Cho, Y;
Berthouze, N;
Julier, S;
(2018)
Automated Inference of Cognitive Stress in-the-Wild.
UCL Interaction Centre: London, UK.
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Abstract
We aim to build technology that combines mobile sensing systems to automatically infer a person’s cognitive stress to provide better and continuous stress management support. Our main innovation is the use of low-cost mobile thermal camera integrated in smartphone or other devices to produce new stress measures. We have developed a robust mobile based tracking system that tracks a person’s breathing pattern by measuring temperature changes around a person’s nostrils region while the person is facing the smartphone. Stress levels are automatically assessed by capturing breathing pattern dynamics through a novel signature based on time and frequency values and using convolutional neural networks to reduce the need to hand craft higher level features. We are now extending the system to integrate multiple sensors (e.g., PPG and GSR) and behavioural information (context). The system is being also adapted to be applied in the context of industry workfloor within the EU H2020 HUMAN research project to support workers during stress inducing tasks. Evaluations are being conducted both in the laboratory and in-the-wild (e.g., industry workfloor).
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