221 results about "Muscle fatigue" patented technology

The invention discloses a peripheral nerve stimulation system based on muscle fatigue prediction and compensation, which is used for acquiring electromyographic signals and predicting muscle fatigue states during nerve stimulation, and timely warning and adjusting stimulation to ensure the stability of muscle strength, and comprises a stimulation output module used for stimulating target nerves to activate target muscles according to stimulation parameters; the acquisition and analysis module is used for synchronously acquiring electromyographic signals of target muscles, preprocessing the electromyographic signals and extracting electromyographic signal features; the fatigue prediction module is used for predicting the fatigue state of the muscle according to the electromyographic signal characteristics; the compensation control module is used for adjusting stimulation parameters according to the fatigue state of the muscle and sending the stimulation parameters to the stimulation output module to ensure that the muscle strength is stable in the stimulation process; and the user interaction module is used for performing stimulation control and setting in real time and displaying the stimulation effect and the muscle fatigue state. Muscle fatigue and strength attenuation in the peripheral nerve stimulation process can be dynamically predicted and compensated in time, and the safety, accuracy and reliability of nerve stimulation are improved.

The invention relates to a muscle strength and muscle fatigue prediction algorithm based on surface electromyogram signals, which comprises the following steps of: (1) acquiring the surface electromyogram signals of a measured muscle area by using a non-invasive sensor, and acquiring muscle mechanics parameters at the same time; (2) carrying out filtering, amplification and noise reduction preprocessing on the collected surface electromyogram signals to eliminate interference and enhance signal quality; and (3) extracting multi-dimensional features from the preprocessed surface electromyogram signals and muscle mechanics parameters, establishing a frequency domain data set by using the feature values, and calibrating the muscle mechanics parameters. The method has the advantage that the prediction accuracy and reliability are enhanced. Muscle strength and muscle fatigue states of different individuals can be reflected more accurately, and accurate prediction of individual differences is realized. The adopted surface electromyogram signal acquisition mode is non-invasive, and a plug-in electrode is not needed, so that the test discomfort is reduced, and the acceptability and participation degree of a testee are increased; by establishing an individual model and combining with real-time signal acquisition, real-time monitoring and prediction of muscle strength and muscle fatigue can be realized, and a user can obtain feedback of a muscle state at any time, so that the exercise intensity, posture and training plan are adjusted, and the training effect is optimized.

The invention relates to a seat self-adaptive adjusting system and method based on multi-modal biological data, and the system comprises a multi-modal data collection module, a data processing and fusion module, a control decision module, and an execution mechanism. Physiological and posture information of a user is collected through a heart rate sensor, a myoelectricity sensor, a skin temperature sensor, a pressure distribution sensor, a three-dimensional posture sensor and other multi-mode sensors, a fatigue index and a posture deviation amount are calculated through feature extraction, normalization and weighted fusion, and the fatigue index and the posture deviation amount are compared with preset threshold values. The control decision module generates adjusting instructions of the backrest angle, the waist support height, the cushion inclination angle and the temperature control power based on fuzzy control and a prediction algorithm, and self-adaptive adjustment of the seat is completed through an execution mechanism. The method can achieve the precise recognition and active intervention of the fatigue and posture change of the user, is suitable for various scenes such as driving, working and rehabilitation, can remarkably improve the comfort, delays the muscle fatigue, and improves the posture health.

The invention relates to the technical field of power-assisted robots, in particular to a self-adaptive control lower limb hydraulic power-assisted exoskeleton robot system which comprises a waist supporting device, a leg supporting device, a foot supporting device, a hydraulic power-assisted assembly, an attitude sensor, a lower limb force sensor, a wearable electromyogram sensor, a step obtaining module and a data analysis module. The system can intelligently adjust the output power of the hydraulic power assisting assembly according to the motion posture of the exoskeleton, the pressure data of the foot supporting device and the wearing target muscle state so as to provide proper walking assisting power and bearing support, and the system can further conduct secondary adjustment and fatigue alarm by monitoring the pace frequency and the muscle fatigue degree in real time. The exoskeleton assisting power output power in the carrying process is gradually adjusted according to the comparison result between the weight of the wearing target and the weight of the to-be-moved object, so that assisting power provided by the exoskeleton robot is effectively matched with the weight of the to-be-moved object when the wearing target carries the object.

