FEATURE GROUPING NORMALIZATION METHOD FOR COGNITIVE STATE RECOGNITION

Abstract

A normalization method in grouped feature data for recognizing human cognitive states, comprising: (1) divide feature data into groups; (2) selecting normalization functions and estimating grouping parameters; (3) building grouped normalization functions, substitute normalization function parameters of each group into its normalization function, the normalization mapping relationship of each group is get; (4) grouped normalization processing, each group uses corresponding normalization function to transfer the feature data to finish feature normalization. The entire feature normalization method can only solve the divers data distribution problem between feature and feature, it can not solve the problem of the large difference of inner data distribution, the grouped normalization methods provided in the invention reserve the advantages of entire feature normalization method, while at the same time, the large inner distribution of feature data is reduced, the accuracy of classification is improved, the grouped normalization method in the invention have strong robustness.

Claims

1. A normalization method in grouped feature data for recognizing human cognitive states, comprising: (1) divide feature data into groups, (1-1) feature data X from A category is XA.sub.ij(i: 1,2,3 . . . , m; j: 1,2, . . . n; m represents user number, n:represents task number of B category), (1-2) feature data X from B category is XB .sub.ij(i: 1,2,3 . . . , m; j: 1,2, . . . n; m represents user number, n:represents task number of B category), (1-3) build feature matrix of X,:X=(XA.sub.ij, XB.sub.ij).sub.m*2n, is composed: $\begin{array}{cc}X=\left[\begin{array}{cccccccc}{\mathrm{XA}}_{11}& {\mathrm{XA}}_{12}& \mathrm{.Math.}& {\mathrm{XA}}_{1.Math.n.Math..Math.1}& {\mathrm{XB}}_{11}& {\mathrm{XB}}_{12}& \mathrm{.Math.}& {\mathrm{XB}}_{1.Math.n.Math..Math.2}\\ {\mathrm{XA}}_{21}& {\mathrm{XA}}_{22}& \mathrm{.Math.}& {\mathrm{XA}}_{2.Math.n.Math..Math.1}& {\mathrm{XB}}_{21}& {\mathrm{XB}}_{22}& \mathrm{.Math.}& {\mathrm{XB}}_{2.Math.n.Math..Math.2}\\ & & & \mathrm{.Math.}.Math..Math.\mathrm{.Math.}& \mathrm{.Math.}.Math..Math.\mathrm{.Math.}& & & \\ {\mathrm{XA}}_{i.Math..Math.1}& {\mathrm{XA}}_{i.Math..Math.2}& \mathrm{.Math.}& {\mathrm{XA}}_{\mathrm{in}.Math..Math.1}& {\mathrm{XB}}_{i.Math..Math.1}& {\mathrm{XB}}_{i.Math..Math.2}& \mathrm{.Math.}& {\mathrm{XB}}_{\mathrm{in}.Math..Math.2}\\ & & & \mathrm{.Math.}& \mathrm{.Math.}& & & \\ {\mathrm{XA}}_{m.Math..Math.1}& {\mathrm{XA}}_{\mathrm{m2}}& \mathrm{.Math.}& {\mathrm{XA}}_{\mathrm{mn}.Math..Math.1}& {\mathrm{XB}}_{m.Math..Math.1}& {\mathrm{XB}}_{m.Math..Math.2}& \mathrm{.Math.}& {\mathrm{XB}}_{\mathrm{mn}.Math..Math.2}\end{array}\right]& \mathrm{formula}.Math..Math.1\end{array}$ (1-4) divide feature X into groups based on user, each line of the matrix is a group, “m” users corresponding “m” lines, divided into “m” groups, the No. i group of feature X is:
