CN112999585A - Auxiliary device for human lumbar vertebra recovery - Google Patents
Auxiliary device for human lumbar vertebra recovery Download PDFInfo
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- CN112999585A CN112999585A CN202110337300.9A CN202110337300A CN112999585A CN 112999585 A CN112999585 A CN 112999585A CN 202110337300 A CN202110337300 A CN 202110337300A CN 112999585 A CN112999585 A CN 112999585A
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B23/00—Exercising apparatus specially adapted for particular parts of the body
- A63B23/02—Exercising apparatus specially adapted for particular parts of the body for the abdomen, the spinal column or the torso muscles related to shoulders (e.g. chest muscles)
- A63B23/0233—Muscles of the back, e.g. by an extension of the body against a resistance, reverse crunch
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- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
- A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/08—Body-protectors for players or sportsmen, i.e. body-protecting accessories affording protection of body parts against blows or collisions
- A63B71/12—Body-protectors for players or sportsmen, i.e. body-protecting accessories affording protection of body parts against blows or collisions for the body or the legs, e.g. for the shoulders
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Abstract
The invention discloses an auxiliary device for human lumbar vertebra recovery; comprises a frame body, a waistband and a control unit; the waist-binding belt is arranged at the center of the interior of the frame body and used for fixing the waist of a rehabilitation patient; a buffer mechanism is arranged between the waistband and the fixed block; by arranging the traction assistance and combining the monitoring system, a doctor can conveniently know the recovery condition of the patient in time, the doctor directly controls the recovery condition of the patient and improves the training plan, and the recovery efficiency is improved; the optimal waist electromyographic signal characteristics are obtained through signal characteristic extraction and classification, waist training strength and muscle fatigue data are obtained after the waist electromyographic signal characteristics are processed by the single chip microcomputer, unstable electromyographic signals are removed, measurement errors of the muscle fatigue are reduced, and training recovery efficiency is improved.
Description
Technical Field
The invention belongs to the field of lumbar vertebra recovery equipment, and particularly relates to an auxiliary device for human lumbar vertebra recovery.
Background
The waist is in human middle part, plays the effect of holding up and down, and the lumbar vertebrae is very important to the human body, also is the position that the health problem appears very easily simultaneously, and along with the rhythm of work is more and more fast, the overload operating condition often appears in human waist, very easily causes various waist diseases because of tired two, and waist rehabilitation training is the basic means of treatment lumbar vertebrae disease, uses this means can improve the cure rate of waist disease, reduces the incidence of lumbar vertebrae disease.
The existing waist rehabilitation training mostly depends on the experience of a trainer to guide the training or the rehabilitation training is carried out by massage and the like, the rehabilitation training equipment is single, and when the equipment is adopted to carry out the rehabilitation training, the equipment lacks the timely communication with a doctor, and the training amount cannot be strengthened or reduced according to the recovery condition in time, so that the rehabilitation training efficiency is lower, and a certain safety risk exists.
Chinese patent application number 202010412187.1 discloses a waist rehabilitation training device for medical care, and relates to the technical field of medical instruments. The waist fixing device comprises a vertical plate, a metal frame, a movable sliding block, a first waist fixing ring, a second waist fixing ring and a hanging ring; the metal frame is fixedly arranged on the vertical plate; the movable sliding block is movably arranged on the vertical plate through a roller; the first waist fixing ring is connected with the movable sliding block in a sliding manner through a sliding rail; the second waist fixing ring is fixed on the connecting column through two elastic ropes; the lifting ring is fixedly connected with the metal frame through a lifting rope; the waist fixing ring and the hanging ring are arranged, and the hanging ring is held by hands to assist in power, so that the waist of the user is trained; the lack of systematic planning and monitoring of training results in inefficient training and difficulty in adjusting the training program based on the degree of recovery.
Chinese patent application No. 201510189821.9 discloses a waist rehabilitation training coupling device and control method: the waist rehabilitation training connecting device comprises an aluminum alloy profile frame and more than 2 waist rehabilitation units. Each waist rehabilitation unit comprises a pneumatic artificial muscle unit, a gravity balance unit and a rehabilitation training connection unit. The control method comprises seven steps, wherein 4 rehabilitation training connection units are controlled to carry out bending rehabilitation training on a patient; the lumbar vertebra is adjusted by only depending on the motor traction rope, the training degree is difficult to control, and the safety of the rehabilitation training is difficult to control.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide an auxiliary device for human lumbar vertebra recovery, which is convenient for a doctor to know the recovery condition of a patient in time by arranging a traction auxiliary combined monitoring system, and the doctor directly controls the recovery condition of the patient and improves a training plan, thereby improving the recovery efficiency; the optimal waist electromyographic signal characteristics are obtained through signal characteristic extraction and classification, waist training strength and muscle fatigue data are obtained after the waist electromyographic signal characteristics are processed by the single chip microcomputer, unstable electromyographic signals are removed after the waist electromyographic signal characteristics are processed, measurement errors of the muscle fatigue are reduced, and training recovery efficiency is improved.
