418 results about "Electromyography" patented technology

The invention discloses an upper limb exoskeleton rehabilitation robot control system and method, and relates to the technical field of rehabilitation robot upper limb control, and the method comprises the steps: collecting multi-modal data such as multi-channel myoelectricity, joint angles, angular velocity and interactive torque; constructing a sliding window feature vector, predicting a next joint angle increment through TCN and Bi-LSTM deep networks, and outputting intention confidence; and based on the confidence coefficient and the muscle fatigue factor, dynamically adjusting an impedance parameter, and combining an impedance control model to generate a power-assisted torque in real time so as to realize power-assisted torque output. By introducing an impedance adjustment mechanism of multi-modal perception, deep time sequence learning and myoelectricity driving, the bottleneck of a traditional upper limb rehabilitation robot in the aspects of intention prediction, parameter adjustment and training individuation is broken through, and the accuracy, safety and active participation degree in the rehabilitation training process are remarkably improved; wide clinical application prospects and industrial transformation values are realized.

The invention relates to a neuromuscular electrical stimulation multi-objective self-tuning optimization method, which comprises the following steps: dynamically establishing a framework for muscle response in a time window sliding modeling mode, and describing short-term excitability change and fatigue accumulation by the framework according to historical stimulation frequency density and total energy integral accumulation variable; introducing a short-time response curve recognition mechanism: after stimulation, according to micro changes of an electromyogram and a force output curve, judging whether a nerve and muscle coupling state is in a deviation trend or not; when the output trend is evolved towards the target improvement direction, current strategy fine tuning is kept; if the trend is stagnated or reversed, the weight combination or the step length is actively adjusted; constructing a multi-target co-integration matrix for analyzing whether the current response changes of different targets are in a consistent direction; when it is detected that two certain targets have an inverse correlation change, the system carries out conflict decomposition processing once; interference compromising is carried out on conflict indexes, and short-term power down-regulation is carried out; and forming a dynamic strategy framework.

The invention relates to a motion evaluation method, device and equipment based on multi-modal physiological data and a medium, and the method comprises the steps: solving the problem of space-time mismatch of the multi-modal data through sampling timestamps of a hardware clock protocol for multi-source physiological signals such as a makeup rate, myoelectricity, blood lactic acid and the like; equipment interference and motion artifacts are eliminated, and the signal quality is improved; dynamic characteristics such as heart rate variability, myoelectricity root mean square and blood lactic acid gradient in the sliding window are calculated; dividing exercise intensity intervals based on the individually calibrated heart rate percentage and the myoelectricity activation degree threshold, and detecting conversion candidate points; and recognizing a motion intensity critical state in real time through a self-adaptive threshold model driven by historical data, and generating a comprehensive evaluation result containing a thermodynamic diagram and an early warning report. According to the method, the limitation of a traditional fixed threshold model is broken through, multi-modal data deep fusion and individual dynamic adaptation are achieved, the exercise intensity critical point detection precision is improved, and real-time decision support is provided for training load optimization and rehabilitation progress evaluation.

The invention provides a multi-signal fusion massage glove teaching system and a control method, and the system comprises a glove body, and a pressure sensing module, a motion sensing module and a myoelectricity sensing module which are disposed on the glove body, and is in electric signal connection with the pressure sensing module, the motion sensing module and the myoelectricity sensing module through a control unit. The control unit is used for analyzing the collected massage pressure, motion trail and electromyographic signals, and synchronous and in-situ collection of force-motion-physiology multi-dimensional information of massage manipulation in a real clinical environment is achieved. The design overcomes the limitation that in the prior art, a real operation scene is separated, and only single-aspect measurement can be carried out, and a hardware foundation is laid for comprehensive and objective quantitative evaluation of the massage manipulation. The integrated structure greatly facilitates the use of an operator, and the system can evaluate the manipulation quality online and give guidance immediately, thereby greatly improving the teaching efficiency and the training effect.

