1289 results about "Myoelectric signal" patented technology

Improved assemblies, systems, and methods provide an MES processor for acquisition and processing of myoelectric signals from muscles, nerves, or central nervous system tissue, or any combination. The MES processor is sized and configured to be implanted subcutaneous a tissue region. The MES processor includes an electrically conductive case of a laser welded titanium material. Control circuitry is located within the case, the control circuitry including a rechargeable power source, a receive coil for receiving an RF magnetic field to recharge the power source, and a microcontroller for control of the MES processor. Improved assemblies, systems, and methods also provide an acquisition and processing system for sensing myoelectric signals from muscles, nerves, or central nervous system tissue, or any combination. The acquisition and processing system provides at least one electrically conductive surface, a lead connected to the electrically conductive surface, and an MES processor electrically connected to the lead.

A model EMG signal is produced from a measured EMG signal that includes a patient's EMG signal and ECG signal by processing the measured EMG signal to produce a logic signal that is in a first binary state in the absence of a P wave, a QRS complex and a T wave of an ECG cycle of the measured EMG signal and that is in a second binary state during at least one of the P wave, the QRS complex and the T wave of the ECG cycle. The measured EMG signal is processed to produce a first envelope signal. The model EMG signal is produced as a function of the first envelope signal when the logic signal is in the first binary state and the absence of the first envelope signal when the logic signal is in the second binary state.

[Problem to be Solved]The problem to be solved by the present invention is to reduce the load applied to the wearer by correcting a parameter in correspondence with detectivity of biosignals.[Means to Solve Problem]The calibration controlling part 162 of the movement assisting apparatus 10 enables the power amplifying part 158 to apply a driving force of the driving source 140 as a load (input torque) from the load generating part 164 to the wearer 12 when the wearer 12 wears the movement assisting wearing device. Then, the wearer 12 applied with the driving force of the driving source 140 generates power from the skeletal muscles by performing a predetermined calibration operation. Accordingly, the physical phenomenon detecting part 142 detects joint angle along with the calibration operation, and the biosignal detecting part 144 detects myoelectric signals. In the parameter correction part 156, a parameter K is corrected based on the difference between the load (input torque) and the driving force (muscular strength) being calculated by the difference deriving part 154 with respect to the phase identified by the phase identifying part 152.

The invention provides a five-degree of freedom exoskeleton-type upper limb recovery robot interactive recovery training control strategy. The strategy comprises two training patterns corresponding to the different recovery period of a patient, i.e. a passive interactive recovery training control strategy and an active-auxiliary interactive recovery training control strategy; during passive movement, a surface myoelectric signal of relevant muscles on a healthy upper limb of a patient is picked up and taken as the movement intention of the patient, thereby controlling a robot to drive a diseased side to realize movement passive training; during active movement, the movement intention of an upper limb of a human body is judged through acquiring in real time the arthrosis force moment generated by a diseased limb acting on the robot during movement, and an applied force is converted into the velocity quantity of the tail end of a mechanical arm by means of a proportional controller, thereby driving the robot to follow the intention so as to carry out recovery active-auxiliary training of the diseased limb. The five-degree of freedom exoskeleton-type upper limb recovery robot interactive recovery training control strategy can provide all-around recovery training movement for a clinic hemiplegia patient, thereby increasing the activeness of the patient in recovery training and the confidence of the patient in recovery; meanwhile, the recovery training control strategy also increases the attractiveness of a recovery process and promotes recovery efficacy.

The invention discloses a myoelectricity feedback based upper limb training method and system. The myoelectricity feedback based upper limb training system orderly combines signal acquisition, mode identification, biological feedback and real-time fatigue evaluation, thereby being capable of helping a user train the movement function of the upper limb. The upper limb training method includes steps of acquiring movement signals and myoelectric signals of the upper limb joint of the user when the user acts correspondingly such as forearm rotation and wrist bending / stretching as completing virtual game tasks, adjusting movement parameters of virtual control targets on the basis of the movement signals acquired by the users, identifying several muscle contraction modes of the user according to the characteristic parameters of the myoelectric signals, and utilizing the identified modes as selection basis for a myoelectric feedback control method. In addition, the upper limb training system is capable of extracting fatigue characteristics in real time according to the acquired myoelectric signals, analyzing fatigue states of muscles so as to judge muscle fatigue and send an alarm during the whole interaction system.

