CN110755084B

CN110755084B - Motion function assessment method and device based on active-passive and staged actions

Info

Publication number: CN110755084B
Application number: CN201911036617.8A
Authority: CN
Inventors: 王红亮; 赵坤坤; 房强
Original assignee: Nanjing Smartsens Electronic Technology Co ltd
Current assignee: Nanjing Smartsens Electronic Technology Co ltd
Priority date: 2019-10-29
Filing date: 2019-10-29
Publication date: 2023-06-23
Anticipated expiration: 2039-10-29
Also published as: CN110755084A

Abstract

The invention discloses a motion function assessment method and corresponding equipment based on active-passive and staged actions, wherein the method comprises the following steps: acquiring 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 degrees of the active movement and the passive movement at different stages in the whole movement stroke, and evaluating the movement function of the limb according to the completion degrees. The invention adopts a mode of processing data in stages, has higher clinical significance of the extracted characteristics, is convenient for clinical analysis, is divided into the separate processing of active action and passive action in data processing, and can compare the action information fed back by the action of a patient at multiple angles, so that the evaluation result is more accurate.

Description

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

Technical Field

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

Background

The motor function evaluation is the basis for a doctor to make a rehabilitation training plan for a cerebral apoplexy hemiplegia patient, can be used as an evaluation means of the curative effects of various treatment schemes, and is vital in the fields of motor rehabilitation and neuroscience. Traditional exercise rehabilitation assessments are based primarily on scales, performed by specialized therapists. The gauge evaluation method is early in development and has proved to be feasible clinically, but has three disadvantages:

1. the evaluation results are greatly affected by the subjective influence of the therapist. Different therapists are empirically assessed, and different therapists may give different assessment results for the same patient, depending on the criteria.

2. And (5) evaluating the precision. The scale type evaluation method gives corresponding scores according to different performances of corresponding actions of the patient, the score gradient is small, and the evaluation result cannot reflect the real situation of the patient; in addition, some fine motion therapists of patients may have difficulty visually capturing, resulting in incomplete assessment results.

3. The existing scale type evaluation method has the defects that a therapist guides a patient to execute a series of actions, one patient is at least matched with one therapist, and the evaluation efficiency is low; meanwhile, a set of evaluation flow is time-consuming and labor-consuming. Taking Fugl-Meyer as an example, the upper limb evaluation process takes at least 30 minutes.

For better management and control of rehabilitation processes, various internet of things sensing techniques and optical techniques are used in the field of rehabilitation evaluation. An automatic exercise rehabilitation assessment model is provided based on a miniature inertial sensor, and feature extraction is carried out on the model by means of kinematic parameters captured in the exercise process, so that the model is trained by healthy people features, and further the extracted patient features are scored. However, existing exercise assessment methods are often based on assessing a certain exercise pattern of the fixed exercise patterns, and do not reflect the rehabilitation situation of the patient well.

Disclosure of Invention

First, the technical problem to be solved

The invention aims to solve the problems that the prior assessment is influenced by subjective or experience of a therapist, the scale type assessment score gradient is small, the assessment precision is influenced, and the exercise rehabilitation assessment is inaccurate due to the fact that the observation is inconvenient for the micro-action of a patient.

(II) technical scheme

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

acquiring limb movement signals of a target object;

processing the limb movement signal to evaluate the movement function of the limb;

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

and extracting the active motion data and the passive motion data in the limb motion signals, obtaining the completion degrees of the active motion and the passive motion at different stages in the whole motion stroke, and evaluating the motion function of the limb according to the completion degrees.

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

and comparing the completion degrees of the active motion and the passive motion at different stages in the whole motion stroke with a preset motion evaluation standard to obtain scores of the motion functions of the limbs.

According to a preferred embodiment of the present invention, the limb movement signal is a movement signal of a plurality of predetermined limb movements, and the score for the movement 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, the degree of completion is determined by the ratio of the degree of movement of a certain joint of the limb to the average value.

According to a preferred embodiment of the invention, the degree of completion 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 stroke are determined by normalizing the time of the limb movement.

According to a preferred embodiment of the invention, the period comprises at least one of the following periods: the whole stage, the beginning stage, the middle stage and the final stage.

According to a preferred embodiment of the present invention, filtering, segmentation and normalization operations are further included for preprocessing after acquisition of the target subject limb movement signals.

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

(III) beneficial effects

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

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

Drawings

FIG. 1 is a flow chart of a digital exercise function assessment method of the present invention;

FIG. 2 is a schematic flow chart of the method for evaluating digital exercise function of the present invention for extracting exercise signal data;

FIG. 3 is a schematic diagram of a specific score evaluation flow chart of the digital exercise function evaluation method of 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 evaluate the limb rehabilitation condition of a patient more accurately and quantitatively, the invention provides a motor function evaluation method and equipment based on active-passive and staged actions.

FIG. 1 is a flow chart of the digitized athletic functionality assessment method of the present invention. As shown in fig. 1, the method of the present invention comprises:

s1, acquiring limb movement signals of a target object.