The invention provides the following technical scheme:
an auxiliary device for lumbar vertebra recovery of a human body; comprises a frame body, a waistband and a control unit; the waist-binding belt is arranged at the center of the interior of the frame body and used for fixing the waist of a rehabilitation patient; a buffer mechanism is arranged between the waistband and the fixed block, the buffer mechanism comprises a sleeve, one end of the sleeve is connected with the fixed block, a guide rod is sleeved inside the other end of the sleeve, the outer wall of one end, arranged inside the sleeve, of the guide rod is bent outwards, the outer wall of one end, close to the guide rod, of the sleeve is bent inwards, the guide rod is prevented from slipping from the inside of the sleeve, and the other end of the guide rod is connected with the waistband through a universal ball head; the guide rod and the sleeve are externally provided with a sleeve, one end of the spring is connected with the fixed block, and the other end of the spring is connected with the waistband;
the auxiliary device for lumbar vertebra recovery further comprises a remote monitoring system, the remote monitoring system and a doctor can directly control the recovery condition of a patient and set a training plan, and the remote monitoring system comprises a single chip microcomputer, a display module, a Bluetooth module, a wifi module, a cloud server, a mobile terminal and an electromyographic signal sensor; a doctor sets a training plan through the mobile terminal, the training plan is transmitted to the control unit through the cloud server, and the patient performs rehabilitation training; when the training intensity set by the doctor is reached, the control unit controls the voice system to send out voice prompt.
Preferably, the rope winding mechanism comprises a shell, a motor is arranged at one end of the shell, an output shaft of the motor is connected with a sliding rod through a coupler, the sliding rod is arranged inside the shell, the sliding rod is rotatably connected with the inner wall of the shell through a bearing, and a bunching wheel is arranged on the sliding rod; the motor is characterized in that a first gear is arranged on an output shaft of the motor, the first gear is connected with a second gear in a meshed mode, one side of the second gear is connected with a screw rod, and the second gear drives the screw rod to rotate.
Preferably, the screw rod is arranged below the sliding rod and is rotationally connected with the inner wall of the shell through a bearing; the wire rod is provided with an internal thread pipe, the internal thread pipe is matched with the wire rod and rotatably sleeved, the outer side wall of the internal thread pipe is connected with a connecting rod, and the other end of the connecting rod is connected with the bunching wheel; the bunching wheel rotates to bunch, and meanwhile, the lead screw drives the internal threaded pipe, so that the bunching wheel is driven to slide on the sliding rod.
Preferably, the diameter of the second gear is larger than that of the first gear; a first rotating wheel is arranged at the top of the fixed block, a displacement sensor is arranged below the first rotating wheel, a second rotating wheel is arranged at the position, close to the middle part, of the fixed block, and the second rotating wheel and the waistband are positioned on the same horizontal plane; the rope is wound through the first rotating wheel and the second rotating wheel through the bunching wheel, and the other end of the rope is connected with the girdling belt; the rope is provided with a tension sensor, and the rope only provides tension and does not provide elasticity, so that the stability is enhanced; the strength of the recovery training is adjusted by controlling the length and the tensile strength of the thread rope.
Preferably, the bottom of support body is equipped with the telescoping cylinder, the running-board is connected to the top of telescoping cylinder, the running-board is established in the forward of beam waist area for fixed recovered patient's foot shares the pressure of patient's waist, prevents that the waist from training in-process secondary damage.
Preferably, in the remote monitoring system, the electromyographic signal sensing is arranged in the waistband, the waist electromyographic signal is obtained through the electromyographic signal sensing of the device, and the electromyographic signal is subjected to preprocessing, feature extraction and signal classification; the optimal waist chicken electrical signal characteristics are obtained through processing, waist training strength and muscle fatigue data are obtained through processing of the single chip microcomputer, unstable myoelectric signals are removed after the myoelectric signals are processed, measurement errors of the muscle fatigue are reduced, and training recovery efficiency is improved.
Preferably, the electromyographic signal feature extracts a multidimensional vector which is hidden in the original electromyographic signal and can represent muscle motion features, and extracts a time domain feature, a frequency domain feature and a time-frequency domain feature of the original electromyographic signal.
Preferably, the extracted electromyographic signal characteristic values are classified, a BP neural network is adopted to classify the time domain characteristic, the frequency domain characteristic and the time-frequency domain characteristic of the original electromyographic signal, and the data error of the electromyographic signal characteristic is reduced.
