589 results about "Muscle group" patented technology

The invention discloses an auxiliary scheme generation method and system for dysphagia rehabilitation, belongs to the technical field of medical rehabilitation intelligent evaluation, and is used for solving the problem of poor optimization of a swallowing training path. According to the method, myoelectricity, tongue pressure, throat images and acoustic signals in the swallowing process are collected through a multi-channel sensing device, a unified multi-mode swallowing behavior feature vector is constructed, obstacle stage recognition and functional grade judgment are achieved in combination with a pre-training model and historical data, and a structured evaluation result is output; matching training actions with high adaptation degree based on the knowledge graph, fusing muscle group matching degree, edge weight and function level factors to generate a personalized training path, and setting strength, frequency and rhythm parameters; in the training process, the motion and myoelectricity deviation is monitored in real time, a prompt signal is output, and the behavior completion degree is recorded; and the rehabilitation progress map is dynamically updated according to feedback, and if the deviation exceeds the limit, the path is automatically reconstructed and doctor intervention is synchronized, so that the rehabilitation efficiency and safety are improved.

The invention relates to the technical field of image recognition, in particular to an image processing method and system for rehabilitation training action analysis. According to the method, a multi-view image sequence is collected based on a binocular camera device, skeleton key point data of a user in a training process is extracted by utilizing a three-dimensional attitude reconstruction technology, and an action three-dimensional time sequence data set is constructed. The method comprises the following steps: firstly, constructing an individual standard power generation characteristic model of a user, modeling a skeleton driving path of a main muscle group, and forming a personalized power generation reference structure; and then a standard rehabilitation action path is matched through a dynamic time warping algorithm, and the attitude deviation under the key frame is identified. And the system dynamically compares the identified non-standard motion mode with the individual model, judges whether abnormal force generation exists or not and outputs the type and the part of the muscle compensation behavior. And finally, multi-modal feedback information with highlighted graphs, voice prompts and character suggestions is generated in combination with an identification result, so that the identification precision and personalized guidance capability of rehabilitation training are remarkably improved.

The invention belongs to the technical field of robot control, and discloses a bionic robot control method and system based on a muscle fiber model.The method comprises the steps that a multi-scale muscle model is constructed based on a scale division mechanism, and the multi-scale muscle model is optimized by means of physiological state variables and fatigue accumulation variables to obtain a muscle fiber model; according to the muscle group cooperation relation and the kinematics model of the robot limbs, geometric structure adaptation is carried out on the model in combination with a deformation compensation strategy; on the basis of the fused multi-sensor data, a muscle fiber model and a model after geometric structure adaptation are utilized to construct a feedback adjustment mechanism; and on the basis of an adaptive neural network algorithm, the muscle fiber model, the model after geometric structure adaptation and a feedback adjustment mechanism are optimized, solving is carried out in combination with a multi-objective optimization algorithm, and command information of the limbs of the bionic robot is obtained and executed. Accurate control over the limb joint position, the torque and the impedance of the bionic robot is achieved.

The invention belongs to the technical field of signal processing, and particularly relates to a lower limb motion intention recognition method and system based on electromyographic signals. The method comprises the following steps: S1, arranging electromyographic sensors on a hip key muscle group, a knee joint key muscle group and an ankle joint key muscle group in sequence, and collecting electromyographic signals; s2, performing time-frequency-space domain feature fusion and dimension reduction processing on the electromyographic signals acquired at the hip key muscle group and the knee joint key muscle group, and performing corresponding motion intention prediction by using a dynamic weight distribution method and a decoder based on an LSTM-attention mechanism; electromyographic signals collected at key muscle groups of ankle joints are processed through a muscle force distribution matrix, and meanwhile a time-varying weight adjustment strategy and impedance control are adopted for corresponding motion intention prediction; s3, according to the predicted motion intention, the system controls a direct-current brushless motor on rehabilitation training equipment, a fuzzy self-adaptive PID control method is adopted to regulate and control joint motion, and a walking posture is simulated.