In the implementation, the target object wears the motion capture device at the corresponding part of the limb, and the limb motion signals of the target object are acquired through data acquisition. For example, when the invention is used for upper limb rehabilitation evaluation, a target object (patient) can be kept in a sitting position and a series of actions (preset actions) are executed, no sequence exists among the actions, each action is executed 3 times, and corresponding signals in the execution process of the actions are acquired and recorded.

The motion signal acquisition can use a miniature motion sensor unit attached to the limb to measure the three-dimensional azimuth angle, angular velocity, joint displacement, velocity and acceleration 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. Because the invention uses separate treatments of active and passive movements in subsequent steps, and the torso compensation phenomenon is very important for assessing the patient's body coordination ability, the invention differs from existing assessment methods in that during the movements, the invention allows the patient to perform certain movements by torso compensation to assess shoulder lifting, shoulder retraction projects.

S2, processing the limb movement signals to evaluate the movement functions of the limbs.

The step of processing the limb-motion signal may be preceded by a pre-processing step of the signal, for example including filtering, splitting, normalizing the signal, etc. to reduce interference of the noise signal. After the preprocessing, the signal is subjected to data processing required for evaluation.

Fig. 2 is a flowchart of the steps for assessing the motor function of a limb. As shown in fig. 2, step S2 includes:

s21, extracting active motion data and passive motion data in the limb motion signals.

The active and passive are not distinguished in the actual measurement process, and the passive means that the patient has abnormal action modes when performing certain actions, such as shoulder forward flexion and shoulder abduction when performing, and then the shoulder forward flexion is active action and the shoulder abduction is passive action. Both actions are obtained at the time of measurement as an evaluation index.

S22, obtaining the completion degrees of the active motion and the passive motion at different stages in the whole motion stroke.

The whole movement travel refers to the movement travel of a limb when completing a certain action, and in order to evaluate the completion condition of a target object on the action more accurately, the invention provides that the whole movement travel is divided into different stages according to time or travel space respectively. 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, not all actions have to be divided into different phases, but different processes may be performed according to the specific situation of the actions. For example, the period includes at least one of the following periods: the whole stage, the beginning stage, the middle stage and the final stage.

The degree of completion refers to a quantitative parameter of the target subject for the case of completion of a certain action, which in one embodiment is determined by the ratio of the degree of movement of a certain joint of the limb to the average value. The degree of completion may be divided into a plurality of levels according to the magnitude of the ratio.

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

Therefore, the evaluation of the movement function of the limb according to the completion degree can realize that the completion degree of the active movement and the passive movement in different stages in the whole movement stroke is compared with the preset movement evaluation standard to obtain the score of the movement function of the limb. And, for the limb movement signal being a movement signal of a plurality of predetermined limb movements, the score for the movement function of the limb is a composite score of the plurality of predetermined limb movements.

Examples

In one embodiment, the invention is based on the scoring criteria of the Fugl-Meyer scale and is modified accordingly. For a specific evaluation action, the invention performs staged processing, and prescribes that the 'start time' refers to the first 1/3 stage (T1) of the previous action, and the 'near prescribed position' refers to the last 1/3 stage (T3) of the action; for non-staged processing actions, use (T) refers to the entire course of action.

In addition, in the Fugl-Meyer scale, qualitative descriptive terms such as "substantially complete", "smoothly complete" and the like are referred to in the scoring criteria. For quantitative evaluation, this embodiment classifies the evaluation actions into active actions and passive actions. For example, assuming that the average value of the mobility of a joint of a limb when a normal person or patient healthy side performs a certain action is m, two decision indexes are defined herein: d and C. D represents the joint motion related to the design motion when the design motion is executed, which is called active motion, and D1, D2 and D3 sequentially represent the active motion with larger and larger activity; c represents compensatory or linked movement caused by movement injury when executing a certain action, which is to be avoided in the normal movement process, and is called passive action, and C1, C2 and C3 sequentially represent that the activity of the passive action is larger and larger. The larger the active motion (closer to the mean) the better, the smaller the passive motion (closer to the mean) the better.

Thus, we can define the degree of completion accordingly. For example, according to the 3 sigma criterion, the available numbers "substantially complete", "smoothly complete", "not complete" are described as follows:

action type	Smoothly finish	Partial completion of	Cannot be completed
				Active action	0.84m-1.16m	0.16m-0.84m	0m-0.16m
Passive action	0m-0.16m	0.16m-1.84m	1.84m-2m

And evaluating the movement function of the limb according to the completion degree to construct an amplification feature matrix, wherein in the test process, a therapist manually scores the full-Meyer scale according to the action completion degree of the patient, so that the full-Meyer scale can be effectively evaluated by using the data measured by the sensor, and the original data and the evaluation result are stored.

After data acquisition, the acquired data may be subjected to a pre-processing analysis. Filtering, segmenting, normalizing and aligning are carried out according to different signal characteristics. The data segmentation mainly extracts effective signals from the actions of multiple measurements, and reduces the data dimension. Normalization essentially cancels out the unit difference between the measurements, for example, normalizes the duration of the action to facilitate subsequent phasing.

