CN112223253B - Exoskeleton system, exoskeleton identification control method, electronic device and storage medium - Google Patents
Exoskeleton system, exoskeleton identification control method, electronic device and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/0006—Exoskeletons, i.e. resembling a human figure
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- 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
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
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Abstract
The application provides an exoskeleton system, an exoskeleton recognition control method, an electronic device and a storage medium, which are applied to the exoskeleton device, wherein the system comprises: the terrain information preprocessing module is used for acquiring terrain preprocessing information according to the acquired terrain information, extracting terrain characteristic information and outputting a terrain identification preprocessing signal; the myoelectricity information preprocessing module is used for acquiring myoelectricity information to obtain myoelectricity preprocessing information, extracting myoelectricity characteristic information and outputting a movement intention identification preprocessing signal; the expert module is used for recognizing the terrain and the motion mode to generate a signal for switching the motion mode or recognizing the gait to generate a gait information signal; and the electromechanical control module generates a control signal for switching the motion mode or generates a gait control signal. The problem that the terrain can not be accurately identified and the movement can not be safely and stably carried out is solved, when different terrain environments are dealt with, the terrain and the movement intention can be quickly and effectively identified, man-machine cooperation is achieved, auxiliary movement is responded, and various work can be smoothly completed.
Description
Technical Field
The present disclosure relates to the field of exoskeleton technologies, and in particular, to an exoskeleton system, an exoskeleton recognition control method, an electronic device and a storage medium.
Background
The exoskeleton system is a companion intelligent equipment system worn on a body, assists a sporter in bearing power assistance and load maneuvering by synchronously 'following' the movement of the human body, effectively improves the carrying and lifting capacity, the carrying and carrying capacity and the quick maneuvering capacity of the sporter, and has wide requirements in the fields of after-loading material loading and carrying guarantee, material companion support guarantee, frontier defense patrol guarantee and the like.
The exoskeleton system is often operated under various terrain conditions, the environmental terrain features have great influence on the movement performance of the exoskeleton system, each terrain provides a challenge for the exoskeleton system to move stably, and the exoskeleton system is required to adopt different movement modes. The exoskeleton system can move at a relatively high speed in an environment with flat terrain and low possibility of slipping, and the terrain environment with loose mud or uneven terrain is unfavorable for the smooth movement of the exoskeleton system, cannot accurately identify the terrain, and cannot safely and stably move in the natural environment.
Content of application
In view of the above-mentioned shortcomings of the prior art, the present application aims to provide an exoskeleton system, an exoskeleton recognition control method, an electronic device and a storage medium, which are used to solve the problems in the prior art that the exoskeleton system cannot accurately recognize the terrain and cannot safely and stably move in the natural environment.
To achieve the above and other related objects, the present application provides an exoskeleton system, applied to an exoskeleton device, comprising: the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the terrain information in the motion environment of the exoskeleton device in real time, preprocessing the terrain information to obtain terrain preprocessing information, extracting terrain characteristic information according to the terrain preprocessing information and outputting corresponding terrain identification preprocessing signals; the electromyographic information preprocessing module is used for acquiring electromyographic information of a wearer in real time, preprocessing the electromyographic information to obtain electromyographic preprocessing information, extracting electromyographic feature information according to the electromyographic preprocessing information and outputting a corresponding movement intention identification preprocessing signal; the expert module is coupled with the terrain information preprocessing module, coupled with the electromyographic information preprocessing module and used for recognizing the terrain and the movement pattern of the received terrain recognition preprocessing signal to generate a switching movement pattern signal or recognizing the gait of the received movement intention recognition preprocessing signal to generate a gait information signal; an electromechanical control module, coupled to the expert module and coupled to the exoskeleton device, for generating a switch motion mode control signal according to the received switch motion mode signal to enable the exoskeleton device to switch motion modes, or generating a gait control signal according to the received gait information signal to enable the exoskeleton device to adjust a motion gait.
In an embodiment of the present application, the terrain information preprocessing module includes: the image acquisition unit is used for acquiring the terrain information in the motion environment in real time; the image preprocessing unit is coupled with the image acquisition unit and used for preprocessing according to the terrain information to obtain terrain preprocessing information; and the terrain feature extraction unit is coupled with the image preprocessing unit and is used for extracting the terrain feature information according to the terrain preprocessing information.
In an embodiment of the present application, the terrain feature information includes terrain morphology feature information and distance information between the current position and the new terrain morphology.
In an embodiment of the present application, the electromyographic information preprocessing module includes: the electromyographic signal acquisition unit is used for acquiring electromyographic information of a wearer in real time; the electromyographic preprocessing unit is coupled with the electromyographic signal acquisition unit and is used for preprocessing the received electromyographic information to obtain the electromyographic preprocessing information; and the electromyographic feature extraction unit is coupled with the electromyographic preprocessing unit and is used for extracting electromyographic signal feature information according to the electromyographic preprocessing information.
