Personalized upper limb rehabilitation training method and system based on human motion model

Abstract

The invention discloses an upper limb personalized rehabilitation training method based on a human body motion model. The method comprises the following steps: S1, fixing wearable positioning sensors on shoulder joints, elbow joints and wrist joints of human body upper limbs through bandages; s2, the computer initializes internal data of the wearable positioning sensor through Bluetooth connection of the wearable positioning sensor; s3, one of the multiple upper limb teaching actions is selected as a target training upper limb teaching action, and the arm posture of the teaching model is changed through built-in data; s4, according to corresponding rehabilitation actions (target training upper limb teaching actions) in the mode selected by the trainee, the computer processes the collected exercise data of the trainee, and the upper limb exercise state of the trainee is mapped to a virtual scene; s5, displaying the action difference between the user arm model and the teaching doctor in the virtual scene for the training personnel, and enabling the training personnel to adjust the actions by themselves; and S6, comparing the joint angle data of the trainee with the standard joint angle data.

Description

Technical Field

[0001] The present invention relates to the technical field of upper limb rehabilitation, and in particular to a personalized upper limb rehabilitation training method and system based on a human body movement model. Background Art

[0002] Brain function damage caused by cerebral palsy, brain trauma, etc. can also lead to limb movement dysfunction, bringing a heavy burden to patients, their families and society.

[0003] Rehabilitation training based on intelligent equipment is an important means to restore patients' motor function, and formulating personalized rehabilitation training prescriptions for different patients' conditions is an important condition to ensure the training effect. However, most of the current rehabilitation training equipment has fixed training tasks and difficulty or is only divided into fixed levels, and cannot be adjusted according to the patient's functional level, resulting in a mismatch between the training content and difficulty and the patient's current functional level. Excessive difficulty will dampen the patient's enthusiasm for training, and too simple will not achieve the best training effect. In addition, some rehabilitation equipment that drives patients to perform passive training has a preset motion trajectory designed according to the normal human range of motion. However, due to the limited range of motion of some joints, patients cannot reach the preset range of motion. Forced training will pose a risk of injuries such as muscle strain and joint dislocation.

[0004] Therefore, it is necessary to personalize the training content, difficulty and movement trajectory according to the patient's limb size parameters and joint range of motion. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an upper limb personalized rehabilitation training method and system based on a human motion model.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The personalized upper limb rehabilitation training method based on the human movement model includes:

[0008] Step S1, fixing the wearable positioning sensor on the shoulder joint, elbow joint and wrist joint of the upper limb of the human body through a strap;

[0009] Step S2, the computer is connected via the Bluetooth MAC address of the wearable positioning sensor to initialize the internal data of the wearable positioning sensor;

[0010] Step S3, selecting one of the multiple upper limb teaching movements as a target training upper limb teaching movement, and changing the arm posture of the teaching model through the built-in data;

[0011] Step S4, according to the corresponding rehabilitation action (target training upper limb teaching action) in the mode selected by the trainee, the computer processes the collected trainee's motion data and maps the trainee's upper limb motion state to the virtual scene;

[0012] Step S5, showing the difference between the user's arm model and the teaching doctor's movements in the virtual scene to the trainer, and the trainer adjusts the movements by himself;

[0013] Step S6, the trainee's joint angle data (upper limb movement parameters) are compared with the standard joint angle data to evaluate the quality of the rehabilitation exercise performed by the trainee.

[0014] Preferably, in step S4, the trainee follows the movement of the teaching doctor model, and the computer correspondingly obtains posture data at multiple joint positions, and uses the received posture data to perform analytical processing to obtain the movement posture vectors at the shoulder joint, elbow joint and wrist joint.

[0015] Preferably, in step S4, corresponding sensor analysis data is assigned to the variables on each joint of the virtual arm model to achieve synchronous movement of the virtual arm and the sensor.

[0016] An upper limb personalized rehabilitation training system based on a human motion model comprises a human motion model, an upper limb rehabilitation mode selection module, a first motion posture generation module, a second motion posture generation module, an upper limb motion data acquisition module and a motion posture display module; the human motion model is connected to the upper limb rehabilitation mode selection module, the first motion posture generation module is respectively connected to the upper limb rehabilitation mode selection module and the motion posture display module, the upper limb motion data acquisition module is connected to the second motion posture generation module, and the second motion posture generation module is connected to the motion posture display module.

