CN202288542U - Artificial limb control device - Google Patents
Artificial limb control device Download PDFInfo
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- CN202288542U CN202288542U CN2011204128947U CN201120412894U CN202288542U CN 202288542 U CN202288542 U CN 202288542U CN 2011204128947 U CN2011204128947 U CN 2011204128947U CN 201120412894 U CN201120412894 U CN 201120412894U CN 202288542 U CN202288542 U CN 202288542U
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Abstract
The utility model relates to an artificial limb control device which comprises a signal acquisition unit, a signal processing unit and an artificial limb driver; the signal acquisition unit comprises a sensor, a pre-processing circuit and an A/D conversion module; the sensor is used for collecting a first bioelectrical signal and a second bioelectrical signal; the pre-processing circuit is used for magnifying and filter processing the collected first bioelectrical signal and second bioelectrical signal and for inputting the collected signals to the signal processing unit through the A/D conversion module; the signal processing unit comprises a microcontroller; the signal processing unit is connected with the pre-processing circuit and is used for pattern classification training and pattern recognition of the first bioelectrical signal and the second bioelectrical signal; and the artificial limb driver is connected with the signal processing unit and is used for driving an artificial limb to accomplish a corresponding action. According to the utility model, both the first bioelectrical signal and the second bioelectrical signal are adopted as information sources to identify spatial arm postures and limb actions of a user, so that the accuracy rate of action identification is ensured under the condition that the spatial arm postures vary, and the stability of limb control is greatly improved.
Description
[technical field]
This utility model relates to the rehabilitation auxiliary implement, particularly relates to a kind of artificial limb control device.
[background technology]
Closely for decades, (Electromyogram EMG) is widely used in artificial upper extremity's the control from the electromyographic signal of limbs surface recording.The myoelectricity artificial upper extremity provides the chance of improving quality of life for the people with disability of upper limb amputation, even can not look like the activity of nature upper limb so freely, myoelectric limb also can help them to recover physiological function some instincts of upper limb, basic, auxiliary its daily life.Present myoelectric limb (for example, German Otto Bock and China Shanghai section give birth to etc.) mostly is to utilize a pair of residual muscle (agonist and Antagonistic muscle) to control a degree of freedom of motion.Yet after the limbs amputation, the myoelectric information source is limited, and the degree of amputation is high more, and residual limb muscle is few more, and needs the limb action of recovery many more.Therefore, realize that with this traditional myoelectricity control mode the multiple degrees of freedom control of artificial limb is very difficult.
Along with the development of electronic technology and microprocessor technology, arise at the historic moment based on the bionical myoelectric limb of mode identification technology.Mode identification technology is intended to utilize the available limited myoelectric information of residual limbs, realizes controlling with intuition freely of artificial limb.But, can find that in the clinical research of myoelectric limb reduce based on the recognition accuracy of the myoelectric limb of mode identification technology, the conversion meeting of user arm space attitude directly causes the bad stability of artificial limb system.This is because user during myoelectric limb, often is placed on certain certain location with arm in training, and arm hangs down in the health outside naturally usually, because the electromyographic signal good reproducibility that obtains in this attitude, natural recognition accuracy is high.Yet in real life, user is in different use scenes, and arm attitude difference is very big, and the bioelectrical signals that records has just produced certain difference, and this has directly caused the artificial limb system recognition effect to reduce.The second, because myoelectric sensor receives the influence of factors such as placement location skew, Skin Resistance variation easily, actual often controlling has a certain distance with the expectation effect.The 3rd, myoelectric sensor expensive, size be bigger than normal, and to be difficult to embed the small space of prosthetic socket also be the one of the main reasons that hinders the clinical process of myoelectric limb.
[utility model content]
Based on this, be necessary to provide a kind of based on the artificial limb control device double source bio-electrical information, high, the good stability of recognition accuracy.
