CN112107397B - Myoelectric signal driven lower limb artificial limb continuous control system - Google Patents

Myoelectric signal driven lower limb artificial limb continuous control system Download PDF

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CN112107397B
CN112107397B CN202011120806.6A CN202011120806A CN112107397B CN 112107397 B CN112107397 B CN 112107397B CN 202011120806 A CN202011120806 A CN 202011120806A CN 112107397 B CN112107397 B CN 112107397B
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lower limb
electromyographic
continuous control
decoder
upper computer
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CN112107397A (en
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李智军
罗玲
高洪波
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University of Science and Technology of China USTC
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    • 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
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • 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
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    • 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
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    • A61F2/60—Artificial legs or feet or parts thereof
    • A61F2002/608—Upper legs
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Abstract

The invention provides a myoelectric signal driven lower limb prosthesis continuous control system. The system comprises a lower limb artificial limb, a myoelectricity sensor and an upper computer. The lower limb prosthesis comprises a receiving cavity, a thigh connecting rod, a knee joint, a shank connecting rod, an ankle joint and a sole. The electromyographic sensor is fixed on the skin surface of the lower limb on the healthy side in a wearable mode. The upper computer comprises an electromyographic decoder and a controller, receives an electromyographic signal from the electromyographic sensor and generates a continuous control instruction for the lower limb prosthesis. The invention solves the problems that the existing lower limb prosthesis related research focuses on intention recognition more and the research on how to convert the exercise intention recognition result into the control instruction suitable for the lower limb prosthesis is less.

Description

Myoelectric signal driven lower limb artificial limb continuous control system
Technical Field
The invention relates to the technical field of lower limb artificial limbs, in particular to a myoelectric signal driven lower limb artificial limb continuous control system.
Background
Due to traffic accidents, natural disasters, cardiovascular and cerebrovascular diseases and the like, tens of thousands of people lose lower limbs every year, which brings great inconvenience to daily life and work of the people. At present, the medical level can not regenerate the residual limb, so that the artificial limb is an indispensable tool for most lower limb amputees. The artificial limb not only can make up the limb defects of the lower limb amputee, but also can help the amputee recover certain mobility.
In recent years, in order to achieve more natural and smooth control of a lower limb prosthesis, a lower limb prosthesis control method incorporating bioelectric signals has become a research focus. Related research mostly focuses on the identification of the movement intention of a wearer, and discrete control of a lower limb prosthesis is completed based on the identification result and achieves certain success. However, natural lower limb movements are not limited to discrete movement patterns, but require continuous movements in which multiple degrees of freedom are coordinated with one another. Therefore, on the basis of accurately recognizing the intention, it is of practical significance to study how to convert the recognition result into a continuous control command suitable for the lower limb prosthesis.
Patent document CN109984875A (application number: 201910359729.0) discloses a bionic mechanical prosthesis and a control method, relating to the technical field of prosthesis, and comprising a prosthesis palm shell, a prosthesis finger mechanism and an external air bag mechanism; the control end of the motor in the artificial limb palm shell is connected with the muscle electric signal sensor through an electric signal; a first pressure sensor and a thermistor are arranged on the inner side of the finger segmented structure; the first pressure sensor is connected with the external air bag mechanism through an electric signal, and the thermistor is connected with the heating body through an electric signal; by arranging the thermistor and the heating body, the prosthetic senses comfortable temperature when the prosthetic acts on the way of riding, so that the prosthetic can be conveniently cared about the other side properly in the daily communication process, and a cold ice machine held by the other side in the holding process is avoided; through setting up external gasbag mechanism, realize that the artificial limb makes user's self can the perception opposite side's dynamics when the etiquette action of traveling, improve the participation sense that the user used, through setting up muscle electricity signal sensor, carry out the action that the artificial limb extends or tightens up.
Disclosure of Invention
In view of the defects in the prior art, the invention aims to provide a lower limb prosthesis continuous control system driven by an electromyographic signal.
