Knee joint rehabilitation auxiliary system based on electronic information hardware technology

Abstract

The present invention discloses a knee joint rehabilitation auxiliary system based on electronic information hardware technology, including a knee joint brace device and a rehabilitation auxiliary server; the knee joint brace device includes a single-chip microcomputer controller and a lower limb anterior pressure sensor, a lower limb posterior pressure sensor and a lower limb triaxial acceleration sensor electrically connected thereto, the single-chip microcomputer controller collects the values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor at preset intervals during rehabilitation guidance training, and sends them to the rehabilitation auxiliary server; the rehabilitation auxiliary server calculates and distinguishes each rehabilitation cycle data set of the lower limb according to the value of the lower limb triaxial acceleration sensor. The present invention can accurately intervene and guide the rehabilitation process of the knee joint by the knee joint brace device, realize the real-time intervention of the hardware technology of the rehabilitation process, and avoid the influence of subjective adjustment.

Description

Technical Field

[0001] The present invention relates to the field of electronic control technology, and in particular to a knee joint rehabilitation auxiliary system based on electronic information hardware technology. Background Art

[0002] In modern knee rehabilitation, muscle training plays a very important role. The role of muscle training in knee rehabilitation is mainly as follows: 1. Strengthen muscle strength: Muscles are a key component in supporting and stabilizing the knee joint. Through muscle training, especially training the quadriceps and hamstrings around the knee joint, the strength and endurance of these muscles can be enhanced. Strong muscles can provide additional support and stability, reduce the burden on the knee joint, and reduce the risk of injury. 2. Restore joint stability: Muscle training can help restore knee stability. When the knee joint is damaged or after surgery, the muscles around the knee joint may become weak due to pain, surgery or long-term inactivity. By conducting targeted training for these muscles, the contraction and control of the muscles can be enhanced, and the stability of the knee joint can be improved. 3. Improve movement control: Muscle training helps improve movement control of the knee joint. Strong muscles can provide better movement control and regulation, making the knee joint more stable and balanced during movement. This is very important for daily activities, exercise and sports rehabilitation. 4. Improve range of motion and flexibility: Through muscle training, the flexibility and stretching ability of the muscles around the knee joint can be increased. This helps improve the knee's range of motion, reduces stiffness, and increases joint flexibility.

[0003] For knee rehabilitation training, a rehabilitation auxiliary system can be used to assist, thereby improving the accuracy of training. Existing knee rehabilitation systems use photographic imaging and neural networks to identify leg rehabilitation movements, such as Chinese invention patents, patent name: A knee rehabilitation movement recognition method and system, patent number: 202311470645.7. The patent discloses a knee rehabilitation movement recognition method and system, wherein the method includes: S1: collecting the knee rehabilitation movement image; S2: preprocessing the collected knee rehabilitation movement image to obtain an image data set for training a knee rehabilitation movement recognition model; S3: inputting the image data set into the knee rehabilitation movement recognition model for training the knee rehabilitation movement recognition model; S4: inputting the image to be recognized into the trained knee rehabilitation movement recognition model to obtain the recognition result of the knee rehabilitation movement. The present invention designs different rehabilitation movement recognition models according to the different complexities of different rehabilitation movements, so that the recognition model of each movement can focus more on the characteristics of the movement itself, and also improves the accuracy of whether the movement is standardized.

[0004] However, this patent only relies on image acquisition and recognition, and cannot intervene in and guide the knee joint rehabilitation process. Even if the movements themselves are not in place, they can only be changed through the user's subjective adjustments, and the effect is not ideal. Summary of the invention

[0005] Therefore, it is necessary to provide a knee joint rehabilitation assistance system based on electronic information hardware technology to solve the problem that the assistance effect of the existing knee joint rehabilitation assistance system is not ideal.