A kind of artificial limb control device; It is characterized in that; Comprise: signal gathering unit, comprise the pick off, pre-process circuit and the A/D modular converter that connect successively, said pick off is used to gather first bioelectrical signals and second bioelectrical signals at deformed limb place; Said pre-process circuit to first bioelectrical signals and second bioelectrical signals that collects amplify, after the Filtering Processing, through said A/D modular converter input signal processing unit; Signal processing unit comprises microcontroller, and said signal processing unit connects said pre-process circuit, is used for the pattern classification training and the pattern recognition of first bioelectrical signals and second bioelectrical signals; The artificial limb driver connects said signal processing unit, drives artificial limb according to the output result of said signal processing unit and accomplishes corresponding action.
Preferably; Said first bioelectrical signals is an electromyographic signal; Said second bioelectrical signals is the moving signal of flesh; Said pick off comprises the electromyographic signal pick off and the moving signal transducer of flesh that is used to gather the moving signal of flesh that is used to gather electromyographic signal, and the moving signal transducer of said flesh is a 3-axis acceleration sensor.
Preferably, said electromyographic signal pick off is a kind of in Ag-AgCl viscose glue formula, metal sensor and the textile electrode.
Preferably, the quantity of said electromyographic signal pick off is more than 3, and the quantity of the moving signal transducer of said flesh is more than 1.
Preferably, the moving signal transducer of said electromyographic signal pick off and flesh is attached on the muscle surface of a place or many places in these three positions of shoulder, upper arm and forearm.
Preferably, said microcontroller connects said A/D modular converter, and said pattern classification training comprises the characteristic information that extracts said first bioelectrical signals and second bioelectrical signals, pattern classification CALCULATION OF PARAMETERS and storage.
Preferably; Said microcontroller at first carries out arm space attitude type identification according to the characteristic information of said pattern classification parameter and said second bioelectrical signals; Select the pattern classification parameter of first bioelectrical signals then based on arm space gesture recognition result, carry out the limb action type identification and recognition result is exported to said artificial limb driver according to the characteristic information of said first bioelectrical signals.
Preferably; Said microcontroller comprises the micro controller module of two parallel connections; A micro controller module extracts the characteristic information of said first bioelectrical signals and carries out the limb action type identification; Another micro controller module extracts the characteristic information of said second bioelectrical signals and carries out arm space attitude type identification, and the recognition result of microcontroller is exported to said artificial limb driver.
Preferably, said pre-process circuit comprises amplifying circuit, filter circuit and the rectification circuit that connects successively.
Above-mentioned artificial limb control method and system discern the type of action and the spatial attitude of limbs respectively according to two information sources, drive artificial limb according to the spatial attitude of limbs and type of action and accomplish corresponding action.Make system under the situation that the arm space attitude changes, the safety action recognition accuracy, thus greatly improved the control stability of artificial limb system.
[description of drawings]
Fig. 1 is the flow chart of artificial limb control method among the embodiment;
Fig. 2 is the structural representation of artificial limb control device among the embodiment;
Fig. 3 is the structural representation of signal gathering unit among the embodiment.
[specific embodiment]
For purpose, the feature and advantage that make this utility model can be more obviously understandable, the specific embodiment of this utility model is done detailed explanation below in conjunction with accompanying drawing.
Fig. 1 is the flow chart of artificial limb control method among the embodiment, comprises the following steps:
S110, first bioelectrical signals and second bioelectrical signals of collection deformed limb skin surface.
With respect to the electromyographic signal of using in the conventional prosthesis, flesh move signal (Mechanomyography, MMG) have cost low, do not receive Skin Resistance variable effect, the miniature advantages such as prosthetic socket that conveniently are positioned over.It is a kind of mechanical signal that characterizes the muscle surface microvibration, and when muscle contraction, the electrical activity that associated movement neuronal activation moving cell is produced can cause the machinery or the mechanics vibration of muscle fibers contract.It is in 1986 that the MMG signal is used for artificial limb control the earliest, and people such as Barry advise using the control signal of MMG signal as artificial limb, and the system of their development can successfully distinguish crooked wrist and stretch these two actions of wrist, and system demonstrates good stability.Scholar's such as Silva, Hong-Bo Xie research has in recent years not only confirmed the necessity of MMG signal application in clinical artificial limb control, and has confirmed that MMG is a kind of feasible method fully as the control signal of artificial limb.