The invention discloses a wearable hand function rehabilitation robot, which is mainly used for assisting the repeated movement function rehabilitation training of the patient with hand movement function disorder which is caused by stroke, brain trauma, spinal cord injury and peripheral nerve injury in communities or families. The robot system extracts the active movement will of the patient by detecting the multi-channel surface myoelectric signals of the affected hand and obtains the state of the affected limb by combining the data which is measured by an angle and force sensor to carry out the rehabilitation training of the affected hand by pneumatic muscle contraction assistance by using the intelligent control algorithm on the basis. The rehabilitation robot has multiple degrees of freedom, which can assist the affected hand to carry out multi-joint complex movement and inosculate the multi-sensor data information fusion during the rehabilitation process to be further used for the evaluation of rehabilitation effect, and the activity and the training interest of the patient can be improved by using the rehabilitation treatment virtual environment on a computer. The invention has the advantages of simple structure, flexible movement, safety and reliability, which can not only realize the rehabilitation training of the movement function of the affected hand, but can also be in line with the physiologic structure characteristics of human hands. The invention is more comfortable to wear.

The invention discloses an upper limb exoskeleton rehabilitation robot control method based on a radial basis neural network. The method includes the steps that a human body upper limb musculoskeletal model is established; upper limb muscle myoelectric signals and upper limb movement data are collected, the movement data are imported into the upper limb musculoskeletal model, upper limb joint torque is obtained, the radial basis neural network is established and a neural network model is given out; the patient movement intention is recognized, the joint angular speed is subjected to fusion analysis, the result is used for recognizing the training object joint stretching state, and the limb movement intention is determined; and myoelectric signals and joint angles in affected side rehabilitation training are collected in real time, affected side joint torque is obtained through the neural network, joint torque needing to be compensated by an exoskeleton mechanical arm is calculated, myoelectric signal fatigue characteristics are analyzed, the compensation torque magnitude can be adjusted by classifying the degree of fatigue, and a torque controller can be controlled to achieve the effect that an upper limb rehabilitation robot assists patients in rehabilitation training by combining the movement intention. By means of the upper limb exoskeleton rehabilitation robot control method, the rehabilitation training process is made to be more suitable for the patients, man-machine interaction is strengthened, and the rehabilitation effect is improved.

The invention relates to a walk-aiding exoskeleton robot system and a control method, which belong to the technical field of rehabilitation engineering. The system comprises a hanging support, a moving platform, joints, protecting sleeves, a sensor module, a signal acquisition module, a central processing module and a motion control module, wherein the hanging support is fixed on the moving platform, the joints are connected with the hanging support to form an exoskeleton robot, the sensor module, the signal acquisition module, the central processing module and the motion control module are sequentially connected, the sensor module is used for acquiring joint angles, the interacting force of the exoskeleton robot and the human being and the myoelectric signals of the muscles of the human body, the signal acquisition module carries out signal conditioning and digital-to-analog conversion, the central processing module carries out action generation and the reverse solution of motion, and transmits an action command to the motion control module, and the motion control module is connected with the exoskeleton robot and generates a pulse signal to control the coordinated motion of the exoskeleton robot. The invention realizes the synchronous motion of the exoskeleton robot and the human body and real-time active control.

The invention relates to an artificial hand controlled by myoelectric and brain, comprising myoelectric and brain electrode, myoelectric and brain signal treatment module, A / D conversion data collection, myoelectric signal moving module identification and track prediction module, electrical artificial hand, touch and feel sensor, systematic feedback stimulation device, force and speed moderating module. The controlling method comprises following steps: collecting and enlarging myoelectric signal; collecting characteristic and identifying module; checking caught object condition, if the objection is not caught, a certain kind of physical stimulation signal will be sent to operator; checking brain information; outputting information to force and speed moderating module; output information controlling the electrical artificial hand. The invention overcomes limit of only taking myoelectric signal as signal source.