The invention discloses a lower limb muscle fatigue assessment method and system based on multi-modal physiological signals, and relates to the technical field of biomedical engineering, target muscles are selected, sEMG signals, AUS signals and respiratory flux VE data are synchronously collected, and RPE is recorded; preprocessing various signals to remove interference and abnormal points; extracting sEMG time-frequency domain features and AUS muscle thickness features; fusing features and constructing vectors in a standardized manner; dividing five fatigue grades; and a data set is divided according to stratified sampling, a model is optimized through five-fold cross validation, and real-time features are input to output a fatigue level. Multi-modal physiological signals are fused, fatigue evaluation accuracy is improved, different fatigue stages are finely adapted, muscle load and energy consumption are reduced through personalized adaption and a dynamic adjustment strategy, rehabilitation training safety and comfort are improved, and efficient recovery of lower limb movement functions is assisted.

The embodiment of the invention discloses a waist muscle fatigue detection method and device, equipment and a medium. The method comprises the following steps: acquiring electromyographic signals and temperature data of waist muscles of a target user based on waist detection equipment, and acquiring a first fatigue degree grade of subjective evaluation of the target user; determining a second fatigue degree grade of the waist muscles according to the electromyographic signals and the temperature data; determining a target fatigue degree grade of a target user according to the first fatigue degree grade and the second fatigue degree grade; adjusting the working mode of the intelligent mattress according to the target fatigue degree grade; wherein the waist detection device is associated with the intelligent mattress. According to the technical scheme, the working mode of the mattress can be intelligently adjusted by monitoring the fatigue grade state of the waist muscles in real time, so that a personalized waist muscle relaxation scheme is provided, and the sleep quality of a target user is improved.

The invention provides an entropy-based muscle fatigue assessment method and application thereof, and particularly relates to the technical field of signal processing, and the method comprises the steps: firstly collecting a surface electromyogram (sEMG) signal, and carrying out the preprocessing; then, fractional calculus is combined with a fluctuation dispersion entropy (FDE) algorithm, and a multi-scale processing process is introduced, so that an RCMFFDE algorithm is innovatively proposed. Performing multi-scale analysis on the preprocessed sEMG signal by using the algorithm to generate a plurality of entropy curves; the entropy curve meeting the preset condition is selected as the final entropy curve, and the final entropy curve can accurately reflect the muscle fatigue state. According to the method, the muscle fatigue degree can be effectively and reliably evaluated, and the method has important kinematics and medical significance for preventing human body muscle diseases and sports injuries.

The invention provides an electrical stimulation thumbtack needle system and method, and the method comprises the steps: determining the change gradient of the muscle contraction intensity of a target user through the median frequency feature of an electromyographic signal on the skin surface of the target user, and further determining the muscle fatigue feature of the target user; determining the change trend of the skin resistance of the target user according to the fluctuation characteristics of the skin resistance in the skin resistance data of the target user and the environmental temperature and humidity of the target user, and determining the tolerance index of the target user to electrical stimulation according to the muscle fatigue characteristics and the change trend of the skin resistance; the parameter deviation of each thumbtack needle of the electrical stimulation thumbtack needle equipment in the electrical stimulation process is determined; and based on all the parameter deviations, adjusting electrical stimulation parameter information of the electrical stimulation thumbtack needle equipment, and controlling the electrical stimulation thumbtack needle equipment to continuously perform electrical stimulation on the target user. By adopting the scheme, the stimulation parameters of the electrical stimulation thumbtack needle equipment can be dynamically adjusted according to the real-time physiological data of the user.

The invention provides an exercise load multi-objective optimization method based on an anti-gravity rehabilitation treadmill. The exercise load multi-objective optimization method comprises the steps of obtaining gait kinematics data, plantar pressure distribution data, electromyographic signal data and pain scores of lower limbs of a patient; based on the data, a gait asymmetry index and a myoelectricity activation sequence consistency index are screened out, the gait asymmetry index and the myoelectricity activation sequence consistency index are combined with pain scores to serve as key features, and a function grading model is constructed to obtain function grades; establishing a multi-objective optimization problem by taking minimization of a pain score, maximization of joint activity and electromyographic activation sequence consistency and minimization of muscle fatigue as objectives and taking non-overrun of articular cartilage contact stress as a constraint; an NSGA-III algorithm is adopted to solve the problem, and a Pareto optimal solution set of the weight reduction proportion and the speed of the treadmill is obtained; through a PPO reinforcement learning strategy network, taking the real-time state of a patient as input, and selecting and dynamically adjusting treadmill parameters in a Pareto solution set; and issuing the adjusted parameters to a treadmill execution mechanism in real time.