In the present embodiment, gather the type of action that electromyographic signal is used to discern limbs.In other embodiments,, still have the other biological signal of telecommunication to comprise certain movable information equally, also can be used as the information source of present technique except the application surface electromyographic signal.The for example EEG signals of human-machine interface technology and the peripheral nerve signal of telecommunication, and intrusive mood deep layer electromyographic signal.In addition, in the present embodiment, gather the spatial attitude of the moving signal identification of flesh limbs.In other embodiments, except using the moving signal of flesh, can also adopt goniometer to be used to write down joint angles information.
The moving signal of electromyographic signal and flesh is gathered through electromyographic signal pick off and the moving signal transducer of flesh respectively.According to the different situations of amputee's deformed limb situation and expectation recovery action number, the number of the moving signal transducer of electromyographic signal pick off and flesh and installation position are with different.Generally speaking, the electromyographic signal pick off adopts 3-12, and the moving signal transducer of flesh adopts 1～3, the signal of a passage of each sensor acquisition.When being applied to upper extremity prosthesis, the moving signal transducer of electromyographic signal pick off and flesh is attached on the muscle surface of shoulder, upper arm, forearm and hand.When being applied to artificial leg, the moving signal transducer of electromyographic signal pick off and flesh is attached at rectus femoris, vastus lateralis, biceps femoris and tensor fasciae latae etc. and locates.In the present embodiment, gather electromyographic signal and the moving signal of flesh through integrated 3-axis acceleration sensor.This pick off is the moving signal transducer of a kind of myoelectricity-three axial musculature.In other embodiments, the moving signal transducer of flesh can adopt piezoelectricity touch sensor, Electret Condencer Microphone, laser displacement sensor etc.The electromyographic signal pick off can adopt Ag-AgCl viscose glue formula, metal sensor or textile electrode etc.
In other embodiments, first bioelectrical signals and second bioelectrical signals can all be electromyographic signals, or first bioelectrical signals and second bioelectrical signals all are the moving signals of flesh.
S120 carries out pretreatment to first bioelectrical signals and second bioelectrical signals.
In the present embodiment, be after electromyographic signal is amplified, carry out the bandpass filtering treatment that bandwidth is the 5-450 hertz (signal that promptly keeps the 5-450 hertz) again.The moving signal of flesh is carried out the bandpass filtering treatment that bandwidth is the 5-200 hertz.Can also carry out rectification after the filtering handles.Sample frequency is set to the 500-1000 hertz.
S130 extracts the characteristic information of first bioelectrical signals and second bioelectrical signals.
Adopting data analysis window that signal is carried out feature extraction, is to extract electromyographic signal and moving signal temporal signatures and/or the frequency domain character relevant with limb motion of flesh respectively in the present embodiment.Data analysis window can have overlapping or zero lap; From each data analysis window, extract the time-frequency and/or the frequency domain character of myoelectricity (flesh is moving) signal; Combine the characteristic vector of the characteristic information that forms this passage myoelectricity (flesh is moving) signal, the combination of eigenvectors of all passage myoelectricities (flesh is moving) signal is myoelectricity (flesh is a moving) eigenmatrix.
Because the moving signal of the flesh particularly moving signal of three axial musculatures is the responsive low frequency mechanical signal of a kind of ten minutes; The temporal signatures parameter of the moving signal of flesh can adopt amplitude maximum, average, variance, integrated value (IMMG), root-mean-square value (RMS), and the rise time (Rise-Time) of the moving signal of flesh, time-histories (Duration) etc.The moving signal frequency-domain characteristic parameter of flesh can adopt frequency of average power (MPF), middle bit frequency (MF), mid frequency (CF) etc.
