A hand movement function evaluation device and method

Abstract

The application discloses a hand movement function evaluation device, characterized in that the device comprises a fixed support and a universal joint connector arranged on the fixed support, one end of the universal joint connector away from the fixed support is connected with a finger sleeve, the finger sleeve is provided with a tension sensor in cooperation with the universal joint connector support, and an inertial sensor is arranged in the finger sleeve. The application can measure the dynamic and kinematic information in the hand movement process, quantitatively evaluate the hand movement function in stages, and can also extend the measurement of hand movement indexes such as pinching force, finger frequency and tremor. Compared with the existing hand movement function evaluation and training device, the measurement form based on the natural gripping behavior is more beneficial to exert the muscle and nerve potential, the structure is simple, the complexity of using a hand robot is avoided, various sensors are flexibly installed, various parameters of the gripping action are collected, and the intelligence and specialization of the traditional mechanical training device are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hand movement function assessment, and in particular to a hand movement function assessment device and method. Background Art

[0002] The hand is one of the most important organs in human life and production, with two major functions: sensation and movement. In terms of movement, it can complete a series of force movements and fine movements such as lifting, touching, pushing, grasping, and pinching with two fingers through the coordinated control of muscles and nerves, thus meeting the needs of daily activities. However, due to the influence of some diseases, such as hand trauma, burns, spinal cord injury, stroke, brain trauma, upper limb fractures, etc., it often causes varying degrees of hand function impairment, which in turn affects the quality of life. Therefore, the assessment and characterization of hand function, functional reconstruction, and rehabilitation training have important clinical significance and social value.

[0003] Currently, hand function is primarily assessed clinically using scales, including the Carroll Hand Function Assessment, the Jebsen Manual Ability Test, and the ADL (Advanced Learning) Ability Test. These methods typically use different tasks and assess hand function based on the quality of the test-taker's performance. While these methods are highly practical and provide comprehensive and scientific assessments, they are also highly subjective and time-consuming. In recent years, with the rapid development of sensing technology, bionics, and artificial intelligence, wearable hand robots have been increasingly used in rehabilitation. However, these devices are expensive, complex, and restrictive, making them difficult to widely adopt, let alone introduce into homes. Therefore, developing intelligent, home-use devices that can quantify and accurately characterize, accurately train, and are simple and easy to use has become a key focus in rehabilitation. Summary of the Invention

[0004] The object of the present invention is to provide a hand movement device evaluation device and method to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A hand motor function assessment device includes a fixed bracket and a universal joint connector arranged on the fixed bracket, wherein the universal joint connector is connected to a finger cuff at one end away from the fixed bracket, the finger cuff and the universal joint connector bracket are provided with tension sensors, and an inertial sensor is provided in the finger cuff.

[0007] As a further solution of the present invention: five universal joint connectors are provided, and the five universal joint connectors are arranged in coordination with the fingers.

[0008] As a further solution of the present invention: a spring is provided between the tension sensor and the finger sleeve.

[0009] As a further solution of the present invention: the finger cuff is detachably connected to the tension sensor.

[0010] As a further solution of the present invention: a main control circuit is provided on the fixing bracket and is in signal communication with the tension sensor and the inertial sensor.

[0011] A method for assessing hand motor function comprises the following steps:

[0012] S1. The patient inserts his finger into the finger cuff (5) and performs a hand overall function test and a hand single function test;

[0013] S2. Obtaining parameters: during the process of the patient's overall hand function test and the individual hand function test, respectively obtaining kinematic parameters and dynamic parameters of the patient's overall hand function test and the individual hand function test through the tension sensor (3) and the inertial sensor (6);

[0014] S3, data processing, processing the parameters obtained in S2 through the main control circuit (7), and obtaining the patient's hand overall function index and hand single function index;

[0015] S4, inputting the patient's hand overall function index and the hand individual function index in step S3 into the hand function evaluation function and outputting the hand evaluation result;

[0016] The overall functional index includes grasping function, and the individual functional index includes pinching strength, finger tremor, and finger frequency;

[0017] The hand function evaluation function is: Score=A*grip function+B*pinch strength+C*finger frequency+D*finger tremor, where A, B, C, and D are weight coefficients, and A+B+C+D=1.

[0018] As a further solution of the present invention: the overall hand function test is a gripping finger cuff, and the single hand fixation test is a pinching finger cuff.

[0019] As a further solution of the present invention: the dynamic parameters include the final tension, and the kinematic parameters include the initial acceleration, the average velocity, and the final bending angle.

