CN107684501B - Elbow joint action continuous identification method based on surface myoelectricity - Google Patents
Elbow joint action continuous identification method based on surface myoelectricity Download PDFInfo
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- 238000013016 damping Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
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- 238000005859 coupling reaction Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000002567 electromyography Methods 0.000 description 1
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Abstract
The invention relates to an elbow joint action continuous identification method based on surface myoelectricity, which comprises the following steps: acquiring and preprocessing elbow joint motion related electromyographic signals to obtain a biceps brachii electromyographic signal and a triceps brachii electromyographic signal at a certain moment; calculating the myoelectric energy characteristics of the biceps brachii muscle of the elbow joint and the triceps brachii muscle at the moment to obtain the myoelectric energy characteristics of the biceps brachii muscle of the elbow joint and the triceps brachii muscle at the moment; according to the myoelectric energy characteristics of the biceps brachii and the triceps brachii of the elbow joint at the moment, the elbow joint action at the moment is identified; and returning to the second step, and identifying the elbow joint action at the next moment until the whole identification process is finished. The method has the advantages of simple algorithm, high accuracy and robustness and the like. The bending and stretching motion amplitude of the elbow joint of the upper limb can be determined at any time by a wearer according to the real-time physical strength and other conditions of the upper limb, and a better assistance effect and man-machine experience are achieved.
Description
Technical Field
The invention relates to the technical field of elbow joint motion recognition, in particular to an elbow joint motion continuous recognition method based on surface myoelectricity.
Background
The upper limbs of the human body are the main limbs of the human body engaged in various complex and fine sports operations, and the movement function and the ability of the upper limbs have decisive influence on the operation efficiency of the human body. The upper limb exoskeleton is a wearable assistance mechanism, and has extremely important application values in the field of rehabilitation medicine and the field of military, for example, the exoskeleton can assist or replace doctors to complete upper limb rehabilitation training, assist a human body to carry heavy objects or complete tasks with high repeatability for upper limb participation. The upper limb exoskeleton is a complex electromechanical system with strong man-machine coupling, and the timely and accurate identification of the human motion intention is the basis for the upper limb exoskeleton to exert the assistance effect and is also the key for realizing efficient man-machine cooperation. For the exoskeleton of the upper limbs, the timely and accurate identification of the elbow joint action is a basic requirement. Many movements of the upper limbs of the human body involve flexion and extension movements of elbow joints, so that the real-time and rapid identification of the flexion and extension movements of the elbow joints has extremely important significance for the development and promotion of the upper limb exoskeleton technology.
The surface electromyogram signal is a real-time bioelectric signal generated by the control of muscle contraction by the central nervous system of the human body, and directly and fully reflects the movement intention of the human body. Therefore, the human body movement intention can be quickly identified through the surface electromyographic signals. Compared with other identification methods, the human motion action identification method based on the electromyographic signals can realize better man-machine cooperativity.
At present, there are some elbow joint flexion and extension motion identifications based on surface electromyographic signals, for example, by extracting time domain or frequency domain characteristics of the electromyographic signals in the elbow joint flexion and extension motion process, and using classifiers such as neural networks and support vector machines to complete elbow joint flexion and extension motion identifications. However, these identification methods realize the classification and identification of discrete motion modes, do not realize the continuous identification of elbow joint flexion and extension motions, and cannot realize the procedural control of the upper limb exoskeleton elbow joint, thereby severely limiting the assistance effect of the upper limb exoskeleton. Therefore, a method for continuously identifying the flexion and extension actions of the elbow joint is needed, and the problem of the over-program control of the elbow joint in the upper limb exoskeleton is solved. Compared with discrete action mode classification identification, continuous action identification can realize control over the upper limb exoskeleton process, and improve the control precision and control capability of the upper limb exoskeleton, so that a wearer can determine the bending and stretching motion amplitude of the elbow joint of the upper limb at any time according to the real-time physical strength and other conditions of the upper limb, and better assistance effect and man-machine experience are achieved.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a method for continuously identifying elbow joint movements based on surface myoelectricity, so as to solve the problems that the existing measuring device cannot perform real-time measurement, and the measuring process is complex and has low stability, and how to improve the control accuracy and control capability for the upper limb exoskeleton and achieve better assistance effect and human-computer experience.