The invention provides a myoelectric signal driven lower limb prosthesis continuous control system, which comprises a lower limb prosthesis 3, a myoelectric sensor 1 and an upper computer 2;
the lower limb prosthesis 3 is in wireless communication with the upper computer 2, and the electromyographic sensor 1 is in wireless communication with the upper computer 2 and sends the electromyographic signals to the upper computer 2 in real time.
Preferably, the lower limb prosthesis 3 includes: socket 31, thigh link 35, knee joint 32, shank link 36, ankle joint 33, and sole 34;
the socket 31 is used to put the lower limb prosthesis 3 on the stump of the lower limb amputee;
the thigh connecting rod 35 is rotationally connected with the shank connecting rod 36 through the knee joint 32;
the lower leg connecting rod 36 is rotationally connected with the sole 34 through the ankle joint 33; the knee joint 32 and the ankle joint 33 are active joints, and a motor is respectively arranged in the active joints;
the driver of the motor communicates with the upper computer 2 in a wireless manner.
Preferably, the electromyographic sensor 1 is fixed on the skin surface of the lower limb of the healthy side in a wearable manner;
the electromyographic sensor 1 comprises electromyographic signal acquisition, amplification, filtering and A/D conversion functions.
Preferably, the upper computer 2 comprises a myoelectric decoder 21 and a controller 22;
the upper computer 2 receives the electromyographic signals from the electromyographic sensor 1 and generates continuous control instructions;
the upper computer 2 transmits a continuous control command to a motor driver of the lower limb prosthesis 3 in a wireless mode, and receives state feedback from a motor.
Preferably, the electromyographic decoder 21 decodes the received electromyographic signals, and obtains posterior probability reflecting the movement intention of the wearer through signal preprocessing, feature extraction and classification.
Preferably, the signal preprocessing is used for filtering the electromyographic signals to remove interference such as direct current components, high-frequency noise, power supply noise and the like;
the feature extraction adopts a sliding window to calculate the time domain features of the preprocessed electromyographic signals;
the classification adopts a pre-trained classifier model to identify the current myoelectric mode and outputs the probability of each mode; 2 classifier models are respectively a knee joint 32 motion mode classifier and an ankle joint 33 motion mode classifier;
the two classifiers adopt a parallel mechanism to synchronously output classification results;
preferably, determining the motion modes of the knee joint 32 and the ankle joint 33 of the lower limb artificial limb 3 according to a walking gait cycle chart;
the motion modes of the knee joint 32 are divided into a knee bending motion mode and a knee stretching motion mode;
the ankle joint 33 movement modes are divided into a plantarflexion movement mode and a dorsiflexion movement mode;
the healthy lower limb knee joint and ankle joint movement patterns are divided according to the movement patterns of the knee joint 32 and the ankle joint 33 of the lower limb prosthesis 3.
Preferably, the controller 1 converts the posterior probability into the continuous control command based on a dynamic model, which is defined as formula (1):
Δzt＝φ[αFself(zt-1)+(1-α)Fdecodet(yt)] (1)
wherein, Δ ztRepresents the state increment of the lower limb prosthesis 3 at the time t, Fself(zt-1) Representing a force associated with a state of moment on said lower limb prosthesis 3, Fdecoder(yt) The myoelectric decoder 21 outputs y representing the current timetThe associated forces, phi governing the overall movement speed of the lower limb prosthesis 3, alpha governing FselfAnd FdecoderThe ratio of contribution of (a);
the dynamic model simultaneously considers the self state of the system and the decoding result of the movement intention, can convert the discrete electromyographic pattern classification result of the lower limb of the healthy side into the continuous joint angular velocity of the lower limb prosthesis 3 of the affected side, and realizes the continuous control of the lower limb prosthesis 3.
Preferably, the state of the system itself is the state z of the lower limb prosthesis 3tIn [0,1 ]]Within the range;
the motor intention decoding result is the output of the myoelectric decoder 21, denoted yt；
The discrete electromyogram pattern classification result of the healthy lower limb means the output result of the classifier in the electromyogram decoder 21, and is the probability of each category, and the probability values are discrete.
