CN201227336Y - Electric artificial hand controlled by brain electricity and muscle electricity - Google Patents
Electric artificial hand controlled by brain electricity and muscle electricity Download PDFInfo
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- CN201227336Y CN201227336Y CNU2008201220719U CN200820122071U CN201227336Y CN 201227336 Y CN201227336 Y CN 201227336Y CN U2008201220719 U CNU2008201220719 U CN U2008201220719U CN 200820122071 U CN200820122071 U CN 200820122071U CN 201227336 Y CN201227336 Y CN 201227336Y
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Abstract
The utility model relates to an electromotion artificial hand controlled by brain electricity and myoelectricity together. The accurate rate of the mode treatment of the current myoelectricity artificial hand with three degrees of freedom is not high enough. The electromotion artificial hand comprises a plurality of myoelectricity pick-up sensors and brain electricity pick-up sensors, wherein secondary-treatment output terminals of the myoelectricity pick-up sensors and the brain electricity pick-up sensors are connected with input terminals of A/D conversion circuits. Three motors are respectively connected with corresponding driving circuits, and microprocessors are respectively in signal connection with the output terminals of the A/D conversion circuits and the input terminals of the driving circuits. The myoelectricity pick-up sensors and the brain electricity pick-up sensors respectively collect surficial myoelectricity signals from each point of human remained arms and brain electricity signals from the vertex and ears of human bodies. After treated, all of the signals are input to the microprocessors for further treatment. The three motors are used for controlling the artificial hand with three degrees of freedom. The electromotion artificial hand merges the brain electricity signals and the myoelectricity signals, realizes control after mode identification and has high accurate rate and reliable action control.
Description
Technical field
This utility model belongs to information and control technology field, relate to and a kind of scalp brain electricity and surface myoelectric information are controlled the technology of manually doing evil through another person, specifically be after brain electricity/electromyographic signal is merged, identification obtain doing evil through another person a plurality of patterns of action realize the multiple degrees of freedom electrically powered hand of control in real time.
Background technology
Electromyographic signal (EMG) is a kind of bioelectrical signals of following musculation, is the signal of telecommunication root of musculation, has wherein contained the various information of musculation, comprises and the corresponding limb action pattern of musculation.Surface electromyogram signal (SEMG) then be on the EMG of shallow-layer muscle and the nerve trunk electrical activity at the comprehensive effect of skin surface.Because SEMG has the non-intruding characteristic on measuring, the individuality that is implemented detection is had no pain and characteristics easily, obtained application widely at numerous areas such as clinical medicine, sports medical science, electronic artificial limbs.
EEG signals (EEG) is that the synapse of cerebral cortex neural cell group is transmitted signal and caused and the reflection of potential change can reflect the conscious activity that brain is autonomous or bring out, and is closely related with the action behavior of reality.At present the collection of EEG signals is had two kinds of intrusive mood and non-intrusion types, the intrusive mood eeg signal acquisition uses implanted electrode, and non-intrusion type then adopts scalp electrode.The implant electrode method is microelectrode to be implanted a kind of method of measuring EEG signals in the cranial cavity by microsurgery, the microelectrode of implanting intracranial has higher space and frequency resolution, can detect near the neuronic electrical activity information of minority of electrode, polarization is good, the signal to noise ratio height.Non-intrusion type scalp brain power technology is harmless fully to human body, but owing to be not in brain neuron, directly to extract, but extract EEG signals from scalp, and weak output signal, noise is big.To the analysis and research of the EEG signals brain-machine interaction (BCI) that makes it to be born, the key of brain-machine interaction is to people's cognition and EEG signals collection that thinking activities produces, analyzes and extract feature, by the classification of feature, optimization etc. being realized the control to machine.The brain-machine interaction technology is expanded the ability of people's equipment control to external world, communication for information greatly.
Myoelectric limb detects its electromyographic signal according to the musculation on the deformed limb, obtains the control signal of artificial limb after treatment, reaches the purpose of controlling.Because myoelectric limb has the action nature, the characteristics that bionical performance is good have become bionical artificial limb ideal control signal source.Electromyographic signal is risen in the bioelectric of neuromuscular system motor unit, when it goes to control limbs as the normal person, musculation corresponding on the deformed limb is followed electromyographic signal, collect the electromyographic signal of the skin surface that flexes a muscle through picking up the fax sense, by obtaining the action pattern of limbs after handling.Because faint property, aliasing and the low signal-to-noise ratio of surface electromyogram signal cause becoming very difficult from the action of few channel surface electromyographic signal identification multi-mode.Although the single-degree-of-freedom EMG-controlling prosthetic hand that obtains two action patterns from the two-way surface electromyogram signal is practicability, but the multi-freedom degree muscle-electric artificial hand commercialization of control is unsatisfactory in real time, and its key issue is that the accuracy that the multiple degrees of freedom pattern is handled in real time waits further raising.At present, the mode treatment accuracy 85% of Three Degree Of Freedom EMG-controlling prosthetic hand is to be difficult to drop into practical application, because the uncertain action of any one of doing evil through another person might cause beyond thought injury to the people with disability.