[0017] Preferably, the human motion model is used to store multiple upper limb teaching movements and upper limb movement parameters corresponding to each of the upper limb teaching movements.

[0018] Preferably, the upper limb rehabilitation mode selection module is used to select an upper limb teaching movement from the human motion model as a target training upper limb teaching movement.

[0019] Preferably, the first movement posture generation module is used to generate the upper limb movement posture of the teaching doctor model according to the upper limb movement parameters corresponding to the target training upper limb teaching movement.

[0020] Preferably, the upper limb motion data acquisition module is used to collect the upper limb motion data of the trainee, and send the collected upper limb motion data to the second motion posture generation module.

[0021] Preferably, the second motion posture generation module is used to generate the upper limb motion posture of the training personnel model according to the upper limb motion data.

[0022] Preferably, the movement posture display module is used to display the upper limb movement posture of the teaching doctor model based on virtual reality according to the upper limb teaching action selected by the upper limb rehabilitation mode selection module. The movement posture display module is also used to overlap the upper limb movement posture of the teaching doctor model when displaying the upper limb movement posture of the trainer model based on virtual reality. Both the trainer model and the teaching doctor model are virtual human models.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention designs a personalized upper limb rehabilitation training method and system based on a human motion model. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0026] Figure 1 A schematic flow chart of the method proposed in the present invention;

[0027] Figure 2 This is a schematic diagram of the system flow proposed by the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0029] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the features defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more; in addition, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a communication between the two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] Reference Figure 1 and Figure 2 As shown, the upper limb personalized rehabilitation training method based on the human body motion model includes: step S1, fixing the wearable positioning sensor on the shoulder joint, elbow joint and wrist joint of the human upper limb by means of a strap; step S2, the computer initializes the internal data of the wearable positioning sensor through the Bluetooth MAC address connection of the wearable positioning sensor; step S3, selecting one from multiple upper limb teaching movements as the target training upper limb teaching movement, and changing the arm posture of the teaching model through the built-in data; step S4, according to the corresponding rehabilitation movement (target training upper limb teaching movement) in the mode selected by the trainee, the computer processes the collected training personnel's motion data, and maps the training personnel's upper limb motion state to the virtual scene; step S5, showing the training personnel the difference in movement between the user's arm model and the teaching doctor in the virtual scene, and the training personnel adjust the movement by themselves; step S6, comparing the training personnel's joint angle data (upper limb movement parameters) with the standard joint angle data, and evaluating the quality of the rehabilitation movement performed by the training personnel.

[0031] Among them, in step S4, the trainee follows the movement of the teaching doctor model, and the computer obtains the posture data at multiple joint positions accordingly, and uses the received posture data to perform analysis and processing to obtain the motion posture vectors at the shoulder joint, elbow joint and wrist joint; in step S4, the corresponding sensor analysis data is assigned to the variables on each joint of the virtual arm model to achieve synchronous movement of the virtual arm and the sensor.

[0032] An upper limb personalized rehabilitation training system based on a human motion model comprises a human motion model, an upper limb rehabilitation mode selection module, a first motion posture generation module, a second motion posture generation module, an upper limb motion data acquisition module and a motion posture display module; the human motion model is connected to the upper limb rehabilitation mode selection module, the first motion posture generation module is respectively connected to the upper limb rehabilitation mode selection module and the motion posture display module, the upper limb motion data acquisition module is connected to the second motion posture generation module, and the second motion posture generation module is connected to the motion posture display module.

[0033] The human motion model is used to store multiple upper limb teaching movements and upper limb movement parameters corresponding to each of the upper limb teaching movements; the upper limb rehabilitation mode selection module is used to select an upper limb teaching movement from the human motion model as a target training upper limb teaching movement.

[0034] The first motion posture generation module is used to generate the upper limb motion posture of the teaching doctor model according to the upper limb motion parameters corresponding to the target training upper limb teaching action; the upper limb motion data acquisition module is used to collect the upper limb motion data of the trainee and send the collected upper limb motion data to the second motion posture generation module.