The purpose of the invention is mainly realized by the following technical scheme:
an elbow joint action continuous identification method based on surface myoelectricity comprises the following steps:
s1, acquiring and preprocessing elbow joint motion related electromyographic signals to obtain a biceps brachii electromyographic signal and a triceps brachii electromyographic signal at a certain moment;
s2, calculating the myoelectric energy characteristics of the elbow joint biceps brachii muscle and the triceps brachii muscle at the moment to obtain the myoelectric energy characteristics of the elbow joint biceps brachii muscle at the moment and the myoelectric energy characteristics of the elbow joint triceps brachii muscle at the moment;
s3, according to the myoelectric energy characteristics of the biceps brachii and the triceps brachii of the elbow joint at the moment, identifying the elbow joint action at the moment;
and S4, returning to the step S2, and identifying the elbow joint action at the next moment until the whole identification process is finished.
In the method, electromyographic signals are collected once every time t, and are counted for 1 time until the identification process is finished, and the number is counted for N times, wherein N is more than or equal to 2;
at the time N after the time n.t after the identification process begins, the electromyographic signal of the biceps brachii is x (N), the electromyographic signal of the triceps brachii is y (N), wherein N is more than or equal to 0 and less than or equal to N, and N is an integer.
Step S1 specifically includes:
giving initial values to the biceps brachii myoelectric signal and the triceps brachii myoelectric signal before the identification process is started; at the moment i after the identification process is started, i is more than or equal to 2 and less than or equal to N, and a biceps brachii myoelectric signal and a triceps brachii myoelectric signal which are related to elbow joint flexion and extension movement of an upper limb exoskeleton wearer are continuously acquired through a multi-channel myoelectric signal acquisition device; preprocessing the biceps brachii electromyographic signals and the triceps brachii electromyographic signals by an electromyographic signal filtering preprocessing module, and filtering interference and noise; finally obtaining a preprocessed biceps brachii electromyogram signal x (i) and a triceps brachii electromyogram signal y (i) at the moment i.
In step S1, the assigning initial values to the biceps muscle myoelectric signal and the triceps muscle myoelectric signal before the start of the identification process includes:
at time i equal to 0, biceps myoelectric signal x (0) and triceps myoelectric signal y (0);
at the moment i is 1, biceps muscle myoelectric signal x (1), triceps muscle myoelectric signal y (1), biceps muscle myoelectric energy characteristic Ex(1) Brachial triceps myoelectric energy characteristic Ey(1)。
In step S1, a butterworth band-pass filter with a cutoff frequency of 30-350Hz is used to filter the collected electromyographic signals, and interference and noise are filtered out.
Step S2 specifically includes:
calculating energy characteristics of the preprocessed biceps brachii electromyographic signals x (i) and triceps brachii electromyographic signals y (i) at the moment i by using an energy characteristic extraction module; the electromyographic energy of biceps brachii muscle is characterized by Ex(i) The myoelectric energy of the triceps brachii is characterized by Ey(i)。
In step S2, the biceps brachii muscle electrical energy characteristic Ex(i) Satisfies the following conditions:
Ex(i)＝ψ[x(i-1)]＝x2(i-1)-x(i)x(i-2)；
brachial triceps myoelectric energy characteristic Ey(i) Satisfies the following conditions:
Ey(i)＝ψ[y(i-1)]＝y2(i-1)-y(i)y(i-2)。
step S3 specifically includes:
s3.1, if the variation of the electromyographic energy characteristic of the biceps brachii at the moment i relative to the electromyographic energy characteristic of the biceps brachii at the moment i-1 and the variation of the electromyographic energy characteristic of the triceps brachii at the moment i relative to the electromyographic energy characteristic of the triceps brachii at the moment i-1 are both smaller than 20%, stopping the motion of the elbow joint at the moment i, and performing a step S4, otherwise performing a step S3.2;
s3.2, if the electromyographic energy characteristic of the biceps brachii at the moment i is increased by more than 1.5 times relative to the electromyographic energy characteristic of the biceps brachii at the moment i-1, or the electromyographic energy characteristic of the triceps brachii at the moment i is increased by more than 1.5 times relative to the electromyographic energy characteristic of the triceps brachii at the moment i-1, performing a step S3.3, otherwise, performing a step S4;
s3.3, if the electromyographic energy characteristic of the biceps brachii at the moment i is more than 1.5 times of the electromyographic energy characteristic of the triceps brachii at the moment i, bending the elbow joint upwards at the moment i, and performing the step S4, otherwise, performing the step S3.4;
and S3.4, if the electromyographic energy characteristic of the brachial triceps at the moment i is more than 1.5 times of the electromyographic energy characteristic of the biceps brachii at the moment i, extending the elbow joint downwards at the moment i, and performing the step S4, otherwise, stopping the movement of the elbow joint at the moment i, and performing the step S4.