In an embodiment of the present application, the expert module includes: the terrain and motion pattern recognition unit is used for establishing a correlation relation library of the terrain form and the corresponding motion pattern by utilizing a terrain and motion pattern recognition technology; recognizing the terrain form according to the terrain recognition preprocessing information; rapidly searching an association relation library of the terrain and the movement mode according to the terrain form, and matching the association relation library with the movement mode corresponding to the terrain form; calculating the time required for reaching the new terrain in the current movement mode; generating the switching motion mode signal before the required time is reached; andor the gait recognition unit is used for recognizing the preprocessing signal off-line training action classification model according to the received movement intention; establishing a gait sample base according to various trained action classification models; rapidly matching an action type corresponding to the movement intention identification preprocessing signal through the gait sample library; and generating a gait information signal according to the motion type.
To achieve the above and other related objects, the present application provides an exoskeleton identification control method applied to an exoskeleton system applied to the exoskeleton device, the system comprising: the device comprises a terrain information preprocessing module, a myoelectricity preprocessing module, a movement intention recognition preprocessing signal generating module and a movement intention recognition preprocessing signal generating module, wherein the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring terrain information in the movement environment of the exoskeleton device in real time, preprocessing the terrain information to obtain the terrain preprocessing information, extracting the terrain characteristic information according to the terrain preprocessing information and outputting the corresponding terrain recognition preprocessing signal myoelectricity information, and the movement intention recognition preprocessing signal generating module is used for acquiring myoelectricity information of a wearer in real time, preprocessing the myoelectricity information to obtain myoelectricity preprocessing information, extracting the myoelectricity characteristic information according to the myoelectricity preprocessing information and outputting the corresponding movement intention recognition preprocessing signal; the method comprises the following steps: generating a switching movement mode signal through terrain and movement mode identification according to the received terrain identification preprocessing signal, or generating a gait information signal through gait identification according to the received movement intention identification preprocessing signal; generating a switching motion mode control signal to cause the exoskeleton device to switch motion modes according to the switching motion mode signal, or generating a gait control signal to cause the exoskeleton device to adjust a motion gait according to the received gait information signal.
In an embodiment of the present application, generating a signal for switching a motion mode through terrain and motion mode recognition according to the received terrain recognition preprocessing information includes: establishing an association relation library of the terrain form and the corresponding motion mode by utilizing a terrain and motion mode identification technology; recognizing the terrain form according to the terrain recognition preprocessing information; rapidly searching an association relation library of the terrain and the movement mode according to the terrain form, and matching the association relation library with the movement mode corresponding to the terrain form; calculating the time required for reaching the new terrain in the current movement mode; generating the switching motion pattern signal before the desired time is reached.
In an embodiment of the present application, the generating a gait information signal through gait recognition according to the received exercise intention recognition preprocessing signal includes: identifying a preprocessing signal offline training action classification model according to the received movement intention; establishing a gait sample base according to various trained action classification models; rapidly matching an action type corresponding to the movement intention identification preprocessing signal through the gait sample library; generating a gait information signal according to the motion type;
to achieve the above and other related objects, the present application provides an electronic device, comprising: one or more communicators for communicating with the outside; one or more memories for storing computer programs; one or more processors configured to execute the computer program to perform the exoskeleton identification control method.
To achieve the above and other related objects, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the exoskeleton recognition control method.
As described above, the exoskeleton system, the exoskeleton recognition control method, the electronic device and the storage medium according to the present application have the following advantages: the problem of in the prior art the exoskeletal system can not accurate discernment to the relief to can not move safely, stably in natural environment is solved, when dealing with different relief environment, quick, effective discernment relief and motion intention discernment move and reach man-machine cooperation, respond to the auxiliary motion, in order to guarantee to accomplish each item work smoothly.
Drawings
Fig. 1 is a schematic structural diagram of an exoskeleton system according to an embodiment of the present application.
Fig. 2 is a flowchart illustrating an exoskeleton recognition control method according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It is noted that in the following description, reference is made to the accompanying drawings which illustrate several embodiments of the present application. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "over," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Throughout the specification, when a part is referred to as being "coupled" to another part, this includes not only a case of being "directly connected" but also a case of being "indirectly connected" with another element interposed therebetween. In addition, when a certain part is referred to as "including" a certain component, unless otherwise stated, other components are not excluded, but it means that other components may be included.
The terms first, second, third, etc. are used herein to describe various elements, components, regions, layers and/or sections, but are not limited thereto. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first part, component, region, layer or section discussed below could be termed a second part, component, region, layer or section without departing from the scope of the present application.
Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," and/or "comprising," when used in this specification, specify the presence of stated features, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions or operations are inherently mutually exclusive in some way.
The exoskeleton system can move at a relatively high speed in an environment with a flat terrain and low possibility of skidding, and a loose muddy or uneven terrain environment is unfavorable for the smooth movement of the exoskeleton system, cannot accurately identify the terrain, and cannot safely and stably move in a natural environment.
