A finger anti-compensation training system and method based on dynamic floating detection

Abstract

The application discloses a finger anti-compensation training system and method based on dynamic floating detection, and belongs to the technical field of finger motor function training. The system comprises a master control unit, a display interaction unit, a five-finger independent training key unit, a single-key floating driving unit and a high-precision pressure acquisition unit; during training, the target finger key is locked and stationary, and the non-target finger key is subjected to up-down reciprocating floating movement; according to the dynamic pressure change characteristics in the floating process, the explicit compensation of finger lifting off the key surface and the implicit compensation of finger sticking to the key are simultaneously recognized; and a hierarchical pass training process is set. The application makes up for the detection short board of the static pressure detection scheme, does not need individual calibration, and can be adapted to the basic skill training of musical instruments and the general hand function training scene.

Description

Technical Field

[0001] This invention relates to the fields of finger motor function training and human-computer interaction training technology, specifically to a finger anti-compensation training system and method that can identify implicit finger compensatory tension, used to correct finger compensatory force habits and train the ability of a single finger to support independently, and can be applied to the basic skill correction training of musical instruments such as piano. Background Technology

[0002] Independent finger control is a core skill in musical instrument playing and hand function training, requiring that when a single finger provides support, the muscles of the other fingers remain relaxed. Existing finger training technologies mainly fall into four categories, each with varying shortcomings in terms of equipment structure and testing methods: The first type is the spring-resistance finger training device, which relies on a built-in spring to provide feedback on the pressing force. It can only simulate the feel of pressing a key and train the pressing force of the fingers. This type of device has no pressure detection capability and no corresponding compensation state judgment method. It cannot identify the muscle compensation state of non-force-using fingers at all, nor can it distinguish the difference between naturally relaxed fingers and fingers that are tense when pressed against the keys. The training quality depends entirely on the user's own judgment, which can easily lead to incorrect force application habits. The second type is passive finger movement devices, which use mechanical structures to drive the fingers to complete flexion and extension movements. This type of solution is only suitable for early rehabilitation assistance for limb dysfunction. During training, the fingers passively follow the movement and cannot develop active support and control abilities. It also completely lacks compensatory recognition functions and corresponding detection methods, making it unsuitable for training scenarios such as playing musical instruments that require active and precise control of force. The third category is wearable electromyography (EMG) sensing solutions, which use EMG electrodes to collect muscle electrical signals to determine muscle tension. While this solution can theoretically identify muscle compensation, it faces significant obstacles to practical application: There are obvious individual differences in muscle structure, skin impedance, and force exertion patterns among different users; the baseline value of muscle relaxation varies from person to person; and personalized calibration is required for each new user, resulting in high adaptation and maintenance costs, making it difficult to widely apply in civilian musical instrument training scenarios. Furthermore, the detection method relies on EMG feature modeling, leading to high algorithm complexity. The fourth type is the static pressure-based finger training system, which uses a fixed pressure sensor structure. The keys remain stationary, and the system determines whether the finger has lifted off the key surface by collecting static pressure values ​​and relying on a fixed threshold. This type of solution also has significant drawbacks: when the trainee's finger muscles are tense and compensating, the finger can remain pressed against the key surface without lifting off, and the pressure values ​​still fall within the preset relaxation range. The system cannot recognize the compensatory state of finger muscle tension; moreover, the detection method is only based on static pressure thresholds, which can only identify explicit compensation of finger lifting off the key surface, but cannot identify implicit compensation of finger tension while pressed against the key. This causes the training judgment to be distorted, and it cannot truly achieve finger relaxation and independent force exertion training, resulting in poor training effects. Currently, the industry lacks a training system and corresponding dynamic detection method that is simple in structure, requires no individual calibration, and can simultaneously identify both explicit bond compensation and implicit bond compensation. Summary of the Invention

