WO2021174705A1 - 一种多自由度肌电假手控制系统及其使用方法 - Google Patents

一种多自由度肌电假手控制系统及其使用方法 Download PDF

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Publication number
WO2021174705A1
WO2021174705A1 PCT/CN2020/094132 CN2020094132W WO2021174705A1 WO 2021174705 A1 WO2021174705 A1 WO 2021174705A1 CN 2020094132 W CN2020094132 W CN 2020094132W WO 2021174705 A1 WO2021174705 A1 WO 2021174705A1
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degree
wrist
freedom
gesture
circuit board
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PCT/CN2020/094132
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English (en)
French (fr)
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宋爱国
胡旭晖
卫智恺
李会军
徐宝国
曾洪
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东南大学
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Priority to US17/628,753 priority Critical patent/US20220355469A1/en
Publication of WO2021174705A1 publication Critical patent/WO2021174705A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A61F2/72—Bioelectric control, e.g. myoelectric
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K6/00—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00
    • B60K6/20—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs
    • B60K6/22—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs characterised by apparatus, components or means specially adapted for HEVs
    • B60K6/36—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs characterised by apparatus, components or means specially adapted for HEVs characterised by the transmission gearings
    • B60K6/365—Arrangement or mounting of plural diverse prime-movers for mutual or common propulsion, e.g. hybrid propulsion systems comprising electric motors and internal combustion engines ; Control systems therefor, i.e. systems controlling two or more prime movers, or controlling one of these prime movers and any of the transmission, drive or drive units Informative references: mechanical gearings with secondary electric drive F16H3/72; arrangements for handling mechanical energy structurally associated with the dynamo-electric machine H02K7/00; machines comprising structurally interrelated motor and generator parts H02K51/00; dynamo-electric machines not otherwise provided for in H02K see H02K99/00 the prime-movers consisting of electric motors and internal combustion engines, e.g. HEVs characterised by apparatus, components or means specially adapted for HEVs characterised by the transmission gearings with the gears having orbital motion
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • A61F2/58—Elbows; Wrists ; Other joints; Hands
    • A61F2/583—Hands; Wrist joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • A61F2/58—Elbows; Wrists ; Other joints; Hands
    • A61F2/583—Hands; Wrist joints
    • A61F2/585—Wrist joints
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/54—Artificial arms or hands or parts thereof
    • A61F2/58—Elbows; Wrists ; Other joints; Hands
    • A61F2/583—Hands; Wrist joints
    • A61F2/586—Fingers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2002/6836—Gears specially adapted therefor, e.g. reduction gears
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
    • A61F2/68—Operating or control means
    • A61F2/70—Operating or control means electrical
    • A61F2002/701—Operating or control means electrical operated by electrically controlled means, e.g. solenoids or torque motors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A61F2/50—Prostheses not implantable in the body
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    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/45—Nc applications
    • G05B2219/45172—Prosthesis

Definitions

  • the present invention relates to a prosthetic hand control system and a use method thereof, and more specifically, to a multi-degree-of-freedom myoelectric prosthetic hand control system and a use method thereof.
  • the multi-degree-of-freedom myoelectric prosthetic hand control system of the present invention includes a manipulator, a manipulator wrist, a residual limb receiving cavity and a data processor.
  • the manipulator and residual limb receiving cavity are respectively installed at both ends of the mechanical wrist, and the residual limb receiving cavity
  • a multi-channel EMG array electrode sleeve is connected, the multi-channel EMG array electrode sleeve is connected with a control unit circuit board and a battery, and the other end of the control unit circuit board is connected with a manipulator and a manipulator wrist.
  • the data processor sends an instruction to collect the surface EMG signal to the control unit circuit board, so that the multi-channel EMG array electrode sleeve collects the surface EMG signal, and performs neural network processing on the received data to generate a gesture prediction model.
  • the mechanical wrist includes a bevel gear set mechanism, a pulley transmission mechanism, a servo motor and a wrist support frame.
