CN107203272A - Wearable augmented reality task instruction system and method based on myoelectricity cognition technology - Google Patents
Wearable augmented reality task instruction system and method based on myoelectricity cognition technology Download PDFInfo
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- CN107203272A CN107203272A CN201710488276.2A CN201710488276A CN107203272A CN 107203272 A CN107203272 A CN 107203272A CN 201710488276 A CN201710488276 A CN 201710488276A CN 107203272 A CN107203272 A CN 107203272A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T19/00—Manipulating 3D models or images for computer graphics
- G06T19/006—Mixed reality
Abstract
The invention discloses a kind of wearable augmented reality task instruction system and method based on myoelectricity cognition technology, including wearable augmented reality EM equipment module and wearable myoelectricity awareness apparatus module；Wearable myoelectricity awareness apparatus module includes：Motion capture unit；Action recognition unit；Dispensing unit；First network transmission unit；Processing and control element (PCE).The present invention can catch more action messages, and not by Environmental Noise Influence, and this is more extensive in increasingly sophisticated industrial application application prospect to realize that more convenient accurate interaction is laid a good foundation.
Description
Technical field
The present invention relates to augmented reality field, more particularly to a kind of wearable enhancing based on myoelectricity cognition technology are existing
Real task instruction system and method.
Background technology
In field of industrial production, with the development of industry 4.0, various emerging task instruction systems emerge in an endless stream, are based on
The guidance for industry application of augmented reality (Augmented Reality, abbreviation AR) technology is one of them.Augmented reality
The data message that script is difficult to observe by the certain time spatial dimension of real world, by being added to after analog simulation
Real world, is perceived by the mankind, so as to realize the sensory experience of exceeding reality.Wearable augmented reality equipment can follow work
People is in the random changing position of plant site, and can be added to teachings virtual information real world, so as to meet real-time finger
The demand led.
The interactive mode of existing industrial augmented reality application mainly has gesture identification based on image and voice-based
Two kinds of instruction identification.Gesture identification based on image gathers gesture information by camera and is identified, and it had the disadvantage to week
Collarette environmental light is more sensitive, and is limited to the angle of camera；Voice-based instruction is identified by collection manipulator's
Special sound instruction carries out corresponding operation, and major defect is larger by Environmental Noise Influence, particularly in industrial application,
Many scenes are run all under strong ambient noise.
The content of the invention
The purpose of the present invention is exactly that there is provided a kind of wearable increasing based on myoelectricity cognition technology in order to solve above-mentioned problem
Strong reality task instruction system and method, by myoelectricity acquisition electrode, gathers the muscle electric signal during user movement, passes through
The model of limb action recognition training, recognizes the pattern of active user；The action recognition skill that the present invention is perceived based on myoelectricity
Art, can recognize different gestures, the amplitude of fluctuation of arm, not by the light in environment, noise effect.
To achieve the above object, concrete scheme of the invention is as follows：
A kind of wearable augmented reality task instruction system based on myoelectricity cognition technology, including：Wearable augmented reality
EM equipment module and wearable myoelectricity awareness apparatus module；
The wearable myoelectricity awareness apparatus module includes：
Motion capture unit：The electromyographic signal of crawl user's arm in real time, and send after electromyographic signal is encoded to acting
Recognition unit；
Action recognition unit：Electromyographic signal, association analysis current user action are received, and the action event of identification is transmitted
To processing and control element (PCE)；
Dispensing unit：For configuring call parameter during system operation, comprising：Communications address information, user personality training
Model；
First network transmission unit：For realizing the communication with wearable augmented reality EM equipment module；
Processing and control element (PCE)：Dispensing unit data initialization is read when system starts, and the action event of identification is sent
To wearable augmented reality EM equipment module.
Further, the wearable augmented reality EM equipment module includes：
Control unit：The action event recognition result of wearable myoelectricity awareness apparatus module is received, system scenarios are updated；
Recognition unit：The user visual field is caught, by image processing and analyzing user environment, anchor point is found；
Augmented reality unit：The corresponding model of reception control unit instruction displaying or information；
Second network transmitting unit：For realizing wearable augmented reality EM equipment module and wearable myoelectricity awareness apparatus mould
The communication of block.
A kind of wearable augmented reality task instruction method based on myoelectricity cognition technology, including：
(1) limb action training is carried out by limb action Training scene, training result saves as user personality training mould
Type；
(2) set anchor point, the anchor point for true environment location point to virtual scene space mapping point；
(3) the 3D models comprising all devices and parts in virtual scene are set up, deployment 3D models are in virtual scene
The detailed guidance explanation of technological process and each step in position, typing virtual scene；
(4) user visual field environment is analyzed in real time, and according to the position of true environment anchor point, virtual scene content is overlapped onto
In true environment；
(5) record in real time and analyze user's electromyographic signal data, judge whether user is complete by user personality training pattern
Into a defined action；
(6) if completing defined action, action response is made with reference to current scene state；Otherwise, step is returned
Suddenly (5).