[0003] The technical problem to be solved by the present invention This invention aims to overcome the shortcomings of existing technologies, addressing the common deficiencies in current finger training techniques such as lack of detection capabilities, limited training modes, single detection methods, and high adaptation costs. It provides a finger anti-compensation training system and method based on dynamic floating detection. This invention focuses on solving the problem that existing static pressure training systems cannot identify implicit compensation when fingers are touching keys but muscles are tense. It also compensates for the lack of compensation detection capabilities in spring resistance devices, the inability of passive motion devices to train active support capabilities, and the high individual calibration costs of wearable electromyography solutions. Through a dynamically floating mechanical structure combined with a dynamic pressure feature recognition method, it achieves comprehensive detection of finger compensation behavior, improving the accuracy of determining independent finger force exertion training and the system's universality. Technical solution (1) Training system A finger anti-compensation training system based on dynamic floating detection includes a main control unit, a display and interaction unit, a five-finger independent training button unit, a single-key floating drive unit, and a high-precision pressure acquisition unit. Each finger corresponds to a five-finger independent training button unit, and each five-finger independent training button unit is equipped with a high-precision pressure acquisition unit and a single-key floating drive unit; the single-key floating drive unit is used to drive the corresponding training button unit to perform small-amplitude up-and-down reciprocating floating motion. The main control unit is electrically connected to the display interaction unit, each single-key floating drive unit, and each high-precision pressure acquisition unit, respectively. The main control unit executes the following control logic: During training, the training button unit corresponding to the target finger exerting force remains stationary, while the training button units corresponding to the other non-target relaxed fingers are controlled to perform up-and-down reciprocating floating motions; dynamic pressure data output by the high-precision pressure acquisition unit is received, and based on the pressure change characteristics during the floating process, it is determined whether there is compensatory tension in the finger, and training success or failure signals are output to the display interaction unit. (2) Finger anti-compensation training method, applied to the above training system, includes the following steps: S1. Initialize training parameters: Receive the training level selected by the user, set the support force and training duration corresponding to the target finger, and configure the relaxation pressure threshold range. S2, Zoned Motion Control: Lock the training button unit corresponding to the target finger exerting force to a stationary position; control the training button units corresponding to the other non-target relaxed fingers to perform small up-and-down reciprocating floating motions. S3. Collect dynamic pressure signals and perform compensation determination: When the non-target finger is in a completely relaxed state, the finger moves synchronously with the training button unit by its own weight, and the pressure collection value is maintained in the preset relaxation pressure threshold range, which is judged as a qualified relaxation. When non-target finger muscles are tense and compensatory occurs: when the training button unit floats upward, the finger generates reverse resistance, and the pressure value increases abnormally; when the training button unit falls downward, the finger cannot follow the downward, and the pressure value drops abnormally. This is identified as implicit compensatory tension, and the training is judged as a failure. When the pressure of the non-target finger is lower than the lower limit of the relaxation pressure, it is determined that the finger is lifted off the key surface, which is identified as an explicit compensation and the training is judged as a failure. S4, Level 1: If the target finger maintains the set support force and all non-target fingers are judged to be relaxed within the training time, then this training level is passed; the training level is increased to proceed to the next level. Furthermore, the range of pressure thresholds and the level of support can be configured according to the user's age and training level. Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: 1. By adopting a dynamic reciprocating floating training key combined with dynamic pressure feature recognition method, it can identify both explicit compensation when the finger lifts off the key surface and implicit compensation when the finger is on the key but the muscle is tense. This makes up for the shortcomings of traditional static pressure detection schemes that rely solely on fixed thresholds for judgment, and achieves a more comprehensive judgment of compensation status. 2. Each of the five fingers is equipped with an independent pressure acquisition unit and an independent floating drive unit, which can carry out independent support training for any single finger; the system sets up multiple levels of difficulty gradients, and through continuous detection, it constrains users to develop muscle memory of independent finger force exertion and relaxation of adjacent fingers, making the training judgment more objective. 3. The dynamic feature detection method based on pressure sensors does not require electromyography acquisition and individual calibration. It can be used directly by different users. The system has strong versatility and is suitable for various basic instrument training and general hand function training scenarios. It can cover the correction training needs of students with no prior experience as well as professional learners. 4. The training logic of hardware and software collaboration integrates mechanical dynamic structure, pressure acquisition, compensation judgment algorithm and graded training process into a complete training system solution, and the training closed loop is more complete. Attached Figure Description

[0004] Figure 1 This is a schematic diagram showing the overall structure of the finger anti-compensation training system based on dynamic floating detection as described in an embodiment of the present invention. Detailed Implementation

