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MIDAS: Deep learning human action intention prediction from natural eye movement patterns
Our evaluation shows that intention prediction is not a naive result of the data, but rather relies on non-linear temporal processing of gaze cues.
Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform
We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling mobility platforms by decoding how the user looks at the environment to understand where they want to navigate their mobility device.
Towards an Embodied Semantic Fovea: Semantic 3D scene reconstruction from ego-centric eye-tracker videos
Incorporating the physical environment is essential for a complete understanding of human behavior in unconstrained every-day tasks.
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