Therefore, the application provides an exoskeleton system, which is applied to an exoskeleton device and is used for solving the problems that in the prior art, an exoskeleton system cannot accurately identify the terrain and cannot safely and stably move in a natural environment.
The system comprises: the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the terrain information in the motion environment of the exoskeleton device in real time, preprocessing the terrain information to obtain terrain preprocessing information, extracting terrain feature information according to the terrain preprocessing information and outputting corresponding terrain recognition preprocessing signals; the electromyographic information preprocessing module is used for acquiring electromyographic information of a wearer in real time, preprocessing the electromyographic information to obtain electromyographic preprocessing information, extracting electromyographic feature information according to the electromyographic preprocessing information and outputting a corresponding movement intention identification preprocessing signal; the expert module is coupled with the terrain information preprocessing module, coupled with the electromyographic information preprocessing module and used for recognizing the terrain and the movement pattern of the received terrain recognition preprocessing signal to generate a switching movement pattern signal or recognizing the gait of the received movement intention recognition preprocessing signal to generate a gait information signal; an electromechanical control module, coupled to the expert module and coupled to the exoskeleton device, for generating a switch motion mode control signal to cause the exoskeleton device to switch motion modes according to the received switch motion mode signal, or generating a gait control signal to cause the exoskeleton device to adjust a gait for the sport according to the received gait information signal.
The following detailed description of the embodiments of the present application will be made with reference to fig. 1 so that those skilled in the art described in the present application can easily implement the embodiments. The present application may be embodied in many different forms and is not limited to the embodiments described herein.
Fig. 1 is a schematic structural diagram of an exoskeleton system according to an embodiment of the present application.
The exoskeleton system is applied to an exoskeleton device and comprises:
the terrain information preprocessing module 11 is coupled to the exoskeleton device and configured to acquire terrain information in a motion environment of the exoskeleton device, where the terrain information preprocessing module 11 includes a device for acquiring the terrain information in the environment of the exoskeleton device, the terrain information includes information such as image information or pressure information, the terrain information is preprocessed to obtain terrain preprocessing information, and then, according to the terrain preprocessing information, the terrain features are determined, the terrain feature information is extracted, and a corresponding terrain recognition preprocessing signal is output.
The determination mode of the topographic features may include, but is not limited to, one or more of the following methods:
(1) judging the topography characteristics according to the flow direction of the river: the topography becomes lower along the flow direction of the river;
(2) judging the terrain features according to the difference of the unified latitude and temperature: if only from the law of distribution of solar radiation in different latitudes, the temperature at the same latitude should be the same, but will be influenced by the terrain, and the topography is low and the temperature is high.
(3) Judging the terrain features according to the bending direction of the contour lines: the convex height is ridge, the convex low is valley (the contour line is convex towards the high altitude place and has ridge, the contour line is convex towards the low altitude place and has valley, namely river.
(4) And directly judging the terrain features according to the distribution rule of the contour line values seen in the contour map.
(5) Judging the terrain features according to the distribution characteristics of agriculture: the planting industry is often distributed in plain areas, and the forestry and animal husbandry is generally distributed in mountainous or hilly areas.
(6) Judging the terrain features according to the population distribution characteristics: the densely populated areas are usually plains, and the sparsely populated areas are mountainous areas or plateaus.
(7) Judging the terrain features according to the distribution rule of the isobars: high altitude, low altitude and high altitude.
The electromyographic information preprocessing module 12 is configured to collect electromyographic information of a wearer in real time, where the electromyographic information preprocessing module 12 includes a device for collecting the electromyographic information of the wearer, the electromyographic information includes image information or induction information, preprocesses the electromyographic information to obtain electromyographic preprocessing information, determines an electromyographic feature according to the electromyographic preprocessing information, extracts the electromyographic feature information, determines a movement intention of the wearer according to the extracted electromyographic feature information, and outputs a movement intention identification preprocessing signal.
The electromyographic characteristics may be determined by one or more of the following methods:
(1) judging the electromyographic characteristics according to a time domain analysis method;
(2) judging the electromyographic characteristics according to a frequency domain analysis method: the average power frequency and the median frequency of the power spectrum are obtained.
(3) Judging the electromyographic characteristics according to a time-frequency analysis method: obtained by fourier transform, wigner-Williams transform, Choi-Williams transform and wavelet transform.
(4) And judging the electromyographic characteristics according to a nonlinear dynamics analysis method.
The expert module 13 is coupled to the terrain information preprocessing module 11 and coupled to the electromyographic information preprocessing module 12, receives the terrain identification preprocessing signal from the terrain information preprocessing module 11, and performs the movement pattern identification on the terrain identification preprocessing signal to claim to switch the movement pattern signal; or the expert module 13 receives the exercise intention recognition preprocessing signal from the electromyographic information preprocessing module 12, and performs gait recognition on the exercise intention recognition preprocessing signal to generate a gait information signal.
