CN115300325A - Wearable limb rehabilitation training system - Google Patents
Wearable limb rehabilitation training system Download PDFInfo
- Publication number
- CN115300325A CN115300325A CN202210950988.2A CN202210950988A CN115300325A CN 115300325 A CN115300325 A CN 115300325A CN 202210950988 A CN202210950988 A CN 202210950988A CN 115300325 A CN115300325 A CN 115300325A
- Authority
- CN
- China
- Prior art keywords
- module
- training
- signal
- wearable
- rehabilitation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H1/00—Apparatus for passive exercising; Vibrating apparatus ; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
- A61H1/02—Stretching or bending or torsioning apparatus for exercising
-
- 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
- A61B5/25—Bioelectric electrodes therefor
- A61B5/251—Means for maintaining electrode contact with the body
- A61B5/256—Wearable electrodes, e.g. having straps or bands
-
- 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]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- 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
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
-
- 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
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B21/00—Exercising apparatus for developing or strengthening the muscles or joints of the body by working against a counterforce, with or without measuring devices
- A63B21/00178—Exercising apparatus for developing or strengthening the muscles or joints of the body by working against a counterforce, with or without measuring devices for active exercising, the apparatus being also usable for passive exercising
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B21/00—Exercising apparatus for developing or strengthening the muscles or joints of the body by working against a counterforce, with or without measuring devices
- A63B21/00181—Exercising apparatus for developing or strengthening the muscles or joints of the body by working against a counterforce, with or without measuring devices comprising additional means assisting the user to overcome part of the resisting force, i.e. assisted-active exercising
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
- A63B24/0075—Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
- A63B24/0087—Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
-
- 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
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/12—Driving means
- A61H2201/1207—Driving means with electric or magnetic drive
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/16—Physical interface with patient
- A61H2201/1602—Physical interface with patient kind of interface, e.g. head rest, knee support or lumbar support
- A61H2201/165—Wearable interfaces
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/50—Control means thereof
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/50—Control means thereof
- A61H2201/5058—Sensors or detectors
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/50—Control means thereof
- A61H2201/5058—Sensors or detectors
- A61H2201/5069—Angle sensors
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2230/00—Measuring physical parameters of the user
- A61H2230/08—Other bio-electrical signals
- A61H2230/085—Other bio-electrical signals used as a control parameter for the apparatus
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
- A63B2024/0065—Evaluating the fitness, e.g. fitness level or fitness index
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- 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
- A63B24/0087—Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load
- A63B2024/0093—Electric or electronic controls for exercising apparatus of groups A63B21/00 - A63B23/00, e.g. controlling load the load of the exercise apparatus being controlled by performance parameters, e.g. distance or speed
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/18—Inclination, slope or curvature
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2220/00—Measuring of physical parameters relating to sporting activity
- A63B2220/80—Special sensors, transducers or devices therefor
- A63B2220/803—Motion sensors
-
- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B2230/00—Measuring physiological parameters of the user
- A63B2230/08—Measuring physiological parameters of the user other bio-electrical signals
- A63B2230/085—Measuring physiological parameters of the user other bio-electrical signals used as a control parameter for the apparatus
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Physical Education & Sports Medicine (AREA)
- Biophysics (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- Veterinary Medicine (AREA)
- Artificial Intelligence (AREA)
- Pathology (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Psychiatry (AREA)
- Signal Processing (AREA)
- Orthopedic Medicine & Surgery (AREA)
- Fuzzy Systems (AREA)
- Evolutionary Computation (AREA)
- Physiology (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- Human Computer Interaction (AREA)
- Rehabilitation Tools (AREA)
- Epidemiology (AREA)
- Pain & Pain Management (AREA)
- Rehabilitation Therapy (AREA)
- Software Systems (AREA)
Abstract
The invention relates to a wearable limb rehabilitation training system, which comprises: the wearable myoelectricity collecting module, the signal processing module, the main control module, the motion state classifying module, the auxiliary power module and the rehabilitation evaluating module. The invention can ensure the training safety of the rehabilitation trainer and the continuity of the rehabilitation plan by dynamically evaluating the training state of the rehabilitation trainer, and can also evaluate the fatigue state of muscles, adjust the training intensity and ensure the continuous effect of the rehabilitation.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a wearable limb rehabilitation training system.