  • the bevel gear set mechanism uses four bevel gears to mesh with each other to form a cross-shaped arrangement.
  • the left and right bevel gears are installed on the wrist support.
  • the frame is respectively connected with transmission wheels.
  • the belt pulley transmission mechanism is connected to the transmission wheel and is connected with a servo motor.
  • the two horizontal gears in the bevel gear set mechanism are the sun gear, and the sun gear is connected to the transmission wheel through the driving shaft.
  • the sun gear is fixed on the driving shaft through the sun gear top wire, and the transmission wheel is fixed on the driving shaft through the driving shaft top wire.
  • the upper part of the vertical direction is the first planetary gear connected to the manipulator, and the lower part is the second planetary gear, the first planetary gear and A hollow passive shaft passes between the second planetary gears, and deep groove bearings are installed between the hollow passive shaft and the first planetary gear and the second planetary gear.
  • the wrist support frame is composed of a left panel, a right panel, a beam and a bottom plate. The upper end of the panel and the right panel are connected by beams, and the lower end is fixedly connected with the bottom plate.
  • the servo motor is installed on the left and right panels. The servo motor pulley and the transmission wheel are sleeved through a belt, and a pressure roller is fixed on the outside of the belt.
  • the method of using the multi-degree-of-freedom myoelectric prosthetic hand control system of the present invention includes the following steps:
  • the data processor receives the surface EMG signal and inputs it into the neural network algorithm to generate a gesture prediction model
  • the neural network algorithm in step S3 includes the following steps for data processing:
  • (S31) Preprocess the original surface EMG signal to extract the muscle activation signal, and then divide it with a fixed-length time window and use it as the input layer of the unsupervised neural network.
  • the first hidden layer of the network uses the principal component analysis method to compress the time -Spatial characteristics;
  • the second hidden layer uses an autoencoder to learn the muscle signal characteristics of 2N forearm muscles that cooperate with each other when completing different gestures, and generate continuous gesture labels based on the muscle coordination characteristics and the experimental action sequence, where 2N represents the 2N gestures to be recognized Degree of freedom, N is the number of antagonistic muscles in the forearm muscles participating in gesture movement;
  • the third hidden layer fits the muscle synergy feature with the continuous gesture label to generate a regression network.
  • the output layer of the regression network contains N neurons, which respectively output the continuous kinematics and dynamics of the antagonist muscles. Data, where different neurons represent different gestures, and the continuous data output by the neuron represents the strength of the gesture.
  • the present invention has significant advantages: 1. It can recognize continuous gestures and the strength of gestures; 2. It can make gestures with multiple degrees of freedom, and the artificial hand is more dexterous.
  • Figure 1 is a schematic diagram of the installation of the prosthetic hand of the present invention
  • Figure 2 is a schematic diagram of the wrist structure
  • Figure 3 is a schematic diagram of the structure of the bevel gear set mechanism
  • Figure 4 is a schematic diagram of the structure of the wrist support frame
  • Figure 5 is a side view of the wrist structure
  • Figure 6 is a block diagram of a hierarchical neural network
  • Figure 7 is a block diagram of the second hidden layer autoencoder of the gesture recognition algorithm
  • Figure 8 is a schematic diagram of a label self-generation method
  • Figure 9 is a flowchart of a gesture recognition algorithm
  • Figure 10 is a flow chart of the neural network.
  • the multi-degree-of-freedom electromyographic prosthetic hand control system is characterized by including a manipulator, a manipulator wrist 2, a residual limb receiving cavity 1 and a data processor 3.
  • the manipulator and residual limb receiving cavity are respectively installed in the mechanical
  • a multi-channel EMG array electrode sleeve is connected in the receiving cavity of the residual limb.
  • the multi-channel EMG array electrode sleeve is connected with a control unit circuit board and a battery.
  • the other end of the control unit circuit board is connected with the manipulator and the manipulator wrist.
  • the data processor 3 sends an instruction to collect the surface EMG signal to the control unit circuit board, so that the multi-channel EMG array electrode sleeve collects the surface EMG signal, and performs neural network processing on the received data to generate a gesture prediction model.