The mechatronic control module 14 coupled to the expert module 13 and coupled to the exoskeleton device, the mechatronic control module 14 receiving the switching motion mode from the expert module 13 to generate a switching motion mode control signal to cause the exoskeleton device to switch motion modes, or the mechatronic control module 14 receiving the gait information signal from the expert module 13 to generate a gait control signal to cause the exoskeleton device to adjust a locomotor gait.
Optionally, the terrain information preprocessing module 11 includes: the image acquisition unit is used for acquiring the terrain information in the motion environment in real time; the image acquisition unit is a device capable of acquiring the terrain information in the motion environment in real time. The terrain information acquired by the image acquisition unit is information such as image information or pressure information, and is not limited in the application. The image preprocessing unit is coupled with the image acquisition unit and carries out preprocessing according to the terrain information received from the image acquisition unit to obtain terrain preprocessing information; and the terrain feature extraction unit is coupled with the image preprocessing unit and used for judging the terrain features and extracting the terrain feature information according to the received terrain preprocessing information from the image preprocessing unit.
The determination mode of the topographic features may include, but is not limited to, one or more of the following methods:
(1) judging the topography characteristics according to the flow direction of the river: the topography becomes lower along the flow direction of the river;
(2) judging the terrain features according to the difference of the unified latitude and temperature: if only from the law of distribution of solar radiation in different latitudes, the temperature at the same latitude should be the same, but will be influenced by the terrain, and the topography is low and the temperature is high.
(3) Judging the terrain features according to the bending direction of the contour lines: the convex height is ridge, the convex low is valley (the contour line is convex towards the high altitude place and has ridge, the contour line is convex towards the low altitude place and has valley, namely river.
(4) And directly judging the terrain features according to the distribution rule of the contour line values seen in the contour map.
(5) Judging the terrain features according to the distribution characteristics of agriculture: the planting industry is often distributed in plain areas, and the forestry and animal husbandry is generally distributed in mountainous or hilly areas.
(6) Judging the terrain features according to the population distribution characteristics: the densely populated areas are usually plains, and the sparsely populated areas are mountainous areas or plateaus.
(7) Judging the terrain features according to the distribution rule of the isobars: high altitude, low altitude and high altitude.
Optionally, the topography feature information includes topography feature information and distance information between the current position and the new topography.
Optionally, the electromyographic information preprocessing module 12 includes: the electromyographic signal acquisition unit is used for acquiring the electromyographic signal of the wearer in real time; the electromyographic signal acquisition unit is a device capable of acquiring the electromyographic signals of a wearer in real time. The electromyographic information includes information such as image information or sensing information, and is not limited in this application. The electromyographic preprocessing unit is coupled with the electromyographic signal acquisition unit and is used for preprocessing the electromyographic information received from the electromyographic signal acquisition unit to obtain electromyographic preprocessing information; and the myoelectric feature extraction unit is coupled with the myoelectric preprocessing unit and outputs a movement intention identification preprocessing signal according to the received movement intention of the wearer judged by the myoelectric preprocessing unit.
The electromyographic characteristics may be determined by one or more of the following methods:
(1) judging the electromyographic characteristics according to a time domain analysis method;
(2) judging the electromyographic characteristics according to a frequency domain analysis method: the average power frequency and the median frequency of the power spectrum are obtained.
(3) Judging the electromyographic characteristics according to a time-frequency analysis method: obtained by fourier transform, wigner-Williams transform, Choi-Williams transform and wavelet transform.
(4) And judging the electromyographic characteristics according to a nonlinear dynamics analysis method.
Optionally, the expert module 13 includes: the device comprises a terrain and motion pattern recognition unit, wherein the terrain and motion pattern recognition unit utilizes a terrain and motion pattern recognition technology, the pattern recognition is used for processing and analyzing various forms of information for representing objects or phenomena, so that the purposes of describing, identifying, classifying and explaining the objects or phenomena are achieved, and the automatic processing and interpretation of the patterns are researched by a computer through a mathematical technical method. That is, the corresponding association relation library is established by using different pattern recognition algorithms and using the computer connection geography forms and the corresponding movement patterns. The pattern recognition algorithm can be K-Nearest Neighbor algorithm, Bayesian algorithm, principal component Analysis or principal component Analysis method, Linear discriminatant Analysis algorithm and other algorithms. Recognizing the topography form according to the topography recognition preprocessing information, and quickly searching in a glass scraping safety relation library of the topography and motion modes according to the topography form to match the motion mode corresponding to the recognized topography form; and calculating the time required for reaching the new terrain in the current movement mode according to the obtained movement mode corresponding to the terrain, and generating a movement mode switching signal before the required time is reached.
And/or (b) a plurality of,
the gait recognition unit is used for recognizing a preprocessing signal according to the received movement intention from the electromyographic information preprocessing module 12 to carry out an off-line training action classification model; establishing a gait sample base according to various trained action classification and action classification models, matching the exercise intention recognition preprocessing signals in the gait sample base to obtain action types corresponding to the exercise intention recognition preprocessing signals according to the exercise intention recognition preprocessing signals, and generating gait information signals according to the matched exercise types.