Background
In the current society, a large number of people with impaired physical activity caused by injuries exist, and with the aggravation of aging, a large number of old people with weakened physical activity also exist, so that various rehabilitation exercises can be carried out to enhance the physical activity of the people. Rehabilitation training includes a variety of categories such as task rehabilitation training, language rehabilitation training, basic function rehabilitation training, athletic rehabilitation training, and the like. Motor rehabilitation training is an emerging frontier discipline combining sports, health, and medicine. The limb rehabilitation training is carried out on the user needing limb rehabilitation, so that the user can be effectively assisted to regain the motion function of the limb, and the limb rehabilitation training device has great significance for the user. During exercise rehabilitation training, the limb load of a patient cannot exceed a reasonable range in order to prevent secondary injury, but cannot be too low in order to obtain a good training effect.
The wearable auxiliary rehabilitation training robot is designed by imitating the structure of human limbs and assists the disabled and the people with impaired limb mobility to perform limb actions. The rehabilitation assisting system can be worn by normal people to improve the load bearing capacity of the human body. Therefore, in the actual weight training, due to the lack of effective monitoring means, the patient is often difficult to follow the reasonable weight-bearing strength given by the doctor, the rehabilitation effect is affected, and meanwhile, the safety is difficult to guarantee.
The technology of collecting human body electric signals is a new way of establishing an information channel between a human body and a computer. The human body electric signal acquisition technology is used for acquiring and analyzing hand electromyographic signals of a wearer, extracting rich characteristics contained in the electromyographic signals, further judging the hand action state of the wearer, and possibly being used in the fields of prosthesis motion control, clinical hand disease detection, clinical diagnosis of motion injury, athlete training, medical rehabilitation and daily life activity improvement.
Disclosure of Invention
The invention aims to provide a wearable limb rehabilitation training system to assist a patient in rehabilitation training.
In order to achieve the purpose, the invention provides the following scheme:
a wearable limb rehabilitation training system, comprising:
wearable myoelectricity collection module: the system comprises a signal processing module, a data processing module and a data processing module, wherein the signal processing module is used for acquiring myoelectric signals of limb actions of a wearer and sending the myoelectric signals to the signal processing module;
the signal processing module: the wearable electromyographic signal acquisition unit is used for extracting the electromyographic signal provided by the wearable electromyographic signal acquisition unit, generating a motion feedback signal and sending the motion feedback signal to the main control module;
the main control module: the real-time myoelectric signal and the action feedback signal are used for generating a control signal;
a motion state classification module: the system comprises a signal processing module, a signal processing module and a signal processing module, wherein the signal processing module is used for receiving the electromyographic signals, classifying the electromyographic signals by combining a deep learning convolutional neural network algorithm and obtaining biofeedback signals;
an auxiliary power module: the auxiliary power is provided for the limb movement of the wearer according to the control signal sent by the main control module;
a rehabilitation evaluation module: and evaluating the rehabilitation state of the wearer based on the biofeedback signal, sending an adjusting signal to the main control module, correspondingly adjusting the auxiliary power module, and further adjusting the limb movement of the wearer during rehabilitation movement.
Preferably, the wearable myoelectricity acquisition module comprises an electrode patch and a lead wire, wherein the electrode patch is used for acquiring myoelectricity signals of muscles at different parts of a wearer and is connected with the signal processing module through the lead wire.
Preferably, the signal processing module includes a signal adjusting unit, and the signal adjusting unit is configured to perform buffering, amplification, filtering, and level adjustment on the electromyographic signals, and output the adjusted electromyographic voltages to the main control module.
