A training method and system based on intelligent traction net rack

Abstract

This invention relates to the field of rehabilitation training technology, and more particularly to a training method and system based on an intelligent traction net frame. The method includes: collecting the tension force on a target rope; determining whether the tension force reaches a preset value; if not, tightening the target rope; if so, stopping the tightening of the target rope; recording the maximum height the user pulls on the target rope when using a first part to pull a second part; collecting the height the user pulls on the target rope during training; determining whether the height is greater than or equal to a reference height; if so, recording the training as successful; counting the number of successful training sessions for the user; the reference height is positively correlated with the maximum height. This method improves the effectiveness and safety of user training.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation training technology. More specifically, this invention relates to a training method and system based on an intelligent traction frame. Background Technology

[0002] Traction meshwork is an auxiliary device widely used in the field of medical rehabilitation, such as... Figure 1 As shown, the traction frame mainly consists of a bed, a frame, and traction accessories. Users can use the traction frame to perform traction training on specific parts of the body, achieving joint range of motion exercises, muscle training, and relaxation and adjustment training, thereby improving limb function.

[0003] However, the length of existing traction netting ropes / traction lines cannot be automatically adjusted according to the user's actual situation. They are generally set by the therapist based on experience, making them susceptible to subjective factors. Furthermore, the training difficulty is also set by the therapist or the user themselves. This can result in the difficulty being set too low, leading to poor training effects, or too high, causing user fatigue and affecting training efficiency.

[0004] Therefore, how to set the corresponding training difficulty and traction line length according to the actual situation of users in order to improve the training effect is an urgent problem to be solved. Summary of the Invention

[0005] To address the aforementioned technical problem of mediocre training results when using traction net frames, the present invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a training method based on an intelligent traction net frame, comprising: collecting the tension force on a target rope; determining whether the tension force reaches a preset value; if not, tightening the target rope; if so, stopping the tightening of the target rope; recording the maximum height the user pulls on the target rope when using a first part to pull a second part; collecting the height the user pulls on the target rope during training; determining whether the height is greater than or equal to a reference height; if so, recording the training as successful; counting the number of successful training sessions by the user; wherein the reference height is positively correlated with the maximum height.

[0007] Furthermore, the tensile force and the height are collected by sensors.

[0008] Furthermore, the calculation expression for the reference height is:

[0009] h = k × h max ;

[0010] In the formula, h is the reference height, k is the correction coefficient, and h max This refers to the maximum height.

[0011] Furthermore, the method of the present invention also includes: adjusting the correction coefficient according to the proportion of the number of successful training sessions to the total number of training sessions within a preset time period.

[0012] Furthermore, the method of the present invention also includes: adjusting the correction coefficient or training time based on the historical training count and the current training count.

[0013] Furthermore, before training, the method includes: determining the user's training method based on the user's diseased area information, wherein the training method includes active training and passive training.

[0014] Furthermore, in response to the training method being passive training, the correction coefficient of the current training remains unchanged, and the maximum height is a preset height.

[0015] Furthermore, the method of the present invention also includes: displaying or broadcasting the height and the reference height.

[0016] Furthermore, the method of the present invention further includes: outputting a training report, wherein the training report includes at least the number of successful training sessions.

[0017] In a second aspect, the present invention provides a training system based on an intelligent traction grid, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a training method based on an intelligent traction grid as described in the first aspect is implemented.