The invention belongs to the cross technical field of rehabilitation engineering and neural engineering, and relates to a multi-modal signal fused fine exercise rehabilitation evaluation, regulation and control method and system, and each evaluation comprises the following steps: collecting a pressure distribution signal, a surface electromyogram signal and an electroencephalogram signal of a hand fine exercise; performing preprocessing, feature extraction and normalization processing on the three signals to obtain a pressure feature value, a surface myoelectricity feature value and an electroencephalogram feature value; calculating a conversion coefficient of a current rehabilitation stage based on the number of days that the fine motor function of the patient is damaged, and calculating weights of a pressure characteristic value, a surface myoelectricity characteristic value and an electroencephalogram characteristic value based on the conversion coefficient; performing weighted summation on the pressure characteristic value, the surface myoelectricity characteristic value and the electroencephalogram characteristic value to obtain an evaluation score; rehabilitation training parameters are regulated based on the assessment score and the number of days of impaired patient's fine motor function. According to the method, the limitation of traditional single-mode physiological signal evaluation can be broken through, and the comprehensiveness, the accuracy and the personalized adaptation capability of rehabilitation evaluation can be improved.

The invention relates to the technical field of intelligent fall detection, in particular to an old people fall detection and early warning system based on multi-modal data fusion, which comprises a dynamic weight fusion unit, a precursor trigger unit, a multi-level decision unit and a verification feedback unit, the dynamic weight fusion unit collects three-axis acceleration, surface electromyographic signals and millimeter wave radar data, multi-source data association analysis is achieved through a double-variable attenuation model and dynamic threshold calibration, and the precursor trigger unit activates electromyographic high-frequency sampling and joint three-dimensional space included angle monitoring when the exercise intensity is abnormal. And the multi-level decision-making unit constructs a tumble probability algorithm based on a random forest model, and performs three-level verification in combination with acceleration standard deviation, ground contact point density and historical mode matching, thereby realizing accurate discrimination of tumble events, providing a high-reliability intelligent solution for safety protection of old people, reducing the risk of tumble injury, and improving the safety of the old people. And the first-aid response efficiency is improved.

The invention provides an intelligent wearable rehabilitation monitoring and evaluation device after anterior cruciate ligament injury, and relates to the technical field of exercise rehabilitation and intelligent medical monitoring, the device comprises a sensor acquisition module, a signal processing module, a compensation determination module, a difference determination module and an evaluation analysis module, a force-electricity coupling flexible sensor, an IMU and a pressure sensor, the method comprises the following steps: synchronously collecting a muscle force signal, a surface electromyogram signal, an attitude signal and a plantar pressure signal at the same position, performing dynamic filtering, confidence enhancement and personalized compensation processing, extracting a difference feature vector, inputting the difference feature vector into a muscle force prediction model, and outputting a quantitative evaluation result. The portable wearable long-term dynamic monitoring is realized, the core indexes of muscles, joints and gaits are covered in multiple dimensions, the assessment is accurate and real-time, the rehabilitation scheme can be optimized, the risk of re-injury is reduced, and the rehabilitation effect and compliance of a patient are improved.

A wearable device includes an accelerometer, at least three electrodes to detect electromyography signal, a communication circuitry, and a processor circuitry. A user wears a primary and a secondary wearable device on two limbs. When the user performs a two-hand gesture, the wearable devices detect a primary and a secondary movement pattern. The primary device recognizes the two-hand gesture from the primary and secondary movement pattern.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention relates to the technical field of electric acupuncture apparatuses, and discloses a method and a system for automatically adjusting stimulation parameters of an electric acupuncture apparatus. According to the method, body surface electromyographic signals, skin impedance and capillary hemodynamic parameters of a user are collected in real time through a sensor set, feature extraction and analysis are conducted through a time sequence prediction model of a Transform architecture, and physiological feature data are generated. And then, constructing a user physiological state characterization model by adopting a multi-modal neural perception fusion algorithm, and carrying out collaborative optimization on the electrical stimulation parameters through an adaptive quantum particle swarm optimization algorithm to generate a stimulation parameter combination matched with the real-time neuromuscular response characteristics of the user. The system drives electrical stimulation output according to the parameter combination, monitors physiological data changes in real time, realizes closed-loop dynamic balance of stimulation parameters and biological feedback signals through the dynamic parameter compensation module, and improves the treatment effect and safety.