Preferably, the main control module includes:
a storage unit: the system is used for storing standard joint motion height information and standard training angle information of rehabilitation training;
a comparison unit: the system is used for receiving the joint movement height information and the current training angle information of the wearer during current movement, comparing the current height information with the standard joint movement height information, and comparing the current training angle information with the standard training angle information.
Preferably, the main control module further comprises an alarm unit, the alarm unit is connected with the comparison unit, and when the real-time height information is inconsistent with the standard joint movement height information, the alarm unit sends movement height alarm information; the comparison unit is also used for comparing the current training angle information with the standard training angle information, and when the current training angle is larger than the standard training angle, the alarm unit sends motion angle alarm information.
Preferably, the motion state classification module includes a classification unit, the classification unit is configured to classify the electromyographic signals correspondingly to obtain electromyographic signals of different limb portions, and the classification process includes: the wearable electromyographic signal acquisition module is used for acquiring an electromyographic signal of a wearer, the acquired electromyographic signal is periodically transmitted, then the actual voltage value of the electromyographic signal is analyzed through a conversion algorithm, finally a deep learning convolutional neural network model is trained, after the training is finished, the electromyographic signal is sent to the motion state classification module through a Bluetooth wireless transmission circuit, and the actual voltage value of the electromyographic signal is analyzed from the conversion result through the conversion algorithm again to perform online real-time classification.
Preferably, the deep learning convolutional neural network model is subjected to full supervision training through TensorFlow, the initial learning rate of the model is set to be 0.005, the learning rate is exponentially attenuated to prevent the fixed learning rate from failing to obtain the optimal model, 300 periods of cyclic training are performed in total, the Batchsize is 128, and an early stopping mechanism of Earlystopping is set to obtain the model with the optimal learning rate in all periods of training.
Preferably, the auxiliary power module comprises a joint restorer, a motor driving device, a sleeve and a driver, one end of the sleeve is arranged on the outer side of the front end of the joint restorer, an inner threaded hole is formed in the other end of the sleeve, the motor driving device is correspondingly arranged on the outer side of the rear end of the joint restorer, and a transmission screw rod driven by a driving motor is arranged in the motor driving device and extends into the inner threaded hole.
Preferably, the rehabilitation evaluation module is used for evaluating the rehabilitation state of muscles according to the change trend of the rhythm energy of the electromyographic signals in the biofeedback signals, generating a muscle state regulating coefficient to the main control module, and regulating the auxiliary power module through the main control module to regulate the strength in the rehabilitation training process.
The invention has the beneficial effects that:
the invention can ensure the training safety of the rehabilitation trainer and the continuity of the rehabilitation plan by dynamically evaluating the training state of the rehabilitation trainer, and can also evaluate the fatigue state of muscles, adjust the training intensity and ensure the continuous effect of the rehabilitation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a wearable limb rehabilitation training system according to an embodiment of the present invention.
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.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
A wearable limb rehabilitation training system, fig. 1, comprising:
wearable myoelectricity collection module: the system comprises a signal processing module, a data processing module and a data processing module, wherein the signal processing module is used for acquiring myoelectric signals of limb actions of a wearer and sending the myoelectric signals to the signal processing module;
a signal processing module: the wearable electromyographic signal acquisition unit is used for extracting the electromyographic signal provided by the wearable electromyographic signal acquisition unit, generating a motion feedback signal and sending the motion feedback signal to the main control module;
the main control module: the wearable electromyography acquisition module is used for generating a real-time electromyography signal based on the action feedback signal generated by the wearable electromyography acquisition module and the signal processing module;
a motion state classification module: the system comprises a signal processing module, a signal processing module and a signal processing module, wherein the signal processing module is used for receiving the electromyographic signals, classifying the electromyographic signals by combining a deep learning convolutional neural network algorithm and obtaining biofeedback signals;
an auxiliary power module: the auxiliary power is provided for the limb movement of the wearer according to the instruction information sent by the main control module;
a rehabilitation evaluation module: and evaluating the rehabilitation state of the wearer based on the biofeedback signal, sending an adjusting signal to the main control module, correspondingly adjusting the auxiliary power module, and further adjusting the limb movement of the wearer during rehabilitation movement.
