Artificial muscle detection system with sensing function

Abstract

The invention discloses an artificial muscle detection system with a sensing function, and relates to the technical field of artificial muscles, and the artificial muscle detection system comprises a signal collection module, a pain analysis module and an artificial muscle control module, the pain analysis module is used for analyzing the pain degrees of joints when fibers of the artificial muscles are located at different positions in the rehabilitation training process of a patient according to the pain feeling signals, and the artificial muscle control module is used for controlling resistance and auxiliary power when the fibers of the artificial muscles move. The signal collection module comprises an EEG electrode, a brain wave recording module, a waveform display module, a database, a signal transmission module and a position recording unit, the EEG electrode is electrically connected with the brain wave recording module and the waveform display module, and the brain wave recording module is electrically connected with the database.

Description

Technical Field

[0001] The present invention relates to the field of artificial muscle technology, in particular to an artificial muscle detection system with a sensing function. Background Art

[0002] The application of artificial muscles in rehabilitation training is primarily due to their flexible drive, responsiveness, and biomimetic properties, enabling them to assist, replace, or reconstruct the function of affected limbs. Artificial muscles can mimic the contraction and extension of natural muscles. Driven by electricity, heat, humidity, or deformation, they actively stretch or traction limb joints (such as the knee, ankle, and wrist), assisting patients in completing movements such as flexion and extension, and inversion and eversion. These muscles are used for passive training in the early stages of rehabilitation.

[0003] However, artificial muscles used in biomimetic structural actuation cannot provide feedback based on the patient's brainwave signals and can only be manually adjusted by the patient, which results in a lag. Therefore, it is necessary to design an artificial muscle detection system with sensing capabilities that can bidirectionally interact with biosignals. Summary of the Invention

[0004] The object of the present invention is to provide an artificial muscle detection system with sensing function to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an artificial muscle detection system with a perception function, comprising a signal collection module, a pain analysis module, and an artificial muscle control module. The signal collection module is used to use electrodes to collect signals about pain perception in the patient's brain waves. The pain analysis module is used to analyze the degree of pain in the patient's joints when the artificial muscle fibers are in different positions during rehabilitation training based on the pain perception signals. The artificial muscle control module is used to control the resistance and auxiliary power of the artificial muscle fibers during movement.

[0006] According to the above technical solution, the signal collection module includes EEG electrodes, a brain wave recording module, a waveform display module, a database, a signal transmission module, and a position recording unit. The EEG electrodes are electrically connected to the brain wave recording module and the waveform display module, the brain wave recording module is electrically connected to the database, and the database is electrically connected to the signal transmission module.

[0007] The EEG electrodes are used to record electrical activities from different areas of the brain, the brain wave recording module is used to record the monitored brain wave signals, the waveform display module is used to intuitively display two waveforms using a display screen, the database is used to store the monitored brain wave signals, the signal transmission module is used to transmit data, and the position recording unit is used to record the position of the bone joints when the patient kicks his legs and form a trajectory;

[0008] The pain analysis module includes a waveform recognition module, an activity monitoring module, a pain calculation module, a resistance calculation module, and a feedback adjustment module. The waveform recognition module is electrically connected to the activity monitoring module, the activity monitoring module is electrically connected to the pain calculation module, the pain calculation module is electrically connected to the resistance calculation module, the feedback adjustment module is electrically connected to the resistance calculation module, and the waveform recognition module and the activity monitoring module are both electrically connected to the signal transmission module.

[0009] The waveform recognition module is used to classify the monitoring data into two waveforms, alpha waves and beta waves, according to the detected brain wave frequency. The activity monitoring module is used to monitor the amplitudes of the two waveforms to reflect the activity state. The pain calculation module is used to judge the current patient's pain based on the amplitudes of the two waveforms. The resistance calculation module is used to calculate the appropriate fiber resistance based on the calculated pain. The feedback adjustment module is used by the doctor to adjust the resistance based on the patient's actual feedback from rehabilitation training using the resistance calculated by the system.