X.sub.i=(XA.sub.i1 XA.sub.i2 . . . XA.sub.in1 XB.sub.i1 XB.sub.i2 . . . XB.sub.in2) i=1,2, . . . , m   formula 2 (5) Estimate grouping parameters, (2-1) first, select one normalization function; f (parameter 1, parameter 2, . . . parameter k); (2-2) according to the parameter request of normalization function, doing parameter estimation for each group of feature X, “m” grouping parameter is get, “k” represents parameter of X.sub.i in i group, these parameters are: (parameter i1, parameter i2, . . . parameter ik), i=1,2, . . . , m (6) building grouped normalization functions according to (2), building normalization function of each feature X respectively, X.sub.i represents the No. i group (i=1,2, . . . m) normalization function in “m” groups of feature X, normalization parameters of X.sub.i uses corresponding parameters in group i, parameter i1, parameter i2. . . parameter ik, different grouping have different parameters, so that different normalization function is built by different groups, the “m” groups of feature X build “m” normalization functions, the normalization function of group i can be expressed as: f.sub.i (X)i=1,2, . . . , m (7) grouped normalization process p2 according to grouped normalization functions built by (3), doing the grouped normalization process of feature data of X, No. i group (i=1,2, . . . m) in “m” groups of feature X, X.sub.i uses corresponding normalization function in group i f.sub.i (X) to do the grouped normalization process, the approach is: substitute feature data X.sub.i in i group before normalization into normalization function f.sub.i (X), feature data X.sub.i ′ after normalization of No. i group is get, as formula 3, $\begin{array}{cc}{X}_i^{\prime }=X_i.fwdarw.f_i\left(X\right)=\left({\mathrm{XA}}_{i.Math..Math.1}^{\prime }.Math.{\mathrm{XA}}_{i.Math..Math.2}^{\prime }.Math..Math.\mathrm{.Math.}.Math..Math.{\mathrm{XA}}_{\mathrm{in}.Math..Math.1}^{\prime }.Math.{\mathrm{XB}}_{i.Math..Math.1}^{\prime }.Math.{\mathrm{XB}}_{i.Math..Math.2}^{\prime }.Math..Math.\mathrm{.Math.}.Math..Math.{\mathrm{XB}}_{\mathrm{in}.Math..Math.2}^{\prime }\right).Math..Math..Math.{\mathrm{XA}}_{\mathrm{ij}}^{\prime }={\mathrm{XA}}_{\mathrm{ij}}.fwdarw.f_i\left(X\right).Math..Math..Math.i=1,2,\mathrm{.Math.}.Math.,m,j=1,2,\mathrm{.Math.}.Math.,n.Math..Math..Math.{\mathrm{XB}}_{\mathrm{ij}}^{\prime }={\mathrm{XB}}_{\mathrm{ij}}.fwdarw.f_i\left(X\right).Math..Math..Math.i=1,2,\mathrm{.Math.}.Math.,m,j=1,2,\mathrm{.Math.}.Math.,n& \mathrm{formula}.Math..Math.3\end{array}$ XA.sub.ij represents feature data of X in A category before grouped normalization, XB.sub.ij represents feature data of X in B category before grouped normalization, XA.sub.ij′ represents feature data of X in A category after grouped normalization, XB.sub.ij′ represents feature data of X in B category after grouped normalization, after finishing the grouped normalization for each group by using formula 3, the normalization of feature X is finished.

Description

DESCRIPTION OF APPENDED DRAWINGS

[0026] FIG. 1: flow chart of normalization method in grouped feature.

[0027] FIG. 2: 2 types data distribution comparative figure of normalization method in grouped feature.

[0028] FIG. 3: classification effect figure of single feature of normalization method in grouped feature.

[0029] FIG. 4: classification effect figure of combined feature of normalization method in grouped feature.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] The invention will be described in more detail below accompanying the appended drawings with the preferred embodiment.

[0031] FIG. 1 is the flow chart of normalization method in grouped feature, including 4 parts: feature data grouping, selecting normalization function and parameter estimation, building grouped normalization function, normalization treatment of grouped feature data.