  • the mechanical wrist 2 includes a bevel gear set mechanism 4, a belt pulley transmission mechanism 5, a servo motor 7 and a wrist support frame 6.
  • the bevel gear set mechanism 4 uses four bevel gears to mesh with each other to form a cross-shaped arrangement.
  • the two horizontal gears are
  • the sun gear 15 is installed on the wrist support frame 6.
  • the sun gear 15 is connected to the driving wheel 10 through the driving shaft 9.
  • the sun gear 15 is fixed on the driving shaft 9 through the sun gear top wire 12, and the driving wheel 10 is passed through the driving shaft top wire 11
  • Fixed on the driving shaft 9, the upper part of the vertical direction is the first planetary gear 14 connected to the manipulator, the lower part is the second planetary gear 13, and a hollow driven shaft 8 passes between the first planetary gear 14 and the second planetary gear 13.
  • a deep groove bearing is installed between the hollow driven shaft 8 and the first planetary gear 14 and the second planetary gear 13.
  • the wrist support frame 6 is composed of a left panel, a right panel, a beam 16 and a bottom plate 17. The upper ends of the left and right panels are connected by the beam 18, and the lower end is fixedly connected with the bottom plate.
  • the servo motors 7 are installed on the left and right panels.
  • the belt wheel 19 and the transmission wheel 10 are sleeved through a belt 18.
  • a pressure roller 20 is fixed on the outside of the belt 21.
  • the flow of the gesture recognition algorithm is shown in Figure 9.
  • Each gesture needs to be completed three times in a row before making the next gesture.
  • the multi-channel EMG array electrode cuff collects EMG signals and stores them to the control unit circuit board and uploads them to the data processor 3, and inputs the neural network algorithm for processing.
  • the processing flow is shown in Figure 10.
  • the training set is processed by the three-layer network of the hierarchical neural network, as shown in Figure 6.
  • the original EMG signal of 8 channels is preprocessed, and the root mean square RMS average value is used to obtain the activation signal.
  • these 8 activation signals are divided by a fixed-length time window and used as the input layer of the neural network.
  • Each input sample will contain the spatial and temporal information of the array EMG signal.
  • the first hidden layer of the network uses principal component analysis Method to reduce the dimensionality of the input signal.
  • the second hidden layer uses an autoencoder to learn six muscle coordination features to further reduce the feature dimensionality.
  • the third hidden layer fits the muscle coordination features with automatically generated motion intent labels, and finally
  • the output layer of the network contains three neurons, which output continuous motion data with three degrees of freedom.
  • the weight matrix of each hidden layer of the neural network is independently trained and stacked together, layer by layer in the actual process of fitting the deep neural network Fine adjustment, in which the predicted wrist movement information is used to control the manipulator wrist 2, and the hand opening and closing movement information is used to control the manipulator mounted on the manipulator wrist 2.
  • the present invention performs principal component analysis on the time scale of the EMG activation signal of each channel, and the T sample points of the EMG activation signal in the time window are Substitute the features of principal component analysis to generate samples in the form of a sliding window. Set the sliding step length to 1, and the number of sampling points of the surface EMG signal of a single channel to m, then the number of generated samples is m-T+1.
  • the figure describes each Each EMG signal channel selects two principal components to characterize the data in the time window.
  • Figure 7 shows the process of extracting 2N muscle synergy features from the principal component features of the 8 channels in the second hidden layer in Figure 6,
  • 2N represents the 2N gesture degrees of freedom to be recognized, and N is the forearm muscles participating in the gesture movement.
  • N is the forearm muscles participating in the gesture movement.
  • the number of neurons that continue to be input into the neural network is reduced from c ⁇ T to c ⁇ 2.
  • the second layer of neurons is used to extract muscle synergy related Features.
