FiToViz: A Visualisation Approach for Real-Time Risk Situation Awareness

Armando López-Cuevas, Miguel Angel Medina-Pérez, Raúl Monroy, José Emmanuel Ramírez-Márquez, Luis A. Trejo

    Research output: Contribution to journalArticlepeer-review

    13 Scopus citations

    Abstract

    People often face risk-prone situations, that range from a mild event to a severe, life-threatening scenario. Risk situations stem from a number of different scenarios: a health condition, a hazard situation due to a natural disaster, a dangerous situation because one is being subject to a crime or physical violence, among others. The lack of a prompt response, calling for assistance, may severely worsen the consequences. In this paper, we propose a novel visualisation method to track and to identify, in real-time, when a person is under a risk-prone situation. Our visualisation model is capable of providing a decision maker a visual description of the physiological behaviour of an individual, or a group thereof; through it, the decision maker may infer whether further assistance is required, if a risky situation is in progress. Our visualisation is leveraged with a traffic light model of a one-class classifier. This combination allows us to train the decision maker into visualising correct and potential risky or abnormal behaviour.

    Original languageEnglish
    Article number8013066
    Pages (from-to)372-382
    Number of pages11
    JournalIEEE Transactions on Affective Computing
    Volume9
    Issue number3
    DOIs
    StatePublished - 1 Jul 2018

    Keywords

    • IoT
    • Personal risk detection
    • machine learning
    • one-class classification
    • visualisation
    • wearable sensors

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