Preferentially, among the remote monitering system, obtain waist training intensity and muscle fatigue data and show on the LED display screen, send to cloud ware according to the wifi module simultaneously, cloud ware passes through TCP network protocol and sends to doctor's removal end, and doctor in time knows patient's training condition and recovery degree through removing end app to in time change new rehabilitation training plan.
Preferably, the feature extraction method includes the steps of: acquiring original data through an electromyographic signal sensor, segmenting the data during original data processing, extracting a feature body of each segment of data, and selecting time domain, frequency domain and time-frequency domain data features; b, layering the data characteristic signals according to the muscle fatigue and the characteristics of muscle action change during training, finding out the clustering center of each layer by adopting a clustering algorithm for each layer to obtain a data set with more obvious characteristics, and extracting better classification and training results after performing classifier training on each subclass; c, calculating the center of each cluster of data of the new data set through clustering, then calculating Euclidean distance with each layer of clustering center of the database established in the step b, and adding the distance of each clustering center of the new data set; the distance sum is the minimum, and new data is layered; d, after the data in the step c reach a certain layer, judging the data in the step b to belong to a smaller set to the bottommost layer by using the method in the step b, obtaining the muscle fatigue degree during rehabilitation training and the muscle action change data characteristics during training, removing impurity signals and myoelectric signals with weak strength, and improving the accuracy of the data.
After extracting the electromyographic signal characteristics, classifying the data through a BP neural network system, and obtaining an input characteristic vector X, X = [ X ] according to time domain, frequency domain and time-frequency domain data1,x2,…,xn]T(ii) a Output eigenvector Y = [ Y ]1,y2,…,yn]T(ii) a Training electromyographic signal characteristic samples serving as input neural networks, continuously consolidating and correcting each weight and threshold aiming at X memorability data of the samples through processing of a hidden layer, finally obtaining a mapping condition meeting an expected value, reversely calculating errors between a result of an actual output layer and the expected result, reversely transmitting the error until the training result is converged, inputting the converged characteristic data into an STM single chip microcomputer for data processing, performing data interaction with a mobile end app of a training doctor, facilitating the doctor to check the training result and the training intensity at any time, and performing training task distribution and change at the same time, thereby improving the rehabilitation training efficiency; the determination of the number of the hidden layer nodes directly influences the data processing result in the training process, and the number of the hidden layer nodes is large, the classification time is long, and the classification efficiency is low; the number of nodes of the hidden layer is small, the convergence degree of characteristic data is low, and the data classification effect is poor, so that the number k of the nodes of the hidden layer satisfies k = alpha + (m + n)1/2(ii) a In the above formula, m is the number of nodes of the input layer, n is the number of nodes of the output layer, and alpha is an adjusting constant and has a value range of 1-15.
In addition, the method for the remote monitoring system to process the electromyographic signal data comprises the following steps: a, muscle fatigue and muscle action change data during training are obtained from a doctor mobile terminal through a cloud server, and a data threshold value of training characteristics is automatically set by a single chip microcomputer and is displayed through an LED display; b, acquiring electromyographic signals by an electromyographic signal sensor on the waistband, and extracting and classifying signal characteristics to obtain muscle fatigue and muscle action change data during training; comparing the obtained muscle fatigue degree and muscle action change data during training with a set threshold, if the muscle fatigue degree and the muscle action change data are larger than the set threshold, indicating that the training intensity is met, carrying out voice reminding, and simultaneously transmitting rehabilitation training result data to a cloud server through a wifi module and transmitting the rehabilitation training result data to a doctor mobile terminal; and if the measured value is less than the set threshold value, returning to the step B, re-collecting the electromyographic signal data, processing the data, and adding the data with the rehabilitation training data of the previous step until the set threshold value of the training data is reached.
In addition, in the process of rehabilitation training, the rope winding mechanism is controlled by the control unit, the control unit acquires the tensile strength of the rope according to the tensile sensor, acquires the length of the rope in the training process according to the displacement sensor, performs data analysis on the acquired electromyographic signals through the STM single chip microcomputer and calculation and analysis, automatically controls the rotating speed of the motor and realizes automatic training; the motor drives the sliding rod to rotate, the bunching wheel arranged on the sliding rod drives the wire rope to rotate along with the rotation of the sliding rod, the bunching wheel drives the wire rope to indirectly drive the bunching belt to move after passing through the first rotating wheel and the second rotating wheel, the front direction, the rear direction, the left direction and the right direction of the bunching belt are respectively connected with the rope winding mechanisms, and the four groups of rope winding mechanisms jointly realize the movement of front, rear, left and right directions and the bending of waist, so that the training is realized; when the waist band moves by means of the thread ropes, four buffer mechanisms are arranged in the front direction, the rear direction, the left direction and the right direction of the waist band, and the waist band is fixed by connecting the buffer mechanisms with the fixed blocks, so that the phenomenon that the lumbar vertebra is uncomfortable or damaged due to large-amplitude movement and the recovery efficiency is influenced is prevented; in order to further increase the buffering effect, prevent errors and improve the training safety, the spring compresses the spring, the thread pitch d is 1.8-6.5mm, the length l is 260-500mm, and the pitch t is 3.6-10.3 mm; the pitch t and the pitch d meet the condition that t.d is more than or equal to 36 and is less than or equal to 85mm2. When t.d is less than 36, the energy storage provided by the elasticity of the spring is insufficient, and the spring is not easy to reverse after compressionThe spring and buffer effect is poor; when t.d is larger than 85, the spring is too hard to play a role of compression, the training amplitude range is small, and the corresponding training effect cannot be achieved; in order to further improve the buffering effect and achieve a proper training movement amplitude and prevent the trainer from being accidentally injured by an overlarge amplitude, the spring pitch t, the length l and the pitch d satisfy the following relations: d ═ λ · (l/t); wherein, the lambda is the adjusting coefficient of the spring and the value range is 0.66-3.28.