[0020] As a further solution of the present invention: the sampling frequency of the tension sensor and the inertial sensor in S2 is 100 Hz, and the sampling parameters include tension, acceleration, and angular velocity.

[0021] As a further solution of the present invention: S3 comprises the following steps:

[0022] S3.1. Perform five-point mean filtering on the parameters obtained in step S2 to obtain effective parameters at 20 Hz;

[0023] S3.2. Calculate the final tension, initial acceleration, average velocity, final bending angle, pinch force, finger tremor, and finger frequency using the valid parameters in S3.1.

[0024] S3.3. A two-layer neural network model with four inputs and three outputs is established based on the final tension, initial acceleration, average velocity, and final bending angle. The number of neurons in the first layer is greater than four, and the number of neurons in the second layer is three. Based on the above experimental data, parameter training is performed to complete the model construction, judge the overall function of the hand, and obtain the overall function index of the hand. The output is defined as [0 0 0] as excellent, [1 00] as good, [1 1 0] as qualified, [0 1 0] as slightly poor, and [0 0 1] as poor.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. This application measures the dynamic and kinematic information during hand movement to perform a graded and quantitative assessment of hand movement function, and can be expanded to measure hand movement indicators such as pinch strength, finger frequency, and tremor. Compared with existing hand movement function assessment and training devices, this measurement method based on natural grasping behavior is more conducive to the development of muscle and nerve potential, and the test is convenient and highly intelligent.

[0027] 2. Compared with the existing hand function assessment and training devices, the present application has a simple structure on the one hand, avoiding the complexity of using a hand robot; on the other hand, by flexibly installing a variety of sensors to collect various parameters of grasping movements, the intelligence and specialization of traditional mechanical trainers are improved. The present application can provide a simple and effective grasping function training method for patients with neurological diseases and trauma patients with motor dysfunction, and provide scientific evaluation opinions and data basis for clinical diagnosis and clinical rehabilitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Fig. 1 This is a schematic diagram of the structure of the hand movement evaluation device of this application;

[0029] Fig. 2 The hand movement evaluation method logic is used for this application.

[0030] In the figure: 1-fixed bracket, 2-universal joint, 3-tension sensor, 4-spring, 5-finger sleeve, 6-inertial sensor, 7-main control circuit. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0032] See also Figs. 1-2 In an embodiment of the present invention, a hand movement function evaluation device includes a fixed bracket 1 and a universal joint connector 2 arranged on the fixed bracket 1, the universal joint connector 2 is connected to a tension sensor 3 at one end away from the fixed bracket 2, the other end of the tension sensor 3 is fixedly connected to a spring 4, the other end of the spring 4 is fixedly connected to a finger cuff 5, and an inertial sensor 6 is arranged in the finger cuff 5. The universal joint connector 2, the tension sensor 3, the spring 4, the finger cuff 5, and the inertial sensor 6 are provided in five groups to correspond to five fingers. At the same time, the finger cuff 5 adopts a detachable connection, and can be removed for the evaluation of a single hand function. At the same time, a main control circuit 7 is also provided on the fixed bracket 1, and the main control circuit 7 reads and processes the parameters obtained by the tension sensor 2 and the inertial sensor 6, and outputs the evaluation results.

[0033] A method for assessing hand motor function comprises the following steps:

[0034] S1. The patient inserts his finger into the cuff 5 and undergoes a hand overall function test and a hand individual function test. The overall function includes grasping function, and the individual functions include pinch strength, finger tremor, and finger frequency.

[0035] S2. Acquiring parameters. During the overall hand function test and the individual hand function test, kinematic parameters and dynamic parameters of the patient's hand are respectively acquired through the tension sensor 3 and the inertial sensor 6. The sampling frequency of the tension sensor 3 and the inertial sensor 6 is 100 Hz, that is, parameter acquisition is performed every 10 ms. The sampled parameters include tension, acceleration, and angular velocity. Kinematic parameters and dynamic parameters are acquired through the acquired tension, acceleration, and angular velocity. The dynamic parameters include the final tension, and the kinematic parameters include the initial acceleration, average velocity, and final bending angle.

[0036] S3, data processing, the main control circuit 7 processes the parameters obtained in S2 and obtains the overall function index and single function index of the patient's hand. The data processing includes the following steps:

[0037] S3.1. Perform five-point mean filtering on the tension, acceleration, and angular velocity parameters obtained in step S2 to obtain effective parameters at 20 Hz;

[0038] S3.2. Calculate the final tension, initial acceleration, average velocity, final bending angle, pinching force, finger tremor, and finger frequency using the effective parameters of tension, acceleration, and angular velocity in S3.1. Determine overall hand function using the final tension, initial acceleration, average velocity, and final bending angle, and determine individual hand functions using pinching force, finger tremor, and finger frequency. The final tension, initial acceleration, average velocity, and final bending angle evaluation parameters in this embodiment are obtained by conducting experiments on people with different hand motor functions, collecting characteristic data, and using statistical methods such as bilateral T-tests to identify differential parameters among the aforementioned characteristic parameters and perform parameter redundancy processing.