The invention has the following beneficial effects:
1. the invention adopts a cyclic measurement method, measures and calculates the electromyographic energy characteristics by taking the measurement time difference as a step length, reflects the motion characteristics of elbow electromyography approximately in real time and continuously identifies the motion of the elbow joint;
2. according to the invention, the action of elbow muscles is described through myoelectric energy characteristics, and a corresponding calculation formula is combined, so that the accuracy and robustness of the measurement method are improved, compared with the existing measurement method, the measurement stability is improved, the continuity, real-time and effectiveness of measurement are ensured, the over-stroke control of the upper limb exoskeleton can be realized, and the control precision and control capability of the upper limb exoskeleton are improved, so that a wearer can determine the flexion and extension movement amplitude of the elbow joint of the upper limb at any time according to the real-time physical strength and other conditions of the upper limb, and better assistance effect and man-machine experience are achieved;
3. the invention judges the elbow movement according to the change of the myoelectric energy characteristics, so that the measuring process is simpler and more visual, and the error accumulation and the aging lag caused by the complicated measuring process of the existing measuring method are avoided.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a flowchart of a method for continuously identifying elbow joint movements based on surface myoelectricity.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and which together with the embodiments of the invention serve to explain the principles of the invention.
An elbow joint action continuous identification method based on surface myoelectricity comprises the following steps:
s1, acquiring and preprocessing elbow joint motion related electromyographic signals to obtain a biceps brachii electromyographic signal and a triceps brachii electromyographic signal at a certain moment:
collecting electromyographic signals once every time t, counting for 1 time until the identification process is finished, and counting for N times, wherein N is more than or equal to 2;
at the time N after the time n.t after the identification process begins, the electromyographic signal of the biceps brachii is x (N), the electromyographic signal of the triceps brachii is y (N), wherein N is more than or equal to 0 and less than or equal to N, and N is an integer.
Giving initial values to the biceps brachii myoelectric signal and the triceps brachii myoelectric signal before the identification process is started; at the moment i after the identification process is started, i is more than or equal to 2 and less than or equal to N, and a biceps brachii myoelectric signal and a triceps brachii myoelectric signal which are related to elbow joint flexion and extension movement of an upper limb exoskeleton wearer are continuously acquired through a multi-channel myoelectric signal acquisition device; preprocessing the biceps brachii electromyographic signals and the triceps brachii electromyographic signals by an electromyographic signal filtering preprocessing module, and filtering interference and noise; finally obtaining a preprocessed biceps brachii electromyogram signal x (i) and a triceps brachii electromyogram signal y (i) at the moment i.
The giving of initial values to the biceps brachii myoelectric signal and the triceps brachii myoelectric signal before the identification process includes:
at time i equal to 0, biceps myoelectric signal x (0) and triceps myoelectric signal y (0);
at the moment i is 1, biceps muscle myoelectric signal x (1), triceps muscle myoelectric signal y (1), biceps muscle myoelectric energy characteristic Ex(1) Brachial triceps myoelectric energy characteristic Ey(1)。
The electromyographic signal is a physiological signal with non-stationary characteristic in nature, the effective frequency band of the electromyographic signal is within the range of 20-500Hz, and a Butterworth band-pass filter with the cut-off frequency of 30-350Hz is used for filtering the collected electromyographic signal to filter interference and noise.