The wearable myoelectricity acquisition module comprises an electrode patch for acquiring myoelectricity signals and a lead wire, wherein the electrode patch is used for acquiring the myoelectricity signals of muscles at different parts of a wearer and is connected with the signal processing module through the lead wire. The electrode patch and the lead wire thereof (the electrode patch collects sEMG electromyographic signals of different limb muscles, is connected with the signal processing module through the lead wire and a PJ313B interface and is used for collecting and transmitting bioelectricity signals, the electrode patch is pasted on the hand of a wearer, and the muscles collected by the electromyographic collecting module are deltoid muscle, sacrospinalis muscle, biceps brachii muscle, triceps brachii muscle, tibialis anterior muscle, triceps cruris muscle, brachioradialis muscle, flexor carpi radialis muscle, extensor carpi radialis longus muscle, flexor carpi ulnaris muscle and extensor carpi ulnaris muscle.
The signal processing module comprises a signal adjusting unit, the signal adjusting unit is used for buffering, amplifying, filtering and level adjusting the electromyographic signals, and the electromyographic voltages obtained after adjustment are output to the main control module. The signal adjusting unit receives muscle voltage collected by the electrode patch, and the high common mode rejection ratio analog input AD module of the sEMG electromyogram signal is used for measuring the electromyogram sEMG. The adjusting unit further comprises an amplifying circuit and a feedback circuit. The amplifying circuit may be used to amplify the electromyographic signals.
In a further optimized scheme, the main control module comprises:
a storage unit: the device is used for storing joint motion height information and standard training angle information during standard rehabilitation training;
a comparison unit: and the system is used for receiving the motion height information and the current training angle information of each joint of the wearer during current motion and comparing the current height information with the standard motion height information.
The master control module also comprises an alarm unit, the alarm unit is connected with the comparison unit, and when the joint movement height information is inconsistent with the standard joint movement height information, the alarm unit sends movement height alarm information; the comparison unit is also used for comparing the current training angle information with the standard training angle information, and when the current training angle is larger than the standard training angle, the alarm unit is used for sending motion angle alarm information.
The alarm unit judges whether the joint movement height information is consistent with the standard joint movement height information or not according to a preset limb movement data threshold value, if not, the alarm unit sends movement height alarm information, sends an alarm request to one or more terminal equipment APPs associated with the main control module through the communication module, and sends the alarm request to the storage unit through the communication module so as to forward the alarm request to one or more terminal equipment APPs associated with the control chip; when the user carries out excessive training, the current training angle is larger than the standard training angle, the warning module can prompt the user to adjust in time, and the injury of rehabilitation training caused by careless excessive training is avoided.
In a further optimized scheme, the physiological status analysis module comprises a classification unit, the classification unit is used for performing corresponding classification based on the received electromyographic signals to obtain the electromyographic signals of different limb parts, and the classification process comprises the following steps: the method comprises the steps of collecting an electromyographic signal of a wearer through a wearable electromyographic signal collection module, periodically transmitting the collected electromyographic signal, analyzing an actual voltage value of the electromyographic signal through a conversion algorithm, finally training a deep learning convolutional neural network model, sending the electromyographic signal to a physiological state analysis module through a Bluetooth wireless transmission circuit after training is finished, analyzing the actual voltage value of the electromyographic signal from a conversion result through the conversion algorithm again, sending the actual voltage value to the physiological state analysis module, and performing online real-time classification.