[0035] The second motion posture generation module is used to generate the upper limb motion posture of the trainee model according to the upper limb motion data; the motion posture display module is used to display the upper limb motion posture of the teaching doctor model based on virtual reality according to the upper limb teaching action selected by the upper limb rehabilitation mode selection module; the motion posture display module is also used to overlap and display the upper limb motion posture of the teaching doctor model when displaying the upper limb motion posture of the trainee model based on virtual reality; the trainee model and the teaching doctor model are both virtual human body models.

[0036] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0037] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An upper limb personalized rehabilitation training method based on a human motion model, characterized in that: include: Step S1, fixing the wearable positioning sensor on the shoulder joint, elbow joint and wrist joint of the upper limb of the human body through a strap. Step S2: The computer is connected via the Bluetooth MAC address of the wearable positioning sensor to initialize the internal data of the wearable positioning sensor. Step S3, select one upper limb teaching action from multiple upper limb teaching actions as a target training upper limb teaching action, and change the arm posture of the teaching model through built-in data. Step S4, according to the corresponding rehabilitation action (target training upper limb teaching action) in the mode selected by the trainee, the computer processes the collected trainee's motion data and maps the trainee's upper limb motion state into the virtual scene. Step S5, showing the trainees the differences in movements between the user's arm model and the teaching doctor in the virtual scene, and the trainees adjust the movements by themselves. Step S6, the trainee's joint angle data (upper limb movement parameters) are compared with the standard joint angle data to evaluate the quality of the rehabilitation exercise performed by the trainee.

2. The upper limb personalized rehabilitation training method based on the human motion model according to claim 1, characterized in that In step S4, the trainee follows the movement of the teaching doctor model, and the computer obtains the posture data at multiple joint positions accordingly. The received posture data is analyzed and processed to obtain the movement posture vectors at the shoulder joint, elbow joint and wrist joint.

3. The upper limb personalized rehabilitation training method based on the human motion model according to claim 2 is characterized in that ,In the step S4, the corresponding sensor parsing data is assigned to the ,variables on each joint of the virtual arm model to achieve ,synchronous movement of the virtual arm and the sensor.

4. The upper limb personalized rehabilitation training system based on human motion model is characterized by , including a human body movement model, an upper limb rehabilitation mode selection module, a first movement posture generation module, a second movement posture generation module, an upper limb movement data acquisition module and a movement posture display module; the human body movement model is connected to the upper limb rehabilitation mode selection module, the first movement posture generation module is respectively connected to the upper limb rehabilitation mode selection module and the movement posture display module, the upper limb movement data acquisition module is connected to the second movement posture generation module, and the second movement posture generation module is connected to the movement posture display module.

5. The upper limb personalized rehabilitation training system based on the human motion model according to claim 4 is characterized in that The human motion model is used to store multiple upper limb teaching movements and upper limb movement parameters corresponding to each of the upper limb teaching movements.

6. The upper limb personalized rehabilitation training system based on the human motion model according to claim 5, characterized in that ,The upper limb rehabilitation mode selection module is used to select an upper limb teaching movement from the human motion model as a target training upper limb teaching movement.

7. The upper limb personalized rehabilitation training system based on the human motion model according to claim 6, characterized in that ,The first motion posture generation module is used to generate the upper limb motion posture of the teaching doctor model according to the upper limb motion parameters corresponding to the target training upper limb teaching action.

8. The upper limb personalized rehabilitation training system based on the human motion model according to claim 7, characterized in that The upper limb motion data acquisition module is used to collect the upper limb motion data of the trainee and send the collected upper limb motion data to the second motion posture generation module.

9. The upper limb personalized rehabilitation training system based on the human motion model according to claim 4, characterized in that ,The second motion posture generation module is used to generate the upper limb motion posture of the training ,personnel model based on the upper limb motion data.

10. The upper limb personalized rehabilitation training system based on the human motion model according to claim 4, characterized in that The movement posture display module is used to display the upper limb movement posture of the teaching doctor model based on virtual reality according to the upper limb teaching action selected by the upper limb rehabilitation mode selection module. The movement posture display module is also used to overlap the upper limb movement posture of the teaching doctor model when displaying the upper limb movement posture of the trainer model based on virtual reality. The trainer model and the teaching doctor model are both virtual human models.

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