[0018] The beneficial effects of this invention are as follows: The method automatically adjusts the length of the traction rope according to the individual user's situation, allowing for effective training based on that length. This avoids safety issues caused by excessively long or short traction ropes, ensuring user training safety. Furthermore, by assessing the user's actual situation (maximum height), the method matches a suitable training difficulty level, ensuring safe and effective training within the user's capabilities, thus improving training effectiveness. In addition, by dynamically adjusting the baseline height or training difficulty based on the user's training progress, the training intensity is further optimized, better matching the user's abilities and further enhancing training effectiveness. Attached Figure Description

[0019] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0020] Figure 1 This is a schematic illustration of a traction grid structure in the prior art;

[0021] Figure 2 This is a flowchart schematically illustrating a training method based on an intelligent traction grid according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram illustrating the structure of a training system based on an intelligent traction grid according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] It is understood that, in order to implement the method of the present invention, a distance sensor needs to be installed on the intelligent traction frame to collect the distance the traction line / rope moves (the height the user's corresponding part is lifted). In addition, the intelligent traction frame also needs to be equipped with a tension sensor to collect the tension force on the rope.

[0026] Figure 2 This is a flowchart illustrating a training method based on an intelligent traction grid according to an embodiment of the present invention.

[0027] In a first aspect, the present invention provides a training method based on the above-described intelligent traction network structure, the method comprising:

[0028] S101. Determine the training method and training areas.

[0029] Specifically, users can select training methods and the parts to be trained according to their actual training needs. In the training method of the present invention, users can select two parts for coordinated training, that is, the movement of one part will cause the other part to move as well.

[0030] In one embodiment, the training areas include four parts: the left upper limb, the right upper limb, the left lower limb, and the right lower limb. The upper limb may include the knee joint and ankle joint, etc., and the lower limb may include the elbow joint and wrist joint, etc.

[0031] Furthermore, the training methods include active training and passive training. Active training involves the user using a healthy part of their body to move the diseased part during training, with the user providing the traction force. Alternatively, a healthy part can be used to move another healthy part (the active training of this invention is applicable to both diseased and healthy individuals). Passive training involves the intelligent traction frame providing the traction force, i.e., the intelligent traction frame (motor) moves / pulls the part of the body to be trained. In this embodiment, passive training can train only a single part or two parts simultaneously.

[0032] Furthermore, the training method can be determined based on the location of the user's diseased areas. Specifically, if all four areas of the user's body are affected, passive training is performed; if one of the four areas is normal, active training can be chosen. This is because active training requires using the healthy area to pull on the affected (or healthy) areas to facilitate subsequent training.

[0033] During active training, users should select at least two body parts from the four mentioned above for training, based on their needs. It's important to note that at least one body part must be healthy during active training. Furthermore, users can train all four body parts simultaneously, but these training sessions must not conflict. For example, if the user selects the left upper limb and left lower limb for coordinated training, then the next possible combination must be the right upper limb and right lower limb, not the left upper limb and right lower limb, because a healthy body part can only train one other body part, not two.

[0034] By using healthy body parts to train affected areas, users can improve their body coordination and promote limb function recovery. Furthermore, this avoids potential muscle damage caused by direct traction from a motor (such as muscle strain due to excessively high traction settings based on experience). Because the traction is provided by the user, the appropriate traction level can be adjusted or the training stopped immediately if fatigue or pain occurs, ensuring user safety during training.

[0035] Once determined, the user inputs the training method and training area into the system. Upon receiving the training method and training area, the system initializes. Initialization includes determining the target rope for subsequent tightening.

[0036] S102. Collect the tension on the target rope and determine whether the tension has reached a preset value. If not, tighten the target rope; if so, stop tightening the target rope.

[0037] Before training begins, the user lies on the bed frame and places the two body parts to be trained in their corresponding loops. The system collects the tension on the loops / ropes (target ropes) corresponding to the user's body parts to be trained and determines whether the tension is greater than or equal to a preset value. If so, the tightening of the target rope stops; otherwise, it tightens. It should be noted that tightening stops only when the tension on both loops reaches the preset value. Furthermore, the two loops can be connected by a rope, allowing the healthy body part to pull / traction the affected body part for training. In optional embodiments, different ropes can be used for connection, as long as the movement of one loop causes the other loop to move accordingly.

[0038] In one embodiment, the preset value is set to 0.5 kg. In other optional embodiments, those skilled in the art can make adaptive adjustments according to actual needs.