Summary of the invention
The purpose of this utility model is exactly at the deficiencies in the prior art, provides a kind of accuracy rate height, bionical performance good, can control the electrically powered hand of Three Degree Of Freedom control in real time.
Electrically powered hand of the present utility model comprises that the individual myoelectricity of N (2≤N≤4) picks up electric transducer and the individual brain electricity of M (2≤M≤4) picks up electric transducer.Each myoelectricity picks up electric transducer and comprises three the myoelectricity power-collecting electrodes placing on the residual arm of human body and the elementary amplifying circuit of myoelectricity of the localityization that is connected with myoelectricity power-collecting electrode signal; The outfan of the elementary amplifying circuit of myoelectricity connects with corresponding myoelectricity after-treatment circuit respectively, and myoelectricity after-treatment circuit comprises that the back level amplifies and two circuit of filtering, and the outfan of myoelectricity after-treatment circuit is connected with the input of A/D change-over circuit.Each brain electricity picks up the electric elementary amplifying circuit of brain that electric transducer comprises the brain electricity power-collecting electrode that places the human body crown and the reference electrode that places human body ear, is connected with reference electrode with the electric power-collecting electrode of brain, the outfan of the elementary amplifying circuit of brain electricity is connected with EEG signals after-treatment circuit, brain electricity after-treatment circuit has comprised back level amplification and two circuit of filtering, and brain electricity after-treatment circuit output end is connected with the input of A/D change-over circuit.Three motors connect with corresponding drive circuit respectively, and microprocessor is connected with the outfan of A/D change-over circuit, the input end signal of drive circuit respectively.
The drive circuit that myoelectricity in this utility model picks up electric transducer, myoelectricity after-treatment circuit (back level amplification, filter circuit), A/D change-over circuit, microprocessor, motor all adopts existing single-degree-of-freedom EMG-controlling prosthetic hand corresponding devices; The brain electricity picks up electric transducer, brain electricity after-treatment circuit (the back level is amplified, filter circuit) and then adopts existing scalp eeg signal acquisition system corresponding devices.
This utility model merges the identification of hand multi-locomotion mode, realize by brain electricity/electromyographic signal after pattern recognition, the accuracy of identification is all higher than using doing evil through another person of single brain electricity or electromyographic signal control, and action control is reliable, avoided the multi-freedom degree muscle-electric artificial hand of conventional commercial need be by switching the situation just can reach the control of multiple freedom degrees hand-prosthesis, realized the do evil through another person real-time control of six actions of Three Degree Of Freedom under the highly reliable discrimination, the bionical performance of doing evil through another person is obviously improved.
Description of drawings
Fig. 1 is the structural representation of this utility model device;
Fig. 2 is the binary tree structure sketch map of many-valued support vector machine classifier;
Fig. 3 is the multi-mode action recognition block diagram based on brain electricity/myoelectricity.
The specific embodiment
As shown in Figure 1, the electrically powered hand that jointly controls of brain electricity myoelectricity comprises that four myoelectricities that place the residual arm extensor carpi ulnaris m. of human body, flexor carpi ulnaris m., extensor digitorum, the pairing skin surface of pronator quadratus pick up electric transducer 1 and place the C3 that human body head central authorities determine according to 10-20 lead systems, two brain electricity of C4 position to pick up electric transducer 2.Each myoelectricity picks up electric transducer 1 and comprises three the myoelectricity power-collecting electrodes placing on the residual arm of human body and the elementary amplifying circuit of myoelectricity of the localityization that is connected with myoelectricity power-collecting electrode signal; The outfan of the elementary amplifying circuit of myoelectricity connects with corresponding myoelectricity after-treatment circuit 3 respectively, after-treatment circuit 3 comprises the bandpass filtering of 10～500Hz and the trap of 50Hz, obtain the electromyographic signal of effective frequency, the outfan of myoelectricity after-treatment circuit 3 is connected with the input of A/D change-over circuit 5.Each brain electricity picks up the electric elementary amplifying circuit of brain that electric transducer 2 comprises the brain electricity power-collecting electrode that places the human body crown and the reference electrode that places human body ear, is connected with reference electrode with the electric power-collecting electrode of brain, the outfan of the elementary amplifying circuit of brain electricity is connected with brain electricity after-treatment circuit 4, brain electricity after-treatment circuit 4 comprises trap circuit, back level amplifying circuit, the compensating circuit (being used to eliminate common-mode signal) of 50Hz, and brain electricity after-treatment circuit 4 outfans are connected with the input of A/D change-over circuit 5.Three motors 8 are realized six actions of three degree of freedom of electrically powered hands, and each motor 8 connects with corresponding drive circuit 7 respectively, and microprocessor 6 is connected with the outfan of A/D change-over circuit 5, the input end signal of drive circuit 7 respectively.