  • 6 types of muscle synergy features are extracted from the electromyographic activation features, corresponding to wrist valgus, wrist varus, clockwise wrist rotation, counterclockwise wrist rotation, hand open, and hand fist, and the muscle synergy feature value They are all non-negative values, and the weight matrix of the second layer of neurons is obtained by training an autoencoder.
  • the main feature of the autoencoder is that the input neurons and output neurons are exactly the same, and the number of hidden layer neurons is less than the number of input and output neurons.
  • This neural network structure can obtain some potential features in the input data.
  • the text uses the encoding part of the self-encoder to complete the training as the weight matrix of the second layer of the network.
  • the activation function of the encoding process uses the Relu function. Since the input layer contains features with negative values, in order to enable the output layer to recover negative features, the activation function of the decoding process uses Tanh function.
  • the loss function of the autoencoder uses the cross-entropy function; the weight matrix of the encoder is initialized with the Xavier method, which can make the initial weights have a normal distribution with a mean value of 0; the iterative training process uses the pruning algorithm to reduce In case of small over-fitting, the network learning rate decays exponentially with the number of iterations, and the ADAM gradient descent method and Mini-Batch method are used to speed up the training speed. Compared with the non-negative matrix factorization method, the model fitted by this method is The calculation of the nonlinear activation function is improved, so it has a better approximation effect.
  • Figure 8 shows the process of extracting kinematics and dynamics labels from the muscle coordination features obtained in Figure 7.
  • the muscle coordination features learned by the autoencoder cannot directly obtain the desired motion intention, when the 6 coordination features are vector superimposed
