Hand function rehabilitation system controlled by nerve interface

Abstract

The invention provides a hand function rehabilitation system controlled by a neural interface. The hand function rehabilitation system comprises a myoelectricity collecting and processing device, an exoskeleton glove and a control host. The myoelectricity collecting and processing device is connected with the control host and used for collecting myoelectricity signals of a forearm, the myoelectricity collecting and processing device processes and analyzes the myoelectricity signals to obtain control signals, and the control signals are sent to the control host; the control host is connected with the exoskeleton gloves and controls the exoskeleton gloves to make fist-clenching or stretching motions according to the received control signals. According to the hand function rehabilitation system controlled by the neural interface, the action intention of a patient can be recognized, the patient can complete the training action according to the will of the patient, the compensatory ability of the nervous system of the patient is easily stimulated, and therefore the rehabilitation training effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a hand function rehabilitation system controlled by a neural interface. Background Art

[0002] Surface electromyography (SEMG) is a weak electrical signal. By collecting and amplifying the surface electromyography of the human body and converting it into analog-to-digital signals to obtain its signal strength, it can be used as an important basis for judging the degree of human muscle movement. At present, there are more and more patients with finger joint dysfunction due to stroke, joint surgery, various diseases, sports accidents, etc. If exercise intervention is not carried out in time, it is easy for the patient's hand muscles to atrophy and joints to become stiff. At present, the rehabilitation treatment of finger joint dysfunction can be achieved through hand function rehabilitation devices. By driving the patient's fingers to perform continuous grasping movements, the mobility of the finger joints can be improved, the natural resilience of the human body can be stimulated, and the compensatory effect of tissues can be exerted to help patients with finger joint dysfunction to restore their autonomous movement ability as soon as possible.

[0003] The existing hand function rehabilitation device is mainly composed of an exoskeleton pneumatic glove and a pneumatic host. Under the instruction of the controller, the opening and closing of the air pump and the air valve in the host are controlled to drive the exoskeleton pneumatic glove to drive the fingers to perform continuous grasping movements. Under the guidance of the continuous passive motion theory, a certain rehabilitation training effect can be obtained. However, the passive training mode lacks the patient's real active participation, and the rehabilitation training effect is general. Summary of the invention

[0004] The purpose of the present invention is to provide a hand function rehabilitation system controlled by a neural interface, which can identify the patient's movement intention, enable the patient to complete the training movements according to his or her own wishes, and easily stimulate the compensatory ability of the patient's nervous system, thereby improving the rehabilitation training effect.

[0005] To achieve the above-mentioned object, in a first aspect, the present invention provides a hand function rehabilitation system controlled by a neural interface, comprising an electromyographic acquisition and processing device, an exoskeleton glove and a control host;

[0006] The electromyography acquisition and processing device is connected to the control host and is used to collect electromyography signals of the forearm. The electromyography acquisition and processing device processes and analyzes the electromyography signals to obtain control signals, and sends the control signals to the control host.

[0007] The control host is connected to the exoskeleton glove and controls the exoskeleton glove to make a fist or stretch according to the received control signal.

[0008] In some embodiments, the electromyographic acquisition and processing device includes an electromyographic acquisition electrode, a data processing module, and a communication isolation module;

[0009] The electromyographic acquisition electrode is electrically connected to the data processing module, and the electromyographic acquisition electrode is used to collect the electromyographic signal of the forearm and send the electromyographic signal to the data processing module;

[0010] The data processing module amplifies the electromyographic signal and then filters it to obtain a target electromyographic signal;

[0011] The data processing module is provided with an electromyographic signal threshold, and the target electromyographic signal is converted into a digital form and compared with the electromyographic signal threshold to obtain the control signal;

[0012] The communication isolation module is used to send the control signal to the control host.

[0013] In some embodiments, the electromyographic acquisition electrode includes a first electrode, a second electrode, and a reference electrode;

[0014] The first electrode and the second electrode are respectively used to collect the electromyographic signal of the forearm as a differential signal;

[0015] The data processing module amplifies the differential signal and then filters it to obtain the target electromyographic signal; wherein the reference electrode provides a reference level when the differential signal is amplified.

[0016] In some embodiments, the data processing module includes an instrumentation amplifier and a low noise amplifier electrically connected to each other;

[0017] The instrument amplifier amplifies the differential signal and then further amplifies it through the low noise amplifier to obtain an amplified signal.

[0018] In some embodiments, the data processing module further comprises a single chip microcomputer electrically connected to the low noise amplifier;

[0019] The amplified signal is filtered through the software power frequency notch and bandpass in the single chip microcomputer to obtain the target electromyographic signal.

[0020] In some embodiments, before rehabilitation training is performed, the electromyographic signal acquisition and processing device is pre-calibrated to obtain the electromyographic signal threshold.