Compared with the prior art, the invention has the following beneficial effects:
(1) according to the auxiliary device for human lumbar vertebra recovery, the traction assistance is arranged and the monitoring system is combined, so that a doctor can know the recovery condition of a patient in time conveniently, the doctor can directly control the recovery condition of the patient and improve a training plan, and the recovery training efficiency is improved.
(2) According to the auxiliary device for human lumbar vertebra recovery, the optimal waist electromyographic signal characteristics are obtained through signal characteristic extraction and classification, waist training strength and muscle fatigue data are obtained after the waist electromyographic signal characteristics are processed by the single chip microcomputer, unstable electromyographic signals are removed after the electromyographic signal characteristics are processed, the measurement error of the muscle fatigue is reduced, and the training recovery efficiency is improved.
(3) The auxiliary device for human lumbar vertebra recovery carries out feature extraction on the electromyographic signals to obtain muscle fatigue degree during rehabilitation training and muscle action change data features during training, removes impurity signals and electromyographic signals with weak strength, and improves data accuracy.
(4) The auxiliary device for human lumbar vertebra recovery classifies data through a BP neural network system, and improves the convergence of data characteristics through limiting the number of nodes of a hidden layer, thereby improving the efficiency of rehabilitation training.
(5) According to the auxiliary device for human lumbar vertebra recovery, four buffer mechanisms are arranged in the front direction, the rear direction, the left direction and the right direction of a waistband while the waistband moves by means of the cotton ropes, the four buffer mechanisms are connected with a fixing block through the buffer mechanisms, the four buffer mechanisms and the fixing block are combined with each other and act together to fix the waistband, and the phenomenon that the lumbar vertebra is uncomfortable or damaged due to large-amplitude movement is prevented, and recovery efficiency is affected is prevented.
(6) According to the auxiliary device for human lumbar vertebra recovery, the buffering effect is further improved through limitation of the pitch, the length and the thread pitch of the spring, the proper exercise range is achieved, and the trainer is prevented from being accidentally injured due to overlarge range.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic view of the overall structure of the present invention.
Fig. 2 is a top view of the present invention.
Fig. 3 is a schematic view of the roping arrangement according to the invention.
Fig. 4 is a schematic view of the damper mechanism of the present invention.
Fig. 5 is a schematic diagram of electromyographic signal acquisition according to the present invention.
FIG. 6 is a diagram illustrating the classification of electromyographic signal features according to the present invention.
Fig. 7 is a block diagram of the remote control system of the present invention.
FIG. 8 is a block diagram of the single-chip processor of the present invention.
FIG. 9 is a flow chart of electromyographic signal data feature processing according to the present invention.