[0006] To achieve the above-mentioned purpose, the present invention provides a knee joint rehabilitation auxiliary system based on electronic information hardware technology, including a knee joint brace device and a rehabilitation auxiliary server; the knee joint brace device includes a single-chip microcomputer controller and a lower limb anterior pressure sensor, a lower limb posterior pressure sensor and a lower limb triaxial acceleration sensor electrically connected thereto, the single-chip microcomputer controller collects the values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor at preset intervals during rehabilitation guidance training, and sends them to the rehabilitation auxiliary server; the rehabilitation auxiliary server calculates and distinguishes each rehabilitation cycle data set of the lower limb according to the value of the lower limb triaxial acceleration sensor, each rehabilitation cycle data set includes multiple interval time points, and each interval time point corresponds to the value of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor; the rehabilitation auxiliary server trains all the lower limb anterior pressure sensors and lower limb posterior pressure sensors in each rehabilitation cycle data set using a neural network model to obtain a recommended rehabilitation cycle data set through training;

[0007] The rehabilitation assistance server sends the recommended rehabilitation cycle data set to the single-chip microcomputer controller; during rehabilitation assistance training, the single-chip microcomputer controller drives the rotating motor of the knee joint brace device to apply force; and then the single-chip microcomputer controller drives the rotating motor of the knee joint brace device to move according to the values ​​in the recommended rehabilitation cycle data set, when the values ​​of the current lower limb anterior pressure sensor, lower limb posterior pressure sensor and lower limb triaxial acceleration sensor are the same as the values ​​in the recommended rehabilitation cycle data set, so that the value of the lower limb triaxial acceleration sensor changes to the next value in the recommended rehabilitation cycle data set.

[0008] Furthermore, the knee joint brace device corresponds to multiple users, and the rehabilitation assistance server stores multiple users; the rehabilitation assistance server is used to receive rehabilitation evaluation level instructions of the recommended rehabilitation cycle data sets between different users, record the correspondence between the rehabilitation evaluation level in the rehabilitation evaluation level instruction and the recommended rehabilitation cycle data set, and train the multiple recommended rehabilitation cycle data sets with high rehabilitation evaluation levels using a neural network model to obtain a recommended rehabilitation cycle data set with a high rehabilitation evaluation level as an intelligent rehabilitation cycle data set, and send the intelligent rehabilitation cycle data set as an optional option to the knee joint brace devices of different users.

[0009] Furthermore, the rehabilitation evaluation level instruction is collected by the single-chip microcomputer controller and sent to the rehabilitation assistance server, or the rehabilitation assistance server receives it from the computer client.

[0010] Furthermore, the knee joint brace device also includes a group confirmation button. After receiving a signal that the group confirmation button is pressed, the single-chip controller sends the currently collected values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor as a group of data to the rehabilitation assistance server; after the group confirmation button is pressed multiple times, multiple groups of data are sent to the rehabilitation assistance server; the rehabilitation assistance server performs separate training on each group of data, and each group of data corresponds to a recommended rehabilitation cycle data set.

[0011] Furthermore, during rehabilitation-assisted training, the single-chip microcomputer controller sends different voice instructions to the speaker according to the value of the lower limb triaxial acceleration sensor.

[0012] Furthermore, during rehabilitation-assisted training, the single-chip controller sends different prompt information to the indicator according to the lower limb front pressure sensor and the lower limb back pressure sensor.

[0013] Furthermore, the knee joint brace device includes an upper limb brace, a lower limb brace and a rotating motor, the upper limb brace and the lower limb brace are hinged to each other, the rotating motor is arranged at the hinge, the lower limb front pressure sensor is arranged at the front side of the lower limb brace, the lower limb rear pressure sensor is arranged at the rear side of the lower limb brace, the lower limb triaxial acceleration sensor is arranged in the middle of the lower limb brace, and the single-chip microcomputer controller is arranged at the upper limb brace.

[0014] Furthermore, the knee joint brace device also includes an upper limb support frame, which is used to be fixedly connected to the upper limb set and support the upper limb set.

[0015] Furthermore, there are two rotating motors, which are respectively arranged on the left and right sides of the hinge.

[0016] Furthermore, the single chip microcomputer controller sends data to the rehabilitation assistance server through an Internet of Things module or an Internet module.

