WO2017073770A1 - Method of identifying myoelectric pattern employing time series information, and myoelectric prosthetic hand - Google Patents
Method of identifying myoelectric pattern employing time series information, and myoelectric prosthetic hand Download PDFInfo
- Publication number
- WO2017073770A1 WO2017073770A1 PCT/JP2016/082171 JP2016082171W WO2017073770A1 WO 2017073770 A1 WO2017073770 A1 WO 2017073770A1 JP 2016082171 W JP2016082171 W JP 2016082171W WO 2017073770 A1 WO2017073770 A1 WO 2017073770A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- myoelectric
- pattern
- patterns
- identification
- prosthetic hand
- Prior art date
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/50—Prostheses not implantable in the body
- A61F2/54—Artificial arms or hands or parts thereof
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
- A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
- A61F2/50—Prostheses not implantable in the body
- A61F2/68—Operating or control means
- A61F2/70—Operating or control means electrical
- A61F2/72—Bioelectric control, e.g. myoelectric
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
Definitions
- the present invention relates to the identification of myoelectric patterns used in devices controlled by myoelectricity.
- Prosthetic hands are roughly classified into decorative prostheses, active prostheses, and electric prostheses.
- the main purpose of the decorative prosthesis is to reproduce the appearance, and the active prosthesis is to reproduce the functionality. Therefore, the decorative prosthetic hand cannot be moved actively, and the active prosthetic hand is driven by transmitting the movement of the remaining part of the user as power using a wire or the like.
- an electric prosthesis is driven by electric power and a motor.
- Electric prosthetic hands have fewer restrictions on wire handling than active prosthetic hands, and the direction and amount of movement can be freely designed to some extent, and various prosthetic hands such as those with multiple degrees of freedom have been produced.
- a myoelectric prosthetic hand controlled using a surface myoelectric signal for measuring myoelectricity on the skin surface is generally used.
- the myoelectric prosthetic hand is used as a substitute for the hand and has a function of grasping an object, the certainty of the operation is regarded as very important. In particular, in terms of operational certainty, it is desirable that control be performed with a certainty of at least 80% in order to be used practically.
- the myoelectricity does not simply increase the degree of freedom in controlling the myoelectric potential even if the number of channels of the electromyograph is increased. From this background, many myoelectric prostheses often place electromyographs at two locations in the flexor and extensor groups that can be contracted separately. Therefore, it is possible to determine 2-bit, that is, 4 states by contracting the flexor and extensor groups separately, but due to the above-mentioned crosstalk problem, further increases in electromyographs are effective. This is the limit for the number of states. These four states are often assigned to rest, forward rotation, reverse rotation, and mode change. As the number of operations performed with the prosthetic hand increases, the number of mode changes increases and the operation becomes complicated, so training for about 4 weeks is required to use it freely. Therefore, a new control method suitable for increasing the degree of freedom of the prosthetic hand is required.
- Ajiboye et al. Used fuzzy theory to measure and learn EMG 8 times for each EMG pattern, and measured about 97 EMGs in the palm of the wrist, dorsiflexion of the wrist, ulnar displacement, grip, and rest.
- Successful identification with% accuracy (Non-Patent Document 1). Even the cutting person has an accuracy of about 96%, and the identification accuracy can withstand practical use. However, since it is recognized that ease of wearing is important for practical use of the prosthetic hand, it may be problematic to measure and learn each of the five patterns of myoelectricity eight times. Therefore, it is necessary to prepare a method before use and to maintain a high identification rate.
- the number of myoelectric patterns to be identified is 15, which is higher than that of existing myoelectric prostheses, and the classification rate is over 90%. However, the number of electrodes is still 12, which is larger than the existing myoelectric prosthesis.
- a myoelectric identification algorithm having a high number of electrodes and a small calibration time and high identification accuracy is desired.
- the currently available multi-degree-of-freedom myoelectric prosthesis does not require calibration, but there are only a few types of myoelectric patterns that can be identified.
- many myoelectric patterns have been identified, but the number of electrodes required for calibration, the length of time, and the accuracy of identification have become problems, and they have not been put into practical use.
- An object of the present invention is to provide an algorithm that dramatically improves the recognition rate of myoelectric patterns while minimizing time.
- the first invention is: A measurement unit for measuring myoelectricity at regular time intervals; A myoelectric identification unit for identifying which electromyographic pattern of the electromyogram pattern learned in advance should be classified, A buffer for storing a predetermined number of myoelectric patterns identified by the myoelectric identification unit in time series, With reference to the predetermined number of myoelectric patterns stored in the buffer and when an occupation ratio exceeding a certain ratio is recognized in a specific myoelectric pattern, control according to the operation command associated with the myoelectric pattern is performed. It is a myoelectric prosthesis characterized by performing.
- the buffer is a ring buffer, and when the number of myoelectric patterns to be stored reaches the predetermined number, the old myoelectric pattern is discarded. It is a myoelectric prosthesis described in the invention.
- a third invention according to the present application is the myoelectric prosthetic hand according to the second invention, characterized in that when the occupation ratio is 50% or more, control according to the specific myoelectric pattern is performed. .
- the occupation ratio is 100 / (n + 1)% or more when the occupation ratio is 100 / (n + 1)% or more.
- the myoelectric prosthetic hand according to the second or third aspect of the invention is characterized in that control is performed simultaneously in accordance with an operation command corresponding to each of a plurality of myoelectric patterns occupying the same.
- 5th invention which concerns on this application is a myoelectric pattern identification method, and in the apparatus to which myoelectric control is applied, myoelectricity is measured at a fixed time interval, and the myoelectric pattern learned in advance by the apparatus Among the electromyographic patterns to be classified, and the device stores the identified electromyographic patterns in a predetermined number of times in time series, and the device stores the electromyographic patterns.
- the predetermined number of myoelectric patterns when an occupation ratio higher than a certain ratio is recognized in a specific myoelectric pattern, control is performed according to an operation command corresponding to the myoelectric pattern.
- 6th invention which concerns on this application is a myoelectric pattern identification program performed with the processor of the apparatus to which myoelectric control is applied, Comprising: The procedure which measures myoelectricity by a fixed time interval, and was learned beforehand Among the myoelectric patterns, a procedure for identifying which myoelectric pattern the measured electromyogram should be classified into, and a procedure for storing a predetermined number of the identified myoelectric patterns in chronological order are stored. And a procedure for referring to the predetermined number of myoelectric patterns and performing control according to an operation command corresponding to the myoelectric pattern when an occupation ratio equal to or higher than a certain ratio is recognized in the specific myoelectric pattern.
- the present invention it is possible to achieve high-speed learning and a high identification rate with a small number of electrode channels, which is effective for practical control of a multi-degree-of-freedom myoelectric hand.
- (A) is a conventional algorithm
- (b) is a conceptual diagram showing the algorithm of the present invention. It is a conceptual diagram which shows the structure of the neural network used by this invention. It is a flowchart which shows the algorithm of this invention. It is a schematic diagram which shows the measurement site
- (A) is an electromyograph
- (b) is a photograph which shows the state which attached the wet electrode to the electromyograph of (a).
- (A) to (d) are graphs showing comparison of identification when the proposed algorithm is installed and when it is not installed.
- the present invention is a filter algorithm that acts as a second-layer discriminator that further improves the discrimination result by using the myoelectric discrimination algorithm (Non-patent Document 3) announced by Kato et al. In 2007 as the first-layer discriminator. .
- Kato et al.'S myoelectric identification algorithm uses backpropagation (BP) neural networks to identify myoelectric patterns.
- BP backpropagation
- the connection strength between nodes constituting the neural network is learned by back-propagation.
- FIG. 1A is a schematic diagram showing a conventional myoelectric pattern identification algorithm.
- the algorithm proposed in the present invention is shown in a schematic diagram in FIG.
- the time series of the identified myoelectric pattern is stored in a buffer, and the identification rate is improved by performing the second stage identification based on the time series data.
- the first-stage identification uses an algorithm based on a BP neural network that has been used conventionally. In this algorithm, first, an electromyographic waveform is measured and stored in a vector emg. Vector emg stores time-series data measured from 3-channel electromyographs. Next, a feature vector X indicating the frequency distribution of the vector emg is extracted from the vector emg by the feature extraction function GFE.
- This feature vector X is input as a feature quantity to the input layer of the BP neural network, and as a result, any node in the output layer corresponding to a different hand posture is fired and identified (see FIG. 2).
- a mechanism for performing this myoelectric pattern identification process is schematically shown as a myoelectric identifier in FIGS. 1 (a) and 1 (b).
- the output value in the output layer of the BP neural network is vector L and the number of EMG patterns to be identified is M.
- the output of the final BP neural net y, the ID of the myoelectric patterns m i, the ID is the set of vectors L when the myoelectric patterns of m i is identified as the output y and O i
- myoelectric patterns m i is assigned to any orientation of the prosthetic hand is programmed in advance by the experimenter.
- a smoothing process such as moving average is performed on the myoelectric waveform.
- the output identified by the neural network has a discrete posture, a simple smoothing process cannot be used.
- the smoothing if the time width is too large, the responsiveness is lowered, and therefore the time width of the smoothing needs to be kept to a minimum. Therefore, attention was paid to the fact that there is a limited number of actions that may occur simultaneously in the target myoelectric pattern.
- the electromyographic pattern handled in this example is, for example, pronation / extroversion of the forearm, palm flexion / dorsiflexion of the wrist, gripping / opening of the five fingers, bending of the thumb, gripping of the fourth and fifth fingers,
- Nine patterns in a resting state For example, palm dorsiflexion of the wrist can be performed while holding the five fingers, but crouching the wrist while bending the wrist means that the wrist is in an antagonistic state, and the current myoelectric prosthetic hand performs antagonistic control. Since it is not performed, it is not necessary to identify. Based on this, the combined action of the three patterns of forearm pronation or pronation, wrist palm flexion or dorsiflexion, and finger pattern is the largest.
- the myoelectric pattern that can be handled in the present invention is not limited to nine patterns, and may be less than nine patterns or ten or more patterns.
- the algorithm was constructed as shown in Fig. 3 based on the fact that there are 3 patterns of movement that may occur simultaneously when the myoelectric pattern is 9 patterns.
- the output is determined based on the ratio of the myoelectric patterns included in the time series by referring to the time series data of the myoelectric patterns for a certain time stored in the ring buffer. If the number of myoelectric patterns buffered at a certain time is N, the ring buffer buff t at a certain time t is
- the electromyographic pattern ID is stored in y i which is a component of buff t . Thereafter, performed again electromyographic measurements in the next control cycle, after t l s identification is performed of the new myoelectric patterns y, the ring buffer buff is
- the myoelectric pattern is single or includes a plurality of myoelectric patterns. If more than half of the ring buffer buff is an arbitrary myoelectric pattern M p , it is determined that the ring buffer buff is single, and the action associated with the myoelectric pattern M p is output to the prosthetic hand.
- the proposed algorithm refers to N myoelectric patterns stored in the ring buffer buff.
- the control unit issues an operation command associated with the myoelectric pattern M p , and the prosthetic hand operates according to the operation command.