[0010] The artificial muscle control module includes an angle sensor, a resistance sensor, an electronic speed change unit, an auxiliary power unit, and an auxiliary power adjustment module. The angle sensor is electrically connected to the resistance calculation module, the resistance sensor is electrically connected to the electronic speed change unit, and the auxiliary power adjustment module is electrically connected to the auxiliary power unit and the angle sensor.

[0011] The angle sensor is used to detect the angle corresponding to the current position of the fiber, the resistance sensor is used to detect the resistance currently borne by the fiber, the electronic speed change unit is used to adjust the resistance of the fiber by changing the gear ratio, the auxiliary power unit is used to provide auxiliary power to the fiber when the fiber is at a specific angle to help the patient save physical strength, and the auxiliary power adjustment module is used to adjust the size of the auxiliary power and the angle range within which the auxiliary power is applied to the fiber.

[0012] According to the above technical solution, the working method of the system is:

[0013] S1. Place the position recording unit on the patient's joints and perform a leg kicking motion. The doctor will use the patient's joint CT images and the joint's motion trajectory during the kicking motion to identify the specific segments of the motion trajectory that require focused training and the corresponding fiber angle ranges.

[0014] S2. Specified that the auxiliary power unit provides auxiliary power to the fibers at angles outside the range of angles requiring focused training, and that the electronic speed change unit increases resistance at angles within the range of focused training;

[0015] S3. Fix the electrodes on the patient's scalp in a standard position and collect brainwave signals while the patient is in a resting state. The brainwaves are divided into two waveforms: alpha and beta waves. At the same time, the patient is asked to try kicking his legs using artificial muscles, and the amplitude changes of the alpha and beta waves are recorded.

[0016] S4. judging the patient's current pain sensation based on the amplitudes of the two waveforms, calculating appropriate fiber resistance based on the calculated pain sensation, and setting specific parameters of the fiber resistance;

[0017] S5: The patient performs leg-pushing training using artificial muscles, with fiber resistance and auxiliary power adjusted linearly according to the progress of training;

[0018] S6. Adjust the fiber resistance in steps based on the relative change in the patient's pain sensation during the last leg kicking cycle and the pain sensation during the penultimate cycle.

[0019] According to the above technical solution, in S1, the specific method for clarifying the movement trajectory segments to be trained is:

[0020] First, the patient kicks his leg once with the artificial muscle, which is a kicking cycle. The movement trajectory of the patient's bone joints during the entire cycle is recorded. At the same time, the angle sensor is used to record the angle of the fiber corresponding to each position of the movement trajectory, and the movement trajectory is divided into several sections, which are recorded as , is the paragraph number, and the angle is The doctor will identify the sections that need to be trained intensively based on the CT images of the patient's bones and joints and the movements corresponding to each section, and convert them into the angle range that needs to be trained intensively.

[0021] According to the above technical solution, in S4, the specific method for judging the pain sensation is:

[0022] S4-1. Record the average amplitude of the α wave and β wave during a period of time when the patient is at rest, and record it as 、 , compared with the average amplitude of α and β waves of normal people in a static state stored in the database 、 , respectively, to obtain the proportion value, and calculate the impact coefficient of the patient's long-term pain , specifically ;

[0023] S4-2. When the patient tries to kick his legs, record the average amplitude of the α wave and β wave in each section, and record it as 、 , excluding the impact of long-term pain, calculate the pain value of a certain paragraph ,in is the paragraph number, and , is the weight coefficient of α wave, is the weight coefficient of β wave.

[0024] According to the above technical solution, in S4, the appropriate fiber resistance calculation method is: only the sections that need to be trained intensively need to adjust the fiber resistance, and the electronic speed change unit is used to adjust the fiber resistance and the resistance sensor is used to determine the specific value of the adjusted fiber resistance. ,in is the original friction force when the fiber moves, The conversion coefficient between pain and resistance. The higher the pain of a patient in a certain section, the higher the fiber resistance. The smaller.

