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Journal article

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 user is in a car, plane or bike? Critically for ap...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/3161174

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Bhattacharya, S More by this author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Mascolo, C More by this author
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Publisher:
Association for Computing Machinery Publisher's website
Journal:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Journal website
Volume:
1
Issue:
4
Pages:
Article: 157
Publication date:
2018-01-08
Acceptance date:
2017-10-09
DOI:
ISSN:
2474-9567
Pubs id:
pubs:946038
URN:
uri:87c29798-2731-48df-9a4b-e1b1aa9caf1c
UUID:
uuid:87c29798-2731-48df-9a4b-e1b1aa9caf1c
Local pid:
pubs:946038

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