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

    Research output: Contribution to journalArticleAcademicpeer-review

    17 Citations (Scopus)
    57 Downloads (Pure)

    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.
    Original languageUndefined
    Pages (from-to)1-19
    Number of pages19
    JournalSensors (Switserland)
    Volume16
    Issue number8
    DOIs
    Publication statusPublished - 2016

    Keywords

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

    Cite this

    Banos Legran, O., Villalonga, C., Bang, J., Hur, T., Kang, D., Park, S., ... Lee, S. (2016). Human behavior analysis by means of multimodal context mining. Sensors (Switserland), 16(8), 1-19. https://doi.org/10.3390/s16081264