CN114864044A - Hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology - Google Patents

Hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology Download PDF

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CN114864044A
CN114864044A CN202210524747.1A CN202210524747A CN114864044A CN 114864044 A CN114864044 A CN 114864044A CN 202210524747 A CN202210524747 A CN 202210524747A CN 114864044 A CN114864044 A CN 114864044A
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杨帮华
庄皓东
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Shanghai Shaonao Sensor Technology Co ltd
University of Shanghai for Science and Technology
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Abstract

The invention discloses a hand function rehabilitation training system fusing eye movement direction classification and augmented reality technology, which comprises electroencephalogram acquisition equipment, a data analysis module, a data preprocessing module, a convolutional neural network module and a hand function rehabilitation module, wherein the electroencephalogram acquisition equipment acquires original electroencephalogram signals of channels related to eyeball movement of a patient, the data analysis module receives the electroencephalogram signals and analyzes the electroencephalogram signals into data format processable electroencephalogram signals, the data preprocessing module preprocesses the electroencephalogram signals, the convolutional neural network module receives the preprocessed electroencephalogram signals and conducts model training and testing, and after a classification result is obtained, the classification result is used as an instruction for interacting with the hand function rehabilitation module. The invention combines augmented reality and eye movement recognition technology, brings more comfortable rehabilitation experience to the stroke patient, meets the interaction requirements of the patient, and improves the enthusiasm of the patient for participating in rehabilitation.

