Eye Movement Analysis for Activity Recognition Using Electrooculography

TLDR

The study investigates eye movement analysis as a novel sensing modality for activity recognition, developing algorithms to detect saccades, fixations, and blinks and to assess repetitive eye‑movement patterns. EOG signals were recorded from eight participants performing five office activities and a NULL class, from which 90 eye‑movement features were extracted, reduced via mRMR, and classified with a leave‑one‑person‑out SVM. The approach achieved 76.1 % precision and 70.5 % recall, demonstrating the feasibility of eye‑based activity recognition and suggesting its applicability to other hard‑to‑detect activities.

Abstract

In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.