[0025] According to the above technical solution, in S5, the specific method for linearly adjusting the fiber resistance and power is:

[0026] S5-1. Set the number of fiber exercise circles for this training , along with the number of fiber movement circles of leg kick training The more real-time fiber resistance It decreases linearly, ;

[0027] S5-2. The doctor sets the final fiber-assisted power according to the severity of the patient's bone and joint injury. , along with the number of fiber movement circles of leg kick training The more real-time fiber-assisted power Increase, .

[0028] According to the above technical solution, in S6, the stepwise adjustment method of the fiber resistance is as follows: assuming that the current patient has A leg kick cycle, is the average pain value of the patient in the first leg kicking cycle, the average pain value of the patient in the last leg kicking cycle Average pain value during the second to last cycle For comparison, if and When the patient's pain becomes more severe and exceeds the set value, the real-time fiber resistance needs to be reduced in a certain proportion. , if necessary, stop leg-pressing training.

[0029] Compared with the existing technology, the beneficial effects achieved by the present invention are: the present invention monitors the pain felt by patients during the leg kicking training cycle through artificial muscles, thereby obtaining the most appropriate fiber resistance at different positions of the current leg kicking training, so that patients can be trained normally without excessive resistance causing pain due to excessive force, and the pain is detected in real time, and the fiber resistance is significantly reduced when the pain becomes more severe, thereby reducing the risk of aggravated joint damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 It is a schematic diagram of the overall module structure of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1 The present invention provides a technical solution: an artificial muscle detection system with a sensing function, comprising a signal collection module, a pain analysis module, and an artificial muscle control module. The signal collection module is used to collect pain perception signals from the patient's brain waves using electrodes. The pain analysis module is used to analyze the degree of joint pain when the artificial muscle fibers are in different positions during rehabilitation training based on the pain perception signals. The artificial muscle control module is used to control the resistance and auxiliary power of the artificial muscle fibers during movement.

[0034] The signal collection module includes EEG electrodes, a brain wave recording module, a waveform display module, a database, a signal transmission module, and a position recording unit. The EEG electrodes are electrically connected to the brain wave recording module and the waveform display module. The brain wave recording module is electrically connected to the database, and the database is electrically connected to the signal transmission module.

[0035] The EEG electrodes are used to record electrical activities from different areas of the brain. The brainwave recording module is used to record the monitored brainwave signals. The waveform display module is used to visually display two waveforms on the display screen. The database is used to store the monitored brainwave signals. The signal transmission module is used to transmit data. The position recording unit is used to record the position of the bone joints when the patient kicks his legs and form a trajectory.

[0036] The pain analysis module includes a waveform recognition module, an activity monitoring module, a pain calculation module, a resistance calculation module, and a feedback adjustment module. The waveform recognition module is electrically connected to the activity monitoring module, the activity monitoring module is electrically connected to the pain calculation module, the pain calculation module is electrically connected to the resistance calculation module, the feedback adjustment module is electrically connected to the resistance calculation module, and the waveform recognition module and the activity monitoring module are both electrically connected to the signal transmission module.

[0037] The waveform recognition module is used to classify the monitoring data into two waveforms, alpha and beta waves, based on the detected brain wave frequency. The activity monitoring module is used to monitor the amplitudes of the two waveforms to reflect the activity state. The pain calculation module is used to judge the patient's current pain based on the amplitudes of the two waveforms. The resistance calculation module is used to calculate the appropriate fiber resistance based on the calculated pain. The feedback adjustment module is used by the doctor to adjust the resistance based on the patient's actual feedback from rehabilitation training using the resistance calculated by the system.