Preferably, phi is a proportionality coefficient greater than 0, and can be increased or decreased as required, so as to increase or decrease the overall movement speed of the lower limb prosthesis 3;
alpha is in [0,1 ]]Within range of adjustment factors, increasing alpha increases Fself(zt-1) While reducing Fdecoder(yt) The degree of contribution of (c);
reducing alpha can lower Fself(zt-1) While increasing Fdecoder(yt) The degree of contribution of (c).
Compared with the prior art, the invention has the following beneficial effects:
1. the invention identifies the movement intention of the wearer based on the myoelectric signals of the lower limbs with healthy side, and has convenient signal acquisition and high identification accuracy.
2. The invention adjusts the contribution of the myoelectricity decoding result according to the confidence level, and reduces the influence of the movement intention fluctuation of the wearer on the output of the controller.
3. The invention considers the self state of the system, effectively reduces the influence of random disturbance and enhances the robustness of the lower limb prosthesis system.
4. The invention converts the movement intention recognition result into continuous joint angular velocity control instructions, so that the lower limb prosthesis moves more naturally and stably.
5. The invention solves the problems that the existing lower limb prosthesis related research focuses on intention recognition more and the research on how to convert the exercise intention recognition result into the control instruction suitable for the lower limb prosthesis is less.
6. The control method provided by the invention is not only suitable for lower limb artificial limb, but also can be applied to continuous control of other robots such as upper limb artificial limb, exoskeleton and the like.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of the overall structure of a myoelectricity-based lower limb prosthesis continuous control system provided by the invention;
FIG. 2 is a schematic view of a wearing position of the electromyographic sensor on a healthy lower limb provided by the invention;
FIG. 3 is a schematic view of a lower limb prosthesis according to the present invention;
FIG. 4 is a schematic view of a walking gait cycle provided by the invention;
fig. 5 is a schematic diagram of the design principle of the controller provided by the present invention.
In the figure, 1 is an electromyography sensor, 11-18 are electromyography sensors distributed on 8 muscles of a healthy lower limb, 2 is an upper computer, 21 is an electromyography decoder, 22 is a controller, 3 is a lower limb prosthesis, 31 is a socket, 32 is a knee joint, 33 is an ankle joint, 34 is a foot, 35 is a thigh link, and 36 is a shank link.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The invention provides a myoelectric signal driven lower limb prosthesis continuous control system, which comprises a lower limb prosthesis 3, a myoelectric sensor 1 and an upper computer 2;
the lower limb prosthesis 3 is in wireless communication with the upper computer 2, and the electromyographic sensor 1 is in wireless communication with the upper computer 2 and sends the electromyographic signals to the upper computer 2 in real time.
Specifically, the lower limb prosthesis 3 includes: socket 31, thigh link 35, knee joint 32, shank link 36, ankle joint 33, and sole 34;
the socket 31 is used to put the lower limb prosthesis 3 on the stump of the lower limb amputee;
the thigh connecting rod 35 is rotationally connected with the shank connecting rod 36 through the knee joint 32;
the lower leg connecting rod 36 is rotationally connected with the sole 34 through the ankle joint 33; the knee joint 32 and the ankle joint 33 are active joints, and a motor is respectively arranged in the active joints;
the driver of the motor communicates with the upper computer 2 in a wireless manner.
Specifically, the electromyographic sensor 1 is fixed on the skin surface of the lower limb at the healthy side in a wearable manner;
the electromyographic sensor 1 comprises electromyographic signal acquisition, amplification, filtering and A/D conversion functions.
Specifically, the upper computer 2 comprises a myoelectricity decoder 21 and a controller 22;
the upper computer 2 receives the electromyographic signals from the electromyographic sensor 1 and generates continuous control instructions;
the upper computer 2 transmits a continuous control command to a motor driver of the lower limb prosthesis 3 in a wireless mode, and receives state feedback from a motor.
Specifically, the electromyographic decoder 21 decodes the received electromyographic signals, and obtains posterior probabilities reflecting the movement intentions of the wearer through signal preprocessing, feature extraction and classification.
