title: Human Behavior Analysis by Means of Multimodal Context Mining.
abstract: There is sufficient evidence proving the impact that negative lifestyle choices have on people's health and wellness. Changing unhealthy behaviours requires raising people's self-awareness and also providing healthcare experts with a thorough and continuous description of the user's conduct. Several monitoring techniques have been proposed in the past to track users' behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user's context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. oresti@khu.ac.kr.|Telemedicine Group, Center for Telematics and Information Technology, University of Twente, Enschede 7500AE, The Netherlands. oresti@khu.ac.kr.
authors: Oresti
authors: O
authors: Banos
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. o.banoslegran@utwente.nl.|Department of Computer Architecture and Computer Technology, Research Center on Information and Communications Technology, University of Granada, Granada E18071, Spain. o.banoslegran@utwente.nl.
authors: Claudia
authors: C
authors: Villalonga
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. cvillalonga@khu.ac.kr.
authors: Jaehun
authors: J
authors: Bang
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. cvillalonga@correo.ugr.es.
authors: Taeho
authors: T
authors: Hur
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. jhb@oslab.khu.ac.kr.
authors: Donguk
authors: D
authors: Kang
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. hth@oslab.khu.ac.kr.
authors: Sangbeom
authors: S
authors: Park
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. dwkang@oslab.khu.ac.kr.
authors: Thien
authors: T
authors: Huynh-The
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. sbp@oslab.khu.ac.kr.
authors: Vui
authors: V
authors: Le-Ba
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. thienht@oslab.khu.ac.kr.
authors: Muhammad Bilal
authors: MB
authors: Amin
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. lebavui@oslab.khu.ac.kr.
authors: Muhammad Asif
authors: MA
authors: Razzaq
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. mbilalamin@oslab.khu.ac.kr.
authors: Wahajat Ali
authors: WA
authors: Khan
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. cshong@khu.ac.kr.
authors: Choong Seon
authors: CS
authors: Hong
authors: Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. asif.razzaq@oslab.khu.ac.kr.
authors: Sungyoung
authors: S
authors: Lee
mesh_terms: D000465:Algorithms; D001364:Awareness / Q000502:physiology; D002755:Choice Behavior / Q000502:physiology*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D008019:Life Style; D008991:Monitoring, Physiologic / Q000379:methods*
keywords: activity recognition; context awareness; emotion identification; human behaviour; location tracking; machine learning; ontologies