The invention relates to a sports injury real-time early warning and health optimization system based on multi-mode infrared thermal imaging and three-dimensional skeleton posture fusion, and belongs to the field of sports health management. The problems of motion analysis lagging, damage identification missing and cross-scene data splitting of a traditional monitoring means are solved. According to the technical scheme, the system comprises a multi-modal motion data sensing module which synchronously collects human thermodynamic distribution, three-dimensional skeleton postures and surface electromyogram signals through an infrared thermal imaging camera, an RGB-D camera and a bioelectric sensor; an AI driving training optimization engine is used for generating an adjustment instruction in real time based on a muscle thermal gradient and joint trajectory deviation degree dual-threshold model; and the full-scene intelligent management platform executes millisecond damage early warning, metabolic optimization and school-family health data closed-loop management. The technical effects are that the exercise safety guarantee is fundamentally improved, the training is scientifically optimized, the metabolism management accuracy is broken through, and the teaching resource allocation is intensive.

A muscle probe is provided for obtaining electromyography data and optical spectroscopy data from muscle tissue. The muscle probe comprises an elongate needle having an outer wall surrounding a needle interior, the needle interior comprising: a core electromyography electrode; and one or more optical fibres; wherein the needle is arranged to be inserted into a muscle, and further arranged to detect electrical activity from the muscle; and wherein the one or more optical fibres are arranged to direct incident light from a light source toward a target area of the muscle, and further arranged to receive scattered light from the target area. The present disclosure aims to provide a muscle probe to improve the diagnostic pathway for patients with neuromuscular disorders, by developing a minimally invasive bedside test of muscle health.

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 relates to the field of medical instruments, in particular to a percutaneous microflow acupuncture system closed-loop regulation and control method and system based on multi-channel acupoint impedance, and the method comprises the steps: obtaining a first surface electromyogram signal in a human body action period, and constructing a relation between an action behavior and surface electromyogram; determining a multi-channel implementation path of the percutaneous microflow acupuncture system according to the relationship; impedance characteristics of acupuncture points along the multi-channel implementation path are obtained, and percutaneous microflow electrical stimulation operation is set according to the impedance characteristics; acquiring a second surface electromyogram signal during the implementation of the electrical stimulation operation, and adjusting the electrical stimulation operation according to the first surface electromyogram signal and the second surface electromyogram signal; and acquiring a response signal for adjusting the multi-channel implementation path association region during the implementation of the electrical stimulation operation so as to change the multi-channel electrical stimulation mode. According to the invention, path calibration of multiple acupoint associated stimulation areas is implemented through multiple channels, feedback closed-loop stimulation regulation is formed by combining acupoint impedance and electromyographic signals formed during stimulation, and the stimulation efficiency is improved.

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.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention provides a throwing action real-time guidance method based on causal inference and self-adaption, and the method comprises the steps: synchronously collecting the kinematics and electromyography data of an athlete through a multi-mode sensor, constructing and applying a dynamic causal graph model which integrates biomechanical priori knowledge and online self-adaption capability, processing the data in real time to generate deep causal enhancement state representation; and based on the model, performing autoregressive pre-judgment on the future motion trajectory of the athlete, and performing quantitative evaluation on the pre-judged trajectory by using a multi-objective cost function integrating athletic performance, injury risk and technical style. When the evaluation cost exceeds an intervention threshold value, a risk source is traced back to determine a kinematic error, and an error correction instruction is generated according to a preset error-instruction mapping relation; the fatigue state of an athlete is monitored in real time, model parameters are finely adjusted on line, a correction instruction is transmitted to the athlete in real time, and prospective and personalized intelligent guidance is achieved.