[0032] In implanting case, extract visual information during recognition process, 20 tasks of A category (watch images) and 20 tasks of B category (reading text) of 30 users is extracted by Tobii T120 eye movement device (sampling frequency 120 Hz), then, extract four kinds of feature: pupil diameter, saccade amplitude, fixation time and fixation count. After feature extraction, it will move to feature normalization process, takes pupil diameter as an example to introduce the invention in detail. [0033] (1) Feature data grouping of pupil diameter: [0034] (1-1) Calculate pupil diameter data of each A category tasks when 20 tasks of 30 users is carried out, marked as: TA.sub.ij(i=1,2, . . . 30; j=1,2, . . . 20). [0035] (1-2) Calculate pupil diameter data of each B category tasks when 20 tasks of 30 users is carried out, marked as: TB.sub.ij(i=1,2, . . . 30; j=1,2, . . . 20). [0036] (1-3) Build feature matrix of pupil diameter feature T, T=(TA.sub.ij, TB.sub.ij).sub.30*40, is composed as:

[00003]$\begin{array}{cc}T=\left[\begin{array}{cccccccc}{\mathrm{TA}}_{11}& {\mathrm{TA}}_{12}& \mathrm{.Math.}& {\mathrm{TA}}_{120}& {\mathrm{TB}}_{11}& {\mathrm{TB}}_{12}& \mathrm{.Math.}& {\mathrm{TB}}_{120}\\ {\mathrm{TA}}_{21}& {\mathrm{TA}}_{22}& \mathrm{.Math.}& {\mathrm{TA}}_{220}& {\mathrm{TB}}_{21}& {\mathrm{TB}}_{22}& \mathrm{.Math.}& {\mathrm{TB}}_{220}\\ & & & \mathrm{.Math.}.Math..Math.\mathrm{.Math.}& \mathrm{.Math.}.Math..Math.\mathrm{.Math.}& & & \\ {\mathrm{TA}}_{i.Math..Math.1}& {\mathrm{TA}}_{i.Math..Math.2}& \mathrm{.Math.}& {\mathrm{TA}}_{i.Math..Math.2.Math..Math.0}& {\mathrm{TB}}_{i.Math..Math.1}& {\mathrm{TB}}_{i.Math..Math.2}& \mathrm{.Math.}& {\mathrm{TB}}_{i.Math.20}\\ & & & \mathrm{.Math.}& \mathrm{.Math.}& & & \\ {\mathrm{TA}}_{30.Math..Math.1}& {\mathrm{TA}}_{302}& \mathrm{.Math.}& {\mathrm{TA}}_{3020}& {\mathrm{TB}}_{30.Math..Math.1}& {\mathrm{TB}}_{302}& \mathrm{.Math.}& {\mathrm{TB}}_{3020}\end{array}\right]& \mathrm{formula}.Math..Math.4\end{array}$

[0037] The pupil diameter feature T is divided into groups, each line is a group, 30 users corresponding to 30 groups.

[0038] According to this method above, group saccade amplitude, fixation time and fixation count respectively. [0039] (2) Select normalization function and parameter estimation [0040] (2-1) Select a normalization function, the invention take Z-score function as feature normalization function, Z-score function has two parameters, mean value Mean (X.sub.i) and standard deviation std (X.sub.i) the formula can be expressed as:

x.sub.ij′=(x.sub.ij Mean (X.sub.i))/std (X.sub.i)

x.sub.ij∈(TA.sub.ij, TB.sub.ij)

x.sub.ij′∈(TA.sub.ij′, TB.sub.ij′)

i−1,2, . . . , 30, j=1,2, . . . , 20   formula 5

[0041] X′.sub.ij represents No. j normalization value of No. i group X′.sub.i after feature data normalization, X.sub.ij represents No. j value of No. i group X.sub.i before feature data normalization, Mean(X.sub.i) represents the mean value of X.sub.i in No. i group of feature value, std (X.sub.i) represents the standard deviation of X.sub.i in No. i group. [0042] (2-2) According to the grouping results in (1) and the request of parameters in (2-1), estimate the parameters of each group of pupil diameter feature T, get parameters of 30 groups, can be expressed as:

