Deep Learning for Human Activity Recognition in Mobile Computing

被引：57
|
作者：
Plotz, Thomas [1 ]
Guan, Yu [2 ]
机构：
[1] Georgia Tech, Sch Interact Comp, Coll Comp, Atlanta, GA 30332 USA
[2] Newcastle Univ, Sch Comp Sci, Newcastle Upon Tyne, Tyne & Wear, England
来源：
COMPUTER | 2018年 / 51卷 / 05期
基金：
英国工程与自然科学研究理事会;
关键词：
artificial intelligence; complexity; deep learning; embedded systems; HAR; human activity recognition; intelligent systems; machine learning; mobile; mobile and embedded deep learning; modeling; pattern recognition;
D O I：
10.1109/MC.2018.2381112
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：50 / 59
页数：10
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