The invention discloses an electromyographic signal gesture recognition method based on a hidden markov model. The method comprises the following steps of: executing smoothing filtering for electromyographic signals; extracting a multi-feature feature set for each window data through a sliding window, and executing normalization and feature dimension reduction of minimum redundancy maximum correlation criterion for feature vectors; designing three classes of hidden markov model classifiers, and optimizing parameters of the hidden markov model classifiers; obtaining classifier models through training with hidden markov classifier model parameters and training data; inputting test data into the models trained well, and according to likelihood output by each class of hidden markov model, determining that the class corresponding to the maximum likelihood is the recognized class. According to the method provided by the invention, three classes of common hidden markov model classifiers are recognized based on a new feature set. By application of a classification method based on the hidden markov model, different gestures of the same testee can be recognized accurately, and gestures of different testees can be relatively recognized accurately.

The invention discloses an electromyographic signal gesture recognition method based on deep learning and attention mechanisms. The method comprises the following steps: performing noise reduction filtering on electromyographic signals; extracting one classic characteristic set from each wind datum by using a sliding window, and establishing a new electromyographic image based on characteristics;designing a deep learning frame based on a convolutional neural network, a circulation neural network and an attention mechanisms, and optimizing network structure parameters of the deep learning frame; performing training with the designed deep learning frame and the training data so as to obtain a classifier model; inputting testing data into the trained deep learning network model, and according to likelihood of a last layer of output, maximally likelihooding corresponding types, that is, recognition types. By adopting the method, electromyographic gesture signals can be recognized on the basis of new characteristic images and deep learning frames based on attention mechanisms. By adopting the electromyographic signal gesture recognition method based on deep learning and attention mechanisms, multiple different gestures of a same subject can be accurately recognized.

A system for passing myoelectric signals through a suspension liner of a prosthetic device, the suspension liner having an inner surface that is in contact with a user's skin and an outer surface that is adjacent to an outer socket of the prosthetic device. The system in one embodiment includes a flexible conductive electrode insert defining a first portion located on or adjacent the inner surface of the suspension liner such that it touches the user's skin, a second portion passing through the suspension liner, and a third portion on the outer surface of the suspension liner, and an adhesive that adheres at least the second and third portions of the insert to the suspension liner.

Provided are a stroke patient rehabilitation training system and method based on brain myoelectricity and a virtual scene. Control over the virtual rehabilitation scene is achieved through myoelectric signals, and rehabilitation training intensity is adjusted in a self-adaptation mode with a brain myoelectricity fatigue index combined. The design of the virtual rehabilitation scene is completed with the needs of stroke patient rehabilitation training and the advice of a rehabilitation physician combined, the brain fatigue index is provided, and quantitative evaluation on brain region fatigue is achieved. The surface myoelectric signal features under different motion modes of an arm are extracted, the motion intention of a patient is obtained, and control over the virtual rehabilitation scene is achieved. The muscle fatigue and brain fatigue index comprehensive features are extracted, the fatigue state of a rehabilitation patient is obtained, self-adaptation rehabilitation training scene adjusting is achieved, rehabilitation training intensity is relieved or enhanced, and secondary damage caused by improper training is avoided. The system and method have the advantages of being high in safety, high in intelligence and scientific in training, and damage cannot happen easily.

The invention discloses a myoelectric feedback control electric stimulation device and a control method thereof, relating to the field of medical instruments. The control method comprises the steps of: collecting a myoelectric signal of a tested object; transmitting the myoelectric signal to a myoelectric signal amplifying unit; converting the myoelectric signal into a digital myoelectric signal; transmitting the digital myoelectric signal to a signal processing and controlling unit; calculating the myoelectric amplitude of the digital myoelectric signal; obtaining the myoelectric amplitude; and judging whether the myoelectric amplitude equals to the optimal myoelectric amplitude; if yes, not changing a stimulation mode, generating a stimulation waveform of the stimulation mode by an electric stimulation generating unit, and carrying out stimulant therapy on the tested object through stimulating electrodes; and if not, adjusting the stimulation mode by the electric stimulation generating unit and the signal processing and controlling unit, using the adjusted stimulation mode as a stimulation mode for the next time, generating a stimulation waveform of the stimulation mode, and carrying out stimulant therapy on the tested object through the stimulating electrodes. According to the myoelectric feedback control electric stimulation device and the control method thereof, the purpose of achieving the optimal stimulation effect in different stimulation modes is realized through adjustment of the stimulation mode.