[0038] The artificial muscle control module includes an angle sensor, a resistance sensor, an electronic speed change unit, an auxiliary power unit, and an auxiliary power adjustment module. The angle sensor is electrically connected to the resistance calculation module, the resistance sensor is electrically connected to the electronic speed change unit, and the auxiliary power adjustment module is electrically connected to the auxiliary power unit and the angle sensor.

[0039] The angle sensor is used to detect the angle corresponding to the current position of the fiber. The resistance sensor is used to detect the resistance currently borne by the fiber. The electronic speed change unit is used to adjust the resistance of the fiber by changing the gear ratio. The auxiliary power unit is used to provide auxiliary power to the fiber when the fiber is at a specific angle to help the patient save energy. The auxiliary power adjustment module is used to adjust the size of the auxiliary power and the angle range within which the auxiliary power is applied to the fiber.

[0040] The system works as follows:

[0041] S1. Place the position recording unit on the patient's joints and perform a leg kicking motion. The doctor will use the patient's joint CT images and the joint's motion trajectory during the kicking motion to identify the specific segments of the motion trajectory that require focused training and the corresponding fiber angle ranges.

[0042] S2. Specified that the auxiliary power unit provides auxiliary power to the fibers at angles outside the range of angles requiring focused training, and that the electronic speed change unit increases resistance at angles within the range of focused training;

[0043] S3. Fix the electrodes on the patient's scalp in a standard position and collect brainwave signals while the patient is in a resting state. The brainwaves are divided into two waveforms: alpha and beta waves. At the same time, the patient is asked to try kicking on the artificial muscle, and the amplitude changes of the alpha and beta waves are recorded.

[0044] S4. judging the patient's current pain sensation based on the amplitudes of the two waveforms, calculating appropriate fiber resistance based on the calculated pain sensation, and setting specific parameters of the fiber resistance;

[0045] S5: The patient performs leg-pushing training on the artificial muscle, and the fiber resistance and auxiliary power are linearly adjusted according to the progress of the training;

[0046] S6. Adjust the fiber resistance in steps based on the relative change in pain between the patient's last leg kick cycle and the second-to-last cycle.

[0047] In S1, the specific method to identify the movement trajectory segments that need to be trained is as follows:

[0048] First, the patient kicks his leg on the artificial muscle for one cycle, and the movement trajectory of the patient's bone joints during the entire cycle is recorded. At the same time, the angle sensor is used to record the angle of the fiber corresponding to each position of the movement trajectory, and the movement trajectory is divided into several sections, which are recorded as , is the paragraph number, and the angle is , the doctor identifies the sections that need to be focused on based on the CT images of the patient's bones and joints and the corresponding movements of each section, and converts them into the angle range that needs to be focused on;

[0049] In S4, the specific method for judging pain is:

[0050] S4-1. Record the average amplitude of the α wave and β wave during a period of time when the patient is at rest, and record it as 、 , compared with the average amplitude of α and β waves of normal people in a static state stored in the database 、 , respectively, to obtain the proportion value, and calculate the impact coefficient of the patient's long-term pain , specifically ;

[0051] S4-2. When the patient tries to kick his legs, record the average amplitude of the α wave and β wave in each section, and record it as 、 , excluding the impact of long-term pain, calculate the pain value of a certain paragraph ,in is the paragraph number, and , is the weight coefficient of α wave, is the weight coefficient of β wave;

[0052] In S4, the appropriate fiber resistance calculation method is: only the sections that need to be trained intensively need to adjust the fiber resistance, and the electronic speed change unit is used to adjust the fiber resistance, and the resistance sensor is used to determine the specific value of the adjusted fiber resistance. ,in is the original friction force when the fiber moves, The conversion factor between pain and resistance. The higher the pain of a patient in a certain section, the higher the fiber resistance. The smaller;

[0053] In S5, the specific method for linear adjustment of fiber resistance and power is as follows:

[0054] S5-1. Set the number of fiber exercise circles for this training , along with the number of fiber movement circles of leg kick training The more real-time fiber resistance It decreases linearly, ;

