Monitoring system and method for joint rehabilitation training

Abstract

The invention provides a monitoring system and method for joint rehabilitation training.The monitoring system comprises an intelligent glasses terminal and a data acquisition terminal, the intelligent glasses terminal and the data acquisition terminal are connected through wireless communication, the data acquisition terminal comprises an electrode cavity and a sealing cavity, and a plurality of myoelectricity sensor arrays are arranged in the electrode cavity; a second processor, a three-axis acceleration sensor, a three-axis gyroscope and a heart rate sensor are arranged in the sealing cavity, the intelligent glasses terminal comprises a glasses frame, miniature cameras are symmetrically arranged on a left glasses leg and a right glasses leg of the glasses frame, the miniature cameras are connected with the left glasses leg or the right glasses leg through rotating shafts, and the rotating shafts are driven by motors to drive the miniature cameras to rotate. The power supply and the first processor are arranged on the left glasses leg, the loudspeaker is arranged on the right glasses leg, and the microphone is arranged at the nose bridge. In the invention, the intelligent glasses terminal and the data acquisition terminal are adopted to carry out rehabilitation training monitoring on the elbow joint, so that the size of the equipment is reduced, and the intelligence and portability of the equipment are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation monitoring, and in particular to a monitoring system and method for joint rehabilitation training. Background Art

[0002] For patients in the rehabilitation stage, joint movement monitoring is one of the key technologies to improve rehabilitation effects. In traditional rehabilitation equipment, sensors are usually installed on rehabilitation machines or brackets, which makes the system bulky and relies on professional technicians to operate. It has serious deficiencies in intelligence and portability and cannot meet the actual needs of users. Summary of the invention

[0003] In order to solve the technical problem that the rehabilitation equipment in the prior art is bulky and inconvenient to operate, the monitoring system for joint rehabilitation training proposed by the present invention includes a smart glasses terminal and a data acquisition terminal, and the smart glasses terminal and the data acquisition terminal are connected via wireless communication;

[0004] The data acquisition terminal is fixed to the user's wrist through a watch strap, and includes an electrode cavity and a sealed cavity, wherein a plurality of myoelectric sensor arrays are arranged inside the electrode cavity, a flip cover is arranged on the electrode cavity to seal the electrode cavity, and a second processor, a three-axis acceleration sensor, a three-axis gyroscope and a heart rate sensor are arranged in the sealed cavity;

[0005] The smart glasses terminal includes a glasses frame, and micro cameras are symmetrically arranged on the left and right temples of the glasses frame. The micro cameras are connected to the left or right temple via a rotating shaft. The rotating shaft drives the micro cameras to rotate under the drive of a motor. A power supply and a first processor are arranged on the left temple, a speaker is arranged on the right temple, and a microphone is arranged at the bridge of the nose.

[0006] Preferably, the electromyographic sensor array 27 includes a plurality of electrodes arranged in a matrix, using a two-dimensional nanomaterial Ti3C2T X Electrodes were made to detect electrophysiological signals of the biceps and triceps.

[0007] Based on the above-mentioned joint rehabilitation training monitoring system, the joint rehabilitation training monitoring method proposed by the present invention specifically includes the following steps:

[0008] S1. Set up data collection terminal;

[0009] S2, adjust the body to the initial state;

[0010] S3. Collect rehabilitation training data and make intelligent judgments based on the rehabilitation training data.

[0011] Preferably, in S1, the watch strap is adjusted to stably fix the data acquisition terminal on the wrist, the myoelectric sensor array is taken out, and the myoelectric sensor array is fixed at the location of the biceps and triceps.

[0012] Preferably, in S2, according to the wearing position of the data acquisition terminal, the micro camera on the left or right side of the smart glasses terminal is started, and the motor is controlled so that the micro camera can capture the current position of the arm, and the user is reminded through a speaker to lower the arm wearing the data acquisition terminal vertically and close to the body, and the micro camera is used to determine whether the position of the arm meets the requirements.

[0013] Preferably, in S3, the user's rehabilitation training is guided by a speaker, the data acquisition terminal transmits the heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint to the smart glasses terminal, and the first processor evaluates the training effect based on the microphone data, heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint.