[0039] Understandably, the rope length after tightening is adapted to individual user differences (allowing users of different heights to train in a suitable position), avoiding safety issues caused by excessively long or short traction lines. For example, a traction line that is too short may cause excessive pulling on the body, while a traction line that is too long may not be able to effectively stabilize the body, leading to accidents such as falls. This improves the safety and comfort of users training with the traction net frame.

[0040] S103. Record the maximum height the target rope is pulled when the user uses the first pull on the second part.

[0041] The first part represents the user's healthy parts, and the second part represents the user's diseased or healthy parts.

[0042] Specifically, when a user uses a healthy body part to pull the rope within their tolerance range, the maximum height the rope can be pulled is recorded. This maximum height accurately reflects the user's training limit (during this process, the user will immediately stop if they experience pain or other discomfort). Understandably, exceeding this maximum height during training may cause damage to affected areas due to excessive force, or lead to fatigue, thus reducing training efficiency. Therefore, training within this limit ensures safe and effective training for the user.

[0043] Understandably, determining the maximum height is equivalent to assessing the user's capabilities. The greater the maximum height, the greater the user's ability to withstand the load and the greater the traction that can be provided.

[0044] S104. Determine the user's reference height based on the maximum height, and count the number of successful training sessions based on the reference height.

[0045] In one embodiment, the reference height is positively correlated with the maximum height. Specifically, the formula for calculating the reference height is:

[0046] h = k × h max ;

[0047] In the formula, h is the reference height, k is the correction coefficient, and h max This refers to the maximum height.

[0048] The correction coefficient can be set between 0 and 1. In this embodiment, the correction coefficient is set to 0.2. In optional embodiments, those skilled in the art can select the coefficient according to actual needs. When the training time, number of training sessions, or user ability are sufficient, the correction coefficient can exceed 1.

[0049] Furthermore, when a user pulls to a height greater than or equal to a baseline height during training, the user is recorded as having successfully completed one training session. In one embodiment, the baseline height can be displayed or announced to inform the user whether their training was successful. If the training was unsuccessful, the user will be motivated to reach the baseline height, thereby increasing their motivation and ensuring the effectiveness of their training. It can be understood that the baseline height corresponds to the training difficulty; as the baseline height increases, the traction force required by the user to reach it also increases, thus increasing the training difficulty; similarly, as the baseline height decreases, the corresponding difficulty decreases.

[0050] For example, when a user pulls on an affected area with a healthy part of their body, the screen can display the height the user has pulled and the baseline height in real time. If the baseline height is not reached, the user knows they need to continue pulling to succeed, thus ensuring the effectiveness of the training. Alternatively, a voice broadcast method can be chosen. Specifically, when the baseline height is reached, a voice broadcast prompts the user that training has been successful, letting them know whether the training was successful (e.g., "Baseline height reached, training successful once"). Compared to the display method, the voice broadcast method allows users to focus their attention more intently on the training.

[0051] In an optional embodiment, the system may not display or announce anything, allowing users to train according to their own abilities, directly counting the number of successful training sessions, and displaying the results after training is complete.

[0052] By selecting a height lower than the maximum height as the benchmark height, users can train safely, avoiding problems such as fatigue and strain caused by directly using the maximum height or a height higher than the maximum height (which is prone to occur when determining the height based on experience). This ensures the continuity of training, thereby ensuring the user's training safety and effectiveness.

[0053] Furthermore, when determining the training methods and training areas, it is also necessary to determine the training mode, which includes a fixed mode and a progressive mode. The fixed mode refers to a fixed baseline height, while the progressive mode refers to a mode where the baseline height changes after preset conditions are met, thereby dynamically altering the training difficulty for the user.

[0054] It should be noted that during training, users generally need to do multiple sets of training (usually 3 to 4 sets), with each set lasting between 10 and 15 minutes, depending on the actual needs.