S2, calculating myoelectric energy characteristics of the elbow joint biceps brachii muscle and the brachial triceps muscle at the moment i to obtain the myoelectric energy characteristics of the elbow joint biceps brachii muscle at the moment and the myoelectric energy characteristics of the elbow joint triceps muscle at the moment:
calculating energy characteristics of the preprocessed biceps brachii electromyographic signals x (i) and triceps brachii electromyographic signals y (i) at the moment i by using an energy characteristic extraction module; the electromyographic energy of biceps brachii muscle is characterized by Ex(i) The myoelectric energy of the triceps brachii is characterized by Ey(i)。
Electromyographic energy characteristic E of biceps brachiix(i) Satisfies the following conditions:
Ex(i)＝ψ[x(i-1)]＝x2(i-1)-x(i)x(i-2)；
brachial triceps myoelectric energy characteristic Ey(i) Satisfies the following conditions:
Ey(i)＝ψ[y(i-1)]＝y2(i-1)-y(i)y(i-2)。
s3, according to the myoelectric energy characteristics of the biceps brachii and the triceps brachii of the elbow joint at the moment i, identifying the motion of the elbow joint at the moment:
s3.1, if the variation of the electromyographic energy characteristic of the biceps brachii at the moment i relative to the electromyographic energy characteristic of the biceps brachii at the moment i-1 and the variation of the electromyographic energy characteristic of the triceps brachii at the moment i relative to the electromyographic energy characteristic of the triceps brachii at the moment i-1 are both smaller than 20%, stopping the motion of the elbow joint at the moment i, and performing a step S4, otherwise performing a step S3.2;
s3.2, if the electromyographic energy characteristic of the biceps brachii at the moment i is increased by more than 1.5 times relative to the electromyographic energy characteristic of the biceps brachii at the moment i-1, or the electromyographic energy characteristic of the triceps brachii at the moment i is increased by more than 1.5 times relative to the electromyographic energy characteristic of the triceps brachii at the moment i-1, performing a step S3.3, otherwise, performing a step S4;
s3.3, if the electromyographic energy characteristic of the biceps brachii at the moment i is more than 1.5 times of the electromyographic energy characteristic of the triceps brachii at the moment i, bending the elbow joint upwards at the moment i, and performing the step S4, otherwise, performing the step S3.4;
and S3.4, if the electromyographic energy characteristic of the brachial triceps at the moment i is more than 1.5 times of the electromyographic energy characteristic of the biceps brachii at the moment i, extending the elbow joint downwards at the moment i, and performing the step S4, otherwise, stopping the movement of the elbow joint at the moment i, and performing the step S4.
Due to the blocking effects of the self weight, the damping and the like of the exoskeleton on the upper limb, when the elbow joint of a wearer starts to move at any position, a certain force is required to overcome the resistance, the active intensity of the biceps brachii or the triceps brachii at the moment is obviously increased, namely the energy characteristic value of the biceps brachii or the triceps brachii is obviously increased. Moreover, the action of the biceps brachii muscle and the triceps brachii muscle in the process of starting the flexion upward and extension downward movements at any position of the elbow joint has a significant difference, namely the biceps brachii muscle takes the dominant action in the flexion upward movement process of the elbow joint, and the triceps brachii muscle takes the dominant action in the extension downward movement process of the elbow joint. Therefore, the continuous identification of the flexion and extension actions of the elbow joint can be realized through the energy characteristic conditions of the biceps brachii and the triceps brachii when the elbow joint starts to move at any position. When the elbow joint of a wearer starts to move from any position, due to the self resistance of the upper limb exoskeleton, the energy characteristic value of the biceps brachii or the triceps brachii is increased and is k times (k is generally between 1.2 and 3) of the previous moment, and when the energy characteristic value of the biceps brachii is 1.2 to 5 times higher than that of the triceps brachii, the elbow joint starts to bend upwards; when the energy characteristic of the triceps brachii is 1.2 to 5 times higher than the energy characteristic of the biceps brachii, the elbow joint begins to extend downward. And when the energy characteristic fluctuation amplitude of the biceps brachii and the triceps brachii is less than 20% at the same time in the motion process of the elbow joint, judging that the elbow joint stops moving. Since the motion recognition can start from an arbitrary position, continuous recognition is achieved.
And S4, if the identification process is not finished, returning to the step S2, and identifying the elbow joint action at the moment i +1 until the whole identification process is finished.