In the figure: 1. a frame body; 2. a waistband; 3. a rope winding mechanism; 4. a buffer mechanism; 5. a displacement sensor; 6. a first runner; 7. a second runner; 8. a cord; 9. a telescopic cylinder; 10. a foot pedal; 11. a control unit; 12. a fixing plate; 31. a housing; 32. a motor; 33. a first gear; 34. a second gear; 35. a slide bar; 36. a wire bundling wheel; 37. a screw rod; 38. an internally threaded tube; 39. a connecting rod; 41. a sleeve; 42. a guide bar; 43. a spring.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described in detail and completely with reference to the accompanying drawings. It is to be understood that the described embodiments are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1-4, an auxiliary device for lumbar vertebra recovery of a human body; comprises a frame body 1, a waistband 2 and a control unit 11; a fixed block 12 is arranged at the center of the side face of the frame body 1, a plurality of groups of rope winding mechanisms 3 are arranged above the frame body 1, each group of rope winding mechanisms 3 is provided with a rope 8, the rope winding mechanisms 3 are connected with the waist restraining bag through the ropes 8, the waist restraining belt 2 is arranged at the center of the inside of the frame body 1, and the waist restraining belt 2 is used for fixing the waist of a rehabilitation patient; a buffer mechanism 4 is arranged between the waist-belt 2 and the fixed block 12, the buffer mechanism 4 comprises a sleeve 41, one end of the sleeve 41 is connected with the fixed block 12, a guide rod 42 is sleeved inside the other end of the sleeve 41, the outer wall of one end of the guide rod 42 arranged inside the sleeve 41 is bent outwards, the outer wall of one end of the sleeve 41 close to the guide rod 42 is bent inwards, the guide rod 42 is prevented from slipping off from the inside of the sleeve 41, and the other end of the guide rod 42 is connected with the waist-belt 2 through a universal ball head; the guide rod 42 and the sleeve 41 are externally provided with the sleeve 41, one end of the spring 43 is connected with the fixed block 12, and the other end of the spring 43 is connected with the waistband 2;
the auxiliary device for lumbar vertebra recovery further comprises a remote monitoring system, the remote monitoring system and a doctor can directly control the recovery condition of a patient and set a training plan, and the remote monitoring system comprises a single chip microcomputer, a display module, a Bluetooth module, a wifi module, a cloud server, a mobile terminal and an electromyographic signal sensor; a doctor sets a training plan through the mobile terminal, the training plan is transmitted to the control unit 11 through the cloud server, and the patient performs rehabilitation training; when the training intensity set by the doctor is reached, the control unit 11 controls the voice system to send out voice prompt; the rope winding mechanism 3 comprises a shell 31, a motor 32 is arranged at one end of the shell 31, an output shaft of the motor 32 is connected with a sliding rod 35 through a coupler, the sliding rod 35 is arranged inside the shell 31, the sliding rod 35 is rotatably connected with the inner wall of the shell 31 through a bearing, and a bunching wheel 36 is arranged on the sliding rod 35; a first gear 33 is arranged on an output shaft of the motor 32, the first gear 33 is connected with a second gear 34 in a meshing manner, one side of the second gear 34 is connected with a screw rod 37, and the second gear 34 drives the screw rod 37 to rotate.
The screw rod 37 is arranged below the sliding rod 35, and the screw rod 37 is rotatably connected with the inner wall of the shell 31 through a bearing; an internal threaded pipe 38 is arranged on the screw rod 37, the internal threaded pipe 38 is matched with the screw rod 37 and rotatably sleeved with the screw rod 37, a connecting rod 39 is connected to the outer side wall of the internal threaded pipe 38, and the other end of the connecting rod 39 is connected with the bunching wheel 36; the bunching wheel 36 rotates to bunch, and meanwhile, the screw rod 37 drives the internal threaded pipe 38, so that the bunching wheel 36 is driven to slide on the sliding rod 35.
The diameter of the second gear 34 is larger than that of the first gear 33; a first rotating wheel 6 is arranged at the top of the fixed block 12, a displacement sensor 5 is arranged below the first rotating wheel 6, a second rotating wheel is arranged at the position, close to the middle, of the fixed block 12, and the second rotating wheel 7 and the waistband 2 are positioned on the same horizontal plane; the wire 8 is wound through the first rotating wheel 6 and the second rotating wheel 7 through the wire bundling wheel 36, and the other end of the wire 8 is connected with the waistband 2; the rope 8 is provided with a tension sensor, and the rope 8 only provides tension and does not provide elasticity, so that the stability is enhanced; the strength of the rehabilitation training is adjusted by controlling the length and the tensile strength of the thread rope 8.
The bottom of support body 1 is equipped with telescopic cylinder 9, the footboard 10 is connected to telescopic cylinder 9's top, footboard 10 is established in the forward of beam waist area 2 for fixed recovered patient's foot shares the pressure of patient's waist, prevents the waist at training in-process secondary damage.
Example two:
as shown in fig. 7-9, in the remote monitoring system based on the first embodiment, the electromyographic signal sensing is arranged inside the waistband 2, the waist electromyographic signal is obtained through the electromyographic signal sensing, and the electromyographic signal is subjected to preprocessing, feature extraction and signal classification; the optimal waist chicken electrical signal characteristics are obtained through processing, waist training strength and muscle fatigue data are obtained through processing of the single chip microcomputer, unstable myoelectric signals are removed after the myoelectric signals are processed, measurement errors of the muscle fatigue are reduced, and training recovery efficiency is improved; extracting a multidimensional vector which is hidden in an original electromyographic signal and can represent muscle movement characteristics by electromyographic signal characteristics, and extracting time domain characteristics, frequency domain characteristics and time-frequency domain characteristics of the original electromyographic signal; classifying the extracted electromyographic signal characteristic values, and classifying the time domain characteristic, the frequency domain characteristic and the time-frequency domain characteristic of the original electromyographic signal by adopting a BP neural network so as to reduce the data error of the electromyographic signal characteristic.