Then, feature extraction is carried out on the preprocessed data, wherein the feature extraction mainly comprises the mention of kinematic features, and an amplification feature matrix is constructed according to the extracted features. The kinematic features mainly comprise the motion angle, the angular velocity, the angular acceleration, the motion smoothness, the track offset and the like of each joint.

During evaluation, the non-negative characteristic matrix decomposition method is adopted to decompose the amplification characteristic matrix, and the score of each evaluation action is obtained through active-passive and staged processing according to the invention. Further, the evaluation values 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 according to a decision tree. Taking the shoulder abduction 90 degrees, elbow extension, forearm pronation evaluation item (13) as an example, the decision tree is shown in fig. 3.

The abbreviations are illustrated in the following table.

The decision method in the decision tree of fig. 3 is as follows: in the whole movement stage (T), if the shoulder joint abduction is abnormal, or the elbow buckling phenomenon is obvious, or the forearm can not rotate forwards and backwards, marking 0 point; if the shoulder joint abducted and elbow flexed normally, but the forearm rotated forward and backward abnormally, it was marked as 1 minute, and if the shoulder joint, elbow joint and forearm rotated forward and backward normally, it was marked as 2 minutes.

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

The upper limb function evaluation part counts 33 items and 66 points according to the Fugl-Meyer scale. According to the embodiment of the invention, 7 hand evaluation items are removed and 3 reflection inspection items are removed according to the measuring range and the application range of the motion capture unit, and 12 actions are designed for 46 minutes. The action criteria were as per the Fugl-Meyer scale.

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

Sports scoring	Corresponding score of upper limb	Grading	Clinical significance
				<50 min (50%)	<23	I	Severe dyskinesia
50-84 min (35%)	23-38	II	Obvious dyskinesia
				85-95 min (11%)	39-43	III	Moderate dyskinesia
96-99 min (4%)	44-45	IV	Mild dyskinesia

The following describes an embodiment of an electronic device according to the present invention, which may be regarded as a specific physical implementation of the above-described embodiment of the method and apparatus according to the present invention. Details described in relation to the embodiments of the electronic device of the present invention should be considered as additions to the embodiments of the method or apparatus described above; for details not disclosed in the embodiments of the electronic device of the present invention, reference may be made to the above-described method or apparatus embodiments.

The above-mentioned motion function evaluation method based on active-passive and staged actions of the present invention can be implemented 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 merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

As shown in fig. 4, 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 different system components (including memory unit 420 and processing unit 410), a data acquisition unit 440, and the like.

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

The memory unit 420 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 4201 and/or cache memory 4202, and may further include Read Only Memory (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 or some combination of which may include an implementation of a network environment.

Bus 430 may be a local 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 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.), one or more devices that enable a user to interact with the electronic device 400, and/or any device (e.g., router, modem, etc.) that enables the electronic device 400 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 450. Also, electronic device 400 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through 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, other hardware and/or software modules may be used in connection with electronic device 400, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the invention thereto, but to limit the invention thereto, and any modifications, equivalents, improvements and equivalents thereof may be made without departing from the spirit and principles of the invention.

Claims

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

acquiring limb movement signals of a target object;

the method is characterized in that:

extracting active motion data and passive motion data in the limb motion signals to obtain the completion degrees of the active motion and the passive motion at different stages in the whole motion stroke;

evaluating the movement function of the limb according to the completion degree;

the passive action represents an abnormal action mode occurring when some action is performed;

wherein assessing the motor function of the limb according to the degree of completion comprises:

dividing the active motion and the passive motion into different stages in the whole motion stroke according to time or stroke space, determining the time period after normalizing the motion time of the limb, and comparing the completion degree of the active motion and the passive motion in the different stages in the whole motion stroke with a preset motion evaluation standard to obtain the score of the motion function of the limb;

the completion degree refers to a quantitative parameter of the target object for completing a certain action, and is determined by the ratio of the activity degree of a certain joint of a limb to the average value.

2. The method of claim 1, wherein: the whole movement stroke refers to the movement stroke of the limb when a certain action is completed.

3. The method of claim 2, wherein: the limb movement signal is a movement signal of a plurality of predetermined limb movements, and the score for the movement function of the limb is a composite score of the plurality of predetermined limb movements.

4. A method as claimed in claim 3, wherein: the predetermined motion estimation criteria is a Fugl-Meyer based motion estimation criteria.

5. The method of any one of claims 1 to 4, wherein:

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

6. The method of claim 5, wherein: the period includes at least one of the following periods: the whole stage, the beginning stage, the middle stage and the final stage.

7. The method of any one of claims 1 to 4, further comprising filtering, segmentation and normalization operations for preprocessing after acquisition of the target subject limb movement signals.

8. An active-passive, phased action based athletic performance assessment device, wherein the device comprises:

a processor; the method comprises the steps of,

a memory storing computer executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.