The deep learning convolutional neural network model carries out full supervision training on the deep convolutional neural network model through TensorFlow, the initial learning rate of the model is set to be 0.005, the learning rate is attenuated in an exponential mode so as to prevent the fixed learning rate from obtaining the optimal model, 300 periods of cyclic training are carried out totally, the Batchsize is 128, and an early stopping mechanism of Earlystopping is set for obtaining the model with the optimal learning rate in all periods of training.
In the deep convolutional neural network model, forward propagation and backward propagation are mainly utilized. Firstly, analyzing original sEMG electromyographic signal samples through all CNN layers, and forward propagating sEMG electromyographic signal data sets to obtain output values. The error between the output value and the desired value is then calculated to determine the accuracy of the output. Next, the weight values are modified using an error back-propagation process. These two processes are repeatedly performed by the iterative operating system until the loss value of the network is minimized. The weighting values are then modified using a gradient descent algorithm. In order to prevent the final model from being not the optimal model due to excessive modification of the weight, in this embodiment, an early stopping mechanism is set, and a model with the minimum test loss function is set as the currently trained optimal model; the deep convolutional neural network model comprises four branches, and the input of each branch is the digital filtered sEMG electromyographic signal; the four branches are different convolution pooling layers, the four convolution pooling layers run in parallel, time domain and frequency domain features of the four branches are fused before entering the full connection layer, and after feature fusion, 20 hand actions are well classified.
According to the further optimization scheme, the auxiliary power module comprises a joint restorer, a motor driving device, a sleeve and a driver, one end of the sleeve is arranged on the outer side of the front end of the joint restorer, an inner threaded hole is formed in the other end of the sleeve, the motor driving device is correspondingly arranged on the outer side of the rear end of the joint restorer, a transmission screw rod driven by a driving motor is arranged in the motor driving device, and the transmission screw rod extends into the inner threaded hole.
The rehabilitation evaluation module is used for evaluating the rehabilitation state of muscles according to the change trend of the rhythm energy of the electromyographic signals in the biofeedback signals, generating a muscle state regulating coefficient to the main control module, and regulating the auxiliary power module through the main control module to regulate the strength of the rehabilitation training process.
When a wearer wears the rehabilitation training system to perform rehabilitation training, a real-time electromyographic signal is extracted from the rehabilitation training system to generate an electromyographic feedback signal to be sent to the main control module and generate a basic control signal of the auxiliary power module, when the corresponding electromyographic signal is increased, assistance is provided according to the direction confirmed by the electromyographic signal, and the assistance is provided according to the size of the electromyographic signal in proportion; extracting signals of the angle of the limb, the limb movement amplitude and the like, forming a rhythm signal of the limb movement through the signals, and providing the rhythm signal to the main control module; the storage unit and the comparison unit form electromyographic signal energy of each rhythm according to the rhythm and the electromyographic signal, further obtain energy trend changing according to the rhythm, and are used for evaluating muscle fatigue state of a rehabilitee, muscle fatigue state feedback is used, when the electromyographic rhythm energy and the electromyographic rhythm energy change rate are higher than an upper limit or lower than a lower limit, the system gives an alarm signal to remind a rehabilitation guardian to intervene and adjust rehabilitation training time, and the rehabilitation trainer is not in an over-fatigue state of the muscle; the rehabilitation evaluation module evaluates the rehabilitation state of muscles according to the change trend of the electromyographic signals, generates a muscle state regulating coefficient in a body state feedback signal to the main control module, actively adjusts the strength in the rehabilitation training process and ensures the effectiveness of the rehabilitation training.
