Context-Aware Operator Functional State Models for Smart Urban Security Applications

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With ever-expanding cities comes an increased need for smart urban security to ensure the safety and protection of citizens and public assets. While sensors and systems deployed across a city can facilitate the co-ordination of incident and emergency handling, technological advances are still limited by the cognitive capacities of the human operator. At smart city control centres, operators are often faced with cognitive challenges such as information overload, multitasking, interruptions, and fatigue, all of which will stretch cognitive capabilities and compromise task efficiency. Furthermore, in-the-field public safety officials are exposed to similar stressors which can increase the chance of error in often life-threatening situations. In both cases, ambulant real-time monitoring of operator functional state (OFS) can potentially prevent the occurrence of such errors, augmenting performance by assessing the availability of psychophysiological resources required by the task. To this end, Thales Research and Technology Canada has developed a nexus called Sensor-Hub, which integrates multiple sensors to provide real-time situational, behavioural and physiological monitoring for multi-dimensional OFS modelling. Typical dimensions include fatigue, stress, mental workload, physical activity, and tunnel vision. To bridge the Sensor-Hub transition from the laboratory to the field, three critical issues must first be addressed: 1) Mobility: While controlled lab research requires operators to make minimal movements to avoid contaminating sensors, real-world operator movements must be taken into account; 2) Obtrusiveness: The capability to measure multiple modalities through a single device will reduce the many sensors currently used (up to 7), and improve in-the-field acceptability; and 3) Validation within a real-world use case. The proposed project will address these issues and will build on the interdisciplinary expertise of the applicants on cognitive science and biomedical signal processing, thus contributing to the development of innovative context-aware OFS models.