Among the remote monitering system, obtain waist training intensity and muscle fatigue data display on the LED display screen, send to cloud ware according to the wifi module simultaneously, cloud ware passes through TCP network protocol and sends to doctor's removal end, and the doctor in time knows patient's training condition and recovery degree through removing end app to in time change new rehabilitation training plan.
Example three:
as shown in fig. 5 to 6, on the basis of the first embodiment, the feature extraction method includes the following steps: acquiring original data through an electromyographic signal sensor, segmenting the data during original data processing, extracting a feature body of each segment of data, and selecting time domain, frequency domain and time-frequency domain data features; b, layering the data characteristic signals according to the muscle fatigue and the characteristics of muscle action change during training, finding out the clustering center of each layer by adopting a clustering algorithm for each layer to obtain a data set with more obvious characteristics, and extracting better classification and training results after performing classifier training on each subclass; c, calculating the center of each cluster of data of the new data set through clustering, then calculating Euclidean distance with each layer of clustering center of the database established in the step b, and adding the distance of each clustering center of the new data set; the distance sum is the minimum, and new data is layered; d, after the data in the step c reach a certain layer, judging the data in the step b to belong to a smaller set to the bottommost layer by using the method in the step b, obtaining the muscle fatigue degree during rehabilitation training and the muscle action change data characteristics during training, removing impurity signals and myoelectric signals with weak strength, and improving the accuracy of the data.
After extracting the electromyographic signal characteristics, classifying the data through a BP neural network system, and obtaining an input characteristic vector X, X = [ X ] according to time domain, frequency domain and time-frequency domain data1,x2,…,xn]T(ii) a Output eigenvector Y = [ Y ]1,y2,…,yn]T(ii) a Training electromyographic signal characteristic samples serving as input neural networks, continuously consolidating and correcting each weight and threshold aiming at X memorability data of the samples through processing of a hidden layer, finally obtaining a mapping condition meeting an expected value, reversely calculating errors between a result of an actual output layer and the expected result, reversely transmitting the error until the training result is converged, inputting the converged characteristic data into an STM single chip microcomputer for data processing, performing data interaction with a mobile end app of a training doctor, facilitating the doctor to check the training result and the training intensity at any time, and performing training task distribution and change at the same time, thereby improving the rehabilitation training efficiency; the determination of the number of the hidden layer nodes directly influences the data processing result in the training process, and the number of the hidden layer nodes is large, the classification time is long, and the classification efficiency is low; the number of nodes of the hidden layer is small, the convergence degree of characteristic data is low, and the data classification effect is poor, so that the number k of the nodes of the hidden layer meets the condition that k = alpha + m + n1/2(ii) a In the above formula, m is the number of nodes of the input layer, n is the number of nodes of the output layer, and alpha is an adjusting constant and has a value range of 1-15.
Example four
On the basis of the first embodiment, the method for processing the electromyographic signal data by the remote monitoring system comprises the following steps: a, muscle fatigue and muscle action change data during training are obtained from a doctor mobile terminal through a cloud server, and a data threshold value of training characteristics is automatically set by a single chip microcomputer and is displayed through an LED display; b, acquiring electromyographic signals by an electromyographic signal sensor on the waistband 2, and extracting and classifying signal characteristics to obtain muscle fatigue and muscle action change data during training; comparing the obtained muscle fatigue degree and muscle action change data during training with a set threshold, if the muscle fatigue degree and the muscle action change data are larger than the set threshold, indicating that the training intensity is met, carrying out voice reminding, and simultaneously transmitting rehabilitation training result data to a cloud server through a wifi module and transmitting the rehabilitation training result data to a doctor mobile terminal; and if the measured value is less than the set threshold value, returning to the step B, re-collecting the electromyographic signal data, processing the data, and adding the data with the rehabilitation training data of the previous step until the set threshold value of the training data is reached.
