CN110236539B - Human lower limb surface electromyographic signal acquisition and pattern recognition system - Google Patents
Human lower limb surface electromyographic signal acquisition and pattern recognition system Download PDFInfo
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- CN110236539B CN110236539B CN201910659123.9A CN201910659123A CN110236539B CN 110236539 B CN110236539 B CN 110236539B CN 201910659123 A CN201910659123 A CN 201910659123A CN 110236539 B CN110236539 B CN 110236539B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Abstract
The invention discloses a system for collecting myoelectric signals on the surface of lower limbs of a human body, which belongs to the technical field of myoelectric data collection, and particularly relates to a system for collecting myoelectric signals on the surface of the lower limbs of the human body and recognizing patterns, which comprises a shell, an upper pull plate, a main inner frame, a bottom box and a bracket, wherein the upper pull plate and the lower pull plate are inserted into the left side wall and the right side wall of the shell, the system for collecting myoelectric signals on the surface of the lower limbs of the human body can be matched with a leg to limit the position of the leg in the process of collecting the myoelectric signals on the surface of the lower limbs of a patient, a myoelectric collecting sheet and an electric stimulation patch are conveniently fixed with the lower limbs, a myoelectric collecting module, a processing module, a pattern recognition system and a pattern recognition algorithm are arranged in a controller, after characteristic extraction and analysis are carried out by the pattern recognition system and the pattern recognition algorithm, the myoelectric signals can be effectively collected, the pattern behaviors in the process of the myoelectric signals are determined, effective data support is provided, and the practical application effect in the rehabilitation activity is conveniently obtained, the rehabilitation schemes with different strengths can be conveniently formulated by matching with data.
Description
Technical Field
The invention relates to the technical field of electromyographic data acquisition, in particular to a system for acquiring electromyographic signals of the surface of a lower limb of a human body and identifying a pattern.
Background
The electromyographic signals are the superposition of action potentials of motor units in a plurality of muscle fibers in time and space. The surface electromyographic signals are the comprehensive effect of the EMG of superficial muscles and the electrical activity of nerve trunks on the surface of skin, and can reflect the activity of the nerve muscles to a certain extent, so the surface electromyographic signals have important practical values in the aspects of clinical medicine, human-computer efficiency, rehabilitation medicine, sports science and the like.
The electromyographic signal acquisition device is matched with equipment for acquiring the electromyographic signal, the electromyographic signal acquisition is used in medical rehabilitation as one of pre-judging modes, and the electromyographic signal acquisition can be used as an important index for judgment on the premise of judging whether the electromyographic signal of a patient reaches a normal standard.
When the existing electromyographic signal acquisition device is used in the face of lower limbs, due to the fact that the muscle structure of the lower limbs is large and complex, an effective limiting mode for legs is also lacked in the face of the electromyographic signal acquisition process, the legs are not placed stably, stress points can incline, tightening or loosening of the muscles of the legs is caused, the accuracy of electromyographic signal acquisition is affected, the existing electromyographic signal acquisition device cannot identify electromyographic signals generated by different movement modes, mode behaviors in the electromyographic signal generation process cannot be determined, effective data support cannot be provided, the practical application effect in the lower limb rehabilitation activity is fuzzy, and proper rehabilitation schemes with different strengths cannot be formulated by matching with data.
Disclosure of Invention
The invention aims to provide a system for collecting the surface electromyographic signals of the lower limbs of a human body and identifying a pattern, in order to solve the problems that when the existing electromyographic signal acquisition device proposed in the background art is used by facing the lower limbs, because the muscle structures of the lower limbs are many and complex, and an effective limiting mode for the legs is also lacked in the process of collecting the electromyographic signals, the stress points can incline due to unstable placement of the legs, the muscles of the legs are tightened or loosened, the accuracy of collecting the electromyographic signals is influenced, and the prior electromyographic signal acquisition device can not identify the electromyographic signals generated by different movement modes and can not determine the mode behaviors in the electromyographic signal generation process, thus the prior electromyographic signal acquisition device can not provide effective data support, the practical application effect in the lower limb rehabilitation activity is vague, and the problem that proper rehabilitation schemes with different strengths cannot be formulated by matching with data is solved.
