Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Fixation-identification in dynamic scenes: comparing an automated algorithm to manual coding
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ACM Transactions on Applied Perception (TAP)
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Real time eye movement identification protocol
CHI '10 Extended Abstracts on Human Factors in Computing Systems
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Wearable eye tracking for mental health monitoring
Computer Communications
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Real-time 3D gaze analysis in mobile applications
Proceedings of the 2013 Conference on Eye Tracking South Africa
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This paper presents a set of qualitative and quantitative scores designed to assess performance of any eye movement classification algorithm. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (I-HMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the "best" classification algorithm is provided for several applications. General recommendations for the selection of the input parameters for each algorithm are provided.