The invention relates to the technical field of pain assessment, in particular to a paediatric pain assessment system which comprises a muscle group rhythm recognition module, a behavior linkage screening module, a periodic fluctuation extraction module, a behavior physiological difference module and a pain grading judgment module. According to the method, a linkage offset identification path is constructed through an angle relation between facial muscle groups and a starting time sequence, multi-muscle cooperative imbalance performance is identified, a region-level dynamic behavior judgment basis is formed, and a behavior cooperative judgment mechanism is established by utilizing response overlapping conditions of multiple behavior characteristics in the same time period; path signals which do not form effective cooperation are eliminated, the effectiveness of behavior characteristics is enhanced, a continuous trend structure of peak time in rhythm change is introduced, a periodic response sudden change monitoring channel is constructed, the recognition sensitivity of a short-time fluctuation concentration section is improved, and a response mismatch section is extracted based on a trigger time dislocation state between behavior and physiological response, so that the recognition sensitivity of the short-time fluctuation concentration section is improved. And a pain judgment key point is positioned in the time sequence overlapping region.

The invention relates to the technical field of medical rehabilitation, and particularly discloses a finger rehabilitation electrical stimulation parameter control method and system, and the method comprises the steps: obtaining a multi-channel surface electromyogram signal and finger joint kinematics data of a patient; according to the multi-channel surface electromyogram signals, target fine actions of the patient are recognized; according to the target fine motion and the finger joint kinematics data, a multi-muscle group cooperative activation mode and an expected motion trail are obtained through analysis; generating an electrical stimulation parameter combination for different muscle groups according to a multi-muscle group collaborative activation mode; outputting multi-channel electrical stimulation to the patient based on the electrical stimulation parameter combination, and continuously adjusting the electrical stimulation parameter combination on line according to the deviation between the kinematics data of the finger joints and the expected motion trail; the method solves the problems that an existing method is difficult to effectively simulate complex multi-muscle group coordination, complex in parameter adjustment and difficult to adapt to dynamic changes of a patient, and the effect of assisting the patient in complex and fine finger action training is 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 provides a method for predicting the vibration reduction effect of exercise compression pants, and relates to the technical field of functional costume design, and the method comprises the steps: collecting a static costume pressure value of a lower limb target muscle group and soft tissue amplitude data in exercise, constructing a pressure-amplitude mathematical model through data processing, and verifying the model; constructing a human body-clothing finite element model, inputting fabric mechanical parameters into the model, and verifying the effectiveness of the model after iterative optimization; inputting fabric mechanical parameters into the verified finite element model, inputting a pressure prediction value output by the model into the pressure-amplitude mathematical model, and predicting soft tissue amplitude in a dynamic motion scene; according to the predicted soft tissue amplitude in the dynamic motion scene, the vibration reduction effect of the motion compression trousers in the target motion scene is evaluated. The method provided by the invention solves the problem that the vibration reduction effect evaluation of the traditional sports compression pants depends on a large number of repeated physical experiments and is difficult to realize accurate quantitative prediction.

The invention discloses a peripheral-central-peripheral evaluation system based on electroencephalogram-myoelectricity dual-channel bidirectional mixed feedback, and the system is characterized in that a muscle electrical stimulation module is used for providing a current with a specific frequency, and carrying out the electrical stimulation of muscles; the transcranial direct current stimulation module is used for providing current with specific frequency and performing electrical stimulation on the cerebral cortex; the myoelectricity acquisition module and the electroencephalogram acquisition module respectively acquire electric signal data of tested muscles and cerebral cortex and transmit the electric signal data to the upper computer feedback module, and the upper computer feedback module is used for controlling the muscle electrical stimulation module and the transcranial direct current stimulation module to generate electrical stimulation and acquiring data acquired by the myoelectricity acquisition module and the electroencephalogram acquisition module; and the data is evaluated after being processed, and an evaluation result is sent to the terminal display module for real-time display. According to the scheme, in the evaluation process, through a bidirectional mixed feedback mechanism, accurate positioning of cerebral cortex and muscle group stimulation targets is achieved, the electrical stimulation and evaluation effect can be optimized, quantitative evaluation is conducted on the result through a bidirectional evaluation model, scientific quantitative indexes are provided, the influence of individual differences and state changes on the evaluation result is reduced, and the evaluation accuracy is improved. And the reliability and stability of evaluation are improved.