In order to achieve the purpose, the invention provides the following technical scheme: a human lower limb surface electromyographic signal acquisition and pattern recognition system comprises a shell, an upper pull plate, a main inner frame, a bottom box and a support, wherein the upper pull plate and a lower pull plate are inserted into the left side wall and the right side wall of the shell, the inner cavity of the shell is fixedly connected with the main inner frame and an auxiliary inner frame through bolts, the bottom of the shell is fixedly connected with the bottom box through bolts, the bottom of the bottom box is fixedly connected with the support through bolts, the left side wall of the upper pull plate is fixedly connected with an arc plate through screws, the left side wall of the arc plate is bonded with a base plate, the front side wall of the main inner frame is fixedly connected with a door leaf through a hinge, the front side wall of the inner cavity of the main inner frame is fixedly connected with a winder through screws, a lead is wound inside the winder, the top end of the lead is electrically connected with an electromyographic acquisition sheet, and the inner structure of the auxiliary inner frame is the same as that of the main inner frame, the electric stimulation patch is electrically connected to the tail end of the top of a lead inside the auxiliary inner frame, the bottom of an inner cavity of the bottom box is fixedly connected with a controller and a storage battery through screws, the inside of the controller is electrically connected with a myoelectric acquisition module, a processing module, a pattern recognition system and a pattern recognition algorithm through signal lines, the output end of the pattern recognition algorithm is electrically connected with a storage module through signal lines, the output end of the storage module is electrically connected with a wireless transmitter through signal lines, the electric output end of the storage battery is electrically connected with a power release module, the output end of the power release module is electrically connected with the electric stimulation patch, and the electric output end of the storage battery is electrically connected with the myoelectric acquisition module, the processing module, the pattern recognition system and the pattern recognition algorithm.
Preferably, the myoelectricity collection module is electrically connected with a myoelectricity induction module, a signal collection module and a control module through a signal line, the output end of the myoelectricity induction module is electrically connected with the signal collection module, and the output end of the signal collection module is electrically connected with the control module.
Preferably, the processing module is electrically connected to the filtering module, the method module and the preprocessing module through a signal line, an output end of the filtering module is electrically connected to the method module, and an output end of the method module is electrically connected to the preprocessing module.
Preferably, the inside of the pattern recognition system is electrically connected with an action library module, a myoelectricity detection module and a feature extraction module through signal lines, the output end of the action library module is electrically connected with the myoelectricity detection module, and the output end of the myoelectricity detection module is electrically connected with the feature extraction module.
Preferably, the pattern recognition algorithm is electrically connected to a pattern comparison module, a balance detection module and a spectrum analysis module through a signal line, an output end of the pattern comparison module is electrically connected to the balance detection module, and an output end of the balance detection module is electrically connected to the spectrum analysis module.
Preferably, the left side wall and the right side wall of the shell are fixedly connected with a pushing handle through screws, and the circumferential outer wall of the pushing handle is sleeved with a rubber sleeve.
Preferably, a top plate is bonded to the rear side wall of the housing, and an inner container is bonded to the front side wall of the top plate.
Preferably, the number of the upper pulling plate is two, the number of the lower pulling plate is two, springs are sleeved on the outer walls of the circumferences of the upper pulling plate and the lower pulling plate, and the structure of the upper pulling plate is the same as that of the lower pulling plate.
Preferably, the right side wall of support passes through screw fixed connection and has the gyro wheel, the left side wall welding of support has the ring, the circumference inner wall threaded connection of ring has the card post.
Compared with the prior art, the invention has the beneficial effects that: the electromyographic signal acquisition and pattern recognition system for the surface of the lower limb of the human body can effectively accommodate the lower limb of a patient by utilizing an open shell structure in the process of acquiring the electromyographic signal of the surface of the lower limb of the patient through the combined application of accessories and systems, can be matched with springs on an upper pull plate and a lower pull plate to shrink an arc plate, can be matched with a leg part for limiting, and is convenient for a wire telescopic electromyographic acquisition sheet and an electric stimulation patch to be fixed with the lower limb, and is internally provided with an electromyographic acquisition module, a processing module, a pattern recognition system and a pattern recognition algorithm, can be matched with the processing module to process the signal after the electromyographic signal is received by the electromyographic acquisition module, can effectively determine the pattern behavior in the electromyographic signal generation process after the characteristic extraction and the analysis are carried out by the pattern recognition system and the pattern recognition algorithm, the effective data support is provided, the practical application effect in the lower limb rehabilitation activity is convenient to obtain, and rehabilitation schemes with different strengths can be conveniently formulated by matching data.
