A survey of people-centric sensing studies utilizing mobile phone sensors

被引:10
|
作者
Bayindir, Levent [1 ]
机构
[1] Ataturk Univ, Dept Comp Engn, TR-25240 Erzurum, Turkey
关键词
Behavior; sensor; people-centric sensing; smartphone; activity recognition; ACTIVITY RECOGNITION; PHYSICAL-ACTIVITY; SLEEP; CLASSIFICATION; CALIBRATION; PREDICTION;
D O I
10.3233/AIS-170446
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Today's ubiquitous presence of sensors provides a large amount of data which can be analyzed to study human behavior. The last few years saw the birth and diffusion of a new class of sensing systems: smartphones. With a diverse range of embedded sensors, smartphones have now become a commodity, and their capabilities can be leveraged to collect data to be used in different domains, including study of human behavior. This paper presents a review of past research works where mobile phone sensors are used to detect various aspects characterizing human behavior. Methods for automatic recognition of the placement of a mobile phone are first described as useful tools to improve the accuracy of sensing systems. Activity detection, at different abstraction levels from basic body motions to high-level activities, is then surveyed extensively, including studies focusing on detection of transportation mode and characterization of health-related activities such as physical exercise and sleeping. Other related works reviewed in this paper are continuous sensing systems for lifelogging applications, techniques to identify the environment where a user is located, and behavior modeling methods that allow extracting common patterns from behavioral data, studying psychological profiles and predicting future behaviors.
引用
收藏
页码:421 / 448
页数:28
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