[0017] Different from the prior art, the above technical solution requires the user to put on the knee brace device during rehabilitation guidance training, and then a professional rehabilitation physician applies force to the brace device by hand, leading the user to complete multiple rehabilitation movements, including extension and flexion, that is, leg lifting and leg bending. The single-chip microcomputer controller uses the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor to collect the motion state of the rehabilitation movement process, and the lower limb anterior pressure sensor and the lower limb posterior pressure sensor can obtain the force of the lower limb during the rehabilitation movement state. These data will be sent to the rehabilitation auxiliary server, which will perform neural network training to obtain a recommended rehabilitation cycle data set. Then, during rehabilitation auxiliary training, the single-chip microcomputer will drive the rotating motor to apply force, thereby simulating the situation where the rehabilitation physician applies force to the brace device by hand, and completes detection and force adjustment through the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor. In this way, the knee brace device can accurately intervene and guide the rehabilitation process of the knee joint, realize the real-time intervention of hardware technology in the rehabilitation process, and avoid the influence of subjective adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a module schematic diagram of the rehabilitation assistance system described in the specific implementation method;

[0019] Figure 2 It is a structural schematic diagram of the knee joint brace device described in a specific implementation manner;

[0020] Figure 3 A method flow chart for a specific implementation method;

[0021] Figure 4 It is a schematic structural diagram of the knee joint brace device according to another embodiment. DETAILED DESCRIPTION

[0022] In order to explain the technical content, structural features, achieved objectives and effects of the technical solution in detail, the following is a detailed description in conjunction with specific embodiments and accompanying drawings.

[0023] See also Figures 1 to 4, this embodiment provides a knee joint rehabilitation auxiliary system based on electronic information hardware technology, including a knee joint brace device 1 and a rehabilitation auxiliary server 2; the knee joint brace device is used to be worn on the leg of the user's knee joint, and the rehabilitation auxiliary server is used for calculation and data management. The knee joint brace device includes a single-chip microcomputer controller 10 and a lower limb anterior pressure sensor 11, a lower limb posterior pressure sensor 12 and a lower limb triaxial acceleration sensor 13 electrically connected thereto. The lower limb anterior pressure sensor 11 can obtain the pressure value when the lower limb is extended, the lower limb posterior pressure sensor 12 can obtain the pressure value when the lower limb is flexed, and the lower limb triaxial acceleration sensor 13 can obtain the pitch angle of the lower limb. First, in step S301, the single-chip microcomputer controller collects the values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor at a preset interval time (such as 100ms) during rehabilitation guidance training, and sends them to the rehabilitation auxiliary server. The three-axis acceleration sensor can identify the acceleration values ​​of the three axes XYZ, the X axis is the x value, the Y axis is the y value and the Z axis is the z value. These values ​​can be used to calculate the pitch angle of the lower limb flexion and extension process:

[0024]

[0025] Then, in step S302, the rehabilitation assistance server distinguishes each rehabilitation cycle data set of the lower limbs based on the value of the lower limb triaxial acceleration sensor. One extension and flexion of the lower limb is a rehabilitation cycle. By judging the angle change of the pitch angle, the change of the pitch angle from the minimum value to the maximum value and then back to the minimum value is a rehabilitation cycle, and the corresponding data in this time period is a rehabilitation cycle data set. Each rehabilitation cycle data set contains multiple interval time points, and each interval time point corresponds to the value of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor. Then, in step S303, the rehabilitation assistance server trains all the lower limb anterior pressure sensors and lower limb posterior pressure sensors in each rehabilitation cycle data set using a neural network model to obtain a recommended rehabilitation cycle data set.

[0026] The neural network model (either multi-layer perceptron MLP or convolutional neural network CNN) must first be trained. The training uses a training data set and a validation data set for training. The training data set can use three to five time points, which can reflect the situation at adjacent time points and train to obtain the optimal situation. The number of training data sets can be multiple, and the output is the optimal data at one time point.

[0027] The following is an illustration of the training data set. In the data set, the pitch angle calculated by the three-axis acceleration sensor is the original data, and the anterior pressure sensor of the lower limb and the posterior pressure sensor of the lower limb are the original data, which are recorded as follows: the anterior pressure sensor value of the lower limb (P1, in N), the posterior pressure sensor value of the lower limb (P2, in N) and the corresponding angle value (β_opt, in degrees). Take two data sets as an example

[0028] Dataset 1:

[0029] Time point | P1 | P2 | β_opt

[0030] 5.1 | 2.5 | 0 | 29.8

[0031] 5.2 | 3.0 | 0.4 | 30.5

[0032] 5.3 | 2.8 | 0 | 31.4

[0033] Dataset 2:

