Activity recognition using smartphones and wearable devices: Traditional approaches, new solutions

被引:2
|
作者
Iskanderov, Jemshit [1 ]
Guvensan, Mehmet Amac [1 ]
机构
[1] Yildiz Tekn Univ, Elektr Elekt Fak, Bilgisayar Muhendisligi Bolumu, Istanbul, Turkey
来源
PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI | 2019年 / 25卷 / 02期
关键词
Activity recognition; Smartphone; Wearable device; Deep learning; Dataset; Survey; MOBILE ACTIVITY RECOGNITION; CLASSIFIERS; SYSTEM;
D O I
10.5505/pajes.2018.84758
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In recent years, the research on activity recognition has gained speed especially with the development of smart phones and wearable devices. Activities could be categorized into two main groups. simple activities such as walking, running and complex activities such as eating, sleeping, brushing teeth. In this survey paper, articles about activity recognition are examined thoroughly. Sensors and devices used in activity recognition, types of daily activities, application areas, data collection process, training methods, classification algorithms and resource consumption are mentioned in details. The state of the art is elaborated and the existing methods are compared to each other. Later, open data sets are mentioned and studies offering innovative solutions using latest approaches such as deep learning methods are introduced. Finally, still open issues on this area are presented and future work has been discussed.
引用
收藏
页码:223 / 239
页数:17
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