Specifically, the signal preprocessing is used for filtering the electromyographic signals and removing interference such as direct-current components, high-frequency noise, power supply noise and the like;
the feature extraction adopts a sliding window to calculate the time domain features of the preprocessed electromyographic signals;
the classification adopts a pre-trained classifier model to identify the current myoelectric mode and outputs the probability of each mode; 2 classifier models are respectively a knee joint 32 motion mode classifier and an ankle joint 33 motion mode classifier;
the two classifiers adopt a parallel mechanism to synchronously output classification results;
specifically, the motion modes of the knee joint 32 and the ankle joint 33 of the lower limb artificial limb 3 are determined according to a walking gait cycle chart;
the motion modes of the knee joint 32 are divided into a knee bending motion mode and a knee stretching motion mode;
the ankle joint 33 movement modes are divided into a plantarflexion movement mode and a dorsiflexion movement mode;
the healthy lower limb knee joint and ankle joint movement patterns are divided according to the movement patterns of the knee joint 32 and the ankle joint 33 of the lower limb prosthesis 3.
Specifically, the controller 1 converts the posterior probability into a continuous control command based on a dynamic model, which is defined as formula (1):
Δzt＝φ[αFself(zt-1)+(1-α)Fdecoder(yt)] (1)
wherein, Δ ztRepresents the state increment of the lower limb prosthesis 3 at the time t, Fself(zt-1) Representing a force associated with a state of moment on said lower limb prosthesis 3, Fdecoder(yt) The myoelectric decoder 21 outputs y representing the current timetThe associated forces, phi governing the overall movement speed of the lower limb prosthesis 3, alpha governing FselfAnd FdecoderThe ratio of contribution of (a);
the dynamic model simultaneously considers the self state of the system and the decoding result of the movement intention, can convert the discrete electromyographic pattern classification result of the lower limb of the healthy side into the continuous joint angular velocity of the lower limb prosthesis 3 of the affected side, and realizes the continuous control of the lower limb prosthesis 3.
In particular, the state of the system itself is the state z of the lower-limb prosthesis 3tIn [0,1 ]]Within the range;
the motor intention decoding result is the output of the myoelectric decoder 21, denoted as yt;
the discrete electromyogram pattern classification result of the healthy lower limb means the output result of the classifier in the electromyogram decoder 21, and is the probability of each category, and the probability values are discrete.
Specifically, phi is a proportionality coefficient greater than 0, and can be increased or decreased as required, so as to increase or decrease the overall movement speed of the lower limb prosthesis 3;
alpha is in [0,1 ]]Within range of adjustment factors, increasing alpha increases Fself(zt-1) While reducing Fdecoder(yt) The degree of contribution of (c);
reducing alpha can lower Fself(zt-1) While increasing Fdecoder(yt) The degree of contribution of (c).
The present invention will be described more specifically below with reference to preferred examples.
Preferred example 1:
a lower limb artificial limb continuous control system driven by an electromyographic signal comprises a lower limb artificial limb, an electromyographic sensor and an upper computer.
The lower limb prosthesis comprises a receiving cavity, a thigh connecting rod, a knee joint, a shank connecting rod, an ankle joint and a sole. The socket is used for wearing the lower limb prosthesis on a residual limb of a lower limb amputee; the thigh connecting rod is rotationally connected with the shank connecting rod through the knee joint; the shank connecting rod is rotationally connected with the sole through an ankle joint; the knee joint and the ankle joint are active joints, and a motor is respectively arranged in the knee joint and the ankle joint; the driver of the motor communicates with the upper computer in a wireless mode.
The myoelectric sensor is fixed on the skin surface of the lower limb on the healthy side in a wearable mode. The electromyographic sensor comprises electromyographic signal acquisition, amplification, filtering and A/D conversion functions. The electromyographic sensor is communicated with an upper computer in a wireless mode and sends the electromyographic signals to the upper computer in real time.