In principle, similar to the above embodiments, the present application provides an exoskeleton identification control method applied to an exoskeleton system applied to the exoskeleton device, the system including: the device comprises a terrain information preprocessing module, a myoelectricity preprocessing module, a movement intention recognition preprocessing signal generating module and a movement intention recognition preprocessing signal generating module, wherein the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring terrain information in the movement environment of the exoskeleton device in real time, preprocessing the terrain information to obtain the terrain preprocessing information, extracting the terrain characteristic information according to the terrain preprocessing information and outputting the corresponding terrain recognition preprocessing signal myoelectricity information, and the movement intention recognition preprocessing signal generating module is used for acquiring myoelectricity information of a wearer in real time, preprocessing the myoelectricity information to obtain myoelectricity preprocessing information, extracting the myoelectricity characteristic information according to the myoelectricity preprocessing information and outputting the corresponding movement intention recognition preprocessing signal; the method comprises the following steps:
generating a switching movement mode signal through terrain and movement mode identification according to the received terrain identification preprocessing signal, or generating a gait information signal through gait identification according to the received movement intention identification preprocessing signal;
generating a switching motion mode control signal to cause the exoskeleton device to switch motion modes according to the switching motion mode signal, or generating a gait control signal to cause the exoskeleton device to adjust a motion gait according to the received gait information signal.
Specific embodiments are provided below in conjunction with the attached figures:
fig. 2 is a schematic flow chart illustrating an exoskeleton identification control method according to an embodiment of the present application.
The exoskeleton identification control method is applied to an exoskeleton system, the exoskeleton system is applied to the exoskeleton device, and the system comprises: the device comprises a terrain information preprocessing module, a myoelectricity preprocessing module, a movement intention recognition preprocessing signal generating module and a movement intention recognition preprocessing signal generating module, wherein the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring terrain information in the movement environment of the exoskeleton device in real time, preprocessing the terrain information to obtain the terrain preprocessing information, extracting the terrain characteristic information according to the terrain preprocessing information and outputting the corresponding terrain recognition preprocessing signal myoelectricity information, and the movement intention recognition preprocessing signal generating module is used for acquiring myoelectricity information of a wearer in real time, preprocessing the myoelectricity information to obtain myoelectricity preprocessing information, extracting the myoelectricity characteristic information according to the myoelectricity preprocessing information and outputting the corresponding movement intention recognition preprocessing signal;
the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the terrain information in the motion environment of the exoskeleton device, wherein the terrain information preprocessing module comprises a device for acquiring the terrain information in the environment of the exoskeleton device, the terrain information comprises information such as image information or pressure information, the terrain information is preprocessed to obtain terrain preprocessing information, and then the terrain characteristics are judged according to the terrain preprocessing information, the terrain characteristic information is extracted, and corresponding terrain recognition preprocessing signals are output. The terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the movement of the exoskeleton device
The judgment mode of the topographic features can include one or more of the following methods:
(1) judging the topography characteristics according to the flow direction of the river: the topography becomes lower along the flow direction of the river;
(2) judging the terrain features according to the difference of the unified latitude and temperature: if only from the law of distribution of solar radiation in different latitudes, the temperature at the same latitude should be the same, but will be influenced by the terrain, and the topography is low and the temperature is high.
(3) Judging the terrain features according to the bending direction of the contour lines: the convex height is ridge, the convex low is valley (the contour line is convex towards the high altitude place and has ridge, the contour line is convex towards the low altitude place and has valley, namely river.
(4) And directly judging the terrain features according to the distribution rule of the contour line values seen in the contour map.
(5) Judging the terrain features according to the distribution characteristics of agriculture: the planting industry is often distributed in plain areas, and the forestry and animal husbandry is generally distributed in mountainous or hilly areas.
(6) Judging the terrain features according to the population distribution characteristics: the densely populated areas are usually plains, and the sparsely populated areas are mountainous areas or plateaus.
(7) Judging the terrain features according to the distribution rule of the isobars: high altitude, low altitude and high altitude.
The electromyographic information preprocessing module is used for acquiring electromyographic information of a wearer in real time, and comprises a device for acquiring the electromyographic information of the wearer, the electromyographic information comprises image information or induction information, the electromyographic information is preprocessed to obtain electromyographic preprocessing information, then the electromyographic feature is judged according to the electromyographic preprocessing information, the electromyographic feature information is extracted, the movement intention of the wearer is judged according to the extracted electromyographic feature information, and a movement intention identification preprocessing signal is output.
The electromyographic characteristics may be determined by one or more of the following methods:
(1) judging the electromyographic characteristics according to a time domain analysis method;
(2) judging the electromyographic characteristics according to a frequency domain analysis method: the average power frequency and the median frequency of the power spectrum are obtained.
(3) Judging the electromyographic characteristics according to a time-frequency analysis method: obtained by fourier transform, wigner-Williams transform, Choi-Williams transform and wavelet transform.