  • the oscillating waveform shown in Figure 8 will be obtained, in which each peak represents the maximum level of muscle coordination when a certain action is completed, and the troughs on both sides indicate that the muscles are in a resting state, so a complete trough -The crest-trough segment indicates the process from the completion of a certain gesture to the strongest muscle activation and then rest recovery.
  • the kinematic parameter labels of the hands and wrists can be reconstructed with three degrees of freedom.
  • the muscle synergy features and label data calculated by the upper layer network are finally substituted into a feedforward neural network for regression fitting.
  • the obtained network layer is stacked with the network layer calculated in the previous two sections to form the final deep regression model. All network layers are trained and stacked to form the final gesture recognition network and fed back to the control unit circuit board.
  • the actual use is to make the user wear the residual limb receiving cavity 1 and connect the manipulator and the manipulator 2.
  • the user controls the wrist and hand movements of the prosthetic hand by imagining gestures, and the control unit circuit board controls the prosthesis at the same time.
  • Manipulator wrist 2 multiple degrees of freedom movement of the manipulator, the speed of the manipulator movement is regulated by the muscle force predicted by the hierarchical network.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Transplantation (AREA)
  • Mechanical Engineering (AREA)
  • General Health & Medical Sciences (AREA)
  • Cardiology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Vascular Medicine (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Behavior & Ethology (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Biomedical Technology (AREA)
  • Automation & Control Theory (AREA)
  • Robotics (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Orthopedic Medicine & Surgery (AREA)
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Abstract