The invention relates to a detecting and training method of EMG controllability which is used for installing a pattern-recognition-based artificial hand with EMG control and hyperdactylia multiple degrees of freedom. The technical proposal of the method comprises procedures as follows: a signal acquisition module is used for collecting the myoelectric signal data of the surface of a stump; a signal analysis module is used for carrying out preliminary analysis for the collected data to determine that the collected myoelectric signal data of the surface of the stump has separability and the feature value of the optimal separability; an off-line recognition module is used for performing network training and recognizing the data to be recognized to the collected myoelectric signal data of the surface of the stump to determine an optimum classifier, the options of the optimal feature values and the selection of specific parameters; and a sufferer can do control training and on-line training by using an on-line recognition module and a control training module according to the determined combination of the optimal classifier and the options of feature values.

A feedback-control wearable upper-limb electrical stimulation device comprises a plurality of electrical stimulation electrodes (10), a plurality of myoelectric signal sensing elements (20), an electrical stimulation output unit (30), a myoelectric signal retrieval unit (35), a myoelectric signal operation unit (40), and a control module (50). Each electrical stimulation electrode (10) is adhered to or fixed in contact with a human trunk and applies an electrical stimulation signal to the neuromuscular system of the human trunk. Each myoelectric signal sensing element (20) is adhered to or fixed in contact with the human trunk at a corresponding position of the neuromuscular system of the human trunk where each electrical stimulation electrode (10) is disposed in an adhered manner. The electrical stimulation output unit (30) is connected to each electrical stimulation electrode (10) and provides an electrical stimulation signal. The myoelectric signal retrieval unit (35) is connected to each myoelectric signal sensing element (20) and receives a myoelectric signal. The myoelectric signal operation unit (40) is connected to the myoelectric signal retrieval unit (35). The control module (50) is electrically connected to the electrical stimulation output unit (30) and the myoelectric signal operation unit (40). The device first determines, according to the intensity of a myoelectric signal of a human trunk, the intensity of an electrical stimulation signal required for the human trunk to perform a specified action and gives a patient suitable assistance. Therefore, a local disabled limb of a patient can be effectively activated and a patient can be effectively exercised in controlling a diseased limb.

The invention discloses a sitting and lying type lower limb rehabilitation robot, which can respectively carry out a passive training, an assisted training or an active training according to the damage degree or the rehabilitation stage of a patient. The robot comprises a seat, a mechanical arm, a main industrial control box, a man-machine interaction interface, an electrical stimulation handheld switch, an electrical stimulation electrode plate, an electromyographic signal acquisition electrode plate, a functional electrical stimulation instrument and an electromyographic signal acquisition industrial control box. During the passive training, the lower limb of the patient is trained according to a set movement locus; during the assisted training, the main muscle group of the lower limb of the patient is applied with electrical stimulation pulse; according to the movement locus of a tail end, the electrical stimulation pulse is subjected to sequential control to finish the assisted training; during the active training, the electromyographic signal of the corresponding muscle of the patient is collected; and according to different control algorithms, the patient drives robot to realize the active training. According to the sitting and lying type lower limb rehabilitation robot disclosed by the invention, the traditional physical therapy, occupational therapy and kinesitherapy are organically combined, so that the patient rehabilitation effect can be effectively improved, and the desire of the patient to actively participate is enhanced.

The invention provides a rehabilitation robot control method based on electromyography feedback type impedance self-adaption. The method comprises the steps of identifying the joint extension and flexion states of a training object based on electromyography signal characteristics values, plantar pressure signals and angular velocity signals, determining limb movement intention, and giving electromyography signal characteristic quantities used for describing the active level of the muscles of an affected side by using a method of affected side-mirroring-unaffected side; setting a target impedance equation, describing the function relation between extremity movement track differences and extremity stress, and establishing an impedance function capable of self-adaptive adjustment according to the active level of the muscles of the affected side and joint angles; according to initial anticipated static balance force, analyzing electromyography signals to obtain fatigue degree levels, and finely adjusting the anticipated static balance force. Self-adaptive tracking of anticipated tracks of a lower limb rehabilitation robot is realized by using a position controller. By establishing the self-adaptive adjustment impedance function and adjusting the static balance force by level, rehabilitation training processes can adapt to individuals and control processes are more natural, smooth and reliable and safer.