The invention discloses a sign language-voice conversion system, and belongs to the technical field of auxiliary communication and wearable computing. The system comprises a wearable myoelectricity acquisition module used for acquiring double-arm myoelectricity signals when a user executes sign language; the mobile terminal module is wirelessly connected with the acquisition module and is used for receiving and preprocessing the signal and uploading the signal; the cloud processing module is used for receiving the signal, converting the signal into text information through a sign language recognition model, and further calling a voice synthesis service to convert the text into voice data; and the wearable audio output module is used for receiving and playing the voice data. Through an innovative end-to-end hardware system architecture, natural, accurate and real-time translation and voice output of sign language gestures are realized, communication barriers between hearing-impaired people and healthy hearing people are effectively solved, and the system has the advantages of flexible deployment, user friendliness and privacy protection.

The invention provides a multi-mode man-machine interaction chip based on an adaptive threshold pulse neural network. The chip comprises a sensor module used for collecting input data of three modes of a visual signal, a pressure signal and a surface electromyography sEMG signal in real time; the acquisition module is used for performing analog-to-digital conversion and preprocessing on the pressure signal and the surface electromyography sEMG signal to generate a feature tensor; the recognition module comprises three adaptive thresholds SNN converted by a convolutional neural network CNN, respectively processes feature tensors of three modals, performs cross-modal feature fusion, and outputs a motion intention of the robot; the self-adaptive threshold SNN reduces the power consumption while maintaining the calculation precision by dynamically adjusting the spiking neuron membrane potential threshold. According to the method, feature fusion is carried out through input data of three modes in combination with the self-adaptive threshold SNN algorithm, the motion intention of an operator and the environment interaction state can be comprehensively captured, the accuracy and adaptability of robot motion generation are remarkably improved, and the naturalness and reliability of man-machine cooperation are enhanced.

The invention discloses a lower limb exoskeleton robot layered cooperative walking aid control method. The method comprises the steps of data acquisition and preprocessing; human-machine coupling dynamics modeling is carried out, and required moments tau < hip >, tau < knee > and tau < ankle > of the hip joint, the knee joint and the ankle joint are obtained; low-layer joint control, including hip joint moment, knee / ankle virtual impedance moment and friction compensation calculation; middle-layer multi-axis cooperation and gait phase judgment; high-rise energy is optimally distributed, assistance is distributed to three joints in a phase and a safety boundary, meanwhile, the requirements for labor saving and stability are considered, a minimization problem is written in a unified mode, J serves as the total amount, and torque of the hip joint, torque of the knee joint and torque of the ankle joint are solved in a combined mode; according to the step-by-step self-adaption of myoelectricity driving, in order to cope with human body fatigue changes, step-by-step updating is carried out on the virtual stiffness within the movement distance. The method has the advantages of improving man-machine cooperation level, man-machine adaptability and walking aid efficiency.

A device for functional electrical stimulation (FES), neuromuscular electrical stimulation (NMES), and / or in receiving electromyography (EMG) signals includes a sleeve and electrodes. The sleeve is sized and shaped to be worn on a human arm, and comprises a stretchable fabric The sleeve has a distal end disposed on or adjacent a wrist of the human arm when the sleeve is worn on the human arm and a proximal end opposite from the distal end. The electrodes are secured with the sleeve and positioned to contact skin of the human arm when the sleeve is worn on the human arm. The sleeve may include an inner sleeve contact with the skin and an outer sleeve disposed over the inner sleeve. The inner sleeve has openings in which the electrodes are disposed.

The invention discloses a man-machine interaction method and device based on gesture recognition, and relates to the technical field of artificial intelligence. Comprising the steps that standard gesture actions that the thumb pinches different knuckle areas of the palm are bound with input operation instructions, each finger is divided into a fingertip area, a finger pulp area and a finger section area, and the gesture actions that the thumb pinches nine knuckle areas of three fingers are respectively input to the finger section area; the trigger module is used for triggering nine input character keys in the virtual input panel; in the process that the current user operates the virtual input panel, real-time electromyographic signals generated when the hand of the current user is kneaded are collected through an electromyographic sensor, and a first gesture action corresponding to hand kneading of the current user is recognized based on knuckle kneading signal features of the real-time electromyographic signals; and executing an input operation instruction bound with the first gesture action. According to the application, the use limitation of the gesture recognition scheme can be reduced, and the applicable crowd range is expanded.

