muscle electrical stimulation device; and fixing components, disposed on both sides of the electrical stimulation component. The device provided by this invention allows for passive muscle movement through electrical stimulation without requiring active human effort, effectively maintaining muscle activity and blood circulation. It is suitable for muscle function maintenance scenarios in confined spaces, offering convenience and strong applicability.

The utility model provides a face and oral muscle motion detection device, which comprises a headphone, a measuring assembly and a flexible arm, a monitoring sensor is arranged in the measuring assembly, the flexible arm is used for connecting the headphone and the measuring assembly, and the flexible arm is also used for adjusting the detection position of the measuring assembly; a wire is arranged in the flexible arm, and the monitoring sensor is connected with a headphone main controller and a power supply through the wire; the face and oral cavity muscle movement detection device can detect face and oral cavity muscle movement in real time, through monitoring the face and oral cavity muscle movement conditions of a patient, a treatment direction or a test treatment result can be provided for a doctor, objective, accurate and comprehensive measurement data and indexes can be obtained, and the detection accuracy is improved. Therefore, great help is provided for treatment of labial and lingual muscle dysfunction.

A method for recommending a user-customized exercise program according to the present invention may comprise: a first step of providing a first exercise video for a first body area among a plurality of body areas of a user, and extracting a first exercise state evaluation score for each of one or more first detailed body areas included in the first body area on the basis of a joint position and muscle movement of the first body area of the user; a second step of determining a first detailed body area for which second exercise state evaluation information needs to be extracted from at least one of the first detailed body areas on the basis of the first exercise state evaluation score; a third step of recommending a second exercise video corresponding to the first detailed body area for which the second exercise state evaluation information needs to be extracted; and a fourth step of providing the recommended second exercise video and calculating a second exercise state evaluation score on the basis of the joint position and muscle movement of the first detailed body area of the user.

The invention relates to the technical field of artificial intelligence, in particular to a construction method based on an AI muscle model, the model maps a video to a potential space in an original size by using a causal 3DVAE technology, and learns a video denoising process by taking stream matching as a training target; by introducing a multi-modal data fusion technology, more accurate simulation and prediction of muscle movement are realized. According to the method, information of various modes such as texts, images, audios and postures can be comprehensively considered, and therefore dynamic changes of muscles in the movement process can be more comprehensively captured. Through deep learning and a neural network model, the method can automatically extract and integrate data of the modals to generate a vivid muscle movement model. The model not only can be used in the fields of animation production, virtual reality and the like, but also can provide powerful support for medical research, physical training and the like.

The invention discloses a robot joint movement method and system based on muscle bionics, and relates to the technical field of robots, and the robot joint movement method comprises the following steps: selecting a bionic driving material as a joint driving unit; a joint connection mode with multiple degrees of freedom is constructed, and state information of joints is monitored in real time; and a control system is established, and the control system receives a movement instruction and controls joint movement in combination with the state information. By simulating contraction and relaxation mechanisms of human muscles and adopting novel intelligent materials or pneumatic muscles or hydraulic muscles and other driving modes, flexible movement of robot joints is achieved, cost is reduced, movement efficiency is improved, the human muscle movement principle is simulated, and a brand new solution is provided for humanoid robot joint movement.

This invention proposes a method for analyzing muscle movement abnormalities during rehabilitation, relating to the field of muscle movement abnormality detection. First, it extracts asymmetric collaborative offset rates between channels based on a muscle collaborative offset tensor. A normalized collaborative offset matrix is ​​constructed by combining a time weighting factor and a channel pair weighting factor, and a temporal curvature-enhanced offset trend perception is derived. Then, an abnormal response is dynamically integrated using an energy perturbation matrix and a cumulative deviation embedding structure. An asymmetric collaborative modeling path is constructed based on an adaptive adjacency matrix and a perturbation operator matrix. A tensor interaction structure and channel offset mapping are fused to generate an offset field coupling matrix. Finally, a deviation sensitivity response vector and a deviation intensity tensor are generated using a standard template. An abnormality scoring index is calculated by introducing a Gaussian perturbation kernel function, a sign function, and channel direction weights. Model training and optimization are completed using a multilayer perceptron and a cross-entropy loss function.