Drawings
FIG. 1 is a schematic top view of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a schematic view of the internal frame structure of the present invention;
FIG. 4 is a schematic diagram of a pattern recognition system according to the present invention.
In the figure: 100 shells, 110 push handles, 120 top plates, 130 liners, 200 upper pull plates, 210 arc plates, 220 backing plates, 230 lower pull plates, 300 main inner frames, 310 windups, 320 leads, 330 myoelectricity acquisition sheets, 340 auxiliary inner frames, 350 electric stimulation patches, 400 bottom boxes, 410 controllers, 420 storage batteries, 500 supports, 510 rollers and 520 clamping columns.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a human lower limb surface electromyographic signal acquisition and pattern recognition system, which can improve the convenience for fixing legs and improving the stability of electromyographic signal acquisition by the combined application of accessories and systems, is also convenient for recognizing different behavior patterns and making different strength rehabilitation schemes after acquiring data, and please refer to fig. 1-4, and the system comprises a shell 100, an upper pull plate 200, a main inner frame 300, a bottom box 400 and a bracket 500;
referring to fig. 2 again, the left and right sidewalls of the casing 100 are provided with the push handles 110, specifically, the left and right sidewalls of the casing 100 are fixedly connected with the push handles 110 through screws, the circumferential outer wall of the push handles 110 is sleeved with a rubber sleeve, the rear sidewall of the casing 100 is bonded with the top plate 120, and the front sidewall of the top plate 120 is bonded with the inner container 130;
referring to fig. 1-2 again, the left and right side walls of the upper pulling plate 200 are fixedly mounted with the left and right side walls of the housing 100, specifically, the left and right side walls of the housing 100 are inserted with the upper pulling plate 200 and the lower pulling plate 230, the left side wall of the upper pulling plate 200 is fixedly connected with the arc plate 210 through screws, the left side wall of the arc plate 210 is bonded with the backing plate 220, the number of the upper pulling plates 200 is two, the number of the lower pulling plates 230 is two, the circumferential outer walls of the upper pulling plate 200 and the lower pulling plates 230 are sleeved with springs, and the mechanism of the upper pulling plate 200 is the same as the structure of the lower pulling plates 230;
referring to fig. 2-3 again, the left side wall of the main inner frame 300 is fixedly mounted with the left side wall of the inner cavity of the outer shell 100, specifically, the inner cavity of the outer shell 100 is fixedly connected with the main inner frame 300 and the auxiliary inner frame 340 through bolts, the front side wall of the main inner frame 300 is fixedly connected with a door leaf through a hinge, the front side wall of the inner cavity of the main inner frame 300 is fixedly connected with a winder 310 through screws, a wire 320 is wound inside the winder 310, the top end of the wire 320 is electrically connected with a myoelectricity collecting piece 330, the internal structure of the auxiliary inner frame 340 is the same as that of the main inner frame 300, and the top end of the wire 320 inside the auxiliary inner frame 340 is electrically connected with an electrical stimulation patch 350;
referring to fig. 2-4 again, the left and right side walls of the bottom case 400 are fixedly installed with the left and right side walls of the inner cavity of the outer shell 100, specifically, the bottom of the outer shell 100 is fixedly connected with the bottom case 400 through bolts, the bottom of the inner cavity of the bottom case 400 is fixedly connected with the controller 410 and the storage battery 420 through screws, the interior of the controller 410 is electrically connected with the myoelectric collection module, the processing module, the pattern recognition system and the pattern recognition algorithm through signal lines, the output end of the pattern recognition algorithm is electrically connected with the storage module through signal lines, the output end of the storage module is electrically connected with the wireless transmitter through signal lines, the electrical output end of the storage battery 420 is electrically connected with the power release module, the output end of the power release module is electrically connected with the electrical stimulation patch 350, and the electrical output end of the storage battery 420 is electrically connected with the myoelectric collection module, the system comprises a processing module, a pattern recognition system and a pattern recognition algorithm;
referring to fig. 2 again, the top of the bracket 500 is fixedly mounted with the bottom of the bottom case 400, specifically, the bottom of the bottom case 400 is fixedly connected with the bracket 500 through bolts, the right side wall of the bracket 500 is fixedly connected with the roller 510 through screws, the left side wall of the bracket 500 is welded with a ring, and the circumferential inner wall of the ring is in threaded connection with the clamping column 520;