[0055] Specifically, by counting the number of successful training sessions per user, and observing changes in the number of successful sessions, the training effect can be visually demonstrated. Furthermore, the number of successful sessions can also determine whether the current baseline difficulty is suitable for the user. If a user performs many training sessions in a single set, and the success rate is also high, it indicates that the current baseline difficulty (training difficulty) is too low and cannot meet the user's training needs. In this case, the baseline difficulty can be appropriately increased to ensure the user's training effectiveness. Similarly, if a user performs few training sessions in a single set, and the success rate is very low, it indicates that the current baseline difficulty is too high for the user. In this case, the baseline difficulty can be appropriately decreased to ensure the continuity of training and the user's motivation.

[0056] Specifically, after a user completes a training session, if the number of successful training sessions accounts for 95% of the total training sessions, the baseline height can be increased. Conversely, if the number of successful training sessions accounts for 65% of the total training sessions, the baseline height can be decreased. In one embodiment, the baseline height can be adjusted by changing the magnitude of a correction coefficient, for example, by increasing or decreasing the correction coefficient by 0.1 or 0.2. In optional embodiments, those skilled in the art can change the values ​​of the above parameters according to actual needs. Furthermore, the user performs the next training session based on the newly determined baseline height.

[0057] In an optional embodiment, the correction coefficient or correction time can be adjusted based on the number of training iterations in the previous training session (i.e., historical training iterations) and the number of training iterations in the current training session (i.e., current training iterations). It is understood that a significant difference between the number of training iterations in the current training session and the previous training session indicates that the baseline height used in the current training may not be suitable for the user, thus requiring adjustment. Specifically, if the current training iterations are 8 or more fewer than the previous training session, it indicates that the current baseline height is too difficult for the user, easily causing fatigue, thus reducing the number of pulls. In this case, the baseline height can be reduced, the time per training session shortened, or the number of training sets reduced to ensure the user's training effectiveness and motivation. Conversely, if the current training iterations are 5 or more more than the previous training session, it indicates that the current baseline height is less difficult for the user, allowing for good user adaptation and sufficient training capacity, thus increasing the number of pulls. In this case, the baseline height can be increased, the time per training session extended, or the number of training sets increased.

[0058] In another embodiment, the baseline height can be changed by comprehensively considering changes in both the number of successful training sessions and the number of training sessions. Specifically, if both the number of training sessions and the number of successful training sessions show a significant increasing trend, the baseline height can be increased; if both the number of training sessions and the number of successful training sessions show a significant decreasing trend, the baseline height can be decreased; if the number of training sessions increases but the number of successful training sessions decreases, but the change is not significant, the current baseline height can be maintained.

[0059] It is understandable that changing the reference height is equivalent to changing the traction force (a certain traction force is required to reach the reference height), and this traction force also needs to be within the range that the user can bear. Compared with other training methods (such as adjusting the training difficulty by directly adding external weight), the method of this invention ensures both the user's training safety and the training effect by matching the training difficulty based on the maximum height.

[0060] It should be noted that users with affected areas in all four locations can only choose the fixed mode for training. This is because the fixed mode uses a fixed reference height (i.e., the correction coefficient remains unchanged), which can prevent secondary injury to the user. In one embodiment, a motor pulls the user's affected area to a preset height. After reaching the preset height, the motor pulls the user's affected area down, repeating the above process multiple times, thereby achieving passive training for the user. The preset height can be set to 50mm. The traction force can be set according to the user's actual situation, but it must follow the principle of gradual progression (i.e., use a small traction force at the beginning of training, and increase the traction force as the training time and number of training sessions increase).

[0061] Furthermore, users undergoing passive training can omit the maximum height data and directly use a preset height for training. In an optional embodiment, for areas with milder symptoms, when they can provide greater pulling force (e.g., the area with milder symptoms can pull another area a distance exceeding 50mm), the maximum height reached when the area with milder symptoms pulls the other area can also be collected, and both areas can be trained based on this maximum height. By training two areas simultaneously, training efficiency can be improved.

