CN212415731U

CN212415731U - Hand motion function evaluation device

Info

Publication number: CN212415731U
Application number: CN202021104260.0U
Authority: CN
Inventors: 彭伟
Original assignee: Hefei Institutes of Physical Science of CAS
Current assignee: Hefei Institutes of Physical Science of CAS
Priority date: 2020-06-15
Filing date: 2020-06-15
Publication date: 2021-01-29
Anticipated expiration: 2030-06-15

Abstract

The utility model discloses a hand motion function evaluation device, which is characterized in that the hand motion function evaluation device comprises a fixed support and a universal joint connector arranged on the fixed support, one end of the universal joint connector far away from the fixed support is connected with a finger sleeve, the finger sleeve and the universal joint connector support are provided with a tension sensor, an inertial sensor is arranged in the finger sleeve, the hand motion function is evaluated in a grading and quantification manner by measuring dynamics and kinematics information in the hand motion process, hand motion indexes such as pinching force, finger frequency, tremor and the like can be expanded and measured, compared with the existing hand motion function evaluation training device, the measurement form based on natural grasping behavior is more beneficial to the exertion of muscle nerve potential, the structure is simple, the complexity of using a hand robot is avoided, and meanwhile, various parameters of grasping action are collected by flexibly installing various sensors, the intellectualization and specialization of the traditional mechanical trainer are improved.

Description

Hand motion function evaluation device

Technical Field

The utility model relates to a hand motion function aassessment technical field specifically is a hand motion device aassessment device.

Background

The hand is one of the most important organs in human life and production, has two functions of sensation and movement, and can complete a series of force actions and fine actions such as lifting, touching, pushing, grasping, pinching between two fingertips and the like through the cooperative control of muscle nerves in the aspect of movement, thereby meeting the requirements of daily activities. However, due to the effects of some diseases, such as hand trauma, burns, spinal cord injury, stroke, brain trauma, fracture of upper limbs, etc., various degrees of hand function impairment are often caused, which in turn affects quality of life. Therefore, the evaluation representation, function reconstruction and rehabilitation training of the hand function have important clinical significance and social value.

At present, in clinical practice, a scale method is mainly adopted for hand function assessment, specifically, a Carroll hand function assessment method, a Jebsen manual performance measurement method, hand function ADL capability measurement and the like. In recent years, with the rapid development of sensing technology, bionic technology and artificial intelligence, hand-worn robots are increasingly applied to the field of rehabilitation, but the devices are expensive in manufacturing cost, complex in operation, strong in constraint, difficult to popularize in a wide range and difficult to enter families. Therefore, the research and development of intelligent household equipment which can be characterized in a quantified mode, can be trained accurately, is easy to operate and is convenient to use becomes one of important directions in the field of rehabilitation.

SUMMERY OF THE UTILITY MODEL

An object of the utility model is to provide a hand motion device evaluation device to solve the problem that proposes among the above-mentioned background art.

In order to achieve the above object, the utility model provides a following technical scheme:

the utility model provides a hand motion function evaluation device, include the fixed bolster and set up in universal joint connector on the fixed bolster, universal joint connector is kept away from the one end of fixed bolster is connected with the dactylotheca, the dactylotheca with universal joint connector support is equipped with force sensor, be equipped with inertial sensor in the dactylotheca.

As a further aspect of the present invention: the universal joint connector is provided with five, and five the universal joint connector is arranged with the finger cooperation.

As a further aspect of the present invention: and a spring is arranged between the tension sensor and the finger sleeve.

As a further aspect of the present invention: the finger stall is detachably connected with the tension sensor.

As a further aspect of the present invention: and the fixed support is provided with a main control circuit which is in signal communication with the tension sensor and the inertial sensor.

Compared with the prior art, the beneficial effects of the utility model are that:

1. this application is through dynamics and the kinematics information of measuring among the hand motion process, and the hand motion function is appraised in grades and quantized to can expand to measure and hold between the fingers power, indicate frequently, hand motion index such as tremor, compare with current hand motion function appraisal trainer, this kind of measurement form based on natural grasping action more does benefit to and exerts the muscle nerve potential, and the test is convenient, intelligent degree is high.

2. This application compares with current hand function assessment trainer, simple structure on the one hand, the complexity of using hand robot has been avoided, on the other hand, through nimble multiple sensor of installation, gather the various parameters of gripping action, the intellectuality and the specialty of traditional mechanical type training ware have been promoted, this application can be for nervous system disease patient and the traumatic patient who has the dyskinesia, provide a simple effectual gripping function training method, and for clinical diagnosis, clinical rehabilitation provide scientific aassessment suggestion and data basis.

Drawings

Fig. 1 is a schematic structural diagram of a hand motion estimation device according to the present application;

FIG. 2 illustrates the logic of the hand movement assessment method of the present application.

In the figure: 1-a fixed support, 2-a universal joint, 3-a tension sensor, 4-a spring, 5-a finger stall, 6-an inertial sensor and 7-a main control circuit.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative work belong to the protection scope of the present invention.