Temporal signatures parameter for electromyographic signal can adopt average absolute value, G-bar absolute value, sampling point difference in magnitude, zero-crossing rate etc.The frequency domain character parameter of electromyographic signal can adopt frequency of average power, median frequency, crest frequency etc.In the time of can also adopting simultaneously-and the frequency characteristic parameter, comprise the characteristic information as electromyographic signal such as wavelet coefficient, Wigner distribution, entropy.
The time domain method and frequency domain method that characteristic information extraction adopts, can also adopt time-domain and frequency-domain combined techniques, high-order spectrometry and chaos and fractal method etc. in present embodiment.
S140 is according to the spatial attitude and the type of action of characteristic information identification limbs.
In the present embodiment, be to discern through grader, comprise grader cascade and the grader dual mode that walks abreast.Grader is parallel to be the spatial attitude according to the characteristic information identification limbs of second bioelectrical signals, and according to the type of action of the characteristic information identification limbs of first bioelectrical signals, and spatial attitude is the identification of calculating of while (walking abreast) with type of action.The grader cascade then is earlier according to the spatial attitude of the characteristic information identification limbs of second bioelectrical signals, again according to the type of action of the characteristic information identification limbs of the spatial attitude of limbs and first bioelectrical signals.In the present embodiment, first bioelectrical signals is an electromyographic signal, and second bioelectrical signals is the moving signal of flesh.In other embodiments, first bioelectrical signals and second bioelectrical signals can all be electromyographic signals, or first bioelectrical signals and second bioelectrical signals all are the moving signals of flesh.
In the present embodiment; Adopt linear discriminant analysis method (Linear Discriminant Analysis; LDA) analyze the characteristic information of moving signal of flesh and electromyographic signal and move calculated signals and discern the spatial attitude and the type of action of limbs, promptly identify the pattern that user need use according to stored training parameter in advance and real-time electromyographic signal and flesh.Before utilizing the limb action that LDA pattern classifier real-time estimate experimenter wants to carry out; Just before user formally uses this artificial limb; Need characteristic information difference training action grader and spatial attitude grader, make it " remember " type of action and the spatial attitude that is comprised with electromyographic signal and the moving signal of flesh.Realize the training of grader by the training program module.The linear discriminant analysis method makes full use of the classification attaching information, and operation is simple, and required amount of calculation is little, and computation time is short, is prone to embed hardware system and realizes extensive use.And, the research proof, the accuracy of linear discriminant analysis method and other several kinds of main recognition methodss (artificial neural network, gauss hybrid models) no significant difference or identity are better.Except above-mentioned linear discriminant analysis method; Can also adopt other based on the theoretical sorting algorithm of Bayes; Fei Sheer linear discriminant (FisherLinear Discrimination for example; FLD), SVMs (Support Vector Machine, SVM), artificial nerve network classifier (Artificial Neural Network, ANN) and hidden Markov model (Hidden Markov Models; HMM), gauss hybrid models (Gussian Mixture Models, identification maneuver type and spatial attitude such as GMM).
S150 accomplishes corresponding action according to the spatial attitude and/or the type of action driving artificial limb of limbs.
In the grader parallel mode, be to accomplish corresponding action according to the spatial attitude and the type of action driving artificial limb of limbs; In grader cascade mode, be only to accomplish corresponding action, or drive artificial limb completion corresponding action according to the spatial attitude and the type of action of limbs according to the type of action of limbs.
The difference of prosthetic user's muscle contraction strength will change the amplitude of electromyographic signal.In one embodiment, can utilize the emg amplitude size to regulate and control the speed of artificial limb action, amplitude greatly then speed is fast, otherwise then speed is slow.