- the ring buffer buff includes a plurality of myoelectric patterns M p1 , M p2 ...
- the number of operations that occur simultaneously is set to 3, so that the myoelectric pattern occupies at least 1/4 of the ring buffer buff.
- the operation o that is finally output is described as n, the maximum value of the number of operations that can be expressed simultaneously
- the threshold occupancy ratio of the ring buffer buff serving as a reference for simultaneous expression is set to 1 / (n + 1), and the ratio of the ring buffer buff to the ratio is 1 / (n + 1). It is desirable to express all of the above myoelectric patterns simultaneously.
- the reference threshold occupation ratio is not limited to 1 / (n + 1), and can be arbitrarily set.
- the filter algorithm proposed in this study is defined as a function GTS that outputs the output o to the prosthetic hand as a filter result using the time series buff of the identification result as an argument.
- each element in the buff is identified and updated in about 20 ms.
- the algorithm GTS first calculates S i and P i according to equation (7).
- BP Neural Network Learning Procedures and Conditions In the learning process of the myoelectric pattern of the BP neural network, the test subject presses the key of the number of the action he wants to correspond while outputting the myoelectric pattern. Correspondence between movement and number is 0: Rest, 1: Forearm gyrus, 2: Forearm pronation, 3: Wrist palmar flexion, 4: Wrist dorsiflexion, 5: Five finger grip, 6: Five finger open, 7: Mother Finger flexion, 8: 4th and 5th finger flexion.
- a specific myoelectric pattern and a specific prosthetic hand movement are labeled, and a neural network is learned by BP. All the combinations of myoelectric patterns taught in the past and their identifying operations are stored, and consideration was given so that the discrimination rate of the last taught myoelectric pattern would not be significantly increased.
- the time taken to teach one myoelectric pattern varies depending on the similarity between the newly taught myoelectric pattern and the already learned myoelectric pattern, and the first teaching without any other learning data is 100 to 300 ms.
- the second and subsequent teachings are about 100 to 1000 ms because BP learning continues until a certain identification rate is secured for all other learning data.
- FIG. 6 shows a screen displayed on the PC monitor.
- the target posture is displayed on the left side, and the posture identified from the myoelectric pattern is displayed on the right side.
- the subject is given a task to control the electromyographic pattern so that the identified posture matches the target posture.
- Example procedure In the experimental procedure, first, an electromyograph is attached to the subject, and the BP neural network is made to learn the electromyographic pattern of the subject. The subject is then assigned an experimental task and data is collected. Since the myoelectric pattern identification system uses supervised learning using a BP neural network, the system first teaches the subject's myoelectric pattern. Next, the target myoelectric pattern and the identification result are displayed on the PC monitor (see FIG. 6), and the task of matching the identification result to the target myoelectric pattern is performed. The task repeatedly outputs the target myoelectric pattern displayed on the PC monitor for 4 seconds and then weakens for 6 seconds. Each target EMG pattern is displayed five times during one task. At this time, each of the ones with and without the proposed algorithm is performed for about 7 minutes, and the degree of matching of the identified myoelectric pattern with respect to the target myoelectric pattern is evaluated as the discrimination rate.
- the proposed algorithm is evaluated by comparing the proposed system with the developed algorithm with the conventional system without it.
- the identification rates of both the conventional system and the proposed system are shown in FIG.
- the identification rate of the algorithm of the embodiment is 82.5% when all nine patterns (indicated as “all movements” in the right half of FIG. 7) are the identification targets, and seven patterns excluding the forearm pronation and pronation from the identification targets In the case of (left half of Fig. 7), it was 92.9%.
- the trial for all patterns had a one-sided probability of 0.095, and the trial excluding pronation and pronation was 0.37, both exceeding the 5% level.
- the t test was performed as equal variance. As a result, both trials showed a significant difference (p ⁇ 0.001) in the high recognition rate of the proposed method.
- the improvement of the recognition rate was 17.7% for all 9 patterns, and 11.5% for 7 patterns excluding forearm pronation and pronation.
- FIG. 8 shows the time series of identification
- Table 2 below shows the identification rate under each condition.
- FIG. 8 is a log of the myoelectric pattern identified by the algorithm in CSV format, and the interval between samples is 10 to 20 ms.
- the correspondence of the myoelectric pattern and the number in FIG. 8 is as follows: 0: Rest, 1: Forearm supination, 2: Forearm pronation, 3: Wrist palm flexion, 4: Wrist dorsiflexion, 5: Grip of 5 fingers, 6 : 5 finger opening, 7: thumb flexion, 8: 4th finger and 5th finger flexion.
- 8 (a) to 8 (d) are comparisons of identification when the algorithm of the present invention is installed and when it is not installed.
- 8A and 8B are graphs of the subject 1
- FIGS. 8C and 8D are graphs of the subject 2.
- 8A and 8C show the proposed method of the present application
- FIGS. 8B and 8D show the conventional method.
- the sampling interval on the horizontal axis is 10 to 20 ms.
- the top and bottom are differences between subjects, and ⁇ indicates each identification result.
- the change in the target posture is indicated by a solid line.
- the target posture is shown on the display only in the portion where the solid line is horizontal.
- the test subject showed the identification result and the target myoelectric pattern in a representative section in about 7 minutes to output the target myoelectric pattern displayed on the PC monitor. It can be seen from FIG. 8 that the continuity of the myoelectric pattern is maintained and there are few misidentifications as compared with the conventional method.
- the pronation (myoelectric pattern 2) is also identified in the pronation (myoelectric pattern 1) and the opening (myoelectric pattern 6), and the bias is easily identified. It is suggested that it depends.
- the proposed method buffers 15 EMG discriminator outputs with a control period of 10 ms. However, as shown in Fig. 3, the delay time of the proposed method is determined by 1/4 or 1/2 of the buffer. Is estimated to be 40-80ms. Considering that myoelectricity is observed several tens to hundreds of milliseconds before the start of joint movement, it is considered to have sufficient responsiveness to be used as a prosthetic hand.
- the control is performed with a certainty of at least 80%.
- the number of electrodes Non-Patent Document 2
- the length of learning time Non-Patent Document 1
- a discrimination rate of 90% or more is realized when seven patterns excluding pronation and pronation are identified with a small number of electrodes of 3 channels and a short learning time of 1 second or less.
- an identification rate of 82.5% is realized, so that it can be used effectively as a prosthetic hand.
- the recognition rate of over 90% was obtained for 9 patterns of all patterns, and the possibility of a recognition rate of over 90% for all 9 patterns was suggested by proficiency.
- FIGS. 1B and 3 can be executed by a microprocessor mounted on a device such as a myoelectric prosthesis.
- a myoelectric pattern identification program is stored in advance in a memory in the microprocessor, and myoelectric control can be performed by the processor executing the program.
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Veterinary Medicine (AREA)
- Transplantation (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Vascular Medicine (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Cardiology (AREA)
- Biophysics (AREA)
- Physics & Mathematics (AREA)
- Pathology (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Human Computer Interaction (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Orthopedic Medicine & Surgery (AREA)
- Prostheses (AREA)
- Manipulator (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
Provided is an algorithm which dramatically improves the identification rate of myoelectric patterns, while minimizing the number of myoelectric electrodes and the learning time, in order to control a myoelectric prosthetic hand having multiple degrees of freedom.
This myoelectric prosthetic hand is provided with: a measuring unit for measuring a myoelectric pattern at constant time intervals; a myoelectric identification unit which identifies the myoelectric pattern from the measured myoelectricity; and a buffer which stores, in chronological order, a prescribed number of the myoelectric patterns identified by the myoelectric identification unit. With reference to the prescribed number of myoelectric patterns stored in the buffer, if an occupancy rate at least equal to a certain proportion is found, control conforming to a motion command corresponding to the myoelectric pattern found to have an occupancy rate at least equal to the certain proportion is carried out. According to the present invention, rapid learning and a high identification rate can be achieved while employing a low number of electrode channels, and the invention is thus effective for practical control of a myoelectric prosthetic hand having multiple degrees of freedom.
Description
本発明は、筋電で制御される機器で用いられる筋電パターンの識別に関するものである。
The present invention relates to the identification of myoelectric patterns used in devices controlled by myoelectricity.
筋電義手、筋電義足など、筋肉に発生する表面筋電位の出力量によって動作制御を行う機器が開発されている。
Devices such as myoelectric prosthetic hands and myoelectric prostheses have been developed that control motion based on the amount of surface myoelectric potential generated in the muscle.
[義手の背景]
義手は大きく分けて装飾義手、能動義手、電動義手がある。装飾義手は外見の再現、能動義手は機能性の再現を主目的としている。そのため装飾義手は能動的に動かすことができず、能動義手は使用者の残存部位の動きをワイヤなどで動力として伝達し駆動されるため、把持力が弱いことが問題となっている。一方電動義手はその名の通り、電力とモータを動力として駆動される。電動義手は能動義手に比べワイヤの取り回しの制約条件が少なく、運動の方向や量をある程度自由に設計可能であり、多自由度のものなど様々な義手が制作されている。電動義手としては、皮膚表面で筋電を計測する表面筋電信号を用いて制御する筋電義手が一般的である。 [Background of prosthetic hand]
Prosthetic hands are roughly classified into decorative prostheses, active prostheses, and electric prostheses. The main purpose of the decorative prosthesis is to reproduce the appearance, and the active prosthesis is to reproduce the functionality. Therefore, the decorative prosthetic hand cannot be moved actively, and the active prosthetic hand is driven by transmitting the movement of the remaining part of the user as power using a wire or the like. On the other hand, as the name suggests, an electric prosthesis is driven by electric power and a motor. Electric prosthetic hands have fewer restrictions on wire handling than active prosthetic hands, and the direction and amount of movement can be freely designed to some extent, and various prosthetic hands such as those with multiple degrees of freedom have been produced. As an electric prosthetic hand, a myoelectric prosthetic hand controlled using a surface myoelectric signal for measuring myoelectricity on the skin surface is generally used.
義手は大きく分けて装飾義手、能動義手、電動義手がある。装飾義手は外見の再現、能動義手は機能性の再現を主目的としている。そのため装飾義手は能動的に動かすことができず、能動義手は使用者の残存部位の動きをワイヤなどで動力として伝達し駆動されるため、把持力が弱いことが問題となっている。一方電動義手はその名の通り、電力とモータを動力として駆動される。電動義手は能動義手に比べワイヤの取り回しの制約条件が少なく、運動の方向や量をある程度自由に設計可能であり、多自由度のものなど様々な義手が制作されている。電動義手としては、皮膚表面で筋電を計測する表面筋電信号を用いて制御する筋電義手が一般的である。 [Background of prosthetic hand]
Prosthetic hands are roughly classified into decorative prostheses, active prostheses, and electric prostheses. The main purpose of the decorative prosthesis is to reproduce the appearance, and the active prosthesis is to reproduce the functionality. Therefore, the decorative prosthetic hand cannot be moved actively, and the active prosthetic hand is driven by transmitting the movement of the remaining part of the user as power using a wire or the like. On the other hand, as the name suggests, an electric prosthesis is driven by electric power and a motor. Electric prosthetic hands have fewer restrictions on wire handling than active prosthetic hands, and the direction and amount of movement can be freely designed to some extent, and various prosthetic hands such as those with multiple degrees of freedom have been produced. As an electric prosthetic hand, a myoelectric prosthetic hand controlled using a surface myoelectric signal for measuring myoelectricity on the skin surface is generally used.