[0014] Preferably, in S3, the evaluation process of the training effect is to obtain the maximum activity angle and activity speed of the elbow joint according to the posture angle and angular velocity of the elbow joint, judge the joint range of motion according to the maximum activity angle and activity speed of the elbow joint, judge the muscle activation effect based on the electromyographic signal, and judge the user difficulty based on the microphone data and heart rate data.

[0015] Preferably, in S3, the process of determining the range of motion of the joint is that if both the maximum motion angle and the motion speed are greater than a set threshold, it indicates that the range of motion of the elbow joint meets the requirement.

[0016] Preferably, in S3, the specific process of judging the muscle activation effect based on the electromyographic signal is to extract the average absolute value, root mean square value and zero crossing point of the electromyographic signal in the time domain, compare the time domain characteristics of the electromyographic signal with the standard electromyographic signal, and calculate the average value of the difference ratio of each indicator. If the average value is less than the difference threshold, it indicates that the degree of muscle activation is qualified.

[0017] Preferably, in S3, the specific process of judging the user's difficulty based on the microphone data and the heart rate data is that the microphone data is the breathing sound of the user during training, the signal strength of the audio signal in the microphone data is extracted, the standard deviation of the signal strength is calculated, and the standard deviation of the heart rate data is calculated. If the standard deviation of the signal strength and the standard deviation of the heart rate data are both less than the set threshold, it indicates that the user can easily complete the rehabilitation training.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The use of smart glasses terminals and data acquisition terminals for rehabilitation training monitoring of the elbow joint reduces the size of the equipment and greatly improves the intelligence and portability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the structure of the monitoring system of the present invention;

[0021] Figure 2 It is a structural schematic diagram of the smart glasses terminal of the present invention;

[0022] Figure 3 It is a structural schematic diagram of the data acquisition terminal of the present invention.

[0023] Description of the drawings: 11. Glasses frame, 12. Rotating shaft, 13. Miniature camera, 14. Microphone, 15. Power supply, 16. First processor, 17. Speaker, 21. Electrode cavity, 22. Sealed cavity, 23. Second processor, 24. Three-axis acceleration sensor, 25. Three-axis gyroscope, 26. Heart rate sensor, 27. EMG sensor array, 28. Flip cover. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1 As shown, the monitoring system for joint rehabilitation training proposed by the present invention includes a smart glasses terminal and a data acquisition terminal. The smart glasses terminal and the data acquisition terminal are connected via wireless communication. The data acquisition terminal is used to detect motion data such as posture angle and angular velocity during exercise. The smart glasses terminal interacts with the user and makes intelligent judgments on the training effect based on the motion data.

[0026] The data collection terminal is a rectangular structure and is fixed to the user's wrist through a strap. Figure 3As shown, the data acquisition terminal includes an electrode cavity 21 and a sealed cavity 22. A second processor 23, a three-axis acceleration sensor 24, a three-axis gyroscope 25 and a heart rate sensor 26 are arranged in the sealed cavity 22, and the second processor 23 is connected with the three-axis acceleration sensor 24, the three-axis gyroscope 25 and the heart rate sensor 26 through a signal line. The second processor 23 can calculate the posture angle and angular velocity of the elbow joint based on the data of the three-axis acceleration sensor 24 and the three-axis gyroscope 25. The calculation process of the posture angle and angular velocity of the elbow joint is that the second processor calculates the posture angle of the elbow joint based on the data of the three-axis acceleration sensor and the three-axis gyroscope using a quaternion posture solution algorithm. In the calculation process, a Kalman filter is used to perform error correction and noise suppression on the data. After the posture angle is obtained, a numerical differential algorithm is used to calculate the angular velocity of the elbow joint. The heart rate sensor 26 is close to the user's skin and uses photoplethysmography (PPG) technology to detect the user's heart rate. A plurality of myoelectric sensor arrays 27 are arranged inside the electrode cavity 21. The myoelectric sensor arrays 27 are connected to the second processor 23 via a signal line. The electrode cavity 21 is provided with a flip cover 28. A rubber ring is arranged around the flip cover 28 to ensure the sealing of the electrode cavity 21 with the outside world and prevent water vapor, dust, etc. from entering the electrode cavity 21. When performing rehabilitation training, the flip cover 28 is opened, the myoelectric sensor array 27 is taken out, and the myoelectric sensor array 27 is fixed at the position where the biceps and triceps are located. When not performing rehabilitation training, the myoelectric sensor array 27 is placed in the electrode cavity 21 and the flip cover 28 is closed. The myoelectric sensor array 27 includes a plurality of electrodes arranged in a matrix, using a two-dimensional nanomaterial Ti3C2T X Fabrication of electrodes, Ti3C2T X It has excellent electrical, electrochemical and mechanical properties, can form a colloid-stable aqueous dispersion, and does not need to apply gel during detection. The electromyographic sensor array 27 is used to detect the electrophysiological signals (EMG) of the biceps and triceps, and the electrophysiological signals (EMG) can reflect the activation state and contraction strength of the muscle. The second processor 23 transmits the heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint to the smart glasses terminal.