The upper computer comprises a myoelectricity decoder and a controller; the upper computer receives the electromyographic signals from the electromyographic sensor and generates continuous control instructions; and the upper computer transmits the continuous control instruction to a motor driver of the lower limb prosthesis in a wireless mode and receives state feedback from a motor at the same time.
The myoelectric decoder decodes the received myoelectric signals, and posterior probability reflecting the movement intention of the wearer is obtained through signal preprocessing, feature extraction and classification.
The signal preprocessing is used for filtering the electromyographic signals and removing interference such as direct current components, high-frequency noise, power supply noise and the like.
And the characteristic extraction adopts a sliding window to calculate the time domain characteristic of the preprocessed electromyographic signal.
And the classification adopts a pre-trained classifier model to identify the current myoelectric mode and outputs the probability of each mode. There are 2 classifier models, which are knee joint motion pattern classifier and ankle joint motion pattern classifier respectively. The two classifiers adopt a parallel mechanism to synchronously output classification results.
And determining the motion modes of the knee joint and the ankle joint of the lower limb artificial limb according to the walking gait cycle diagram. The knee joint movement modes comprise a knee bending movement mode and a knee extending movement mode; the ankle joint movement patterns are classified into a plantarflexion movement pattern and a dorsiflexion movement pattern.
And dividing the knee joint and ankle joint movement modes of the healthy lower limb according to the knee joint and ankle joint movement modes of the lower limb artificial limb.
The controller converts the posterior probability into a continuous control command based on the dynamic model, and is defined as formula (1).
Δzt＝φ[αFself(zt-1)+(1-α)Fdecoder(yt)] (1)
Wherein, Δ ztRepresenting the increase of the state of said lower limb prosthesis at time t, Fself(zt-1) Representing a force associated with a state of moment on said lower limb prosthesis, Fdecoder(yt) Representation and current time electromyography decoder output ytThe related force, phi, regulates the overall movement speed of the lower limb prosthesis, alpha, regulates FselfAnd FdecoderThe ratio of contribution of (a).
The dynamic model simultaneously considers the self state of the system and the decoding result of the movement intention, can convert the discrete electromyographic pattern classification result of the lower limb of the healthy side into the continuous joint angular velocity of the artificial limb of the lower limb of the affected side, and realizes the continuous control of the artificial limb of the lower limb.
Preferred example 2:
the invention provides an electromyographic signal driven lower limb prosthesis continuous control system, the whole structure of which is shown in figure 1, and the system comprises a lower limb prosthesis 3, an electromyographic sensor 1 and an upper computer 2.
The lower limb prosthesis 3 is structurally shown in fig. 3 and comprises a receiving cavity 31, a thigh link 35, a knee joint 32, a shank link 36, an ankle joint 33 and a sole 34. The socket 31 is used to put the lower limb prosthesis 3 on the stump of the lower limb amputee; the thigh connecting rod 35 is rotationally connected with the shank connecting rod 36 through the knee joint 32; the lower leg connecting rod 36 is rotationally connected with the sole 34 through the ankle joint 33; the knee joint 32 and the ankle joint 33 are active joints, and a motor is respectively arranged in the active joints; the driver of the motor communicates with the upper computer 2 in a wireless manner.
The electromyographic sensor 1 is fixed on the skin surface of the lower limb on the healthy side in a wearable manner, as shown in fig. 2. The number of the electromyographic sensors 1 is 8 (not limited to) and the electromyographic sensors are respectively a thigh medial myoelectric sensor 11, a thigh rectus myoelectric sensor 12, a thigh lateral myoelectric sensor 13, a biceps longhead myoelectric sensor 14, a semitendinosus myoelectric sensor 15, a tibialis anterior myoelectric sensor 16, a gastrocnemius lateral myoelectric sensor 17 and a gastrocnemius medial myoelectric sensor 18 of the lower limb at the healthy side. The electromyographic sensor 1 comprises electromyographic signal acquisition, amplification, filtering and A/D conversion functions. The electromyographic sensor 1 establishes communication with the upper computer 2 in a wireless mode and sends the electromyographic signals to the upper computer 2 in real time.