The control method of this electrically powered hand is: pick up EEG signals that electric transducer picks up and myoelectricity by the brain electricity and pick up the electromyographic signal that electric transducer picks up and convert data acquisition through elementary amplifying circuit, after-treatment circuit, A/D respectively, enter microprocessor, carry out the information processing of multiple motor pattern identification.At first adopt the permutation entropy method to extract and merge the feature of brain electricity and myoelectric information, utilize the many-valued support vector machine of binary tree structure to realize multiple motor pattern identification then.The identification computing of algorithm is finished by microprocessor, and exports three tunnel control signals according to recognition result, drives three motors of doing evil through another person through drive circuit, finishes six action controls that 3-degree-of-freedom electrical is done evil through another person.
The concrete grammar of permutation entropy feature extraction and fusion is: gather four tunnel electromyographic signals and two-way EEG signals, according to the setting of control cycle, respectively each road calculated permutations is made up entropy in each cycle.At first the data of input are chosen the signal of fixed length in the one-period by adding slip Hamming window, window length is got 200ms here, and frame moves into 50ms (myoelectricity is slightly different with the sample frequency of brain electricity, is respectively 2KHz and 1KHz).Brain electricity/electromyographic signal of remembering each road fixed length is one dimension time series { x (t), t=1,2 ... T} gets continuous n sampling point X every a sampling point in sequence
i=[x (i), x (i+1) ..., x (i+n-1)] and constitute subsequence, n is called the number of permutations here, X at random
iCarry out ascending order be arranged with n! Plant the mode of permutation and combination, wherein X
iA certain permutation and combination method can be expressed as:
{ (j
1..., j
c..., j
n), [x (i+j
l-1) ... ,≤x (i+j
c-1)≤... ,≤x (i+j
n-1)] } (1) wherein, j
cC sampling point is at original signal subsequence X in the subsequence of expression ordering back
iIn positional value.
The number of times that various arranging situations in the whole sequence occur is added up, and calculated relative frequency that various arranging situations occur as its probability P
1, P
2..., P
k, k≤n! , calculate permutation entropy by the definition of entropy:
By formula (2) as can be seen, if whole sequence is periodic, its permutation entropy just is 0 so; If white noise sequence, so various permutation and combination situations all can occur with equiprobability, its permutation and combination mean entropy just be lg ( ), because general time series is between periodic sequence and random sequence, thus their permutation entropy scope satisfy 0≤H (n)≤lg (n! ).Therefore, permutation entropy can be carried out normalization:
H′(n)＝H(n)/lg(n！) (3)
Draw from the calculating principle of permutation entropy, the variation of physiology electrical activity when EEG signals and electromyographic signal permutation entropy have directly reflected limb motion, can be used as the validity feature of identification motor pattern, what permutation entropy reflected simultaneously is the size of the probability of time series permutation and combination pattern, and it is irrelevant with the magnitude of signal, EEG signals that the signal amplitude scope is different (0～100 μ V) and electromyographic signal (0～6000 μ V) normalization have been realized, can make up two kinds of signal characteristics easily, finish the information fusion of characteristic layer, last obtaining with the permutation entropy after the normalization at each frame is the sextuple feature of value, with 50ms is to constitute continuous time series at interval, as the description to action pattern.In the calculating of permutation entropy, also need to determine parameter number of permutations n,, under the situation of less amount of calculation, obtain recognition effect preferably when n gets 5.