The invention discloses a lightweight surface electromyogram signal classification method and system. The method comprises the steps that original surface electromyogram signals are acquired; maintaining a dynamic reference vector capable of tracking the change of the physiological baseline of the signal; standardizing the original signal by using the dynamic reference vector, and generating a channel hybrid matrix according to the vector; extracting a local time-frequency characteristic pattern from the standardized signal; transforming the local time-frequency feature map by using a channel hybrid matrix so as to output a collaborative adaptive feature map capable of adapting to muscle collaborative mode change; and through a parallel sequence modeling module fusing a state space model and a multi-head self-attention mechanism, processing the collaborative adaptive feature map to obtain a final action category. The method improves the robustness of the model to individual difference and muscle fatigue, has the advantages of light weight and low delay while maintaining high classification precision, and is suitable for mobile or wearable equipment.

The invention relates to a pelvic floor muscle test data evaluation method, system and equipment, and the method comprises the steps: obtaining test data of pelvic floor muscle in a test period, presetting a boundary value, obtaining a first reference image related to first-class muscle fibers of the pelvic floor muscle, and obtaining a second reference image related to second-class muscle fibers of the pelvic floor muscle, based on the first-class muscle fiber test section and the second-class muscle fiber test section, respectively constructing a first-class muscle test curve and a second-class muscle test curve, and combining the first reference image, the second reference image, the first-class muscle test curve and the second-class muscle test curve to obtain a first-class muscle test curve and a second-class muscle test curve; and first-class muscle strength grading, first-class muscle fatigue degree calculation, second-class muscle strength grading and second-class muscle fatigue degree calculation are carried out respectively. The system and the equipment are used for executing the method. According to the method, muscle strength grading and fatigue calculation can be accurately and quickly performed on the pelvic floor muscle data, human subjective influences are reduced, and the accuracy and efficiency of pelvic floor muscle test data evaluation are improved.

The invention provides a muscle fatigue detection method and device based on a multi-modal physiological information graph, electronic equipment and a storage medium, and relates to the field of biological information. The method comprises the steps that space-time synchronization signals of a target muscle group are acquired, and the space-time synchronization signals comprise electromyographic signals and muscle oxygen signals; performing data preprocessing on the space-time synchronization signal to extract a target characteristic parameter reflecting a muscle fatigue state; performing visualization processing based on the target characteristic parameters to obtain a multi-modal physiological information graph; and inputting the multi-modal physiological information graph as input data into a target detection model to obtain a muscle fatigue classification result. Therefore, the fusion of the multi-modal physiological information graph is realized based on the acquired space-time synchronization signal and the target detection model, then the identification of muscle fatigue is completed, the objectivity and accuracy of waist and back muscle fatigue detection and classification are finally improved, and an objective and effective prevention means is provided for preventing chronic lumbago and acute muscle injury.

The invention discloses a muscle fatigue detection and treatment integrated fabric electronic system, which is characterized in that conductive fabric lines, physiological electrodes, vertical interconnected via holes and patch-type components are integrated on the same fabric substrate, and the conductive fabric lines are arranged on the surfaces of the two sides of the fabric substrate and are communicated with each other through the vertical interconnected via holes; the integrally formed circuit comprises an electrode array and a signal conditioning circuit, the electrode array is used for detecting electromyographic signals and applying transcutaneous electrical stimulation, and the signal conditioning circuit is used for electromyographic signal amplification, electrical stimulation waveform conditioning and overall sequential logic control and has the functions of safety isolation and electrical stimulation waveform detection. Components can be highly integrated on a single piece of fabric through process design, and electromyographic signal acquisition and transcutaneous electrical stimulation are realized. Experiments prove that when the system is worn on the arm, the fatigue process of the biceps brachii muscle can be monitored, fatigue recovery is effectively promoted, and the capacity of the system in closed-loop diagnosis and treatment is shown.

The invention discloses a snore-preventing device capable of being automatically adjusted to resist muscle fatigue. The snore-preventing device comprises a split tooth socket, an induction module, an integrated chip and a release module. The split tooth socket comprises a left rear molar tooth socket, a right rear molar tooth socket and an elastic pressing strip, the shell is formed by polyurethane 3D printing, a resin pressing die tooth socket is embedded in the shell, and a battery, an integrated chip and a signal circuit are arranged in the shell; the sensing modules are symmetrically arranged on the working surface of the inner side of the tooth socket and comprise flexible pressure sensors, humidity monitors and broken contact monitoring devices, the bottoms of the pressure sensors are arc-shaped triangular surfaces, the surfaces of the pressure sensors are covered with cellulose acetate films for resisting bacteria, and humidity monitoring ports are provided with carbon nanofiber pads for resisting moisture. The integrated chip converts the sensing data into an electric signal through the analog-to-digital converter, the signal processor and the transmitter and transmits the electric signal to the release module; after receiving the signal, the release module generates micro-current through an electrode to stimulate muscles, and the snoring symptom is inhibited. The shell is covered with a poly-o-phenylenediamine membrane, so that durability is enhanced, and the whole structure is small and portable and suitable for sleep breathing management.