一种多自由度肌电假手控制系统及其使用方法，该系统包括机械手、机械手腕（2）、残肢接受腔（1）和数据处理器（3），机械手和残肢接受腔（1）分别安装在机械手腕（2）两端，残肢接受腔（1）内连接有多通道肌电阵列电极袖套、控制单元电路板和电池，控制单元电路板另一端连接机械手和机械手腕（2），该系统的使用方法包括以下步骤：(S1)令使用者戴上多通道肌电阵列电极袖套，连接好电池和控制单元电路板；(S2)令使用者完成手势，采集表面肌电信号后上传至数据处理器（3）；(S3)数据处理器（3）接收表面肌电信号并输入神经网络算法生成手势预测模型；(S4)使用者控制机械手腕（2）、机械手的多个自由度运动。该系统能够对连续手势及手势力度进行识别，做出多自由度的手势。

Description

一种多自由度肌电假手控制系统及其使用方法 技术领域
本发明涉及一种假手控制系统及其使用方法，更具体地，涉及一种多自由度肌电假手控制系统及其使用方法。
背景技术
人工假肢的研究可以应用到高端医疗装备、生机电一体化智能机器人、危险环境勘查、灾难救援装备、国防装备以及辅助残疾人进行康复工程训练等多个领域，其科学技术成果可辐射，因此具有重要的战略意义，到目前为止，基于单自由度的仿人型机器人手部结构已经十分成熟，但单自由度并不能不满足仿真假手的灵活度需求，且不存在能够同步识别假手手势和手势力度的算法，使得假手的应用受到了限制。
发明内容
发明目的：本发明的目的是提供一种能够同步识别手势及其力度，并进行多自由度控制的多自由度肌电假手控制系统，本发明的另一目的是提供该系统的使用方法。
技术方案：本发明所述的多自由度肌电假手控制系统包括机械手、机械手腕、残肢接受腔和数据处理器，机械手和残肢接受腔分别安装在机械手腕两端，残肢接受腔内连接有多通道肌电阵列电极袖套，多通道肌电阵列电极袖套连接有控制单元电路板和电池，控制单元电路板另一端连接机械手和机械手腕。数据处理器向控制单元电路板发出采集表面肌电信号的指令，使多通道肌电阵列电极袖套采集表面肌电信号，并通过接收的数据进行神经网络处理，生成手势预测模型。
其中，机械手腕包括锥齿轮组机构、皮带轮传动机构、伺服电机和手腕支撑框，锥齿轮组机构采用四个锥齿轮相互啮合，构成十字型排布，左、右两个锥齿轮安装在手腕支撑框架上，并分别连接有传动轮，皮带轮传动机构连接在传动轮上，并连接有伺服电机；锥齿轮组机构中水平方向的两个齿轮为太阳轮，太阳轮通过主动轴与传动轮相连，太阳轮通过太阳轮顶丝固定在主动轴上，传动轮通过传动轴顶丝固定在主动轴上，垂直方向上部为连接机械手的第一行星齿轮，下部为第二行星齿轮，第一行星齿轮和第二行星齿轮之间穿过一空心被动轴，空心被动轴与第一行星齿轮和第二行星齿轮之间安装有深沟轴承，手腕支撑框架由左面板、右面板、梁和底板构成，左面板、右面板上端通过梁连接，下端与底板固定 连接，伺服电机安装在左、右面板上，伺服电机皮带轮与传动轮通过皮带套接，皮带外侧固定一压轮。
本发明所述的多自由度肌电假手控制系统的使用方法包括以下步骤：
(S1)令使用者戴上多通道肌电阵列电极袖套，然后连接好控制单元电路板、电池；
(S2)令使用者根据实验动作序列完成手势，数据处理器向控制单元电路板发出采集表面肌电信号的指令，控制单元电路板控制多通道肌电阵列电极袖套采集表面肌电信号后储存至控制单元电路板并上传至数据处理器；
(S3)数据处理器接收表面肌电信号并输入神经网络算法生成手势预测模型；
(S4)使用者穿戴上残肢接受腔，并连接好机械手和机械手腕，利用生成的手势预测模型进行实时手势识别，控制单元电路板控制手腕、机械手的多个自由度运动。
其中，步骤S3中神经网络算法对数据处理包括以下步骤：
(S31)对原始表面肌电信号进行预处理以提取肌肉激活信号，然后用固定长度的时间窗口分割并作为无监督神经网络的输入层，网络的第一个隐藏层利用主成分分析方法压缩时间-空间特征；
(S32)第二个隐藏层采用自编码器学习2N个前臂肌肉完成不同手势时相互协同的肌肉信号特征，根据肌肉协同特征和实验动作序列生成连续手势标签，其中2N表示要识别的2N个手势自由度，N为参与手势运动的前臂肌肉中互为拮抗肌肉的个数；
(S33)第三个隐藏层将肌肉协同特征与连续手势标签进行拟合，生成回归网络，回归网络的输出层包含N个神经元，分别输出N对拮抗肌表现出的连续运动学与动力学数据，其中不同神经元表示不同的手势，神经元输出的连续数据表示该手势的力度。
有益效果：本发明与现有技术相比，其显著优点是：1、能够对连续手势进行识别，并对手势力度进行识别；2、能够做出多自由度的手势，假手更加灵巧。
附图说明
图1是本发明假手安装示意图；
图2是手腕结构示意图；
图3是锥齿轮组机构结构示意图；
图4是手腕支撑框架结构示意图；
图5是手腕结构侧视图；
图6是分层神经网络框图；
图7是手势识别算法第二隐层自编码器框图；
图8是标签自生成方法示意图；
图9是手势识别算法流程图；
图10是神经网络流程图。
具体实施方式
