Affiliations:
Department of Computer Science & Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India
Correspondence:
[*]
Corresponding author. C. Jyotsna, Department of Computer Science & Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India. E-mail: c_jyotsna@blr.amrita.edu.
Abstract: Extraction of eye gaze events is highly dependent on automated powerful software that charges exorbitant prices. The proposed open-source intelligent tool StimulEye helps to detect and classify eye gaze events and analyse various metrics related to these events. The algorithms for eye event detection in use today heavily depend on hand-crafted signal features and thresholding, which are computed from the stream of raw gaze data. These algorithms leave most of their parametric decisions on the end user which might result in ambiguity and inaccuracy. StimulEye uses deep learning techniques to automate eye gaze event detection which neither requires manual decision making nor parametric definitions. StimulEye provides an end to end solution which takes raw streams of data from an eye tracker in text form, analyses these to classify the inputs into the events, namely saccades, fixations, and blinks. It provides the user with insights such as scanpath, fixation duration, radii, etc.
Keywords: Eye tracking, fixations, saccades, scanpath, deep learning