[0027] like Figure 2As shown, the smart glasses terminal includes a glasses frame 11, and a micro camera 13 is symmetrically arranged on the left and right temples of the glasses frame 11. The micro camera 13 is connected to the left temple or the right temple through a rotating shaft 12. The rotating shaft 12 can drive the micro camera 13 to rotate under the drive of the motor to adjust the shooting angle. The power supply 15 and the first processor 16 are arranged on the left temple, the speaker 17 is arranged on the right temple, and the microphone 14 is arranged at the bridge of the nose. A lens or a transparent display can be arranged in the frame of the glasses frame 11. When the user is myopic, a lens is arranged in the frame of the glasses frame 11 to meet the daily needs of the user. When the user's vision is normal, a transparent display is arranged in the frame of the glasses frame 11, and training requirements, training data and other information can be displayed through the transparent display. The first processor 16 is connected to the motor, the micro camera 13, the microphone 14, the speaker 17, and the transparent display through a signal line, and the first processor 16 is connected to the second processor 23 for wireless communication.

[0028] Based on the above monitoring system, the monitoring method of joint rehabilitation training specifically includes the following steps:

[0029] S1. Set up a data acquisition terminal. Specifically, adjust the watch strap to stably fix the data acquisition terminal on the wrist, take out the myoelectric sensor array, and fix the myoelectric sensor array at the location of the biceps and triceps.

[0030] S2. Adjust the body to the initial state. Specifically, according to the wearing position of the data collection terminal, start the micro camera on the left or right side of the smart glasses terminal, control the motor so that the micro camera can capture the current position of the arm, and remind the user through the speaker to vertically lower the arm wearing the data collection terminal and close to the body, and determine whether the position of the arm meets the requirements through the micro camera.

[0031] S3, collecting rehabilitation training data, and making intelligent judgments based on the rehabilitation training data. Specifically, the user's rehabilitation training is guided by the speaker, the data collection terminal transmits the heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint to the smart glasses terminal, and the first processor evaluates the training effect based on the microphone data, heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint.

[0032] The evaluation process of the training effect is to obtain the maximum activity angle and activity speed of the elbow joint according to the posture angle and angular velocity of the elbow joint, judge the joint activity according to the maximum activity angle and activity speed of the elbow joint, judge the muscle activation effect based on the electromyographic signal, and judge the user difficulty based on the microphone data and heart rate data. The process of judging the joint activity is that if the maximum activity angle and activity speed are both greater than the set threshold, it indicates that the joint activity of the elbow joint meets the requirements. The specific process of judging the muscle activation effect based on the electromyographic signal is to extract the average absolute value, root mean square value and zero crossing point of the electromyographic signal in the time domain, compare the time domain characteristics of the electromyographic signal with the standard electromyographic signal, and calculate the average value of the difference ratio of each indicator. If the average value is less than the difference threshold, it indicates that the muscle activation degree is qualified. The specific process of judging the user difficulty based on the microphone data and heart rate data is that the microphone data is the breathing sound of the user during the training process, extract the signal strength of the audio signal in the microphone data, calculate the standard deviation of the signal strength, and calculate the standard deviation of the heart rate data. If the standard deviation of the signal strength and the standard deviation of the heart rate data are both less than the set threshold, it indicates that the user can easily complete the rehabilitation training.

[0033] The above disclosure is only the preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the present invention. It should be pointed out that for those skilled in the art, any equivalent changes made to the scheme of the present invention without departing from the design structure and principle of the present invention are considered to be within the protection scope of the present invention.