The application provides a real-time closed-loop adaptive airway pressure regulating system. The system comprises a respirator mask and a host computer; the respirator mask is provided with an airflow monitoring module, a pressure monitoring module, a snore monitoring module, a lower jaw electromyography monitoring module, a forehead transcutaneous carbon dioxide sensing module and a preprocessing module; the host computer is internally provided with an intelligent module; the preprocessing module pre-processes airflow velocity, pressure data, airway vibration signals, electrical signals corresponding to lower jaw and pharyngeal muscle activity and carbon dioxide content measured by the airflow monitoring module, the pressure monitoring module, the snore monitoring module, the lower jaw electromyography monitoring module and the forehead transcutaneous carbon dioxide sensing module in real time; the intelligent module identifies a risk window period of airway collapse of a user in real time according to the pre-processed data and performs adaptive pressure boosting control on the pressure in the airway of the patient after identification. The application reduces the misjudgment rate of newly occurring CSA events of complex sleep apnea after OSA relief.

The invention discloses a postoperative pain degree sensor and an ovum extraction operation pain monitoring system, and belongs to the field of postoperative health monitoring and intelligent nursing equipment. The sensor comprises a flexible substrate, and a skin electric response sensing unit, a temperature sensing unit, a myoelectricity sensing unit, a micro-pressure sensing unit, a signal processing circuit and a wireless communication module which are integrated on the flexible substrate, the monitoring system comprises an edge processing module, a pain index evaluation module, an interaction terminal and a data recording module. The system evaluates the postoperative pain level based on the multi-channel physiological signals, real-time quantification and alarm prompt of the pain state are achieved, and the postoperative nursing response efficiency is improved. The scheme is suitable for assisting pain management after reproductive ovum extraction, and has the advantages of high sensitivity, low delay, structural flexibility and system closed loop.

The invention provides a nerve regulation and control system and method for a spinal cord ventral epidural region, and the system employs a microprocessor to generate multiple paths of pulse width modulation signals, and drives a voltage-controlled current source after amplitude adjustment. Stimulation current with adjustable frequency, pulse width and amplitude is output to act on a spinal cord ventral nerve structure. Real-time monitoring of stimulation current of each channel is realized through combination of voltage sampling and a single-pole multi-throw analog switch, feedback information of a body position, myoelectricity, pressure and other multi-mode sensors is fused, a closed-loop control mechanism is constructed, and dynamic adjustment and individualized optimization of electrical stimulation parameters are realized. The system has the advantages of being more accurate in electrical stimulation positioning, more sensitive in response, more intelligent in control and the like, and is suitable for intelligent rehabilitation intervention in scenes of spinal cord injury, cerebral palsy, neurological dysfunction and the like.

The invention discloses an obstructive sleep apnea event typing method and device based on respiratory center driving characteristics, electronic equipment and a computer readable storage medium, and belongs to the technical field of medical detection technologies and sleep medicine. According to the method, body surface diaphragm electromyographic signals and respiratory airflow signals of a subject are synchronously collected in a non-invasive mode, the body surface diaphragm electromyographic signals are preprocessed to obtain diaphragm electromyographic characteristic values, and the respiratory airflow signals are segmented and subjected to integral processing according to the respiratory cycle to obtain airflow characteristic values; selecting a characteristic value corresponding to at least one respiratory cycle before the event occurs as a reference baseline, calculating a diaphragm myoelectricity change value and an airflow change value of each respiratory cycle in the event relative to the reference baseline, and calculating the diaphragm myoelectricity change value based on a coupling relationship between the airflow change value and the diaphragm myoelectricity change value, and performing central drive dependent or non-central drive dependent judgment on the obstructive sleep apnea event. The method can provide technical support for typing analysis of the obstructive sleep apnea event.