[0055] S5-2. The doctor sets the final fiber-assisted power according to the severity of the patient's bone and joint injury. , along with the number of fiber movement circles of leg kick training The more real-time fiber-assisted power Increase, ;

[0056] In S6, the stepwise adjustment method of fiber resistance is as follows: Assume that the current patient has A leg kick cycle, is the average pain value of the patient in the first leg kicking cycle, the average pain value of the patient in the last leg kicking cycle Average pain value during the second to last cycle For comparison, if and When the patient's pain becomes more severe and exceeds the set value, the real-time fiber resistance needs to be reduced in a certain proportion. , stop leg-pressing training if necessary.

[0057] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0058] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An artificial muscle detection system with sensing function, characterized by: It includes a signal collection module, a pain analysis module, and an artificial muscle control module. The signal collection module is used to use electrodes to collect signals about pain perception in the patient's brain waves. The pain analysis module is used to analyze the degree of pain in the patient's joints when the artificial muscle fibers are in different positions during rehabilitation training based on the pain perception signals. The artificial muscle control module is used to control the resistance and auxiliary power of the artificial muscle fibers during movement.

2. The artificial muscle detection system with sensing function according to claim 1, characterized in that: The signal collection module includes EEG electrodes, a brain wave recording module, a waveform display module, a database, a signal transmission module, and a position recording unit. The EEG electrodes are electrically connected to the brain wave recording module and the waveform display module. The brain wave recording module is electrically connected to the database, and the database is electrically connected to the signal transmission module. The EEG electrodes are used to record electrical activities from different areas of the brain, the brain wave recording module is used to record the monitored brain wave signals, the waveform display module is used to intuitively display two waveforms using a display screen, the database is used to store the monitored brain wave signals, the signal transmission module is used to transmit data, and the position recording unit is used to record the position of the bone joints when the patient kicks his legs and form a trajectory; The pain analysis module includes a waveform recognition module, an activity monitoring module, a pain calculation module, a resistance calculation module, and a feedback adjustment module. The waveform recognition module is electrically connected to the activity monitoring module, the activity monitoring module is electrically connected to the pain calculation module, the pain calculation module is electrically connected to the resistance calculation module, the feedback adjustment module is electrically connected to the resistance calculation module, and the waveform recognition module and the activity monitoring module are both electrically connected to the signal transmission module. The waveform recognition module is used to classify the monitoring data into two waveforms, alpha waves and beta waves, according to the detected brain wave frequency. The activity monitoring module is used to monitor the amplitudes of the two waveforms to reflect the activity state. The pain calculation module is used to judge the current patient's pain based on the amplitudes of the two waveforms. The resistance calculation module is used to calculate the appropriate fiber resistance based on the calculated pain. The feedback adjustment module is used by the doctor to adjust the resistance based on the patient's actual feedback from rehabilitation training using the resistance calculated by the system. The artificial muscle control module includes an angle sensor, a resistance sensor, an electronic speed change unit, an auxiliary power unit, and an auxiliary power adjustment module. The angle sensor is electrically connected to the resistance calculation module, the resistance sensor is electrically connected to the electronic speed change unit, and the auxiliary power adjustment module is electrically connected to the auxiliary power unit and the angle sensor. The angle sensor is used to detect the angle corresponding to the current position of the fiber, the resistance sensor is used to detect the resistance currently borne by the fiber, the electronic speed change unit is used to adjust the resistance of the fiber by changing the gear ratio, the auxiliary power unit is used to provide auxiliary power to the fiber when the fiber is at a specific angle to help the patient save physical strength, and the auxiliary power adjustment module is used to adjust the size of the auxiliary power and the angle range within which the auxiliary power is applied to the fiber.

