484 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 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 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 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 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.

The invention discloses an active constant-speed treatment and training method for patients with neurodegenerative diseases and myasthenia, and the method comprises the steps: promoting nerve remodeling through active constant-speed training, enabling the patients to use active constant-speed training equipment, and adjusting the training speed, the angle range and the impedance torque. The device activates a specific muscle group in a safe and controllable environment through an adaptive impedance training technology, gradually enhances training impedance to improve muscle strength, enhances muscle contraction efficiency through targeted training, promotes neural pathway reconstruction and improves nerve-muscle cooperative control ability, and thus achieves neural function recovery and improvement. Therefore, drug therapy provides neuroprotection and muscle function support for a patient, active constant-speed training further promotes the rehabilitation progress through accurate control and real-time feedback, and meanwhile, the flexibility and individuation of training are improved through the multi-mode man-machine interaction and cloud remote guidance functions, so that the rehabilitation effect of the patient is improved. And the treatment effect and life quality of patients with neurodegenerative diseases and myasthenia are improved.

The utility model provides an abdominal wheel which comprises a main body part, and the main body part comprises a first abdominal wheel main body and a second abdominal wheel main body, the second power roller main body is detachably connected with the first power roller main body, so that the second power roller main body and the first power roller main body are combined into a whole, and a user can combine the second power roller main body and the first power roller main body into a whole for use; according to the power roller, the first power roller body and the second power roller body are separated from each other, so that the first power roller body or the second power roller body is independently used, a user can use the power roller to do exercise in various different modes, different muscle groups of the body are exercised, and the actual use requirements of the user in different scenes are met.

The invention relates to the technical field of nursing robot control, in particular to a control method and system of a nursing robot. The method comprises the following steps that a 3D camera is used for collecting a posture track when a patient gets up, muscle tension change signals associated with the posture track are obtained through an electromyographic signal sensor, and posture associated muscle tension data are obtained through synchronous association processing. Thirdly, the data are analyzed, the muscle group force line unbalance strength is evaluated, and the gravity center shift trend is obtained in combination with gravity center shift regression analysis; finally, robot auxiliary output parameters are generated based on the gravity center shift regression trend and input into a nursing robot control framework, a control scheme suitable for the nursing robot is designed, a control instruction is sent to a robot terminal, and corresponding nursing operation is executed. According to the invention, the control technology of the nursing robot is optimized, so that the control technology of the nursing robot is more perfect.

The invention provides a throat motion monitoring method based on stereoscopic perception and a hybrid coding Transform model. The method comprises the following steps: acquiring throat epidermis motion characteristics through a mechanical motion sensing unit; acquiring features of deep muscle groups of the throat through an electrical characteristic measuring unit; performing data preprocessing on the laryngeal epidermis motion features and the laryngeal deep muscle group features to obtain a data set; the data set is input into an HE-Transform model for feature analysis, and a classification result is obtained; the HE-Transform model is composed of an embedded layer, a Patch division layer, a position coding layer, a Transform layer and a linear layer which are connected in sequence. According to the method, all-directional tracking of the physiological activity of the throat is achieved through the three-dimensional perception technology from the epidermis to the deep layer, and global information and local features of signals are considered through a Transform frame with mixed codes.

A stretching table that provides targeted stretching for lower extremity, lower back and / or lower abdominal muscle groups in a human patient and is capable of being manipulated in a number of different configurations. The stretching table is capable of providing targeted stretching so that certain muscle groups can be stretched, rehabilitated and / or worked on individually. According to one example, the stretching table includes a base member, an upright member connected to the base member, an upper body support member connected to the upright member, and first and second lower body support members connected to the upper body support member via a connection mechanism.

The invention provides a robot navigation path planning method and system which are applied to the technical field of robot navigation, and the method comprises the steps: obtaining a preset navigation path to carry out surface interaction operation (such as ultrasonic scanning, physiotherapy massage and other scenes) on a target muscle group, and extracting a real-time muscle group activation degree index; the deviation between the current surface interaction operation effect and the expected target is quantified through the error signal, and the navigation path parameters of the surface interaction robot are dynamically adjusted by calculating the control quantity to obtain a corrected navigation path instruction; by executing the corrected navigation path instruction, the robot can adjust its behavior according to real-time physiological feedback, so that the surface interaction operation process can be continuously optimized towards the preset target muscle group activation degree target value, thereby improving the effectiveness and individuation degree of physiotherapy. The method has the advantages that the dynamic adjustment of the navigation path is realized through real-time electromyographic signal feedback, and the individuation, the self-adaptability and the effect of the surface interaction operation are improved.

An electromyography (EMG) measurement device includes a garment configured to be worn on an anatomical region of an associated wearer, a plurality of electrodes arranged on the garment to contact skin of the anatomical region when the garment is worn on the anatomical region of the associated wearer, electronics operatively connected with the plurality of electrodes and configured to measure EMG data emanating from the anatomical region, and an electronic processor programmed to derive a contribution of spatial muscle activity of a target muscle or muscle group to the measured EMG data.

The utility model relates to an angle adjusting device, which belongs to the technical field of fitness equipment and comprises a sleeve, a rotating shaft and a handle. The sleeve is connected with the rotating shaft in a sleeved mode, a clamping groove is formed in an inner ring of the top end of the sleeve, the handle is connected with the rotating shaft, a check block is connected to the end of the handle, and the handle can drive the rotating shaft to rotate and drive the check block to be clamped in the clamping groove. Through mutual cooperation of the handle, the sleeve and the rotating shaft, 360-degree rotation of the handle can be achieved, the stepless adjustment effect is achieved, a user can be allowed to adjust the angle of the handle according to own requirements and habits through stepless adjustment, specific muscle groups can be better exercised by properly adjusting the angle of the handle, and the user experience is improved. Therefore, an exercise plan conforming to individual requirements is formulated; the whole device is simple in structure and convenient to operate, stepless adjustment of the angle can be easily achieved only when the device is applied to fitness equipment, and the use requirements of customers are met.

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.