CN110755084A

CN110755084A - Motion function evaluation method and device based on active and passive staged actions

Info

Publication number: CN110755084A
Application number: CN201911036617.8A
Authority: CN
Inventors: 王红亮; 赵坤坤; 房强
Original assignee: Nanjing Maosen Electronics Technology Co Ltd
Current assignee: Nanjing Maosen Electronics Technology Co Ltd
Priority date: 2019-10-29
Filing date: 2019-10-29
Publication date: 2020-02-07
Anticipated expiration: 2039-10-29
Also published as: CN110755084B

Abstract

The invention discloses a motion function evaluation method based on active and passive staged actions and corresponding equipment, wherein the method comprises the following steps: collecting limb movement signals of a target object; and processing the limb movement signals, extracting active movement data and passive movement signal data in the limb movement signals, obtaining the completion degree of the active movement and the passive movement at different stages in the whole movement process, and evaluating the movement function of the limb according to the completion degree. The invention adopts a mode of processing data by stages, has higher clinical significance of extracted characteristics, is convenient for clinical analysis, is divided into respective processing of active action and passive action on data processing, can compare action information fed back by action of a patient from multiple angles, and ensures that an evaluation result is more accurate.

Description

Motion function evaluation method and device based on active and passive staged actions

Technical Field

The invention relates to the technical field of human motion perception, in particular to a motion function evaluation method and equipment based on active and passive staged actions.

Background

The motor function evaluation is the basis for doctors to make a rehabilitation training plan for stroke hemiplegia patients, can also be used as an evaluation means for the curative effect of various treatment schemes, and is of great importance in the fields of motor rehabilitation and neuroscience. Traditional assessment of motor rehabilitation is based primarily on scales, performed by professional therapists. The scale-based assessment method is developed earlier and is clinically validated for feasibility, but has three disadvantages:

first, the evaluation results are greatly affected by the subjectivity of the therapist. Different therapists evaluate empirically, and may give different evaluation results for the same patient depending on different criteria.

And secondly, evaluating the precision. The scale type evaluation method gives corresponding scores according to different performances of corresponding actions of the patient, the grading gradient is small, and the evaluation result cannot reflect the real condition of the patient; furthermore, some fine motor therapists for patients may have difficulty in visual capture, resulting in an incomplete assessment.

In the existing quantitative table type evaluation method, a therapist guides a patient to execute a series of actions, and at least one therapist is required for one patient, so that the evaluation efficiency is low; meanwhile, one set of evaluation process is time-consuming and labor-consuming. In the case of Fugl-Meyer, the upper limb assessment procedure takes at least 30 minutes.

For better management and control of the healing process, various internet of things sensing and optical techniques are used in the field of healing assessment. An automatic motion rehabilitation evaluation model is provided based on a miniature inertial sensor, the model is subjected to feature extraction by relying on kinematic parameters captured in the motion process, the model is trained by the features of healthy people, and then the extracted features of the patient are scored. However, the existing motion estimation methods are often based on estimating a certain motion pattern in a fixed motion pattern, and do not reflect the rehabilitation condition of the patient well.

Disclosure of Invention

Technical problem to be solved

The invention aims to solve the problems that the existing assessment is influenced by the subjectivity or experience of a therapist, the assessment precision is influenced by small scale-type assessment grading gradient, the small actions of a patient are inconvenient to find and observe, the exercise rehabilitation assessment is inaccurate, and the like.

(II) technical scheme

In order to solve the technical problems, the invention provides a motion function evaluation method and equipment based on active and passive staged actions, which comprises the following steps:

collecting limb movement signals of a target object;

processing the limb movement signals to assess the movement function of the limb;

the step of processing the limb movement signal to assess the motor function of the limb further comprises:

and extracting active action data and passive action data in the limb movement signal, obtaining the completion degree of the active action and the passive action at different stages in the whole movement process, and evaluating the movement function of the limb according to the completion degree.

According to a preferred embodiment of the invention, the assessing of the motor function of the limb according to the degree of completion comprises:

and comparing the completion degrees of the active action and the passive action at different stages in the whole movement process with a preset movement evaluation standard to obtain a score of the movement function of the limb.

According to a preferred embodiment of the invention, the limb movement signal is a movement signal of a plurality of predetermined limb movements, and the score for the motor function of the limb is a composite score of the plurality of predetermined limb movements.