The above-described embodiments are only intended to describe the preferred embodiments of the present invention, and not to limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (9)
1. A wearable limb rehabilitation training system, comprising:
wearable myoelectricity collection module: the system comprises a signal processing module, a data acquisition module and a data processing module, wherein the signal processing module is used for acquiring electromyographic signals of limb actions of a wearer and sending the electromyographic signals to the signal processing module;
the signal processing module: the wearable electromyography acquisition unit is used for extracting the electromyography signals provided by the wearable electromyography acquisition unit, generating action feedback signals and sending the action feedback signals to the main control module;
the main control module: the real-time myoelectric signal and the action feedback signal are used for generating a control signal;
a motion state classification module: the system comprises a signal processing module, a signal processing module and a signal processing module, wherein the signal processing module is used for receiving the electromyographic signals, classifying the electromyographic signals by combining a deep learning convolutional neural network algorithm and obtaining biofeedback signals;
an auxiliary power module: the auxiliary power is provided for the limb movement of the wearer according to the control signal sent by the main control module;
a rehabilitation evaluation module: and evaluating the rehabilitation state of the wearer based on the biofeedback signal, sending an adjusting signal to the main control module, correspondingly adjusting the auxiliary power module, and further adjusting the limb movement of the wearer during rehabilitation movement.
2. The wearable limb rehabilitation training system of claim 1, wherein the wearable myoelectric acquisition module comprises an electrode patch and a lead wire, wherein the electrode patch is used for acquiring myoelectric signals of muscles of different parts of a wearer and is connected with the signal processing module through the lead wire.
3. The wearable limb rehabilitation training system of claim 1, wherein the signal processing module comprises a signal adjusting unit, and the signal adjusting unit is used for buffering, amplifying, filtering and level adjusting the electromyographic signals, and outputting the electromyographic voltages obtained after adjustment to the main control module.
4. The wearable limb rehabilitation training system of claim 1, wherein the master control module comprises:
a storage unit: the system is used for storing standard joint motion height information and standard training angle information of rehabilitation training;
a comparison unit: the system is used for receiving the joint movement height information and the current training angle information of the wearer during current movement, comparing the current height information with the standard joint movement height information, and comparing the current training angle information with the standard training angle information.
5. The wearable limb rehabilitation training system of claim 4, wherein the master control module further comprises an alarm unit, the alarm unit is connected with the comparison unit, and when the real-time height information is inconsistent with the standard joint movement height information, the alarm unit sends movement height alarm information; the comparison unit is also used for comparing the current training angle information with the standard training angle information, and when the current training angle is larger than the standard training angle, the alarm unit sends motion angle alarm information.
6. The wearable limb rehabilitation training system of claim 1, wherein the motion state classification module comprises a classification unit, the classification unit is used for correspondingly classifying the electromyographic signals to obtain the electromyographic signals of different limb parts, and the classification process comprises the following steps: the method comprises the steps of collecting electromyographic signals of a wearer through a wearable electromyographic signal collection module, periodically transmitting the collected electromyographic signals, analyzing actual voltage values of the electromyographic signals through a conversion algorithm, finally training a deep learning convolutional neural network model, sending the electromyographic signals to a motion state classification module through a Bluetooth wireless transmission circuit after training is finished, analyzing the actual voltage values of the electromyographic signals from conversion results through the conversion algorithm again, and performing online real-time classification.
7. The wearable limb rehabilitation training system according to claim 6, wherein the deep learning convolutional neural network model is subjected to full supervision training on the deep learning convolutional neural network model through TensorFlow, the initial learning rate of the model is set to be 0.005, the learning rate is exponentially attenuated to prevent the fixed learning rate from obtaining the optimal model, 300 cycles of cyclic training are carried out, the Batchsize is 128, and an early stopping mechanism of Earlystopping is set for obtaining the model with the optimal learning rate in all the training cycles.
8. The wearable limb rehabilitation training system according to claim 1, wherein the auxiliary power module comprises a joint restorer, a motor driving device, a sleeve and a driver, one end of the sleeve is arranged on the outer side of the front end of the joint restorer, the other end of the sleeve is provided with an internal thread hole, the motor driving device is correspondingly arranged on the outer side of the rear end of the joint restorer, a transmission screw rod driven by a driving motor is arranged in the motor driving device, and the transmission screw rod extends into the internal thread hole.