[0021] In some embodiments, the electromyographic acquisition and processing device is pre-calibrated, and the method for obtaining the electromyographic signal threshold includes:

[0022] Wearing the first electrode and the second electrode on the patient's forearm;

[0023] Ask the patient to try to make a fist with all his strength, and the first electrode and the second electrode collect electromyographic signals in real time;

[0024] The electromyographic signal collected in real time is sent to the data processing module for processing to obtain the electromyographic signal threshold.

[0025] In some embodiments, the electromyographic signal collected in real time is sent to the data processing module for processing, and obtaining the electromyographic signal threshold comprises:

[0026] Collecting electromyographic signals in real time within 6 seconds to obtain electromyographic signals for calculating the electromyographic signal threshold;

[0027] Amplifying the electromyographic signal used to calculate the electromyographic signal threshold and filtering it to obtain a filtered signal;

[0028] The filtered signal is converted into an analog-to-digital signal to obtain an ADC value; wherein the ADC values ​​within every 256 milliseconds are summed to obtain each summed value, and the summed value is stored in the data processing module;

[0029] The maximum value in the summed value is taken as the calibration result, and multiplied by a coefficient to obtain the electromyographic signal threshold.

[0030] In some embodiments, during rehabilitation training, the electromyographic acquisition electrode collects electromyographic signals of the forearm in real time, and obtains the target electromyographic signals in real time through the data processing module;

[0031] The real-time target electromyographic signal is converted into analog-to-digital to obtain the ADC value during training; wherein, the ADC value within every 0.5 seconds is summed to obtain the summed value during training, and the summed value during training is compared with the electromyographic signal threshold to obtain the control signal.

[0032] In some embodiments, the control signal includes a fist clenching signal, a hold still signal, and a stretching signal;

[0033] When the sum value during the training is greater than the electromyographic signal threshold, the fist-clenching signal is obtained, and the fist-clenching signal is used to control the exoskeleton glove to make a fist-clenching action;

[0034] When the sum value during training is less than the electromyographic signal threshold, the stretching signal is obtained, and the stretching signal is used to control the exoskeleton glove to perform a stretching action.

[0035] In some embodiments, the electromyography acquisition and processing device further includes a power isolation module, and the power isolation module is used to obtain electrical energy from the control host.

[0036] The beneficial effects of a neural interface-controlled hand function rehabilitation system provided by the present invention are:

[0037] 1. It can actively identify the patient's movement intentions, allowing the patient to complete the training movements according to his or her own wishes, easily stimulate the compensatory ability of the patient's nervous system, and thus improve the effect of rehabilitation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the structure of a hand function rehabilitation system controlled by a neural interface according to an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of the electromyography acquisition and processing device provided by the present invention;

[0040] Figure 3 This is a flow chart of pre-calibration of the hand function rehabilitation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0042] Figure 1 This is a schematic diagram of the structure of a hand function rehabilitation system controlled by a neural interface according to an embodiment of the present invention.

[0043] refer to Figure 1 As shown, an embodiment of the present invention provides a hand function rehabilitation system controlled by a neural interface, including an electromyography acquisition and processing device, an exoskeleton glove and a control host. The exoskeleton glove can be pneumatically controlled. The electromyography acquisition and processing device is connected to the control host, and is used to collect the electromyography signal of the patient's forearm. The electromyography acquisition and processing device processes and analyzes the electromyography signal to obtain a control signal, and sends the control signal to the control host. The control host and the exoskeleton glove can be pneumatically connected, and the control host can be an air pump controller. The control host pneumatically controls the exoskeleton glove to make a fist or stretch according to the received control signal.

[0044] In this embodiment, the electromyographic acquisition and processing device can identify the patient's movement intention, so that the patient can complete the training movement according to his own wishes, thereby improving the rehabilitation training effect.

[0045] Figure 2 This is a schematic diagram of the electromyography acquisition and processing device provided by the present invention.

[0046] refer to Figure 2 As shown, further, the electromyographic acquisition and processing device includes electromyographic acquisition electrodes, a data processing module and a communication isolation module which are electrically connected in sequence.

[0047] Wherein, the electromyographic acquisition electrode is electrically connected to the data processing module. During rehabilitation training, the electromyographic acquisition electrode is used to collect the electromyographic signal of the patient's forearm and send the electromyographic signal to the data processing module. The data processing module amplifies the electromyographic signal and filters it to obtain the target electromyographic signal. The data processing module is provided with an electromyographic signal threshold, and the target electromyographic signal is converted into a numerical value after analog-to-digital conversion and compared with the electromyographic signal threshold to obtain the control signal. The communication isolation module is used to send the control signal to the control host, thereby realizing the control of the exoskeleton glove.