The present disclosure relates to relates to a assistive wearable system for relieving muscle fatigue in the neck and shoulders, the assistive wearable system includes a head gear mounted to cover a head of a user, a wearable platform mounted on an upper body of the user, and a wire tension controller that maintains tension of a wire by being seated on the wearable platform and fixing to operate a semi-active ratchet mechanism structure as a brake to detect a moment a movement of the wire stops as connected to the head gear by the wire, and stop unwinding of the wire, wherein the ratchet mechanism rotates the ratchet based on the unwinding and pulling of the wire.

The invention relates to the technical field of rehabilitation training, and discloses an electromyographic signal feedback-based upper limb rehabilitation training system, which comprises a main frame body, a training mechanism is mounted on the middle side in the main frame body, and the training mechanism comprises a flexion and extension assembly, a fixing assembly, a massage assembly and an auxiliary training assembly, and is used for carrying out rehabilitation training on arms of a user. An electromyographic signal collecting mechanism is installed on the middle side of the interior of the main frame body and used for collecting the training condition of a user, and a feedback display mechanism is installed on one side of the interior of the main frame body and used for feeding back collected data and displaying the training condition in real time. Through the feedback display mechanism, a user and medical staff can know the muscle activity condition in real time, automatic inflation or deflation is achieved through an air bag in the massage assembly, meanwhile, a vibration motor drives a massage plate and a circular massage block to vibrate, muscles and acupuncture points are stimulated, blood circulation is promoted, muscle fatigue and aching pain can be effectively relieved, and the rehabilitation effect is improved.

A method for determining a muscle fatigue of a muscle includes the step of electrostimulating the muscle at different frequencies, wherein the electrostimulation includes, at each frequency, a repetition of pulses during a period of time lower than 5 s. The method further includes the steps of determining forces developed by the muscle in response to the electrostimulations of step (i); and determining a muscle fatigue on basis of the determined forces.

A system and method to control the Na / K pumps for the treatment or prevention of muscle fatigue by applying a synchronization modulation electric field that is effective in synchronizing the Na / K pumps down to individual steps throughout the pumping cycle, thereby synthesizing one ATP for each pumping cycle. The generated ATP molecule compensates the ATP consumed in extrusion of 3 Na ions and pumping 2 K ions such that the net-consumption of ATP for the Na / K pumps controlled by the system and method of the present invention is significantly reduced, theoretically to zero, thereby providing an effective treatment for muscle fatigue.

The invention discloses a degradable, asymmetric-adhesion and non-swelling Janus hydrogel adhesive as well as a preparation method and application thereof, and belongs to the technical field of functional composite materials and preparation thereof. The problems that an existing Janus hydrogel cannot be degraded, is weak in adhesive force, adheres and is high in swelling are solved. Polyethylene glycol with double bonds, sodium alginate and chitosan are used for physical and covalent cross-linking, a degradable hydrogel substrate is constructed and then soaked in a phosphate buffer solution for swelling equilibrium, then a solution containing chitosan is used for preparing an adhesion coating, and the degradable and asymmetrically-adhered non-swelling Janus hydrogel adhesive is obtained. The adhesive can be applied to the biomedical fields of wound healing anti-adhesion, in-vivo degradability, intestinal repair and the like, and can also be used for adhesion of bioelectronics, epidermal physiological signal monitoring sensors, epidermal muscle fatigue monitoring sensors, rat subcutaneous muscle physiological signal monitoring sensors and muscle fatigue monitoring sensors.

The invention relates to the technical field of rehabilitation physiological monitoring, and discloses a nerve-muscle recovery detection method based on HRV recovery rate and sEMG frequency spectrum declination, and the method comprises the steps: firstly obtaining an effective RR interval sequence subjected to quality gating and a steady state sEMG segment of a specific muscle group; then, extracting systemic HRV recovery features and local muscle fatigue features in parallel, and respectively generating a normalized HRV recovery rate score and a myoelectricity recovery score; through multi-level fusion, a global nerve recovery score, a local muscle recovery score array and a comprehensive nerve-muscle recovery score are integrated; and finally, based on the multi-dimensional evaluation results, generating personalized training suggestions. Therefore, the nerve-muscle system preparation degree of an individual can be comprehensively and accurately reflected, and a scientific basis is provided for optimizing a training plan and preventing sport injuries.