Description

Hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology
Technical Field
The invention relates to a hand function rehabilitation training system, in particular to a hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology.
Background
With the aging of the population in China, the incidence of cerebral apoplexy is increasing, the daily activities of patients are seriously affected, and the basic hand motions such as grasping, holding, finger-pointing and the like are difficult to complete in main clinical manifestations. In order to promote the hand function rehabilitation of stroke patients more effectively, a rehabilitation training system based on Brain-Computer Interface (BCI) technology is developed and is more and more widely applied to clinical treatment.
However, patients in the early stage of rehabilitation mainly have prone positions, and need to go through a long adaptation and familiarity stage to formally enter a rehabilitation course, and the attention of the patients is often hard to concentrate during the period, so that the rehabilitation process and the rehabilitation effect are influenced. The reason is that the traditional computer display method is difficult to improve the enthusiasm of the patient for actively participating in the rehabilitation training. Even the emergence of virtual reality technique can provide the lifelike experience of certain degree, still can bring motion sickness and tired problem for the patient, still can influence the comfort level that the patient carried out hand rehabilitation training at last. Augmented reality is a novel display technology which is continuously developed on the basis of virtual reality, can solve the problem of vertigo of people in the virtual reality, helps the integration of brain high-level centers by stimulating a plurality of sense organs, establishes a new information receiving and processing channel, activates a coupling mechanism between two brain hemispheres by combining a bilateral cooperative rehabilitation training method, and is more beneficial to hand function rehabilitation.
In addition, the patient has daily interactive demand in the early stage of rehabilitation, the conventional hand function rehabilitation system cannot meet the daily interactive demand of the patient, the patient is difficult to independently complete tasks such as rehabilitation training and the like, but a rehabilitation therapist is required to complete a large amount of auxiliary work, so that the labor cost is extremely high. The intention expression based on the bioelectrical signal is a natural and convenient man-machine interaction mode, and the movement of the eyeball is one of the main modes, so that the eye movement is a proper control instruction. In fact, before entering the BCI rehabilitation course, the patient can realize human-computer interaction through eye movement, and the interaction requirements are met.
The eye movement can be captured in various ways, but the traditional method has low applicability to patients in early stage of rehabilitation, such as: firstly, the method for measuring the electrooculogram by adopting the patch electrode mode has the advantages that the electrode is pasted around eyes to reduce the visual field, so that the electrode is easy to cause discomfort of facial muscles after being worn for a long time, and simultaneously, the signal quality is influenced, and the electrode is not suitable for the rehabilitation process; secondly, the eye tracker captures the eye movement by means of computer image, but not only the equipment cost but also the learning cost of the rehabilitation therapist are required to be borne.
Disclosure of Invention
In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a hand function rehabilitation training system combining eye movement direction classification and augmented reality technology.
The invention solves the technical problems through the following technical scheme: a hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology is characterized by comprising electroencephalogram acquisition equipment, a data analysis module, a data preprocessing module, a convolutional neural network module and a hand function rehabilitation module, wherein the electroencephalogram acquisition equipment acquires original electroencephalogram signals of channels related to eyeball movement of a patient, the data analysis module receives the electroencephalogram signals and analyzes the electroencephalogram signals into electroencephalogram signals with a data format capable of being processed, the data preprocessing module preprocesses the electroencephalogram signals, the convolutional neural network module receives the preprocessed electroencephalogram signals and conducts model training and testing, and after classification results are obtained, the classification results are used as instructions interacting with the hand function rehabilitation module.
Preferably, the head of the patient wears electroencephalogram acquisition equipment and augmented reality equipment, and the affected hand wears pneumatic gloves.
Preferably, the patient selects an action to be rehabilitated at a system interface in the augmented reality device, thereby entering a corresponding rehabilitation scenario.
Preferably, the electroencephalogram acquisition equipment uses an international 10-20 lead distribution mode.
Preferably, the data preprocessing module employs the following algorithm: removing the baseline algorithm, normalizing the algorithm and fitting the algorithm by the least square method.
Preferably, the convolutional neural network module comprises a convolutional neural network model, and the convolutional neural network model comprises the following seven layers: an input layer, a batch normalization layer, a convolution layer, a max pooling layer, a flattening layer, a full link layer, and an output layer.
The positive progress effects of the invention are as follows: in order to reduce the rehabilitation cost, solve the vertigo of the virtual reality technology, meet the interaction requirements of patients and improve the enthusiasm of the patients for participating in rehabilitation, the invention combines the augmented reality and the eye movement recognition technology and brings more comfortable rehabilitation experience to stroke patients. The equipment is light and friendly, and is convenient for the patient in the early stage of cerebral apoplexy rehabilitation to use in a prone position at any time and any place, the display part adopts an augmented reality technology, and a digital interactive rehabilitation scene is superposed under the condition that the surrounding real environment is visible, so that the equipment brings stronger interest and guidance for the patient, and the enthusiasm of the patient for rehabilitation training is further aroused. Based on the technical background of the stroke rehabilitation training system, the invention can obtain the eye movement data of the patient in the early stage of rehabilitation without additional equipment, and is more accurate and more stable. The invention also takes the eyeball motion as a control instruction to interact with an augmented reality scene, realizes technical innovation, and can meet the interaction requirement of the initial patient to a certain extent while promoting the attention performance of the patient. The eye movement direction recognition algorithm adopted by the invention can obtain higher accuracy, can verify the feasibility of the method, further ensures the accuracy of the eye movement control command, brings better rehabilitation experience to patients, and has great practicability.
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Fig. 1 is a block diagram of a hand function rehabilitation training system incorporating eye movement direction classification and augmented reality technology according to the present invention.
