Deep Learning for Human Activity Recognition in Mobile Computing
By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's c...
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| Vydáno v: | Computer (Long Beach, Calif.) Ročník 51; číslo 5; s. 50 - 59 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9162, 1558-0814 |
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| Abstract | By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field's core application domains, including the ongoing transformation of human activity recognition technology through machine learning. |
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| AbstractList | By leveraging advances in deep learning, challenging pattern recognition problems have been solved in computer vision, speech recognition, natural language processing, and more. Mobile computing has also adopted these powerful modeling approaches, delivering astonishing success in the field’s core application domains, including the ongoing transformation of human activity recognition technology through machine learning. |
| Author | Plotz, Thomas Guan, Yu |
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| Cites_doi | 10.1109/MPRV.2017.2940968 10.1145/2499621 10.1371/journal.pone.0169649 10.1145/2971763.2971764 10.1109/CIT/IUCC/DASC/PICOM.2015.170 10.1007/s00779-010-0293-9 10.1145/3090076 10.1145/2750858.2807534 10.1038/nature14539 10.3390/s16010115 10.1145/2493988.2494353 10.4108/icst.mobicase.2014.257786 |
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| SubjectTerms | Analytical models artificial intelligence complexity Computation Computational modeling Computer vision Data mining Data models Deep learning embedded systems Feature extraction HAR Human activity recognition intelligent systems Machine learning mobile mobile and embedded deep learning Mobile computing modeling Natural language processing Pattern recognition Speech recognition Voice recognition |
| Title | Deep Learning for Human Activity Recognition in Mobile Computing |
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