The application discloses a lower limb muscle fatigue factor analysis method fusing multiple muscle shape features, and comprises the following steps: acquiring muscle shape features of a measured position based on three-dimensional coordinates of marker points of motion capture, constructing a multidimensional feature vector through the muscle shape features, and screening sensitive features sensitive to a fatigue state through variance analysis; obtaining combined features with strong sensitivity to the fatigue state and high interpretability through fatigue correlation analysis and joint feature screening; fusing the combined features into multidimensional fusion features through principal component analysis dimension reduction processing, and obtaining a fatigue factor based on the multidimensional fusion features. The application discloses a lower limb muscle fatigue factor analysis method fusing multiple muscle shape features, a system and a storage medium, synergic shape changes of the vastus lateralis muscle and the vastus medialis muscle are fused through a three-dimensional cone body volume model, fatigue degree quantitative indexes and fatigue cause contribution degrees are output in combination with an interpretable statistical model, and the lower limb muscle fatigue cause is explained.

The invention relates to the field of medicine patches, in particular to an adjuvant therapy medicine patch which comprises the following medicines: eucommia ulmoides, divaricate saposhnikovia root, kadsura pepper stem, rhizoma corydalis, scorpion, radix astragali, bighead atractylodes rhizome, radix aconiti carmichaeli, earthworm, rhizoma zingiberis, fructus evodiae, dipsacus root, snake, gentiana macrophylla, asarum, agastache rugosus, carragheen, cortex dictamni, cinnamon, radix paeoniae rubra, radix sophorae flavescentis, semen coicis and philippine flemingia root. The prepared medicine patch has the effects of resisting fatigue, improving sleep, relieving pain and repairing soft tissue texture fascia, and is an ideal health-care rehabilitation medicine patch; the patch is good in effect, saves medicinal material resources, can treat diseases and even difficult miscellaneous diseases without taking medicines or taking less medicines for injection, and can enhance resistance, relieve muscle fatigue and mental fatigue and prolong life of people after being applied for a long time.

The invention provides an intelligent fatigue recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: evaluating a fatigue index of a target user through two dimensions, i.e., a target conduction velocity and a target fractal dimension, the target conduction velocity being capable of reflecting a muscle fiber function, the target fractal dimension being capable of reflecting neural control complexity, and the target fractal dimension being capable of reflecting neural control complexity; one-sidedness of single index evaluation is avoided, and muscle fatigue and central fatigue can be distinguished more accurately; through multi-channel and multi-time-scale analysis, signal characteristics of different muscle areas and different contraction rhythms are covered, and errors of a single channel and a time scale are reduced. Furthermore, the peripheral fatigue index and the central fatigue index of the target user are calculated in combination with the baseline conduction velocity and the baseline fractal dimension obtained based on the steady state surface electromyogram signal of the target user, and the method can adapt to the muscle basic states of different users, so that the evaluation result is more personalized and higher in accuracy.

The utility model belongs to the technical field of battery detection, and particularly relates to an automatic switching device and automatic detection equipment. The automatic switching device comprises a conveying structure which comprises a supporting rack, a material carrying sliding base arranged on the supporting rack in a sliding mode and a driving assembly used for driving the material carrying sliding base to slide in the preset direction; the electric connection structure is located at the electric connection station and comprises a clamping assembly and an electrode assembly which are connected with the supporting rack; a plurality of wire harnesses are arranged on the material carrying sliding base in the preset direction at intervals, the driving assembly drives one wire harness to be located at the electric connection station, and the clamping assembly clamps the wire harnesses and the electrode assembly so that the wire harnesses can be electrically connected with the electrode assembly. According to the utility model, different wire harnesses can be conveyed to an electric connection station without manual switching of the wire harnesses, so that the muscle fatigue of workers is avoided, the switching efficiency of the wire harnesses is improved, and the measurement accuracy is also improved.