如图1～图5所示，多自由度肌电假手控制系统，其特征在于，包括机械手、机械手腕2、残肢接受腔1和数据处理器3，机械手和残肢接受腔分别安装在机械手腕两端，残肢接受腔内连接有多通道肌电阵列电极袖套，多通道肌电阵列电极袖套连接有控制单元电路板和电池，控制单元电路板另一端连接机械手和机械手腕。数据处理器3向控制单元电路板发出采集表面肌电信号的指令，使多通道肌电阵列电极袖套采集表面肌电信号，并通过接收的数据进行神经网络处理，生成手势预测模型。
机械手腕2包括锥齿轮组机构4、皮带轮传动机构5、伺服电机7和手腕支撑框架6，锥齿轮组机构4采用四个锥齿轮相互啮合，构成十字型排布，水平方向的两个齿轮为太阳轮15，安装在手腕支撑框架6上，太阳轮15通过主动轴9与传动轮10相连，太阳轮15通过太阳轮顶丝12固定在主动轴9上，传动轮10通过传动轴顶丝11固定在主动轴9上，垂直方向上部为连接机械手的第一行星齿轮14，下部为第二行星齿轮13，第一行星齿轮14和第二行星齿轮13之间穿过一空心被动轴8。空心被动轴8与第一行星齿轮14和第二行星齿轮13之间安装有深沟轴承。手腕支撑框架6由左面板、右面板、梁16和底板17构成，左面板、右面板上端通过梁18连接，下端与底板固定连接，所述伺服电机7安装在左、右面板上，伺服电机皮带轮19与传动轮10通过皮带18套接。皮带21外侧固定一压轮20。
手势识别算法流程如图9所示，使用时，先将控制单元电路板、电池与多通道肌电阵列电极袖套相连，令使用者穿戴上多通道肌电阵列电极袖套，令使用者依次完成手腕外翻，手腕外旋、手张开、手腕内翻、手腕内旋、手握拳共计六个动作，由腕翻、腕旋、手开合三个自由度的动作组成，每个动作从开始到结束持续3秒，之后手处于放松状态并持续3秒，每种手势需连续完成3次再做下一个手势，当所有手势都做完后视为一轮采集结束，同一对象需要采集三轮数据，多通道肌电阵列电极袖套采集肌电信号后储存至控制单元电路板并上传至数据处理器3，输入神经网络算法进行处理，处理流程如图10所示。肌电数据收集完 成后，训练集被分层神经网络的三层网络加工，如图6所示，首先对8个通道的原始肌电信号进行预处理，采用均方根RMS均值来获得激活信号，然后，这8个激活信号被固定长度的时间窗口分割并作为神经网络的输入层，每个输入样本将包含阵列肌电信号的空间和时间信息，网络的第一个隐藏层利用主成分分析方法来降低输入信号的维度，第二个隐藏层采用自编码器学习六个肌肉协同特征以进一步降低特征维度，第三个隐藏层将肌肉协同特征与自动生成的运动意图标签进行拟合，最终网络的输出层包含三个神经元，分别输出三个自由度的连续运动数据，各个神经网络隐藏层的权值矩阵是独立训练再堆叠在一起，在实际拟合深度神经网络过程中进行逐层精调，其中预测出的手腕运动信息用于控制机械手腕2，手开合运动信息用于控制安装于机械手腕2上的机械手。
设图6中的时间窗内包含T个样本点，阵列肌电传感器的个数为c,则网络输入层神经元的个数为c×T。为了从冗余信息中获取有代表性的时间和空间信息，本发明对每个通道的肌电激活信号进行时间尺度上的主成分分析，将时间窗内的T个肌电激活信号采样点为代入主成分分析的特征，以滑动窗口的形式生成样本，设滑动步长为1，单个通道表面肌电信号的采样点数为m，则生成样本数为m-T+1，图中描述了每个肌电信号通道选取两个主成分来表征该时间窗内的数据。
图7表示图6中第二隐层从8个通道的主成分特征中提取2N种肌肉协同特征的过程，2N表示要识别的2N个手势自由度，N为参与手势运动的前臂肌肉中，互为拮抗肌肉的个数，经过第一层网络的加工后，继续输入神经网络的神经元个数由c×T降为c×2，进一步的，第二层神经元用于提取肌肉协同相关的特征，本实施例中从肌电激活特征中提取6种肌肉协同特征，分别对应手腕外翻、手腕内翻、顺时针腕旋、逆时针腕旋、手打开和手握拳，且肌肉协同特征值均为非负值，第二层神经元的权值矩阵通过训练一个自编码器得到。自编码器的主要特征在于输入神经元与输出神经元完全一致，且隐藏层神经元个数小于输入输出神经元个数，该神经网络结构可以获得输入数据中某些潜在的特征。从网络的输入层到隐藏层称为编码过程，从隐藏层到输出层称为解码过程，文本使用自编码器完成训练后的编码部分作为第二层网络的权值矩阵。为了使隐层神经元均为非负值，编码过程的激活函数使用Relu函数，由于输入层包含数值为负的特征，为了使输出层也能复原出负值特征，解码过程的激活函数使用Tanh函数。自编码器的损失函数使用交叉熵cross entropy函数；编码器的权值矩阵使用Xavier法进行初 始化，该方法能够使初始权值呈均值为0的正态分布；迭代训练过程中使用剪枝算法减小过拟合情况，网络学习率随迭代次数指数衰减、并采用ADAM梯度下降法和Mini-Batch法加快训练速度，与非负矩阵因式分解方法相比，该方法拟合出的模型由于经过了非线性激活函数的运算，因此具有更好的逼近效果。