[筋電義手の要求仕様と本研究の対象要素]
筋電義手を使用するに当たり最も重要と言われている要素としては、重量、操作の確実性、装着の容易さ、把持力、自由度が挙げられる。特に自由度と操作の確実性と装着の容易さの両立が困難とされている。 [Required specifications of myoelectric prosthetic hand and target elements of this study]
Elements that are said to be the most important in using a myoelectric hand include weight, certainty of operation, ease of wearing, gripping force, and degree of freedom. In particular, it is difficult to achieve both freedom, reliability of operation, and ease of mounting.
筋電義手を使用するに当たり最も重要と言われている要素としては、重量、操作の確実性、装着の容易さ、把持力、自由度が挙げられる。特に自由度と操作の確実性と装着の容易さの両立が困難とされている。 [Required specifications of myoelectric prosthetic hand and target elements of this study]
Elements that are said to be the most important in using a myoelectric hand include weight, certainty of operation, ease of wearing, gripping force, and degree of freedom. In particular, it is difficult to achieve both freedom, reliability of operation, and ease of mounting.
筋電義手は手の代替として用いられ物体を把持する機能を持つため、操作の確実性は非常に重要視される。特に操作の確実性において、実用的に用いられるためには少なくとも80%以上の確実性を持って制御が行われることが望ましい。
Since the myoelectric prosthetic hand is used as a substitute for the hand and has a function of grasping an object, the certainty of the operation is regarded as very important. In particular, in terms of operational certainty, it is desirable that control be performed with a certainty of at least 80% in order to be used practically.
装着の容易さについては、義手の生体への物理的接合に関するものとキャリブレーションに関するものが挙げられる。義手は日常的に用いるものであるため、装着は簡便で即座に行われることが求められる。物理的接合については、筋電義手の多くはソケット内側に筋電計を配置し、断端をソケットに挿入するだけで装着が完了する。またキャリブレーションについては、装着するごとに設定する必要が極小なことと、設定パラメータ数が極小なことから、筋電位の振幅の閾値を設定するのみで制御が可能なものが主流である。
As for the ease of wearing, there are those related to physical bonding to the living body of the prosthetic hand and those related to calibration. Since prosthetic hands are used on a daily basis, wearing is required to be simple and immediate. For physical joints, many myoelectric prosthetic hands can be installed simply by placing an electromyograph inside the socket and inserting the stump into the socket. As for calibration, it is mainstream that calibration can be controlled only by setting a threshold value of myoelectric potential amplitude because it is necessary to set each time it is worn and the number of setting parameters is extremely small.
[多自由度筋電義手における制御方式]
多自由度化に伴い、多自由度の手指を自由に動かすために制御自由度も向上させる必要があるが、現在普及する多自由度筋電義手の制御方式としてはコマンド方式が主流である。コマンド方式では2つの筋電計を掌屈筋群と背屈筋群に配置し、筋電のパターンを脱力、掌屈、背屈、拮抗の4つに分け、それらの筋電パターンを特定の順序で入力し特定の動作を行わせる。例えば、伸筋と屈筋を瞬間的に2度拮抗させ、屈筋に力を入れると前腕が回内する、といった具合である。このような操作体系を取るのは、筋電が個人により様々な波形を示し、力の入れ方や力の加減をもとに制御すると、使用できる切断者が限定されるためである。そのため筋電計1チャンネルごとに、明確に区別可能で信頼して用いることのできる筋電のOn/Offを用いることが一般的である。しかしながら、表面筋電位は筋の電位を皮膚表面で計測するため、近傍の筋の信号が混ざり合うクロストークを生じる。そのため表面筋電位から筋1つ1つの電位を算出することは困難であることと、筋電計を多く皮膚表面に配置しても、近傍の筋電計には同様な信号が計測される。筋電は、筋の電位を筋電計のチャンネルの数を増加させても制御自由度は単純増加しない。このような背景から、多くの筋電義手は、別々に収縮させることのできる屈筋群と伸筋群に2箇所に筋電計を配置することが多い。そのため、屈筋群と伸筋群を別々に収縮させることで2ビット、つまり4状態の判別を行うことができるが、前述のクロストークの問題により、これ以上の筋電計の増加は効果的でなく、状態数としてはこれが限界である。それら4状態は安静、正回転、逆回転、モード変更に割り当てられることが多い。義手で行う動作を増やせば増やすほどモード変更の回数が増え、操作が煩雑になることから、自由に使用するには4週間ほどのトレーニングが必要となってしまう。そのため、義手の多自由度化に適した新たな制御方法が必要とされている。 [Control method for multi-degree-of-freedom myoelectric hand]
As the number of degrees of freedom increases, it is necessary to improve the degree of freedom of control in order to move the fingers of the degree of freedom freely. In the command method, two electromyographs are placed in the palm flexor muscle group and the dorsiflexor muscle group, and the myoelectric pattern is divided into four categories: weakness, palm flexion, dorsiflexion, and antagonism. Enter and perform a specific action. For example, the extensor and flexor muscles are momentarily antagonized twice, and the forearm prolapses when force is applied to the flexor muscles. The reason why such an operation system is adopted is that, when myoelectric waves show various waveforms depending on the individual, and control is performed based on how to apply force and how much force is applied, the number of amputees that can be used is limited. Therefore, it is common to use On / Off of myoelectricity that can be clearly distinguished and used reliably for each channel of the electromyograph. However, since the surface myoelectric potential measures the electric potential of the muscle on the skin surface, crosstalk occurs in which signals of nearby muscles are mixed. For this reason, it is difficult to calculate the potential of each muscle from the surface electromyogram, and even if many electromyographs are arranged on the skin surface, similar signals are measured in nearby electromyographs. The myoelectricity does not simply increase the degree of freedom in controlling the myoelectric potential even if the number of channels of the electromyograph is increased. From this background, many myoelectric prostheses often place electromyographs at two locations in the flexor and extensor groups that can be contracted separately. Therefore, it is possible to determine 2-bit, that is, 4 states by contracting the flexor and extensor groups separately, but due to the above-mentioned crosstalk problem, further increases in electromyographs are effective. This is the limit for the number of states. These four states are often assigned to rest, forward rotation, reverse rotation, and mode change. As the number of operations performed with the prosthetic hand increases, the number of mode changes increases and the operation becomes complicated, so training for about 4 weeks is required to use it freely. Therefore, a new control method suitable for increasing the degree of freedom of the prosthetic hand is required.
多自由度化に伴い、多自由度の手指を自由に動かすために制御自由度も向上させる必要があるが、現在普及する多自由度筋電義手の制御方式としてはコマンド方式が主流である。コマンド方式では2つの筋電計を掌屈筋群と背屈筋群に配置し、筋電のパターンを脱力、掌屈、背屈、拮抗の4つに分け、それらの筋電パターンを特定の順序で入力し特定の動作を行わせる。例えば、伸筋と屈筋を瞬間的に2度拮抗させ、屈筋に力を入れると前腕が回内する、といった具合である。このような操作体系を取るのは、筋電が個人により様々な波形を示し、力の入れ方や力の加減をもとに制御すると、使用できる切断者が限定されるためである。そのため筋電計1チャンネルごとに、明確に区別可能で信頼して用いることのできる筋電のOn/Offを用いることが一般的である。しかしながら、表面筋電位は筋の電位を皮膚表面で計測するため、近傍の筋の信号が混ざり合うクロストークを生じる。そのため表面筋電位から筋1つ1つの電位を算出することは困難であることと、筋電計を多く皮膚表面に配置しても、近傍の筋電計には同様な信号が計測される。筋電は、筋の電位を筋電計のチャンネルの数を増加させても制御自由度は単純増加しない。このような背景から、多くの筋電義手は、別々に収縮させることのできる屈筋群と伸筋群に2箇所に筋電計を配置することが多い。そのため、屈筋群と伸筋群を別々に収縮させることで2ビット、つまり4状態の判別を行うことができるが、前述のクロストークの問題により、これ以上の筋電計の増加は効果的でなく、状態数としてはこれが限界である。それら4状態は安静、正回転、逆回転、モード変更に割り当てられることが多い。義手で行う動作を増やせば増やすほどモード変更の回数が増え、操作が煩雑になることから、自由に使用するには4週間ほどのトレーニングが必要となってしまう。そのため、義手の多自由度化に適した新たな制御方法が必要とされている。 [Control method for multi-degree-of-freedom myoelectric hand]
As the number of degrees of freedom increases, it is necessary to improve the degree of freedom of control in order to move the fingers of the degree of freedom freely. In the command method, two electromyographs are placed in the palm flexor muscle group and the dorsiflexor muscle group, and the myoelectric pattern is divided into four categories: weakness, palm flexion, dorsiflexion, and antagonism. Enter and perform a specific action. For example, the extensor and flexor muscles are momentarily antagonized twice, and the forearm prolapses when force is applied to the flexor muscles. The reason why such an operation system is adopted is that, when myoelectric waves show various waveforms depending on the individual, and control is performed based on how to apply force and how much force is applied, the number of amputees that can be used is limited. Therefore, it is common to use On / Off of myoelectricity that can be clearly distinguished and used reliably for each channel of the electromyograph. However, since the surface myoelectric potential measures the electric potential of the muscle on the skin surface, crosstalk occurs in which signals of nearby muscles are mixed. For this reason, it is difficult to calculate the potential of each muscle from the surface electromyogram, and even if many electromyographs are arranged on the skin surface, similar signals are measured in nearby electromyographs. The myoelectricity does not simply increase the degree of freedom in controlling the myoelectric potential even if the number of channels of the electromyograph is increased. From this background, many myoelectric prostheses often place electromyographs at two locations in the flexor and extensor groups that can be contracted separately. Therefore, it is possible to determine 2-bit, that is, 4 states by contracting the flexor and extensor groups separately, but due to the above-mentioned crosstalk problem, further increases in electromyographs are effective. This is the limit for the number of states. These four states are often assigned to rest, forward rotation, reverse rotation, and mode change. As the number of operations performed with the prosthetic hand increases, the number of mode changes increases and the operation becomes complicated, so training for about 4 weeks is required to use it freely. Therefore, a new control method suitable for increasing the degree of freedom of the prosthetic hand is required.
[多自由度筋電義手の制御における先行研究]
そこで研究レベルでは、表面筋電から直接使用者の手指姿勢を推定するという試みが行われてきた。その多くは、筋電パターンを手指の運動種ごと複数のクラスに分類し学習し、計測された筋電パターンがいずれのクラスに属するかを識別することで義手の動作を実現するものである。 [Previous research on control of multi-degree-of-freedom myoelectric hand]
At the research level, attempts have been made to estimate the user's finger posture directly from surface EMG. In many of them, the myoelectric pattern is classified into a plurality of classes for each kind of finger movement and learned, and the class of the measured myoelectric pattern belongs to which class to realize the action of the prosthetic hand.