[0048] In some embodiments, the electromyographic acquisition electrode includes a first electrode, a second electrode, and a reference electrode. During rehabilitation training, the first electrode and the second electrode are attached to the patient's forearm to respectively collect electromyographic signals of the patient's forearm, and the two collected electromyographic signals are sent to the data processing module as differential signals. The data processing module amplifies the differential signal and then filters it to obtain the target electromyographic signal. The reference electrode provides a reference level when the differential signal is amplified.

[0049] Specifically, the data processing module includes an instrument amplifier and a low-noise amplifier electrically connected to each other. The instrument amplifier amplifies the differential signal by 50 times and then amplifies it by 50 times through the low-noise amplifier to obtain an amplified signal. The amplified signal is then filtered through a 50 Hz power frequency notch and a 10-400 Hz bandpass filter by software in the single-chip microcomputer to remove power frequency interference in the amplified signal and obtain the target electromyography signal.

[0050] It should be noted that, since each patient has a different physique, before rehabilitation training, the electromyographic acquisition and processing device is pre-calibrated for each patient to obtain the electromyographic signal threshold corresponding to the patient.

[0051] Figure 3 This is a flow chart of pre-calibration of the hand function rehabilitation system according to an embodiment of the present invention.

[0052] refer to Figure 3 As shown, the electromyographic acquisition and processing device is pre-calibrated, and the method for obtaining the electromyographic signal threshold includes:

[0053] S301: Wear the first electrode and the second electrode on the patient's forearm.

[0054] The electromyographic acquisition and processing device may be provided with a switch button and an indicator light. When the switch button is pressed, the indicator light will light up, indicating that the first electrode and the second electrode are acquiring electromyographic signals.

[0055] S302: Ask the patient to try to make a fist with all his strength, and the first electrode and the second electrode collect electromyographic signals in real time.

[0056] In this step, during the electromyographic signal acquisition process, the patient is asked to try to make a fist with all his strength to obtain the electromyographic signal when the patient wants to make a fist.

[0057] S303: Sending the electromyographic signal collected in real time to the data processing module for processing to obtain the electromyographic signal threshold.

[0058] In this step, the real-time acquisition of the electromyographic signal generally lasts for less than 6 seconds to obtain the electromyographic signal used to calculate the electromyographic signal threshold.

[0059] Similarly, the electromyographic signal used to calculate the electromyographic signal threshold is amplified (amplified twice respectively) and then filtered to obtain a filtered signal;

[0060] The filtered signal is converted into an analog-to-digital signal to obtain an ADC value; wherein, the ADC values ​​within every 256 milliseconds are summed to obtain each summed value, and the summed value is stored in the data processing module.

[0061] The maximum value in the summed value is taken as the calibration result, and multiplied by a coefficient to obtain the electromyographic signal threshold.

[0062] In this embodiment, the coefficient is 0.8, that is, the maximum value in the summed value is multiplied by 0.8 to obtain the electromyographic signal threshold, and the calculated electromyographic signal threshold is stored in the single chip microcomputer.

[0063] When 6 seconds are up, the indicator light will go out, indicating that the electromyographic signal threshold has been calculated.

[0064] It is understandable that, in some embodiments, the time for collecting electromyographic signals may also be continuous within 5 seconds, 7 seconds or 8 seconds, and the specific time may be adaptively adjusted according to actual use.

[0065] After the electromyographic signal threshold is calculated, the patient can be arranged to undergo rehabilitation training. During the rehabilitation training, the electromyographic acquisition electrode collects the electromyographic signal of the forearm in real time, and obtains the target electromyographic signal in real time through the data processing module.

[0066] The real-time target electromyographic signal is converted into an analog-to-digital signal to obtain an ADC value during training; wherein, during rehabilitation training, the ADC values ​​obtained every 0.5 seconds are summed to obtain the summed value during training, and the summed value during training is compared with the electromyographic signal threshold to obtain the control signal, so that it can be determined based on the control signal whether the patient wants to make a fist or stretch the palm.

[0067] In some embodiments, the control signal includes a fist-clenching signal and an extension signal. When the sum value during the training is greater than the electromyographic signal threshold, the fist-clenching signal is obtained, and the fist-clenching signal is transmitted to the control host through the communication isolation module, so that the control host controls the exoskeleton glove to make a fist-clenching action.

[0068] When the sum value during training is less than the electromyographic signal threshold, the stretching signal is obtained, and the stretching signal is transmitted to the control host through the communication isolation module, so that the control host controls the exoskeleton glove to perform a stretching action.

[0069] In some embodiments, the electromyography acquisition and processing device further includes a power isolation module, and the power isolation module is used to obtain electrical energy from the control host.