Claims

1. A monitoring system for joint rehabilitation training, characterized in that: The monitoring system includes a smart glasses terminal and a data collection terminal, and the smart glasses terminal and the data collection terminal are connected via wireless communication; The data acquisition terminal is fixed to the user's wrist through a watch strap, and includes an electrode cavity and a sealed cavity, wherein a plurality of myoelectric sensor arrays are arranged inside the electrode cavity, a flip cover is arranged on the electrode cavity to seal the electrode cavity, and a second processor, a three-axis acceleration sensor, a three-axis gyroscope and a heart rate sensor are arranged in the sealed cavity; The smart glasses terminal includes a glasses frame, and micro cameras are symmetrically arranged on the left and right temples of the glasses frame. The micro cameras are connected to the left or right temple via a rotating shaft. The rotating shaft drives the micro cameras to rotate under the drive of a motor. A power supply and a first processor are arranged on the left temple, a speaker is arranged on the right temple, and a microphone is arranged at the bridge of the nose.

2. The monitoring system according to claim 1, characterized in that: The myoelectric sensor array 27 includes a plurality of electrodes arranged in a matrix, and is made of two-dimensional nanomaterial Ti3C2T X Electrodes were made to detect electrophysiological signals of the biceps and triceps.

3. A joint rehabilitation training monitoring method, based on the joint rehabilitation training monitoring system according to any one of claims 1-2, characterized in that: The monitoring method specifically comprises the following steps: S1. Set up data collection terminal; S2, adjust the body to the initial state; S3. Collect rehabilitation training data and make intelligent judgments based on the rehabilitation training data.

4. The monitoring method according to claim 3, characterized in that: In S1, the watch strap is adjusted to stably fix the data acquisition terminal on the wrist, the myoelectric sensor array is taken out, and the myoelectric sensor array is fixed at the position where the biceps and triceps are located.

5. The monitoring method according to claim 3, characterized in that: In S2, according to the wearing position of the data acquisition terminal, the micro camera on the left or right side of the smart glasses terminal is started, and the motor is controlled so that the micro camera can capture the current position of the arm. The user is reminded through a speaker to move the arm wearing the data acquisition terminal vertically downward and close to the body, and the micro camera is used to determine whether the position of the arm meets the requirements.

6. The monitoring method according to claim 3, characterized in that: In S3, the user's rehabilitation training is guided by a speaker, the data acquisition terminal transmits the heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint to the smart glasses terminal, and the first processor evaluates the training effect based on the microphone data, heart rate data, electromyographic signals, and the posture angle and angular velocity of the elbow joint.

7. The monitoring method according to claim 6, characterized in that: In S3, the evaluation process of the training effect is to obtain the maximum activity angle and activity speed of the elbow joint according to the posture angle and angular velocity of the elbow joint, judge the joint activity according to the maximum activity angle and activity speed of the elbow joint, judge the muscle activation effect based on the electromyographic signal, and judge the user difficulty based on the microphone data and heart rate data.

8. The monitoring method according to claim 7, characterized in that: In S3, the process of determining the range of motion of the elbow joint is as follows: if the maximum motion angle and the motion speed are both greater than a set threshold, it indicates that the range of motion of the elbow joint meets the requirement.

9. The monitoring method according to claim 7, characterized in that: In S3, the specific process of judging the muscle activation effect based on the electromyographic signal is to extract the average absolute value, root mean square value and zero crossing point of the electromyographic signal in the time domain, compare the time domain characteristics of the electromyographic signal with the standard electromyographic signal, and calculate the average value of the difference ratio of each indicator. If the average value is less than the difference threshold, it indicates that the degree of muscle activation is qualified.

10. The monitoring method according to claim 7, characterized in that: In S3, the specific process of judging the user's difficulty based on the microphone data and the heart rate data is as follows: the microphone data is the breathing sound of the user during training, the signal strength of the audio signal in the microphone data is extracted, the standard deviation of the signal strength is calculated, and the standard deviation of the heart rate data is calculated. If the standard deviation of the signal strength and the standard deviation of the heart rate data are both less than the set threshold, it indicates that the user can easily complete the rehabilitation training.

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