[0039] S3.3. The overall hand function is judged based on the final tension, initial acceleration, average speed, and final bending angle, and the overall hand function index is obtained. In this embodiment, a two-layer neural network model with 4 inputs and 3 outputs is established based on the four index parameters. The number of neurons in the first layer is greater than 4, and the number of neurons in the second layer is 3. Based on the above experimental data, parameter training is performed to complete the model construction, and the output is defined as [0 0 0] as excellent, [1 00] as good, [1 1 0] as qualified, [0 1 0] as slightly poor, and [0 0 1] as poor. Based on this model, a hierarchical quantitative evaluation of the hand grasping motor function is achieved. In addition, the neural network model is a common technical means in the technical field of this application, and therefore will not be described in detail here.

[0040] S4. Input the patient's overall hand function index and individual function index in step S3 into the hand function evaluation function and output the hand evaluation result; the hand evaluation function is: Score = A*grasp function + B*pinch strength + C*finger frequency + D*finger tremor, where A, B, C, and D are weight coefficients, and A+B+C+D=1.

[0041] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0042] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. An assessment method using a hand motor function assessment device, characterized in that: The invention comprises a fixed bracket (1) and a universal joint connector (2) arranged on the fixed bracket (1), wherein one end of the universal joint connector (2) away from the fixed bracket (1) is connected to a finger sleeve (5), the finger sleeve (5) and the bracket of the universal joint connector (2) are provided with a tension sensor (3), and an inertial sensor (6) is provided in the finger sleeve (5). The evaluation method comprises the following steps: S1. The patient inserts his finger into the finger cuff (5) and performs a hand overall function test and a hand single function test; S2. Obtaining parameters: during the process of the patient's overall hand function test and the individual hand function test, respectively obtaining kinematic parameters and dynamic parameters of the patient's overall hand function test and the individual hand function test through the tension sensor (3) and the inertial sensor (6); S3, data processing, processing the parameters obtained in S2 through the main control circuit (7), and obtaining the patient's hand overall function index and hand single function index; S4, inputting the patient's hand overall function index and the hand individual function index in step S3 into the hand function evaluation function and outputting the hand evaluation result; The overall functional index includes grasping function, and the individual functional index includes pinching strength, finger tremor, and finger frequency; The hand function evaluation function is: Score=A*grip function+B*pinch strength+C*finger frequency+D*finger tremor, where A, B, C, and D are weight coefficients, and A+B+C+D=1.

2. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: There are five universal joint connectors (2), and the five universal joint connectors (2) are arranged in coordination with the fingers.

3. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: A spring (4) is provided between the tension sensor (3) and the finger sleeve (5).

4. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: The finger sleeve (5) is detachably connected to the tension sensor (3).

5. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: The fixed bracket (1) is provided with a main control circuit (7) in signal communication with the tension sensor (3) and the inertia sensor (6).

6. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: The overall hand function test is a gripping finger sleeve (5), and the single hand fixation test is a pinching finger sleeve (5).

7. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: The dynamic parameters include the final tension, and the kinematic parameters include the initial acceleration, the average velocity, and the final bending angle.

8. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 1, wherein: The sampling frequency of the tension sensor (3) and the inertial sensor (6) in S2 is 100 Hz, and the sampling parameters include tension, acceleration, and angular velocity.

9. The method for evaluating hand motor function using a hand motor function evaluation device according to claim 8, wherein: The S3 includes the following steps: S3.

1. Perform five-point mean filtering on the parameters obtained in step S2 to obtain effective parameters at 20 Hz; S3.

2. Calculate the final tension, initial acceleration, average velocity, final bending angle, pinch force, finger tremor, and finger frequency using the valid parameters in S3.

1. S3.

3. A two-layer neural network model with four inputs and three outputs is established based on the final tension, initial acceleration, average velocity, and final bending angle. The number of neurons in the first layer is greater than four, and the number of neurons in the second layer is three. Based on the above experimental data, parameter training is performed to complete the model construction, judge the overall function of the hand, and obtain the overall function index of the hand. The output is defined as [0 0 0] for excellent, [1 0 0] for good, [1 1 0] for qualified, [0 1 0] for slightly poor, and [0 0 1] for poor.

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