Referring to fig. 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 disposed on the fixed bracket 1, one end of the universal joint connector 2 away from the fixed bracket 1 is connected with a tension sensor 3, the other end of the tension sensor 3 is fixedly connected with a spring 4, the other end of the spring 4 is fixedly connected with a finger sleeve 5, an inertial sensor 6 is disposed in the finger sleeve 5, five groups of the universal joint connector 2, the tension sensor 3, the spring 4, the finger sleeve 5, and the inertial sensor 6 are disposed to correspond to five fingers, meanwhile, the finger sleeve 5 is detachably connected, and can be detached for evaluating a single function of a hand, a main control circuit 7 is disposed on the fixed bracket 1, parameters obtained by the tension sensor 2 and the inertial sensor 6 are read and processed by the main control circuit 7, and meanwhile, outputting an evaluation result.

The using process of the embodiment comprises the following steps:

s1, inserting fingers of a patient into the finger sleeve 5, and performing hand integral function inspection and hand single function inspection, wherein the integral function comprises a gripping function, and the single function comprises pinching force, finger tremor and finger frequency;

s2, acquiring parameters, wherein in the process of hand overall function inspection and hand single function inspection of a patient, kinematic parameters and kinetic parameters in the process of hand overall function inspection and hand single function inspection of the patient 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 100Hz, namely, parameter acquisition is carried out at intervals of 10ms, and the sampling parameters comprise tension, acceleration and angular speed; acquiring kinematic parameters and kinetic parameters through the acquired tension, acceleration and angular velocity, wherein the kinetic parameters comprise termination tension, and the kinematic parameters comprise initial acceleration, average velocity and termination bending angle;

s3, processing the parameters acquired in S2 through the main control circuit 7, and acquiring the whole function index and the single function index of the hand of the patient, wherein the data processing comprises the following steps:

s3.1, carrying out five-point mean filtering processing on the parameters of the tension, the acceleration and the angular velocity obtained in the step S2 to obtain effective parameters of 20 Hz;

s3.2, calculating effective parameters of the tension, the acceleration and the angular speed in the S3.1 to obtain termination tension, initial acceleration, average speed, termination bending angle, pinching force, finger tremor and finger frequency, judging the overall function of the hand through the termination tension, the initial acceleration, the average speed and the termination bending angle, and judging the single function of the hand through the pinching force, the finger tremor and the finger frequency, wherein the evaluation parameters of the termination tension, the initial acceleration, the average speed and the termination bending angle in the embodiment are obtained by carrying out experiments aiming at people with different hand motion functions, acquiring characteristic data, searching difference parameters in the characteristic parameters through statistical methods such as bilateral T test and the like, and carrying out redundancy processing on the parameters;

s3.3, judging the overall function of the hand according to the termination tension, the initial acceleration, the average speed and the termination bending angle and obtaining the overall function index of the hand, in the embodiment, establishing a double-layer neural network model with 4 inputs and 3 outputs according to four index parameters, wherein the number of the first layer of neurons is more than 4, the number of the second layer of neurons is 3, performing parameter training according to the experimental data to complete model construction, defining the output [ 000 ] as excellent, [ 100 ] as good, [ 110 ] as qualified, [ 010 ] as slightly poor and [ 001 ] as poor, and realizing the hierarchical quantitative evaluation of the hand grasping motion function based on the model; in addition, the neural network model is a common technical means in the technical field of the application, and therefore, the details are not repeated herein;

s4, inputting the whole function index and the single function index of the patient hand in the step S3 into a hand function evaluation function and outputting a hand evaluation result; the hand evaluation function is: score ═ a grasping function + B × + pinching force + C × + frequency + D × + tremor, where A, B, C, D is the weighting factor, and a + B + C + D ═ 1.

It is obvious to a person skilled in the art that the invention is not restricted to details of the above-described exemplary embodiments, but that it can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.

Claims

1. The hand motion function assessment device is characterized by comprising a fixed support (1) and a universal joint connector (2) arranged on the fixed support (1), wherein the universal joint connector (2) is far away from one end of the fixed support (1) and is connected with a finger stall (5), the finger stall (5) and the universal joint connector (2) are provided with a tension sensor (3), and an inertial sensor (6) is arranged in the finger stall (5).

2. A hand motion assessment device according to claim 1, wherein five said universal joint connectors (2) are provided and five said universal joint connectors (2) are arranged to cooperate with fingers.

3. A hand motion assessment device according to claim 1, wherein a spring (4) is provided between the tension sensor (3) and the finger cuff (5).

4. A hand motion assessment device according to claim 1, wherein said finger cuff (5) is removably connected to said tension sensor (3).

5. A hand motion function assessment device according to claim 1, wherein the fixing support (1) is provided with a main control circuit (7) in signal communication with the tension sensor (3) and the inertial sensor (6).