[0034] Time point | P1 | P2 | β_opt

[0035] 5.1 | 2.8 | 0.1 | 29.5

[0036] 5.2 | 2.9 | 0 | 30.2

[0037] 5.3 | 3.1 | 0 | 31.1

[0038] The above data set can be collected by the knee brace device and sent to the rehabilitation assistance server. Multiple data can be collected at the corresponding time point position as the training data set input, and the preferred data set to be output is:

[0039] Time point | P1 | P2 | β_opt

[0040] 5.2 | 5 | 0 | 30

[0041] The above data set reflects the preferred pressure value at the 30-degree position during the extension of the lower limb. Multiple data sets can be collected and sent to the neural network for pre-training to obtain a standard neural network model. The network model reflects the data at three time points and generates data at one time point. During the training process, the anterior lower limb pressure sensor value, the posterior lower limb pressure sensor value and the pitch angle value of the training data are input, and the output of the network is calculated by forward propagation. Then, the output of the network is compared with the actual preferred anterior lower limb pressure sensor value, the preferred posterior lower limb pressure sensor value and the preferred pitch angle value to calculate the loss (error). Commonly used loss functions include root mean square error (RMSE) and mean absolute error (MAE). Next, the weights and biases of the network are adjusted through the back propagation algorithm and optimizer (such as gradient descent) to gradually reduce the loss. That is, back propagation and parameter update are performed. Finally, the model is evaluated: the trained neural network model is evaluated using the validation set. By inputting the validation set, the trained model is used for prediction and compared with the actual preferred value. The error between the predicted value and the true value can be calculated, and the accuracy of the model can be measured using indicators such as root mean square error (RMSE) and mean absolute error (MAE). Based on the evaluation results, the hyperparameters of the network, such as the learning rate, the number and size of convolutional layers, etc., can be adjusted to improve the performance of the model.

[0042] The real-time data set collected can then use the pre-trained neural network to calculate the preferred lower limb anterior pressure sensor value, the preferred lower limb posterior pressure sensor value and the preferred pitch angle value one by one according to the time point, and use it as a recommended rehabilitation cycle data set. A recommended rehabilitation cycle data set includes all time points in extension and flexion and the preferred lower limb anterior pressure sensor value, the preferred lower limb posterior pressure sensor value and the preferred pitch angle value corresponding to the time point.

[0043] Then, in step S304, the rehabilitation auxiliary server sends the recommended rehabilitation cycle data set to the single-chip controller. And in step S305, during the rehabilitation auxiliary training, the user can try rehabilitation training at home. The single-chip controller drives the rotating motor of the knee joint brace device to apply force, where the force is to apply resistance, and the rotating motor enters a braking state, such as short-circuiting the two ends of the rotating motor. The user can stretch his legs for rehabilitation training, which will apply pressure to the anterior pressure sensor of the upper limb. Then, in step S306, the single-chip controller drives the rotating motor of the knee joint brace device to move according to the values ​​in the recommended rehabilitation cycle data set, when the values ​​of the current anterior pressure sensor of the lower limb, the posterior pressure sensor of the lower limb and the triaxial acceleration sensor of the lower limb are the same as the values ​​in the recommended rehabilitation cycle data set, so that the value of the triaxial acceleration sensor of the lower limb changes to the next value in the recommended rehabilitation cycle data set. That is, the rotating motor can rotate, drive the lower limbs to extend forward and maintain a certain force, and the user's legs can be trained under force. In particular, when flexing backward, without the rotating motor of the knee joint brace device of the present invention, the user's legs will not be subjected to force due to gravity, and the existing rehabilitation training will not meet the rehabilitation training requirements.