when the myoelectric stimulation device is used specifically, firstly, the device is moved to the position of a patient through a support 500 with rollers 510 at the lower part, the patient selects to stand or sit, the lower limb is placed in the inner container of the shell 100, at the moment, the upper pull plate 200 and the lower pull plate 230 can be pulled, the position of the arc plate 210 is changed by matching with a spring, after the lower limb completely enters, the leg can be fixed by loosening the pull plate, secondly, the lead 320 is pulled and is extended after being matched with the winder 310 to move, the winder 310 is provided with a fixer, a door leaf can be opened to press the fixer to ensure that the lead 320 is blocked and can not retract, after the myoelectric collection piece 330 and the electric stimulation patch 350 are respectively fixed at proper positions, the surface myoelectric signals are collected through the controller 410, a myoelectric collection module, a processing module, a mode recognition system and a mode recognition algorithm are arranged in the controller 410, after the electromyographic signal is received by the electromyographic signal acquisition module, the electromyographic signal is processed by the processing module in a matching way, after feature extraction and analysis are carried out by utilizing a pattern recognition system and a pattern recognition algorithm, the pattern behavior in the electromyographic signal generation process can be effectively determined after the electromyographic signal is acquired, effective data support is provided, the practical application effect in lower limb rehabilitation activity can be conveniently obtained, and rehabilitation schemes with different intensities can be conveniently formulated by matching with data.
Referring to fig. 4 again, in order to perform myoelectric collection in a coordinated manner, specifically, the myoelectric collection module is electrically connected to the myoelectric induction module, the signal collection module and the control module through a signal line, an output end of the myoelectric induction module is electrically connected to the signal collection module, and an output end of the signal collection module is electrically connected to the control module.
Referring to fig. 4 again, in order to perform data processing in a coordinated manner, specifically, the interior of the processing module is electrically connected to the filtering module, the method module and the preprocessing module through the signal line, the output end of the filtering module is electrically connected to the method module, and the output end of the method module is electrically connected to the preprocessing module.
Referring to fig. 4 again, in order to perform feature processing and effective calculation, specifically, the inside of the pattern recognition system is electrically connected to the action library module, the myoelectricity detection module and the feature extraction module through signal lines, the output end of the action library module is electrically connected to the myoelectricity detection module, the output end of the myoelectricity detection module is electrically connected to the feature extraction module, the inside of the pattern recognition algorithm is electrically connected to the pattern comparison module, the equalization detection module and the spectrum analysis module through signal lines, the output end of the pattern comparison module is electrically connected to the equalization detection module, and the output end of the equalization detection module is electrically connected to the spectrum analysis module.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the embodiments disclosed herein may be used in any combination, provided that there is no structural conflict, and the combinations are not exhaustively described in this specification merely for the sake of brevity and conservation of resources. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (9)
1. A human lower limb surface electromyographic signal acquisition and pattern recognition system is characterized in that: the novel rolling machine comprises a shell (100), an upper pulling plate (200), a main inner frame (300), a bottom box (400) and a support (500), wherein the left side wall and the right side wall of the shell (100) are connected with the upper pulling plate (200) and a lower pulling plate (230) in an inserting mode, an inner cavity of the shell (100) is fixedly connected with the main inner frame (300) and an auxiliary inner frame (340) through bolts, the bottom of the shell (100) is fixedly connected with the bottom box (400) through bolts, the bottom of the bottom box (400) is fixedly connected with the support (500) through bolts, the left side wall of the upper pulling plate (200) is fixedly connected with an arc plate (210) through screws, a backing plate (220) is bonded on the left side wall of the arc plate (210), the front side wall of the main inner frame (300) is fixedly connected with a door leaf through a hinge, the front side wall of the inner cavity of the main inner frame (300) is fixedly connected with a rolling machine (310) through screws, and a lead (320) is wound inside the rolling machine (310), the tail end of the top of the lead (320) is electrically connected with a myoelectricity acquisition sheet (330), the internal structure of the auxiliary inner frame (340) is the same as that of the main inner frame (300), the tail end of the top of the lead (320) in the auxiliary inner frame (340) is electrically connected with an electric stimulation patch (350), the bottom of the inner cavity of the bottom box (400) is fixedly connected with a controller (410) and a storage battery (420) through screws, the inside of the controller (410) is electrically connected with a myoelectricity acquisition module, a processing module, a pattern recognition system and a pattern recognition algorithm through signal wires, the output end of the pattern recognition algorithm is electrically connected with a storage module through a signal wire, the output end of the storage module is electrically connected with a wireless transmitter through a signal wire, the electric output end of the storage battery (420) is electrically connected with an electric power release module, and the output end of the electric power release module is electrically connected with the electric stimulation patch (350), the electrical output end of the storage battery (420) is electrically connected with the myoelectricity acquisition module, the processing module, the pattern recognition system and the pattern recognition algorithm.
2. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the myoelectricity induction and control system is characterized in that a myoelectricity induction module, a signal acquisition module and a control module are electrically connected to the interior of the myoelectricity acquisition module through a signal line, the output end of the myoelectricity induction module is electrically connected with the signal acquisition module, and the output end of the signal acquisition module is electrically connected with the control module.
3. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the interior of the processing module is electrically connected with the filtering module, the method module and the preprocessing module through signal lines, the output end of the filtering module is electrically connected with the method module, and the output end of the method module is electrically connected with the preprocessing module.
4. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the internal part of the pattern recognition system is electrically connected with an action library module, a myoelectricity detection module and a feature extraction module through signal lines, the output end of the action library module is electrically connected with the myoelectricity detection module, and the output end of the myoelectricity detection module is electrically connected with the feature extraction module.
5. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the mode identification algorithm is characterized in that a mode comparison module, a balance detection module and a spectrum analysis module are electrically connected to the interior of the mode identification algorithm through signal lines, the output end of the mode comparison module is electrically connected with the balance detection module, and the output end of the balance detection module is electrically connected with the spectrum analysis module.
6. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the left side wall and the right side wall of the shell (100) are fixedly connected with a pushing handle (110) through screws, and the circumferential outer wall of the pushing handle (110) is sleeved with a rubber sleeve.
7. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the rear side wall of the shell (100) is bonded with a top plate (120), and the front side wall of the top plate (120) is bonded with an inner container (130).
8. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the number of the upper pulling plate (200) is two, the number of the lower pulling plate (230) is two, springs are sleeved on the circumferential outer walls of the upper pulling plate (200) and the lower pulling plate (230), and the structure of the upper pulling plate (200) is the same as that of the lower pulling plate (230).
9. The system for collecting electromyographic signals on the surface of the lower limb of a human body and recognizing the pattern according to claim 1, wherein: the right side wall of support (500) passes through screw fixed connection and has gyro wheel (510), the left side wall welding of support (500) has the ring, the circumference inner wall threaded connection of ring has card post (520).
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| CN105849788A (en) * | 2013-10-09 | 2016-08-10 | Mc10股份有限公司 | Utility gear including conformal sensors |
| CN107874763A (en) * | 2017-12-11 | 2018-04-06 | 无锡智开医疗机器人有限公司 | A kind of lower limb data collecting system |
| CN109940584A (en) * | 2019-03-25 | 2019-06-28 | 杭州程天科技发展有限公司 | The detection method that a kind of exoskeleton robot and its detection human motion are intended to |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US10231851B2 (en) * | 2014-08-29 | 2019-03-19 | Conor J. MADDRY | Pneumatic electromyographic exoskeleton |
| JP6301862B2 (en) * | 2015-03-04 | 2018-03-28 | 上銀科技股▲分▼有限公司 | Lower leg exercise device and control method thereof |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105849788A (en) * | 2013-10-09 | 2016-08-10 | Mc10股份有限公司 | Utility gear including conformal sensors |
| CN107874763A (en) * | 2017-12-11 | 2018-04-06 | 无锡智开医疗机器人有限公司 | A kind of lower limb data collecting system |
| CN109940584A (en) * | 2019-03-25 | 2019-06-28 | 杭州程天科技发展有限公司 | The detection method that a kind of exoskeleton robot and its detection human motion are intended to |
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