Through 5 BE amputation persons respectively 5 different spatial positions (attitude) carry out 6 common basic actss (in the wrist receipts, wrist abduction, wrist outward turning, wrist inward turning and hands opens, closure) experiment, prove that above-mentioned cascade/concurrency control method is feasible, effective.Respectively adopt the moving signal transducer of 8 integrated myoelectricity-fleshes to gather the electromyographic signal and the moving signal of flesh of experimenter's deformed limb and strong side respectively in the experiment.For the deformed limb side, the moving signal of flesh to the everything type at the average error discrimination of 5 spatial attitudes below 0.03%.And the moving signal of flesh under all spatial attitudes to the average error discrimination of 6 basic actss below 8%.For all experimenters, the bat of cascade control RM is more than 93%, and the bat of parallel control RM is more than 95%.Strong side of contrast and amputated side, the control performance of strong side slightly is superior to amputated side.This explanation, above-mentioned artificial limb control method based on the moving fusion of myoelectricity-flesh can realize the accurate action control of upper limb in the greater room scope.
The people with disability controls artificial limb or wants more accurately to control easily artificial limb, thus because of the signal source of its mutilation control very limited.Electromyographic signal can provide the detailed information of limb action, but muscle fatigue, electrode move, sweat etc. and can influence the performance that myoelectricity is controlled.Above-mentioned artificial limb control method has increased the moving signal of flesh as information source.It does not receive the Skin Resistance variable effect, can remedy the defective of above-mentioned electromyographic signal.Flesh moves the signal transducer relative low price, and compact size is convenient nested to the limited space of prosthetic socket.
Because under the different spaces attitude; Some specific muscle groups need remain retracted; Even if so carry out same action, the moving information of myoelectric information and flesh all has bigger different, thus artificial limb in clinical controlled in real-time often the type of action discrimination reduce, bad stability.Above-mentioned artificial limb control method not only can be differentiated the arm space attitude of user, and can improve the recognition accuracy of limb action type, strengthens the stability of artificial limb control.
Fig. 2 is the structural representation of artificial limb control device among the embodiment.Comprise signal gathering unit 210, signal processing unit 230 and artificial limb driver 250.Fig. 3 is the structural representation of signal gathering unit among the embodiment.Signal gathering unit 210 comprises pick off 212, pre-process circuit 214 and the A/D modular converter 216 that connects successively.
Pick off 212 is used to gather first bioelectrical signals and second bioelectrical signals of deformed limb skin surface.In the present embodiment, first bioelectrical signals is an electromyographic signal, and second bioelectrical signals is the moving signal of flesh.In other embodiments,, still have the other biological signal of telecommunication to comprise certain movable information equally, also can be used as the information source of present technique except the application surface electromyographic signal.The for example EEG signals of human-machine interface technology and the peripheral nerve signal of telecommunication, and intrusive mood deep layer electromyographic signal.In addition, first bioelectrical signals and second bioelectrical signals can all be electromyographic signals, or first bioelectrical signals and second bioelectrical signals all are the moving signals of flesh.
The moving signal of electromyographic signal and flesh is gathered through electromyographic signal pick off and the moving signal transducer of flesh respectively.According to the different situations of amputee's deformed limb situation and expectation recovery action number, the number of the moving signal transducer of electromyographic signal pick off and flesh and installation position are with different.Generally speaking, the electromyographic signal pick off adopts 3-12, and the moving signal transducer of flesh adopts 1～3, and each pick off 212 is gathered the signal of a passage.The moving signal transducer of electromyographic signal pick off and flesh is attached on the muscle surface of a place or many places in these three positions of shoulder, upper arm and forearm.In the present embodiment, gather electromyographic signal and the moving signal of flesh through integrated 3-axis acceleration sensor.This pick off is the moving signal transducer of a kind of myoelectricity-three axial musculature.In other embodiments, the moving signal transducer of flesh can adopt piezoelectricity touch sensor, Electret Condencer Microphone, laser displacement sensor etc.The electromyographic signal pick off can adopt Ag-AgCl viscose glue formula, metal sensor or textile electrode etc.