[0044] In some embodiments, the knee brace device corresponds to multiple users, and multiple users can correspond to different knee brace devices. The rehabilitation auxiliary server stores multiple users; the rehabilitation auxiliary server is used to receive rehabilitation evaluation level instructions for the recommended rehabilitation cycle data sets between different users. The evaluation level can be excellent, good, ineffective, etc. The correspondence between the rehabilitation evaluation level in the rehabilitation evaluation level instruction and the recommended rehabilitation cycle data set is recorded, and the multiple recommended rehabilitation cycle data sets with high rehabilitation evaluation levels are trained using a neural network model. A recommended rehabilitation cycle data set with a high rehabilitation evaluation level is obtained through training as an intelligent rehabilitation cycle data set, and the intelligent rehabilitation cycle data set is sent to knee brace devices of different users as an option. In this way, rehabilitation cycle data sets with good evaluation results from different users can be integrated, and intelligent rehabilitation training can be performed using this data set, without the need to go to a rehabilitation physician for the first rehabilitation training.

[0045] In the above embodiment, the rehabilitation evaluation level instruction is collected by the single-chip microcomputer controller and sent to the rehabilitation auxiliary server, such as the single-chip microcomputer controller collects the rehabilitation evaluation level clicked on the electronic screen. Or the rehabilitation auxiliary server receives it from the computer client, which can be a mobile client, a web client or an installed client. After the user performs rehabilitation training, the rehabilitation training evaluation result is input on the client, and the rehabilitation auxiliary server can collect it.

[0046] Furthermore, the knee brace device also includes a group confirmation button. After receiving the group confirmation button being pressed, the single-chip controller sends the currently collected values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor as a group of data to the rehabilitation assistance server; after the group confirmation button is pressed multiple times, multiple groups of data are sent to the rehabilitation assistance server; the rehabilitation assistance server performs separate training on each group of data, and each group of data corresponds to a recommended rehabilitation cycle data set. These multiple groups of data correspond to different actions of rehabilitation training. For example, the first group is trained with greater forward extension strength, and the second group is trained with greater backward flexion strength. Targeted training can be carried out.

[0047] Furthermore, during the rehabilitation training, the single-chip controller sends different voice commands to the speaker according to the value of the lower limb triaxial acceleration sensor. For example, when the angle becomes larger, a voice command of extension can be issued to guide the user to perform extension training, and then when the angle becomes smaller, a voice command of flexion can be issued to guide the user to perform flexion training.

[0048] Furthermore, during rehabilitation training, the single-chip controller sends different prompt information to the indicator according to the lower limb anterior pressure sensor and the lower limb posterior pressure sensor. If the pressure does not meet the standard, it will display green, and after the pressure gradually reaches the standard, it will display red, prompting the user to standardize the force.

[0049] Furthermore, the knee brace device includes an upper limb brace 14, a lower limb brace 15 and a rotating motor 16, the upper limb brace and the lower limb brace are hinged to each other, the rotating motor is arranged at the hinge, the lower limb front pressure sensor is arranged at the front of the lower limb brace, the lower limb rear pressure sensor is arranged at the rear of the lower limb brace, the lower limb triaxial acceleration sensor is arranged in the middle of the lower limb brace, and the single-chip microcomputer controller is arranged at the upper limb brace. In this way, a complete knee brace device is realized, which is easy to carry and move. Of course, the knee brace device needs to be powered, and an external power supply can be used for power supply.

[0050] Furthermore, if Figure 4 As shown, the knee joint brace device further comprises an upper limb support frame 17, which is used to be fixedly connected to the upper limb sleeve and support the upper limb sleeve, so as to achieve support for the upper limb.

[0051] In some embodiments, there are two rotating motors, which are respectively arranged on the left and right sides of the hinge, so that double-sided acceptance can be achieved. When the rotating motors are controlled, they must be controlled at the same time to ensure that the forces on both sides are the same.

[0052] Furthermore, the single chip controller sends data to the rehabilitation assistance server through an Internet of Things module or an Internet module. The present invention can realize Internet of Things connection or Internet connection and realize mobile interconnection.

[0053] It should be noted that, although the above embodiments have been described in this article, the patent protection scope of the present invention is not limited thereby. Therefore, based on the innovative concept of the present invention, changes and modifications made to the embodiments described herein, or equivalent structures or equivalent process changes made using the contents of the present invention specification and drawings, directly or indirectly applying the above technical solutions to other related technical fields, are all included in the patent protection scope of the present invention.