Further, the order according to the step of technological process, points out user's current procedures contents, specific in virtual scene
Mode of operation and need operate true environment in location point.
Further, in the step (1), the process of setting up of user personality training pattern is specially：
1) setting sample frequency and sampling way, according to user's current action gather user's electromyographic signal data, as with
The initial data of family individual character training pattern；
2) characteristic vector pickup is carried out to the initial data；
3) using the characteristic vector of extraction as the input layer of depth fully-connected network, the recognition training acted.
Further, the step 1) in, user's electromyographic signal data of collection include：Hand signals, forearm signal, on
Arm signal.
Further, the step 2) in, the feature per signal extraction all the way includes：Root mean square, peak factor, kurtosis with
And wavelet-packet energy.
Further, in the step (6), action response logic is specifically included：
When instructing user to tighten up a screw, if system receives user and tightened up a screw action event, and motionless action is twisted
During state, system assert that current operation has been completed, and the operation of user's next step is instructed automatically.
Further, in the step (6), action response logic is specifically included：
When showing the picture of job instruction, if system receives the gesture that the five fingers open, picture can amplify；If
System receives the gesture that the five fingers close up, and picture can reduce；If system receives gesture of clenching fist, picture can be followed before and after fist
Left and right is moved up and down.
Further, in the step (6), action response logic is specifically included：As upward other finger grippings boxer of forefinger
During gesture, the warming-up exercise before clicking on is represented, now the cursor in system can follow gesture to move；When forefinger is bent, represent a little
Event is hit, now system handles click event according to the position of cursor.
Beneficial effect of the present invention：
The present invention is gathered the muscle electric signal during user movement, known by limb action by myoelectricity acquisition electrode
The model do not trained, recognizes the pattern of active user.The action recognition technology perceived based on myoelectricity, can be recognized different
Gesture, the amplitude of fluctuation of arm, not by the light in environment, noise effect.Wearable myoelectricity awareness apparatus, is worn on workman
Arm, daily operation is not influenceed.
Present system and method, should in increasingly sophisticated industry to realize that more convenient accurate interaction is laid a good foundation
It is more extensive with field application prospect.
Brief description of the drawings
Fig. 1 is each module connection diagram in the system of the present invention；
Fig. 2 is user personality training pattern training flow chart of the present invention；
Fig. 3 is the structural representation of depth fully-connected network of the present invention.
Embodiment：
The present invention is described in detail below in conjunction with the accompanying drawings：
The invention discloses a kind of wearable augmented reality task instruction system based on myoelectricity cognition technology, such as Fig. 1 institutes
Show, including：Wearable augmented reality EM equipment module and wearable myoelectricity awareness apparatus module；
Wearable myoelectricity awareness apparatus module includes：
Motion capture unit：It is worn on the electromyographic signal of crawl user's arm, and myoelectricity is believed in real time on the body of user
Number coding after send to action recognition unit；
Action recognition unit：Electromyographic signal, association analysis current user action are received, and the action event of identification is transmitted
To processing and control element (PCE)；
Dispensing unit：For configuring call parameter during system operation, comprising：Communications address information, user personality training
Model；
First network transmission unit：With the second communication unit radio communication, for realize with wearable augmented reality equipment
The communication of module；
Processing and control element (PCE)：Dispensing unit data initialization is read when system starts, will by first network transmission unit
The action event of identification is sent to wearable augmented reality EM equipment module.
Wearable augmented reality EM equipment module includes：
Control unit：Network service is carried out by the second network transmitting unit and wearable myoelectricity awareness apparatus module, connect
The recognition result of recognition unit is received, system scenarios are updated；
Recognition unit：The user visual field is caught by camera, user environment is analyzed using image processing techniques, positioning is found
Point；
Augmented reality unit：The corresponding model of reception control unit instruction displaying or information；
Second network transmitting unit：With the first communication unit radio communication, for realizing wearable augmented reality equipment mould
The communication of block and wearable myoelectricity awareness apparatus module.
On the other hand, the invention discloses a kind of wearable augmented reality task instruction side based on myoelectricity cognition technology
Method, specifically includes following steps：
(1) limb action is trained：It is different that different user produces electromyographic signal for same action.In order to improve the course of work
Middle user action recognition accuracy, system provides limb action Training scene, and training result is saved as into user personality instruction
Practice model.