Pre-process circuit 214 connects pick off 212; Comprise the amplifying circuit, filter circuit and the rectification circuit that connect successively; First bioelectrical signals and second bioelectrical signals that are used for pick off 212 is gathered carry out signal amplification and filter rectification processing, then signal are passed through A/D modular converter 216 input signal processing units 230.
Signal processing unit 230 comprises microcontroller, is used for the pattern classification training and the pattern recognition of first bioelectrical signals and second bioelectrical signals.
The pattern classification training comprises the characteristic information that extracts said first bioelectrical signals and second bioelectrical signals, pattern classification CALCULATION OF PARAMETERS and storage.Adopt data analysis window that signal is carried out feature extraction in the present embodiment, extract the temporal signatures and/or the frequency domain character of the moving signal of electromyographic signal and flesh respectively.Data analysis window can have overlapping or zero lap; From each data analysis window, extract the time-frequency and/or the frequency domain character of myoelectricity (flesh is moving) signal; Combine the characteristic vector of the characteristic information that forms this passage myoelectricity (flesh is moving) signal, the combination of eigenvectors of all passage myoelectricities (flesh is moving) signal is myoelectricity (flesh is a moving) eigenmatrix.
Because the moving signal of the flesh particularly moving signal of three axial musculatures is the responsive low frequency mechanical signal of a kind of ten minutes; The temporal signatures parameter of the moving signal of flesh can adopt amplitude maximum, average, variance, integrated value (IMMG), root-mean-square value (RMS), and the rise time (Rise-Time) of the moving signal of flesh, time-histories (Duration) etc.The moving signal frequency-domain characteristic parameter of flesh can adopt frequency of average power (MPF), middle bit frequency (MF), mid frequency (CF) etc.
Temporal signatures parameter for electromyographic signal can adopt average absolute value, G-bar absolute value, sampling point difference in magnitude, zero-crossing rate etc.The frequency domain character parameter of electromyographic signal can adopt frequency of average power, median frequency, crest frequency etc.In the time of can also adopting simultaneously-and the frequency characteristic parameter, comprise the characteristic information as electromyographic signal such as wavelet coefficient, Wigner distribution, entropy.
The time domain method and frequency domain method that characteristic information extraction adopts, can also adopt time-domain and frequency-domain combined techniques, high-order spectrometry and chaos and fractal method etc. in present embodiment.
The artificial limb control device comprises grader cascade structure and two kinds of framework forms of grader parallel organization.In the grader cascade structure; Microcontroller at first carries out arm space attitude type identification according to the characteristic information of the pattern classification parameter and second bioelectrical signals; Select the pattern classification parameter of first bioelectrical signals then based on arm space gesture recognition result; Characteristic information according to first bioelectrical signals carries out the limb action type identification, and recognition result is exported to artificial limb driver 250.In the grader parallel organization; Microcontroller comprises the micro controller module of two parallel connections; A micro controller module extracts the characteristic information of first bioelectrical signals and carries out the limb action type identification; The another one micro controller module extracts the characteristic information of second bioelectrical signals and carries out arm space attitude type identification, and the recognition result of limb action type and spatial attitude type is exported to artificial limb driver 250.As aforementioned, first bioelectrical signals is preferably electromyographic signal, and second bioelectrical signals is preferably the moving signal of flesh.In other embodiments, first bioelectrical signals and second bioelectrical signals can all be electromyographic signals, or first bioelectrical signals and second bioelectrical signals all are the moving signals of flesh.
Grader cascade structure and grader parallel organization are the framework form of single kind signal.In other embodiments, can also use of the input of the moving signal combination of electromyographic signal and flesh as single grader or two graders.