In the rehabilitation training process, the rope winding mechanism 3 is controlled by the control unit 11, the control unit 11 acquires the tensile strength of the rope 8 according to the tensile sensor, acquires the length of the rope 8 in the training process according to the displacement sensor 5, performs data analysis on the acquired electromyographic signals through the STM single chip microcomputer and calculation and analysis, and automatically controls the rotating speed of the motor 32 to realize automatic training; the motor 32 drives the sliding rod 35 to rotate, the bunching wheel 36 arranged on the sliding rod 35 rotates along with the rotation of the sliding rod 35, the bunching wheel 36 drives the cord 8 to indirectly drive the girdling belt 2 to move after passing through the first rotating wheel 6 and the second rotating wheel 7, the front, the rear, the left and the right directions of the girdling belt 2 are all connected with the cord winding mechanisms 3, and the four groups of cord winding mechanisms 3 jointly realize the movement of front, rear, left and right directions and the bending of waist, so as to realize the training; the four buffer mechanisms 4 are arranged in the front, back, left and right directions of the waistband 2 while the movement of the cotton rope 8 is depended on, the waistband 2 is fixed by connecting the buffer mechanisms 4 with the fixed block 12, and the phenomenon that the lumbar vertebra is uncomfortable or damaged due to large-amplitude movement and the recovery efficiency is influenced is prevented; in order to further increase the buffering effect, prevent errors,the safety of training is improved, the spring 43 compresses the spring 43, the thread pitch d is 1.8-6.5mm, the length l is 260-500mm, and the pitch t is 3.6-10.3 mm; the pitch t and the pitch d meet the condition that t.d is more than or equal to 36 and is less than or equal to 85mm2. When t.d is less than 36, the energy storage provided by the elasticity of the spring 43 is insufficient, the spring is not easy to rebound after compression, and the buffering effect is poor; when t.d is more than 85, the spring 43 is too hard to play a role of compression, the training amplitude range is small, and the corresponding training effect cannot be achieved; in order to further improve the buffering effect and achieve a proper training movement amplitude and prevent the trainer from being accidentally injured by an overlarge amplitude, the pitch t, the length l and the pitch d of the spring 43 satisfy the following relations: d ═ λ · (l/t); wherein, the lambda is the adjusting coefficient of the spring 43 and the value range is 0.66-3.28.
The device obtained by the technical scheme is an auxiliary device for human lumbar vertebra recovery, a traction auxiliary combined monitoring system is arranged, so that a doctor can know the recovery condition of a patient in time conveniently, the doctor directly controls the recovery condition of the patient and improves a training plan, and the recovery training efficiency is improved; the optimal waist electromyographic signal characteristics are obtained through signal characteristic extraction and classification, waist training strength and muscle fatigue data are obtained after the waist electromyographic signal characteristics are processed by a single chip microcomputer, unstable electromyographic signals are removed after the waist electromyographic signal characteristics are processed, measurement errors of the muscle fatigue are reduced, and training recovery efficiency is improved; feature extraction is carried out on the electromyographic signals to obtain muscle fatigue degree during rehabilitation training and muscle action change data features during training, impurity signals and electromyographic signals with weak strength are removed, and data accuracy is improved; the data are classified through a BP neural network system, and the convergence of data characteristics is improved through limiting the number of nodes of a hidden layer, so that the rehabilitation training efficiency is improved; when the waistband moves by means of the thread ropes, four buffer mechanisms are arranged in the front direction, the rear direction, the left direction and the right direction of the waistband, the four buffer mechanisms are connected with the fixed block through the buffer mechanisms, the four buffer mechanisms are mutually combined and act together to fix the waistband, and the phenomenon that the recovery efficiency is influenced due to the fact that the waistband is uncomfortable or damaged due to large-amplitude movement is prevented; through the limitation to the pitch, the length and the thread pitch of the spring, the buffer effect is further improved, the proper exercise range is achieved, and the trainer is prevented from being accidentally injured due to overlarge range.
Other technical solutions not described in detail in the present invention are prior art in the field, and are not described herein again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and it will be apparent to those skilled in the art that various modifications and variations can be made in the present invention; any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An auxiliary device for lumbar vertebra recovery of a human body; comprises a frame body (1), a waistband (2) and a control unit (11); the waist binding belt is characterized in that a fixed block (12) is arranged at the center of the side face of the frame body (1), a plurality of groups of rope winding mechanisms (3) are arranged above the frame body (1), each group of rope winding mechanisms (3) is provided with a rope (8), the rope winding mechanisms (3) are connected with the waist binding bag through the ropes (8), the waist binding belt (2) is arranged at the center of the inside of the frame body (1), and the waist binding belt (2) is used for fixing the waist of a rehabilitation patient; a buffer mechanism (4) is arranged between the waistband (2) and the fixed block (12), the buffer mechanism (4) comprises a sleeve (41), one end of the sleeve (41) is connected with the fixed block (12), a guide rod (42) is sleeved in the other end of the sleeve (41), the outer wall of one end, arranged in the sleeve (41), of the guide rod (42) is bent outwards, the outer wall of one end, close to the guide rod (42), of the sleeve (41) is bent inwards, the guide rod (42) is prevented from slipping out of the inner part of the sleeve (41), and the other end of the guide rod (42) is connected with the waistband (2) through a universal ball head; the guide rod (42) and the sleeve (41) are externally provided with the sleeve (41), one end of the spring (43) is connected with the fixed block (12), and the other end of the spring (43) is connected with the waistband (2);
the auxiliary device for lumbar vertebra recovery further comprises a remote monitoring system, the remote monitoring system and a doctor can directly control the recovery condition of a patient and set a training plan, and the remote monitoring system comprises a single chip microcomputer, a display module, a Bluetooth module, a wifi module, a cloud server, a mobile terminal and an electromyographic signal sensor; a doctor sets a training plan through the mobile terminal, and transmits the training plan to the control unit (11) through the cloud server, so that the patient can perform rehabilitation training; when the training intensity set by the doctor is reached, the control unit (11) controls the voice system to send out voice prompt.
2. The auxiliary device for human lumbar vertebra recovery of claim 1, wherein the rope winding mechanism (3) comprises a housing (31), one end of the housing (31) is provided with a motor (32), an output shaft of the motor (32) is connected with a sliding rod (35) through a coupling, the sliding rod (35) is arranged inside the housing (31), the sliding rod (35) is rotatably connected with the inner wall of the housing (31) through a bearing, and the sliding rod (35) is provided with a bunching wheel (36); the motor is characterized in that an output shaft of the motor (32) is provided with a first gear (33), the first gear (33) is meshed with a second gear (34), one side of the second gear (34) is connected with a screw rod (37), and the second gear (34) drives the screw rod (37) to rotate.
3. The auxiliary device for human lumbar vertebra recovery as claimed in claim 2, wherein said lead screw (37) is disposed below said slide rod (35), said lead screw (37) is rotatably connected with the inner wall of the housing (31) through a bearing; an internal threaded pipe (38) is arranged on the screw rod (37), the internal threaded pipe (38) is matched with the screw rod (37) and rotatably sleeved with the screw rod, the outer side wall of the internal threaded pipe (38) is connected with a connecting rod (39), and the other end of the connecting rod (39) is connected with the bunching wheel (36); the bunching wheel (36) rotates to bunch, and meanwhile, the lead screw (37) drives the internal threaded pipe (38), so that the bunching wheel (36) is driven to slide on the sliding rod (35).
4. An aid for human lumbar spine recovery as claimed in claim 3, characterized in that said second gear (34) has a diameter greater than the diameter of the first gear (33); a first rotating wheel (6) is arranged at the top of the fixed block (12), a displacement sensor (5) is arranged below the first rotating wheel (6), a second rotating wheel is arranged at the position, close to the middle part, of the fixed block (12), and the second rotating wheel (7) and the waistband (2) are located on the same horizontal plane; the cord (8) is wound through the first rotating wheel (6) and the second rotating wheel (7) through the cord wheel (36), and the other end of the cord (8) is connected with the waistband (2); the tension sensor is arranged on the wire rope (8), and the wire rope (8) only provides tension and does not provide elasticity, so that the stability is enhanced; the strength of the recovery training is adjusted by controlling the length and the tensile strength of the thread rope (8).
5. The auxiliary device for human lumbar vertebra rehabilitation according to claim 1, characterized in that a telescopic cylinder (9) is arranged at the bottom of the frame body (1), a pedal plate (10) is connected above the telescopic cylinder (9), and the pedal plate (10) is arranged in the forward direction of the waist belt (2) and used for fixing the feet of a rehabilitation patient, sharing the pressure of the waist of the patient and preventing the waist from secondary damage in the training process.
6. The auxiliary device for human lumbar vertebra recovery according to claim 1, wherein in the remote monitoring system, the electromyographic signal sensing is arranged inside the waist belt (2), the waist electromyographic signal is obtained through the electromyographic signal sensing, and the electromyographic signal is preprocessed, extracted and classified; the optimal waist electromyographic signal characteristic is obtained through processing, waist training strength and muscle fatigue degree data are obtained through processing of the single chip microcomputer, unstable electromyographic signals are removed after the electromyographic signal processing, measurement errors of the muscle fatigue degree are reduced, and training recovery efficiency is improved.
7. The auxiliary device for human lumbar vertebra recovery as claimed in claim 6, wherein the electromyographic signal features are extracted from multi-dimensional vectors capable of representing muscle movement features hidden in the original electromyographic signals, and time domain features, frequency domain features, and time-frequency domain features of the original electromyographic signals are extracted.
8. The auxiliary device for human lumbar vertebra recovery as claimed in claim 7, wherein the extracted electromyographic signal characteristic values are classified, and a BP neural network is adopted to classify time domain characteristics, frequency domain characteristics and time-frequency domain characteristics of the original electromyographic signals, so as to reduce data errors of the electromyographic signal characteristics.
9. The auxiliary device for human lumbar vertebra recovery as claimed in claim 6, wherein in the remote monitoring system, the obtained data of the training intensity of the waist and the muscle fatigue degree are displayed on the LED display screen, and are sent to the cloud server according to the wifi module, the cloud server is sent to the mobile terminal of the doctor through the TCP network protocol, and the doctor can timely know the training condition and the recovery degree of the patient through the mobile terminal app, so as to timely change a new rehabilitation training plan.
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