The invention relates to the technical field of rehabilitation medical treatment, and discloses a muscle group stress analysis method and device, a terminal and a storage medium, and the method comprises the steps: respectively obtaining motion data collected by inertial measurement units arranged on the body of a user, and recognizing gait events based on the motion data corresponding to the inertial measurement units of the lower limbs, dividing the continuous motion data into a plurality of gait cycles according to the gait event; fusing the motion data acquired by each inertial measurement unit to obtain a three-dimensional posture of each preset segment of the body, and constructing a three-dimensional motion track of the spine area of the user in combination with a human kinematics chain model; processing the three-dimensional motion track and the known muscle group stress data by adopting a singular value decomposition embedding regression method to obtain a preliminary target muscle group stress curve of each gait cycle; and correcting the initial target muscle group stress curve based on a human body multi-rigid-body dynamic model in combination with a control cost function to obtain a final target muscle group stress curve.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to the technical field of dynamic tracking, in particular to a human dance dynamic tracking method and system, and the method comprises the following steps: selecting a target stable isotope as a marker according to atomic weight and chemical properties, mixing the target stable isotope with a proper amount of carrier, and smearing the mixture on key joints and muscle groups of a dancer to generate a key marker combination. According to the invention, through cooperation with specially developed isotope labeling detection equipment, the identification effect under various illumination and background conditions is optimized, the data accuracy and tracking continuity are ensured, and the accurate three-dimensional motion model is constructed, so that the real-time reaction speed and the motion response precision of the model are improved; in addition, key action nodes are allowed to be extracted from the model and used for dynamically tracking and evaluating the coherence of the actions, through deep action analysis, the dancer and the coach can obtain detailed technical evaluation and improvement guidance, and the quality of dance performance and the technical level of the dancer are effectively improved.

The invention discloses a flexible exoskeleton execution device and a flexible exoskeleton system for upper limb training, and relates to the technical field of robots. The problems that an existing exoskeleton robot is complex in mechanical structure, large in size, heavy in weight and large in inertia due to the fact that rigid driving is adopted for achieving actions such as joint bending and stretching or unfolding and folding are solved. The device comprises a bandage, a forward line wheel, a reverse line wheel, a line wheel driving mechanism and Bowden lines, the bandage is fixed to a limb, one end of one Bowden line is wound around the forward line wheel, the other end of the Bowden line is fixed to the bandage, one end of the other Bowden line is reversely wound around the reverse line wheel, and the other end of the other Bowden line is fixed to the bandage; the line wheel driving mechanism is arranged on the trunk and can drive the forward line wheel and the reverse line wheel to rotate reversely in the pre-tightening stage so as to achieve line winding and pre-tightening, the forward line wheel and the reverse line wheel are driven to rotate synchronously in the same direction in the working stage, and correction force is applied to the force exerting muscle group through the two Bowden lines which are wound and unwound alternately so as to correct the action of a wearer.

The invention discloses a self-adaptive neuromuscular regulation and control device based on myoelectricity feedback. The self-adaptive neuromuscular regulation and control device comprises a myoelectricity sensor attached to the skin surface of a target muscle group. The establishing module is used for establishing an electromyographic signal matrix and respectively establishing a space weight matrix for describing a muscle cooperative activation mode and an activation matrix for describing muscle time sequence activation intensity; and iterating the space weight matrix and the activation matrix to obtain the space weight matrix and the activation matrix after iteration. The time sequence compensation module is used for extracting a target muscle collaborative activation curve from the iterated activation matrix; matching the activation curve with a health template, and determining an activation peak point according to a matching result; and according to the activation peak point, determining whether an activation time difference anomaly exists, and when the activation time difference anomaly exists, performing electrical stimulation compensation based on a time sequence according to the activation time difference anomaly parameters in the next period. The electrical stimulation can be effectively corrected according to the effect, and the neuromuscular regulation and control effect is further improved.