(4) And judging the electromyographic characteristics according to a nonlinear dynamics analysis method.
The method comprises the following steps:
step S21: generating a switching movement mode signal through terrain and movement mode identification according to the received terrain identification preprocessing signal, or generating a gait information signal through gait identification according to the received movement intention identification preprocessing signal;
optionally, receiving the terrain recognition preprocessing signal, and performing motion pattern recognition on the terrain recognition preprocessing signal to claim to switch the motion pattern signal; or receiving the movement intention identification preprocessing signal, and carrying out gait identification on the movement intention identification preprocessing signal to generate a gait information signal.
Step S21: generating a switching motion mode control signal to cause the exoskeleton device to switch motion modes according to the switching motion mode signal, or generating a gait control signal to cause the exoskeleton device to adjust a motion gait according to the received gait information signal.
Optionally, a switching motion mode control signal is received from the switching motion mode to switch the exoskeleton device to the motion mode, or a gait information signal is received to generate a gait control signal to enable the exoskeleton device to adjust the gait of the sport.
Optionally, the generating of the switching motion mode signal by the received terrain recognition preprocessed signal through terrain and motion mode recognition includes:
by means of the terrain and motion pattern recognition technology, the pattern recognition is used for processing and analyzing various forms of information for representing objects or phenomena, so that the purposes of describing, identifying, classifying and explaining the objects or phenomena are achieved, and automatic processing and interpretation of the patterns are researched by a computer through a mathematical technical method. That is, the corresponding association relation library is established by using different pattern recognition algorithms and using the computer connection geography forms and the corresponding movement patterns. The pattern recognition algorithm can be K-Nearest Neighbor algorithm, Bayesian algorithm, principal component Analysis or principal component Analysis method, Linear discriminatant Analysis algorithm and other algorithms. Recognizing the terrain form according to the terrain recognition preprocessing information, quickly searching in a glass scraping relation library of the terrain and the movement mode according to the terrain form, and matching the movement mode corresponding to the recognized terrain form; and calculating the time required for reaching the new terrain in the current movement mode according to the obtained movement mode corresponding to the terrain, and generating a movement mode switching signal before the required time is reached.
Optionally, the step of generating the gait information signal through gait recognition according to the received exercise intention recognition preprocessing signal includes: receiving a movement intention recognition preprocessing signal to perform an offline training movement classification model; establishing a gait sample base according to various trained action classification and action classification models, matching the exercise intention recognition preprocessing signals in the gait sample base to obtain action types corresponding to the exercise intention recognition preprocessing signals according to the exercise intention recognition preprocessing signals, and generating gait information signals according to the matched exercise types.
Fig. 3 is a schematic structural diagram of an electronic device 30 in the embodiment of the present application.
The electronic device 30 includes: one or more memories 31, one or more processors 32, and one or more communicators 33, the memories 31 being for storing computer programs; the processor 32 runs a computer program to implement the exoskeleton identification control method as shown in fig. 2, and the communicator 33 is coupled to the processor 32 for communicating with the outside.
The communicator 33 is a network communicator for accessing the cloud through a communication network. The communication network may be the internet, one or more intranets, Local Area Networks (LANs), wide area networks (WLANs), Storage Area Networks (SANs), etc., or a suitable combination thereof, and the communicator 33 may be a wired or wireless circuit module conforming to any of the network protocols, preferably, the communicator 33 is a wireless circuit module.
Optionally, the number of the memories 31 may be one or more, the number of the processors 32 may be one or more, the number of the communicators 33 may be one or more, and fig. 3 illustrates one example.
Optionally, the processor 32 in the electronic device 30 may load one or more instructions corresponding to the processes of the application program into the memory 31 according to the steps shown in fig. 2, and the processor 32 executes the application program stored in the memory 31, so as to implement various functions in the exoskeleton recognition control method shown in fig. 2.
Optionally, the memory 31 may include, but is not limited to, a high speed random access memory, a non-volatile memory. Such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices; the Processor 32 may include, but is not limited to, a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
Optionally, the Processor 32 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
The present application further provides a computer readable storage medium storing a computer program which when executed implements the exoskeleton identification control method as shown in fig. 3. The computer-readable storage medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (compact disc-read only memories), magneto-optical disks, ROMs (read-only memories), RAMs (random access memories), EPROMs (erasable programmable read only memories), EEPROMs (electrically erasable programmable read only memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. The computer readable storage medium may be a product that is not accessed by the computer device or may be a component that is used by an accessed computer device.