そこで研究レベルでは、表面筋電から直接使用者の手指姿勢を推定するという試みが行われてきた。その多くは、筋電パターンを手指の運動種ごと複数のクラスに分類し学習し、計測された筋電パターンがいずれのクラスに属するかを識別することで義手の動作を実現するものである。 [Previous research on control of multi-degree-of-freedom myoelectric hand]
At the research level, attempts have been made to estimate the user's finger posture directly from surface EMG. In many of them, the myoelectric pattern is classified into a plurality of classes for each kind of finger movement and learned, and the class of the measured myoelectric pattern belongs to which class to realize the action of the prosthetic hand.
Ajiboyeらはファジー理論を用い、筋電1パターンにつき8回筋電を計測し学習することで、健常者の手首の掌背屈、尺骨偏位、握り、安静の5パターンの筋電を約97%の精度で識別することに成功した(非特許文献1)。切断者においても約96%の精度を出しており、識別精度は実用に耐えうるものである。しかしながら、義手を実用的に用いるためには装着の容易さが重要と認知されているため、5パターンの筋電それぞれを8回計測し学習させることが問題となる可能性がある。そのため、使用前の準備が簡便で、高い識別率を維持可能な手法が必要となる。3自由度を約93%の精度で推定し、さらに複数の自由度を同時に動かす複合動作においても約89%の精度を出すことに成功した例もあるが、その手法では1つの動作につき筋電パターンの学習が3秒間と高速で、筋電計測もAjiboyeらに比べ半分の4回で十分である。また、用いている電極が6つであり、一般的な筋電義手の2電極に比べると多くなっていることから、装着の際に電極の位置合わせが困難となる可能性がある。そこで識別器を二段構成とし、一段目の識別器で、識別率は低い大まかな識別を行い、二段目で識別率を向上させるフィルタアルゴリズムを展開するという手法も用いられてきた(非特許文献2)。この手法では5~15%の識別率向上が見られた。識別対象とする筋電パターン数は15と既存の筋電義手に比べ高く識別率も90%を超えている。しかしながら、依然電極数は12と既存の筋電義手に比べ多くなっている。より少ない電極数、少ないキャリブレーション時間で高い識別精度を有する筋電識別アルゴリズムが望まれる。
Ajiboye et al. Used fuzzy theory to measure and learn EMG 8 times for each EMG pattern, and measured about 97 EMGs in the palm of the wrist, dorsiflexion of the wrist, ulnar displacement, grip, and rest. Successful identification with% accuracy (Non-Patent Document 1). Even the cutting person has an accuracy of about 96%, and the identification accuracy can withstand practical use. However, since it is recognized that ease of wearing is important for practical use of the prosthetic hand, it may be problematic to measure and learn each of the five patterns of myoelectricity eight times. Therefore, it is necessary to prepare a method before use and to maintain a high identification rate. In some cases, we estimated 3 degrees of freedom with an accuracy of about 93% and succeeded in achieving an accuracy of about 89% even in a combined motion that moves multiple degrees of freedom simultaneously. Pattern learning is as fast as 3 seconds, and myoelectric measurement is half as much as Ajiboye et al. In addition, the number of electrodes used is six, which is larger than that of two common myoelectric prosthetic hands, which may make it difficult to align the electrodes when worn. Therefore, a method has been used in which the classifier is composed of two stages, the first stage classifier performs rough classification with a low classification rate, and a filter algorithm is developed to improve the classification rate in the second stage (non-patented). Reference 2). This method showed an improvement in recognition rate of 5-15%. The number of myoelectric patterns to be identified is 15, which is higher than that of existing myoelectric prostheses, and the classification rate is over 90%. However, the number of electrodes is still 12, which is larger than the existing myoelectric prosthesis. A myoelectric identification algorithm having a high number of electrodes and a small calibration time and high identification accuracy is desired.
現在市販されている多自由度筋電義手は、キャリブレーションの必要がない反面、識別可能な筋電パターンは数種類程度に留まっている。研究段階においては多数の筋電パターン識別が実現されているが、キャリブレーションに必要な電極の多さ、時間の長さや識別精度が問題となり、実用化に至っていない。
The currently available multi-degree-of-freedom myoelectric prosthesis does not require calibration, but there are only a few types of myoelectric patterns that can be identified. In the research stage, many myoelectric patterns have been identified, but the number of electrodes required for calibration, the length of time, and the accuracy of identification have become problems, and they have not been put into practical use.
筋電のパターン識別において、クラス数を増大させればより多くの動作が実現できるが、識別クラス数が増加すれば識別率は減少してしまう。識別率を向上させるためには筋電電極数や学習時間の増加が必要であるが、実用性が失われる恐れがある。
In the myoelectric pattern identification, if the number of classes is increased, more operations can be realized, but if the number of identification classes is increased, the identification rate decreases. In order to improve the identification rate, it is necessary to increase the number of myoelectric electrodes and learning time, but there is a possibility that practicality may be lost.
筋電電極数と学習時間を制限したときに識別器が十分に学習されず識別が安定しない問題を解決するべく、本発明では多自由度筋電義手制御のために、筋電電極数と学習時間を最小限にしながら、筋電パターンの識別率を飛躍的に向上させるアルゴリズムを提供することを目的とする。
In order to solve the problem that the discriminator is not sufficiently learned and the discrimination is not stable when the number of myoelectric electrodes and the learning time are limited, in the present invention, in order to control the multi-degree-of-freedom myoelectric prosthetic hand, An object of the present invention is to provide an algorithm that dramatically improves the recognition rate of myoelectric patterns while minimizing time.
本出願に係る第1の発明は、
筋電を一定の時間間隔で計測するための計測部と、
あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別する筋電識別部と、
前記筋電識別部で識別された筋電パターンを、時系列順に所定数だけ保存するバッファと、を備え、
バッファに保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合には、その筋電パターンに関連付けられた動作指令にしたがった制御を行うことを特徴とする筋電義手である。 The first invention according to the present application is:
A measurement unit for measuring myoelectricity at regular time intervals;
A myoelectric identification unit for identifying which electromyographic pattern of the electromyogram pattern learned in advance should be classified,
A buffer for storing a predetermined number of myoelectric patterns identified by the myoelectric identification unit in time series,
With reference to the predetermined number of myoelectric patterns stored in the buffer and when an occupation ratio exceeding a certain ratio is recognized in a specific myoelectric pattern, control according to the operation command associated with the myoelectric pattern is performed. It is a myoelectric prosthesis characterized by performing.
筋電を一定の時間間隔で計測するための計測部と、
あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別する筋電識別部と、
前記筋電識別部で識別された筋電パターンを、時系列順に所定数だけ保存するバッファと、を備え、
バッファに保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合には、その筋電パターンに関連付けられた動作指令にしたがった制御を行うことを特徴とする筋電義手である。 The first invention according to the present application is:
A measurement unit for measuring myoelectricity at regular time intervals;
A myoelectric identification unit for identifying which electromyographic pattern of the electromyogram pattern learned in advance should be classified,
A buffer for storing a predetermined number of myoelectric patterns identified by the myoelectric identification unit in time series,
With reference to the predetermined number of myoelectric patterns stored in the buffer and when an occupation ratio exceeding a certain ratio is recognized in a specific myoelectric pattern, control according to the operation command associated with the myoelectric pattern is performed. It is a myoelectric prosthesis characterized by performing.
本出願に係る第2の発明は、前記バッファがリングバッファであり、保存すべき筋電パターンが前記所定数に達した場合には、古い筋電パターンから破棄することを特徴とする第1の発明に記載の筋電義手である。
According to a second invention of the present application, the buffer is a ring buffer, and when the number of myoelectric patterns to be stored reaches the predetermined number, the old myoelectric pattern is discarded. It is a myoelectric prosthesis described in the invention.
本出願に係る第3の発明は、前記占有率が50%以上のときに、前記特定の筋電パターンにしたがった制御を行うことを特徴とする第2の発明に記載の筋電義手である。
A third invention according to the present application is the myoelectric prosthetic hand according to the second invention, characterized in that when the occupation ratio is 50% or more, control according to the specific myoelectric pattern is performed. .
本出願に係る第4の発明は、前記筋電義手において同時に発現可能な動作をnパターンと設定した場合には、前記占有率が100/(n+1)%以上のときに、前記占有率を占める複数の筋電パターンのそれぞれに対応した動作指令にしたがった制御を同時に行うことを特徴とする第2の発明または第3の発明に記載の筋電義手である。
According to a fourth aspect of the present application, in the case where an action that can be manifested simultaneously in the myoelectric prosthetic hand is set as an n pattern, the occupation ratio is 100 / (n + 1)% or more when the occupation ratio is 100 / (n + 1)% or more. The myoelectric prosthetic hand according to the second or third aspect of the invention is characterized in that control is performed simultaneously in accordance with an operation command corresponding to each of a plurality of myoelectric patterns occupying the same.
本出願に係る第5の発明は、筋電パターン識別方法であり、筋電制御が適用される機器において、筋電を一定の時間間隔で計測し、前記機器で、あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別し、前記機器において、識別された筋電パターンを、時系列順に所定数だけ保存し、前記機器において、保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合に、その筋電パターンに対応した動作指令にしたがった制御を行う。
5th invention which concerns on this application is a myoelectric pattern identification method, and in the apparatus to which myoelectric control is applied, myoelectricity is measured at a fixed time interval, and the myoelectric pattern learned in advance by the apparatus Among the electromyographic patterns to be classified, and the device stores the identified electromyographic patterns in a predetermined number of times in time series, and the device stores the electromyographic patterns. With reference to the predetermined number of myoelectric patterns, when an occupation ratio higher than a certain ratio is recognized in a specific myoelectric pattern, control is performed according to an operation command corresponding to the myoelectric pattern.
本出願に係る第6の発明は、筋電制御が適用される機器のプロセッサで実行される筋電パターン識別プログラムであって、筋電を一定の時間間隔で計測する手順と、あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別する手順と、識別された筋電パターンを、時系列順に所定数だけ保存する手順と、保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合に、その筋電パターンに対応した動作指令にしたがった制御を行う手順、を含む。
6th invention which concerns on this application is a myoelectric pattern identification program performed with the processor of the apparatus to which myoelectric control is applied, Comprising: The procedure which measures myoelectricity by a fixed time interval, and was learned beforehand Among the myoelectric patterns, a procedure for identifying which myoelectric pattern the measured electromyogram should be classified into, and a procedure for storing a predetermined number of the identified myoelectric patterns in chronological order are stored. And a procedure for referring to the predetermined number of myoelectric patterns and performing control according to an operation command corresponding to the myoelectric pattern when an occupation ratio equal to or higher than a certain ratio is recognized in the specific myoelectric pattern.
