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

L Bayındır - Journal of Ambient Intelligence and Smart …, 2017 - content.iospress.com
Journal of Ambient Intelligence and Smart Environments, 2017content.iospress.com
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 …
Abstract
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.
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