According to a preferred embodiment of the invention, the predetermined motion estimation criterion is based on the Fugl-Meyer motion estimation criterion.

According to a preferred embodiment of the invention said degree of completion is determined by the ratio of the activity of a certain joint of the limb with respect to the average value.

According to a preferred embodiment of the present invention, the completion degree is divided into a plurality of levels according to the magnitude of the ratio.

According to a preferred embodiment of the invention, the different phases of the whole movement path are determined by normalizing the time of the limb movement.

According to a preferred embodiment of the invention, the period of time comprises at least one of the following periods of time: whole stage, initial stage, intermediate stage, final stage.

According to the preferred embodiment of the invention, the method further comprises the operations of filtering, segmenting and normalizing after acquiring the target object limb motion signal for preprocessing.

The present invention also provides an electronic device, comprising: a processor; and a memory storing computer-executable instructions that, when executed, cause the processor to perform the method.

(III) advantageous effects

1) The invention adopts a mode of processing data by stages, has higher clinical significance of extracted features and is convenient for clinical analysis.

2) The data extraction of the invention is divided into active extraction and passive extraction, and the action information fed back by the action of the patient can be compared in multiple angles, so that the data is more accurate.

Drawings

FIG. 1 is a schematic flow chart of a digital exercise function evaluation method according to the present invention;

FIG. 2 is a schematic flow chart of the method for digital motion function evaluation according to the present invention for extracting motion signal data;

FIG. 3 is a flowchart illustrating a specific evaluation score of the method for evaluating a digital exercise function according to the present invention.

Fig. 4 is a block diagram of an exemplary embodiment of an electronic device according to the present invention.

Detailed Description

In order to more accurately and quantitatively evaluate the limb rehabilitation condition of a patient, the invention provides an active and passive staged action-based motion function evaluation method and equipment.

FIG. 1 is a flow chart of a method for digital motor function assessment in accordance with the present invention. As shown in fig. 1, the method of the present invention comprises:

and S1, acquiring limb movement signals of the target object.

In specific implementation, the target object wears the motion capture equipment on the corresponding part of the limb, and the limb motion signal of the target object is acquired through data acquisition. For example, when the invention is used for upper limb rehabilitation evaluation, the target object (patient) can be kept in a sitting position and a series of actions (preset actions) are executed, the actions are not in sequence, each action is executed for 3 times, and corresponding signals in the action execution process are collected and recorded.

The motion signal acquisition can use a miniature motion sensor unit attached to the limb to measure the three-dimensional azimuth angle, the angular velocity, the displacement, the velocity and the acceleration of the joint of the limb in real time; the motion sensor unit includes a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional magnetometer, and the like. The invention uses the respective treatment of active action and passive action in the subsequent steps, and the trunk compensation phenomenon is very important for the body coordination capacity evaluation of the patient, so the invention is different from the existing evaluation method in that the invention allows the patient to realize certain actions by means of trunk compensation in the action process so as to evaluate the shoulder lifting and shoulder retraction projects.

And S2, processing the limb movement signal to evaluate the movement function of the limb.

The step of processing the limb movement signal may be preceded by a pre-processing step of the signal, for example comprising filtering, segmenting, normalizing, etc. the signal to reduce interference of noise signals. After the preprocessing, the signals are subjected to the data processing required for evaluation.

Fig. 2 is a flow chart of the steps of evaluating the motor function of a limb. As shown in fig. 2, step S2 includes:

and S21, extracting active motion data and passive motion data in the limb motion signal.

In the actual measurement process, active and passive actions are not distinguished, passive action refers to an abnormal action mode when a patient performs certain actions, for example, for the action of bending the shoulder forward, the shoulder abduction occurs during the execution, the shoulder forward bending is the active action, and the shoulder abduction is the passive action. Both actions are obtained at the time of measurement as evaluation indexes.

And S22, obtaining the completion degree of the active action and the passive action at different stages in the whole movement stroke.

The whole movement stroke refers to the movement stroke of the limb when completing a certain action, and in order to more accurately evaluate the completion condition of the target object on the action, the invention provides that the active action and the passive action are respectively divided into different stages in the whole movement stroke according to time or stroke space. In a specific implementation, the different phases may be determined by a period of time normalized to the time of the limb movement. However, for a series of actions, it is not necessary for all actions to be divided into different stages, but different processing may be performed according to the particular situation of the action. For example, the period of time includes at least one of the following periods of time: whole stage, initial stage, intermediate stage, final stage.

