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Automated Mental Stress Recognition through Mobile Thermal Imaging

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Mental stress is a critical problem in our modern society. This form of stress strongly affects our well being, and technology is needed to help us to manage health problems. The ability to automatically recognize a person’s mental stress can be fundamental in supporting stress and health management. This research focuses on the use of mobile thermal imaging, a new and less explored sensor, to merge the measurement of multiple physiological signatures into one sensor and to build a reliable mental stress automatic recognition model. Mobile thermal imaging has greater potentials for real-world applications given that it is small and light weight, and requires low computation cost. To make mobile thermal imaging a robust multimodal stress sensor, we have so far contributed: i) a new robust respiration tracking method; and ii) a novel respiration-based automatic stress recognition model based on convolutional neural networks. We are currently investigating new thermal signatures and formulating a research framework to fuse multiple thermal signatures for more reliable stress recognition outcomes.

Short Bio: Youngjun is currently pursuing his PhD at the UCL Interaction Centre (PALS in Faculty of Brain Sciences | Department of Computer Sciences), University College London (UCL) under the supervision of Prof. Dr. Nadia Berthouze (primary supervisor), Dr. Simon Julier and Dr. Nicolai Marquardt (secondary supervisors). He received a MSc in Robotics from KAIST (Korea Advanced Institute of Science and Technology) in 2011. Before joining UCLIC in November 2015, he worked as a Senior Research Engineer at the LG Electronics CTO Research division, Seoul (2011-2015) and was a PI of 4D Touch Project, which has been successfully commercialising a 3D input and gesture recognition technology in collaboration with major automobile manufacturers in the world. His research interests include designing the principles, techniques, and technologies for the next generation of brain-computer interfaces that extend the interactive space and enhance human perception, and understand affects and psychophysiology. In his PhD research, he has been exploring (and made possible) the use of low cost, mobile, biomedical thermography (1) to extract stress-related physiological signatures in mobile contexts and (2) to automatically monitor a person’s mental stress level. The aim is to develop technology-based intervention to support a person’s psychological needs and wellbeing. His work on affective computing, biomedical technology, machine learning, human-computer interaction and haptics has produced more than 50 patent publications (30 Granted) and 10 refereed academic papers.

This talk is part of the Rainbow Group Seminars series.

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