The training of limb action is the basis for realizing finer action recognition, user personality training pattern of the present invention
Training process is as shown in Fig. 2 idiographic flow is as follows：
Data prediction part mainly includes collection, framing and the feature extraction of signal, is carried out by motion capture unit
The collection of signal, the feature extraction of signal is carried out by action recognition unit.
According to actual needs, setting signal sample rate f s scope is 50Hz~1000Hz, the wearable device gathered
Signal mainly includes the signal of following limb part：
1) hand signals, each finger exports 2 road signals, both hands 20 tunnel altogether；
2) forearm signal, the road signal of forearm 8, both arms 16 tunnel altogether are gathered using annular wearable device；
3) upper arm signal, the road signal of upper arm 8, both arms 16 tunnel altogether are gathered using annular wearable device；
Processing is identified in collection 1s data every time, and according to the sample rate and signal way, each gathered data amount is
Fs × (20+16+16), this signal constitutes the matrix of fs × 52, and next step feature extraction is carried out as initial data.
Can be x=[x with a vector representation per road signal (fs point)1,x2,…,xn], n=fs, the feature of extraction is such as
It is lower described：
1) root mean square
2) peak factor
3) kurtosis
Wherein μ is x average, and σ is x standard deviation, and E () represents to average.
4) wavelet-packet energy
3 layers of WAVELET PACKET DECOMPOSITION are carried out using haar small echos, by signal decomposition to 8 subbands, each subband letter are then calculated
Number energy accounts for the ratio of gross energy, exports 8 dimensional features.
More than, 11 dimensional features are extracted altogether per road signal, the characteristic sequence of 52 road signals is arranged, and obtain one 572 dimension
Characteristic vector, this characteristic vector is as the input layer of depth fully-connected network, the recognition training acted.
Depth fully-connected network structure used in the present invention comprising multiple hidden layers is as shown in figure 3, to recognize 8 actions
Exemplified by, specifically include：
1st layer be input layer, neuron number 572, activation primitive uses ReLU；
2nd layer to the 6th layer neuron number is followed successively by 600,500,300,150,50, and activation primitive is ReLU, each
Adding proportion prevents over-fitting for 0.25 Dropout after full articulamentum.
Last layer is output layer, and dimension is that the action number of tags to be recognized plus 1, such as to 8 actions of identification, then defeated
Go out dimension for 9, the action of additional 1 tag representation it is unidentified go out, be used as loss function using cross entropy.
(2) anchor point is set：Anchor point refers to the location point of true environment to the mapping point in virtual scene space.
Use image analysis technology, it is desirable to have an identifier is used as anchor point so that in wearable augmented reality equipment
Can completely it be photographed in the camera image of system.Identifier can be a pictures (position for being flattened on anchor point), can
Can be that (object can not be too big, in anchor point for a three-dimensional body to be a Background (background pictures clapped at anchor point)
Place's camera can be photographed completely).The setting of anchor point is realized by control unit.
(3) scene construction：Use the 3D models comprising all devices and parts, portion in 3 d modeling software design scenario
Affix one's name to the detailed guidance explanation of technological process and each step of the 3D models in the position of scene, typing scene.
(4) task instruction：The recognition unit of wearable augmented reality EM equipment module analyzes user visual field environment in real time, according to
The position of true environment anchor point, the virtual scene content in system is overlapped onto in true environment.System is according to technological process
The step of order, that points out that user's current procedures contents, specific mode of operation and needing operate in virtual scene is true
Location point in environment.
(5) limb action is recognized：The motion capture unit of wearable myoelectricity awareness apparatus module records user's myoelectricity in real time
Signal, user personality training pattern analysis user electromyographic signal data of the action recognition unit in configuration module, finally sentences
Whether disconnected user completed a defined action just now.If completing defined action, processing and control element (PCE) passes through
One network transmitting unit, the action event is sent to wearable augmented reality EM equipment module in real time.
(6) action response：Wearable augmented reality device systems receive action event, with reference to current scene condition responsive
Action.
The algorithm logic of response action is as follows：
1) when wearable augmented reality device systems are instructing user to tighten up a screw, if system receives user and tightened up a screw
When action event is with motionless operating state is twisted, system assert that current operation has been completed, and user's next step will be instructed automatically
Operation；
2) when wearable augmented reality device systems are showing the picture of job instruction, if system receives the five fingers
The gesture opened, picture can amplify；If system receives the gesture that the five fingers close up, picture can reduce；If system is received
Clench fist gesture, picture can follow fist all around to move up and down；
3) when forefinger other finger grippings boxer's gesture upwards, the warming-up exercise before clicking on is represented, now the cursor in system
Gesture can be followed to move；When forefinger is bent, click event is represented, now system handles click event according to the position of cursor.