The degree of completion refers to a quantitative parameter of the target object for the case that a certain action is completed, and in a specific embodiment, the degree of completion is determined by the ratio of the degree of motion of a certain joint of the limb relative to the average value. The completion degree can be divided into a plurality of levels according to the size of the ratio.

And S23, evaluating the movement function of the limb according to the completion degree.

Therefore, the evaluation of the motion function of the limb according to the completion degree can realize that the completion degrees of the active action and the passive action at different stages in the whole motion process are compared with the preset motion evaluation standard to obtain the score of the motion function of the limb. And wherein the limb motion signal is a motion signal of a plurality of predetermined limb actions and the score for the motor function of the limb is a composite score of the plurality of predetermined limb actions.

Examples

In one embodiment, the present invention is based on the scoring criteria of the Fugl-Meyer scale with corresponding modifications. For a particular evaluation action, the invention performs a staged process and specifies that "start" refers to the first 1/3 stage of the previous action (T1) and "approach to the specified position" refers to the last 1/3 stage of the action (T3); for non-staged processing actions, the whole action process is denoted by (T).

In addition, in the Fugl-Meyer scale, qualitative descriptive words such as "basic completion", "smooth completion" and the like are involved in the scoring criteria. For quantitative evaluation, the embodiment classifies evaluation actions into active actions and passive actions. For example, assuming that the average value of the degree of motion of a certain joint of a limb when a normal person or a healthy side of a patient performs a certain motion is m, two determination indexes are defined herein: d and C. D represents the joint motion related to the designed motion when the motion is executed according to the designed motion, and is a main observation quantity, namely the motion is called as the active motion, and D1, D2 and D3 sequentially represent that the activity of the active motion is larger and larger; c represents compensatory or involvement movements due to sports injury which should be avoided during normal sports when a certain movement is performed, which is called passive movement, and C1, C2 and C3 sequentially represent that the passive movement has more and more activity. The larger the active motion (closer to the mean) the better, the smaller the passive motion (closer to the mean) the better.

Therefore, we can define the completeness accordingly. For example, according to the 3 σ criterion, "substantially complete", "successfully complete", "not complete" can be numerically described as follows:

type of action	Successfully complete the process	Is partially completed	Can not complete
				Active action	0.84m-1.16m	0.16m-0.84m	0m-0.16m
Passive actuation	0m-0.16m	0.16m-1.84m	1.84m-2m

And in the testing process, a therapist mainly digitalizes the scoring standard of the Fugl-Meyer scale according to the manual scoring of the action completion degree of the patient, so that the effective evaluation can be carried out by using the data measured by the sensor, and the original data and the evaluation result are stored.

After data acquisition, the acquired data can be preprocessed and analyzed. And carrying out filtering, segmenting, normalizing and aligning operations aiming at different signal characteristics. The data segmentation mainly extracts effective signals from actions of multiple measurements, and reduces data dimensions. Normalization essentially cancels unit differences between measurements, e.g., normalizing for the duration of an action for subsequent staging.

And then, extracting features of the preprocessed data, wherein the extraction mainly comprises the reference of kinematic features, and constructing an amplification feature matrix according to the extracted features. The kinematic characteristics mainly comprise motion angles, angular velocities, angular accelerations, motion smoothness, track offsets and the like of all joints.

During evaluation, the amplification feature matrix is decomposed by a non-negative feature matrix decomposition method, and the score of each evaluation action is obtained according to the active and passive and staged processing of the method. Further, the evaluation scores may be normalized based on the total score of each action.

The following table is an example of an evaluation digital description:

the specific evaluation of a score is performed in accordance with a decision tree. Taking the shoulder abduction 90 degrees, elbow extension, forearm pronation assessment term (13) as an example, the decision tree is shown in fig. 3.

The abbreviations are as shown in the table below.

The decision method in the decision tree of fig. 3 is as follows: in the whole motion stage (T), if the shoulder joint abduction is abnormal, or the elbow flexion phenomenon is obvious, or the forearm can not rotate forwards or backwards, 0 point is recorded; if the shoulder joint abduction and elbow flexion are normal, but the forearm pronation and supination are abnormal, it is scored as 1 point, and if the shoulder joint, elbow joint and forearm pronation and supination are all normal, it is scored as 2 points.