图8表示从图7中得到的肌肉协同特征中提取运动学和动力学标签的过程，自编码器学习到的肌肉协同特征虽然不能直接得到期望的运动意图，但当6个协同特征经过矢量叠加运算后，将得到图8中所示的震荡波形图，其中每一个波峰表示完成某一动作时肌肉协同程度达到的最大值，两侧的波谷表示肌肉协同处于静息状态，因此一个完整的波谷-波峰-波谷段表示某手势完成至最强肌肉激活程度再到静息恢复的过程，通过搜索波峰和波谷位置可以重构出手部、腕部共三个自由度的运动学参数标签。在得到标签数据后，最后将上一层网络计算得到的肌肉协同特征和标签数据代入一个前馈神经网络进行回归拟合。得到的网络层再与是前两节计算得到的网络层进行堆叠，即形成了最终的深层回归模型，所有网络层训练完成并堆叠起来形成最终的手势识别网络并反馈给控制单元电路板。实际使用是，令使用者穿戴上残肢接受腔1，并连接好机械手和机械手腕2，使用者通过想象手势动作来控制假肢手的腕部和手部动作，控制单元电路板同时控制假肢的机械手腕2、机械手的多个自由度运动，机械手运动的速度由分层网络预测的肌肉力调控。

Claims (8)

  1. 一种多自由度肌电假手控制系统，其特征在于，包括机械手、机械手腕(2)、残肢接受腔(1)和数据处理器(3)，所述机械手和残肢接受腔分别安装在机械手腕两端，所述残肢接受腔内连接有多通道肌电阵列电极袖套，所述多通道肌电阵列电极袖套连接有控制单元电路板和电池，所述控制单元电路板另一端连接机械手和机械手腕，所述数据处理器向控制单元电路板发出采集表面肌电信号的指令使多通道肌电阵列电极袖套采集表面肌电信号并接受数据进行处理生成手势预测模型。
  2. 根据权利要求1所述的多自由度肌电假手控制系统，其特征在于，所述机械手腕(2)包括锥齿轮组机构(4)、皮带轮传动机构(5)、伺服电机(7)和手腕支撑框架(6)，所述锥齿轮组机构(4)采用四个锥齿轮相互啮合，构成十字型排布，左、右两个锥齿轮安装在手腕支撑框架(6)上，并分别连接有传动轮(10)，所述皮带轮传动机构(5)连接在传动轮(10)上，并连接有伺服电机(7)。
  3. 根据权利要求2所述的多自由度肌电假手控制系统，其特征在于，所述锥齿轮组机构(4)中水平方向的两个齿轮为太阳轮(15)，太阳轮(15)通过主动轴(9)与传动轮(10)相连，太阳轮(15)通过太阳轮顶丝(12)固定在主动轴(9)上，传动轮(10)通过传动轴顶丝(11)固定在主动轴(9)上，垂直方向上部为连接机械手的第一行星齿轮(14)，下部为第二行星齿轮(13)，所述第一行星齿轮(14)和第二行星齿轮(13)之间穿过一空心被动轴(8)。
  4. 根据权利要求3所述的多自由度肌电假手控制系统，其特征在于，所述空心被动轴(8)与第一行星齿轮(14)和第二行星齿轮(13)之间安装有深沟轴承。
  5. 根据权利要求2所述的多自由度肌电假手控制系统，其特征在于，所述手腕支撑框架(6)由左面板、右面板、梁(16)和底板(17)构成，所述左面板、右面板上端通过梁(18)连接，下端与底板固定连接，所述伺服电机(7)安装在左、右面板上，伺服电机皮带轮(19)与传动轮(10)通过皮带(18)套接。
  6. 根据权利要求5所述的多自由度肌电假手控制系统，其特征在于，所述皮带(21)外侧固定一压轮(20)。
  7. 一种权利要求1所述的多自由度肌电假手控制系统的使用方法，其特征在于，包括以下步骤：
    (S1)令使用者戴上多通道肌电阵列电极袖套，然后连接好控制单元电路板、 电池；
    (S2)令使用者根据实验动作序列完成手势，数据处理器(3)向控制单元电路板发出采集表面肌电信号的指令，控制单元电路板控制多通道肌电阵列电极袖套采集表面肌电信号后储存至控制单元电路板并上传至数据处理器(3)；
    (S3)数据处理器(3)接收表面肌电信号并输入神经网络算法生成手势预测模型；
    (S4)使用者穿戴上残肢接受腔，并连接好机械手和机械手腕，利用生成的手势预测模型进行实时手势识别，控制单元电路板控制手腕、机械手的多个自由度运动。
  8. 根据权利要求7所述的多自由度肌电假手控制系统的使用方法，其特征在于，步骤S3中神经网络算法对数据处理包括以下步骤：
    (S31)对原始表面肌电信号进行预处理以提取肌肉激活信号，然后用固定长度的时间窗口分割并作为无监督神经网络的输入层，网络的第一个隐藏层利用主成分分析方法压缩时间-空间特征；
    (S32)第二个隐藏层采用自编码器学习2N个前臂肌肉完成不同手势时相互协同的肌肉信号特征，根据肌肉协同特征和实验动作序列生成连续手势标签，其中2N表示要识别的2N个手势自由度，N为参与手势运动的前臂肌肉中互为拮抗肌肉的个数；
    (S33)第三个隐藏层将肌肉协同特征与连续手势标签进行拟合，生成回归网络，回归网络的输出层包含N个神经元，分别输出N对拮抗肌表现出的连续运动学与动力学数据，其中不同神经元表示不同的手势，神经元输出的连续数据表示该手势的力度。
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