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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Bibliographic Details
Published in:Computer (Long Beach, Calif.) Vol. 51; no. 5; pp. 50 - 59
Main Authors: Plotz, Thomas, Guan, Yu
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:0018-9162, 1558-0814
Online Access:Get full text
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Summary: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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ISSN:0018-9162
1558-0814
DOI:10.1109/MC.2018.2381112