Above-mentioned scene construction, task instruction and action response are realized by augmented reality unit.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, not to present invention protection model
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deform still within protection scope of the present invention that creative work can make.
Claims (10)
1. a kind of wearable augmented reality task instruction system based on myoelectricity cognition technology, it is characterised in that including：It is wearable
Augmented reality EM equipment module and wearable myoelectricity awareness apparatus module；
The wearable myoelectricity awareness apparatus module includes：
Motion capture unit：The electromyographic signal of crawl user's arm, and sending to action recognition after electromyographic signal is encoded in real time
Unit；
Action recognition unit：Electromyographic signal, association analysis current user action are received, and the action event of identification is transmitted to place
Manage control unit；
Dispensing unit：For configuring call parameter during system operation, comprising：Communications address information, user personality training pattern；
First network transmission unit：For realizing the communication with wearable augmented reality EM equipment module；
Processing and control element (PCE)：Dispensing unit data initialization is read when system starts, and the action event of identification is sent to can
Dress augmented reality EM equipment module.
2. a kind of wearable augmented reality task instruction system based on myoelectricity cognition technology as claimed in claim 1, it is special
Levy and be, the wearable augmented reality EM equipment module includes：
Control unit：The action event recognition result of wearable myoelectricity awareness apparatus module is received, system scenarios are updated；
Recognition unit：The user visual field is caught, by image processing and analyzing user environment, anchor point is found；
Augmented reality unit：The corresponding model of reception control unit instruction displaying or information；
Second network transmitting unit：For realizing wearable augmented reality EM equipment module and wearable myoelectricity awareness apparatus module
Communication.
3. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology, it is characterised in that including：
(1) limb action training is carried out by limb action Training scene, training result saves as user personality training pattern；
(2) set anchor point, the anchor point for true environment location point to virtual scene space mapping point；
(3) the 3D models comprising all devices and parts in virtual scene are set up, 3D models are disposed in the position of virtual scene,
The detailed guidance explanation of technological process and each step in typing virtual scene；
(4) user visual field environment is analyzed in real time, and according to the position of true environment anchor point, virtual scene content is overlapped onto truly
In environment；
(5) record in real time and analyze user's electromyographic signal data, judge whether user completes by user personality training pattern
One defined action；
(6) if completing defined action, action response is made with reference to current scene state；Otherwise, return to step
(5)。
4. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 3, it is special
Levy and be, the order according to the step of technological process points out user's current procedures content, specific mode of operation in virtual scene
And need the location point in the true environment of operation.
5. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 3, it is special
Levy and be, in the step (1), the process of setting up of user personality training pattern is specially：
1) setting sample frequency and sampling way, gather user's electromyographic signal data according to user's current action, are used as user
The initial data of property training pattern；
2) characteristic vector pickup is carried out to the initial data；
3) using the characteristic vector of extraction as the input layer of depth fully-connected network, the recognition training acted.
6. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 5, it is special
Levy and be, the step 1) in, user's electromyographic signal data of collection include：Hand signals, forearm signal, upper arm signal.
7. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 5, it is special
Levy and be, the step 2) in, the feature per signal extraction all the way includes：Root mean square, peak factor, kurtosis and wavelet packet energy
Amount.
8. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 3, it is special
Levy and be, in the step (6), action response logic is specifically included：
When instructing user to tighten up a screw, if system receives user and tightened up a screw action event, and motionless operating state is twisted
When, system assert that current operation has been completed, and the operation of user's next step is instructed automatically.
9. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 3, it is special
Levy and be, in the step (6), action response logic is specifically included：
When showing the picture of job instruction, if system receives the gesture that the five fingers open, picture can amplify；If system
The gesture that the five fingers close up is received, picture can reduce；If system receives gesture of clenching fist, picture can follow fist all around
Move up and down.
10. a kind of wearable augmented reality task instruction method based on myoelectricity cognition technology as claimed in claim 3, it is special
Levy and be, in the step (6), action response logic is specifically included：When forefinger other finger grippings boxer's gesture upwards, represent a little
Warming-up exercise before hitting, now the cursor in system gesture can be followed to move；When forefinger is bent, click event is represented, now
System handles click event according to the position of cursor.
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| US11907423B2 (en) | 2019-11-25 | 2024-02-20 | Meta Platforms Technologies, Llc | Systems and methods for contextualized interactions with an environment |
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