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


JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, vol.9, no.4, pp.421-448, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 9 Issue: 4
  • Publication Date: 2017
  • Doi Number: 10.3233/ais-170446
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.421-448
  • Keywords: Behavior, sensor, people-centric sensing, smartphone, activity recognition, ACTIVITY RECOGNITION, SLEEP, CLASSIFICATION, CALIBRATION, PREDICTION
  • Kocaeli University Affiliated: No


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.