Journal
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
Volume 9, Issue 3, Pages 372-382Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAFFC.2017.2741478
Keywords
Personal risk detection; visualisation; wearable sensors; machine learning; one-class classification; IoT
Funding
- US National Science Foundation
- CRISP Type 2/Collaborative Research: Resilience Analytics: A Data-Driven Approach for Enhanced Interdependent Network Resilience [1541165]
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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.
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