A method and apparatus for conducting electromyography of a deep muscle non-invasively are provided. An array of suitable surface electromyography electrodes is arranged in one or more rings encircling a part of the human body in which a deep muscle being investigated is located. The potential of at least selected electrodes relative to another electrode selected from a common reference electrode (mono-polar) and other electrodes (bi-polar) in the array is recorded and the data is processed in respect of the recorded potentials of at least some of said selected electrodes in order to determine (optionally using approximations or algorithms, or both) the contribution being made by at least the deep muscle being investigated. Typically, this is done mathematically by resolving the electromyography signals into their constituent components using a suitable technique. Preferably, this is integrated with a static muscle imaging device that may use the same electrodes to obtain a static tomogram of the muscles encircled.

The invention relates to an electromyographic signal gait recognition method for optimizing a support vector machine based on a genetic algorithm. According to the electromyographic signal gait recognition method, the penalty parameter and the kernel function parameter of the support vector machine are optimized with the genetic algorithm, the performance of the support vector machine is accordingly optimized, and the efficiency and the accuracy of the support vector machine for recognizing lower limb movement gaits based on electromyographic signals are improved. The electromyographic signal gait recognition method includes the steps of firstly, carrying out de-noising processing on the collected lower limb electromyographic signals with a wavelet modulus maximum de-noising method; secondly, extracting the time domain characteristics of the de-noised electromyographic signals to form characteristic samples; thirdly, optimizing parameters of the support vector machine with the genetic algorithm to obtain a set of optimal parameters with the minimum errors, and constructing a classifier through the parameters; finally, inputting a characteristic sample set into the optimized classifier for gait recognition. The electromyographic signal gait recognition method is easy to operate, rapid in calculation and high in recognition rate, and has the application value and the broad prospects in the human body lower limb gait recognition field.

The invention discloses a human body forearm surface electromyogram signal acquisition and pattern recognition system comprising an acquisition circuit, a PCI (programmable communication interface) data acquisition card and a signal processing and motion recognition unit, wherein the acquisition circuit is used for acquiring, filtering and amplifying a human body forearm surface electromyogram signal, the PCI data acquisition card is used for carrying out AD (analog-to-digital) sampling conversion on an acquired analog electromyogram signal to obtain a digital electromyogram signal, and the signal processing and motion recognition unit is used for processing electromyogram signals acquired from four muscles, namely brachioradial muscle, extensor carpi radialis longus, musculus extensor carpi ulnaris and musculus flexor carpi radialis of the forearm of the right hand of the human body, extracting the characteristics of the electromyogram signals and recognizing six motions, namely making a fist by a wrist of the human body, stretching out the hands, turning the hands down, turning the hands up, turning the hands inward and turning the hands outward by combining a support vector machine. According to the invention, a surface electromyogram (SEMG) online mode pattern recognition study platform with low cost, good instantaneity and high recognition rate is realized.

The invention provides an upper limb rehabilitation exoskeleton control method based on lower limb gaits. The method comprises following steps: 1), data collection: electromyographic sensors and motion sensors located on lower limbs as well as pressure sensors located on soles of feet collect electromyographic signals, joint angles and sole contact force of different gait categories when a subject walks normally; 2), data pre-processing: the collected electromyographic signals, joint angles and sole contact force are pre-processed; 3), gait recognition; 4), upper limb swinging position matching: corresponding upper limb swinging positions are matched according to the recognized gait categories; 5), exoskeleton control: corresponding control commands are sent to an upper limb rehabilitation exoskeleton driver according to the obtained upper limb swinging positions for performing upper limb rehabilitation exoskeleton control, and the upper limbs are driven to perform rehabilitation training. According to the method, coordination of the upper limbs and the lower limbs is taken into consideration, and the rehabilitation effect is better.

The invention relates to a rehabilitation training system of amputation upper limb based on virtual reality, comprising the following parts: myoelectric signal detection and processing, amputation upper limb modeling and virtual reality scene interaction. The myoelectric signal detection and processing is realized by the steps of extracting, amplifying, filtering, A / D-transferring and multipath-acquiring myielectric signals on a stump by using a myoelectric tester ; extracting, extracting Rubust features of the myielectric signals and recognizing a fast and effective upper limb movement gesture together with an on-line learning method; the amputation upper limb modeling is realized by the steps of carrying out three-dimensional reconstruction on the amputation upper limb by adopting a three-dimensional parametric grid model and using a photo of a healthy upper limb, and realizing motion simulation of a virtual hand by taking the tracked upper limb movement parameters as model driven data; and the virtual reality scene interaction is realized by the steps of carrying out real three-dimensional interactional scene modeling and realizing real time interaction between the stump musclemovement and the three-dimensional scene through myoelectricity. The rehabilitation training system is mainly used to assist an upper limb amputated patient to carry out necessary adaptive training before installing an artificial limb so as to help the patient to adapt to the usage of the artificial limb as soon as possible.