Classical support vector machine is the classification at two class problems, for multiple motor pattern identification problem of the present utility model, must re-construct many-valued grader and find the solution.If on the basis of classical support vector machine theory, re-construct multi-class disaggregated model and realize many-valued classification, the object function of selection will be very complicated, realize difficulty, and computation complexity is also very high, thereby less use.Another kind of building method is to realize many-valued classification by making up a plurality of two-value sub-classifiers, and this utility model is realized multiple motor pattern identification by the many-valued grader of structure binary tree structure.The structure of binary tree adopts the thought of classifying after the first cluster, and the class of being separated by farthest with other classes is split at first.
Many-valued support vector machine method is specifically: make up binary tree structure, at first will carry out cluster and ordering is handled to the sextuple permutation entropy feature samples set that the wearer that does evil through another person extracts under all kinds of motor patterns.Comprise a for one
1, a
2..., a
mThe classification problem of m class at first defines a altogether
iAnd a
jEuclidean distance between nearest two samples of two apoplexy due to endogenous wind is as between class distance, that is:
δ
i，j＝min{‖x
a-x
b‖，x
a∈a
i，x
b∈a
j} (4)
Each class is calculated distance value with other classes respectively, and these values are renumberd arrangement by ascending order, for example, for a
iClass can obtain the between class distance value δ of m-1 and other classes
I, j(j=1,2 ..., m, j ≠ i), be by ascending sequence arrangement:
Secondly relatively
(i=1,2 ..., value m) also sorts to corresponding class by descending order, if equate, then continues relatively
Size, go down successively; If all values all equate, then the medium and small class row front of class label, the row back that the class label is big.Finally obtain a b that rearranges to all classes
1, b
2..., b
m, can generate as shown in Figure 2 binary tree, the m=7 among the figure by class label ordering.
After all kinds of orderings, just can construct the optimum hyperplane of each node in the binary tree, at first with b
1Class action pattern sample is positive sample set, b
2..., b
mClass action pattern sample is the negative sample collection, the two-value support vector machine sub-classifier at structure root node place, and the inner product function of support vector machine adopts radially basic inner product function.Then with b
2Class action pattern sample is positive sample set, b
3..., b
mClass action pattern sample is the negative sample collection, the two-value sub-classifier of second interior nodes of structure.Go down successively, can obtain taxonomic structure based on the multi-class support vector machine of binary tree.
Utilize said method, discern to be divided into based on the motor pattern of brain electricity/myoelectricity and learn and discern two stages.As shown in Figure 3, band arrow solid line is a cognitive phase among the figure, and band arrow dotted line is a learning phase.At first calculate the permutation entropy of brain electricity/electromyographic signal, carry out Feature Fusion generating feature vector then, use the characteristic vector of known action pattern class to constitute sample set at learning phase and construct the binary tree sort device.At cognitive phase, then will extract each characteristic vector that merges in the characteristic sequence of back and be input to grader successively, begin to carry out step by step the two-value classification from root node, up to the action pattern classification of determining that each is corresponding constantly.
When the people with disability who wears the Three Degree Of Freedom EMG-controlling prosthetic hand needs the artificial hand controlled action, because " phantom limb " that the people with disability had sense, only need as being intended to control, healthy people do corresponding action, brain electricity/EMG-controlling prosthetic hand will be according to the two-way brain electricity and four tunnel electromyographic signals of corresponding hand motion, on judgement, base of recognition, artificial hand controlled is finished corresponding action: turn over motor control consciousness on the wrist, do evil through another person to finish synchronously and stretch the wrist action; Turn over motor control consciousness under the wrist, do evil through another person to finish synchronously and bend the wrist action; Wrist outward turning motor control consciousness is done evil through another person and is finished the wrist outward turning synchronously; Wrist inward turning motor control consciousness is done evil through another person and is finished the wrist inward turning synchronously; The five fingers stretching control consciousness, the hand of doing evil through another person opens; The motor control of clenching fist consciousness, the hand of doing evil through another person closes up; Hand loosens consciousness, does evil through another person and does not do any action.Avoid the multi-freedom degree muscle-electric artificial hand of conventional commercial need be, realized the do evil through another person real-time control of six actions of Three Degree Of Freedom by repeatedly switching the situation just can reach the control of multiple freedom degrees hand-prosthesis.