In this embodiment, the limb movement signal is a movement signal of a plurality of predetermined limb movements, and the score of the movement function of the limb is a composite score of the plurality of predetermined limb movements.

The upper limb function was assessed in part for 33 items on the Fugl-Meyer scale and 66 points were calculated. According to the measuring range and the application range of the used motion capture unit, 7 items of the hand evaluation item are removed, 3 items of the reflection inspection are removed, and 12 actions are designed in total, wherein the total number of the actions is 46. The standard of action was as required by the Fugl-Meyer scale.

In this embodiment, the completion degree is determined by a ratio of the activity of a certain joint of the limb to an average value, the completion degree may be divided into a plurality of levels according to the size of the ratio, and the evaluation result is a total sum of evaluation scores. The score obtained was mapped to the score of the Fugl-Meyer scale based on the score of the upper limb on the Fugl-Meyer scale and the relationship between the exercise score and clinical significance, as shown in the table below.

Exercise scoring	Score corresponding to upper limb	Grading	Clinical significance
				<50 points (50%)	<23	I	Severe movement disorder
50-84 points (35%)	23-38	II	Apparent dyskinesia
				85-95 points (11%)	39-43	III	Moderate dyskinesia
96-99 points (4%)	44-45	IV	Mild movement disorder

In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.

The motion function evaluation method based on active and passive staged actions can be realized in different devices. Fig. 4 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. An electronic device 400 according to an embodiment of the invention is described below with reference to fig. 4. The electronic device 400 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.

As shown in fig. 4, the components of electronic device 400 may include, but are not limited to: at least one processing unit 410, at least one memory unit 420, a bus 430 connecting the various system components (including the memory unit 420 and the processing unit 410), a data acquisition unit 440, and the like.

Wherein the storage unit stores program code executable by the processing unit 410 to cause the processing unit 410 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 410 may perform the steps as shown in fig. 1 and 2. The data acquisition unit 440 is used for acquiring the limb movement signal of the target object. Which may be various inertial sensors.

The storage unit 420 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)4201 and/or a cache memory unit 4202, and may further include a read only memory unit (ROM) 4203.

The storage unit 420 may also include a program/utility 4204 having a set (at least one) of program modules 4205, such program modules 4205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 430 may be any bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 400 may also communicate with one or more external devices 500 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 400, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 400 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 450. Also, the electronic device 400 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 460. The network adapter 460 may communicate with other modules of the electronic device 400 via the bus 430. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for motion function assessment based on active-passive, staged actions, the method comprising:

collecting limb movement signals of a target object;

the method is characterized in that: the step of processing the limb movement signal to assess the movement function of the limb further comprises:

2. The method for digitized assessment of motor function based on active-passive staged actions according to claim 1, characterized by: evaluating the motor function of the limb according to the completeness comprises: and comparing the completion degrees of the active action and the passive action at different stages in the whole movement process with a preset movement evaluation standard to obtain a score of the movement function of the limb.

3. The method of claim 2, wherein the method comprises: the limb motion signal is a motion signal of a plurality of predetermined limb actions and the score for the motor function of the limb is a composite score of the plurality of predetermined limb actions.

4. The method of claim 3, wherein the method comprises: the predetermined motion assessment criterion is a Fugl-Meyer based motion assessment criterion.

5. The method for digitized assessment of motor function based on active-passive staged action according to any of claims 1 to 4, characterized by: the completion is determined by the ratio of the activity of a certain joint of the limb relative to the average.

6. The method of claim 5, wherein the method comprises:

the completion degree is divided into a plurality of levels according to the size of the ratio.

7. The method for digitized assessment of motor function based on active-passive staged action according to any of claims 1 to 4, characterized by: the different phases of the whole movement stroke are determined by the time period after the time of the limb movement is normalized.

8. The method of claim 7, wherein the method comprises: the time period comprises at least one of the following time periods: whole stage, initial stage, intermediate stage, final stage.

9. The method for digitized motion function assessment based on active-passive staged action according to any of claims 1 to 4, further comprising filtering, segmentation and normalization operations for preprocessing after acquiring the target object limb motion signal.

10. An active-passive staged action-based athletic function assessment device, wherein the device comprises:

a processor; and the number of the first and second groups,

a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of joint activity and motion coordination based motor function assessment according to any of claims 1-9.