The utility model relates to the technical field of bicycle manufacturing, and discloses a bicycle adjustable saddle which comprises a bicycle frame, a connecting rod is connected to the inner wall of the bicycle frame in a sliding mode, the inner wall of the connecting rod is connected with the bicycle frame through a lifting assembly so as to be used for adjusting the height of a saddle body, and a connecting frame is fixedly connected to the top end of the connecting rod. The inner wall of the connecting frame is slidably connected with a sliding frame, the inner wall of the sliding frame is rotatably connected with a rotary knob, the rear end of the rotary knob is connected with the connecting frame through a movable assembly so as to be used for adjusting the front-back position of the saddle body, and a buckle assembly is arranged on the outer wall of the rotary knob. By means of the rotatable design and the liftable design, the height and the angle of the saddle body can be adjusted, the height is adjusted to ensure that leg muscles are evenly used, unnecessary muscle fatigue is reduced, the angle is adjusted to ensure that the buttocks and the spine are located at the comfortable positions, discomfort caused by long-time riding is reduced, and the riding comfort is improved. And the riding comfort and efficiency are improved.

The utility model relates to the technical field of medical auxiliary equipment, and provides an anti-gripping limb binding device which comprises a glove body, the glove body comprises a palm part and finger parts, and a gripping block is arranged outside the side, located on the palm, of the palm part; the side, attached to the palm, of the holding block is arranged to be an arc face, the interior of the holding block is hollow, a binding unit is arranged in the holding block and comprises a binding belt, and the binding belt penetrates through the surface of the holding block, is connected with the finger part and is used for tensioning the finger part on the surface of the holding block; the wrist restraining assembly comprises a lower restraining plate, and the lower restraining plate is arranged on the holding block, located at the end, away from the finger part, of the palm part and used for being tightly attached to the wrist and limiting the position between the palm and the wrist. The hand of a patient can be kept in a relaxed half-holding state in the long-time binding process, hand muscle fatigue is avoided, meanwhile, the positions of fingers and the relative angle between the palm and the wrist can be fixed, the palm is prevented from hooking a pipeline, and safety is improved.

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.

This invention discloses an intelligent exercise assessment and early warning system based on multi-source biosignal fusion, belonging to the field of exercise risk early warning technology. It collects multi-source data, including electromyography (EMG), heart rate, blood oxygen, skin conductance, and acceleration, from various wearable sensors to assess muscle fatigue, cardiovascular load, exercise status, and environmental factors. After preprocessing, the system uses quantitative models such as the muscle fatigue correction index and the cardiovascular load dynamic coefficient to calculate a comprehensive exercise risk early warning index by integrating physiological state and environmental factors. This results in a normalized risk classification, corresponding to low, medium, and high risk levels and marked with different color icons. This system overcomes the limitations of single signals, considering the synergistic effects of multi-dimensional physiological indicators and the influence of environmental factors, improving the accuracy of assessment in complex scenarios. It can effectively intervene before risks develop and is suitable for scenarios such as mass fitness and professional training. It is integrated into lightweight wearable devices without interfering with the exercise process.

The invention provides a human-machine cooperation assembly planning method based on human body real-time muscle fatigue, and relates to the technical field of human-machine cooperation assembly manufacturing, and the method comprises the steps: constructing a muscle fatigue index model through a multi-angle motion image set, a human body skeletal muscle model and a biomechanical data set; training the deep learning model through the sample assembly action image set, and constructing an action category recognition model; according to the actual assembly action image set, the human skeletal muscle model, the muscle fatigue index model and the action category recognition model, the actual assembly time consumption and the actual fatigue accumulated value of the person are obtained through calculation; and based on the actual assembly time consumption of the robot, the actual assembly time consumption of the person and the actual fatigue accumulated value, an optimal population is obtained through calculation of a multi-target genetic algorithm, and an optimized man-machine cooperation assembly process is obtained through the optimal population. According to the method, human muscle fatigue and assembling action time consumption are monitored in a non-invasive and real-time mode, the man-machine cooperation assembling procedure is dynamically adjusted, the man-machine cooperation assembling efficiency is improved, human muscle fatigue is reduced, and meanwhile the method is more suitable for the actual assembling environment.

The invention provides an intelligent sportswear muscle fatigue degree dynamic evaluation method and system, and relates to the technical field of intelligent sportswear, and the method comprises the steps: collecting muscle pressure, surface temperature and electric signal data, extracting a multi-dimensional feature vector through time-frequency domain decomposition, constructing a dynamic connection network through an adaptive neural synaptic algorithm, and carrying out the dynamic evaluation of the muscle fatigue degree of the intelligent sportswear. And establishing a fatigue evaluation function, determining a fatigue value of the muscle group, dynamically updating a personalized fatigue threshold value, and performing real-time monitoring.