This utility model is realized six actions that Three Degree Of Freedom does evil through another person, the identification of seven kinds of patterns by C3 in the brain that receives the corresponding SEMG of four vastus meat (extensor carpi ulnaris m., flexor carpi ulnaris m., extensor digitorum and pronator quadratus), can standard 10-20 lead systems determines with international electroencephalography, C4 two-way EEG signals by fusion treatment.Six actions refer to opening of doing evil through another person, close up, wrist is stretched, wrist is bent, wrist outward turning, wrist inward turning; Seven kinds of patterns are meant that six corresponding patterns of action add the attonity pattern; During no sports consciousness, four vastus meat are in relaxed state, and brain is attonity consciousness also, does evil through another person and be failure to actuate.
Claims (1)
1, the electrically powered hand that jointly controls of a kind of brain electricity myoelectricity, comprise that N myoelectricity picks up electric transducer and M brain electricity picks up electric transducer, wherein 2≤N≤4,2≤M≤4 is characterized in that: each myoelectricity picks up electric transducer and comprises three the myoelectricity power-collecting electrodes placing on the residual arm of human body and the elementary amplifying circuit of myoelectricity of the localityization that is connected with myoelectricity power-collecting electrode signal; The outfan of the elementary amplifying circuit of myoelectricity connects with corresponding myoelectricity after-treatment circuit respectively, and myoelectricity after-treatment circuit comprises that the back level amplifies and two circuit of filtering, and the outfan of myoelectricity after-treatment circuit is connected with the input of A/D change-over circuit; Each brain electricity picks up the electric elementary amplifying circuit of brain that electric transducer comprises the brain electricity power-collecting electrode that places the human body crown and the reference electrode that places human body ear, is connected with reference electrode with the electric power-collecting electrode of brain, the outfan of the elementary amplifying circuit of brain electricity is connected with EEG signals after-treatment circuit, brain electricity after-treatment circuit has comprised back level amplification and two circuit of filtering, and brain electricity after-treatment circuit output end is connected with the input of A/D change-over circuit; Three motors connect with corresponding drive circuit respectively, and microprocessor is connected with the outfan of A/D change-over circuit, the input end signal of drive circuit respectively.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101836909A (en) * | 2010-04-23 | 2010-09-22 | 上海科生假肢有限公司 | Multipath control signal source for multi-freedom upper artificial limb and related control method and device |
| CN101569569B (en) * | 2009-06-12 | 2011-05-11 | 东北大学 | Interface system of human brain and manipulator in micro-power wireless communication mode |
| CN102319130A (en) * | 2011-07-21 | 2012-01-18 | 山东科技大学 | Control system and method for triggering multi-degree-of-freedom movement of upper artificial limbs by using toe |
| CN102429748A (en) * | 2011-12-01 | 2012-05-02 | 上海理工大学 | Holding speed controllable intelligent myoelectric prosthetic hand control circuit |
| CN102488514A (en) * | 2011-12-09 | 2012-06-13 | 天津大学 | Method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities |
-
2008
- 2008-08-01 CN CNU2008201220719U patent/CN201227336Y/en not_active Expired - Fee Related
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101569569B (en) * | 2009-06-12 | 2011-05-11 | 东北大学 | Interface system of human brain and manipulator in micro-power wireless communication mode |
| CN101836909A (en) * | 2010-04-23 | 2010-09-22 | 上海科生假肢有限公司 | Multipath control signal source for multi-freedom upper artificial limb and related control method and device |
| CN101836909B (en) * | 2010-04-23 | 2012-05-23 | 上海科生假肢有限公司 | Control method of one freedom degree or multi-freedom upper artificial limb |
| CN102319130A (en) * | 2011-07-21 | 2012-01-18 | 山东科技大学 | Control system and method for triggering multi-degree-of-freedom movement of upper artificial limbs by using toe |
| CN102319130B (en) * | 2011-07-21 | 2014-03-26 | 山东科技大学 | Control system and method for triggering multi-degree-of-freedom movement of upper artificial limbs by using toe |
| CN102429748A (en) * | 2011-12-01 | 2012-05-02 | 上海理工大学 | Holding speed controllable intelligent myoelectric prosthetic hand control circuit |
| CN102429748B (en) * | 2011-12-01 | 2014-06-18 | 上海理工大学 | Holding speed controllable intelligent myoelectric prosthetic hand control circuit |
| CN102488514A (en) * | 2011-12-09 | 2012-06-13 | 天津大学 | Method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities |
| CN102488514B (en) * | 2011-12-09 | 2013-10-23 | 天津大学 | Method for analyzing relativity between electroencephalograph and myoelectricity based on autonomous and stimulation movement modalities |
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| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20090429 Termination date: 20110801 |