本発明によれば、少ない電極チャンネルでありながら、高速な学習と高い識別率を達成することができるため、多自由度筋電義手の実用的な制御に有効である。
According to the present invention, it is possible to achieve high-speed learning and a high identification rate with a small number of electrode channels, which is effective for practical control of a multi-degree-of-freedom myoelectric hand.
以下、本発明を適用した具体的な実施の形態について詳細に説明する。
Hereinafter, specific embodiments to which the present invention is applied will be described in detail.
本発明は、加藤らが2007年に発表した筋電識別アルゴリズム(非特許文献3)を第1層の識別器とし、その識別結果を更に向上させる第2層の識別器として振る舞うフィルタアルゴリズムである。加藤らの筋電識別アルゴリズムではバックプロパゲーション(BP)ニューラルネットを用いて筋電パターンを識別している。この識別率を向上させるため、出力時系列の事象と相互背反性を有する特徴に着目することにより、BPニューラルネットのフィルタリングを行うアルゴリズムを提案する。BPニューラルネットでは、ニューラルネットワークを構成するノード間の結合強度をバックプロパゲーションにより学習する。本発明では、BPニューラルネットの構造を全く変化させることなく、識別率を飛躍的に向上させる階層化フィルタアルゴリズムを開発した。まず、従来の筋電パターン識別のアルゴリズムを模式図で表したのが図1(a)である。これに対し、本発明で提案するアルゴリズムを図1(b)に模式図で表示した。
The present invention is a filter algorithm that acts as a second-layer discriminator that further improves the discrimination result by using the myoelectric discrimination algorithm (Non-patent Document 3) announced by Kato et al. In 2007 as the first-layer discriminator. . Kato et al.'S myoelectric identification algorithm uses backpropagation (BP) neural networks to identify myoelectric patterns. In order to improve this discrimination rate, we propose an algorithm for filtering BP neural networks by focusing on features that are mutually contradictory to events in the output time series. In the BP neural network, the connection strength between nodes constituting the neural network is learned by back-propagation. In the present invention, a hierarchized filter algorithm has been developed that dramatically improves the identification rate without changing the structure of the BP neural network at all. First, FIG. 1A is a schematic diagram showing a conventional myoelectric pattern identification algorithm. On the other hand, the algorithm proposed in the present invention is shown in a schematic diagram in FIG.
本発明では、識別された筋電パターンの時系列をバッファに保存しておき、その時系列データを元に2段階目の識別を行うことにより識別率を向上させる。1段階目の識別には、従来から用いられてきたBPニューラルネットワークによるアルゴリズムを用いる。このアルゴリズムでは、まず筋電波形を計測しベクトルemgに保存する。ベクトルemgは3チャンネルの筋電計から計測された時系列データが格納されている。次に特徴抽出関数GFEによって、ベクトルemgからベクトルemgの周波数分布を示す特徴ベクトルXが抽出される。
In the present invention, the time series of the identified myoelectric pattern is stored in a buffer, and the identification rate is improved by performing the second stage identification based on the time series data. The first-stage identification uses an algorithm based on a BP neural network that has been used conventionally. In this algorithm, first, an electromyographic waveform is measured and stored in a vector emg. Vector emg stores time-series data measured from 3-channel electromyographs. Next, a feature vector X indicating the frequency distribution of the vector emg is extracted from the vector emg by the feature extraction function GFE.
ここで、BPニューラルネットの出力層における出力値をベクトルL、識別する筋電パターンの数をMとすると
Suppose here that the output value in the output layer of the BP neural network is vector L and the number of EMG patterns to be identified is M.
筋電パターンが9パターンの場合には同時に発現する可能性のある動作は3パターンであることを踏まえ、図3のようにアルゴリズムを構築した。このアルゴリズムでは、リングバッファに保存した一定時間の筋電パターンの時系列データを参照し、時系列に含まれる筋電パターンの割合を元に出力を決定するものである。一定時間にバッファされる筋電パターンの数をNとすると、ある時刻tにおけるリングバッファbufftは
The algorithm was constructed as shown in Fig. 3 based on the fact that there are 3 patterns of movement that may occur simultaneously when the myoelectric pattern is 9 patterns. In this algorithm, the output is determined based on the ratio of the myoelectric patterns included in the time series by referring to the time series data of the myoelectric patterns for a certain time stored in the ring buffer. If the number of myoelectric patterns buffered at a certain time is N, the ring buffer buff t at a certain time t is
すなわち、まず提案するアルゴリズムが、リングバッファbuffに保存されたN個の筋電パターンを参照する。そして、半数以上が筋電パターンMpである場合には、制御部は筋電パターンMpに対応付けられた動作指令を出し、義手はその動作指令の通りの動作をする。
That is, first, the proposed algorithm refers to N myoelectric patterns stored in the ring buffer buff. When more than half are the myoelectric patterns M p , the control unit issues an operation command associated with the myoelectric pattern M p , and the prosthetic hand operates according to the operation command.
リングバッファbuffに複数の筋電パターンMp1、Mp2…を含む場合、先に述べたように同時に発現する動作数を3としたため、少なくともリングバッファbuffの1/4以上を占めた筋電パターンMp1、Mp2…をすべて出力する。ここで、一般化のために、最終的に出力される動作oを、同時に発現する動作数の最大値をnとして記述すると
When the ring buffer buff includes a plurality of myoelectric patterns M p1 , M p2 ..., The number of operations that occur simultaneously is set to 3, so that the myoelectric pattern occupies at least 1/4 of the ring buffer buff. Output all M p1 , M p2 . Here, for generalization, if the operation o that is finally output is described as n, the maximum value of the number of operations that can be expressed simultaneously
なお、同時に発現する動作数がnである場合には、同時発現の基準となるリングバッファbuffの閾占有率を1/(n+1)に設定し、リングバッファbuffに占める割合が1/(n+1)以上の筋電パターンの全てを同時に発現するのが望ましい。しかし、基準となる閾占有率は1/(n+1)に限られず、任意に設定することもできる。
When the number of operations that are simultaneously expressed is n, the threshold occupancy ratio of the ring buffer buff serving as a reference for simultaneous expression is set to 1 / (n + 1), and the ratio of the ring buffer buff to the ratio is 1 / (n + 1). It is desirable to express all of the above myoelectric patterns simultaneously. However, the reference threshold occupation ratio is not limited to 1 / (n + 1), and can be arbitrarily set.
本研究で提案するフィルタアルゴリズムを、識別結果の時系列buffを引数としてフィルタ結果としての義手への出力oを出力する関数GTSとして定義すると
Suppose that the filter algorithm proposed in this study is defined as a function GTS that outputs the output o to the prosthetic hand as a filter result using the time series buff of the identification result as an argument.
[筋電パターン識別の平滑化]
本発明で提案したアルゴリズムGTSについて、バッファ長N = 6、同時に発現する動作数の最大値n = 3とする場合に、buffが式(11)のように与えられると仮定する。 [Smooth EMG pattern identification]
For the algorithm GTS proposed in the present invention, when the buffer length is N = 6 and the maximum number n of simultaneous operations is n = 3, it is assumed that buff is given by Equation (11).
本発明で提案したアルゴリズムGTSについて、バッファ長N = 6、同時に発現する動作数の最大値n = 3とする場合に、buffが式(11)のように与えられると仮定する。 [Smooth EMG pattern identification]
For the algorithm GTS proposed in the present invention, when the buffer length is N = 6 and the maximum number n of simultaneous operations is n = 3, it is assumed that buff is given by Equation (11).
[複数動作への対応]
手首を掌屈させながら第4指及び第5指を曲げるなど、複数の筋電パターンを同時に出力した場合、それらの筋電パターンが多く現れるため、このときのbuffは [Support for multiple operations]
When multiple myoelectric patterns are output at the same time, such as bending the 4th and 5th fingers while palm flexing the wrist, many of those myoelectric patterns appear, so the buff at this time is
手首を掌屈させながら第4指及び第5指を曲げるなど、複数の筋電パターンを同時に出力した場合、それらの筋電パターンが多く現れるため、このときのbuffは [Support for multiple operations]
When multiple myoelectric patterns are output at the same time, such as bending the 4th and 5th fingers while palm flexing the wrist, many of those myoelectric patterns appear, so the buff at this time is
[適用範囲]
本発明で提案されるアルゴリズムは、定常的に筋電を発揮する場合に有効である。しかし、逆に瞬間的に筋電を発揮する場合においては適用することが困難である。例えば、タッピング動作など短時間にスパイク状の筋電が出力される場合、buffは脱力の筋電パターンをm0として [Scope of application]
The algorithm proposed in the present invention is effective when the myoelectricity is constantly exerted. However, it is difficult to apply in the case where myoelectricity is instantaneously exhibited. For example, if spiked myoelectricity is output in a short time such as tapping operation, buff sets the weak myoelectric pattern as m 0
本発明で提案されるアルゴリズムは、定常的に筋電を発揮する場合に有効である。しかし、逆に瞬間的に筋電を発揮する場合においては適用することが困難である。例えば、タッピング動作など短時間にスパイク状の筋電が出力される場合、buffは脱力の筋電パターンをm0として [Scope of application]
The algorithm proposed in the present invention is effective when the myoelectricity is constantly exerted. However, it is difficult to apply in the case where myoelectricity is instantaneously exhibited. For example, if spiked myoelectricity is output in a short time such as tapping operation, buff sets the weak myoelectric pattern as m 0
[実験目的]
実験により本発明である筋電パターン識別のアルゴリズムを評価した。提案アルゴリズムを搭載した筋電パターン識別が、従来の筋電パターン識別に比べてどれほど精度向上が図れるかを検証した。 [Experimental purpose]
Through experiments, the myoelectric pattern identification algorithm according to the present invention was evaluated. We verified how much the electromyographic pattern identification equipped with the proposed algorithm could improve the accuracy compared to the conventional electromyographic pattern identification.
実験により本発明である筋電パターン識別のアルゴリズムを評価した。提案アルゴリズムを搭載した筋電パターン識別が、従来の筋電パターン識別に比べてどれほど精度向上が図れるかを検証した。 [Experimental purpose]
Through experiments, the myoelectric pattern identification algorithm according to the present invention was evaluated. We verified how much the electromyographic pattern identification equipped with the proposed algorithm could improve the accuracy compared to the conventional electromyographic pattern identification.
[被験者と実験セットアップ]
本実験は、筋電義手の操作経験のある20代健常者2名を対象とし実施された。筋電計の貼り付け位置は前腕部の伸筋群、屈筋群、長母指屈筋の3箇所(図4参照)であり、基準電極は肘部とした。被験者はPCのモニタを無理のない体勢で見られるよう椅子に座る。筋電計測における各諸元は表1の通りであり、計測は図5の湿式表面筋電計を用いた。筋電パターンのリングバッファbuffのバッファ長Nは15とした。なお、筋電計測と筋電パターンの識別を経て、リングバッファが更新されるのにかかる時間は約10msである。 [Subject and experiment setup]
This experiment was conducted with 2 healthy people in their 20s who had experience with the operation of myoelectric prostheses. The electromyograph was attached at three positions (see FIG. 4) of the forearm extensor group, flexor group, and long thumb flexor, and the reference electrode was the elbow. The subject sits in a chair so that the PC monitor can be seen in a comfortable posture. Each item in the myoelectric measurement is as shown in Table 1, and the wet surface electromyograph shown in FIG. 5 was used for the measurement. The buffer length N of the myoelectric pattern ring buffer buff was set to 15. Note that the time required for the ring buffer to be updated after the electromyogram measurement and the myoelectric pattern identification is about 10 ms.