The upper computer 2 comprises a myoelectricity decoder 21 and a controller 22; the upper computer 2 receives the electromyographic signals from the electromyographic sensor 1 and generates continuous control instructions; the upper computer 2 transmits a continuous control command to a motor driver of the lower limb prosthesis 3 in a wireless mode, and receives state feedback from a motor.
The electromyographic decoder 21 decodes the received electromyographic signals, and the posterior probability reflecting the movement intention of the wearer is obtained through signal preprocessing, feature extraction and classification.
The signal preprocessing is used for filtering the electromyographic signals and removing interference such as direct current components, high-frequency noise, power supply noise and the like. The filtering method is any one or more of time domain filtering, frequency domain filtering and time-frequency domain filtering.
And the characteristic extraction adopts a sliding window to calculate the time domain characteristic of the preprocessed electromyographic signal. The characteristics are any one or more of root mean square value, average absolute value, wavelength, Wilson amplitude, number of zero-crossing points and slope change number.
And the classification adopts a pre-trained classifier model to identify the current myoelectric mode and outputs the probability of each mode. There are 2 classifier models, which are knee joint 32 motion pattern classifier and ankle joint 33 motion pattern classifier, respectively. The two classifiers adopt a parallel mechanism to synchronously output classification results. The classifier model is any one or combination of a support vector machine model, a linear discriminant analysis model, a Gaussian mixture model, a hidden Markov model, a random forest model and a neural network model.
The movement patterns of the knee joint 32 and the ankle joint 33 of the lower limb artificial limb 3 are determined according to the walking gait cycle chart shown in figure 4. Let the left lower limb be healthy side and the right lower limb be affected side. The movement patterns of the knee joint 32 of the lower limb prosthesis 3 are divided into two types: the knee bending movement mode comprises a double-support phase early stage, a double-support phase later stage and a right swing phase early stage; the knee stretching movement mode covers the earlier stage of the right support phase, the middle stage of the right support phase, the later stage of the right support phase, the middle stage of the right swing phase and the later stage of the right swing phase. The ankle joint 33 motion patterns fall into two categories: plantar flexion movement patterns, covering the early and late phases of double strut; the dorsiflexion motion mode comprises a right support phase prophase, a right support phase metaphase, a right support phase anaphase, a right swing phase prophase, a right swing phase metaphase and a right swing phase anaphase.
The healthy lower limb knee joint and ankle joint movement patterns are divided according to the movement patterns of the knee joint 32 and the ankle joint 33 of the lower limb prosthesis 3. Let the left lower limb be healthy side and the right lower limb be affected side. A first knee joint movement mode for side health corresponds to a knee bending movement mode for the affected side, and comprises a double-support-phase early stage, a double-support-phase later stage and a left-support-phase early stage; a healthy side knee joint movement mode II corresponds to a diseased side knee extension movement mode and comprises a left swing phase early stage, a left swing phase middle stage, a left swing phase later stage, a left support phase middle stage and a left support phase later stage; a healthy side ankle joint movement mode I corresponds to a diseased side plantarflexion movement mode, and comprises a double-support phase early stage and a double-support phase later stage; and the side-strengthening ankle joint movement mode II corresponds to the dorsiflexion movement mode of the affected side, and comprises a left swing phase early stage, a left swing phase middle stage, a left swing phase later stage, a left support phase early stage, a left support phase middle stage and a left support phase later stage.
The controller 22 converts the posterior probability into a continuous control command, defined as equation (1), based on the dynamic model.
Δzt＝φ[αFself(zt-1)+(1-α)Fdecoder(yt)] (1)
Wherein, Δ ztRepresents the state increment of the lower limb prosthesis 3 at the time t, Fself(zt-1) Representing said lower extremity prosthesis3 force related to the state at the last moment, Fdecoder(yt) The myoelectric decoder 21 outputs y representing the current timetThe associated forces, phi governing the overall movement speed of the lower limb prosthesis 3, alpha governing FselfAnd FdecoderThe ratio of contribution of (a).