The invention provides a scoliosis rehabilitation process nursing method, and belongs to the technical field of scoliosis rehabilitation nursing, and the method comprises the following steps: firstly, collecting scoliosis posture data and medical image examination results of a patient, and establishing a rehabilitation training database containing spinal curvature, scoliosis types, heart rate variability and electromyographic signal reference values; and making a training plan according to the initial state information, wherein the training plan comprises an action sequence, intensity and duration. In the process that the core muscle group intensive training device is used for executing training, the spine position change of a patient is continuously monitored, and meanwhile physiological parameters such as heart rate variability and electromyographic signals are collected to form a feedback data set. The system calculates a fatigue coefficient through a fatigue degree evaluation function, and automatically adjusts the training intensity and the action sequence when the fatigue coefficient exceeds a preset threshold value. During training, daily life posture data are collected through the intelligent somatosensory equipment, the training database is continuously updated in combination with spine position change data in training, and the operation is circularly executed until the spine curvature reaches a preset rehabilitation target value.

The invention relates to a chin electrical stimulation combined upper airway muscle group training system and method based on bioelectric feedback, and belongs to the field of upper airway muscle group training. Comprising a pressure detection module used for detecting a pressure value in an upper respiratory tract muscle group training process; the myoelectricity acquisition module is used for detecting myoelectricity numerical values in the training process of the upper respiratory tract muscle group; the electrical stimulation output module is used for stimulating partial nerve excitation and muscle contraction of the muscle group of the upper respiratory tract of the trainee; the signal conditioning module is used for carrying out preprocessing and analog-to-digital conversion on signals transmitted among the modules; and the control module is used for controlling signal interaction in the whole training process. And the display interaction terminal is used for interacting with the trainee. Based on bioelectricity feedback, a new individualized electrical stimulation and upper airway muscle group training treatment mode is newly established, accurate electrical stimulation and training intensity are formulated, and fatigue and injury are avoided.

The invention relates to the field of upper limb joint rehabilitation training, in particular to an upper limb joint rehabilitation training method and system based on an exoskeleton mechanical arm, and the method comprises the steps: collecting sEMG signals of a target muscle group of a user in real time in a training process; calculating the root mean square of the amplitude of the sEMG signal in the current time window; if the root mean square is smaller than a muscle force insufficient threshold value, a training mode is switched to a full-assistance mode; if the root mean square is not smaller than the muscle force insufficient threshold value and smaller than the normal muscle force threshold value, the training mode is switched to a semi-assistance mode; if the root mean square is larger than or equal to the normal muscle force threshold value, the training mode is switched to an anti-resistance training mode. The rehabilitation training effect of the upper limb joints can be effectively improved.

The invention relates to the technical field of rehabilitation medical treatment and neural engineering, and particularly discloses an epidural electrical stimulation lower limb rehabilitation system and method based on a brain-computer interface, the epidural electrical stimulation lower limb rehabilitation system comprises a weight reduction suspension device, an epidural electrical stimulation module and a brain-computer interface module, the epidural electrical stimulation module controls left and right electrical stimulation through a program controller, and the brain-computer interface module comprises a wireless electroencephalogram acquisition device and an interaction terminal and is used for acquiring electroencephalogram signals of a movement area of a patient and presenting a virtual gait scene and voice prompt at a computer end. The method comprises the steps of electroencephalogram signal collection and feature extraction, motion intention judgment, stimulation control and feedback backtracking, and by combining a motion intention decoding result with electrical stimulation output, activation of a lower limb target muscle group is achieved, a training closed loop is constructed, and the method is used for postoperative rehabilitation of a spinal cord injury patient.