本実験は、筋電義手の操作経験のある20代健常者2名を対象とし実施された。筋電計の貼り付け位置は前腕部の伸筋群、屈筋群、長母指屈筋の3箇所(図4参照)であり、基準電極は肘部とした。被験者はPCのモニタを無理のない体勢で見られるよう椅子に座る。筋電計測における各諸元は表1の通りであり、計測は図5の湿式表面筋電計を用いた。筋電パターンのリングバッファbuffのバッファ長Nは15とした。なお、筋電計測と筋電パターンの識別を経て、リングバッファが更新されるのにかかる時間は約10msである。 [Subject and experiment setup]
This experiment was conducted with 2 healthy people in their 20s who had experience with the operation of myoelectric prostheses. The electromyograph was attached at three positions (see FIG. 4) of the forearm extensor group, flexor group, and long thumb flexor, and the reference electrode was the elbow. The subject sits in a chair so that the PC monitor can be seen in a comfortable posture. Each item in the myoelectric measurement is as shown in Table 1, and the wet surface electromyograph shown in FIG. 5 was used for the measurement. The buffer length N of the myoelectric pattern ring buffer buff was set to 15. Note that the time required for the ring buffer to be updated after the electromyogram measurement and the myoelectric pattern identification is about 10 ms.
BPニューラルネットワークの筋電パターンの学習プロセスは、被験者が筋電パターンを出力しながら対応させたい動作の番号のキーを押す。動作と番号の対応は、0:安静、1:前腕回外、2:前腕回内、3:手首掌屈、4:手首背屈、5:5指握り、6:5指開き、7:母指屈曲、8:第4指及び第5指屈曲となっている。この作業により特定の筋電パターンと特定の義手の動作がラベリングされ、BPによりニューラルネットが学習される。過去に教示した筋電パターンとその識別動作の組は全て記憶されており、最後に教示した筋電パターンの識別率が有意に高くならないよう配慮した。
In the learning process of the myoelectric pattern of the BP neural network, the test subject presses the key of the number of the action he wants to correspond while outputting the myoelectric pattern. Correspondence between movement and number is 0: Rest, 1: Forearm gyrus, 2: Forearm pronation, 3: Wrist palmar flexion, 4: Wrist dorsiflexion, 5: Five finger grip, 6: Five finger open, 7: Mother Finger flexion, 8: 4th and 5th finger flexion. By this work, a specific myoelectric pattern and a specific prosthetic hand movement are labeled, and a neural network is learned by BP. All the combinations of myoelectric patterns taught in the past and their identifying operations are stored, and consideration was given so that the discrimination rate of the last taught myoelectric pattern would not be significantly increased.
教示の条件としては、各動作に割り当てる筋電パターンを1回ずつ教示した後、学習が不十分だとみなされた筋電パターンにつき追加1回までの追学習を許容した。筋電パターン1つの教示にかかる時間は、新たに教示された筋電パターンとすでに学習された筋電パターンとの類似度により変動し、他に学習データのない1度目の教示は100~300ms、2度目以降の教示は、他の全ての学習データに対し一定の識別率が担保されるまでBP学習が継続するため100~1000msほどとなる。
As teaching conditions, after the myoelectric pattern to be assigned to each motion was taught once, additional learning was allowed up to one additional time for the myoelectric pattern considered to be insufficient. The time taken to teach one myoelectric pattern varies depending on the similarity between the newly taught myoelectric pattern and the already learned myoelectric pattern, and the first teaching without any other learning data is 100 to 300 ms. The second and subsequent teachings are about 100 to 1000 ms because BP learning continues until a certain identification rate is secured for all other learning data.
図6は、PCのモニタ上に映し出される画面を示す。左側に目標姿勢、右側に筋電パターンから識別された姿勢が表示される。被験者には目標姿勢と識別された姿勢が一致するよう筋電パターンをコントロールするタスクが与えられる。
FIG. 6 shows a screen displayed on the PC monitor. The target posture is displayed on the left side, and the posture identified from the myoelectric pattern is displayed on the right side. The subject is given a task to control the electromyographic pattern so that the identified posture matches the target posture.
[実験手順]
実験手順は、まず筋電計が被験者に装着され、被験者の筋電パターンをBPニューラルネットに学習させる。次に被験者に実験タスクが課せられ、データが収集される。筋電パターンの識別系はBPニューラルネットを用いた教師あり学習を用いているため、まず被験者の筋電パターンをシステムに教示する。次にPCモニタ上に目標となる手の筋電パターンと識別結果が表示され(図6参照)、目標筋電パターンに識別結果を一致させるタスクを行う。タスクは、4秒間PCモニタ上に表示された目標筋電パターンを出力し、次に6秒間脱力することを繰り返す。1タスク中に目標筋電パターンはそれぞれ5回表示される。この際、提案アルゴリズムを搭載したものと搭載していないものそれぞれで約7分間行い、目標となる筋電パターンに対する識別された筋電パターンの一致の程度を識別率として評価する。 [Experimental procedure]
In the experimental procedure, first, an electromyograph is attached to the subject, and the BP neural network is made to learn the electromyographic pattern of the subject. The subject is then assigned an experimental task and data is collected. Since the myoelectric pattern identification system uses supervised learning using a BP neural network, the system first teaches the subject's myoelectric pattern. Next, the target myoelectric pattern and the identification result are displayed on the PC monitor (see FIG. 6), and the task of matching the identification result to the target myoelectric pattern is performed. The task repeatedly outputs the target myoelectric pattern displayed on the PC monitor for 4 seconds and then weakens for 6 seconds. Each target EMG pattern is displayed five times during one task. At this time, each of the ones with and without the proposed algorithm is performed for about 7 minutes, and the degree of matching of the identified myoelectric pattern with respect to the target myoelectric pattern is evaluated as the discrimination rate.
実験手順は、まず筋電計が被験者に装着され、被験者の筋電パターンをBPニューラルネットに学習させる。次に被験者に実験タスクが課せられ、データが収集される。筋電パターンの識別系はBPニューラルネットを用いた教師あり学習を用いているため、まず被験者の筋電パターンをシステムに教示する。次にPCモニタ上に目標となる手の筋電パターンと識別結果が表示され(図6参照)、目標筋電パターンに識別結果を一致させるタスクを行う。タスクは、4秒間PCモニタ上に表示された目標筋電パターンを出力し、次に6秒間脱力することを繰り返す。1タスク中に目標筋電パターンはそれぞれ5回表示される。この際、提案アルゴリズムを搭載したものと搭載していないものそれぞれで約7分間行い、目標となる筋電パターンに対する識別された筋電パターンの一致の程度を識別率として評価する。 [Experimental procedure]
In the experimental procedure, first, an electromyograph is attached to the subject, and the BP neural network is made to learn the electromyographic pattern of the subject. The subject is then assigned an experimental task and data is collected. Since the myoelectric pattern identification system uses supervised learning using a BP neural network, the system first teaches the subject's myoelectric pattern. Next, the target myoelectric pattern and the identification result are displayed on the PC monitor (see FIG. 6), and the task of matching the identification result to the target myoelectric pattern is performed. The task repeatedly outputs the target myoelectric pattern displayed on the PC monitor for 4 seconds and then weakens for 6 seconds. Each target EMG pattern is displayed five times during one task. At this time, each of the ones with and without the proposed algorithm is performed for about 7 minutes, and the degree of matching of the identified myoelectric pattern with respect to the target myoelectric pattern is evaluated as the discrimination rate.
[実験結果]
開発されたアルゴリズムを搭載した提案システムと、非搭載の従来システムを比較することで提案アルゴリズムの評価を行う。まず従来システムと提案システム両方の識別率を図7に示す。実施例のアルゴリズムの識別率は全9パターン(図7の右半分で「全動作」と表記)を識別対象とした場合は82.5%、前腕の回外・回内を識別対象から除いた7パターンの場合(図7の左半分)は92.9%となった。実施例のアルゴリズム搭載と非搭載の試行でF検定を行ったところ、全パターン対象の試行は片側確率0.095、回外・回内を除く試行は0.37であり、ともに5%水準を上回っているため、等分散としてt検定を行った。その結果どちらの試行も提案手法の識別率が高いことに有意差(p < 0.001)が認められた。識別率の向上は全9パターン対象の場合17.7%、前腕の回外・回内を除く7パターンの場合11.5%となった。 [Experimental result]
The proposed algorithm is evaluated by comparing the proposed system with the developed algorithm with the conventional system without it. First, the identification rates of both the conventional system and the proposed system are shown in FIG. The identification rate of the algorithm of the embodiment is 82.5% when all nine patterns (indicated as “all movements” in the right half of FIG. 7) are the identification targets, and seven patterns excluding the forearm pronation and pronation from the identification targets In the case of (left half of Fig. 7), it was 92.9%. When the F test was performed with trials with and without the algorithm of the example, the trial for all patterns had a one-sided probability of 0.095, and the trial excluding pronation and pronation was 0.37, both exceeding the 5% level. The t test was performed as equal variance. As a result, both trials showed a significant difference (p <0.001) in the high recognition rate of the proposed method. The improvement of the recognition rate was 17.7% for all 9 patterns, and 11.5% for 7 patterns excluding forearm pronation and pronation.
開発されたアルゴリズムを搭載した提案システムと、非搭載の従来システムを比較することで提案アルゴリズムの評価を行う。まず従来システムと提案システム両方の識別率を図7に示す。実施例のアルゴリズムの識別率は全9パターン(図7の右半分で「全動作」と表記)を識別対象とした場合は82.5%、前腕の回外・回内を識別対象から除いた7パターンの場合(図7の左半分)は92.9%となった。実施例のアルゴリズム搭載と非搭載の試行でF検定を行ったところ、全パターン対象の試行は片側確率0.095、回外・回内を除く試行は0.37であり、ともに5%水準を上回っているため、等分散としてt検定を行った。その結果どちらの試行も提案手法の識別率が高いことに有意差(p < 0.001)が認められた。識別率の向上は全9パターン対象の場合17.7%、前腕の回外・回内を除く7パターンの場合11.5%となった。 [Experimental result]
The proposed algorithm is evaluated by comparing the proposed system with the developed algorithm with the conventional system without it. First, the identification rates of both the conventional system and the proposed system are shown in FIG. The identification rate of the algorithm of the embodiment is 82.5% when all nine patterns (indicated as “all movements” in the right half of FIG. 7) are the identification targets, and seven patterns excluding the forearm pronation and pronation from the identification targets In the case of (left half of Fig. 7), it was 92.9%. When the F test was performed with trials with and without the algorithm of the example, the trial for all patterns had a one-sided probability of 0.095, and the trial excluding pronation and pronation was 0.37, both exceeding the 5% level. The t test was performed as equal variance. As a result, both trials showed a significant difference (p <0.001) in the high recognition rate of the proposed method. The improvement of the recognition rate was 17.7% for all 9 patterns, and 11.5% for 7 patterns excluding forearm pronation and pronation.