In the present embodiment; Adopt linear discriminant analysis method (Linear DiscriminantAnalysis; LDA) analytical characteristic information and spatial attitude and the type of action of relatively discerning limbs according to stored training result in advance promptly identify the pattern that user need use.Before utilizing the limb action that LDA pattern classifier real-time estimate experimenter wants to carry out; Just before user formally uses this artificial limb; Need characteristic information difference training action grader and spatial attitude grader, make it " remember " type of action and the spatial attitude that is comprised with first bioelectrical signals and second bioelectrical signals.Realize the training of grader by the training program module.
The linear discriminant analysis method makes full use of the classification attaching information, and operation is simple, and required amount of calculation is little, and computation time is short, is prone to embed hardware system and realizes extensive use.And, the research proof, the accuracy of linear discriminant analysis method and other several kinds of main recognition methodss (artificial neural network, gauss hybrid models) no significant difference or identity are better.Except above-mentioned linear discriminant analysis method; Can also adopt other based on the theoretical sorting algorithm of Bayes; Fei Sheer linear discriminant (Fisher Linear Discrimination for example; FLD), SVMs (Support Vector Machine, SVM), artificial nerve network classifier (Artificial NeuralNetwork, ANN) and hidden Markov model (Hidden Markov Models; HMM), gauss hybrid models (Gussian Mixture Models, identification maneuver type and spatial attitude such as GMM).
Artificial limb driver 250 connects said signal processing unit 230, drives artificial limb according to the output result of signal processing unit 230 and accomplishes corresponding action.
The above embodiment has only expressed several kinds of embodiments of this utility model, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to this utility model claim.Should be pointed out that for the person of ordinary skill of the art under the prerequisite that does not break away from this utility model design, can also make some distortion and improvement, these all belong to the protection domain of this utility model.Therefore, the protection domain of this utility model patent should be as the criterion with accompanying claims.
Claims (9)
1. an artificial limb control device is characterized in that, comprising:
Signal gathering unit; Comprise the pick off, pre-process circuit and the A/D modular converter that connect successively; Said pick off is used to gather first bioelectrical signals and second bioelectrical signals at deformed limb place; Said pre-process circuit to first bioelectrical signals and second bioelectrical signals that collects amplify, after the Filtering Processing, through said A/D modular converter input signal processing unit;
Signal processing unit comprises microcontroller, and said signal processing unit connects said pre-process circuit, is used for the pattern classification training and the pattern recognition of first bioelectrical signals and second bioelectrical signals;
The artificial limb driver connects said signal processing unit, drives artificial limb according to the output result of said signal processing unit and accomplishes corresponding action.
2. artificial limb control device according to claim 1; It is characterized in that; Said first bioelectrical signals is an electromyographic signal; Said second bioelectrical signals is the moving signal of flesh, and said pick off comprises the electromyographic signal pick off and the moving signal transducer of flesh that is used to gather the moving signal of flesh that is used to gather electromyographic signal, and the moving signal transducer of said flesh is a 3-axis acceleration sensor.
3. artificial limb control device according to claim 2 is characterized in that, said electromyographic signal pick off is a kind of in Ag-AgCl viscose glue formula, metal sensor and the textile electrode.
4. artificial limb control device according to claim 2 is characterized in that, the quantity of said electromyographic signal pick off is more than 3, and the quantity of the moving signal transducer of said flesh is more than 1.
5. artificial limb control device according to claim 2 is characterized in that, the moving signal transducer of said electromyographic signal pick off and flesh is attached on the muscle surface of a place or many places in these three positions of shoulder, upper arm and forearm.
6. according to any described artificial limb control device among the claim 1-5; It is characterized in that; Said microcontroller connects said A/D modular converter; Said pattern classification training comprises the characteristic information that extracts said first bioelectrical signals and second bioelectrical signals, pattern classification CALCULATION OF PARAMETERS and storage.