Claims

1. A knee joint rehabilitation auxiliary system based on electronic information hardware technology, characterized by: It comprises a knee brace device and a rehabilitation assistance server; the knee brace device comprises a single-chip microcomputer controller and a lower limb anterior pressure sensor, a lower limb posterior pressure sensor and a lower limb triaxial acceleration sensor electrically connected thereto, the lower limb triaxial acceleration sensor acquires the pitch angle of the lower limb, the single-chip microcomputer controller collects the values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor at preset intervals during rehabilitation guidance training, and sends them to the rehabilitation assistance server; the rehabilitation assistance server calculates and distinguishes each rehabilitation cycle data set of the lower limb according to the value of the lower limb triaxial acceleration sensor, each rehabilitation cycle data set comprises a plurality of interval time points, and each interval time point corresponds to the values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor; The rehabilitation auxiliary server trains all the lower limb anterior pressure sensors and lower limb posterior pressure sensors in each rehabilitation cycle data set using a neural network model to obtain a recommended rehabilitation cycle data set; The rehabilitation assistance server sends the recommended rehabilitation cycle data set to the single-chip controller; during rehabilitation assistance training, the single-chip controller drives the rotating motor of the knee joint brace device to apply force; Then, the single-chip microcomputer controller drives the rotary motor of the knee joint brace device to move according to the values ​​in the recommended rehabilitation cycle data set, when the values ​​of the current lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor are the same as the values ​​in the recommended rehabilitation cycle data set, so that the value of the lower limb triaxial acceleration sensor changes to the next value in the recommended rehabilitation cycle data set; The knee joint brace device corresponds to a plurality of users, and the rehabilitation assistance server stores a plurality of users; the rehabilitation assistance server is used to receive rehabilitation evaluation level instructions of the recommended rehabilitation cycle data sets between different users, record the corresponding relationship between the rehabilitation evaluation level in the rehabilitation evaluation level instruction and the recommended rehabilitation cycle data set, train the plurality of the recommended rehabilitation cycle data sets with high rehabilitation evaluation levels using a neural network model, obtain a recommended rehabilitation cycle data set with high rehabilitation evaluation level as an intelligent rehabilitation cycle data set through training, and send the intelligent rehabilitation cycle data set as an option to the knee joint brace devices of different users; The knee joint brace device also includes a group confirmation button. After receiving a signal that the group confirmation button has been pressed, the single-chip controller sends the currently collected values ​​of the lower limb anterior pressure sensor, the lower limb posterior pressure sensor and the lower limb triaxial acceleration sensor as a group of data to the rehabilitation assistance server; after the group confirmation button is pressed multiple times, multiple groups of data are sent to the rehabilitation assistance server; the rehabilitation assistance server performs separate training on each group of data, and each group of data corresponds to a recommended rehabilitation cycle data set.

2. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 1 is characterized in that: The rehabilitation evaluation level instruction is collected by the single-chip microcomputer controller and sent to the rehabilitation assistance server, or the rehabilitation assistance server receives it from the computer client.

3. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 1 is characterized in that: During rehabilitation-assisted training, the single-chip microcomputer controller sends different voice instructions to the speaker according to the value of the lower limb triaxial acceleration sensor.

4. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 1 is characterized in that: During the rehabilitation auxiliary training, the single chip controller sends different prompt information to the indicator according to the lower limb front pressure sensor and the lower limb back pressure sensor.

5. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 1 is characterized in that: The knee joint brace device comprises an upper limb sleeve, a lower limb sleeve and a rotating motor, wherein the upper limb sleeve and the lower limb sleeve are hinged to each other, the rotating motor is arranged at the hinge, the lower limb front side pressure sensor is arranged at the front side of the lower limb sleeve, the lower limb rear side pressure sensor is arranged at the rear side of the lower limb sleeve, the lower limb triaxial acceleration sensor is arranged in the middle of the lower limb sleeve, and the single-chip microcomputer controller is arranged at the upper limb sleeve.

6. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 5 is characterized in that: The knee joint protector device also includes an upper limb support frame, which is used to be fixedly connected to the upper limb set and support the upper limb set.

7. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 5 is characterized in that: There are two rotating motors, which are respectively arranged on the left and right sides of the hinge.

8. The knee joint rehabilitation auxiliary system based on electronic information hardware technology according to claim 1 is characterized in that: The single chip microcomputer controller sends data to the rehabilitation assistance server through an Internet of Things module or an Internet module.

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