図8に識別の時系列、下記の表2に各条件での識別率を示す。図8はアルゴリズムにより識別された筋電パターンをCSV形式でロギングしたものであり、サンプル間は10~20msである。なお、図8においての筋電パターンと番号の対応は、0:安静、1:前腕回外、2:前腕回内、3:手首掌屈、4:手首背屈、5:5指握り、6:5指開き、7:母指屈曲、8:第4指及び第5指屈曲である。
Fig. 8 shows the time series of identification, and Table 2 below shows the identification rate under each condition. FIG. 8 is a log of the myoelectric pattern identified by the algorithm in CSV format, and the interval between samples is 10 to 20 ms. In addition, the correspondence of the myoelectric pattern and the number in FIG. 8 is as follows: 0: Rest, 1: Forearm supination, 2: Forearm pronation, 3: Wrist palm flexion, 4: Wrist dorsiflexion, 5: Grip of 5 fingers, 6 : 5 finger opening, 7: thumb flexion, 8: 4th finger and 5th finger flexion.
図8(a)~(d)は本発明のアルゴリズムを搭載した場合と搭載していない場合の識別の比較である。図8(a)(b)は被験者1のグラフ、図8(c)(d)は被験者2のグラフである。図8(a)(c)が本出願の提案手法、図8(b)(d)が従来手法である。横軸のサンプリング間隔は10~20msである。上下は被験者ごとの違いであり、○が各識別結果を示している。目標姿勢の変化は実線で示している。目標姿勢がディスプレイに提示されているのは実線が水平の部分のみである。被験者が、PCモニタ上に表示される目標筋電パターンを出力する約7分間のうち代表的な区間における識別結果と目標筋電パターンを示した。図8から、従来手法に比べ、筋電パターンの連続性が保たれ、誤識別が少ないことが見て取れる。
8 (a) to 8 (d) are comparisons of identification when the algorithm of the present invention is installed and when it is not installed. 8A and 8B are graphs of the subject 1, and FIGS. 8C and 8D are graphs of the subject 2. 8A and 8C show the proposed method of the present application, and FIGS. 8B and 8D show the conventional method. The sampling interval on the horizontal axis is 10 to 20 ms. The top and bottom are differences between subjects, and ○ indicates each identification result. The change in the target posture is indicated by a solid line. The target posture is shown on the display only in the portion where the solid line is horizontal. The test subject showed the identification result and the target myoelectric pattern in a representative section in about 7 minutes to output the target myoelectric pattern displayed on the PC monitor. It can be seen from FIG. 8 that the continuity of the myoelectric pattern is maintained and there are few misidentifications as compared with the conventional method.
[識別率について]
実験の結果、回外・回内を含めるか否かで識別率が若干変わることがわかった。回外・回内を含めない場合の識別率は90%を超える結果となった。また、回外・回内を含めた場合であっても識別率は82.5%となった。識別の時系列を示した図8(a)の被験者1に注目すると、握り(筋電パターン5)時に回外(筋電パターン1)と誤識別しているのが見受けられる。握りの際に回外方向に力が入っており、握りと回外の識別がつきにくかった可能性がある。また図8(c)の被験者2に着目すると、回内(筋電パターン2)が回外(筋電パターン1)と開き(筋電パターン6)においても識別されており、識別されやすくバイアスがかかっていることが示唆される。
[制御周期について]
提案手法は制御周期10msの筋電識別器の出力を15個分バッファしているが、図3に示すようにバッファの1/4または1/2で判断がなされるため、提案手法の遅れ時間は40~80msと見積もれる。筋電は、関節運動開始の数十~百ミリ秒早く観測されることを考えれば、義手として使うためには十分な応答性を持っていると考えられる。 [About identification rate]
As a result of the experiment, it was found that the discrimination rate changed slightly depending on whether or not pronation and pronation were included. The classification rate when the pronation and pronation were not included exceeded 90%. The identification rate was 82.5% even when including pronation and pronation. When attention is paid to the subject 1 in FIG. 8 (a) showing the time series of identification, it can be seen that the hand is misidentified as gyrus (myoelectric pattern 1) when grasping (myoelectric pattern 5). There is a possibility that it is difficult to distinguish between gripping and pronation because there is force in the pronation direction when grasping. In addition, paying attention to the subject 2 in FIG. 8C, the pronation (myoelectric pattern 2) is also identified in the pronation (myoelectric pattern 1) and the opening (myoelectric pattern 6), and the bias is easily identified. It is suggested that it depends.
[Control cycle]
The proposed method buffers 15 EMG discriminator outputs with a control period of 10 ms. However, as shown in Fig. 3, the delay time of the proposed method is determined by 1/4 or 1/2 of the buffer. Is estimated to be 40-80ms. Considering that myoelectricity is observed several tens to hundreds of milliseconds before the start of joint movement, it is considered to have sufficient responsiveness to be used as a prosthetic hand.
実験の結果、回外・回内を含めるか否かで識別率が若干変わることがわかった。回外・回内を含めない場合の識別率は90%を超える結果となった。また、回外・回内を含めた場合であっても識別率は82.5%となった。識別の時系列を示した図8(a)の被験者1に注目すると、握り(筋電パターン5)時に回外(筋電パターン1)と誤識別しているのが見受けられる。握りの際に回外方向に力が入っており、握りと回外の識別がつきにくかった可能性がある。また図8(c)の被験者2に着目すると、回内(筋電パターン2)が回外(筋電パターン1)と開き(筋電パターン6)においても識別されており、識別されやすくバイアスがかかっていることが示唆される。
[制御周期について]
提案手法は制御周期10msの筋電識別器の出力を15個分バッファしているが、図3に示すようにバッファの1/4または1/2で判断がなされるため、提案手法の遅れ時間は40~80msと見積もれる。筋電は、関節運動開始の数十~百ミリ秒早く観測されることを考えれば、義手として使うためには十分な応答性を持っていると考えられる。 [About identification rate]
As a result of the experiment, it was found that the discrimination rate changed slightly depending on whether or not pronation and pronation were included. The classification rate when the pronation and pronation were not included exceeded 90%. The identification rate was 82.5% even when including pronation and pronation. When attention is paid to the subject 1 in FIG. 8 (a) showing the time series of identification, it can be seen that the hand is misidentified as gyrus (myoelectric pattern 1) when grasping (myoelectric pattern 5). There is a possibility that it is difficult to distinguish between gripping and pronation because there is force in the pronation direction when grasping. In addition, paying attention to the subject 2 in FIG. 8C, the pronation (myoelectric pattern 2) is also identified in the pronation (myoelectric pattern 1) and the opening (myoelectric pattern 6), and the bias is easily identified. It is suggested that it depends.
[Control cycle]
The proposed method buffers 15 EMG discriminator outputs with a control period of 10 ms. However, as shown in Fig. 3, the delay time of the proposed method is determined by 1/4 or 1/2 of the buffer. Is estimated to be 40-80ms. Considering that myoelectricity is observed several tens to hundreds of milliseconds before the start of joint movement, it is considered to have sufficient responsiveness to be used as a prosthetic hand.
本発明では、識別結果の時系列に注目し2段階の識別を行うことで、演算コストを大きく増大させることなく、3つの電極から7パターンを90%、9パターンを82.5%という実用的な識別率をもって識別することのできるシステムを構築した。また、識別器の学習時間も7パターン全ての教示で十数秒足らずであり、実用に耐えうる多自由度義手のための制御方式が実現に近づいた。
In the present invention, by focusing on the time series of the identification results and performing two-stage identification, practical identification of 7 patterns from 3 electrodes, 90% and 9 patterns, 82.5%, without greatly increasing the calculation cost. We built a system that can be identified at a rate. In addition, the learning time of the classifier is less than a dozen seconds with all seven patterns taught, and a control system for a multi-degree-of-freedom prosthesis that can withstand practical use is nearing realization.
なお、図1(b)及び図3のアルゴリズムは、筋電義肢等の機器に実装したマイクロプロセッサによって実行可能である。この場合、マイクロプロセッサ内のメモリに筋電パターン識別プログラムをあらかじめ格納し、プロセッサがプログラムを実行することで筋電制御を行うことができる。
Note that the algorithms shown in FIGS. 1B and 3 can be executed by a microprocessor mounted on a device such as a myoelectric prosthesis. In this case, a myoelectric pattern identification program is stored in advance in a memory in the microprocessor, and myoelectric control can be performed by the processor executing the program.
近年、筋電義手は多自由度化へのニーズが高まってきており、高機能な多自由度筋電義手の研究発表が相次いでいる。また、筋電制御の義足への適用も検討されている。しかしながら筋電のノイズ対策や皮膚の電気的性質の変動など種々に存在する問題の難しさから、実質的な制御自由度の向上が妨げられてきた。そのため、多自由度のロボットハンドを筋電義手として機能させるためには、生体の特性変動を情報処理的に吸収し、安定して複数の筋電パターンを識別可能な方法論が必要とされる。本出願では筋電パターンの識別率を向上させるアルゴリズムを提案する。識別器の出力時系列に着目し、識別器自体の構造を全く変化させることなく、識別器の後方に位置することで識別率を飛躍的に向上させるフィルタアルゴリズムを開発した。評価実験では、開発されたアルゴリズムを搭載した識別系と非搭載の識別系で、筋電パターンの識別率を比較した。表面電極3チャンネル、1クラスあたりの学習時間は1秒以内で、識別クラス数を7、9個の2条件設定し、パターン識別の出力を10~20msでサンプリングした。その結果クラス数7の場合は11.5%、9の場合は17.7%の精度向上が図られた。クラス数9では提案手法による識別率は82.5%、クラス数7では92.9%となった。少ない電極チャンネルでありながら、高速な学習と高い識別率が達成されており、多自由度筋電義手の実用的な制御に有効性の高い方法論の提案となっている。
In recent years, there is an increasing need for multi-degree-of-freedom myoelectric prostheses, and a series of research announcements of high-performance multi-degree-of-freedom myoelectric prostheses. Application of myoelectric control to prosthetic legs is also being studied. However, the difficulty of various problems such as countermeasures against myoelectric noise and changes in the electrical properties of the skin has hindered substantial improvement in control freedom. Therefore, in order to make a multi-degree-of-freedom robot hand function as a myoelectric prosthetic hand, a methodology capable of absorbing a characteristic change of a living body in information processing and stably identifying a plurality of myoelectric patterns is required. This application proposes an algorithm that improves the recognition rate of myoelectric patterns. Focusing on the output time series of the classifier, we developed a filter algorithm that dramatically improves the classification rate by being positioned behind the classifier without changing the structure of the classifier itself. In the evaluation experiment, the discrimination rate of the myoelectric pattern was compared between the discriminating system equipped with the developed algorithm and the discriminating system not equipped. The surface electrode 3 channels, the learning time per class was within 1 second, the number of identification classes was set to 2 and 9 conditions, and the pattern identification output was sampled at 10-20 ms. As a result, the accuracy was improved by 11.5% for 7 classes and 17.7% for 9 classes. The classification rate by the proposed method was 82.5% for 9 classes and 92.9% for 7 classes. Although there are few electrode channels, high-speed learning and a high identification rate have been achieved, and it has been proposed a methodology that is highly effective for practical control of a multi-degree-of-freedom myoelectric hand.
