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


BAYINDIR L.

JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, cilt.9, sa.4, ss.421-448, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 4
  • Basım Tarihi: 2017
  • Doi Numarası: 10.3233/ais-170446
  • Dergi Adı: JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.421-448
  • Anahtar Kelimeler: Behavior, sensor, people-centric sensing, smartphone, activity recognition, ACTIVITY RECOGNITION, SLEEP, CLASSIFICATION, CALIBRATION, PREDICTION
  • Kocaeli Üniversitesi Adresli: Hayır

Özet

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.