The invention discloses an exoskeleton robot system and a kinematics extremity detection-based control method, and relates to an exoskeleton robot system and a control method to solve the problems that an accurate control effect cannot be achieved by the traditional electromyographic signal detection method for assisting in improving the walk of a human body, and man-machine interaction performance is low and an effective control method is lacked in the aspect of resisting self weight in the process of assisting in doing walk of the human body. The exoskeleton robot system comprises an upper body back, a left leg and a right leg; the left leg and the right leg respectively comprise a hip driving system, a knee driving system, a foot wearing system and a binding device; the exoskeleton robot system also comprises insole plates, a back detection shell, a nine-axis flight attitude measurement module, an industrial control computer, two thin film pressure sensors, two first angle sensors, two second angle sensors, two three-axis inclination sensors and three sets of three-dimensional contact force detection device. The exoskeleton robot system and the method are used for assisting in walking and performing detection and control of a moving posture of the human body.

The invention provides a brain electrical signal and physiological signal fused fatigue detection system. Brain electrical signals closely associated with metal state, more capable of reflecting activities of the brain itself and having higher time resolution are adopted, other physiological methods of electrocatdiogram, electrooculogram, electromyography, breath, blood oxygen and the like are infused, the task status can be monitored in real time, efficiency of the fatigue detection system is improved, and comprehensive and dynamic advantage combination of the system is fully realized. The brain electrical signal and physiological signal fused fatigue detection system comprises a signal acquisition unit used for acquiring human body signals, the signal acquisition unit comprises a brain electrical acquisition unit used for acquiring the brain electrical signals, an electrocatdiogram acquisition unit used for acquiring electrocatdiogram signals, an electromyography acquisition unit used for acquiring electromyography signals, a signal processing unit used for receiving and processing the human body signals and a fatigue calibration unit used for calibrating fatigue according to the signals processed through the signal processing unit.

The invention provides an upper limb rehabilitation system and method based on a myoelectric signal and a virtual reality interaction technology. The system comprises a myoelectric signal acquiring and processing part, a virtual reality man-machine interaction part and a muscle function evaluation part, wherein the myoelectric signal acquiring and processing part is composed of a data acquiring module, a signal processing module and a model control module; the virtual reality man-machine interaction part is composed of an upper computer virtual environment module and a force feedback device module; the muscle function evaluation part is composed of a muscular tension quantitative evaluation module and a muscle cooperativeness quantitative evaluation module. According to the rehabilitation method, myoelectric control is used so that a patient subjective intention can be reflected better; a patient keeps initiative in a rehabilitation process by using a virtual reality technology, and the portability, the safety and the effectiveness of rehabilitation trainings are improved. According to the upper limb rehabilitation system and method, an existing clinical rehabilitation evaluation manner can be combined and a muscle function state of the patient is objectively evaluated, so that rehabilitation training standards are provided for the patient and evidences for formulating a therapeutic scheme are provided for rehabilitation doctors.

The invention relates to a movement control system and method for a multi-shaft mechanical arm, the movement of the mechanical arm can be controlled by users through a natural gesture and an arm motion. According to the movement control system and method for the multi-shaft mechanical arm, the physical gesture is recognized and the arm posture is calculated and solved through detection of myoelectric signals and IMU motion information on the surface of the human arm; the mechanical arm is controlled to present the arm motion of the human body through establishment of an arm-mechanical arm physical relationship model; and the grabbing operation of a tail end actuator of the mechanical arm is controlled by the natural gesture. The movement control system and method for the multi-shaft mechanical arm can be widely used for the field of remote operations of the mechanical arm, the teaching and the like; and the intelligent control over a robot is realized, and the aims of human-computer interaction and intelligent interaction are achieved.