The utility model discloses a shoulder joint rehabilitation training device which aims at solving the problems that an existing traditional method is limited in effect, unicity, lack of individuation and the like. The training device combines multiple functions of the seat assembly, the damping rocker arm assembly, the shoulder limiting assembly, the tension training assembly and the like, multiple muscle groups including triceps brachii muscle, deltoid muscle, trapezius muscle, pectoralis major muscle, latissimus dorsi muscle, teres major muscle, biceps brachii muscle and the like can be exercised at the same time, and comprehensive shoulder joint rehabilitation training is achieved. Through adjustable parts such as a cushion lifting stand column and a shoulder pressing width adjusting connecting rod, the trainer can be adjusted according to individual differences of users, and individuation and comfort of training are ensured. Meanwhile, the adjustable two-way damping oil cylinder is adopted, a user can adjust the training resistance according to needs, and rehabilitation training of different intensities is achieved. Therefore, the shoulder joint rehabilitation training device has higher rehabilitation effect and practicability, and provides a more effective rehabilitation scheme for patients with shoulder problems.

The invention provides a pelvic floor muscle function exercise method and system based on remote digital guidance, and relates to the technical field of remote rehabilitation guidance, and the method comprises the steps: arranging a multi-channel micro sensor array at a preset position of a pelvic floor muscle group, and collecting electromyographic signals and tissue deformation data in resting and contraction states; generating a biomechanical model through Fourier transform and multi-dimensional mapping calculation; performing convolutional neural network analysis on the model and a standard physiological database to generate functional damage positioning data; the remote medical data center calculates a neural pathway activation parameter based on the injury positioning data, generates a training instruction and converts the training instruction into a biological feedback control signal; collecting real-time biomechanical data in the training process, calculating a muscle group synergy efficiency value and generating a fatigue characteristic curve; training parameters are adjusted in real time through wavelet transformation and deep reinforcement learning network analysis, a training scheme iteration closed loop is formed, and precise remote guidance of pelvic floor functional rehabilitation is achieved.

The invention discloses a neuromuscular electrical stimulation rehabilitation control method and system based on electroencephalogram intention recognition, and relates to the technical field of intelligent rehabilitation control, and the method comprises the steps: collecting electroencephalogram signal data of a user in a rehabilitation training process, processing the electroencephalogram signal data, constructing an electroencephalogram intention discrimination model, and dynamically recognizing the current motion intention category of the user. Outputting a corresponding intention signal and a confidence coefficient; and outputting a neuromuscular electrical stimulation parameter set by adopting a neuromuscular electrical stimulation parameter optimization model according to the identified intention signal, the confidence coefficient and the current myoelectricity state, sending the neuromuscular electrical stimulation parameter set to neuromuscular electrical stimulation equipment, and controlling the target muscle group to perform electrical stimulation. According to the method disclosed by the invention, by constructing the electroencephalogram intention real-time identification and neuromuscular electrical stimulation closed-loop optimization control method, high-quality preprocessing of electroencephalogram signals, dynamic discrimination of intention and self-adaptive optimization of individualized electrical stimulation parameters are realized, and the motion intention and the myoelectricity state of the user can be accurately matched.

An isokinetic control module and method for incorporation on both new and existing exercise equipment. The control module replaces standard friction and hydraulic resistance components offering constant speed of the moveable elements of the machine rather than a selected resistance or weight. The constant user-generated resistance prevents injury and enhances strength training of the targeted muscle groups by maintaining constant strain on those muscles throughout the exercise. This method and device incorporates both a PCFC valve and a check valve device, removing all resistance the moment the user stops applying force to the machine. This enhances safety and facilitates the rapid return of the lifting element to its home position between repetitions. An optional graphical display is also disclosed.

