Algorithm for eye-movement signal processing
Implements a generic nonparametric statistical algorithm to analyze eye-movement trajectory data.
LPiTrack(xy_mat, m = c(3, 5, 15), p = 10)
xy_mat |
A matrix with first column as x-coordinates and second column as y-coordinates of trajectory data. |
m |
A vector of three items. |
p |
The lag-order for vector autoregressive model to be fitted to the data to extract temporal features in the data. |
This function simultaneously extracts all Temporal-Spatial-Static
features from the trajectory data integrating LPTime,
LP.comoment and LP.moment functions.
LPTime fits VAR model on the LP transformed
series to capture the joint (horizontal and vertical) dynamics of the eye-movement pattern.
LP.moment is applied on the series r(t) (where we define
r^2(t) \,=\, X^2(t) + Y^2(t)), X(t), Y(t), and their first
and second order differences to capture the static
pattern. LP.comoment is applied on the following three series: (r(t), Δ r(t)),
(X(t), Y(t)) and (Δ X(t),Δ Y(t)) to extract
nonparametric copula-based spatial fixation patterns.
A vector representation of LP features for the trajectory data, which can be used as covariates (signatures) for subsequent prediction modelling.
Shinjini Nandi
Mukhopadhyay, S. and Nandi, S. (2015). LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification.
library(LPTime) data(EyeTrack.sample) head(LPiTrack(as.matrix(EyeTrack.sample), m = c(4,5, 15), p=3))
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