[0005] The present invention will be further described below with reference to specific embodiments. A finger anti-compensation training system based on dynamic floating detection is disclosed. The main control unit is a microcontroller; the display and interaction unit is a display screen; the five-finger independent training button unit adopts a simulated piano white key structure to adapt to piano performance training scenarios; the single-key floating drive unit adopts a micro geared motor; and the high-precision pressure acquisition unit is a weight sensor. Example Training Method Flow: The user selects the training level, and the system loads the corresponding parameters. Example configuration: relaxation pressure range 2g-60g, training support intensity 60g-600g, divided into ten levels. This set of parameters is only an implementation example and can be modified according to users of different ages and skill levels. The system selects the target finger to exert force, and the training button corresponding to the target finger remains stationary; the training buttons corresponding to the other four fingers continuously move up and down in small amplitude. The pressure sensor continuously collects dynamic pressure. If the relaxed finger moves synchronously with the button and the pressure remains stable in the 2-60g range, the relaxation is considered successful. If the pressure rises or falls abnormally during the floating process, it is considered implicit compensation and the training fails. If the pressure is below 2g, it is considered that the finger is lifted off the button, indicating explicit compensation and the training fails. If the target finger maintains a stable pressure level within the specified time while the other fingers remain relaxed throughout, this level is passed; you can then proceed to a higher intensity level for further training.

Claims

1. A finger anti-compensation training system based on dynamic float detection, characterized in that, It includes a main control unit, a display and interaction unit, a five-finger independent training button unit, a single-button floating drive unit, and a high-precision pressure acquisition unit; Each finger corresponds to a five-finger independent training button unit, and each five-finger independent training button unit is equipped with a high-precision pressure acquisition unit and a single-key floating drive unit; the single-key floating drive unit is used to drive the corresponding training button unit to perform small-amplitude up-and-down reciprocating floating motion. The main control unit is electrically connected to the display interaction unit, each single-key floating drive unit, and each high-precision pressure acquisition unit, respectively. The main control unit executes the following control logic: During training, the training button unit corresponding to the target finger exerting force remains stationary, while the training button units corresponding to the other non-target relaxed fingers are controlled to perform up-and-down reciprocating floating motions; dynamic pressure data output by the high-precision pressure acquisition unit is received, and compensation is determined based on the pressure change characteristics during the floating process: when the pressure of the non-target finger is lower than the lower limit of the relaxation pressure, it is determined to be an explicit compensation of finger lifting off; when the pressure rises or falls abnormally during the floating process, it is determined to be an implicit compensation of finger sticking to the key tightly, and the training success or failure signal is output to the display interaction unit.

2. The training system according to claim 1, characterized in that, The main control unit can be configured with a relaxation pressure threshold range and multi-level training support force parameters.

3. The training system according to claim 1, characterized in that, The training key unit simulates the white key structure of a piano.

4. A finger anti-compensation training method, applied to the training system according to any one of claims 1-3, characterized in that, Includes the following steps: S1. Initialize training parameters: Receive the training level selected by the user, set the support force and training duration corresponding to the target finger, and configure the relaxation pressure threshold range. S2, Zoned Motion Control: Lock the training button unit corresponding to the target finger exerting force to a stationary position; control the training button units corresponding to the other non-target relaxed fingers to perform small up-and-down reciprocating floating motions. S3. Collect dynamic pressure signals and perform compensation determination: When the non-target finger is in a completely relaxed state, the finger moves synchronously with the training button unit by its own weight, and the pressure collection value is maintained in the preset relaxation pressure threshold range, which is judged as a qualified relaxation. When the muscles of the non-target finger are tense and compensatory occurs: when the training button unit floats upward, the finger generates reverse resistance, and the pressure value increases abnormally. When the training button unit falls downwards, the finger cannot follow the downward movement, and the pressure value drops abnormally. This is identified as implicit compensatory tension, and the training is judged to have failed. When the pressure of the non-target finger is lower than the lower limit of the relaxation pressure, it is determined that the finger is lifted off the key surface, which is identified as an explicit compensation and the training is judged as a failure. S4, Level 1: The target finger maintains the set support force continuously, and all non-target fingers are judged to be relaxed and qualified within the training time. Then the training is passed. Raise the training level and proceed to the next level of training.

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