この出願は、2015年10月29日に日本国特許庁に出願された特許出願第2015-212840号に基づき、その全内容を含むものである。
This application is based on Patent Application No. 2015-212840 filed with the Japan Patent Office on October 29, 2015, and includes the entire contents thereof.
Claims (6)
- 筋電を一定の時間間隔で計測するための計測部と、
あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別する筋電識別部と、
前記筋電識別部で識別された筋電パターンを、時系列順に所定数だけ保存するバッファと、を備え、
バッファに保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合には、その筋電パターンに対応した動作指令にしたがった制御を行うことを特徴とする筋電義手。 A measurement unit for measuring myoelectricity at regular time intervals;
A myoelectric identification unit for identifying which electromyographic pattern of the electromyogram pattern learned in advance should be classified,
A buffer for storing a predetermined number of myoelectric patterns identified by the myoelectric identification unit in time series,
With reference to the predetermined number of myoelectric patterns stored in the buffer, if an occupation ratio exceeding a certain ratio is recognized in a specific myoelectric pattern, control is performed according to an operation command corresponding to the myoelectric pattern. A myoelectric prosthesis characterized by that. - 前記バッファがリングバッファであり、保存すべき筋電パターンが前記所定数に達した場合には、古い筋電パターンから破棄することを特徴とする請求項1に記載の筋電義手。 The myoelectric prosthetic hand according to claim 1, wherein when the buffer is a ring buffer and the number of myoelectric patterns to be stored reaches the predetermined number, the old myoelectric pattern is discarded.
- 前記占有率が50%以上のときに、前記特定の筋電パターンに対応した動作指令にしたがった制御を行うことを特徴とする請求項2に記載の筋電義手。 The myoelectric prosthetic hand according to claim 2, wherein when the occupation ratio is 50% or more, control according to an operation command corresponding to the specific myoelectric pattern is performed.
- 前記筋電義手において同時に発現可能な筋電パターンをnパターンと設定した場合には、前記占有率が100/(n+1)%以上のときに、前記占有率を占める複数の筋電パターンのそれぞれに対応した動作指令にしたがった制御を同時に行うことを特徴とする請求項2または3に記載の筋電義手。 When the myoelectric pattern that can be expressed simultaneously in the myoelectric prosthetic hand is set as n pattern, each of the plurality of myoelectric patterns that occupy the occupation rate when the occupation rate is 100 / (n + 1)% or more. 4. The myoelectric prosthetic hand according to claim 2, wherein control according to a corresponding operation command is performed simultaneously.
- 筋電制御が適用される機器において、筋電を一定の時間間隔で計測し、
前記機器で、あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別し、
前記機器において、識別された筋電パターンを、時系列順に所定数だけ保存し、
前記機器において、保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合に、その筋電パターンに対応した動作指令にしたがった制御を行う、
ことを特徴とする筋電パターン識別方法。 In devices to which EMG control is applied, measure EMG at regular time intervals,
With the device, among the electromyographic patterns learned in advance, identify which myoelectric pattern should be classified into the myoelectric pattern,
In the device, the identified myoelectric patterns are stored in a predetermined number of times in chronological order,
In the device, referring to the predetermined number of stored myoelectric patterns, and when an occupation ratio higher than a certain ratio is recognized in a specific myoelectric pattern, control according to an operation command corresponding to the myoelectric pattern is performed. Do,
A myoelectric pattern identification method characterized by the above. - 筋電制御が適用される機器のプロセッサで実行される筋電パターン識別プログラムであって、
筋電を一定の時間間隔で計測する手順と、
あらかじめ学習された筋電パターンのうち、計測された筋電がいずれの筋電パターンに分類されるべきかを識別する手順と、
識別された筋電パターンを、時系列順に所定数だけ保存する手順と、
保存された前記所定数の筋電パターンを参照し、特定の筋電パターンにある割合以上の占有率が認められる場合に、その筋電パターンに対応した動作指令にしたがった制御を行う手順、
を含むことを特徴とする筋電パターン識別プログラム。 A myoelectric pattern identification program executed by a processor of a device to which myoelectric control is applied,
Procedures for measuring myoelectricity at regular time intervals;
A procedure for identifying which electromyographic pattern should be classified among the pre-trained myoelectric patterns,
A procedure for storing a predetermined number of identified myoelectric patterns in chronological order,
A procedure for referring to the predetermined number of stored myoelectric patterns and performing control according to an operation command corresponding to the myoelectric pattern when an occupation ratio of a certain ratio or more in a specific myoelectric pattern is recognized,
A myoelectric pattern identification program comprising:
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2015-212840 | 2015-10-29 | ||
JP2015212840A JP2019004915A (en) | 2015-10-29 | 2015-10-29 | Myoelectric pattern identification method using time series information and myoelectric artificial hand |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2017073770A1 true WO2017073770A1 (en) | 2017-05-04 |
Family
ID=58630397
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/JP2016/082171 WO2017073770A1 (en) | 2015-10-29 | 2016-10-28 | Method of identifying myoelectric pattern employing time series information, and myoelectric prosthetic hand |
Country Status (2)
Country | Link |
---|---|
JP (1) | JP2019004915A (en) |
WO (1) | WO2017073770A1 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109598219A (en) * | 2018-11-20 | 2019-04-09 | 中国科学技术大学 | A kind of adaptive electrode method for registering for robust myoelectric control |
WO2019107554A1 (en) * | 2017-11-30 | 2019-06-06 | 株式会社メルティンMmi | System for identifying information represented by biological signals |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP6991622B1 (en) * | 2021-04-21 | 2022-01-14 | 株式会社メルティンMmi | Methods, systems, and electrode structures for acquiring electrical signals from multiple nerves in a living body, and methods for manufacturing the electrode structures. |
WO2023286216A1 (en) * | 2021-07-14 | 2023-01-19 | 日本電信電話株式会社 | Teaching device, teaching method, and program |
-
2015
- 2015-10-29 JP JP2015212840A patent/JP2019004915A/en active Pending
-
2016
- 2016-10-28 WO PCT/JP2016/082171 patent/WO2017073770A1/en active Application Filing
Non-Patent Citations (2)
Title |
---|
KASUYA, MASAHIRO ET AL.: "Analysis and Optimization of Novel Post- processing Method for Myoelectric Pattern Recognition", 2015 IEEE INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR, August 2015 (2015-08-01), pages 985 - 990, XP055380256 * |
MASAHIRO KASUYA ET AL.: "Development of a Novel Post-processing Algorithm for Myoelectric Pattern Classification", TRANSACTIONS OF JAPANESE SOCIETY FOR MEDICAL AND BIOLOGICAL ENGINEERING, vol. 53, no. 4, August 2015 (2015-08-01), pages 217 - 224, XP055380255, ISSN: 1347-443X * |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2019107554A1 (en) * | 2017-11-30 | 2019-06-06 | 株式会社メルティンMmi | System for identifying information represented by biological signals |
JPWO2019107554A1 (en) * | 2017-11-30 | 2019-12-12 | 株式会社メルティンMmi | System for identifying information represented by a biological signal |
JP2020096851A (en) * | 2017-11-30 | 2020-06-25 | 株式会社メルティンMmi | System for identifying information indicated by biological signal |
CN111565680A (en) * | 2017-11-30 | 2020-08-21 | 梅尔廷Mmi株式会社 | System for recognizing information represented by biological signal |
CN111565680B (en) * | 2017-11-30 | 2021-11-02 | 梅尔廷Mmi株式会社 | System for recognizing information represented by biological signal |
US11166667B2 (en) | 2017-11-30 | 2021-11-09 | Meltin Mmi Co., Ltd. | System for identifying information represented by biological signals |
JP7477309B2 (en) | 2017-11-30 | 2024-05-01 | 株式会社メルティンMmi | System for identifying information represented by biological signals |
CN109598219A (en) * | 2018-11-20 | 2019-04-09 | 中国科学技术大学 | A kind of adaptive electrode method for registering for robust myoelectric control |
CN109598219B (en) * | 2018-11-20 | 2022-09-02 | 中国科学技术大学 | Adaptive electrode registration method for robust electromyography control |
Also Published As
Publication number | Publication date |
---|---|
JP2019004915A (en) | 2019-01-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Waris et al. | Multiday evaluation of techniques for EMG-based classification of hand motions | |
Soares et al. | The development of a virtual myoelectric prosthesis controlled by an EMG pattern recognition system based on neural networks | |
Kanitz et al. | Classification of transient myoelectric signals for the control of multi-grasp hand prostheses | |
WO2017073770A1 (en) | Method of identifying myoelectric pattern employing time series information, and myoelectric prosthetic hand | |
US10632003B2 (en) | Systems and methods for postural control of a multi-function prosthesis | |
Chen et al. | Exploring the relation between EMG sampling frequency and hand motion recognition accuracy | |
Cipriani et al. | Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: preliminary study | |
Das et al. | A review on the advancements in the field of upper limb prosthesis | |
US9907489B2 (en) | Systems and methods for hierarchical pattern recognition for simultaneous control of multiple-degree of freedom movements for prosthetics | |
JP7477309B2 (en) | System for identifying information represented by biological signals | |
Lauretti et al. | Fusion of M-IMU and EMG signals for the control of trans-humeral prostheses | |
Wołczowski et al. | Human–machine interface in bioprosthesis control using EMG signal classification | |
George et al. | Inexpensive surface electromyography sleeve with consistent electrode placement enables dexterous and stable prosthetic control through deep learning | |
Engdahl et al. | First demonstration of functional task performance using a sonomyographic prosthesis: a case study | |
Alshamsi et al. | Development of a local prosthetic limb using artificial intelligence | |
Raurale | Acquisition and processing real-time EMG signals for prosthesis active hand movements | |
KR100994408B1 (en) | Method and device for deducting pinch force, method and device for discriminating muscle to deduct pinch force | |
CN111616848B (en) | Five-degree-of-freedom upper arm prosthesis control system based on FSM | |
Ahmad et al. | Classification of surface electromyographic signal using fuzzy logic for prosthesis control application | |
Farrell et al. | Pilot comparison of surface vs. implanted EMG for multifunctional prosthesis control | |
Ossaba et al. | Open source multichannel EMG armband design | |
Putra et al. | Pattern recognition of electromyography (EMG) signal for wrist movement using learning vector quantization (LVQ) | |
Shima et al. | An MMG-based human-assisting manipulator using acceleration sensors | |
Cipriani et al. | Preliminary study on the influence of inertia and weight of the prosthesis on the EMG pattern recognition robustness | |
Rusydi et al. | Robot mobile control based on three EMG signals using an artificial neural network |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 16859998 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 16859998 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: JP |