The invention discloses an orthopedic surgery navigation drilling and implant placement intention real-time optimization method based on multi-modal perception, and relates to the technical field of computer-aided surgery, man-machine interaction and intelligent medical equipment. Multi-modal signals such as electroencephalogram, eye movement and myoelectricity of a doctor are collected in real time through XR glasses and an intelligent surgical drill; and inputting the pre-trained micro-world model to generate a current intention vector and predict a future bone structure trajectory. The intention level is judged by calculating the energy distance between the intention and the master template and an emotional value function: when the intention meets the master level condition, the system automatically generates an XR green optimal path and assists in operation; and when the intention is identified as the risk intention, triggering the skykeeper system to lock the equipment and send out an alarm. The system supports a data federation learning evolutionary model, and immediately destroys an original multi-mode signal after encoding to guarantee data security. According to the invention, the real-time monitoring, evaluation and guidance of the operation intention are realized, and the accuracy and safety of the operation are improved.

The invention discloses a lower limb muscle force deep learning model evaluation method based on high-density electromyographic signals. The method comprises the steps that S1, the lower limb electromyographic signals of a human body are obtained through wearable monitoring equipment and converted into discharge time sequence data; s2, a deep learning hybrid model containing a convolutional neural network CNN and a recurrent neural network LSTM is constructed, a lower limb muscle strength sample database is constructed, the lower limb muscle strength sample database comprises lower limb muscle strength sample data and corresponding lower limb muscle strength label data, and the deep learning hybrid model performs model training by using the lower limb muscle strength sample database; and S3, obtaining discharge time sequence data of a to-be-detected human body, inputting the trained deep learning hybrid model, and outputting lower limb muscle force data of the to-be-detected human body. The lower limb muscle strength data and the lower limb muscle strength level can be accurately predicted and evaluated by acquiring the lower limb muscle electric signals in a contact non-destructive mode, and level-stage quantitative evaluation can be conveniently carried out in clinical diagnosis and rehabilitation training.

The invention discloses an upper limb exoskeleton control method which comprises the steps that a monocular vision sensor is fixed to the wrist of a user, a lens faces the trunk of the user, and spatial position information of the wrist is collected; reversely calculating the pose of the sensor according to the feature points at the wrist by using an EPnP algorithm to obtain a rotation matrix and a translation vector; synchronously collecting surface electromyogram signals, and predicting intention through feature extraction and a classification model; performing time alignment on the visual data and the myoelectricity data, performing multi-modal fusion, and predicting the tail end position and speed at a future moment; mapping the three-dimensional motion to a two-dimensional plane, simplifying the three-dimensional motion into a two-link inverse kinematics problem, and analyzing to obtain a joint angle and an angular velocity; and the torque of the exoskeleton motor is adjusted in real time according to the joint angle, the angular speed and the electromyographic activation intensity. According to the upper limb exoskeleton control method, the vision sensor is reversely installed to the wrist and faces the body, and the problem that the monocular vision scale is uncertain is solved in the mode that monocular vision is added with kinematics constraint.

The invention relates to the technical field of brain-computer interfaces, in particular to a wearable helmet brain-computer control system and a wearable helmet brain-computer control method. The wearable helmet brain-computer control system comprises a multi-mode sensor assembly, an inertial measurement unit, an inertial measurement unit, an inertial measurement unit and a brain-computer interface control module, and the multi-mode sensor assembly is used for synchronously collecting electroencephalogram, electrocardiogram and electromyogram physiological signals and head movement data of the inertial measurement unit; the motion artifact removing module is used for predicting motion artifacts through a Hammerstein-Wiener model on the basis of IMU (Inertial Measurement Unit) data, separating artifact components in mixed electroencephalogram signals by combining an independent component analysis model, and reconstructing pure electroencephalogram signals; the central processor is used for carrying out feature extraction and decoding on the pure electroencephalogram signal and generating a control instruction; the control instruction interface is used for outputting the control instruction to external equipment; the storage module is used for storing an original signal and a processing result; the wireless transmission module is used for realizing data interaction with a terminal; the core problems that a traditional device is poor in signal quality and unstable in control in a dynamic environment are effectively solved.