To sum up, the exoskeleton system, the exoskeleton recognition control method, the electronic device and the storage medium are applied to the exoskeleton device, and the system comprises: the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the terrain information in the motion environment of the exoskeleton device in real time, preprocessing the terrain information to obtain terrain preprocessing information, extracting terrain feature information according to the terrain preprocessing information and outputting corresponding terrain recognition preprocessing signals; the electromyographic information preprocessing module is used for acquiring electromyographic information of a wearer in real time, preprocessing the electromyographic information to obtain electromyographic preprocessing information, extracting electromyographic feature information according to the electromyographic preprocessing information and outputting a corresponding movement intention identification preprocessing signal; the expert module is coupled with the terrain information preprocessing module, coupled with the electromyographic information preprocessing module and used for recognizing the terrain and the movement pattern of the received terrain recognition preprocessing signal to generate a switching movement pattern signal or recognizing the gait of the received movement intention recognition preprocessing signal to generate a gait information signal; an electromechanical control module, coupled to the expert module and coupled to the exoskeleton device, for generating a switch motion mode control signal to cause the exoskeleton device to switch motion modes according to the received switch motion mode signal, or generating a gait control signal to cause the exoskeleton device to adjust a gait for the sport according to the received gait information signal. The problem of in the prior art the exoskeletal system can not accurate discernment to the relief to can not move safely, stably in natural environment is solved, when dealing with different relief environment, quick, effective discernment relief and motion intention discernment move and reach man-machine cooperation, respond to the auxiliary motion, in order to guarantee to accomplish each item work smoothly. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.
Claims (7)
1. An exoskeleton system for use in an exoskeleton device, the system comprising:
the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the terrain information in the motion environment of the exoskeleton device in real time, preprocessing the terrain information to obtain terrain preprocessing information, judging terrain features according to the terrain preprocessing information, extracting the terrain feature information and outputting corresponding terrain recognition preprocessing signals; wherein the topography information comprises image information or pressure information;
the electromyographic information preprocessing module is used for acquiring electromyographic information of a wearer in real time, preprocessing the electromyographic information to obtain electromyographic preprocessing information, judging electromyographic characteristics according to the electromyographic preprocessing information, extracting electromyographic characteristic information, judging the movement intention of the wearer and outputting a corresponding movement intention identification preprocessing signal; wherein the electromyographic information includes: image information or sensing information;
the expert module is coupled with the terrain information preprocessing module, coupled with the electromyographic information preprocessing module and used for carrying out terrain and motion mode recognition on the received terrain recognition preprocessing signal to generate a switching motion mode signal or carrying out gait recognition on the received motion intention recognition preprocessing signal to generate a gait information signal; wherein the expert module comprises:
the terrain and motion pattern recognition unit is used for establishing a correlation relation library of the terrain form and the corresponding motion pattern by utilizing a terrain and motion pattern recognition technology; recognizing the topography form according to the topography recognition preprocessing information; rapidly searching an association relation library of the terrain and the movement mode according to the terrain form, and matching the association relation library with the movement mode corresponding to the terrain form; calculating the time required for reaching the new terrain in the current movement mode; generating the switching motion mode signal before the required time is reached;
the gait recognition unit is used for recognizing the preprocessing signal off-line training action classification model according to the received movement intention; establishing a gait sample base according to various trained action classification models; rapidly matching an action type corresponding to the movement intention identification preprocessing signal through the gait sample library; generating a gait information signal according to the action type;
an electromechanical control module, coupled to the expert module and coupled to the exoskeleton device, for generating a switch motion mode control signal to cause the exoskeleton device to switch motion modes according to the received switch motion mode signal, or generating a gait control signal to cause the exoskeleton device to adjust a gait for the sport according to the received gait information signal;
wherein, the judgment mode of the topographic features is one or more of the following methods:
(1) judging the topography characteristics according to the flow direction of the river;
(2) judging the topographic features according to the difference of the unified latitude and temperature;
(3) judging the terrain features according to the bending direction of the contour lines;
(4) directly judging the terrain features according to the distribution rule of the contour line values seen in the contour map;
(5) judging the terrain features according to the distribution characteristics of agriculture;
(6) judging the terrain features according to the population distribution characteristics;
(7) judging the terrain features according to the distribution rule of the isobars;
the electromyographic characteristics are judged in one or more of the following ways:
(1) judging the electromyographic characteristics according to a time domain analysis method;
(2) judging the electromyographic characteristics according to a frequency domain analysis method;
(3) and judging the electromyographic characteristics according to a nonlinear dynamics analysis method.
2. The exoskeleton system of claim 1, wherein said terrain information preprocessing module comprises:
the image acquisition unit is used for acquiring the terrain information in the motion environment in real time;
the image preprocessing unit is coupled with the image acquisition unit and used for preprocessing according to the terrain information to obtain terrain preprocessing information;
and the terrain feature extraction unit is coupled with the image preprocessing unit and is used for extracting the terrain feature information according to the terrain preprocessing information.
3. The exoskeleton system of claim 1 wherein said topographical feature information includes topographical feature information and distance information between the current location and the new topographical feature.
4. The exoskeleton system of claim 1, wherein the electromyographic information preprocessing module comprises:
the electromyographic signal acquisition unit is used for acquiring electromyographic information of a wearer in real time;
the electromyographic preprocessing unit is coupled with the electromyographic signal acquisition unit and is used for preprocessing the received electromyographic information to obtain the electromyographic preprocessing information;
and the electromyographic feature extraction unit is coupled with the electromyographic preprocessing unit and is used for extracting electromyographic signal feature information according to the electromyographic preprocessing information.
