Human behavior analysis by means of multimodal context mining

Oresti Banos Legran, Claudia Villalonga, Jaehun Bang, Taeho Hur, Donguk Kang, Sangbeom Park, Thien Hyunh-The, Vui Le-Ba, Muhammad Bilal Amin, Muhammad Asif Razzaq, Wajahat Ali Khan, Choong Seon Hong, Sungyoung Lee

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    29 Citations (Scopus)
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    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.
    Original languageUndefined
    Pages (from-to)1-19
    Number of pages19
    JournalSensors (Switzerland)
    Issue number8
    Publication statusPublished - 2016


    • emotion identification
    • Ontologies
    • location tracking
    • human behaviour
    • Machine Learning
    • METIS-318537
    • Context awareness
    • IR-101595
    • Activity Recognition
    • EWI-27259

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