3. The artificial muscle detection system with sensing function according to claim 2, characterized in that: The system works as follows: S1. Place the position recording unit on the patient's joints and perform a leg kicking motion. The doctor will use the patient's joint CT images and the joint's motion trajectory during the kicking motion to identify the specific segments of the motion trajectory that require focused training and the corresponding fiber angle ranges. S2. Specified that the auxiliary power unit provides auxiliary power to the fibers at angles outside the range of angles requiring focused training, and that the electronic speed change unit increases resistance at angles within the range of focused training; S3. Fix the electrodes on the patient's scalp in a standard position and collect brainwave signals while the patient is in a resting state. The brainwaves are divided into two waveforms: alpha and beta waves. At the same time, the patient is asked to try kicking his legs using artificial muscles, and the amplitude changes of the alpha and beta waves are recorded. S4. judging the patient's current pain sensation based on the amplitudes of the two waveforms, calculating appropriate fiber resistance based on the calculated pain sensation, and setting specific parameters of the fiber resistance; S5: The patient performs leg-pushing training using artificial muscles, with fiber resistance and auxiliary power adjusted linearly according to the progress of training; S6. Adjust the fiber resistance in steps based on the relative change in the patient's pain sensation during the last leg kicking cycle and the pain sensation during the penultimate cycle.

4. The artificial muscle detection system with sensing function according to claim 3, characterized in that: In S1, the specific method for clarifying the movement trajectory segments to be trained is as follows: First, the patient kicks his leg once with the artificial muscle, which is a kicking cycle. The movement trajectory of the patient's bone joints during the entire cycle is recorded. At the same time, the angle sensor is used to record the angle of the fiber corresponding to each position of the movement trajectory, and the movement trajectory is divided into several sections, which are recorded as , is the paragraph number, and the angle is The doctor will identify the sections that need to be trained intensively based on the CT images of the patient's bones and joints and the movements corresponding to each section, and convert them into the angle range that needs to be trained intensively.

5. The artificial muscle detection system with sensing function according to claim 4, characterized in that: In S4, the specific method for judging pain sensation is: S4-1. Record the average amplitude of the α wave and β wave during a period of time when the patient is at rest, and record it as 、 , compared with the average amplitude of α and β waves of normal people in a static state stored in the database 、 , respectively, to obtain the proportion value, and calculate the impact coefficient of the patient's long-term pain , specifically ; S4-2. When the patient tries to kick his legs, record the average amplitude of the α wave and β wave in each section, and record it as 、 , excluding the impact of long-term pain, calculate the pain value of a certain paragraph ,in is the paragraph number, and , is the weight coefficient of α wave, is the weight coefficient of β wave.

6. The artificial muscle detection system with sensing function according to claim 5, characterized in that: In the above S4, the appropriate fiber resistance calculation method is as follows: only the sections that need to be focused on are required to adjust the fiber resistance, and the electronic speed change unit is used to adjust and the resistance sensor is used to determine the specific value of the adjusted fiber resistance. ,in is the original friction force when the fiber moves, The conversion coefficient between pain and resistance. The higher the pain of a patient in a certain section, the higher the fiber resistance. The smaller.

7. The artificial muscle detection system with sensing function according to claim 6, characterized in that: In S5, the specific method for linearly adjusting the fiber resistance and power is: S5-1. Set the number of fiber exercise circles for this training , along with the number of fiber movement circles of leg kick training The more real-time fiber resistance It decreases linearly, ; S5-2. The doctor sets the final fiber-assisted power according to the severity of the patient's bone and joint injury. , along with the number of fiber movement circles of leg kick training The more real-time fiber-assisted power Increase, .

8. The artificial muscle detection system with sensing function according to claim 7, characterized in that: In said S6, the stepwise adjustment method of the fiber resistance is as follows: assuming that the current patient has A leg kick cycle, is the average pain value of the patient in the first leg kicking cycle, the average pain value of the patient in the last leg kicking cycle Average pain value during the second to last cycle For comparison, if and When the patient's pain becomes more severe and exceeds the set value, the real-time fiber resistance needs to be reduced in a certain proportion. , if necessary, stop leg-pressing training.

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