Multimodal Deep Learning for Activity and Context Recognition

Abstract
Wearables and mobile devices see the world through the lens of half a dozen low-power sensors, such as, barometers, accelerometers, microphones and proximity detectors. But differences between sensors ranging from sampling rates, discrete and continuous data or even the data type itself make principled approaches to integrating these streams challenging. How, for example, is barometric pressure best combined with an audio sample to infer if a...
Paper Details
Title
Multimodal Deep Learning for Activity and Context Recognition
Published Date
Jan 8, 2018
Volume
1
Issue
4
Pages
157
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