Fig. 2 is a schematic diagram of a hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
As shown in figure 1, the hand function rehabilitation training system integrating the eye movement direction classification and the augmented reality technology comprises an electroencephalogram acquisition device 1, a data analysis module 2, a data preprocessing module 3, a convolutional neural network module 4 and a hand function rehabilitation module 5, wherein the electroencephalogram acquisition device acquires original electroencephalogram signals of channels related to eyeball movement of a patient, the data analysis module receives the electroencephalogram signals and analyzes the electroencephalogram signals into electroencephalogram signals with a data format capable of being processed, the data preprocessing module preprocesses the electroencephalogram signals, the convolutional neural network module receives the preprocessed electroencephalogram signals and conducts model training and testing, and after a classification result is obtained, the classification result is used as an instruction for interacting with the hand function rehabilitation module.
As shown in fig. 2, the head of the patient is worn with an electroencephalogram acquisition device and an augmented reality device, and the affected hand is worn with a pneumatic glove. The patient selects an action to be rehabilitated at the system interface in the augmented reality device, thereby entering a corresponding rehabilitation scenario. In the recovered scene through the specified time of vision and hearing suggestion patient in the progress bar demonstration, accomplish the training action of requirement in the scene, for example snatch the strawberry in the recovered scene to this side position through the affected side hand, the patient uses healthy side hand to snatch the strawberry in the recovered scene to this side position, and pneumatic gloves starts when accomplishing this action, drives affected side hand and carries out same gripping action training. Visual feedback is provided to the patient indicating that the completion is correct or not. Can play arbitrary one of four kinds of background music automatically in the recovered scene, the patient carries out song switching selection to it through eyeball's upper and lower side-to-side motion, and every direction respectively represents a song.
The electroencephalogram acquisition equipment adopts an international 10-20 lead distribution mode, acquires electroencephalogram data of four channels including frontal lobe positions F7, T3, F8 and T4, and has a sampling rate of 300 Hz.
The data preprocessing module adopts the following algorithm:
first, remove baseline algorithm: the EEG signal from each channel of the respective electrodes can be represented as follows (1):
S(m,n)＝[s(m,1)s(m,2)…s(m,n-1)s(m,n)]………………(1)
where S is the EEG signal, m is the serial number of the EEG channel, n is the nth sample point in the discrete time series, and S (m, n) represents the amplitude of the EEG signal for the m channel in the nth sample point.
Subtracting the average of the amplitudes at all the sampling points in the channel from the amplitude at each sampling point achieves removing the baseline, namely the equations (2) and (3):
X i ＝S i -μ(S)………………(2)
Figure BDA0003643728160000051
wherein, X i Is the baseline-removed value, S, of each channel signal i Is the original value of each channel signal and μ (S) is the average value of each channel signal.
Secondly, a normalization algorithm: in order to make the model more accurate and accelerate the convergence rate of the learning algorithm in the subsequent model training stage, feature scaling, i.e., normalization, needs to be performed on the data from which the baseline is removed. Since EEG data contains positive and negative polarities, to preserve such features, normalization is performed using equation (4):
Figure BDA0003643728160000052
wherein, X normalized Is a normalized value; x max Is X before normalization i The maximum value of the absolute value of (a), i.e., + Max (abs (X) i ))；X min Is X i Negative of the maximum absolute value of (a), i.e., -Max (abs (X) i )). Respectively combine X max And X min Conversion to equation (.4) yields the following results, as in equation (5):
Figure BDA0003643728160000053
thirdly, a least square fitting algorithm: finally, the best matching function of the data is found by minimizing the sum of squared errors based on the least square method, and a polynomial is used for fitting the original data, and the problem is that: for a given set (x) i ,y i ) I is 0,1,2, …, n, and k-th order (k) is obtained to minimize the total error Q<n) is as in formula (6) and (7):
Figure BDA0003643728160000054
Figure BDA0003643728160000055
a 0 is the polynomial coefficient a found j K is an integer within n, and j is a term in the summation symbol.
Wherein the total error Q is considered to be with respect to a j (j-0, 1,2, …, k). Thus, the above problem can be equivalent to a multivariate function extremum problem, as shown in equations (6) and (7):
Figure BDA0003643728160000061
from formula (8), we can obtain a j (j ═ 0,1,2, …, k) of a fitting polynomial p (x).
The convolutional neural network module 4 includes a convolutional neural network model, which includes seven layers: an input layer, a batch normalization layer, a convolution layer, a max pooling layer, a flattening layer, a full link layer, and an output layer.
An input layer: the input values are in a matrix with the pre-processed EEG data, with dimensions [900 × 4 × 1 ]. 4 is the number of channels selected previously, 900 is the sample point data for each channel, and 1 is the data depth.
And (3) rolling layers: setting a 2D convolution kernel containing time and channels, wherein the size is [10 multiplied by 2], the convolution step size is [2 multiplied by 1], the output number of the filter is 20, padding is adopted to carry out 0 filling on input data, and the activation function is ELU. The output shape is [450 × 4 × 20 ].
Batch standardization layer: the output size of the previous layer is not changed, and the input and the output are [450 × 4 × 20 ]. The parameters of the batch standardization are all default configurations.
Maximum pooling layer: set the maximum pooling window of size [10 × 2], maximum pooling step [2 × 1 ]. The output shape is [221 × 3 × 20 ].
Flattening the layer: vectorizing the input size, and stacking into a one-dimensional large vector output with the shape of [13260 ].
Full connection layer: the goal is four-classification, so set the output space dimension to 4, the activation function to Softmax, and the output shape to [4 ].
An output layer: and outputting the classification results of the four classes.
In the system, the eye movement is a control instruction, and by means of an augmented reality interface, a patient switches music tracks in a scene by controlling the movement of eyeballs and performs hand function rehabilitation training by matching with pneumatic gloves of a device terminal, so that the system is particularly suitable for the initial rehabilitation training of stroke patients. The invention utilizes the eyeball motion electric signal component in the electroencephalogram signal to recognize and classify the eye movements in the upper, lower, left and right directions through preprocessing and a convolutional neural network. The invention provides a visual augmented reality interface and a rehabilitation scene for a patient, and the patient can realize music switching in each scene through the movement of eyeballs in four directions, namely up, down, left and right.
While there have been shown and described what are at present considered the fundamental principles and essential features of the invention and its advantages, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. 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 (6)