The invention provides a rehabilitation training feedback system and method. The method comprises the following steps: acquiring a surface electromyogram signal; a plurality of activation trend indexes are determined through the surface electromyogram signals, and then muscle force activation points of different muscle areas are extracted from the surface electromyogram signals according to all the activation trend indexes; performing collaborative analysis on the basis of a function collaborative relationship in combination with all muscle force activation points to obtain time sequence response tensors between all motion joints and corresponding muscle areas when motion responses are inconsistent; according to the joint angle information and the time sequence response tensor, constructing a functional coupling graph, and further performing kinematic chain simulation on a rehabilitation training process through the functional coupling graph to obtain a response consistency index; and performing feedback control on the rehabilitation training process of the patient according to the response consistency index in combination with a preset consistency threshold. By means of the scheme, feedback control can be conducted on rehabilitation training of the patient based on the space-time coupling relation between muscle group microstructure changes and mechanical conduction characteristics.

The invention relates to the technical field of signal processing, and provides a muscle state analysis method and device based on electrical impedance measurement, equipment and a storage medium. The muscle state analysis method based on electrical impedance measurement comprises the following steps: acquiring electrical impedance data and myoelectricity data of a target muscle group; extracting electrical impedance characteristics based on the electrical impedance data, and extracting myoelectricity characteristics based on the myoelectricity data; fusing the electrical impedance feature and the myoelectricity feature to obtain a fused feature vector; and inputting the fusion feature vector into a pre-constructed muscle state evaluation model for analysis to obtain a muscle state. By combining the myoelectricity data and the electrical impedance data, the muscle state is accurately evaluated from multiple dimensions, and therefore the accuracy and reliability of muscle state evaluation are improved.

A rigid-flexible integrated multifunctional lower limb rehabilitation training device comprises a base (100), a flexion and extension support (200), a hip and knee joint flexion and extension autonomous training control mechanism (300), an ankle joint dorsal extension and plantar flexion training mechanism (400), a hip and ankle joint inward and outward rotation autonomous training mechanism (500) and a hip joint adduction and abduction autonomous training mechanism (600). Mainly comprises a flexion and extension hand pull rope (310), a pedal plate (320) and a pedal pull rope (330); a support plate (410) and an elastic member (420); the internal and external rotation training device comprises a support frame (510), an internal and external rotation training arc-shaped track (520), an internal and external rotation roller (450) and an internal and external rotation training pull rope (530), the gear (620) is meshed with the adduction and abduction training arc-shaped track (610), and the adduction and abduction training pull rope (630) is arranged on the adduction and abduction training arc-shaped track (610). The lower limb core muscle group rehabilitation training device integrates multiple modes such as multiple joints, multiple degrees of freedom, passive, assisting and active as soon as possible in the lying position, can simulate gait training in the lying position, and effectively promotes early rehabilitation of a patient.

The invention discloses an extendable air bag type anorectal function evaluation probe, which comprises an expandable support layer, an air bag and a probe body, the adhesive layer is arranged between the outer surface of the expandable support layer and the substrate layer and is used for connecting the two; the substrate layer is attached to the surface of the adhesion layer; the flexible electrode array film layer is attached to the surface of the substrate layer; the outer insulating film layer covers the surface of the flexible electrode array film layer, and electrodes in the flexible electrode array film layer are exposed in the air through electrode holes of the outer insulating film layer; and the pipeline is connected with the cavity formed by the expandable supporting layer through the input port. The probe expands after being filled with gas or liquid through a pipeline, and can adapt to huge volume change before and after being put into the anus, so that the comfort of a testee is improved, and comprehensive and accurate evaluation on functions of anorectal muscle groups can be realized.

