Artificial Intelligence for Mental Wellbeing Monitoring

With sensing technology becoming pervasive in our everyday life, the ability to monitor human mental states and well-being has become important in human computer interaction.

Amongst such states, high level mental stress or mental workload is a common problem affecting mental, physical health and life in our modern society. With disabled people for instance, even basic everyday tasks such as using public transport to go shopping can induce high level stress. However, people are often not aware of their stress level until it becomes too high. Long exposure to this situation leads to further impairment of their mental capability, such as attention, memory and decision-making. The ability to automatically recognise a person’s mental stress is fundamental to the personalisation of better and continuous stress management support. Studies have shown that mental stress could be automatically assessed through the use of these physiological sensing technologies, in turn opening new potential ways for mental wellbeing management support strategies. However, to date, such available technologies had been relatively heavy, expensive, fragile, or restrict mobility, movement and measurement environments, limiting their use.

In this research project, we have been focusing on building new low-cost approaches to more reliable mental wellbeing measurements using mobile sensing technology, supporting unconstrained and potentially a variety of everyday situations.

Principal Investigator: Dr. Youngjun Cho

Published Academic Papers:

• Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging. In the proceeding of International Conference on Affective Computing and Intelligent Interaction (ACII). (2019). https://arxiv.org/abs/1905.05144
• Instant Stress: Detection of Perceived Mental Stress Through Smartphone Photoplethysmography and Thermal Imaging. (2019). https://mental.jmir.org/2019/4/e10140/