7. artificial limb control device according to claim 6; It is characterized in that; Said microcontroller is used for carrying out arm space attitude type identification according to the characteristic information of said pattern classification parameter and said second bioelectrical signals; Select the pattern classification parameter of first bioelectrical signals then based on arm space gesture recognition result, carry out the limb action type identification and recognition result is exported to said artificial limb driver according to the characteristic information of said first bioelectrical signals.
8. artificial limb control device according to claim 6; It is characterized in that; Said microcontroller comprises the micro controller module of two parallel connections; A micro controller module extracts the characteristic information of said first bioelectrical signals and carries out the limb action type identification, and another micro controller module extracts the characteristic information of said second bioelectrical signals and carries out arm space attitude type identification, and the recognition result of microcontroller is exported to said artificial limb driver.
9. artificial limb control device according to claim 1 is characterized in that, said pre-process circuit comprises amplifying circuit, filter circuit and the rectification circuit that connects successively.
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| CN103970271A (en) * | 2014-04-04 | 2014-08-06 | 浙江大学 | Daily activity identifying method with exercising and physiology sensing data fused |
| CN104586390A (en) * | 2014-12-18 | 2015-05-06 | 中国科学院深圳先进技术研究院 | Information processing method and related equipment |
| CN105686827A (en) * | 2016-03-22 | 2016-06-22 | 浙江大学 | Microcontroller based electromyogram signal processing and feature extraction method |
| CN105943207A (en) * | 2016-06-24 | 2016-09-21 | 吉林大学 | Intelligent artificial limb movement system based on idiodynamics and control methods thereof |
| CN108742957A (en) * | 2018-06-22 | 2018-11-06 | 上海交通大学 | A kind of artificial limb control method of multi-sensor fusion |
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| CN112057212A (en) * | 2020-08-03 | 2020-12-11 | 桂林电子科技大学 | Artificial limb system based on deep learning |
| CN114668564A (en) * | 2022-05-26 | 2022-06-28 | 深圳市心流科技有限公司 | Method for dynamically adjusting sampling frequency based on electromyographic signal data |
| CN114676737A (en) * | 2022-05-26 | 2022-06-28 | 深圳市心流科技有限公司 | Dynamic regulation method for sampling frequency of electromyographic signal |
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| CN103970271A (en) * | 2014-04-04 | 2014-08-06 | 浙江大学 | Daily activity identifying method with exercising and physiology sensing data fused |
| CN104586390A (en) * | 2014-12-18 | 2015-05-06 | 中国科学院深圳先进技术研究院 | Information processing method and related equipment |
| CN105686827A (en) * | 2016-03-22 | 2016-06-22 | 浙江大学 | Microcontroller based electromyogram signal processing and feature extraction method |
| CN105686827B (en) * | 2016-03-22 | 2018-12-18 | 浙江大学 | A kind of electromyography signal processing and feature extracting method based on microcontroller |
| CN105943207A (en) * | 2016-06-24 | 2016-09-21 | 吉林大学 | Intelligent artificial limb movement system based on idiodynamics and control methods thereof |
| CN108742957A (en) * | 2018-06-22 | 2018-11-06 | 上海交通大学 | A kind of artificial limb control method of multi-sensor fusion |
| CN109381184A (en) * | 2018-10-15 | 2019-02-26 | 刘丹 | A kind of wearable smart machine control method that auxiliary is carried |
| CN111297354A (en) * | 2020-02-17 | 2020-06-19 | 中国人民解放军军事科学院军事医学研究院 | Myoelectric and pressure combined hybrid sensor system with self-calibration function |
| CN112057212A (en) * | 2020-08-03 | 2020-12-11 | 桂林电子科技大学 | Artificial limb system based on deep learning |
| CN114668564A (en) * | 2022-05-26 | 2022-06-28 | 深圳市心流科技有限公司 | Method for dynamically adjusting sampling frequency based on electromyographic signal data |
| CN114676737A (en) * | 2022-05-26 | 2022-06-28 | 深圳市心流科技有限公司 | Dynamic regulation method for sampling frequency of electromyographic signal |
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