1. A hand function rehabilitation training system integrating eye movement direction classification and augmented reality technology is characterized by comprising electroencephalogram acquisition equipment, a data analysis module, a data preprocessing module, a convolutional neural network module and a hand function rehabilitation module, wherein the electroencephalogram acquisition equipment acquires original electroencephalogram signals of channels related to eyeball movement of a patient, the data analysis module receives the electroencephalogram signals and analyzes the electroencephalogram signals into electroencephalogram signals with a data format capable of being processed, the data preprocessing module preprocesses the electroencephalogram signals, the convolutional neural network module receives the preprocessed electroencephalogram signals and conducts model training and testing, and after classification results are obtained, the classification results are used as instructions interacting with the hand function rehabilitation module.
2. The system of claim 1, wherein the patient wears an electroencephalogram acquisition device and an augmented reality device on the head and pneumatic gloves on the affected hand.
3. The system of claim 2, wherein the patient selects an action to be rehabilitated at a system interface of the augmented reality device to enter a corresponding rehabilitation scenario.
4. The eye movement direction classification and augmented reality fused hand function rehabilitation training system according to claim 1, wherein the electroencephalogram acquisition device uses an international 10-20 lead distribution mode.
5. The system of claim 1, wherein the data preprocessing module employs the following algorithm: removing the baseline algorithm, normalizing the algorithm and fitting the algorithm by the least square method.
6. The system of claim 1, wherein the convolutional neural network module comprises a convolutional neural network model, and wherein the convolutional neural network model comprises the following seven layers: an input layer, a batch normalization layer, a convolution layer, a max pooling layer, a flattening layer, a full link layer, and an output layer.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113274032A (en) * 2021-04-29 2021-08-20 上海大学 Cerebral apoplexy rehabilitation training system and method based on SSVEP + MI brain-computer interface

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113274032A (en) * 2021-04-29 2021-08-20 上海大学 Cerebral apoplexy rehabilitation training system and method based on SSVEP + MI brain-computer interface

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* Cited by examiner, † Cited by third party
Title
HAODONG ZHUANG ETC: "EEG Based Eye Movements Multi-Classification Using Convolutional Neural Network", 《PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE》, 31 July 2021 (2021-07-31), pages 7191 - 7195, XP033981130, DOI: 10.23919/CCC52363.2021.9550039 *

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