FselfThe design principle is shown in fig. 5 (a). An interval around 0.5 (gray area) is defined as an unintentional control state, and the other intervals (white area) are defined as an intentional control state. FselfThe effects of the intentional control state and the unintentional control state are introduced into the controller. The attractor within each region generates an attractive force and the repeller adjacent the region generates a repulsive force, thus FselfIs defined as formula (2).
Figure BDA0002731964480000091
Where ω adjusts the intentional control range and the unintentional control range.
FdecoderThe design principle is shown in fig. 5(b), which reflects the influence of the current myoelectricity decoding result on the system, and plays the roles of enhancing the contribution of the high confidence result and weakening the contribution of the low confidence result, so that FdecoderIs defined as formula (3).
Figure BDA0002731964480000092
Wherein, the sigma is used for adjusting the contribution degree of the decoding results with different confidence degrees.
And (4) calculating the expected joint angular velocity through the formula (4) and generating a continuous control command for the lower limb artificial limb 3.
Figure BDA0002731964480000093
Wherein q istIs the actual joint angle at time t, qdtFor the desired joint angle, is ztLinear mapping over a range of joint angles.
The dynamic model simultaneously considers the self state of the system and the decoding result of the movement intention, can convert the discrete electromyographic pattern classification result of the lower limb of the healthy side into the continuous joint angular velocity of the lower limb prosthesis 3 of the affected side, and realizes the continuous control of the lower limb prosthesis 3.
In the description of the present application, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present application and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present application.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (9)

1. A lower limb artificial limb continuous control system driven by an electromyographic signal is characterized by comprising a lower limb artificial limb (3), an electromyographic sensor (1) and an upper computer (2);
the lower limb prosthesis (3) is in wireless communication with the upper computer (2), and the myoelectric sensor (1) is in wireless communication with the upper computer (2) and sends myoelectric signals to the upper computer (2) in real time;
the upper computer (2) comprises a myoelectricity decoder (21) and a controller (22);
the controller (22) converts the posterior probability into a continuous control command based on a dynamic model, which is defined as formula (1):
△zt＝φ[αFself(zt-1)+(1α)Fdecoder(yt)] (1)
wherein, Δ ztRepresents the state increment of the lower limb prosthesis (3) at the time t, Fself(zt-1) Representing a force related to a state of said lower limb prosthesis (3) at a moment in time, Fdecoder(yt) The myoelectricity decoder (21) outputs y representing the current timetThe related forces, phi regulating the overall movement speed of the lower limb prosthesis (3), alpha regulating FselfAnd FdecoderThe ratio of contribution of (a);
the dynamic model simultaneously considers the self state of the system and the decoding result of the movement intention, can convert the discrete electromyographic pattern classification result of the lower limb of the healthy side into the continuous joint angular velocity of the lower limb prosthesis (3) of the affected side, and realizes the continuous control of the lower limb prosthesis (3).
2. An electromyographic signal driven lower extremity prosthetic continuous control system according to claim 1, wherein the lower extremity prosthetic (3) comprises: a socket (31), a thigh link (35), a knee joint (32), a shank link (36), an ankle joint (33) and a sole (34);
the socket (31) is used for wearing the lower limb prosthesis (3) on the stump of a lower limb amputee;
the thigh connecting rod (35) is rotationally connected with the shank connecting rod (36) through the knee joint (32);
the shank connecting rod (36) is rotationally connected with the sole (34) through an ankle joint (33); the knee joint (32) and the ankle joint (33) are both active joints, and the motors are respectively arranged in the active joints;
the driver of the motor communicates with the upper computer (2) in a wireless mode.
3. An electromyographic signal driven lower extremity prosthetic continuous control system according to claim 1, wherein the electromyographic sensor (1) is wearable affixed to a healthy side lower extremity skin surface;
the electromyographic sensor (1) comprises electromyographic signal acquisition, amplification, filtering and A/D conversion functions.
4. An electromyographic signal driven lower extremity prosthetic continuous control system according to claim 1, wherein the upper computer (2) receives an electromyographic signal from the electromyographic sensor (1) and generates a continuous control command; the upper computer (2) transmits a continuous control command to a motor driver of the lower limb prosthesis (3) in a wireless mode, and receives state feedback from a motor.