TABLE-US-00001 (i) Mean(X.sub.i) std(X.sub.i) 1 3.585 0.272 2 3.788 0.561 3 3.880 0.199 4 4.563 0.340 5 3.388 0.400 6 3.501 0.358 7 3.926 0.246 8 3.744 0.238 9 4.652 1.587 10 4.092 0.274 11 3.536 0.263 12 2.871 0.182 13 3.805 0.491 14 5.196 0.401 15 4.388 0.320 16 3.827 0.493 17 4.135 0.667 18 3.807 0.386 19 3.739 0.487 20 3.521 0.394 21 3.885 0.275 22 4.275 0.409 23 4.149 0.500 24 3.313 0.533 25 3.163 0.219 26 4.854 0.465 27 3.276 0.232 28 4.477 0.404 29 4.518 0.465 30 3.508 0.268 [0043] (3) Building grouped normalization function. [0044] This case use Z-score function as feature normalization function, building grouped normalization function for each group of pupil feature T, in the 30 groups of feature T, the parameter usage of No. i (i=1,2, . . . 30) group of feature corresponding to the statistic parameters in No. i group, different normalization function of different groups are built, 30 normalization function of 30 pupil diameter feature are built, for example, grouped normalization function of group 1 in formula 4, can be expressed as:

[00004]$\begin{array}{cc}{x}_{1.Math.j}^{\prime }=\left(x_{1.Math.j}-3.585\right).Math.\text{/}.Math.\underset{10}{0.272}.Math..Math.x_{1.Math.j}\in \left({\mathrm{TA}}_{1.Math.j},{\mathrm{TB}}_{1.Math.j}\right).Math..Math.x_{1.Math.j}^{\prime }\in \left({\mathrm{TA}}_{1.Math.j}^{\prime },{\mathrm{TB}}_{1.Math.j}^{\prime }\right).Math..Math.j=1,2,\mathrm{.Math.}.Math.,20& \mathrm{formula}.Math..Math.6\end{array}$ [0045] x′.sub.ij represents pupil diameter data of group 1 after grouped normalization, X.sub.1j represents pupil diameter data of group 1 before grouped normalization, 3.585 is mean value of group 1, 0.272 is standard deviation og group 1, TA.sub.1j, TB.sub.1j represents pupil diameter feature data of A and B category before normalization respectively, [0046] TA.sub.1j′, TB.sub.1j′, represents pupil diameter feature data of A and B category after normalization respectively. [0047] (4) Grouped normalization process [0048] Using grouped normalization function of pupil diameter feature in (3), doing the grouped normalization process of feature data of pupil diameter feature, the normalization process of [0049] No. i group (i=1, 2, . . . , 30) in 30 groups of pupil diameter feature using corresponding No. i normalization function to normalize. After finishing 30 groups normalization of feature data, pupil diameter feature matrix T′ is obtained, as formula 7. Then according to the method above to do the normalization processes of saccade amplitude, fixation time and fixation count.