In summary, the embodiment of the invention provides an elbow joint action continuous identification method based on surface myoelectricity, the elbow joint action continuous identification method fully utilizes the blocking effects of the self weight, damping and the like of the exoskeleton of the upper limb, and completes the continuous identification of the elbow joint action by extracting and analyzing the myoelectricity signal energy characteristics of the muscle related to the elbow joint in real time. The extracted energy characteristics simultaneously contain frequency and amplitude information of the myoelectric signals, and the energy characteristics in the rest state and the action state have high distinguishability and reliability, particularly for the myoelectric signals with low signal-to-noise ratio.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (4)
1. An elbow joint action continuous identification method based on surface myoelectricity is characterized by comprising the following steps:
s1, acquiring and preprocessing elbow joint motion related electromyographic signals to obtain a biceps brachii electromyographic signal and a triceps brachii electromyographic signal at a certain moment;
collecting electromyographic signals once every time t, counting for 1 time until the identification process is finished, and counting for N times, wherein N is more than or equal to 2;
at the time N after the time n.t after the identification process begins, the electromyographic signal of the biceps brachii is x (N), the electromyographic signal of the triceps brachii is y (N), wherein N is more than or equal to 0 and less than or equal to N and is an integer;
the giving of initial values to the biceps brachii myoelectric signal and the triceps brachii myoelectric signal before the identification process includes:
at time i equal to 0, biceps myoelectric signal x (0) and triceps myoelectric signal y (0);
at the moment i is 1, biceps muscle myoelectric signal x (1), triceps muscle myoelectric signal y (1), biceps muscle myoelectric energy characteristic Ex(1) Brachial triceps myoelectric energy characteristic Ey(1)；
At the moment i after the identification process is started, i is more than or equal to 2 and less than or equal to N, and a biceps brachii myoelectric signal and a triceps brachii myoelectric signal which are related to elbow joint flexion and extension movement of an upper limb exoskeleton wearer are continuously acquired through a multi-channel myoelectric signal acquisition device; preprocessing the biceps brachii electromyographic signals and the triceps brachii electromyographic signals by an electromyographic signal filtering preprocessing module, and filtering interference and noise; finally obtaining preprocessed biceps brachii electromyographic signals x (i) and triceps brachii electromyographic signals y (i) at the moment i;
s2, calculating the myoelectric energy characteristics of the elbow joint biceps brachii muscle and the triceps brachii muscle at the moment to obtain the myoelectric energy characteristics of the elbow joint biceps brachii muscle at the moment and the myoelectric energy characteristics of the elbow joint triceps brachii muscle at the moment;
s3, according to the myoelectric energy characteristics of the biceps brachii and the triceps brachii of the elbow joint at the moment, identifying the elbow joint action at the moment;
the step S3 specifically includes:
s3.1, if the variation of the electromyographic energy characteristic of the biceps brachii at the moment i relative to the electromyographic energy characteristic of the biceps brachii at the moment i-1 and the variation of the electromyographic energy characteristic of the triceps brachii at the moment i relative to the electromyographic energy characteristic of the triceps brachii at the moment i-1 are both smaller than 20%, stopping the motion of the elbow joint at the moment i, and performing a step S4, otherwise performing a step S3.2;
s3.2, if the electromyographic energy characteristic of the biceps brachii at the moment i is increased by more than 1.5 times relative to the electromyographic energy characteristic of the biceps brachii at the moment i-1, or the electromyographic energy characteristic of the triceps brachii at the moment i is increased by more than 1.5 times relative to the electromyographic energy characteristic of the triceps brachii at the moment i-1, performing a step S3.3, otherwise, performing a step S4;
s3.3, if the electromyographic energy characteristic of the biceps brachii at the moment i is more than 1.5 times of the electromyographic energy characteristic of the triceps brachii at the moment i, bending the elbow joint upwards at the moment i, and performing the step S4, otherwise, performing the step S3.4;
s3.4, if the electromyographic energy characteristic of the brachial triceps at the moment i is more than 1.5 times of the electromyographic energy characteristic of the biceps brachii at the moment i, extending the elbow joint downwards at the moment i, and performing the step S4, otherwise, stopping the movement of the elbow joint at the moment i, and performing the step S4;
and S4, returning to the step S2, and identifying the elbow joint action at the next moment until the whole identification process is finished.
2. The method according to claim 1, wherein the step S1 is implemented by filtering the collected electromyographic signals using a butterworth band-pass filter with a cut-off frequency of 30-350Hz to remove interference and noise.
3. The method according to claim 1 or 2, wherein the step S2 specifically is:
calculating energy characteristics of the preprocessed biceps brachii electromyographic signals x (i) and triceps brachii electromyographic signals y (i) at the moment i by using an energy characteristic extraction module; the electromyographic energy of biceps brachii muscle is characterized by Ex(i) The myoelectric energy of the triceps brachii is characterized by Ey(i)。
4. Method according to claim 3, characterized in that in step S2, the biceps muscle electrical energy characteristic Ex(i) Satisfies the following conditions:
Ex(i)＝ψ[x(i-1)]＝x2(i-1)-x(i)x(i-2)；
brachial triceps myoelectric energy characteristic Ey(i) Satisfies the following conditions:
Ey(i)＝ψ[y(i-1)]＝y2(i-1)-y(i)y(i-2)。
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