5. An electromyographic signal driven lower extremity prosthetic continuous control system according to claim 4, wherein the electromyographic decoder (21) decodes the received electromyographic signal to obtain a posterior probability reflecting the wearer's motor intent by signal pre-processing, feature extraction and classification.
6. The system of claim 5, wherein the signal pre-processing filters the electromyographic signals to remove DC components, high frequency noise, and power noise interference;
the feature extraction adopts a sliding window to calculate the time domain features of the preprocessed electromyographic signals;
the classification adopts a pre-trained classifier model to identify the current myoelectric mode and outputs the probability of each mode; the number of the classifier models is 2, and the classifier models are a knee joint (32) motion mode classifier and an ankle joint (33) motion mode classifier respectively;
the two classifiers adopt a parallel mechanism to synchronously output classification results;
7. an electromyographic signal driven lower extremity prosthetic continuous control system according to claim 6, wherein knee joint (32) and ankle joint (33) movement patterns of the lower extremity prosthetic (3) are determined from a walking gait cycle map;
the motion modes of the knee joint (32) are divided into a knee bending motion mode and a knee stretching motion mode;
the motion modes of the ankle joint (33) are divided into a plantar flexion motion mode and a dorsiflexion motion mode;
the knee joint movement mode and the ankle joint movement mode of the lower limb prosthesis (3) on the healthy side are divided according to the movement modes of the knee joint (32) and the ankle joint (33).
8. Electromyographic signal-driven lower extremity prosthetic continuous control system according to claim 1, characterized in that the system's own state is the state z of the lower extremity prosthetic (3)tIn [0,1 ]]Within the range;
the motor intention decoding result is the output of the myoelectric decoder (21), denoted yt；
The discrete electromyogram pattern classification result of the lower limb at the healthy side refers to the output result of a classifier in an electromyogram decoder (21), is the probability of each category, and the probability values are discrete.
9. An electromyographic signal driven continuous control system for a lower extremity prosthetic of claim 1, wherein φ is a proportionality coefficient greater than 0, which can be increased or decreased as required to increase or decrease the overall locomotion speed of the lower extremity prosthetic (3);
alpha is in [0,1 ]]Within range of adjustment factors, increasing alpha increases Fself(zt-1) While reducing Fdecoder(yt) The degree of contribution of (c);
reducing alpha can lower Fself(zt-1) While increasing Fdecoder(yt) The degree of contribution of (c).
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150262076A1 (en) * 2013-06-24 2015-09-17 Rehabilitation Institute Of Chicago Ambulation Prediction Controller for Lower Limb Assistive Device
CN110141239A (en) * 2019-05-30 2019-08-20 东北大学 A kind of motion intention identification and installation method for lower limb exoskeleton
CN110755070A (en) * 2019-08-28 2020-02-07 北京精密机电控制设备研究所 Multi-sensor fusion-based lower limb movement pose rapid prediction system and method
CN110974497A (en) * 2019-12-30 2020-04-10 南方科技大学 Electric artificial limb control system and control method
CN111557828A (en) * 2020-04-29 2020-08-21 天津科技大学 Active stroke lower limb rehabilitation robot control method based on healthy side coupling

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150262076A1 (en) * 2013-06-24 2015-09-17 Rehabilitation Institute Of Chicago Ambulation Prediction Controller for Lower Limb Assistive Device
CN110141239A (en) * 2019-05-30 2019-08-20 东北大学 A kind of motion intention identification and installation method for lower limb exoskeleton
CN110755070A (en) * 2019-08-28 2020-02-07 北京精密机电控制设备研究所 Multi-sensor fusion-based lower limb movement pose rapid prediction system and method
CN110974497A (en) * 2019-12-30 2020-04-10 南方科技大学 Electric artificial limb control system and control method
CN111557828A (en) * 2020-04-29 2020-08-21 天津科技大学 Active stroke lower limb rehabilitation robot control method based on healthy side coupling

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