[0070] In this embodiment, a lithium battery is provided in the control host to provide electric energy. And by isolating the power isolation module and the communication isolation module, signal crosstalk can be eliminated and the influence of power supply and communication on electromyography acquisition can be reduced.

[0071] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A hand function rehabilitation system controlled by a neural interface, It is characterized in that It includes electromyography acquisition and processing device, exoskeleton gloves and control host; The electromyography acquisition and processing device is connected to the control host and is used to collect electromyography signals of the forearm. The electromyography acquisition and processing device processes and analyzes the electromyography signals to obtain control signals, and sends the control signals to the control host. The control host is connected to the exoskeleton glove and controls the exoskeleton glove to make a fist or stretch according to the received control signal.

2. The hand function rehabilitation system according to claim 1, It is characterized in that The electromyography acquisition and processing device comprises an electromyography acquisition electrode, a data processing module and a communication isolation module; The electromyographic acquisition electrode is electrically connected to the data processing module, and the electromyographic acquisition electrode is used to collect the electromyographic signal of the forearm and send the electromyographic signal to the data processing module; The data processing module amplifies the electromyographic signal and then filters it to obtain a target electromyographic signal; The data processing module is provided with an electromyographic signal threshold, and the target electromyographic signal is converted into a digital form and compared with the electromyographic signal threshold to obtain the control signal; The communication isolation module is used to send the control signal to the control host.

3. The hand function rehabilitation system according to claim 2, It is characterized in that The electromyography acquisition electrode comprises a first electrode, a second electrode and a reference electrode; The first electrode and the second electrode are respectively used to collect the electromyographic signal of the forearm as a differential signal; The data processing module amplifies the differential signal and then filters it to obtain the target electromyographic signal; wherein the reference electrode provides a reference level when the differential signal is amplified.

4. The hand function rehabilitation system according to claim 3, It is characterized in that The data processing module includes an instrumentation amplifier and a low noise amplifier electrically connected to each other; The instrument amplifier amplifies the differential signal and then further amplifies it through the low noise amplifier to obtain an amplified signal.

5. The hand function rehabilitation system according to claim 4, It is characterized in that The data processing module also includes a single chip microcomputer electrically connected to the low noise amplifier; The amplified signal is filtered through the software power frequency notch and bandpass in the single chip microcomputer to obtain the target electromyographic signal.

6. The hand function rehabilitation system according to claim 5, It is characterized in that Before the rehabilitation training is performed, the electromyographic signal acquisition and processing device is pre-calibrated to obtain the electromyographic signal threshold.

7. The hand function rehabilitation system according to claim 6, It is characterized in that The electromyographic acquisition and processing device is pre-calibrated, and the method for obtaining the electromyographic signal threshold comprises: Wearing the first electrode and the second electrode on the patient's forearm; Ask the patient to try to make a fist with all his strength, and the first electrode and the second electrode collect electromyographic signals in real time; The electromyographic signal collected in real time is sent to the data processing module for processing to obtain the electromyographic signal threshold.

8. The hand function rehabilitation system according to claim 7, It is characterized in that The real-time collected electromyographic signal is sent to the data processing module for processing, and the electromyographic signal threshold is obtained, which includes: Collecting electromyographic signals in real time within 6 seconds to obtain electromyographic signals for calculating the electromyographic signal threshold; Amplifying the electromyographic signal used to calculate the electromyographic signal threshold and filtering it to obtain a filtered signal; The filtered signal is converted into an analog-to-digital signal to obtain an ADC value; wherein the ADC values ​​within every 256 milliseconds are summed to obtain each summed value, and the summed value is stored in the data processing module; The maximum value in the summed value is taken as the calibration result, and multiplied by a coefficient to obtain the electromyographic signal threshold.

9. The hand function rehabilitation system according to claim 2, It is characterized in that During rehabilitation training, the electromyographic acquisition electrode collects the electromyographic signal of the forearm in real time, and obtains the real-time target electromyographic signal through the data processing module; The real-time target electromyographic signal is converted into analog-to-digital to obtain the ADC value during training; wherein, the ADC value within every 0.5 seconds is summed to obtain the summed value during training, and the summed value during training is compared with the electromyographic signal threshold to obtain the control signal.

10. The hand function rehabilitation system according to claim 9, It is characterized in that The control signal includes a fist-clenching signal and a stretching signal; When the sum value during the training is greater than the electromyographic signal threshold, the fist-clenching signal is obtained, and the fist-clenching signal is used to control the exoskeleton glove to make a fist-clenching action; When the sum value during training is less than the electromyographic signal threshold, the stretching signal is obtained, and the stretching signal is used to control the exoskeleton glove to perform a stretching action.

11. The hand function rehabilitation system according to claim 1, It is characterized in that The electromyography acquisition and processing device also includes a power isolation module, and the power isolation module is used to obtain electrical energy from the control host.

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