9. The wearable limb rehabilitation training system of claim 8, wherein the rehabilitation evaluation module is configured to evaluate a rehabilitation status of a muscle according to a rhythm energy variation trend of an electromyographic signal in the biofeedback signal, generate a muscle status adjustment coefficient to the main control module, and adjust the auxiliary power module through the main control module to adjust the strength of the rehabilitation training process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210950988.2A CN115300325A (en) | 2022-08-09 | 2022-08-09 | Wearable limb rehabilitation training system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210950988.2A CN115300325A (en) | 2022-08-09 | 2022-08-09 | Wearable limb rehabilitation training system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115300325A true CN115300325A (en) | 2022-11-08 |
Family
ID=83861684
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210950988.2A Pending CN115300325A (en) | 2022-08-09 | 2022-08-09 | Wearable limb rehabilitation training system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115300325A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116269450A (en) * | 2023-03-21 | 2023-06-23 | 苏州海臻医疗器械有限公司 | Patient limb rehabilitation state evaluation system and method based on electromyographic signals |
-
2022
- 2022-08-09 CN CN202210950988.2A patent/CN115300325A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116269450A (en) * | 2023-03-21 | 2023-06-23 | 苏州海臻医疗器械有限公司 | Patient limb rehabilitation state evaluation system and method based on electromyographic signals |
| CN116269450B (en) * | 2023-03-21 | 2023-12-19 | 苏州海臻医疗器械有限公司 | Patient limb rehabilitation state evaluation system and method based on electromyographic signals |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Merletti et al. | Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions | |
| CN114748080B (en) | Method and system for detecting and quantifying sensory-motor function | |
| CN105054927A (en) | Biological quantitative assessment method for active participation degree in lower limb rehabilitation system | |
| CN113101134B (en) | Child lower limb movement auxiliary rehabilitation system based on power exoskeleton | |
| CN106730766A (en) | A kind of evaluation method of the adult motor's load based on heart rate data | |
| CN111317600A (en) | Artificial limb control method, device, system, equipment and storage medium | |
| CN110339024A (en) | Lower limb exoskeleton robot and its real-time gait switching method and storage device | |
| CN115300325A (en) | Wearable limb rehabilitation training system | |
| Xiong et al. | Measurement and analysis of human infant crawling for rehabilitation: a narrative review | |
| KR101492478B1 (en) | Locomotion Mode Pattern Recognition System using Fusion Sensor | |
| Ingraham et al. | Using wearable physiological sensors to predict energy expenditure | |
| KR20170098058A (en) | Personalized rehabilitation system based on biometrics | |
| CN117503153A (en) | Patient postoperative rehabilitation evaluation method based on artificial intelligence | |
| CN113397569A (en) | Intelligent knee joint neuromuscular assessment control system | |
| Chandrapal et al. | Preliminary evaluation of a lower-limb exoskeleton-stair climbing | |
| Ahmad et al. | Development of wearable electromyogram for the physical fatigue detection during aerobic activity | |
| JP2003325467A (en) | Intention communication assisting device and software for assisting intention communication | |
| CN203208022U (en) | Electroencephalograph | |
| Ahmad et al. | Monitoring and prediction of exhaustion threshold during aerobic exercise based on physiological system using artificial neural network | |
| RU2766764C1 (en) | Method for assessing muscular fatigue based on control of synergy patterns and device for implementation thereof | |
| Jeong et al. | Recognition of lower limb muscle EMG patterns by using neural networks during the postural balance control | |
| CN114145889B (en) | Intelligent artificial limb perception feedback adjusting system | |
| Zhang et al. | Simulation study on changes of EMG and physiological parameters of athletes under training state based on nano biomechanics analysis | |
| Zhu et al. | A method to improve the robustness of grasping pattern recognition for prosthetic hands | |
| Yu et al. | Analysis of stable semg features for bilateral upper limb motion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |