TY - JOUR
T1 - FiToViz
T2 - A Visualisation Approach for Real-Time Risk Situation Awareness
AU - López-Cuevas, Armando
AU - Medina-Pérez, Miguel Angel
AU - Monroy, Raúl
AU - Ramírez-Márquez, José Emmanuel
AU - Trejo, Luis A.
N1 - Publisher Copyright:
© 2010-2012 IEEE.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
KW - IoT
KW - Personal risk detection
KW - machine learning
KW - one-class classification
KW - visualisation
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85028461604&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028461604&partnerID=8YFLogxK
U2 - 10.1109/TAFFC.2017.2741478
DO - 10.1109/TAFFC.2017.2741478
M3 - Article
AN - SCOPUS:85028461604
SN - 1949-3045
VL - 9
SP - 372
EP - 382
JO - IEEE Transactions on Affective Computing
JF - IEEE Transactions on Affective Computing
IS - 3
M1 - 8013066
ER -