[0062] In one embodiment, the method of the present invention further includes: outputting a training report, which includes the user's basic information, training method, training body part, training time, number of successful training sessions per set, total number of training sessions per set, number of successful training sessions per unit time, etc., which can be set according to actual needs by those skilled in the art. The training report provides a clear and intuitive understanding of the user's training status, and analysis of historical training reports can provide a reference for the reference height, number of training sets, training time, etc., used in the next training session. For example, if the previous two historical training reports show that the number of training sessions per set has increased and the success rate has also increased, then in the next training session, the training time can be appropriately extended, the number of training sets increased, or the reference height increased, etc.

[0063] It is understood that the method of this invention has a wide range of applications, applicable not only to normal individuals and those with mild illnesses (who have both normal and diseased areas among the four body parts), but also to those with severe illnesses (who have diseased areas in all four body parts). Furthermore, both normal and mildly ill individuals have their own specific training parameters (different maximum heights and different baseline heights), resulting in more targeted training.

[0064] Figure 3 This is a schematic diagram illustrating the structure of a training system based on an intelligent traction grid according to this embodiment.

[0065] In a second aspect, the present invention also provides a training method system based on an intelligent traction network frame. For example... Figure 3 As shown, the training system based on the intelligent traction grid includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a training method based on the intelligent traction grid according to the first aspect of the present invention.

[0066] The training system based on the intelligent traction grid also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0067] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions that can be stored or otherwise maintained by such a computer-readable medium.

[0068] In the description of this specification, "multiple" means at least two, such as two, three or more, unless otherwise explicitly specified. Furthermore, the steps described above are for clarity only; in implementation, they can be combined into one step or some steps can be broken down into multiple steps, as long as they include the same logical relationships.

[0069] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A training system based on an intelligent traction grid, characterized in that, The system includes a processor and a memory, the memory storing computer program instructions. When the processor executes the computer program instructions, it implements a training method based on an intelligent traction grid structure. The method includes: The tension in the rope used to collect the data; Determine whether the tension has reached a preset value. If not, tighten the target rope; if so, stop tightening the target rope. Record the maximum height the target rope is pulled when the user uses the first part to pull the second part; The height of the target rope is collected when the user pulls it during training. Determine whether the height is greater than or equal to the reference height. If so, record the training as successful and count the number of successful training sessions for the user. The reference height is positively correlated with the maximum height. The formula for calculating the reference height is: ； In the formula, The reference height is, As a correction factor, k ranges from 0 to 1. The maximum height; If the difference between the number of training sessions in the current training session and the number of training sessions in the previous training session is greater than the first threshold, increase the baseline height or extend the training time for a single session; if the difference between the number of training sessions in the previous training session and the number of training sessions in the current training session is greater than the second threshold, decrease the baseline height or shorten the training time for a single session.

2. The training system based on an intelligent traction network frame according to claim 1, characterized in that, The tensile force and the height are collected by sensors.

3. The training system based on an intelligent traction grid according to claim 1, characterized in that, Also includes: The correction coefficient is adjusted according to the proportion of successful training sessions to the total number of training sessions within a preset time period.

4. The training system based on an intelligent traction grid according to claim 1, characterized in that, Before training, the process also includes: determining the user's training system based on the user's diseased area information, wherein the training system includes active training and passive training.

5. The training system based on an intelligent traction grid according to claim 4, characterized in that, In response to the training system being passive training, the correction coefficient for the current training remains unchanged, and the maximum height is a preset height.

6. The training system based on an intelligent traction grid according to claim 1, characterized in that, Also includes: Display or announce the height and the reference height.

7. The training system based on an intelligent traction grid according to claim 1, characterized in that, Also includes: Output a training report, which includes at least the number of successful training sessions.

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