5. An exoskeleton recognition control method is applied to an exoskeleton system, wherein the exoskeleton system is applied to an exoskeleton device, and the system comprises: the terrain information preprocessing module is coupled with the exoskeleton device and used for acquiring the terrain information in the motion environment of the exoskeleton device in real time, preprocessing the terrain information to obtain terrain preprocessing information, judging terrain features according to the terrain preprocessing information, extracting the terrain feature information and outputting corresponding terrain recognition preprocessing signals; wherein the topography information comprises image information or pressure information; the electromyographic information preprocessing module is used for acquiring electromyographic information of a wearer in real time, preprocessing the electromyographic information to obtain electromyographic preprocessing information, judging electromyographic characteristics according to the electromyographic preprocessing information, extracting electromyographic characteristic information, judging the movement intention of the wearer and outputting a corresponding movement intention identification preprocessing signal; wherein the electromyographic information includes: image information or sensing information; the method comprises the following steps:
generating a switching motion mode signal through terrain and motion mode recognition according to the received terrain recognition preprocessing signal, or generating a gait information signal through gait recognition according to the received motion intention recognition preprocessing signal;
generating a switching motion mode control signal according to the switching motion mode signal to cause the exoskeleton device to switch a motion mode, or generating a gait control signal according to the received gait information signal to cause the exoskeleton device to adjust a motion gait;
wherein, the generating of the switching movement mode signal through the terrain and movement mode identification according to the received terrain identification preprocessing signal comprises: establishing an association relation library of the topography and the corresponding motion mode by using the topography and motion mode identification technology; recognizing the terrain form according to the terrain recognition preprocessing information; rapidly searching an association relation library of the terrain and the movement mode according to the terrain form, and matching the association relation library with the movement mode corresponding to the terrain form; calculating the time required for reaching the new terrain in the current movement mode; generating the switching motion mode signal before the required time is reached;
the step of generating the gait information signal through gait recognition according to the received movement intention recognition preprocessing signal comprises the following steps: identifying a preprocessing signal offline training action classification model according to the received movement intention; establishing a gait sample base according to various trained action classification models; rapidly matching an action type corresponding to the movement intention identification preprocessing signal through the gait sample library; generating a gait information signal according to the action type;
the judgment mode of the topographic features is one or more of the following methods:
(1) judging the topography characteristics according to the flow direction of the river;
(2) judging the topographic features according to the difference of the unified latitude and temperature;
(3) judging the terrain features according to the bending direction of the contour lines;
(4) directly judging the terrain features according to the distribution rule of the contour line values seen in the contour map;
(5) judging the terrain features according to the distribution characteristics of agriculture;
(6) judging the terrain features according to the population distribution characteristics;
(7) judging the terrain features according to the distribution rule of the isobars;
the electromyographic characteristics are judged in one or more of the following ways:
(1) judging the electromyographic characteristics according to a time domain analysis method;
(2) judging the electromyographic characteristics according to a frequency domain analysis method;
(3) and judging the electromyographic characteristics according to a nonlinear dynamics analysis method.
6. An electronic device, comprising:
one or more communicators for communicating with the outside;
one or more memories for storing computer programs;
one or more processors for executing the computer program to perform the exoskeleton recognition control method of claim 5.
7. A computer storage medium, characterized in that a computer program is stored which, when running, implements the exoskeleton recognition control method as claimed in claim 5.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005253650A (en) * | 2004-03-11 | 2005-09-22 | Yoshiyuki Yamaumi | Body-worn movement aid, calibration device for body-worn movement aid and calibration program |
| CN103984962A (en) * | 2014-05-30 | 2014-08-13 | 河北工业大学 | Exoskeleton walking mode identification method based on electromyographic signals |
| CN104635732A (en) * | 2013-11-08 | 2015-05-20 | 三星电子株式会社 | Walk-assistive robot and method of controlling the same |
| CN105549733A (en) * | 2015-12-08 | 2016-05-04 | 西安交通大学 | Brain-computer interface system and method based on steady state visual evoked in intelligent space |
-
2019
- 2019-07-15 CN CN201910637170.3A patent/CN112223253B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005253650A (en) * | 2004-03-11 | 2005-09-22 | Yoshiyuki Yamaumi | Body-worn movement aid, calibration device for body-worn movement aid and calibration program |
| CN104635732A (en) * | 2013-11-08 | 2015-05-20 | 三星电子株式会社 | Walk-assistive robot and method of controlling the same |
| CN103984962A (en) * | 2014-05-30 | 2014-08-13 | 河北工业大学 | Exoskeleton walking mode identification method based on electromyographic signals |
| CN105549733A (en) * | 2015-12-08 | 2016-05-04 | 西安交通大学 | Brain-computer interface system and method based on steady state visual evoked in intelligent space |
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