[00005]$\begin{array}{cc}{T}^{\prime }=\left[\begin{array}{cccccccc}{\mathrm{TA}}_{11}^{\prime }& {\mathrm{TA}}_{12}^{\prime }& \mathrm{.Math.}& {\mathrm{TA}}_{120}^{\prime }& {\mathrm{TB}}_{11}^{\prime }& {\mathrm{TB}}_{12}^{\prime }& \mathrm{.Math.}& {\mathrm{TB}}_{120}^{\prime }\\ {\mathrm{TA}}_{21}^{\prime }& {\mathrm{TA}}_{22}^{\prime }& \mathrm{.Math.}& {\mathrm{TA}}_{220}^{\prime }& {\mathrm{TB}}_{21}^{\prime }& {\mathrm{TB}}_{22}^{\prime }& \mathrm{.Math.}& {\mathrm{TB}}_{220}^{\prime }\\ & & & \mathrm{.Math.}.Math..Math.\mathrm{.Math.}& \mathrm{.Math.}.Math..Math.\mathrm{.Math.}& & & \\ {\mathrm{TA}}_{i.Math..Math.1}^{\prime }& {\mathrm{TA}}_{i.Math..Math.2}^{\prime }& \mathrm{.Math.}& {\mathrm{TA}}_{i.Math..Math.20}^{\prime }& {\mathrm{TB}}_{i.Math..Math.1}^{\prime }& {\mathrm{TB}}_{i.Math..Math.2}^{\prime }& \mathrm{.Math.}& {\mathrm{TB}}_{i.Math..Math.20}^{\prime }\\ & & & \mathrm{.Math.}& \mathrm{.Math.}& & & \\ {\mathrm{TA}}_{30.Math..Math.1}^{\prime }& {\mathrm{TA}}_{302}^{\prime }& \mathrm{.Math.}& {\mathrm{TA}}_{3020}^{\prime }& {\mathrm{TB}}_{30.Math..Math.1}^{\prime }& {\mathrm{TB}}_{302}^{\prime }& \mathrm{.Math.}& {\mathrm{TB}}_{3020}^{\prime }\end{array}\right]& \mathrm{formula}.Math..Math.7\end{array}$ [0050] (5) Evaluation of normalization method in the invention [0051] (5-1) FIG. 2 is a comparative result of Log-normal distribution fitting between feature grouped normalization (FIG. 2a) and feature entire normalization (FIG. 2b) which is disclosed in the invention. The result shows, when using feature entire normalization method, the mean difference between A and B feature is 0.92, when using feature grouped normalization method in the invention, the mean difference between A and B feature increases to 1.63, which is 1.77 times as former one. The bigger the mean difference between A and B feature is, the further the distribution distance it has and the smaller the overlapping degree is, so that the better recognition effect is reached. What's more, as for inner category standard deviation, when using feature entire normalization method, the standard deviation of A feature is 0.96, when using feature grouped normalization method in the invention, the standard deviation of A feature decreases to 0.55 which is 0.57 times as the former one, the standard deviation of B feature using feature grouped normalization method is 0.69 times as the former one. No matter A or B feature, when using the method in the invention, their inner category standard deviation are decrease, it indicates that distribution range of inner feature is decrease, at the same time, overlapping degree is decrease between two kinds of feature. Using the invention method, the distribution distance between two kinds of feature is becoming large, and distribution range is decrease of each kinds of feature, in another word, the diversity problem inner feature is solved by using normalization method in the invention, so that the overlapping degree of feature is decreased. [0052] (5-2) FIG. 3 is a comparative result of classification between feature grouped normalization and feature entire normalization which is disclosed in the invention. This case uses 4 kinds of normalization function (Max-Min, Z-score, Median, tanh) corresponding to 4 kinds of feature, pupil diameter (FIG. 3a), saccade amplitude (FIG. 3b), fixation time (FIG. 3c), fixation count (FIG. 3d), to do feature entire normalization and feature grouped normalization disclosed in the invention, after that, using support vector machine based on the recognition accuracy of mode classification of single feature, result shows, no matter which kind of feature or the normalization function is, the recognition accuracy of invention is higher than feature entire normalization. [0053] (5-3) FIG. 4 shows, after using feature grouped normalization in the invention or feature entire normalization for each feature based on different normalization method, combined these features (pupil diameter+saccade amplitude+fixation time+fixation count), and from the recognition accuracy results of mode classification, no matter which kind of function is used, the combined recognition rate of the invention is higher than feature entire normalization method. The classification recognition accuracy data and combined feature recognition accuracy data based on single feature which is disclosed by the invention shows, the feature grouped normalization method in the invention is not only solved diversity distribution problem of inner feature data, but also solve the diversity problem between features, the advantages of entire normalization are reserved. The grouped normalization method in the invention compare with feature entire normalization method has strong robustness.