The invention provides an upper respiratory tract muscle group mechanical property measuring and evaluating method based on signal characteristics. Firstly, a force signal of an upper respiratory tract muscle group is collected through a sensor; after the signals are subjected to voltage amplification filtering and digital processing, key characteristic variables including the maximum muscle force, the muscle force stability, the total acting amount, the fatigue index and the overall testing time are generated. In order to improve the evaluation accuracy, a multi-level machine learning model is introduced, and the multi-level machine learning model comprises a basic TabTransform structure, a contrast learning framework and a meta-learning technology so as to realize deep analysis of a feature complex interaction relationship. In the model training process, through comparative learning of unlabeled data, the feature representation capability is enhanced; an MAML framework is adopted to quickly adapt to a small amount of labeled data, and a comprehensive target of disease grading and curative effect prediction is taken into account by utilizing a multi-task learning strategy. Finally, the performance index of the model on the verification set is remarkably improved, and efficient evaluation and rapid deployment can be achieved in practical application.

The invention relates to a limb-sensor-stimulator arrangement comprising a sensor part (1) to be fastened in a shoe, or in or to a sock (2), or to a cuff (35), comprising a sensor sole (30) or sensors (4) in the sole of a sock (2) or a cuff, in each case for a foot (17) of a patient; wherein: the sensor part (1) comprises sensors (4) in gait-line-typical sole portions (3) of the sole (30); and each sole portion (3) has at least one sensor (4) which is or can be connected to at least one controller (6) via a data link (5); and the controller (6) itself is connected to stimulators / actuators (7) for controlled transmission of information to the patient; at least two, preferably four, and at most thirteen gait-line-typical sole portions (3) are formed in the sensor part (1), each of which has at least one sensor (4) which is operatively connected to one stimulator (7) or to a plurality of stimulators (7a, 7b, 7n) via the data link (5) and via the controller (6); or some of the sensors are connected to one stimulator (7) each; and the first stimulators (7a) and the second stimulators (7b) or the nth stimulators (7n) are designed such that, when used, they can be fastened to muscle areas (8) on a leg (19) or to other muscle areas (8) of the patient body (12) - in particular at the upper end of the calf - and can emit independent flat muscle-stimulating signals - instead of conventional neural stimulating signals - to the patient body (12), which muscle-stimulating signals are triggered by sensor activities of the sensors (4); and each stimulator (7) is designed to be vibrationally muscle-stimulating - in the sense of a muscle-activating, and simultaneously therapeutic, intervention on the muscle - for each determined muscle group (8) with which it is associated.

An automatic analysis system for fitness training comprises a database, a thermal image capturing unit and an analysis device. The database stores a training course and a target muscle group data. The thermal image capturing unit captures a first thermal image of the user after performing the training course. The analysis device is connected to the database and the thermal image capturing unit, and further comprises a muscle identifying module, a muscle temperature calculating module, and a comparing module. The muscle identifying module identifies the muscle groups of the user. The muscle temperature calculating module calculates muscle temperatures of the muscle groups respectively, and obtain a main training muscle group while the user is performing the training course. The comparing module compares the main training muscle group with the target muscle group data, and then generates a warning signal when the main training muscle group does not match the target muscle group data.

The invention relates to the technical field of postpartum rehabilitation equipment, and provides a pelvic floor muscle repairing instrument, a pelvic floor electromyographic signal interference removing method and related equipment. The pelvic floor muscle electromyographic acquisition module is configured to acquire pelvic floor muscle surface electromyographic signals containing adjacent muscle group interference to obtain target electromyographic signals; the interference muscle group monitoring module is configured to collect physiological activity signals of muscle groups adjacent to pelvic floor muscles so as to obtain interference reference signals; the posture sensing module is configured to collect a body posture time sequence signal related to pelvic floor muscle activity so as to obtain a scene auxiliary signal; and the signal processing module is configured to perform suppression processing on adjacent muscle group interference in the target electromyographic signal based on the interference reference signal and the scene auxiliary signal, and output a purified pelvic floor electromyographic signal. Pelvic floor muscle real signals and dynamic interference are distinguished through the scene auxiliary signals obtained by the posture sensing module, and the purity and reliability of output signals of the pelvic floor muscle repairing instrument are improved.